diff --git a/LICENSE b/LICENSE index c2b0d72663b55..820f14dbdeed0 100644 --- a/LICENSE +++ b/LICENSE @@ -263,7 +263,7 @@ The text of each license is also included at licenses/LICENSE-[project].txt. (New BSD license) Protocol Buffer Java API (org.spark-project.protobuf:protobuf-java:2.4.1-shaded - http://code.google.com/p/protobuf) (The BSD License) Fortran to Java ARPACK (net.sourceforge.f2j:arpack_combined_all:0.1 - http://f2j.sourceforge.net) (The BSD License) xmlenc Library (xmlenc:xmlenc:0.52 - http://xmlenc.sourceforge.net) - (The New BSD License) Py4J (net.sf.py4j:py4j:0.10.6 - http://py4j.sourceforge.net/) + (The New BSD License) Py4J (net.sf.py4j:py4j:0.10.7 - http://py4j.sourceforge.net/) (Two-clause BSD-style license) JUnit-Interface (com.novocode:junit-interface:0.10 - http://github.com/szeiger/junit-interface/) (BSD licence) sbt and sbt-launch-lib.bash (BSD 3 Clause) d3.min.js (https://github.com/mbostock/d3/blob/master/LICENSE) diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION index 6d46c31906260..8df26351614ec 100644 --- a/R/pkg/DESCRIPTION +++ b/R/pkg/DESCRIPTION @@ -1,6 +1,6 @@ Package: SparkR Type: Package -Version: 2.3.0 +Version: 2.3.2 Title: R Frontend for Apache Spark Description: Provides an R Frontend for Apache Spark. Authors@R: c(person("Shivaram", "Venkataraman", role = c("aut", "cre"), @@ -13,6 +13,7 @@ Authors@R: c(person("Shivaram", "Venkataraman", role = c("aut", "cre"), License: Apache License (== 2.0) URL: http://www.apache.org/ http://spark.apache.org/ BugReports: http://spark.apache.org/contributing.html +SystemRequirements: Java (== 8) Depends: R (>= 3.0), methods diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE index 3219c6f0cc47b..c51eb0f39c4b1 100644 --- a/R/pkg/NAMESPACE +++ b/R/pkg/NAMESPACE @@ -179,6 +179,7 @@ exportMethods("arrange", "with", "withColumn", "withColumnRenamed", + "withWatermark", "write.df", "write.jdbc", "write.json", diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index fe238f6dd4eb0..41c3c3a89fa72 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -2090,7 +2090,8 @@ setMethod("selectExpr", #' #' @param x a SparkDataFrame. #' @param colName a column name. -#' @param col a Column expression, or an atomic vector in the length of 1 as literal value. +#' @param col a Column expression (which must refer only to this SparkDataFrame), or an atomic +#' vector in the length of 1 as literal value. #' @return A SparkDataFrame with the new column added or the existing column replaced. #' @family SparkDataFrame functions #' @aliases withColumn,SparkDataFrame,character-method @@ -2853,7 +2854,7 @@ setMethod("intersect", #' except #' #' Return a new SparkDataFrame containing rows in this SparkDataFrame -#' but not in another SparkDataFrame. This is equivalent to \code{EXCEPT} in SQL. +#' but not in another SparkDataFrame. This is equivalent to \code{EXCEPT DISTINCT} in SQL. #' #' @param x a SparkDataFrame. #' @param y a SparkDataFrame. @@ -3054,10 +3055,10 @@ setMethod("describe", #' \item stddev #' \item min #' \item max -#' \item arbitrary approximate percentiles specified as a percentage (eg, "75%") +#' \item arbitrary approximate percentiles specified as a percentage (eg, "75\%") #' } #' If no statistics are given, this function computes count, mean, stddev, min, -#' approximate quartiles (percentiles at 25%, 50%, and 75%), and max. +#' approximate quartiles (percentiles at 25\%, 50\%, and 75\%), and max. #' This function is meant for exploratory data analysis, as we make no guarantee about the #' backward compatibility of the schema of the resulting Dataset. If you want to #' programmatically compute summary statistics, use the \code{agg} function instead. @@ -3661,7 +3662,8 @@ setMethod("getNumPartitions", #' isStreaming #' #' Returns TRUE if this SparkDataFrame contains one or more sources that continuously return data -#' as it arrives. +#' as it arrives. A dataset that reads data from a streaming source must be executed as a +#' \code{StreamingQuery} using \code{write.stream}. #' #' @param x A SparkDataFrame #' @return TRUE if this SparkDataFrame is from a streaming source @@ -3707,7 +3709,17 @@ setMethod("isStreaming", #' @param df a streaming SparkDataFrame. #' @param source a name for external data source. #' @param outputMode one of 'append', 'complete', 'update'. -#' @param ... additional argument(s) passed to the method. +#' @param partitionBy a name or a list of names of columns to partition the output by on the file +#' system. If specified, the output is laid out on the file system similar to Hive's +#' partitioning scheme. +#' @param trigger.processingTime a processing time interval as a string, e.g. '5 seconds', +#' '1 minute'. This is a trigger that runs a query periodically based on the processing +#' time. If value is '0 seconds', the query will run as fast as possible, this is the +#' default. Only one trigger can be set. +#' @param trigger.once a logical, must be set to \code{TRUE}. This is a trigger that processes only +#' one batch of data in a streaming query then terminates the query. Only one trigger can be +#' set. +#' @param ... additional external data source specific named options. #' #' @family SparkDataFrame functions #' @seealso \link{read.stream} @@ -3725,7 +3737,8 @@ setMethod("isStreaming", #' # console #' q <- write.stream(wordCounts, "console", outputMode = "complete") #' # text stream -#' q <- write.stream(df, "text", path = "/home/user/out", checkpointLocation = "/home/user/cp") +#' q <- write.stream(df, "text", path = "/home/user/out", checkpointLocation = "/home/user/cp" +#' partitionBy = c("year", "month"), trigger.processingTime = "30 seconds") #' # memory stream #' q <- write.stream(wordCounts, "memory", queryName = "outs", outputMode = "complete") #' head(sql("SELECT * from outs")) @@ -3737,7 +3750,8 @@ setMethod("isStreaming", #' @note experimental setMethod("write.stream", signature(df = "SparkDataFrame"), - function(df, source = NULL, outputMode = NULL, ...) { + function(df, source = NULL, outputMode = NULL, partitionBy = NULL, + trigger.processingTime = NULL, trigger.once = NULL, ...) { if (!is.null(source) && !is.character(source)) { stop("source should be character, NULL or omitted. It is the data source specified ", "in 'spark.sql.sources.default' configuration by default.") @@ -3748,12 +3762,43 @@ setMethod("write.stream", if (is.null(source)) { source <- getDefaultSqlSource() } + cols <- NULL + if (!is.null(partitionBy)) { + if (!all(sapply(partitionBy, function(c) { is.character(c) }))) { + stop("All partitionBy column names should be characters.") + } + cols <- as.list(partitionBy) + } + jtrigger <- NULL + if (!is.null(trigger.processingTime) && !is.na(trigger.processingTime)) { + if (!is.null(trigger.once)) { + stop("Multiple triggers not allowed.") + } + interval <- as.character(trigger.processingTime) + if (nchar(interval) == 0) { + stop("Value for trigger.processingTime must be a non-empty string.") + } + jtrigger <- handledCallJStatic("org.apache.spark.sql.streaming.Trigger", + "ProcessingTime", + interval) + } else if (!is.null(trigger.once) && !is.na(trigger.once)) { + if (!is.logical(trigger.once) || !trigger.once) { + stop("Value for trigger.once must be TRUE.") + } + jtrigger <- callJStatic("org.apache.spark.sql.streaming.Trigger", "Once") + } options <- varargsToStrEnv(...) write <- handledCallJMethod(df@sdf, "writeStream") write <- callJMethod(write, "format", source) if (!is.null(outputMode)) { write <- callJMethod(write, "outputMode", outputMode) } + if (!is.null(cols)) { + write <- callJMethod(write, "partitionBy", cols) + } + if (!is.null(jtrigger)) { + write <- callJMethod(write, "trigger", jtrigger) + } write <- callJMethod(write, "options", options) ssq <- handledCallJMethod(write, "start") streamingQuery(ssq) @@ -3967,3 +4012,47 @@ setMethod("broadcast", sdf <- callJStatic("org.apache.spark.sql.functions", "broadcast", x@sdf) dataFrame(sdf) }) + +#' withWatermark +#' +#' Defines an event time watermark for this streaming SparkDataFrame. A watermark tracks a point in +#' time before which we assume no more late data is going to arrive. +#' +#' Spark will use this watermark for several purposes: +#' \itemize{ +#' \item To know when a given time window aggregation can be finalized and thus can be emitted +#' when using output modes that do not allow updates. +#' \item To minimize the amount of state that we need to keep for on-going aggregations. +#' } +#' The current watermark is computed by looking at the \code{MAX(eventTime)} seen across +#' all of the partitions in the query minus a user specified \code{delayThreshold}. Due to the cost +#' of coordinating this value across partitions, the actual watermark used is only guaranteed +#' to be at least \code{delayThreshold} behind the actual event time. In some cases we may still +#' process records that arrive more than \code{delayThreshold} late. +#' +#' @param x a streaming SparkDataFrame +#' @param eventTime a string specifying the name of the Column that contains the event time of the +#' row. +#' @param delayThreshold a string specifying the minimum delay to wait to data to arrive late, +#' relative to the latest record that has been processed in the form of an +#' interval (e.g. "1 minute" or "5 hours"). NOTE: This should not be negative. +#' @return a SparkDataFrame. +#' @aliases withWatermark,SparkDataFrame,character,character-method +#' @family SparkDataFrame functions +#' @rdname withWatermark +#' @name withWatermark +#' @export +#' @examples +#' \dontrun{ +#' sparkR.session() +#' schema <- structType(structField("time", "timestamp"), structField("value", "double")) +#' df <- read.stream("json", path = jsonDir, schema = schema, maxFilesPerTrigger = 1) +#' df <- withWatermark(df, "time", "10 minutes") +#' } +#' @note withWatermark since 2.3.0 +setMethod("withWatermark", + signature(x = "SparkDataFrame", eventTime = "character", delayThreshold = "character"), + function(x, eventTime, delayThreshold) { + sdf <- callJMethod(x@sdf, "withWatermark", eventTime, delayThreshold) + dataFrame(sdf) + }) diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R index 3b7f71bbbffb8..b8e6bb302b856 100644 --- a/R/pkg/R/SQLContext.R +++ b/R/pkg/R/SQLContext.R @@ -727,7 +727,9 @@ read.jdbc <- function(url, tableName, #' @param schema The data schema defined in structType or a DDL-formatted string, this is #' required for file-based streaming data source #' @param ... additional external data source specific named options, for instance \code{path} for -#' file-based streaming data source +#' file-based streaming data source. \code{timeZone} to indicate a timezone to be used to +#' parse timestamps in the JSON/CSV data sources or partition values; If it isn't set, it +#' uses the default value, session local timezone. #' @return SparkDataFrame #' @rdname read.stream #' @name read.stream diff --git a/R/pkg/R/client.R b/R/pkg/R/client.R index 9d82814211bc5..4c87f64e7f0e1 100644 --- a/R/pkg/R/client.R +++ b/R/pkg/R/client.R @@ -19,7 +19,7 @@ # Creates a SparkR client connection object # if one doesn't already exist -connectBackend <- function(hostname, port, timeout) { +connectBackend <- function(hostname, port, timeout, authSecret) { if (exists(".sparkRcon", envir = .sparkREnv)) { if (isOpen(.sparkREnv[[".sparkRCon"]])) { cat("SparkRBackend client connection already exists\n") @@ -29,7 +29,7 @@ connectBackend <- function(hostname, port, timeout) { con <- socketConnection(host = hostname, port = port, server = FALSE, blocking = TRUE, open = "wb", timeout = timeout) - + doServerAuth(con, authSecret) assign(".sparkRCon", con, envir = .sparkREnv) con } @@ -60,6 +60,41 @@ generateSparkSubmitArgs <- function(args, sparkHome, jars, sparkSubmitOpts, pack combinedArgs } +checkJavaVersion <- function() { + javaBin <- "java" + javaHome <- Sys.getenv("JAVA_HOME") + javaReqs <- utils::packageDescription(utils::packageName(), fields = c("SystemRequirements")) + sparkJavaVersion <- as.numeric(tail(strsplit(javaReqs, "[(=)]")[[1]], n = 1L)) + if (javaHome != "") { + javaBin <- file.path(javaHome, "bin", javaBin) + } + + # If java is missing from PATH, we get an error in Unix and a warning in Windows + javaVersionOut <- tryCatch( + launchScript(javaBin, "-version", wait = TRUE, stdout = TRUE, stderr = TRUE), + error = function(e) { + stop("Java version check failed. Please make sure Java is installed", + " and set JAVA_HOME to point to the installation directory.", e) + }, + warning = function(w) { + stop("Java version check failed. Please make sure Java is installed", + " and set JAVA_HOME to point to the installation directory.", w) + }) + javaVersionFilter <- Filter( + function(x) { + grepl(" version", x) + }, javaVersionOut) + + javaVersionStr <- strsplit(javaVersionFilter[[1]], "[\"]")[[1L]][2] + # javaVersionStr is of the form 1.8.0_92. + # Extract 8 from it to compare to sparkJavaVersion + javaVersionNum <- as.integer(strsplit(javaVersionStr, "[.]")[[1L]][2]) + if (javaVersionNum != sparkJavaVersion) { + stop(paste("Java version", sparkJavaVersion, "is required for this package; found version:", + javaVersionStr)) + } +} + launchBackend <- function(args, sparkHome, jars, sparkSubmitOpts, packages) { sparkSubmitBinName <- determineSparkSubmitBin() if (sparkHome != "") { @@ -67,6 +102,7 @@ launchBackend <- function(args, sparkHome, jars, sparkSubmitOpts, packages) { } else { sparkSubmitBin <- sparkSubmitBinName } + combinedArgs <- generateSparkSubmitArgs(args, sparkHome, jars, sparkSubmitOpts, packages) cat("Launching java with spark-submit command", sparkSubmitBin, combinedArgs, "\n") invisible(launchScript(sparkSubmitBin, combinedArgs)) diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R index 3095adb918b67..3d6d9f9746ee6 100644 --- a/R/pkg/R/column.R +++ b/R/pkg/R/column.R @@ -164,12 +164,18 @@ setMethod("alias", #' @aliases substr,Column-method #' #' @param x a Column. -#' @param start starting position. +#' @param start starting position. It should be 1-base. #' @param stop ending position. +#' @examples +#' \dontrun{ +#' df <- createDataFrame(list(list(a="abcdef"))) +#' collect(select(df, substr(df$a, 1, 4))) # the result is `abcd`. +#' collect(select(df, substr(df$a, 2, 4))) # the result is `bcd`. +#' } #' @note substr since 1.4.0 setMethod("substr", signature(x = "Column"), function(x, start, stop) { - jc <- callJMethod(x@jc, "substr", as.integer(start - 1), as.integer(stop - start + 1)) + jc <- callJMethod(x@jc, "substr", as.integer(start), as.integer(stop - start + 1)) column(jc) }) diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R index a90f7d381026b..cb03f1667629f 100644 --- a/R/pkg/R/deserialize.R +++ b/R/pkg/R/deserialize.R @@ -60,14 +60,18 @@ readTypedObject <- function(con, type) { stop(paste("Unsupported type for deserialization", type))) } -readString <- function(con) { - stringLen <- readInt(con) - raw <- readBin(con, raw(), stringLen, endian = "big") +readStringData <- function(con, len) { + raw <- readBin(con, raw(), len, endian = "big") string <- rawToChar(raw) Encoding(string) <- "UTF-8" string } +readString <- function(con) { + stringLen <- readInt(con) + readStringData(con, stringLen) +} + readInt <- function(con) { readBin(con, integer(), n = 1, endian = "big") } diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R index 55365a41d774b..29ee146ab14f9 100644 --- a/R/pkg/R/functions.R +++ b/R/pkg/R/functions.R @@ -278,8 +278,8 @@ setMethod("abs", }) #' @details -#' \code{acos}: Computes the cosine inverse of the given value; the returned angle is in -#' the range 0.0 through pi. +#' \code{acos}: Returns the inverse cosine of the given value, +#' as if computed by \code{java.lang.Math.acos()} #' #' @rdname column_math_functions #' @export @@ -334,8 +334,8 @@ setMethod("ascii", }) #' @details -#' \code{asin}: Computes the sine inverse of the given value; the returned angle is in -#' the range -pi/2 through pi/2. +#' \code{asin}: Returns the inverse sine of the given value, +#' as if computed by \code{java.lang.Math.asin()} #' #' @rdname column_math_functions #' @export @@ -349,8 +349,8 @@ setMethod("asin", }) #' @details -#' \code{atan}: Computes the tangent inverse of the given value; the returned angle is in the range -#' -pi/2 through pi/2. +#' \code{atan}: Returns the inverse tangent of the given value, +#' as if computed by \code{java.lang.Math.atan()} #' #' @rdname column_math_functions #' @export @@ -613,7 +613,8 @@ setMethod("covar_pop", signature(col1 = "characterOrColumn", col2 = "characterOr }) #' @details -#' \code{cos}: Computes the cosine of the given value. Units in radians. +#' \code{cos}: Returns the cosine of the given value, +#' as if computed by \code{java.lang.Math.cos()}. Units in radians. #' #' @rdname column_math_functions #' @aliases cos cos,Column-method @@ -627,7 +628,8 @@ setMethod("cos", }) #' @details -#' \code{cosh}: Computes the hyperbolic cosine of the given value. +#' \code{cosh}: Returns the hyperbolic cosine of the given value, +#' as if computed by \code{java.lang.Math.cosh()}. #' #' @rdname column_math_functions #' @aliases cosh cosh,Column-method @@ -1026,7 +1028,9 @@ setMethod("last_day", }) #' @details -#' \code{length}: Computes the length of a given string or binary column. +#' \code{length}: Computes the character length of a string data or number of bytes +#' of a binary data. The length of string data includes the trailing spaces. +#' The length of binary data includes binary zeros. #' #' @rdname column_string_functions #' @aliases length length,Column-method @@ -1461,7 +1465,8 @@ setMethod("sign", signature(x = "Column"), }) #' @details -#' \code{sin}: Computes the sine of the given value. Units in radians. +#' \code{sin}: Returns the sine of the given value, +#' as if computed by \code{java.lang.Math.sin()}. Units in radians. #' #' @rdname column_math_functions #' @aliases sin sin,Column-method @@ -1475,7 +1480,8 @@ setMethod("sin", }) #' @details -#' \code{sinh}: Computes the hyperbolic sine of the given value. +#' \code{sinh}: Returns the hyperbolic sine of the given value, +#' as if computed by \code{java.lang.Math.sinh()}. #' #' @rdname column_math_functions #' @aliases sinh sinh,Column-method @@ -1651,7 +1657,9 @@ setMethod("sumDistinct", }) #' @details -#' \code{tan}: Computes the tangent of the given value. Units in radians. +#' \code{tan}: Returns the tangent of the given value, +#' as if computed by \code{java.lang.Math.tan()}. +#' Units in radians. #' #' @rdname column_math_functions #' @aliases tan tan,Column-method @@ -1665,7 +1673,8 @@ setMethod("tan", }) #' @details -#' \code{tanh}: Computes the hyperbolic tangent of the given value. +#' \code{tanh}: Returns the hyperbolic tangent of the given value, +#' as if computed by \code{java.lang.Math.tanh()}. #' #' @rdname column_math_functions #' @aliases tanh tanh,Column-method @@ -1971,7 +1980,8 @@ setMethod("year", #' @details #' \code{atan2}: Returns the angle theta from the conversion of rectangular coordinates -#' (x, y) to polar coordinates (r, theta). Units in radians. +#' (x, y) to polar coordinates (r, theta), +#' as if computed by \code{java.lang.Math.atan2()}. Units in radians. #' #' @rdname column_math_functions #' @aliases atan2 atan2,Column-method diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R index 5369c32544e5e..cffc9ab5f2bea 100644 --- a/R/pkg/R/generics.R +++ b/R/pkg/R/generics.R @@ -762,7 +762,7 @@ setGeneric("summarize", function(x, ...) { standardGeneric("summarize") }) #' @export setGeneric("summary", function(object, ...) { standardGeneric("summary") }) -setGeneric("toJSON", function(x) { standardGeneric("toJSON") }) +setGeneric("toJSON", function(x, ...) { standardGeneric("toJSON") }) setGeneric("toRDD", function(x) { standardGeneric("toRDD") }) @@ -799,6 +799,12 @@ setGeneric("withColumn", function(x, colName, col) { standardGeneric("withColumn setGeneric("withColumnRenamed", function(x, existingCol, newCol) { standardGeneric("withColumnRenamed") }) +#' @rdname withWatermark +#' @export +setGeneric("withWatermark", function(x, eventTime, delayThreshold) { + standardGeneric("withWatermark") +}) + #' @rdname write.df #' @export setGeneric("write.df", function(df, path = NULL, ...) { standardGeneric("write.df") }) diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R index 7cd072a1d6f89..f6e9b1357561b 100644 --- a/R/pkg/R/mllib_classification.R +++ b/R/pkg/R/mllib_classification.R @@ -279,11 +279,24 @@ function(object, path, overwrite = FALSE) { #' savedModel <- read.ml(path) #' summary(savedModel) #' -#' # multinomial logistic regression +#' # binary logistic regression against two classes with +#' # upperBoundsOnCoefficients and upperBoundsOnIntercepts +#' ubc <- matrix(c(1.0, 0.0, 1.0, 0.0), nrow = 1, ncol = 4) +#' model <- spark.logit(training, Species ~ ., +#' upperBoundsOnCoefficients = ubc, +#' upperBoundsOnIntercepts = 1.0) #' +#' # multinomial logistic regression #' model <- spark.logit(training, Class ~ ., regParam = 0.5) #' summary <- summary(model) #' +#' # multinomial logistic regression with +#' # lowerBoundsOnCoefficients and lowerBoundsOnIntercepts +#' lbc <- matrix(c(0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0, -1.0), nrow = 2, ncol = 4) +#' lbi <- as.array(c(0.0, 0.0)) +#' model <- spark.logit(training, Species ~ ., family = "multinomial", +#' lowerBoundsOnCoefficients = lbc, +#' lowerBoundsOnIntercepts = lbi) #' } #' @note spark.logit since 2.1.0 setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula"), diff --git a/R/pkg/R/mllib_recommendation.R b/R/pkg/R/mllib_recommendation.R index fa794249085d7..5441c4a4022a9 100644 --- a/R/pkg/R/mllib_recommendation.R +++ b/R/pkg/R/mllib_recommendation.R @@ -48,6 +48,8 @@ setClass("ALSModel", representation(jobj = "jobj")) #' @param numUserBlocks number of user blocks used to parallelize computation (> 0). #' @param numItemBlocks number of item blocks used to parallelize computation (> 0). #' @param checkpointInterval number of checkpoint intervals (>= 1) or disable checkpoint (-1). +#' Note: this setting will be ignored if the checkpoint directory is not +#' set. #' @param ... additional argument(s) passed to the method. #' @return \code{spark.als} returns a fitted ALS model. #' @rdname spark.als diff --git a/R/pkg/R/mllib_tree.R b/R/pkg/R/mllib_tree.R index 89a58bf0aadae..4e5ddf22ee16d 100644 --- a/R/pkg/R/mllib_tree.R +++ b/R/pkg/R/mllib_tree.R @@ -161,6 +161,8 @@ print.summary.decisionTree <- function(x) { #' >= 1. #' @param minInfoGain Minimum information gain for a split to be considered at a tree node. #' @param checkpointInterval Param for set checkpoint interval (>= 1) or disable checkpoint (-1). +#' Note: this setting will be ignored if the checkpoint directory is not +#' set. #' @param maxMemoryInMB Maximum memory in MB allocated to histogram aggregation. #' @param cacheNodeIds If FALSE, the algorithm will pass trees to executors to match instances with #' nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching @@ -382,6 +384,8 @@ setMethod("write.ml", signature(object = "GBTClassificationModel", path = "chara #' @param minInstancesPerNode Minimum number of instances each child must have after split. #' @param minInfoGain Minimum information gain for a split to be considered at a tree node. #' @param checkpointInterval Param for set checkpoint interval (>= 1) or disable checkpoint (-1). +#' Note: this setting will be ignored if the checkpoint directory is not +#' set. #' @param maxMemoryInMB Maximum memory in MB allocated to histogram aggregation. #' @param cacheNodeIds If FALSE, the algorithm will pass trees to executors to match instances with #' nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching @@ -595,6 +599,8 @@ setMethod("write.ml", signature(object = "RandomForestClassificationModel", path #' @param minInstancesPerNode Minimum number of instances each child must have after split. #' @param minInfoGain Minimum information gain for a split to be considered at a tree node. #' @param checkpointInterval Param for set checkpoint interval (>= 1) or disable checkpoint (-1). +#' Note: this setting will be ignored if the checkpoint directory is not +#' set. #' @param maxMemoryInMB Maximum memory in MB allocated to histogram aggregation. #' @param cacheNodeIds If FALSE, the algorithm will pass trees to executors to match instances with #' nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R index 3bbf60d9b668c..263b9b576c0c5 100644 --- a/R/pkg/R/serialize.R +++ b/R/pkg/R/serialize.R @@ -30,14 +30,17 @@ # POSIXct,POSIXlt -> Time # # list[T] -> Array[T], where T is one of above mentioned types +# Multi-element vector of any of the above (except raw) -> Array[T] # environment -> Map[String, T], where T is a native type # jobj -> Object, where jobj is an object created in the backend # nolint end getSerdeType <- function(object) { type <- class(object)[[1]] - if (type != "list") { - type + if (is.atomic(object) & !is.raw(object) & length(object) > 1) { + "array" + } else if (type != "list") { + type } else { # Check if all elements are of same type elemType <- unique(sapply(object, function(elem) { getSerdeType(elem) })) @@ -50,9 +53,7 @@ getSerdeType <- function(object) { } writeObject <- function(con, object, writeType = TRUE) { - # NOTE: In R vectors have same type as objects. So we don't support - # passing in vectors as arrays and instead require arrays to be passed - # as lists. + # NOTE: In R vectors have same type as objects type <- class(object)[[1]] # class of POSIXlt is c("POSIXlt", "POSIXt") # Checking types is needed here, since 'is.na' only handles atomic vectors, # lists and pairlists diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R index 965471f3b07a0..266fa46525e0a 100644 --- a/R/pkg/R/sparkR.R +++ b/R/pkg/R/sparkR.R @@ -161,11 +161,16 @@ sparkR.sparkContext <- function( " please use the --packages commandline instead", sep = ",")) } backendPort <- existingPort + authSecret <- Sys.getenv("SPARKR_BACKEND_AUTH_SECRET") + if (nchar(authSecret) == 0) { + stop("Auth secret not provided in environment.") + } } else { path <- tempfile(pattern = "backend_port") submitOps <- getClientModeSparkSubmitOpts( Sys.getenv("SPARKR_SUBMIT_ARGS", "sparkr-shell"), sparkEnvirMap) + checkJavaVersion() launchBackend( args = path, sparkHome = sparkHome, @@ -189,16 +194,27 @@ sparkR.sparkContext <- function( monitorPort <- readInt(f) rLibPath <- readString(f) connectionTimeout <- readInt(f) + + # Don't use readString() so that we can provide a useful + # error message if the R and Java versions are mismatched. + authSecretLen <- readInt(f) + if (length(authSecretLen) == 0 || authSecretLen == 0) { + stop("Unexpected EOF in JVM connection data. Mismatched versions?") + } + authSecret <- readStringData(f, authSecretLen) close(f) file.remove(path) if (length(backendPort) == 0 || backendPort == 0 || length(monitorPort) == 0 || monitorPort == 0 || - length(rLibPath) != 1) { + length(rLibPath) != 1 || length(authSecret) == 0) { stop("JVM failed to launch") } - assign(".monitorConn", - socketConnection(port = monitorPort, timeout = connectionTimeout), - envir = .sparkREnv) + + monitorConn <- socketConnection(port = monitorPort, blocking = TRUE, + timeout = connectionTimeout, open = "wb") + doServerAuth(monitorConn, authSecret) + + assign(".monitorConn", monitorConn, envir = .sparkREnv) assign(".backendLaunched", 1, envir = .sparkREnv) if (rLibPath != "") { assign(".libPath", rLibPath, envir = .sparkREnv) @@ -208,7 +224,7 @@ sparkR.sparkContext <- function( .sparkREnv$backendPort <- backendPort tryCatch({ - connectBackend("localhost", backendPort, timeout = connectionTimeout) + connectBackend("localhost", backendPort, timeout = connectionTimeout, authSecret = authSecret) }, error = function(err) { stop("Failed to connect JVM\n") @@ -694,3 +710,17 @@ sparkCheckInstall <- function(sparkHome, master, deployMode) { NULL } } + +# Utility function for sending auth data over a socket and checking the server's reply. +doServerAuth <- function(con, authSecret) { + if (nchar(authSecret) == 0) { + stop("Auth secret not provided.") + } + writeString(con, authSecret) + flush(con) + reply <- readString(con) + if (reply != "ok") { + close(con) + stop("Unexpected reply from server.") + } +} diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R index 164cd6d01a347..493a50d0ba5cc 100644 --- a/R/pkg/R/utils.R +++ b/R/pkg/R/utils.R @@ -747,7 +747,7 @@ varargsToJProperties <- function(...) { props } -launchScript <- function(script, combinedArgs, wait = FALSE) { +launchScript <- function(script, combinedArgs, wait = FALSE, stdout = "", stderr = "") { if (.Platform$OS.type == "windows") { scriptWithArgs <- paste(script, combinedArgs, sep = " ") # on Windows, intern = F seems to mean output to the console. (documentation on this is missing) @@ -757,7 +757,7 @@ launchScript <- function(script, combinedArgs, wait = FALSE) { # stdout = F means discard output # stdout = "" means to its console (default) # Note that the console of this child process might not be the same as the running R process. - system2(script, combinedArgs, stdout = "", wait = wait) + system2(script, combinedArgs, stdout = stdout, wait = wait, stderr = stderr) } } diff --git a/R/pkg/inst/worker/daemon.R b/R/pkg/inst/worker/daemon.R index 2e31dc5f728cd..fb9db63b07cd0 100644 --- a/R/pkg/inst/worker/daemon.R +++ b/R/pkg/inst/worker/daemon.R @@ -28,7 +28,9 @@ suppressPackageStartupMessages(library(SparkR)) port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT")) inputCon <- socketConnection( - port = port, open = "rb", blocking = TRUE, timeout = connectionTimeout) + port = port, open = "wb", blocking = TRUE, timeout = connectionTimeout) + +SparkR:::doServerAuth(inputCon, Sys.getenv("SPARKR_WORKER_SECRET")) # Waits indefinitely for a socket connecion by default. selectTimeout <- NULL diff --git a/R/pkg/inst/worker/worker.R b/R/pkg/inst/worker/worker.R index 00789d815bba8..ba458d2b9ddfb 100644 --- a/R/pkg/inst/worker/worker.R +++ b/R/pkg/inst/worker/worker.R @@ -100,9 +100,12 @@ suppressPackageStartupMessages(library(SparkR)) port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT")) inputCon <- socketConnection( - port = port, blocking = TRUE, open = "rb", timeout = connectionTimeout) + port = port, blocking = TRUE, open = "wb", timeout = connectionTimeout) +SparkR:::doServerAuth(inputCon, Sys.getenv("SPARKR_WORKER_SECRET")) + outputCon <- socketConnection( port = port, blocking = TRUE, open = "wb", timeout = connectionTimeout) +SparkR:::doServerAuth(outputCon, Sys.getenv("SPARKR_WORKER_SECRET")) # read the index of the current partition inside the RDD partition <- SparkR:::readInt(inputCon) diff --git a/R/pkg/tests/fulltests/test_Serde.R b/R/pkg/tests/fulltests/test_Serde.R index 6bbd201bf1d82..3577929323b8b 100644 --- a/R/pkg/tests/fulltests/test_Serde.R +++ b/R/pkg/tests/fulltests/test_Serde.R @@ -37,6 +37,53 @@ test_that("SerDe of primitive types", { expect_equal(class(x), "character") }) +test_that("SerDe of multi-element primitive vectors inside R data.frame", { + # vector of integers embedded in R data.frame + indices <- 1L:3L + myDf <- data.frame(indices) + myDf$data <- list(rep(0L, 3L)) + mySparkDf <- as.DataFrame(myDf) + myResultingDf <- collect(mySparkDf) + myDfListedData <- data.frame(indices) + myDfListedData$data <- list(as.list(rep(0L, 3L))) + expect_equal(myResultingDf, myDfListedData) + expect_equal(class(myResultingDf[["data"]][[1]]), "list") + expect_equal(class(myResultingDf[["data"]][[1]][[1]]), "integer") + + # vector of numeric embedded in R data.frame + myDf <- data.frame(indices) + myDf$data <- list(rep(0, 3L)) + mySparkDf <- as.DataFrame(myDf) + myResultingDf <- collect(mySparkDf) + myDfListedData <- data.frame(indices) + myDfListedData$data <- list(as.list(rep(0, 3L))) + expect_equal(myResultingDf, myDfListedData) + expect_equal(class(myResultingDf[["data"]][[1]]), "list") + expect_equal(class(myResultingDf[["data"]][[1]][[1]]), "numeric") + + # vector of logical embedded in R data.frame + myDf <- data.frame(indices) + myDf$data <- list(rep(TRUE, 3L)) + mySparkDf <- as.DataFrame(myDf) + myResultingDf <- collect(mySparkDf) + myDfListedData <- data.frame(indices) + myDfListedData$data <- list(as.list(rep(TRUE, 3L))) + expect_equal(myResultingDf, myDfListedData) + expect_equal(class(myResultingDf[["data"]][[1]]), "list") + expect_equal(class(myResultingDf[["data"]][[1]][[1]]), "logical") + + # vector of character embedded in R data.frame + myDf <- data.frame(indices) + myDf$data <- list(rep("abc", 3L)) + mySparkDf <- as.DataFrame(myDf) + myResultingDf <- collect(mySparkDf) + myDfListedData <- data.frame(indices) + myDfListedData$data <- list(as.list(rep("abc", 3L))) + expect_equal(myResultingDf, myDfListedData) + expect_equal(class(myResultingDf[["data"]][[1]]), "list") + expect_equal(class(myResultingDf[["data"]][[1]][[1]]), "character") +}) + test_that("SerDe of list of primitive types", { x <- list(1L, 2L, 3L) y <- callJStatic("SparkRHandler", "echo", x) diff --git a/R/pkg/tests/fulltests/test_mllib_classification.R b/R/pkg/tests/fulltests/test_mllib_classification.R index ad47717ddc12f..a46c47dccd02e 100644 --- a/R/pkg/tests/fulltests/test_mllib_classification.R +++ b/R/pkg/tests/fulltests/test_mllib_classification.R @@ -124,7 +124,7 @@ test_that("spark.logit", { # Petal.Width 0.42122607 # nolint end - # Test multinomial logistic regression againt three classes + # Test multinomial logistic regression against three classes df <- suppressWarnings(createDataFrame(iris)) model <- spark.logit(df, Species ~ ., regParam = 0.5) summary <- summary(model) @@ -196,7 +196,7 @@ test_that("spark.logit", { # # nolint end - # Test multinomial logistic regression againt two classes + # Test multinomial logistic regression against two classes df <- suppressWarnings(createDataFrame(iris)) training <- df[df$Species %in% c("versicolor", "virginica"), ] model <- spark.logit(training, Species ~ ., regParam = 0.5, family = "multinomial") @@ -208,7 +208,7 @@ test_that("spark.logit", { expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1)) expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1)) - # Test binomial logistic regression againt two classes + # Test binomial logistic regression against two classes model <- spark.logit(training, Species ~ ., regParam = 0.5) summary <- summary(model) coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04) @@ -239,7 +239,7 @@ test_that("spark.logit", { prediction2 <- collect(select(predict(model2, df2), "prediction")) expect_equal(sort(prediction2$prediction), c("0.0", "0.0", "0.0", "0.0", "0.0")) - # Test binomial logistic regression againt two classes with upperBoundsOnCoefficients + # Test binomial logistic regression against two classes with upperBoundsOnCoefficients # and upperBoundsOnIntercepts u <- matrix(c(1.0, 0.0, 1.0, 0.0), nrow = 1, ncol = 4) model <- spark.logit(training, Species ~ ., upperBoundsOnCoefficients = u, @@ -252,7 +252,7 @@ test_that("spark.logit", { expect_error(spark.logit(training, Species ~ ., upperBoundsOnCoefficients = as.array(c(1, 2)), upperBoundsOnIntercepts = 1.0)) - # Test binomial logistic regression againt two classes with lowerBoundsOnCoefficients + # Test binomial logistic regression against two classes with lowerBoundsOnCoefficients # and lowerBoundsOnIntercepts l <- matrix(c(0.0, -1.0, 0.0, -1.0), nrow = 1, ncol = 4) model <- spark.logit(training, Species ~ ., lowerBoundsOnCoefficients = l, diff --git a/R/pkg/tests/fulltests/test_sparkSQL.R b/R/pkg/tests/fulltests/test_sparkSQL.R index 5197838eaac66..a73811ed4a978 100644 --- a/R/pkg/tests/fulltests/test_sparkSQL.R +++ b/R/pkg/tests/fulltests/test_sparkSQL.R @@ -1649,6 +1649,7 @@ test_that("string operators", { expect_false(first(select(df, startsWith(df$name, "m")))[[1]]) expect_true(first(select(df, endsWith(df$name, "el")))[[1]]) expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi") + expect_equal(first(select(df, substr(df$name, 4, 6)))[[1]], "hae") if (as.numeric(R.version$major) >= 3 && as.numeric(R.version$minor) >= 3) { expect_true(startsWith("Hello World", "Hello")) expect_false(endsWith("Hello World", "a")) @@ -2185,8 +2186,8 @@ test_that("join(), crossJoin() and merge() on a DataFrame", { expect_equal(count(where(join(df, df2), df$name == df2$name)), 3) # cartesian join expect_error(tryCatch(count(join(df, df2)), error = function(e) { stop(e) }), - paste0(".*(org.apache.spark.sql.AnalysisException: Detected cartesian product for", - " INNER join between logical plans).*")) + paste0(".*(org.apache.spark.sql.AnalysisException: Detected implicit cartesian", + " product for INNER join between logical plans).*")) joined <- crossJoin(df, df2) expect_equal(names(joined), c("age", "name", "name", "test")) diff --git a/R/pkg/tests/fulltests/test_streaming.R b/R/pkg/tests/fulltests/test_streaming.R index 54f40bbd5f517..bfb1a046490ec 100644 --- a/R/pkg/tests/fulltests/test_streaming.R +++ b/R/pkg/tests/fulltests/test_streaming.R @@ -172,6 +172,113 @@ test_that("Terminated by error", { stopQuery(q) }) +test_that("PartitionBy", { + parquetPath <- tempfile(pattern = "sparkr-test", fileext = ".parquet") + checkpointPath <- tempfile(pattern = "sparkr-test", fileext = ".checkpoint") + textPath <- tempfile(pattern = "sparkr-test", fileext = ".text") + df <- read.df(jsonPath, "json", stringSchema) + write.df(df, parquetPath, "parquet", "overwrite") + + df <- read.stream(path = parquetPath, schema = stringSchema) + + expect_error(write.stream(df, "json", path = textPath, checkpointLocation = "append", + partitionBy = c(1, 2)), + "All partitionBy column names should be characters") + + q <- write.stream(df, "json", path = textPath, checkpointLocation = "append", + partitionBy = "name") + awaitTermination(q, 5 * 1000) + callJMethod(q@ssq, "processAllAvailable") + + dirs <- list.files(textPath) + expect_equal(length(dirs[substring(dirs, 1, nchar("name=")) == "name="]), 3) + + unlink(checkpointPath) + unlink(textPath) + unlink(parquetPath) +}) + +test_that("Watermark", { + parquetPath <- tempfile(pattern = "sparkr-test", fileext = ".parquet") + schema <- structType(structField("value", "string")) + t <- Sys.time() + df <- as.DataFrame(lapply(list(t), as.character), schema) + write.df(df, parquetPath, "parquet", "append") + df <- read.stream(path = parquetPath, schema = "value STRING") + df <- withColumn(df, "eventTime", cast(df$value, "timestamp")) + df <- withWatermark(df, "eventTime", "10 seconds") + counts <- count(group_by(df, "eventTime")) + q <- write.stream(counts, "memory", queryName = "times", outputMode = "append") + + # first events + df <- as.DataFrame(lapply(list(t + 1, t, t + 2), as.character), schema) + write.df(df, parquetPath, "parquet", "append") + awaitTermination(q, 5 * 1000) + callJMethod(q@ssq, "processAllAvailable") + + # advance watermark to 15 + df <- as.DataFrame(lapply(list(t + 25), as.character), schema) + write.df(df, parquetPath, "parquet", "append") + awaitTermination(q, 5 * 1000) + callJMethod(q@ssq, "processAllAvailable") + + # old events, should be dropped + df <- as.DataFrame(lapply(list(t), as.character), schema) + write.df(df, parquetPath, "parquet", "append") + awaitTermination(q, 5 * 1000) + callJMethod(q@ssq, "processAllAvailable") + + # evict events less than previous watermark + df <- as.DataFrame(lapply(list(t + 25), as.character), schema) + write.df(df, parquetPath, "parquet", "append") + awaitTermination(q, 5 * 1000) + callJMethod(q@ssq, "processAllAvailable") + + times <- collect(sql("SELECT * FROM times")) + # looks like write timing can affect the first bucket; but it should be t + expect_equal(times[order(times$eventTime), ][1, 2], 2) + + stopQuery(q) + unlink(parquetPath) +}) + +test_that("Trigger", { + parquetPath <- tempfile(pattern = "sparkr-test", fileext = ".parquet") + schema <- structType(structField("value", "string")) + df <- as.DataFrame(lapply(list(Sys.time()), as.character), schema) + write.df(df, parquetPath, "parquet", "append") + df <- read.stream(path = parquetPath, schema = "value STRING") + + expect_error(write.stream(df, "memory", queryName = "times", outputMode = "append", + trigger.processingTime = "", trigger.once = ""), "Multiple triggers not allowed.") + + expect_error(write.stream(df, "memory", queryName = "times", outputMode = "append", + trigger.processingTime = ""), + "Value for trigger.processingTime must be a non-empty string.") + + expect_error(write.stream(df, "memory", queryName = "times", outputMode = "append", + trigger.processingTime = "invalid"), "illegal argument") + + expect_error(write.stream(df, "memory", queryName = "times", outputMode = "append", + trigger.once = ""), "Value for trigger.once must be TRUE.") + + expect_error(write.stream(df, "memory", queryName = "times", outputMode = "append", + trigger.once = FALSE), "Value for trigger.once must be TRUE.") + + q <- write.stream(df, "memory", queryName = "times", outputMode = "append", trigger.once = TRUE) + awaitTermination(q, 5 * 1000) + callJMethod(q@ssq, "processAllAvailable") + df <- as.DataFrame(lapply(list(Sys.time()), as.character), schema) + write.df(df, parquetPath, "parquet", "append") + awaitTermination(q, 5 * 1000) + callJMethod(q@ssq, "processAllAvailable") + + expect_equal(nrow(collect(sql("SELECT * FROM times"))), 1) + + stopQuery(q) + unlink(parquetPath) +}) + unlink(jsonPath) unlink(jsonPathNa) diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd index 2e662424b25f2..d4713de7806a1 100644 --- a/R/pkg/vignettes/sparkr-vignettes.Rmd +++ b/R/pkg/vignettes/sparkr-vignettes.Rmd @@ -46,7 +46,7 @@ Sys.setenv("_JAVA_OPTIONS" = paste("-XX:-UsePerfData", old_java_opt, sep = " ")) ## Overview -SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. With Spark `r packageVersion("SparkR")`, SparkR provides a distributed data frame implementation that supports data processing operations like selection, filtering, aggregation etc. and distributed machine learning using [MLlib](http://spark.apache.org/mllib/). +SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. With Spark `r packageVersion("SparkR")`, SparkR provides a distributed data frame implementation that supports data processing operations like selection, filtering, aggregation etc. and distributed machine learning using [MLlib](https://spark.apache.org/mllib/). ## Getting Started @@ -132,7 +132,7 @@ sparkR.session.stop() Different from many other R packages, to use SparkR, you need an additional installation of Apache Spark. The Spark installation will be used to run a backend process that will compile and execute SparkR programs. -After installing the SparkR package, you can call `sparkR.session` as explained in the previous section to start and it will check for the Spark installation. If you are working with SparkR from an interactive shell (eg. R, RStudio) then Spark is downloaded and cached automatically if it is not found. Alternatively, we provide an easy-to-use function `install.spark` for running this manually. If you don't have Spark installed on the computer, you may download it from [Apache Spark Website](http://spark.apache.org/downloads.html). +After installing the SparkR package, you can call `sparkR.session` as explained in the previous section to start and it will check for the Spark installation. If you are working with SparkR from an interactive shell (eg. R, RStudio) then Spark is downloaded and cached automatically if it is not found. Alternatively, we provide an easy-to-use function `install.spark` for running this manually. If you don't have Spark installed on the computer, you may download it from [Apache Spark Website](https://spark.apache.org/downloads.html). ```{r, eval=FALSE} install.spark() @@ -147,7 +147,7 @@ sparkR.session(sparkHome = "/HOME/spark") ### Spark Session {#SetupSparkSession} -In addition to `sparkHome`, many other options can be specified in `sparkR.session`. For a complete list, see [Starting up: SparkSession](http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession) and [SparkR API doc](http://spark.apache.org/docs/latest/api/R/sparkR.session.html). +In addition to `sparkHome`, many other options can be specified in `sparkR.session`. For a complete list, see [Starting up: SparkSession](https://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession) and [SparkR API doc](https://spark.apache.org/docs/latest/api/R/sparkR.session.html). In particular, the following Spark driver properties can be set in `sparkConfig`. @@ -169,7 +169,7 @@ sparkR.session(spark.sql.warehouse.dir = spark_warehouse_path) #### Cluster Mode -SparkR can connect to remote Spark clusters. [Cluster Mode Overview](http://spark.apache.org/docs/latest/cluster-overview.html) is a good introduction to different Spark cluster modes. +SparkR can connect to remote Spark clusters. [Cluster Mode Overview](https://spark.apache.org/docs/latest/cluster-overview.html) is a good introduction to different Spark cluster modes. When connecting SparkR to a remote Spark cluster, make sure that the Spark version and Hadoop version on the machine match the corresponding versions on the cluster. Current SparkR package is compatible with ```{r, echo=FALSE, tidy = TRUE} @@ -177,7 +177,7 @@ paste("Spark", packageVersion("SparkR")) ``` It should be used both on the local computer and on the remote cluster. -To connect, pass the URL of the master node to `sparkR.session`. A complete list can be seen in [Spark Master URLs](http://spark.apache.org/docs/latest/submitting-applications.html#master-urls). +To connect, pass the URL of the master node to `sparkR.session`. A complete list can be seen in [Spark Master URLs](https://spark.apache.org/docs/latest/submitting-applications.html#master-urls). For example, to connect to a local standalone Spark master, we can call ```{r, eval=FALSE} @@ -317,7 +317,7 @@ A common flow of grouping and aggregation is 2. Feed the `GroupedData` object to `agg` or `summarize` functions, with some provided aggregation functions to compute a number within each group. -A number of widely used functions are supported to aggregate data after grouping, including `avg`, `countDistinct`, `count`, `first`, `kurtosis`, `last`, `max`, `mean`, `min`, `sd`, `skewness`, `stddev_pop`, `stddev_samp`, `sumDistinct`, `sum`, `var_pop`, `var_samp`, `var`. See the [API doc for `mean`](http://spark.apache.org/docs/latest/api/R/mean.html) and other `agg_funcs` linked there. +A number of widely used functions are supported to aggregate data after grouping, including `avg`, `countDistinct`, `count`, `first`, `kurtosis`, `last`, `max`, `mean`, `min`, `sd`, `skewness`, `stddev_pop`, `stddev_samp`, `sumDistinct`, `sum`, `var_pop`, `var_samp`, `var`. See the [API doc for aggregate functions](https://spark.apache.org/docs/latest/api/R/column_aggregate_functions.html) linked there. For example we can compute a histogram of the number of cylinders in the `mtcars` dataset as shown below. @@ -935,7 +935,7 @@ perplexity #### Alternating Least Squares -`spark.als` learns latent factors in [collaborative filtering](https://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering) via [alternating least squares](http://dl.acm.org/citation.cfm?id=1608614). +`spark.als` learns latent factors in [collaborative filtering](https://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering) via [alternating least squares](https://dl.acm.org/citation.cfm?id=1608614). There are multiple options that can be configured in `spark.als`, including `rank`, `reg`, and `nonnegative`. For a complete list, refer to the help file. @@ -1042,7 +1042,7 @@ unlink(modelPath) ## Structured Streaming -SparkR supports the Structured Streaming API (experimental). +SparkR supports the Structured Streaming API. You can check the Structured Streaming Programming Guide for [an introduction](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#programming-model) to its programming model and basic concepts. @@ -1171,11 +1171,11 @@ env | map ## References -* [Spark Cluster Mode Overview](http://spark.apache.org/docs/latest/cluster-overview.html) +* [Spark Cluster Mode Overview](https://spark.apache.org/docs/latest/cluster-overview.html) -* [Submitting Spark Applications](http://spark.apache.org/docs/latest/submitting-applications.html) +* [Submitting Spark Applications](https://spark.apache.org/docs/latest/submitting-applications.html) -* [Machine Learning Library Guide (MLlib)](http://spark.apache.org/docs/latest/ml-guide.html) +* [Machine Learning Library Guide (MLlib)](https://spark.apache.org/docs/latest/ml-guide.html) * [SparkR: Scaling R Programs with Spark](https://people.csail.mit.edu/matei/papers/2016/sigmod_sparkr.pdf), Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, Xiangrui Meng, Reynold Xin, Ali Ghodsi, Michael Franklin, Ion Stoica, and Matei Zaharia. SIGMOD 2016. June 2016. diff --git a/assembly/pom.xml b/assembly/pom.xml index b3b4239771bc3..02bf39bcb96f3 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/bin/docker-image-tool.sh b/bin/docker-image-tool.sh new file mode 100755 index 0000000000000..0d0f564bb8b9b --- /dev/null +++ b/bin/docker-image-tool.sh @@ -0,0 +1,146 @@ +#!/usr/bin/env bash + +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# This script builds and pushes docker images when run from a release of Spark +# with Kubernetes support. + +function error { + echo "$@" 1>&2 + exit 1 +} + +if [ -z "${SPARK_HOME}" ]; then + SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" +fi +. "${SPARK_HOME}/bin/load-spark-env.sh" + +function image_ref { + local image="$1" + local add_repo="${2:-1}" + if [ $add_repo = 1 ] && [ -n "$REPO" ]; then + image="$REPO/$image" + fi + if [ -n "$TAG" ]; then + image="$image:$TAG" + fi + echo "$image" +} + +function build { + local BUILD_ARGS + local IMG_PATH + + if [ ! -f "$SPARK_HOME/RELEASE" ]; then + # Set image build arguments accordingly if this is a source repo and not a distribution archive. + IMG_PATH=resource-managers/kubernetes/docker/src/main/dockerfiles + BUILD_ARGS=( + --build-arg + img_path=$IMG_PATH + --build-arg + spark_jars=assembly/target/scala-$SPARK_SCALA_VERSION/jars + ) + else + # Not passed as an argument to docker, but used to validate the Spark directory. + IMG_PATH="kubernetes/dockerfiles" + BUILD_ARGS=() + fi + + if [ ! -d "$IMG_PATH" ]; then + error "Cannot find docker image. This script must be run from a runnable distribution of Apache Spark." + fi + + docker build "${BUILD_ARGS[@]}" \ + -t $(image_ref spark) \ + -f "$IMG_PATH/spark/Dockerfile" . +} + +function push { + docker push "$(image_ref spark)" +} + +function usage { + cat </dev/null; then + error "Cannot find minikube." + fi + eval $(minikube docker-env) + ;; + esac +done + +case "${@: -1}" in + build) + build + ;; + push) + if [ -z "$REPO" ]; then + usage + exit 1 + fi + push + ;; + *) + usage + exit 1 + ;; +esac diff --git a/bin/pyspark b/bin/pyspark index dd286277c1fc1..5d5affb1f97c3 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -25,14 +25,14 @@ source "${SPARK_HOME}"/bin/load-spark-env.sh export _SPARK_CMD_USAGE="Usage: ./bin/pyspark [options]" # In Spark 2.0, IPYTHON and IPYTHON_OPTS are removed and pyspark fails to launch if either option -# is set in the user's environment. Instead, users should set PYSPARK_DRIVER_PYTHON=ipython +# is set in the user's environment. Instead, users should set PYSPARK_DRIVER_PYTHON=ipython # to use IPython and set PYSPARK_DRIVER_PYTHON_OPTS to pass options when starting the Python driver # (e.g. PYSPARK_DRIVER_PYTHON_OPTS='notebook'). This supports full customization of the IPython # and executor Python executables. # Fail noisily if removed options are set if [[ -n "$IPYTHON" || -n "$IPYTHON_OPTS" ]]; then - echo "Error in pyspark startup:" + echo "Error in pyspark startup:" echo "IPYTHON and IPYTHON_OPTS are removed in Spark 2.0+. Remove these from the environment and set PYSPARK_DRIVER_PYTHON and PYSPARK_DRIVER_PYTHON_OPTS instead." exit 1 fi @@ -57,7 +57,7 @@ export PYSPARK_PYTHON # Add the PySpark classes to the Python path: export PYTHONPATH="${SPARK_HOME}/python/:$PYTHONPATH" -export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.6-src.zip:$PYTHONPATH" +export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.7-src.zip:$PYTHONPATH" # Load the PySpark shell.py script when ./pyspark is used interactively: export OLD_PYTHONSTARTUP="$PYTHONSTARTUP" diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd index 663670f2fddaf..15fa910c277b3 100644 --- a/bin/pyspark2.cmd +++ b/bin/pyspark2.cmd @@ -30,7 +30,7 @@ if "x%PYSPARK_DRIVER_PYTHON%"=="x" ( ) set PYTHONPATH=%SPARK_HOME%\python;%PYTHONPATH% -set PYTHONPATH=%SPARK_HOME%\python\lib\py4j-0.10.6-src.zip;%PYTHONPATH% +set PYTHONPATH=%SPARK_HOME%\python\lib\py4j-0.10.7-src.zip;%PYTHONPATH% set OLD_PYTHONSTARTUP=%PYTHONSTARTUP% set PYTHONSTARTUP=%SPARK_HOME%\python\pyspark\shell.py diff --git a/common/kvstore/pom.xml b/common/kvstore/pom.xml index cf93d41cd77cf..646fdfb23ef19 100644 --- a/common/kvstore/pom.xml +++ b/common/kvstore/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java b/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java index 4f9e10ca20066..0e491efac9181 100644 --- a/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java +++ b/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java @@ -83,6 +83,7 @@ public LevelDB(File path, KVStoreSerializer serializer) throws Exception { if (versionData != null) { long version = serializer.deserializeLong(versionData); if (version != STORE_VERSION) { + close(); throw new UnsupportedStoreVersionException(); } } else { diff --git a/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDBTypeInfo.java b/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDBTypeInfo.java index 232ee41dd0b1f..f4d359234cb9e 100644 --- a/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDBTypeInfo.java +++ b/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDBTypeInfo.java @@ -493,7 +493,7 @@ byte[] toKey(Object value, byte prefix) { byte[] key = new byte[bytes * 2 + 2]; long longValue = ((Number) value).longValue(); key[0] = prefix; - key[1] = longValue > 0 ? POSITIVE_MARKER : NEGATIVE_MARKER; + key[1] = longValue >= 0 ? POSITIVE_MARKER : NEGATIVE_MARKER; for (int i = 0; i < key.length - 2; i++) { int masked = (int) ((longValue >>> (4 * i)) & 0xF); diff --git a/common/kvstore/src/test/java/org/apache/spark/util/kvstore/DBIteratorSuite.java b/common/kvstore/src/test/java/org/apache/spark/util/kvstore/DBIteratorSuite.java index 9a81f86812cde..1e062437d1803 100644 --- a/common/kvstore/src/test/java/org/apache/spark/util/kvstore/DBIteratorSuite.java +++ b/common/kvstore/src/test/java/org/apache/spark/util/kvstore/DBIteratorSuite.java @@ -73,7 +73,9 @@ default BaseComparator reverse() { private static final BaseComparator NATURAL_ORDER = (t1, t2) -> t1.key.compareTo(t2.key); private static final BaseComparator REF_INDEX_ORDER = (t1, t2) -> t1.id.compareTo(t2.id); private static final BaseComparator COPY_INDEX_ORDER = (t1, t2) -> t1.name.compareTo(t2.name); - private static final BaseComparator NUMERIC_INDEX_ORDER = (t1, t2) -> t1.num - t2.num; + private static final BaseComparator NUMERIC_INDEX_ORDER = (t1, t2) -> { + return Integer.valueOf(t1.num).compareTo(t2.num); + }; private static final BaseComparator CHILD_INDEX_ORDER = (t1, t2) -> t1.child.compareTo(t2.child); /** @@ -112,7 +114,8 @@ public void setup() throws Exception { t.key = "key" + i; t.id = "id" + i; t.name = "name" + RND.nextInt(MAX_ENTRIES); - t.num = RND.nextInt(MAX_ENTRIES); + // Force one item to have an integer value of zero to test the fix for SPARK-23103. + t.num = (i != 0) ? (int) RND.nextLong() : 0; t.child = "child" + (i % MIN_ENTRIES); allEntries.add(t); } diff --git a/common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java b/common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java index 2b07d249d2022..b8123ac81d29a 100644 --- a/common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java +++ b/common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java @@ -21,6 +21,8 @@ import java.util.Arrays; import java.util.List; import java.util.NoSuchElementException; +import java.util.stream.Collectors; +import java.util.stream.StreamSupport; import org.apache.commons.io.FileUtils; import org.iq80.leveldb.DBIterator; @@ -74,11 +76,7 @@ public void testReopenAndVersionCheckDb() throws Exception { @Test public void testObjectWriteReadDelete() throws Exception { - CustomType1 t = new CustomType1(); - t.key = "key"; - t.id = "id"; - t.name = "name"; - t.child = "child"; + CustomType1 t = createCustomType1(1); try { db.read(CustomType1.class, t.key); @@ -106,17 +104,9 @@ public void testObjectWriteReadDelete() throws Exception { @Test public void testMultipleObjectWriteReadDelete() throws Exception { - CustomType1 t1 = new CustomType1(); - t1.key = "key1"; - t1.id = "id"; - t1.name = "name1"; - t1.child = "child1"; - - CustomType1 t2 = new CustomType1(); - t2.key = "key2"; - t2.id = "id"; - t2.name = "name2"; - t2.child = "child2"; + CustomType1 t1 = createCustomType1(1); + CustomType1 t2 = createCustomType1(2); + t2.id = t1.id; db.write(t1); db.write(t2); @@ -142,11 +132,7 @@ public void testMultipleObjectWriteReadDelete() throws Exception { @Test public void testMultipleTypesWriteReadDelete() throws Exception { - CustomType1 t1 = new CustomType1(); - t1.key = "1"; - t1.id = "id"; - t1.name = "name1"; - t1.child = "child1"; + CustomType1 t1 = createCustomType1(1); IntKeyType t2 = new IntKeyType(); t2.key = 2; @@ -188,10 +174,7 @@ public void testMultipleTypesWriteReadDelete() throws Exception { public void testMetadata() throws Exception { assertNull(db.getMetadata(CustomType1.class)); - CustomType1 t = new CustomType1(); - t.id = "id"; - t.name = "name"; - t.child = "child"; + CustomType1 t = createCustomType1(1); db.setMetadata(t); assertEquals(t, db.getMetadata(CustomType1.class)); @@ -202,11 +185,7 @@ public void testMetadata() throws Exception { @Test public void testUpdate() throws Exception { - CustomType1 t = new CustomType1(); - t.key = "key"; - t.id = "id"; - t.name = "name"; - t.child = "child"; + CustomType1 t = createCustomType1(1); db.write(t); @@ -222,13 +201,7 @@ public void testUpdate() throws Exception { @Test public void testSkip() throws Exception { for (int i = 0; i < 10; i++) { - CustomType1 t = new CustomType1(); - t.key = "key" + i; - t.id = "id" + i; - t.name = "name" + i; - t.child = "child" + i; - - db.write(t); + db.write(createCustomType1(i)); } KVStoreIterator it = db.view(CustomType1.class).closeableIterator(); @@ -240,6 +213,36 @@ public void testSkip() throws Exception { assertFalse(it.hasNext()); } + @Test + public void testNegativeIndexValues() throws Exception { + List expected = Arrays.asList(-100, -50, 0, 50, 100); + + expected.stream().forEach(i -> { + try { + db.write(createCustomType1(i)); + } catch (Exception e) { + throw new RuntimeException(e); + } + }); + + List results = StreamSupport + .stream(db.view(CustomType1.class).index("int").spliterator(), false) + .map(e -> e.num) + .collect(Collectors.toList()); + + assertEquals(expected, results); + } + + private CustomType1 createCustomType1(int i) { + CustomType1 t = new CustomType1(); + t.key = "key" + i; + t.id = "id" + i; + t.name = "name" + i; + t.num = i; + t.child = "child" + i; + return t; + } + private int countKeys(Class type) throws Exception { byte[] prefix = db.getTypeInfo(type).keyPrefix(); int count = 0; diff --git a/common/network-common/pom.xml b/common/network-common/pom.xml index 18cbdadd224ab..76c7dcf52203f 100644 --- a/common/network-common/pom.xml +++ b/common/network-common/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/common/network-common/src/main/java/org/apache/spark/network/buffer/FileSegmentManagedBuffer.java b/common/network-common/src/main/java/org/apache/spark/network/buffer/FileSegmentManagedBuffer.java index c20fab83c3460..8b8f9892847c3 100644 --- a/common/network-common/src/main/java/org/apache/spark/network/buffer/FileSegmentManagedBuffer.java +++ b/common/network-common/src/main/java/org/apache/spark/network/buffer/FileSegmentManagedBuffer.java @@ -24,6 +24,7 @@ import java.io.RandomAccessFile; import java.nio.ByteBuffer; import java.nio.channels.FileChannel; +import java.nio.file.StandardOpenOption; import com.google.common.base.Objects; import com.google.common.io.ByteStreams; @@ -132,7 +133,7 @@ public Object convertToNetty() throws IOException { if (conf.lazyFileDescriptor()) { return new DefaultFileRegion(file, offset, length); } else { - FileChannel fileChannel = new FileInputStream(file).getChannel(); + FileChannel fileChannel = FileChannel.open(file.toPath(), StandardOpenOption.READ); return new DefaultFileRegion(fileChannel, offset, length); } } diff --git a/common/network-common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java b/common/network-common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java index a5337656cbd84..e7b66a6f33a82 100644 --- a/common/network-common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java +++ b/common/network-common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java @@ -137,30 +137,15 @@ protected void deallocate() { } private int copyByteBuf(ByteBuf buf, WritableByteChannel target) throws IOException { - ByteBuffer buffer = buf.nioBuffer(); - int written = (buffer.remaining() <= NIO_BUFFER_LIMIT) ? - target.write(buffer) : writeNioBuffer(target, buffer); + // SPARK-24578: cap the sub-region's size of returned nio buffer to improve the performance + // for the case that the passed-in buffer has too many components. + int length = Math.min(buf.readableBytes(), NIO_BUFFER_LIMIT); + ByteBuffer buffer = buf.nioBuffer(buf.readerIndex(), length); + int written = target.write(buffer); buf.skipBytes(written); return written; } - private int writeNioBuffer( - WritableByteChannel writeCh, - ByteBuffer buf) throws IOException { - int originalLimit = buf.limit(); - int ret = 0; - - try { - int ioSize = Math.min(buf.remaining(), NIO_BUFFER_LIMIT); - buf.limit(buf.position() + ioSize); - ret = writeCh.write(buf); - } finally { - buf.limit(originalLimit); - } - - return ret; - } - @Override public MessageWithHeader touch(Object o) { super.touch(o); diff --git a/common/network-shuffle/pom.xml b/common/network-shuffle/pom.xml index 9968480ab7658..f2661fedd6f75 100644 --- a/common/network-shuffle/pom.xml +++ b/common/network-shuffle/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/common/network-shuffle/src/main/java/org/apache/spark/network/shuffle/OneForOneBlockFetcher.java b/common/network-shuffle/src/main/java/org/apache/spark/network/shuffle/OneForOneBlockFetcher.java index 9cac7d00cc6b6..0bc571874f07c 100644 --- a/common/network-shuffle/src/main/java/org/apache/spark/network/shuffle/OneForOneBlockFetcher.java +++ b/common/network-shuffle/src/main/java/org/apache/spark/network/shuffle/OneForOneBlockFetcher.java @@ -171,7 +171,9 @@ private class DownloadCallback implements StreamCallback { @Override public void onData(String streamId, ByteBuffer buf) throws IOException { - channel.write(buf); + while (buf.hasRemaining()) { + channel.write(buf); + } } @Override diff --git a/common/network-shuffle/src/main/java/org/apache/spark/network/shuffle/ShuffleIndexInformation.java b/common/network-shuffle/src/main/java/org/apache/spark/network/shuffle/ShuffleIndexInformation.java index eacf485344b76..386738ece51a6 100644 --- a/common/network-shuffle/src/main/java/org/apache/spark/network/shuffle/ShuffleIndexInformation.java +++ b/common/network-shuffle/src/main/java/org/apache/spark/network/shuffle/ShuffleIndexInformation.java @@ -19,10 +19,10 @@ import java.io.DataInputStream; import java.io.File; -import java.io.FileInputStream; import java.io.IOException; import java.nio.ByteBuffer; import java.nio.LongBuffer; +import java.nio.file.Files; /** * Keeps the index information for a particular map output @@ -39,7 +39,7 @@ public ShuffleIndexInformation(File indexFile) throws IOException { offsets = buffer.asLongBuffer(); DataInputStream dis = null; try { - dis = new DataInputStream(new FileInputStream(indexFile)); + dis = new DataInputStream(Files.newInputStream(indexFile.toPath())); dis.readFully(buffer.array()); } finally { if (dis != null) { diff --git a/common/network-yarn/pom.xml b/common/network-yarn/pom.xml index ec2db6e5bb88c..229d4667169d2 100644 --- a/common/network-yarn/pom.xml +++ b/common/network-yarn/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/common/sketch/pom.xml b/common/sketch/pom.xml index 2d59c71cc3757..febec1897c59c 100644 --- a/common/sketch/pom.xml +++ b/common/sketch/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/common/sketch/src/main/java/org/apache/spark/util/sketch/Murmur3_x86_32.java b/common/sketch/src/main/java/org/apache/spark/util/sketch/Murmur3_x86_32.java index a61ce4fb7241d..e83b331391e39 100644 --- a/common/sketch/src/main/java/org/apache/spark/util/sketch/Murmur3_x86_32.java +++ b/common/sketch/src/main/java/org/apache/spark/util/sketch/Murmur3_x86_32.java @@ -60,6 +60,8 @@ public static int hashUnsafeWords(Object base, long offset, int lengthInBytes, i } public static int hashUnsafeBytes(Object base, long offset, int lengthInBytes, int seed) { + // This is not compatible with original and another implementations. + // But remain it for backward compatibility for the components existing before 2.3. assert (lengthInBytes >= 0): "lengthInBytes cannot be negative"; int lengthAligned = lengthInBytes - lengthInBytes % 4; int h1 = hashBytesByInt(base, offset, lengthAligned, seed); @@ -71,6 +73,20 @@ public static int hashUnsafeBytes(Object base, long offset, int lengthInBytes, i return fmix(h1, lengthInBytes); } + public static int hashUnsafeBytes2(Object base, long offset, int lengthInBytes, int seed) { + // This is compatible with original and another implementations. + // Use this method for new components after Spark 2.3. + assert (lengthInBytes >= 0): "lengthInBytes cannot be negative"; + int lengthAligned = lengthInBytes - lengthInBytes % 4; + int h1 = hashBytesByInt(base, offset, lengthAligned, seed); + int k1 = 0; + for (int i = lengthAligned, shift = 0; i < lengthInBytes; i++, shift += 8) { + k1 ^= (Platform.getByte(base, offset + i) & 0xFF) << shift; + } + h1 ^= mixK1(k1); + return fmix(h1, lengthInBytes); + } + private static int hashBytesByInt(Object base, long offset, int lengthInBytes, int seed) { assert (lengthInBytes % 4 == 0); int h1 = seed; diff --git a/common/tags/pom.xml b/common/tags/pom.xml index f7e586ee777e1..4dec96f86d9c3 100644 --- a/common/tags/pom.xml +++ b/common/tags/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/common/unsafe/pom.xml b/common/unsafe/pom.xml index a3772a2620088..7187313c5c2f7 100644 --- a/common/unsafe/pom.xml +++ b/common/unsafe/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/common/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java b/common/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java index 5e7ee480cafd1..d239de6083ad0 100644 --- a/common/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java +++ b/common/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java @@ -60,6 +60,8 @@ public static int hashUnsafeWords(Object base, long offset, int lengthInBytes, i } public static int hashUnsafeBytes(Object base, long offset, int lengthInBytes, int seed) { + // This is not compatible with original and another implementations. + // But remain it for backward compatibility for the components existing before 2.3. assert (lengthInBytes >= 0): "lengthInBytes cannot be negative"; int lengthAligned = lengthInBytes - lengthInBytes % 4; int h1 = hashBytesByInt(base, offset, lengthAligned, seed); @@ -71,6 +73,20 @@ public static int hashUnsafeBytes(Object base, long offset, int lengthInBytes, i return fmix(h1, lengthInBytes); } + public static int hashUnsafeBytes2(Object base, long offset, int lengthInBytes, int seed) { + // This is compatible with original and another implementations. + // Use this method for new components after Spark 2.3. + assert (lengthInBytes >= 0): "lengthInBytes cannot be negative"; + int lengthAligned = lengthInBytes - lengthInBytes % 4; + int h1 = hashBytesByInt(base, offset, lengthAligned, seed); + int k1 = 0; + for (int i = lengthAligned, shift = 0; i < lengthInBytes; i++, shift += 8) { + k1 ^= (Platform.getByte(base, offset + i) & 0xFF) << shift; + } + h1 ^= mixK1(k1); + return fmix(h1, lengthInBytes); + } + private static int hashBytesByInt(Object base, long offset, int lengthInBytes, int seed) { assert (lengthInBytes % 4 == 0); int h1 = seed; diff --git a/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java b/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java index cc9cc429643ad..a9603c1aba051 100644 --- a/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java +++ b/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java @@ -31,8 +31,7 @@ public class HeapMemoryAllocator implements MemoryAllocator { @GuardedBy("this") - private final Map>> bufferPoolsBySize = - new HashMap<>(); + private final Map>> bufferPoolsBySize = new HashMap<>(); private static final int POOLING_THRESHOLD_BYTES = 1024 * 1024; @@ -49,13 +48,14 @@ private boolean shouldPool(long size) { public MemoryBlock allocate(long size) throws OutOfMemoryError { if (shouldPool(size)) { synchronized (this) { - final LinkedList> pool = bufferPoolsBySize.get(size); + final LinkedList> pool = bufferPoolsBySize.get(size); if (pool != null) { while (!pool.isEmpty()) { - final WeakReference blockReference = pool.pop(); - final MemoryBlock memory = blockReference.get(); - if (memory != null) { - assert (memory.size() == size); + final WeakReference arrayReference = pool.pop(); + final long[] array = arrayReference.get(); + if (array != null) { + assert (array.length * 8L >= size); + MemoryBlock memory = new MemoryBlock(array, Platform.LONG_ARRAY_OFFSET, size); if (MemoryAllocator.MEMORY_DEBUG_FILL_ENABLED) { memory.fill(MemoryAllocator.MEMORY_DEBUG_FILL_CLEAN_VALUE); } @@ -76,18 +76,36 @@ public MemoryBlock allocate(long size) throws OutOfMemoryError { @Override public void free(MemoryBlock memory) { + assert (memory.obj != null) : + "baseObject was null; are you trying to use the on-heap allocator to free off-heap memory?"; + assert (memory.pageNumber != MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER) : + "page has already been freed"; + assert ((memory.pageNumber == MemoryBlock.NO_PAGE_NUMBER) + || (memory.pageNumber == MemoryBlock.FREED_IN_TMM_PAGE_NUMBER)) : + "TMM-allocated pages must first be freed via TMM.freePage(), not directly in allocator " + + "free()"; + final long size = memory.size(); if (MemoryAllocator.MEMORY_DEBUG_FILL_ENABLED) { memory.fill(MemoryAllocator.MEMORY_DEBUG_FILL_FREED_VALUE); } + + // Mark the page as freed (so we can detect double-frees). + memory.pageNumber = MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER; + + // As an additional layer of defense against use-after-free bugs, we mutate the + // MemoryBlock to null out its reference to the long[] array. + long[] array = (long[]) memory.obj; + memory.setObjAndOffset(null, 0); + if (shouldPool(size)) { synchronized (this) { - LinkedList> pool = bufferPoolsBySize.get(size); + LinkedList> pool = bufferPoolsBySize.get(size); if (pool == null) { pool = new LinkedList<>(); bufferPoolsBySize.put(size, pool); } - pool.add(new WeakReference<>(memory)); + pool.add(new WeakReference<>(array)); } } else { // Do nothing diff --git a/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java b/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java index cd1d378bc1470..c333857358d30 100644 --- a/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java +++ b/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java @@ -26,6 +26,25 @@ */ public class MemoryBlock extends MemoryLocation { + /** Special `pageNumber` value for pages which were not allocated by TaskMemoryManagers */ + public static final int NO_PAGE_NUMBER = -1; + + /** + * Special `pageNumber` value for marking pages that have been freed in the TaskMemoryManager. + * We set `pageNumber` to this value in TaskMemoryManager.freePage() so that MemoryAllocator + * can detect if pages which were allocated by TaskMemoryManager have been freed in the TMM + * before being passed to MemoryAllocator.free() (it is an error to allocate a page in + * TaskMemoryManager and then directly free it in a MemoryAllocator without going through + * the TMM freePage() call). + */ + public static final int FREED_IN_TMM_PAGE_NUMBER = -2; + + /** + * Special `pageNumber` value for pages that have been freed by the MemoryAllocator. This allows + * us to detect double-frees. + */ + public static final int FREED_IN_ALLOCATOR_PAGE_NUMBER = -3; + private final long length; /** @@ -33,7 +52,7 @@ public class MemoryBlock extends MemoryLocation { * TaskMemoryManager. This field is public so that it can be modified by the TaskMemoryManager, * which lives in a different package. */ - public int pageNumber = -1; + public int pageNumber = NO_PAGE_NUMBER; public MemoryBlock(@Nullable Object obj, long offset, long length) { super(obj, offset); diff --git a/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java b/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java index 55bcdf1ed7b06..4368fb615ba1e 100644 --- a/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java +++ b/common/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java @@ -38,9 +38,20 @@ public MemoryBlock allocate(long size) throws OutOfMemoryError { public void free(MemoryBlock memory) { assert (memory.obj == null) : "baseObject not null; are you trying to use the off-heap allocator to free on-heap memory?"; + assert (memory.pageNumber != MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER) : + "page has already been freed"; + assert ((memory.pageNumber == MemoryBlock.NO_PAGE_NUMBER) + || (memory.pageNumber == MemoryBlock.FREED_IN_TMM_PAGE_NUMBER)) : + "TMM-allocated pages must be freed via TMM.freePage(), not directly in allocator free()"; + if (MemoryAllocator.MEMORY_DEBUG_FILL_ENABLED) { memory.fill(MemoryAllocator.MEMORY_DEBUG_FILL_FREED_VALUE); } Platform.freeMemory(memory.offset); + // As an additional layer of defense against use-after-free bugs, we mutate the + // MemoryBlock to reset its pointer. + memory.offset = 0; + // Mark the page as freed (so we can detect double-frees). + memory.pageNumber = MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER; } } diff --git a/common/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/common/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java index b0d0c44823e68..5d468aed42337 100644 --- a/common/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java +++ b/common/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java @@ -57,12 +57,43 @@ public final class UTF8String implements Comparable, Externalizable, public Object getBaseObject() { return base; } public long getBaseOffset() { return offset; } - private static int[] bytesOfCodePointInUTF8 = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, - 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, - 4, 4, 4, 4, 4, 4, 4, 4, - 5, 5, 5, 5, - 6, 6}; + /** + * A char in UTF-8 encoding can take 1-4 bytes depending on the first byte which + * indicates the size of the char. See Unicode standard in page 126, Table 3-6: + * http://www.unicode.org/versions/Unicode10.0.0/UnicodeStandard-10.0.pdf + * + * Binary Hex Comments + * 0xxxxxxx 0x00..0x7F Only byte of a 1-byte character encoding + * 10xxxxxx 0x80..0xBF Continuation bytes (1-3 continuation bytes) + * 110xxxxx 0xC0..0xDF First byte of a 2-byte character encoding + * 1110xxxx 0xE0..0xEF First byte of a 3-byte character encoding + * 11110xxx 0xF0..0xF7 First byte of a 4-byte character encoding + * + * As a consequence of the well-formedness conditions specified in + * Table 3-7 (page 126), the following byte values are disallowed in UTF-8: + * C0–C1, F5–FF. + */ + private static byte[] bytesOfCodePointInUTF8 = { + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // 0x00..0x0F + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // 0x10..0x1F + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // 0x20..0x2F + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // 0x30..0x3F + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // 0x40..0x4F + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // 0x50..0x5F + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // 0x60..0x6F + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // 0x70..0x7F + // Continuation bytes cannot appear as the first byte + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 0x80..0x8F + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 0x90..0x9F + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 0xA0..0xAF + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 0xB0..0xBF + 0, 0, // 0xC0..0xC1 - disallowed in UTF-8 + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, // 0xC2..0xCF + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, // 0xD0..0xDF + 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, // 0xE0..0xEF + 4, 4, 4, 4, 4, // 0xF0..0xF4 + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 // 0xF5..0xFF - disallowed in UTF-8 + }; private static final boolean IS_LITTLE_ENDIAN = ByteOrder.nativeOrder() == ByteOrder.LITTLE_ENDIAN; @@ -187,8 +218,9 @@ public void writeTo(OutputStream out) throws IOException { * @param b The first byte of a code point */ private static int numBytesForFirstByte(final byte b) { - final int offset = (b & 0xFF) - 192; - return (offset >= 0) ? bytesOfCodePointInUTF8[offset] : 1; + final int offset = b & 0xFF; + byte numBytes = bytesOfCodePointInUTF8[offset]; + return (numBytes == 0) ? 1: numBytes; // Skip the first byte disallowed in UTF-8 } /** diff --git a/common/unsafe/src/test/java/org/apache/spark/unsafe/PlatformUtilSuite.java b/common/unsafe/src/test/java/org/apache/spark/unsafe/PlatformUtilSuite.java index 4b141339ec816..62854837b05ed 100644 --- a/common/unsafe/src/test/java/org/apache/spark/unsafe/PlatformUtilSuite.java +++ b/common/unsafe/src/test/java/org/apache/spark/unsafe/PlatformUtilSuite.java @@ -62,6 +62,52 @@ public void overlappingCopyMemory() { } } + @Test + public void onHeapMemoryAllocatorPoolingReUsesLongArrays() { + MemoryBlock block1 = MemoryAllocator.HEAP.allocate(1024 * 1024); + Object baseObject1 = block1.getBaseObject(); + MemoryAllocator.HEAP.free(block1); + MemoryBlock block2 = MemoryAllocator.HEAP.allocate(1024 * 1024); + Object baseObject2 = block2.getBaseObject(); + Assert.assertSame(baseObject1, baseObject2); + MemoryAllocator.HEAP.free(block2); + } + + @Test + public void freeingOnHeapMemoryBlockResetsBaseObjectAndOffset() { + MemoryBlock block = MemoryAllocator.HEAP.allocate(1024); + Assert.assertNotNull(block.getBaseObject()); + MemoryAllocator.HEAP.free(block); + Assert.assertNull(block.getBaseObject()); + Assert.assertEquals(0, block.getBaseOffset()); + Assert.assertEquals(MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER, block.pageNumber); + } + + @Test + public void freeingOffHeapMemoryBlockResetsOffset() { + MemoryBlock block = MemoryAllocator.UNSAFE.allocate(1024); + Assert.assertNull(block.getBaseObject()); + Assert.assertNotEquals(0, block.getBaseOffset()); + MemoryAllocator.UNSAFE.free(block); + Assert.assertNull(block.getBaseObject()); + Assert.assertEquals(0, block.getBaseOffset()); + Assert.assertEquals(MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER, block.pageNumber); + } + + @Test(expected = AssertionError.class) + public void onHeapMemoryAllocatorThrowsAssertionErrorOnDoubleFree() { + MemoryBlock block = MemoryAllocator.HEAP.allocate(1024); + MemoryAllocator.HEAP.free(block); + MemoryAllocator.HEAP.free(block); + } + + @Test(expected = AssertionError.class) + public void offHeapMemoryAllocatorThrowsAssertionErrorOnDoubleFree() { + MemoryBlock block = MemoryAllocator.UNSAFE.allocate(1024); + MemoryAllocator.UNSAFE.free(block); + MemoryAllocator.UNSAFE.free(block); + } + @Test public void memoryDebugFillEnabledInTest() { Assert.assertTrue(MemoryAllocator.MEMORY_DEBUG_FILL_ENABLED); @@ -71,9 +117,11 @@ public void memoryDebugFillEnabledInTest() { MemoryAllocator.MEMORY_DEBUG_FILL_CLEAN_VALUE); MemoryBlock onheap1 = MemoryAllocator.HEAP.allocate(1024 * 1024); + Object onheap1BaseObject = onheap1.getBaseObject(); + long onheap1BaseOffset = onheap1.getBaseOffset(); MemoryAllocator.HEAP.free(onheap1); Assert.assertEquals( - Platform.getByte(onheap1.getBaseObject(), onheap1.getBaseOffset()), + Platform.getByte(onheap1BaseObject, onheap1BaseOffset), MemoryAllocator.MEMORY_DEBUG_FILL_FREED_VALUE); MemoryBlock onheap2 = MemoryAllocator.HEAP.allocate(1024 * 1024); Assert.assertEquals( diff --git a/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java b/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java index e759cb33b3e6a..6348a73bf3895 100644 --- a/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java +++ b/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java @@ -22,6 +22,8 @@ import java.util.Random; import java.util.Set; +import scala.util.hashing.MurmurHash3$; + import org.apache.spark.unsafe.Platform; import org.junit.Assert; import org.junit.Test; @@ -51,6 +53,23 @@ public void testKnownLongInputs() { Assert.assertEquals(-2106506049, hasher.hashLong(Long.MAX_VALUE)); } + // SPARK-23381 Check whether the hash of the byte array is the same as another implementations + @Test + public void testKnownBytesInputs() { + byte[] test = "test".getBytes(StandardCharsets.UTF_8); + Assert.assertEquals(MurmurHash3$.MODULE$.bytesHash(test, 0), + Murmur3_x86_32.hashUnsafeBytes2(test, Platform.BYTE_ARRAY_OFFSET, test.length, 0)); + byte[] test1 = "test1".getBytes(StandardCharsets.UTF_8); + Assert.assertEquals(MurmurHash3$.MODULE$.bytesHash(test1, 0), + Murmur3_x86_32.hashUnsafeBytes2(test1, Platform.BYTE_ARRAY_OFFSET, test1.length, 0)); + byte[] te = "te".getBytes(StandardCharsets.UTF_8); + Assert.assertEquals(MurmurHash3$.MODULE$.bytesHash(te, 0), + Murmur3_x86_32.hashUnsafeBytes2(te, Platform.BYTE_ARRAY_OFFSET, te.length, 0)); + byte[] tes = "tes".getBytes(StandardCharsets.UTF_8); + Assert.assertEquals(MurmurHash3$.MODULE$.bytesHash(tes, 0), + Murmur3_x86_32.hashUnsafeBytes2(tes, Platform.BYTE_ARRAY_OFFSET, tes.length, 0)); + } + @Test public void randomizedStressTest() { int size = 65536; diff --git a/common/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/common/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java index 9b303fa5bc6c5..7c34d419574ef 100644 --- a/common/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java +++ b/common/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java @@ -58,8 +58,12 @@ private static void checkBasic(String str, int len) { @Test public void basicTest() { checkBasic("", 0); - checkBasic("hello", 5); + checkBasic("¡", 1); // 2 bytes char + checkBasic("ку", 2); // 2 * 2 bytes chars + checkBasic("hello", 5); // 5 * 1 byte chars checkBasic("大 千 世 界", 7); + checkBasic("︽﹋％", 3); // 3 * 3 bytes chars + checkBasic("\uD83E\uDD19", 1); // 4 bytes char } @Test @@ -791,4 +795,21 @@ public void trimRightWithTrimString() { assertEquals(fromString("头"), fromString("头a???/").trimRight(fromString("数?/*&^%a"))); assertEquals(fromString("头"), fromString("头数b数数 [").trimRight(fromString(" []数b"))); } + + @Test + public void skipWrongFirstByte() { + int[] wrongFirstBytes = { + 0x80, 0x9F, 0xBF, // Skip Continuation bytes + 0xC0, 0xC2, // 0xC0..0xC1 - disallowed in UTF-8 + // 0xF5..0xFF - disallowed in UTF-8 + 0xF5, 0xF6, 0xF7, 0xF8, 0xF9, + 0xFA, 0xFB, 0xFC, 0xFD, 0xFE, 0xFF + }; + byte[] c = new byte[1]; + + for (int i = 0; i < wrongFirstBytes.length; ++i) { + c[0] = (byte)wrongFirstBytes[i]; + assertEquals(fromBytes(c).numChars(), 1); + } + } } diff --git a/core/pom.xml b/core/pom.xml index 0a5bd958fc9c5..a9cc91f87566b 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml @@ -344,7 +344,7 @@ net.sf.py4j py4j - 0.10.6 + 0.10.7 org.apache.spark diff --git a/core/src/main/java/org/apache/spark/memory/TaskMemoryManager.java b/core/src/main/java/org/apache/spark/memory/TaskMemoryManager.java index e8d3730daa7a4..632d718062212 100644 --- a/core/src/main/java/org/apache/spark/memory/TaskMemoryManager.java +++ b/core/src/main/java/org/apache/spark/memory/TaskMemoryManager.java @@ -321,8 +321,12 @@ public MemoryBlock allocatePage(long size, MemoryConsumer consumer) { * Free a block of memory allocated via {@link TaskMemoryManager#allocatePage}. */ public void freePage(MemoryBlock page, MemoryConsumer consumer) { - assert (page.pageNumber != -1) : + assert (page.pageNumber != MemoryBlock.NO_PAGE_NUMBER) : "Called freePage() on memory that wasn't allocated with allocatePage()"; + assert (page.pageNumber != MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER) : + "Called freePage() on a memory block that has already been freed"; + assert (page.pageNumber != MemoryBlock.FREED_IN_TMM_PAGE_NUMBER) : + "Called freePage() on a memory block that has already been freed"; assert(allocatedPages.get(page.pageNumber)); pageTable[page.pageNumber] = null; synchronized (this) { @@ -332,6 +336,10 @@ public void freePage(MemoryBlock page, MemoryConsumer consumer) { logger.trace("Freed page number {} ({} bytes)", page.pageNumber, page.size()); } long pageSize = page.size(); + // Clear the page number before passing the block to the MemoryAllocator's free(). + // Doing this allows the MemoryAllocator to detect when a TaskMemoryManager-managed + // page has been inappropriately directly freed without calling TMM.freePage(). + page.pageNumber = MemoryBlock.FREED_IN_TMM_PAGE_NUMBER; memoryManager.tungstenMemoryAllocator().free(page); releaseExecutionMemory(pageSize, consumer); } @@ -358,7 +366,7 @@ public long encodePageNumberAndOffset(MemoryBlock page, long offsetInPage) { @VisibleForTesting public static long encodePageNumberAndOffset(int pageNumber, long offsetInPage) { - assert (pageNumber != -1) : "encodePageNumberAndOffset called with invalid page"; + assert (pageNumber >= 0) : "encodePageNumberAndOffset called with invalid page"; return (((long) pageNumber) << OFFSET_BITS) | (offsetInPage & MASK_LONG_LOWER_51_BITS); } @@ -424,6 +432,7 @@ public long cleanUpAllAllocatedMemory() { for (MemoryBlock page : pageTable) { if (page != null) { logger.debug("unreleased page: " + page + " in task " + taskAttemptId); + page.pageNumber = MemoryBlock.FREED_IN_TMM_PAGE_NUMBER; memoryManager.tungstenMemoryAllocator().free(page); } } diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java index 5f0045507aaab..9a767dd739b91 100644 --- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java +++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java @@ -703,7 +703,7 @@ public boolean append(Object kbase, long koff, int klen, Object vbase, long voff // must be stored in the same memory page. // (8 byte key length) (key) (value) (8 byte pointer to next value) int uaoSize = UnsafeAlignedOffset.getUaoSize(); - final long recordLength = (2 * uaoSize) + klen + vlen + 8; + final long recordLength = (2L * uaoSize) + klen + vlen + 8; if (currentPage == null || currentPage.size() - pageCursor < recordLength) { if (!acquireNewPage(recordLength + uaoSize)) { return false; diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordComparator.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordComparator.java index 09e4258792204..02b5de8e128c9 100644 --- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordComparator.java +++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordComparator.java @@ -32,6 +32,8 @@ public abstract class RecordComparator { public abstract int compare( Object leftBaseObject, long leftBaseOffset, + int leftBaseLength, Object rightBaseObject, - long rightBaseOffset); + long rightBaseOffset, + int rightBaseLength); } diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java index 951d076420ee6..b3c27d83da172 100644 --- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java +++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java @@ -62,12 +62,13 @@ public int compare(RecordPointerAndKeyPrefix r1, RecordPointerAndKeyPrefix r2) { int uaoSize = UnsafeAlignedOffset.getUaoSize(); if (prefixComparisonResult == 0) { final Object baseObject1 = memoryManager.getPage(r1.recordPointer); - // skip length final long baseOffset1 = memoryManager.getOffsetInPage(r1.recordPointer) + uaoSize; + final int baseLength1 = UnsafeAlignedOffset.getSize(baseObject1, baseOffset1 - uaoSize); final Object baseObject2 = memoryManager.getPage(r2.recordPointer); - // skip length final long baseOffset2 = memoryManager.getOffsetInPage(r2.recordPointer) + uaoSize; - return recordComparator.compare(baseObject1, baseOffset1, baseObject2, baseOffset2); + final int baseLength2 = UnsafeAlignedOffset.getSize(baseObject2, baseOffset2 - uaoSize); + return recordComparator.compare(baseObject1, baseOffset1, baseLength1, baseObject2, + baseOffset2, baseLength2); } else { return prefixComparisonResult; } diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java index cf4dfde86ca91..ff0dcc259a4ad 100644 --- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java +++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java @@ -35,8 +35,8 @@ final class UnsafeSorterSpillMerger { prefixComparator.compare(left.getKeyPrefix(), right.getKeyPrefix()); if (prefixComparisonResult == 0) { return recordComparator.compare( - left.getBaseObject(), left.getBaseOffset(), - right.getBaseObject(), right.getBaseOffset()); + left.getBaseObject(), left.getBaseOffset(), left.getRecordLength(), + right.getBaseObject(), right.getBaseOffset(), right.getRecordLength()); } else { return prefixComparisonResult; } diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java index e2f48e5508af6..2c53c8d809d2e 100644 --- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java +++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java @@ -76,8 +76,10 @@ public UnsafeSorterSpillReader( SparkEnv.get() == null ? 0.5 : SparkEnv.get().conf().getDouble("spark.unsafe.sorter.spill.read.ahead.fraction", 0.5); + // SPARK-23310: Disable read-ahead input stream, because it is causing lock contention and perf + // regression for TPC-DS queries. final boolean readAheadEnabled = SparkEnv.get() != null && - SparkEnv.get().conf().getBoolean("spark.unsafe.sorter.spill.read.ahead.enabled", true); + SparkEnv.get().conf().getBoolean("spark.unsafe.sorter.spill.read.ahead.enabled", false); final InputStream bs = new NioBufferedFileInputStream(file, (int) bufferSizeBytes); diff --git a/core/src/main/resources/org/apache/spark/ui/static/historypage.js b/core/src/main/resources/org/apache/spark/ui/static/historypage.js index 2cde66b081a1c..16d59bedf1fef 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/historypage.js +++ b/core/src/main/resources/org/apache/spark/ui/static/historypage.js @@ -108,7 +108,7 @@ $(document).ready(function() { requestedIncomplete = getParameterByName("showIncomplete", searchString); requestedIncomplete = (requestedIncomplete == "true" ? true : false); - $.getJSON("api/v1/applications?limit=" + appLimit, function(response,status,jqXHR) { + $.getJSON(uiRoot + "/api/v1/applications?limit=" + appLimit, function(response,status,jqXHR) { var array = []; var hasMultipleAttempts = false; for (i in response) { @@ -146,7 +146,7 @@ $(document).ready(function() { "showCompletedColumns": !requestedIncomplete, } - $.get("static/historypage-template.html", function(template) { + $.get(uiRoot + "/static/historypage-template.html", function(template) { var sibling = historySummary.prev(); historySummary.detach(); var apps = $(Mustache.render($(template).filter("#history-summary-template").html(),data)); diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala index 9112d93a86b2a..63d87b4cd385c 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala @@ -55,18 +55,18 @@ private[spark] trait ExecutorAllocationClient { /** * Request that the cluster manager kill the specified executors. * - * When asking the executor to be replaced, the executor loss is considered a failure, and - * killed tasks that are running on the executor will count towards the failure limits. If no - * replacement is being requested, then the tasks will not count towards the limit. - * * @param executorIds identifiers of executors to kill - * @param replace whether to replace the killed executors with new ones, default false + * @param adjustTargetNumExecutors whether the target number of executors will be adjusted down + * after these executors have been killed + * @param countFailures if there are tasks running on the executors when they are killed, whether + * to count those failures toward task failure limits * @param force whether to force kill busy executors, default false * @return the ids of the executors acknowledged by the cluster manager to be removed. */ def killExecutors( executorIds: Seq[String], - replace: Boolean = false, + adjustTargetNumExecutors: Boolean, + countFailures: Boolean, force: Boolean = false): Seq[String] /** @@ -81,7 +81,8 @@ private[spark] trait ExecutorAllocationClient { * @return whether the request is acknowledged by the cluster manager. */ def killExecutor(executorId: String): Boolean = { - val killedExecutors = killExecutors(Seq(executorId)) + val killedExecutors = killExecutors(Seq(executorId), adjustTargetNumExecutors = true, + countFailures = false) killedExecutors.nonEmpty && killedExecutors(0).equals(executorId) } } diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala index 2e00dc8b49dd5..189d91333c045 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala @@ -29,6 +29,7 @@ import org.apache.spark.internal.Logging import org.apache.spark.internal.config.{DYN_ALLOCATION_MAX_EXECUTORS, DYN_ALLOCATION_MIN_EXECUTORS} import org.apache.spark.metrics.source.Source import org.apache.spark.scheduler._ +import org.apache.spark.storage.BlockManagerMaster import org.apache.spark.util.{Clock, SystemClock, ThreadUtils, Utils} /** @@ -81,7 +82,8 @@ import org.apache.spark.util.{Clock, SystemClock, ThreadUtils, Utils} private[spark] class ExecutorAllocationManager( client: ExecutorAllocationClient, listenerBus: LiveListenerBus, - conf: SparkConf) + conf: SparkConf, + blockManagerMaster: BlockManagerMaster) extends Logging { allocationManager => @@ -151,7 +153,7 @@ private[spark] class ExecutorAllocationManager( private var clock: Clock = new SystemClock() // Listener for Spark events that impact the allocation policy - private val listener = new ExecutorAllocationListener + val listener = new ExecutorAllocationListener // Executor that handles the scheduling task. private val executor = @@ -195,8 +197,11 @@ private[spark] class ExecutorAllocationManager( throw new SparkException( "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout must be > 0!") } - if (executorIdleTimeoutS <= 0) { - throw new SparkException("spark.dynamicAllocation.executorIdleTimeout must be > 0!") + if (executorIdleTimeoutS < 0) { + throw new SparkException("spark.dynamicAllocation.executorIdleTimeout must be >= 0!") + } + if (cachedExecutorIdleTimeoutS < 0) { + throw new SparkException("spark.dynamicAllocation.cachedExecutorIdleTimeout must be >= 0!") } // Require external shuffle service for dynamic allocation // Otherwise, we may lose shuffle files when killing executors @@ -331,6 +336,11 @@ private[spark] class ExecutorAllocationManager( // If the new target has not changed, avoid sending a message to the cluster manager if (numExecutorsTarget < oldNumExecutorsTarget) { + // We lower the target number of executors but don't actively kill any yet. Killing is + // controlled separately by an idle timeout. It's still helpful to reduce the target number + // in case an executor just happens to get lost (eg., bad hardware, or the cluster manager + // preempts it) -- in that case, there is no point in trying to immediately get a new + // executor, since we wouldn't even use it yet. client.requestTotalExecutors(numExecutorsTarget, localityAwareTasks, hostToLocalTaskCount) logDebug(s"Lowering target number of executors to $numExecutorsTarget (previously " + s"$oldNumExecutorsTarget) because not all requested executors are actually needed") @@ -452,7 +462,10 @@ private[spark] class ExecutorAllocationManager( val executorsRemoved = if (testing) { executorIdsToBeRemoved } else { - client.killExecutors(executorIdsToBeRemoved) + // We don't want to change our target number of executors, because we already did that + // when the task backlog decreased. + client.killExecutors(executorIdsToBeRemoved, adjustTargetNumExecutors = false, + countFailures = false, force = false) } // [SPARK-21834] killExecutors api reduces the target number of executors. // So we need to update the target with desired value. @@ -572,7 +585,7 @@ private[spark] class ExecutorAllocationManager( // Note that it is not necessary to query the executors since all the cached // blocks we are concerned with are reported to the driver. Note that this // does not include broadcast blocks. - val hasCachedBlocks = SparkEnv.get.blockManager.master.hasCachedBlocks(executorId) + val hasCachedBlocks = blockManagerMaster.hasCachedBlocks(executorId) val now = clock.getTimeMillis() val timeout = { if (hasCachedBlocks) { @@ -607,7 +620,7 @@ private[spark] class ExecutorAllocationManager( * This class is intentionally conservative in its assumptions about the relative ordering * and consistency of events returned by the listener. */ - private class ExecutorAllocationListener extends SparkListener { + private[spark] class ExecutorAllocationListener extends SparkListener { private val stageIdToNumTasks = new mutable.HashMap[Int, Int] // Number of running tasks per stage including speculative tasks. diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala index 437bbaae1968b..c940cb25d478b 100644 --- a/core/src/main/scala/org/apache/spark/Partitioner.scala +++ b/core/src/main/scala/org/apache/spark/Partitioner.scala @@ -43,17 +43,19 @@ object Partitioner { /** * Choose a partitioner to use for a cogroup-like operation between a number of RDDs. * - * If any of the RDDs already has a partitioner, and the number of partitions of the - * partitioner is either greater than or is less than and within a single order of - * magnitude of the max number of upstream partitions, choose that one. + * If spark.default.parallelism is set, we'll use the value of SparkContext defaultParallelism + * as the default partitions number, otherwise we'll use the max number of upstream partitions. * - * Otherwise, we use a default HashPartitioner. For the number of partitions, if - * spark.default.parallelism is set, then we'll use the value from SparkContext - * defaultParallelism, otherwise we'll use the max number of upstream partitions. + * When available, we choose the partitioner from rdds with maximum number of partitions. If this + * partitioner is eligible (number of partitions within an order of maximum number of partitions + * in rdds), or has partition number higher than default partitions number - we use this + * partitioner. * - * Unless spark.default.parallelism is set, the number of partitions will be the - * same as the number of partitions in the largest upstream RDD, as this should - * be least likely to cause out-of-memory errors. + * Otherwise, we'll use a new HashPartitioner with the default partitions number. + * + * Unless spark.default.parallelism is set, the number of partitions will be the same as the + * number of partitions in the largest upstream RDD, as this should be least likely to cause + * out-of-memory errors. * * We use two method parameters (rdd, others) to enforce callers passing at least 1 RDD. */ @@ -67,31 +69,32 @@ object Partitioner { None } - if (isEligiblePartitioner(hasMaxPartitioner, rdds)) { + val defaultNumPartitions = if (rdd.context.conf.contains("spark.default.parallelism")) { + rdd.context.defaultParallelism + } else { + rdds.map(_.partitions.length).max + } + + // If the existing max partitioner is an eligible one, or its partitions number is larger + // than the default number of partitions, use the existing partitioner. + if (hasMaxPartitioner.nonEmpty && (isEligiblePartitioner(hasMaxPartitioner.get, rdds) || + defaultNumPartitions < hasMaxPartitioner.get.getNumPartitions)) { hasMaxPartitioner.get.partitioner.get } else { - if (rdd.context.conf.contains("spark.default.parallelism")) { - new HashPartitioner(rdd.context.defaultParallelism) - } else { - new HashPartitioner(rdds.map(_.partitions.length).max) - } + new HashPartitioner(defaultNumPartitions) } } /** - * Returns true if the number of partitions of the RDD is either greater - * than or is less than and within a single order of magnitude of the - * max number of upstream partitions; - * otherwise, returns false + * Returns true if the number of partitions of the RDD is either greater than or is less than and + * within a single order of magnitude of the max number of upstream partitions, otherwise returns + * false. */ private def isEligiblePartitioner( - hasMaxPartitioner: Option[RDD[_]], + hasMaxPartitioner: RDD[_], rdds: Seq[RDD[_]]): Boolean = { - if (hasMaxPartitioner.isEmpty) { - return false - } val maxPartitions = rdds.map(_.partitions.length).max - log10(maxPartitions) - log10(hasMaxPartitioner.get.getNumPartitions) < 1 + log10(maxPartitions) - log10(hasMaxPartitioner.getNumPartitions) < 1 } } diff --git a/core/src/main/scala/org/apache/spark/SSLOptions.scala b/core/src/main/scala/org/apache/spark/SSLOptions.scala index 477b01968c6ef..04c38f12acc78 100644 --- a/core/src/main/scala/org/apache/spark/SSLOptions.scala +++ b/core/src/main/scala/org/apache/spark/SSLOptions.scala @@ -128,7 +128,7 @@ private[spark] case class SSLOptions( } /** Returns a string representation of this SSLOptions with all the passwords masked. */ - override def toString: String = s"SSLOptions{enabled=$enabled, " + + override def toString: String = s"SSLOptions{enabled=$enabled, port=$port, " + s"keyStore=$keyStore, keyStorePassword=${keyStorePassword.map(_ => "xxx")}, " + s"trustStore=$trustStore, trustStorePassword=${trustStorePassword.map(_ => "xxx")}, " + s"protocol=$protocol, enabledAlgorithms=$enabledAlgorithms}" @@ -142,6 +142,7 @@ private[spark] object SSLOptions extends Logging { * * The following settings are allowed: * $ - `[ns].enabled` - `true` or `false`, to enable or disable SSL respectively + * $ - `[ns].port` - the port where to bind the SSL server * $ - `[ns].keyStore` - a path to the key-store file; can be relative to the current directory * $ - `[ns].keyStorePassword` - a password to the key-store file * $ - `[ns].keyPassword` - a password to the private key diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala index 4c1dbe3ffb4ad..00cebc4e6bcbe 100644 --- a/core/src/main/scala/org/apache/spark/SecurityManager.scala +++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala @@ -17,14 +17,12 @@ package org.apache.spark -import java.lang.{Byte => JByte} import java.net.{Authenticator, PasswordAuthentication} import java.nio.charset.StandardCharsets.UTF_8 -import java.security.{KeyStore, SecureRandom} +import java.security.KeyStore import java.security.cert.X509Certificate import javax.net.ssl._ -import com.google.common.hash.HashCodes import com.google.common.io.Files import org.apache.hadoop.io.Text import org.apache.hadoop.security.{Credentials, UserGroupInformation} @@ -227,6 +225,7 @@ private[spark] class SecurityManager( setViewAclsGroups(sparkConf.get("spark.ui.view.acls.groups", "")); setModifyAclsGroups(sparkConf.get("spark.modify.acls.groups", "")); + private var secretKey: String = _ logInfo("SecurityManager: authentication " + (if (authOn) "enabled" else "disabled") + "; ui acls " + (if (aclsOn) "enabled" else "disabled") + "; users with view permissions: " + viewAcls.toString() + @@ -504,6 +503,12 @@ private[spark] class SecurityManager( val creds = UserGroupInformation.getCurrentUser().getCredentials() Option(creds.getSecretKey(SECRET_LOOKUP_KEY)) .map { bytes => new String(bytes, UTF_8) } + // Secret key may not be found in current UGI's credentials. + // This happens when UGI is refreshed in the driver side by UGI's loginFromKeytab but not + // copy secret key from original UGI to the new one. This exists in ThriftServer's Hive + // logic. So as a workaround, storing secret key in a local variable to make it visible + // in different context. + .orElse(Option(secretKey)) .orElse(Option(sparkConf.getenv(ENV_AUTH_SECRET))) .orElse(sparkConf.getOption(SPARK_AUTH_SECRET_CONF)) .getOrElse { @@ -535,13 +540,9 @@ private[spark] class SecurityManager( return } - val rnd = new SecureRandom() - val length = sparkConf.getInt("spark.authenticate.secretBitLength", 256) / JByte.SIZE - val secretBytes = new Array[Byte](length) - rnd.nextBytes(secretBytes) - + secretKey = Utils.createSecret(sparkConf) val creds = new Credentials() - creds.addSecretKey(SECRET_LOOKUP_KEY, secretBytes) + creds.addSecretKey(SECRET_LOOKUP_KEY, secretKey.getBytes(UTF_8)) UserGroupInformation.getCurrentUser().addCredentials(creds) } diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index d77303e6fdf8b..f53b2bed74c6e 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -640,9 +640,9 @@ private[spark] object SparkConf extends Logging { translation = s => s"${s.toLong * 10}s")), "spark.reducer.maxSizeInFlight" -> Seq( AlternateConfig("spark.reducer.maxMbInFlight", "1.4")), - "spark.kryoserializer.buffer" -> - Seq(AlternateConfig("spark.kryoserializer.buffer.mb", "1.4", - translation = s => s"${(s.toDouble * 1000).toInt}k")), + "spark.kryoserializer.buffer" -> Seq( + AlternateConfig("spark.kryoserializer.buffer.mb", "1.4", + translation = s => s"${(s.toDouble * 1000).toInt}k")), "spark.kryoserializer.buffer.max" -> Seq( AlternateConfig("spark.kryoserializer.buffer.max.mb", "1.4")), "spark.shuffle.file.buffer" -> Seq( diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 31f3cb9dfa0ae..f5b560c9e345f 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -533,7 +533,8 @@ class SparkContext(config: SparkConf) extends Logging { schedulerBackend match { case b: ExecutorAllocationClient => Some(new ExecutorAllocationManager( - schedulerBackend.asInstanceOf[ExecutorAllocationClient], listenerBus, _conf)) + schedulerBackend.asInstanceOf[ExecutorAllocationClient], listenerBus, _conf, + _env.blockManager.master)) case _ => None } @@ -1632,6 +1633,8 @@ class SparkContext(config: SparkConf) extends Logging { * :: DeveloperApi :: * Request that the cluster manager kill the specified executors. * + * This is not supported when dynamic allocation is turned on. + * * @note This is an indication to the cluster manager that the application wishes to adjust * its resource usage downwards. If the application wishes to replace the executors it kills * through this method with new ones, it should follow up explicitly with a call to @@ -1643,7 +1646,10 @@ class SparkContext(config: SparkConf) extends Logging { def killExecutors(executorIds: Seq[String]): Boolean = { schedulerBackend match { case b: ExecutorAllocationClient => - b.killExecutors(executorIds, replace = false, force = true).nonEmpty + require(executorAllocationManager.isEmpty, + "killExecutors() unsupported with Dynamic Allocation turned on") + b.killExecutors(executorIds, adjustTargetNumExecutors = true, countFailures = false, + force = true).nonEmpty case _ => logWarning("Killing executors is not supported by current scheduler.") false @@ -1681,7 +1687,8 @@ class SparkContext(config: SparkConf) extends Logging { private[spark] def killAndReplaceExecutor(executorId: String): Boolean = { schedulerBackend match { case b: ExecutorAllocationClient => - b.killExecutors(Seq(executorId), replace = true, force = true).nonEmpty + b.killExecutors(Seq(executorId), adjustTargetNumExecutors = false, countFailures = true, + force = true).nonEmpty case _ => logWarning("Killing executors is not supported by current scheduler.") false @@ -2276,7 +2283,7 @@ class SparkContext(config: SparkConf) extends Logging { } /** - * Clean a closure to make it ready to be serialized and send to tasks + * Clean a closure to make it ready to be serialized and sent to tasks * (removes unreferenced variables in $outer's, updates REPL variables) * If checkSerializable is set, clean will also proactively * check to see if f is serializable and throw a SparkException diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala index 0b87cd503d4fa..69739745aa6cf 100644 --- a/core/src/main/scala/org/apache/spark/TaskContext.scala +++ b/core/src/main/scala/org/apache/spark/TaskContext.scala @@ -66,7 +66,7 @@ object TaskContext { * An empty task context that does not represent an actual task. This is only used in tests. */ private[spark] def empty(): TaskContextImpl = { - new TaskContextImpl(0, 0, 0, 0, null, new Properties, null) + new TaskContextImpl(0, 0, 0, 0, 0, null, new Properties, null) } } @@ -150,6 +150,13 @@ abstract class TaskContext extends Serializable { */ def stageId(): Int + /** + * How many times the stage that this task belongs to has been attempted. The first stage attempt + * will be assigned stageAttemptNumber = 0, and subsequent attempts will have increasing attempt + * numbers. + */ + def stageAttemptNumber(): Int + /** * The ID of the RDD partition that is computed by this task. */ diff --git a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala index 01d8973e1bb06..cccd3ea457ba4 100644 --- a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala +++ b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala @@ -41,8 +41,9 @@ import org.apache.spark.util._ * `TaskMetrics` & `MetricsSystem` objects are not thread safe. */ private[spark] class TaskContextImpl( - val stageId: Int, - val partitionId: Int, + override val stageId: Int, + override val stageAttemptNumber: Int, + override val partitionId: Int, override val taskAttemptId: Long, override val attemptNumber: Int, override val taskMemoryManager: TaskMemoryManager, diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonGatewayServer.scala b/core/src/main/scala/org/apache/spark/api/python/PythonGatewayServer.scala index 11f2432575d84..9ddc4a4910180 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonGatewayServer.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonGatewayServer.scala @@ -17,26 +17,39 @@ package org.apache.spark.api.python -import java.io.DataOutputStream -import java.net.Socket +import java.io.{DataOutputStream, File, FileOutputStream} +import java.net.InetAddress +import java.nio.charset.StandardCharsets.UTF_8 +import java.nio.file.Files import py4j.GatewayServer +import org.apache.spark.SparkConf import org.apache.spark.internal.Logging import org.apache.spark.util.Utils /** - * Process that starts a Py4J GatewayServer on an ephemeral port and communicates the bound port - * back to its caller via a callback port specified by the caller. + * Process that starts a Py4J GatewayServer on an ephemeral port. * * This process is launched (via SparkSubmit) by the PySpark driver (see java_gateway.py). */ private[spark] object PythonGatewayServer extends Logging { initializeLogIfNecessary(true) - def main(args: Array[String]): Unit = Utils.tryOrExit { - // Start a GatewayServer on an ephemeral port - val gatewayServer: GatewayServer = new GatewayServer(null, 0) + def main(args: Array[String]): Unit = { + val secret = Utils.createSecret(new SparkConf()) + + // Start a GatewayServer on an ephemeral port. Make sure the callback client is configured + // with the same secret, in case the app needs callbacks from the JVM to the underlying + // python processes. + val localhost = InetAddress.getLoopbackAddress() + val gatewayServer: GatewayServer = new GatewayServer.GatewayServerBuilder() + .authToken(secret) + .javaPort(0) + .javaAddress(localhost) + .callbackClient(GatewayServer.DEFAULT_PYTHON_PORT, localhost, secret) + .build() + gatewayServer.start() val boundPort: Int = gatewayServer.getListeningPort if (boundPort == -1) { @@ -46,15 +59,24 @@ private[spark] object PythonGatewayServer extends Logging { logDebug(s"Started PythonGatewayServer on port $boundPort") } - // Communicate the bound port back to the caller via the caller-specified callback port - val callbackHost = sys.env("_PYSPARK_DRIVER_CALLBACK_HOST") - val callbackPort = sys.env("_PYSPARK_DRIVER_CALLBACK_PORT").toInt - logDebug(s"Communicating GatewayServer port to Python driver at $callbackHost:$callbackPort") - val callbackSocket = new Socket(callbackHost, callbackPort) - val dos = new DataOutputStream(callbackSocket.getOutputStream) + // Communicate the connection information back to the python process by writing the + // information in the requested file. This needs to match the read side in java_gateway.py. + val connectionInfoPath = new File(sys.env("_PYSPARK_DRIVER_CONN_INFO_PATH")) + val tmpPath = Files.createTempFile(connectionInfoPath.getParentFile().toPath(), + "connection", ".info").toFile() + + val dos = new DataOutputStream(new FileOutputStream(tmpPath)) dos.writeInt(boundPort) + + val secretBytes = secret.getBytes(UTF_8) + dos.writeInt(secretBytes.length) + dos.write(secretBytes, 0, secretBytes.length) dos.close() - callbackSocket.close() + + if (!tmpPath.renameTo(connectionInfoPath)) { + logError(s"Unable to write connection information to $connectionInfoPath.") + System.exit(1) + } // Exit on EOF or broken pipe to ensure that this process dies when the Python driver dies: while (System.in.read() != -1) { diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index f6293c0dc5091..a1ee2f7d1b119 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -38,6 +38,7 @@ import org.apache.spark.broadcast.Broadcast import org.apache.spark.input.PortableDataStream import org.apache.spark.internal.Logging import org.apache.spark.rdd.RDD +import org.apache.spark.security.SocketAuthHelper import org.apache.spark.util._ @@ -107,6 +108,12 @@ private[spark] object PythonRDD extends Logging { // remember the broadcasts sent to each worker private val workerBroadcasts = new mutable.WeakHashMap[Socket, mutable.Set[Long]]() + // Authentication helper used when serving iterator data. + private lazy val authHelper = { + val conf = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf()) + new SocketAuthHelper(conf) + } + def getWorkerBroadcasts(worker: Socket): mutable.Set[Long] = { synchronized { workerBroadcasts.getOrElseUpdate(worker, new mutable.HashSet[Long]()) @@ -129,12 +136,13 @@ private[spark] object PythonRDD extends Logging { * (effectively a collect()), but allows you to run on a certain subset of partitions, * or to enable local execution. * - * @return the port number of a local socket which serves the data collected from this job. + * @return 2-tuple (as a Java array) with the port number of a local socket which serves the + * data collected from this job, and the secret for authentication. */ def runJob( sc: SparkContext, rdd: JavaRDD[Array[Byte]], - partitions: JArrayList[Int]): Int = { + partitions: JArrayList[Int]): Array[Any] = { type ByteArray = Array[Byte] type UnrolledPartition = Array[ByteArray] val allPartitions: Array[UnrolledPartition] = @@ -147,13 +155,14 @@ private[spark] object PythonRDD extends Logging { /** * A helper function to collect an RDD as an iterator, then serve it via socket. * - * @return the port number of a local socket which serves the data collected from this job. + * @return 2-tuple (as a Java array) with the port number of a local socket which serves the + * data collected from this job, and the secret for authentication. */ - def collectAndServe[T](rdd: RDD[T]): Int = { + def collectAndServe[T](rdd: RDD[T]): Array[Any] = { serveIterator(rdd.collect().iterator, s"serve RDD ${rdd.id}") } - def toLocalIteratorAndServe[T](rdd: RDD[T]): Int = { + def toLocalIteratorAndServe[T](rdd: RDD[T]): Array[Any] = { serveIterator(rdd.toLocalIterator, s"serve toLocalIterator") } @@ -384,8 +393,11 @@ private[spark] object PythonRDD extends Logging { * and send them into this connection. * * The thread will terminate after all the data are sent or any exceptions happen. + * + * @return 2-tuple (as a Java array) with the port number of a local socket which serves the + * data collected from this job, and the secret for authentication. */ - def serveIterator[T](items: Iterator[T], threadName: String): Int = { + def serveIterator(items: Iterator[_], threadName: String): Array[Any] = { val serverSocket = new ServerSocket(0, 1, InetAddress.getByName("localhost")) // Close the socket if no connection in 15 seconds serverSocket.setSoTimeout(15000) @@ -395,11 +407,14 @@ private[spark] object PythonRDD extends Logging { override def run() { try { val sock = serverSocket.accept() + authHelper.authClient(sock) + val out = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream)) Utils.tryWithSafeFinally { writeIteratorToStream(items, out) } { out.close() + sock.close() } } catch { case NonFatal(e) => @@ -410,7 +425,7 @@ private[spark] object PythonRDD extends Logging { } }.start() - serverSocket.getLocalPort + Array(serverSocket.getLocalPort, authHelper.secret) } private def getMergedConf(confAsMap: java.util.HashMap[String, String], diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala index 1ec0e717fac29..719ce5b9e3698 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala @@ -37,14 +37,14 @@ private[spark] object PythonEvalType { val SQL_BATCHED_UDF = 100 - val SQL_PANDAS_SCALAR_UDF = 200 - val SQL_PANDAS_GROUP_MAP_UDF = 201 + val SQL_SCALAR_PANDAS_UDF = 200 + val SQL_GROUPED_MAP_PANDAS_UDF = 201 def toString(pythonEvalType: Int): String = pythonEvalType match { case NON_UDF => "NON_UDF" case SQL_BATCHED_UDF => "SQL_BATCHED_UDF" - case SQL_PANDAS_SCALAR_UDF => "SQL_PANDAS_SCALAR_UDF" - case SQL_PANDAS_GROUP_MAP_UDF => "SQL_PANDAS_GROUP_MAP_UDF" + case SQL_SCALAR_PANDAS_UDF => "SQL_SCALAR_PANDAS_UDF" + case SQL_GROUPED_MAP_PANDAS_UDF => "SQL_GROUPED_MAP_PANDAS_UDF" } } diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala index 92e228a9dd10c..27a5e19f96a14 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala @@ -32,7 +32,7 @@ private[spark] object PythonUtils { val pythonPath = new ArrayBuffer[String] for (sparkHome <- sys.env.get("SPARK_HOME")) { pythonPath += Seq(sparkHome, "python", "lib", "pyspark.zip").mkString(File.separator) - pythonPath += Seq(sparkHome, "python", "lib", "py4j-0.10.6-src.zip").mkString(File.separator) + pythonPath += Seq(sparkHome, "python", "lib", "py4j-0.10.7-src.zip").mkString(File.separator) } pythonPath ++= SparkContext.jarOfObject(this) pythonPath.mkString(File.pathSeparator) diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala index f53c6178047f5..949aa445537a6 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala @@ -27,6 +27,7 @@ import scala.collection.mutable import org.apache.spark._ import org.apache.spark.internal.Logging +import org.apache.spark.security.SocketAuthHelper import org.apache.spark.util.{RedirectThread, Utils} private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String, String]) @@ -45,6 +46,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String !System.getProperty("os.name").startsWith("Windows") && useDaemonEnabled } + + private val authHelper = new SocketAuthHelper(SparkEnv.get.conf) + var daemon: Process = null val daemonHost = InetAddress.getByAddress(Array(127, 0, 0, 1)) var daemonPort: Int = 0 @@ -85,6 +89,8 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String if (pid < 0) { throw new IllegalStateException("Python daemon failed to launch worker with code " + pid) } + + authHelper.authToServer(socket) daemonWorkers.put(socket, pid) socket } @@ -122,25 +128,24 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String workerEnv.put("PYTHONPATH", pythonPath) // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u: workerEnv.put("PYTHONUNBUFFERED", "YES") + workerEnv.put("PYTHON_WORKER_FACTORY_PORT", serverSocket.getLocalPort.toString) + workerEnv.put("PYTHON_WORKER_FACTORY_SECRET", authHelper.secret) val worker = pb.start() // Redirect worker stdout and stderr redirectStreamsToStderr(worker.getInputStream, worker.getErrorStream) - // Tell the worker our port - val out = new OutputStreamWriter(worker.getOutputStream, StandardCharsets.UTF_8) - out.write(serverSocket.getLocalPort + "\n") - out.flush() - - // Wait for it to connect to our socket + // Wait for it to connect to our socket, and validate the auth secret. serverSocket.setSoTimeout(10000) + try { val socket = serverSocket.accept() + authHelper.authClient(socket) simpleWorkers.put(socket, worker) return socket } catch { case e: Exception => - throw new SparkException("Python worker did not connect back in time", e) + throw new SparkException("Python worker failed to connect back.", e) } } finally { if (serverSocket != null) { @@ -163,6 +168,7 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String val workerEnv = pb.environment() workerEnv.putAll(envVars.asJava) workerEnv.put("PYTHONPATH", pythonPath) + workerEnv.put("PYTHON_WORKER_FACTORY_SECRET", authHelper.secret) // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u: workerEnv.put("PYTHONUNBUFFERED", "YES") daemon = pb.start() @@ -172,7 +178,6 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String // Redirect daemon stdout and stderr redirectStreamsToStderr(in, daemon.getErrorStream) - } catch { case e: Exception => diff --git a/core/src/main/scala/org/apache/spark/api/r/RAuthHelper.scala b/core/src/main/scala/org/apache/spark/api/r/RAuthHelper.scala new file mode 100644 index 0000000000000..ac6826a9ec774 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/RAuthHelper.scala @@ -0,0 +1,38 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io.{DataInputStream, DataOutputStream} +import java.net.Socket + +import org.apache.spark.SparkConf +import org.apache.spark.security.SocketAuthHelper + +private[spark] class RAuthHelper(conf: SparkConf) extends SocketAuthHelper(conf) { + + override protected def readUtf8(s: Socket): String = { + SerDe.readString(new DataInputStream(s.getInputStream())) + } + + override protected def writeUtf8(str: String, s: Socket): Unit = { + val out = s.getOutputStream() + SerDe.writeString(new DataOutputStream(out), str) + out.flush() + } + +} diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackend.scala b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala index 2d1152a036449..3b2e809408e0f 100644 --- a/core/src/main/scala/org/apache/spark/api/r/RBackend.scala +++ b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala @@ -17,8 +17,8 @@ package org.apache.spark.api.r -import java.io.{DataOutputStream, File, FileOutputStream, IOException} -import java.net.{InetAddress, InetSocketAddress, ServerSocket} +import java.io.{DataInputStream, DataOutputStream, File, FileOutputStream, IOException} +import java.net.{InetAddress, InetSocketAddress, ServerSocket, Socket} import java.util.concurrent.TimeUnit import io.netty.bootstrap.ServerBootstrap @@ -32,6 +32,8 @@ import io.netty.handler.timeout.ReadTimeoutHandler import org.apache.spark.SparkConf import org.apache.spark.internal.Logging +import org.apache.spark.network.util.JavaUtils +import org.apache.spark.util.Utils /** * Netty-based backend server that is used to communicate between R and Java. @@ -45,7 +47,7 @@ private[spark] class RBackend { /** Tracks JVM objects returned to R for this RBackend instance. */ private[r] val jvmObjectTracker = new JVMObjectTracker - def init(): Int = { + def init(): (Int, RAuthHelper) = { val conf = new SparkConf() val backendConnectionTimeout = conf.getInt( "spark.r.backendConnectionTimeout", SparkRDefaults.DEFAULT_CONNECTION_TIMEOUT) @@ -53,6 +55,7 @@ private[spark] class RBackend { conf.getInt("spark.r.numRBackendThreads", SparkRDefaults.DEFAULT_NUM_RBACKEND_THREADS)) val workerGroup = bossGroup val handler = new RBackendHandler(this) + val authHelper = new RAuthHelper(conf) bootstrap = new ServerBootstrap() .group(bossGroup, workerGroup) @@ -71,13 +74,16 @@ private[spark] class RBackend { new LengthFieldBasedFrameDecoder(Integer.MAX_VALUE, 0, 4, 0, 4)) .addLast("decoder", new ByteArrayDecoder()) .addLast("readTimeoutHandler", new ReadTimeoutHandler(backendConnectionTimeout)) + .addLast(new RBackendAuthHandler(authHelper.secret)) .addLast("handler", handler) } }) channelFuture = bootstrap.bind(new InetSocketAddress("localhost", 0)) channelFuture.syncUninterruptibly() - channelFuture.channel().localAddress().asInstanceOf[InetSocketAddress].getPort() + + val port = channelFuture.channel().localAddress().asInstanceOf[InetSocketAddress].getPort() + (port, authHelper) } def run(): Unit = { @@ -116,7 +122,7 @@ private[spark] object RBackend extends Logging { val sparkRBackend = new RBackend() try { // bind to random port - val boundPort = sparkRBackend.init() + val (boundPort, authHelper) = sparkRBackend.init() val serverSocket = new ServerSocket(0, 1, InetAddress.getByName("localhost")) val listenPort = serverSocket.getLocalPort() // Connection timeout is set by socket client. To make it configurable we will pass the @@ -133,6 +139,7 @@ private[spark] object RBackend extends Logging { dos.writeInt(listenPort) SerDe.writeString(dos, RUtils.rPackages.getOrElse("")) dos.writeInt(backendConnectionTimeout) + SerDe.writeString(dos, authHelper.secret) dos.close() f.renameTo(new File(path)) @@ -144,12 +151,35 @@ private[spark] object RBackend extends Logging { val buf = new Array[Byte](1024) // shutdown JVM if R does not connect back in 10 seconds serverSocket.setSoTimeout(10000) + + // Wait for the R process to connect back, ignoring any failed auth attempts. Allow + // a max number of connection attempts to avoid looping forever. try { - val inSocket = serverSocket.accept() + var remainingAttempts = 10 + var inSocket: Socket = null + while (inSocket == null) { + inSocket = serverSocket.accept() + try { + authHelper.authClient(inSocket) + } catch { + case e: Exception => + remainingAttempts -= 1 + if (remainingAttempts == 0) { + val msg = "Too many failed authentication attempts." + logError(msg) + throw new IllegalStateException(msg) + } + logInfo("Client connection failed authentication.") + inSocket = null + } + } + serverSocket.close() + // wait for the end of socket, closed if R process die inSocket.getInputStream().read(buf) } finally { + serverSocket.close() sparkRBackend.close() System.exit(0) } @@ -165,4 +195,5 @@ private[spark] object RBackend extends Logging { } System.exit(0) } + } diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendAuthHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendAuthHandler.scala new file mode 100644 index 0000000000000..4162e4a6c7476 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/RBackendAuthHandler.scala @@ -0,0 +1,55 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io.{ByteArrayOutputStream, DataOutputStream} +import java.nio.charset.StandardCharsets.UTF_8 + +import io.netty.channel.{Channel, ChannelHandlerContext, SimpleChannelInboundHandler} + +import org.apache.spark.internal.Logging +import org.apache.spark.util.Utils + +/** + * Authentication handler for connections from the R process. + */ +private class RBackendAuthHandler(secret: String) + extends SimpleChannelInboundHandler[Array[Byte]] with Logging { + + override def channelRead0(ctx: ChannelHandlerContext, msg: Array[Byte]): Unit = { + // The R code adds a null terminator to serialized strings, so ignore it here. + val clientSecret = new String(msg, 0, msg.length - 1, UTF_8) + try { + require(secret == clientSecret, "Auth secret mismatch.") + ctx.pipeline().remove(this) + writeReply("ok", ctx.channel()) + } catch { + case e: Exception => + logInfo("Authentication failure.", e) + writeReply("err", ctx.channel()) + ctx.close() + } + } + + private def writeReply(reply: String, chan: Channel): Unit = { + val out = new ByteArrayOutputStream() + SerDe.writeString(new DataOutputStream(out), reply) + chan.writeAndFlush(out.toByteArray()) + } + +} diff --git a/core/src/main/scala/org/apache/spark/api/r/RRunner.scala b/core/src/main/scala/org/apache/spark/api/r/RRunner.scala index 88118392003e8..e7fdc3963945a 100644 --- a/core/src/main/scala/org/apache/spark/api/r/RRunner.scala +++ b/core/src/main/scala/org/apache/spark/api/r/RRunner.scala @@ -74,14 +74,19 @@ private[spark] class RRunner[U]( // the socket used to send out the input of task serverSocket.setSoTimeout(10000) - val inSocket = serverSocket.accept() - startStdinThread(inSocket.getOutputStream(), inputIterator, partitionIndex) - - // the socket used to receive the output of task - val outSocket = serverSocket.accept() - val inputStream = new BufferedInputStream(outSocket.getInputStream) - dataStream = new DataInputStream(inputStream) - serverSocket.close() + dataStream = try { + val inSocket = serverSocket.accept() + RRunner.authHelper.authClient(inSocket) + startStdinThread(inSocket.getOutputStream(), inputIterator, partitionIndex) + + // the socket used to receive the output of task + val outSocket = serverSocket.accept() + RRunner.authHelper.authClient(outSocket) + val inputStream = new BufferedInputStream(outSocket.getInputStream) + new DataInputStream(inputStream) + } finally { + serverSocket.close() + } try { return new Iterator[U] { @@ -315,6 +320,11 @@ private[r] object RRunner { private[this] var errThread: BufferedStreamThread = _ private[this] var daemonChannel: DataOutputStream = _ + private lazy val authHelper = { + val conf = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf()) + new RAuthHelper(conf) + } + /** * Start a thread to print the process's stderr to ours */ @@ -349,6 +359,7 @@ private[r] object RRunner { pb.environment().put("SPARKR_BACKEND_CONNECTION_TIMEOUT", rConnectionTimeout.toString) pb.environment().put("SPARKR_SPARKFILES_ROOT_DIR", SparkFiles.getRootDirectory()) pb.environment().put("SPARKR_IS_RUNNING_ON_WORKER", "TRUE") + pb.environment().put("SPARKR_WORKER_SECRET", authHelper.secret) pb.redirectErrorStream(true) // redirect stderr into stdout val proc = pb.start() val errThread = startStdoutThread(proc) @@ -370,8 +381,12 @@ private[r] object RRunner { // the socket used to send out the input of task serverSocket.setSoTimeout(10000) val sock = serverSocket.accept() - daemonChannel = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream)) - serverSocket.close() + try { + authHelper.authClient(sock) + daemonChannel = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream)) + } finally { + serverSocket.close() + } } try { daemonChannel.writeInt(port) diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala index e88988fe03b2e..8d7a4a353a792 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala @@ -21,6 +21,8 @@ import java.util.concurrent.atomic.AtomicLong import scala.reflect.ClassTag +import org.apache.commons.collections.map.{AbstractReferenceMap, ReferenceMap} + import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.internal.Logging @@ -52,6 +54,10 @@ private[spark] class BroadcastManager( private val nextBroadcastId = new AtomicLong(0) + private[broadcast] val cachedValues = { + new ReferenceMap(AbstractReferenceMap.HARD, AbstractReferenceMap.WEAK) + } + def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean): Broadcast[T] = { broadcastFactory.newBroadcast[T](value_, isLocal, nextBroadcastId.getAndIncrement()) } diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala index 7aecd3c9668ea..e125095cf4777 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -206,36 +206,50 @@ private[spark] class TorrentBroadcast[T: ClassTag](obj: T, id: Long) private def readBroadcastBlock(): T = Utils.tryOrIOException { TorrentBroadcast.synchronized { - setConf(SparkEnv.get.conf) - val blockManager = SparkEnv.get.blockManager - blockManager.getLocalValues(broadcastId) match { - case Some(blockResult) => - if (blockResult.data.hasNext) { - val x = blockResult.data.next().asInstanceOf[T] - releaseLock(broadcastId) - x - } else { - throw new SparkException(s"Failed to get locally stored broadcast data: $broadcastId") - } - case None => - logInfo("Started reading broadcast variable " + id) - val startTimeMs = System.currentTimeMillis() - val blocks = readBlocks() - logInfo("Reading broadcast variable " + id + " took" + Utils.getUsedTimeMs(startTimeMs)) - - try { - val obj = TorrentBroadcast.unBlockifyObject[T]( - blocks.map(_.toInputStream()), SparkEnv.get.serializer, compressionCodec) - // Store the merged copy in BlockManager so other tasks on this executor don't - // need to re-fetch it. - val storageLevel = StorageLevel.MEMORY_AND_DISK - if (!blockManager.putSingle(broadcastId, obj, storageLevel, tellMaster = false)) { - throw new SparkException(s"Failed to store $broadcastId in BlockManager") + val broadcastCache = SparkEnv.get.broadcastManager.cachedValues + + Option(broadcastCache.get(broadcastId)).map(_.asInstanceOf[T]).getOrElse { + setConf(SparkEnv.get.conf) + val blockManager = SparkEnv.get.blockManager + blockManager.getLocalValues(broadcastId) match { + case Some(blockResult) => + if (blockResult.data.hasNext) { + val x = blockResult.data.next().asInstanceOf[T] + releaseLock(broadcastId) + + if (x != null) { + broadcastCache.put(broadcastId, x) + } + + x + } else { + throw new SparkException(s"Failed to get locally stored broadcast data: $broadcastId") } - obj - } finally { - blocks.foreach(_.dispose()) - } + case None => + logInfo("Started reading broadcast variable " + id) + val startTimeMs = System.currentTimeMillis() + val blocks = readBlocks() + logInfo("Reading broadcast variable " + id + " took" + Utils.getUsedTimeMs(startTimeMs)) + + try { + val obj = TorrentBroadcast.unBlockifyObject[T]( + blocks.map(_.toInputStream()), SparkEnv.get.serializer, compressionCodec) + // Store the merged copy in BlockManager so other tasks on this executor don't + // need to re-fetch it. + val storageLevel = StorageLevel.MEMORY_AND_DISK + if (!blockManager.putSingle(broadcastId, obj, storageLevel, tellMaster = false)) { + throw new SparkException(s"Failed to store $broadcastId in BlockManager") + } + + if (obj != null) { + broadcastCache.put(broadcastId, obj) + } + + obj + } finally { + blocks.foreach(_.dispose()) + } + } } } } diff --git a/core/src/main/scala/org/apache/spark/deploy/DependencyUtils.scala b/core/src/main/scala/org/apache/spark/deploy/DependencyUtils.scala index ecc82d7ac8001..fac834a70b893 100644 --- a/core/src/main/scala/org/apache/spark/deploy/DependencyUtils.scala +++ b/core/src/main/scala/org/apache/spark/deploy/DependencyUtils.scala @@ -18,12 +18,13 @@ package org.apache.spark.deploy import java.io.File +import java.net.URI import org.apache.commons.lang3.StringUtils import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path} -import org.apache.spark.{SecurityManager, SparkConf} +import org.apache.spark.{SecurityManager, SparkConf, SparkException} import org.apache.spark.util.{MutableURLClassLoader, Utils} private[deploy] object DependencyUtils { @@ -32,7 +33,8 @@ private[deploy] object DependencyUtils { packagesExclusions: String, packages: String, repositories: String, - ivyRepoPath: String): String = { + ivyRepoPath: String, + ivySettingsPath: Option[String]): String = { val exclusions: Seq[String] = if (!StringUtils.isBlank(packagesExclusions)) { packagesExclusions.split(",") @@ -40,10 +42,12 @@ private[deploy] object DependencyUtils { Nil } // Create the IvySettings, either load from file or build defaults - val ivySettings = sys.props.get("spark.jars.ivySettings").map { ivySettingsFile => - SparkSubmitUtils.loadIvySettings(ivySettingsFile, Option(repositories), Option(ivyRepoPath)) - }.getOrElse { - SparkSubmitUtils.buildIvySettings(Option(repositories), Option(ivyRepoPath)) + val ivySettings = ivySettingsPath match { + case Some(path) => + SparkSubmitUtils.loadIvySettings(path, Option(repositories), Option(ivyRepoPath)) + + case None => + SparkSubmitUtils.buildIvySettings(Option(repositories), Option(ivyRepoPath)) } SparkSubmitUtils.resolveMavenCoordinates(packages, ivySettings, exclusions = exclusions) @@ -137,16 +141,31 @@ private[deploy] object DependencyUtils { def resolveGlobPaths(paths: String, hadoopConf: Configuration): String = { require(paths != null, "paths cannot be null.") Utils.stringToSeq(paths).flatMap { path => - val uri = Utils.resolveURI(path) - uri.getScheme match { - case "local" | "http" | "https" | "ftp" => Array(path) - case _ => - val fs = FileSystem.get(uri, hadoopConf) - Option(fs.globStatus(new Path(uri))).map { status => - status.filter(_.isFile).map(_.getPath.toUri.toString) - }.getOrElse(Array(path)) + val (base, fragment) = splitOnFragment(path) + (resolveGlobPath(base, hadoopConf), fragment) match { + case (resolved, Some(_)) if resolved.length > 1 => throw new SparkException( + s"${base.toString} resolves ambiguously to multiple files: ${resolved.mkString(",")}") + case (resolved, Some(namedAs)) => resolved.map(_ + "#" + namedAs) + case (resolved, _) => resolved } }.mkString(",") } + private def splitOnFragment(path: String): (URI, Option[String]) = { + val uri = Utils.resolveURI(path) + val withoutFragment = new URI(uri.getScheme, uri.getSchemeSpecificPart, null) + (withoutFragment, Option(uri.getFragment)) + } + + private def resolveGlobPath(uri: URI, hadoopConf: Configuration): Array[String] = { + uri.getScheme match { + case "local" | "http" | "https" | "ftp" => Array(uri.toString) + case _ => + val fs = FileSystem.get(uri, hadoopConf) + Option(fs.globStatus(new Path(uri))).map { status => + status.filter(_.isFile).map(_.getPath.toUri.toString) + }.getOrElse(Array(uri.toString)) + } + } + } diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala index 7aca305783a7f..ccb30e205ca40 100644 --- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala @@ -18,7 +18,8 @@ package org.apache.spark.deploy import java.io.File -import java.net.URI +import java.net.{InetAddress, URI} +import java.nio.file.Files import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer @@ -39,6 +40,7 @@ object PythonRunner { val pyFiles = args(1) val otherArgs = args.slice(2, args.length) val sparkConf = new SparkConf() + val secret = Utils.createSecret(sparkConf) val pythonExec = sparkConf.get(PYSPARK_DRIVER_PYTHON) .orElse(sparkConf.get(PYSPARK_PYTHON)) .orElse(sys.env.get("PYSPARK_DRIVER_PYTHON")) @@ -47,11 +49,17 @@ object PythonRunner { // Format python file paths before adding them to the PYTHONPATH val formattedPythonFile = formatPath(pythonFile) - val formattedPyFiles = formatPaths(pyFiles) + val formattedPyFiles = resolvePyFiles(formatPaths(pyFiles)) // Launch a Py4J gateway server for the process to connect to; this will let it see our // Java system properties and such - val gatewayServer = new py4j.GatewayServer(null, 0) + val localhost = InetAddress.getLoopbackAddress() + val gatewayServer = new py4j.GatewayServer.GatewayServerBuilder() + .authToken(secret) + .javaPort(0) + .javaAddress(localhost) + .callbackClient(py4j.GatewayServer.DEFAULT_PYTHON_PORT, localhost, secret) + .build() val thread = new Thread(new Runnable() { override def run(): Unit = Utils.logUncaughtExceptions { gatewayServer.start() @@ -82,6 +90,7 @@ object PythonRunner { // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u: env.put("PYTHONUNBUFFERED", "YES") // value is needed to be set to a non-empty string env.put("PYSPARK_GATEWAY_PORT", "" + gatewayServer.getListeningPort) + env.put("PYSPARK_GATEWAY_SECRET", secret) // pass conf spark.pyspark.python to python process, the only way to pass info to // python process is through environment variable. sparkConf.get(PYSPARK_PYTHON).foreach(env.put("PYSPARK_PYTHON", _)) @@ -145,4 +154,30 @@ object PythonRunner { .map { p => formatPath(p, testWindows) } } + /** + * Resolves the ".py" files. ".py" file should not be added as is because PYTHONPATH does + * not expect a file. This method creates a temporary directory and puts the ".py" files + * if exist in the given paths. + */ + private def resolvePyFiles(pyFiles: Array[String]): Array[String] = { + lazy val dest = Utils.createTempDir(namePrefix = "localPyFiles") + pyFiles.flatMap { pyFile => + // In case of client with submit, the python paths should be set before context + // initialization because the context initialization can be done later. + // We will copy the local ".py" files because ".py" file shouldn't be added + // alone but its parent directory in PYTHONPATH. See SPARK-24384. + if (pyFile.endsWith(".py")) { + val source = new File(pyFile) + if (source.exists() && source.isFile && source.canRead) { + Files.copy(source.toPath, new File(dest, source.getName).toPath) + Some(dest.getAbsolutePath) + } else { + // Don't have to add it if it doesn't exist or isn't readable. + None + } + } else { + Some(pyFile) + } + }.distinct + } } diff --git a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala index 6eb53a8252205..e86b362639e57 100644 --- a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala @@ -68,10 +68,13 @@ object RRunner { // Java system properties etc. val sparkRBackend = new RBackend() @volatile var sparkRBackendPort = 0 + @volatile var sparkRBackendSecret: String = null val initialized = new Semaphore(0) val sparkRBackendThread = new Thread("SparkR backend") { override def run() { - sparkRBackendPort = sparkRBackend.init() + val (port, authHelper) = sparkRBackend.init() + sparkRBackendPort = port + sparkRBackendSecret = authHelper.secret initialized.release() sparkRBackend.run() } @@ -91,6 +94,7 @@ object RRunner { env.put("SPARKR_PACKAGE_DIR", rPackageDir.mkString(",")) env.put("R_PROFILE_USER", Seq(rPackageDir(0), "SparkR", "profile", "general.R").mkString(File.separator)) + env.put("SPARKR_BACKEND_AUTH_SECRET", sparkRBackendSecret) builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize val process = builder.start() diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index e14f9845e6db6..177295fb7af0f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -111,7 +111,9 @@ class SparkHadoopUtil extends Logging { * subsystems. */ def newConfiguration(conf: SparkConf): Configuration = { - SparkHadoopUtil.newConfiguration(conf) + val hadoopConf = SparkHadoopUtil.newConfiguration(conf) + hadoopConf.addResource(SparkHadoopUtil.SPARK_HADOOP_CONF_FILE) + hadoopConf } /** @@ -435,6 +437,13 @@ object SparkHadoopUtil { */ private[spark] val UPDATE_INPUT_METRICS_INTERVAL_RECORDS = 1000 + /** + * Name of the file containing the gateway's Hadoop configuration, to be overlayed on top of the + * cluster's Hadoop config. It is up to the Spark code launching the application to create + * this file if it's desired. If the file doesn't exist, it will just be ignored. + */ + private[spark] val SPARK_HADOOP_CONF_FILE = "__spark_hadoop_conf__.xml" + def get: SparkHadoopUtil = instance /** diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index cbe1f2c3e08a1..d1347f1fdd2be 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -22,6 +22,7 @@ import java.lang.reflect.{InvocationTargetException, Modifier, UndeclaredThrowab import java.net.URL import java.security.PrivilegedExceptionAction import java.text.ParseException +import java.util.UUID import scala.annotation.tailrec import scala.collection.mutable.{ArrayBuffer, HashMap, Map} @@ -245,6 +246,19 @@ object SparkSubmit extends CommandLineUtils with Logging { args: SparkSubmitArguments, conf: Option[HadoopConfiguration] = None) : (Seq[String], Seq[String], SparkConf, String) = { + try { + doPrepareSubmitEnvironment(args, conf) + } catch { + case e: SparkException => + printErrorAndExit(e.getMessage) + throw e + } + } + + private def doPrepareSubmitEnvironment( + args: SparkSubmitArguments, + conf: Option[HadoopConfiguration] = None) + : (Seq[String], Seq[String], SparkConf, String) = { // Return values val childArgs = new ArrayBuffer[String]() val childClasspath = new ArrayBuffer[String]() @@ -348,7 +362,8 @@ object SparkSubmit extends CommandLineUtils with Logging { // Resolve maven dependencies if there are any and add classpath to jars. Add them to py-files // too for packages that include Python code val resolvedMavenCoordinates = DependencyUtils.resolveMavenDependencies( - args.packagesExclusions, args.packages, args.repositories, args.ivyRepoPath) + args.packagesExclusions, args.packages, args.repositories, args.ivyRepoPath, + args.ivySettingsPath) if (!StringUtils.isBlank(resolvedMavenCoordinates)) { args.jars = mergeFileLists(args.jars, resolvedMavenCoordinates) @@ -584,10 +599,11 @@ object SparkSubmit extends CommandLineUtils with Logging { confKey = "spark.executor.memory"), OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES, confKey = "spark.cores.max"), - OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, ALL_DEPLOY_MODES, + OptionAssigner(args.files, LOCAL | STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES, confKey = "spark.files"), OptionAssigner(args.jars, LOCAL, CLIENT, confKey = "spark.jars"), - OptionAssigner(args.jars, STANDALONE | MESOS, ALL_DEPLOY_MODES, confKey = "spark.jars"), + OptionAssigner(args.jars, STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES, + confKey = "spark.jars"), OptionAssigner(args.driverMemory, STANDALONE | MESOS | YARN | KUBERNETES, CLUSTER, confKey = "spark.driver.memory"), OptionAssigner(args.driverCores, STANDALONE | MESOS | YARN | KUBERNETES, CLUSTER, @@ -1194,7 +1210,33 @@ private[spark] object SparkSubmitUtils { /** A nice function to use in tests as well. Values are dummy strings. */ def getModuleDescriptor: DefaultModuleDescriptor = DefaultModuleDescriptor.newDefaultInstance( - ModuleRevisionId.newInstance("org.apache.spark", "spark-submit-parent", "1.0")) + // Include UUID in module name, so multiple clients resolving maven coordinate at the same time + // do not modify the same resolution file concurrently. + ModuleRevisionId.newInstance("org.apache.spark", + s"spark-submit-parent-${UUID.randomUUID.toString}", + "1.0")) + + /** + * Clear ivy resolution from current launch. The resolution file is usually at + * ~/.ivy2/org.apache.spark-spark-submit-parent-$UUID-default.xml, + * ~/.ivy2/resolved-org.apache.spark-spark-submit-parent-$UUID-1.0.xml, and + * ~/.ivy2/resolved-org.apache.spark-spark-submit-parent-$UUID-1.0.properties. + * Since each launch will have its own resolution files created, delete them after + * each resolution to prevent accumulation of these files in the ivy cache dir. + */ + private def clearIvyResolutionFiles( + mdId: ModuleRevisionId, + ivySettings: IvySettings, + ivyConfName: String): Unit = { + val currentResolutionFiles = Seq( + s"${mdId.getOrganisation}-${mdId.getName}-$ivyConfName.xml", + s"resolved-${mdId.getOrganisation}-${mdId.getName}-${mdId.getRevision}.xml", + s"resolved-${mdId.getOrganisation}-${mdId.getName}-${mdId.getRevision}.properties" + ) + currentResolutionFiles.foreach { filename => + new File(ivySettings.getDefaultCache, filename).delete() + } + } /** * Resolves any dependencies that were supplied through maven coordinates @@ -1245,14 +1287,6 @@ private[spark] object SparkSubmitUtils { // A Module descriptor must be specified. Entries are dummy strings val md = getModuleDescriptor - // clear ivy resolution from previous launches. The resolution file is usually at - // ~/.ivy2/org.apache.spark-spark-submit-parent-default.xml. In between runs, this file - // leads to confusion with Ivy when the files can no longer be found at the repository - // declared in that file/ - val mdId = md.getModuleRevisionId - val previousResolution = new File(ivySettings.getDefaultCache, - s"${mdId.getOrganisation}-${mdId.getName}-$ivyConfName.xml") - if (previousResolution.exists) previousResolution.delete md.setDefaultConf(ivyConfName) @@ -1273,7 +1307,10 @@ private[spark] object SparkSubmitUtils { packagesDirectory.getAbsolutePath + File.separator + "[organization]_[artifact]-[revision](-[classifier]).[ext]", retrieveOptions.setConfs(Array(ivyConfName))) - resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) + val paths = resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) + val mdId = md.getModuleRevisionId + clearIvyResolutionFiles(mdId, ivySettings, ivyConfName) + paths } finally { System.setOut(sysOut) } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index 9db7a1fe3106d..7e19417538656 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -63,6 +63,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S var packages: String = null var repositories: String = null var ivyRepoPath: String = null + var ivySettingsPath: Option[String] = None var packagesExclusions: String = null var verbose: Boolean = false var isPython: Boolean = false @@ -184,6 +185,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S jars = Option(jars).orElse(sparkProperties.get("spark.jars")).orNull files = Option(files).orElse(sparkProperties.get("spark.files")).orNull ivyRepoPath = sparkProperties.get("spark.jars.ivy").orNull + ivySettingsPath = sparkProperties.get("spark.jars.ivySettings") packages = Option(packages).orElse(sparkProperties.get("spark.jars.packages")).orNull packagesExclusions = Option(packagesExclusions) .orElse(sparkProperties.get("spark.jars.excludes")).orNull diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 94c80ebd55e74..ace6d9e00c838 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -19,7 +19,7 @@ package org.apache.spark.deploy.history import java.io.{File, FileNotFoundException, IOException} import java.util.{Date, ServiceLoader, UUID} -import java.util.concurrent.{Executors, ExecutorService, Future, TimeUnit} +import java.util.concurrent.{ExecutorService, TimeUnit} import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.JavaConverters._ @@ -29,7 +29,7 @@ import scala.xml.Node import com.fasterxml.jackson.annotation.JsonIgnore import com.google.common.io.ByteStreams -import com.google.common.util.concurrent.{MoreExecutors, ThreadFactoryBuilder} +import com.google.common.util.concurrent.MoreExecutors import org.apache.hadoop.fs.{FileStatus, Path} import org.apache.hadoop.fs.permission.FsAction import org.apache.hadoop.hdfs.DistributedFileSystem @@ -116,8 +116,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) // Used by check event thread and clean log thread. // Scheduled thread pool size must be one, otherwise it will have concurrent issues about fs // and applications between check task and clean task. - private val pool = Executors.newScheduledThreadPool(1, new ThreadFactoryBuilder() - .setNameFormat("spark-history-task-%d").setDaemon(true).build()) + private val pool = ThreadUtils.newDaemonSingleThreadScheduledExecutor("spark-history-task-%d") // The modification time of the newest log detected during the last scan. Currently only // used for logging msgs (logs are re-scanned based on file size, rather than modtime) @@ -174,7 +173,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) * Fixed size thread pool to fetch and parse log files. */ private val replayExecutor: ExecutorService = { - if (!conf.contains("spark.testing")) { + if (!Utils.isTesting) { ThreadUtils.newDaemonFixedThreadPool(NUM_PROCESSING_THREADS, "log-replay-executor") } else { MoreExecutors.sameThreadExecutor() @@ -275,7 +274,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) try { Some(load(appId).toApplicationInfo()) } catch { - case e: NoSuchElementException => + case _: NoSuchElementException => None } } @@ -405,49 +404,70 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) try { val newLastScanTime = getNewLastScanTime() logDebug(s"Scanning $logDir with lastScanTime==$lastScanTime") - // scan for modified applications, replay and merge them - val logInfos = Option(fs.listStatus(new Path(logDir))).map(_.toSeq).getOrElse(Nil) + + val updated = Option(fs.listStatus(new Path(logDir))).map(_.toSeq).getOrElse(Nil) .filter { entry => !entry.isDirectory() && // FsHistoryProvider generates a hidden file which can't be read. Accidentally // reading a garbage file is safe, but we would log an error which can be scary to // the end-user. !entry.getPath().getName().startsWith(".") && - SparkHadoopUtil.get.checkAccessPermission(entry, FsAction.READ) && - recordedFileSize(entry.getPath()) < entry.getLen() + SparkHadoopUtil.get.checkAccessPermission(entry, FsAction.READ) + } + .filter { entry => + try { + val info = listing.read(classOf[LogInfo], entry.getPath().toString()) + if (info.fileSize < entry.getLen()) { + // Log size has changed, it should be parsed. + true + } else { + // If the SHS view has a valid application, update the time the file was last seen so + // that the entry is not deleted from the SHS listing. + if (info.appId.isDefined) { + listing.write(info.copy(lastProcessed = newLastScanTime)) + } + false + } + } catch { + case _: NoSuchElementException => + // If the file is currently not being tracked by the SHS, add an entry for it and try + // to parse it. This will allow the cleaner code to detect the file as stale later on + // if it was not possible to parse it. + listing.write(LogInfo(entry.getPath().toString(), newLastScanTime, None, None, + entry.getLen())) + entry.getLen() > 0 + } } .sortWith { case (entry1, entry2) => entry1.getModificationTime() > entry2.getModificationTime() } - if (logInfos.nonEmpty) { - logDebug(s"New/updated attempts found: ${logInfos.size} ${logInfos.map(_.getPath)}") + if (updated.nonEmpty) { + logDebug(s"New/updated attempts found: ${updated.size} ${updated.map(_.getPath)}") } - var tasks = mutable.ListBuffer[Future[_]]() - - try { - for (file <- logInfos) { - tasks += replayExecutor.submit(new Runnable { - override def run(): Unit = mergeApplicationListing(file) + val tasks = updated.map { entry => + try { + replayExecutor.submit(new Runnable { + override def run(): Unit = mergeApplicationListing(entry, newLastScanTime) }) + } catch { + // let the iteration over the updated entries break, since an exception on + // replayExecutor.submit (..) indicates the ExecutorService is unable + // to take any more submissions at this time + case e: Exception => + logError(s"Exception while submitting event log for replay", e) + null } - } catch { - // let the iteration over logInfos break, since an exception on - // replayExecutor.submit (..) indicates the ExecutorService is unable - // to take any more submissions at this time - - case e: Exception => - logError(s"Exception while submitting event log for replay", e) - } + }.filter(_ != null) pendingReplayTasksCount.addAndGet(tasks.size) + // Wait for all tasks to finish. This makes sure that checkForLogs + // is not scheduled again while some tasks are already running in + // the replayExecutor. tasks.foreach { task => try { - // Wait for all tasks to finish. This makes sure that checkForLogs - // is not scheduled again while some tasks are already running in - // the replayExecutor. task.get() } catch { case e: InterruptedException => @@ -459,13 +479,70 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) } } + // Delete all information about applications whose log files disappeared from storage. + // This is done by identifying the event logs which were not touched by the current + // directory scan. + // + // Only entries with valid applications are cleaned up here. Cleaning up invalid log + // files is done by the periodic cleaner task. + val stale = listing.view(classOf[LogInfo]) + .index("lastProcessed") + .last(newLastScanTime - 1) + .asScala + .toList + stale.foreach { log => + log.appId.foreach { appId => + cleanAppData(appId, log.attemptId, log.logPath) + listing.delete(classOf[LogInfo], log.logPath) + } + } + lastScanTime.set(newLastScanTime) } catch { case e: Exception => logError("Exception in checking for event log updates", e) } } - private def getNewLastScanTime(): Long = { + private def cleanAppData(appId: String, attemptId: Option[String], logPath: String): Unit = { + try { + val app = load(appId) + val (attempt, others) = app.attempts.partition(_.info.attemptId == attemptId) + + assert(attempt.isEmpty || attempt.size == 1) + val isStale = attempt.headOption.exists { a => + if (a.logPath != new Path(logPath).getName()) { + // If the log file name does not match, then probably the old log file was from an + // in progress application. Just return that the app should be left alone. + false + } else { + val maybeUI = synchronized { + activeUIs.remove(appId -> attemptId) + } + + maybeUI.foreach { ui => + ui.invalidate() + ui.ui.store.close() + } + + diskManager.foreach(_.release(appId, attemptId, delete = true)) + true + } + } + + if (isStale) { + if (others.nonEmpty) { + val newAppInfo = new ApplicationInfoWrapper(app.info, others) + listing.write(newAppInfo) + } else { + listing.delete(classOf[ApplicationInfoWrapper], appId) + } + } + } catch { + case _: NoSuchElementException => + } + } + + private[history] def getNewLastScanTime(): Long = { val fileName = "." + UUID.randomUUID().toString val path = new Path(logDir, fileName) val fos = fs.create(path) @@ -530,7 +607,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) /** * Replay the given log file, saving the application in the listing db. */ - protected def mergeApplicationListing(fileStatus: FileStatus): Unit = { + protected def mergeApplicationListing(fileStatus: FileStatus, scanTime: Long): Unit = { val eventsFilter: ReplayEventsFilter = { eventString => eventString.startsWith(APPL_START_EVENT_PREFIX) || eventString.startsWith(APPL_END_EVENT_PREFIX) || @@ -544,73 +621,78 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) bus.addListener(listener) replay(fileStatus, bus, eventsFilter = eventsFilter) - listener.applicationInfo.foreach { app => - // Invalidate the existing UI for the reloaded app attempt, if any. See LoadedAppUI for a - // discussion on the UI lifecycle. - synchronized { - activeUIs.get((app.info.id, app.attempts.head.info.attemptId)).foreach { ui => - ui.invalidate() - ui.ui.store.close() + val (appId, attemptId) = listener.applicationInfo match { + case Some(app) => + // Invalidate the existing UI for the reloaded app attempt, if any. See LoadedAppUI for a + // discussion on the UI lifecycle. + synchronized { + activeUIs.get((app.info.id, app.attempts.head.info.attemptId)).foreach { ui => + ui.invalidate() + ui.ui.store.close() + } } - } - addListing(app) + addListing(app) + (Some(app.info.id), app.attempts.head.info.attemptId) + + case _ => + // If the app hasn't written down its app ID to the logs, still record the entry in the + // listing db, with an empty ID. This will make the log eligible for deletion if the app + // does not make progress after the configured max log age. + (None, None) } - listing.write(new LogInfo(logPath.toString(), fileStatus.getLen())) + listing.write(LogInfo(logPath.toString(), scanTime, appId, attemptId, fileStatus.getLen())) } /** * Delete event logs from the log directory according to the clean policy defined by the user. */ - private[history] def cleanLogs(): Unit = { - var iterator: Option[KVStoreIterator[ApplicationInfoWrapper]] = None - try { - val maxTime = clock.getTimeMillis() - conf.get(MAX_LOG_AGE_S) * 1000 - - // Iterate descending over all applications whose oldest attempt happened before maxTime. - iterator = Some(listing.view(classOf[ApplicationInfoWrapper]) - .index("oldestAttempt") - .reverse() - .first(maxTime) - .closeableIterator()) - - iterator.get.asScala.foreach { app => - // Applications may have multiple attempts, some of which may not need to be deleted yet. - val (remaining, toDelete) = app.attempts.partition { attempt => - attempt.info.lastUpdated.getTime() >= maxTime - } + private[history] def cleanLogs(): Unit = Utils.tryLog { + val maxTime = clock.getTimeMillis() - conf.get(MAX_LOG_AGE_S) * 1000 - if (remaining.nonEmpty) { - val newApp = new ApplicationInfoWrapper(app.info, remaining) - listing.write(newApp) - } + val expired = listing.view(classOf[ApplicationInfoWrapper]) + .index("oldestAttempt") + .reverse() + .first(maxTime) + .asScala + .toList + expired.foreach { app => + // Applications may have multiple attempts, some of which may not need to be deleted yet. + val (remaining, toDelete) = app.attempts.partition { attempt => + attempt.info.lastUpdated.getTime() >= maxTime + } - toDelete.foreach { attempt => - val logPath = new Path(logDir, attempt.logPath) - try { - listing.delete(classOf[LogInfo], logPath.toString()) - } catch { - case _: NoSuchElementException => - logDebug(s"Log info entry for $logPath not found.") - } - try { - fs.delete(logPath, true) - } catch { - case e: AccessControlException => - logInfo(s"No permission to delete ${attempt.logPath}, ignoring.") - case t: IOException => - logError(s"IOException in cleaning ${attempt.logPath}", t) - } - } + if (remaining.nonEmpty) { + val newApp = new ApplicationInfoWrapper(app.info, remaining) + listing.write(newApp) + } - if (remaining.isEmpty) { - listing.delete(app.getClass(), app.id) - } + toDelete.foreach { attempt => + logInfo(s"Deleting expired event log for ${attempt.logPath}") + val logPath = new Path(logDir, attempt.logPath) + listing.delete(classOf[LogInfo], logPath.toString()) + cleanAppData(app.id, attempt.info.attemptId, logPath.toString()) + deleteLog(logPath) + } + + if (remaining.isEmpty) { + listing.delete(app.getClass(), app.id) + } + } + + // Delete log files that don't have a valid application and exceed the configured max age. + val stale = listing.view(classOf[LogInfo]) + .index("lastProcessed") + .reverse() + .first(maxTime) + .asScala + .toList + stale.foreach { log => + if (log.appId.isEmpty) { + logInfo(s"Deleting invalid / corrupt event log ${log.logPath}") + deleteLog(new Path(log.logPath)) + listing.delete(classOf[LogInfo], log.logPath) } - } catch { - case t: Exception => logError("Exception while cleaning logs", t) - } finally { - iterator.foreach(_.close()) } } @@ -631,12 +713,9 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) // an error the other way -- if we report a size bigger (ie later) than the file that is // actually read, we may never refresh the app. FileStatus is guaranteed to be static // after it's created, so we get a file size that is no bigger than what is actually read. - val logInput = EventLoggingListener.openEventLog(logPath, fs) - try { - bus.replay(logInput, logPath.toString, !isCompleted, eventsFilter) + Utils.tryWithResource(EventLoggingListener.openEventLog(logPath, fs)) { in => + bus.replay(in, logPath.toString, !isCompleted, eventsFilter) logInfo(s"Finished parsing $logPath") - } finally { - logInput.close() } } @@ -703,18 +782,6 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) | application count=$count}""".stripMargin } - /** - * Return the last known size of the given event log, recorded the last time the file - * system scanner detected a change in the file. - */ - private def recordedFileSize(log: Path): Long = { - try { - listing.read(classOf[LogInfo], log.toString()).fileSize - } catch { - case _: NoSuchElementException => 0L - } - } - private def load(appId: String): ApplicationInfoWrapper = { listing.read(classOf[ApplicationInfoWrapper], appId) } @@ -773,11 +840,8 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) logInfo(s"Leasing disk manager space for app $appId / ${attempt.info.attemptId}...") val lease = dm.lease(status.getLen(), isCompressed) val newStorePath = try { - val store = KVUtils.open(lease.tmpPath, metadata) - try { + Utils.tryWithResource(KVUtils.open(lease.tmpPath, metadata)) { store => rebuildAppStore(store, status, attempt.info.lastUpdated.getTime()) - } finally { - store.close() } lease.commit(appId, attempt.info.attemptId) } catch { @@ -806,6 +870,17 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) throw new NoSuchElementException(s"Cannot find attempt $attemptId of $appId.")) } + private def deleteLog(log: Path): Unit = { + try { + fs.delete(log, true) + } catch { + case _: AccessControlException => + logInfo(s"No permission to delete $log, ignoring.") + case ioe: IOException => + logError(s"IOException in cleaning $log", ioe) + } + } + } private[history] object FsHistoryProvider { @@ -832,8 +907,16 @@ private[history] case class FsHistoryProviderMetadata( uiVersion: Long, logDir: String) +/** + * Tracking info for event logs detected in the configured log directory. Tracks both valid and + * invalid logs (e.g. unparseable logs, recorded as logs with no app ID) so that the cleaner + * can know what log files are safe to delete. + */ private[history] case class LogInfo( @KVIndexParam logPath: String, + @KVIndexParam("lastProcessed") lastProcessed: Long, + appId: Option[String], + attemptId: Option[String], fileSize: Long) private[history] class AttemptInfoWrapper( diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala index 5d62a7d8bebb4..6fc12d721e6f1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala @@ -37,7 +37,8 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") val lastUpdatedTime = parent.getLastUpdatedTime() val providerConfig = parent.getProviderConfig() val content = - + ++ +
    @@ -65,7 +66,6 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") if (allAppsSize > 0) { ++
    ++ - ++ ++ } else if (requestedIncomplete) { diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala index 75484f5c9f30f..06540420399e0 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala @@ -28,6 +28,7 @@ import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder} import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.deploy.history.config.HISTORY_SERVER_UI_PORT import org.apache.spark.internal.Logging import org.apache.spark.internal.config._ import org.apache.spark.status.api.v1.{ApiRootResource, ApplicationInfo, UIRoot} @@ -149,14 +150,17 @@ class HistoryServer( ui: SparkUI, completed: Boolean) { assert(serverInfo.isDefined, "HistoryServer must be bound before attaching SparkUIs") - ui.getHandlers.foreach(attachHandler) - addFilters(ui.getHandlers, conf) + handlers.synchronized { + ui.getHandlers.foreach(attachHandler) + } } /** Detach a reconstructed UI from this server. Only valid after bind(). */ override def detachSparkUI(appId: String, attemptId: Option[String], ui: SparkUI): Unit = { assert(serverInfo.isDefined, "HistoryServer must be bound before detaching SparkUIs") - ui.getHandlers.foreach(detachHandler) + handlers.synchronized { + ui.getHandlers.foreach(detachHandler) + } provider.onUIDetached(appId, attemptId, ui) } @@ -276,7 +280,7 @@ object HistoryServer extends Logging { .newInstance(conf) .asInstanceOf[ApplicationHistoryProvider] - val port = conf.getInt("spark.history.ui.port", 18080) + val port = conf.get(HISTORY_SERVER_UI_PORT) val server = new HistoryServer(conf, provider, securityManager, port) server.bind() diff --git a/core/src/main/scala/org/apache/spark/deploy/history/config.scala b/core/src/main/scala/org/apache/spark/deploy/history/config.scala index 22b6d49d8e2a4..efdbf672bb52f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/config.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/config.scala @@ -44,4 +44,9 @@ private[spark] object config { .bytesConf(ByteUnit.BYTE) .createWithDefaultString("10g") + val HISTORY_SERVER_UI_PORT = ConfigBuilder("spark.history.ui.port") + .doc("Web UI port to bind Spark History Server") + .intConf + .createWithDefault(18080) + } diff --git a/core/src/main/scala/org/apache/spark/deploy/security/HadoopDelegationTokenManager.scala b/core/src/main/scala/org/apache/spark/deploy/security/HadoopDelegationTokenManager.scala index 116a686fe1480..5151df00476f9 100644 --- a/core/src/main/scala/org/apache/spark/deploy/security/HadoopDelegationTokenManager.scala +++ b/core/src/main/scala/org/apache/spark/deploy/security/HadoopDelegationTokenManager.scala @@ -64,9 +64,9 @@ private[spark] class HadoopDelegationTokenManager( } private def getDelegationTokenProviders: Map[String, HadoopDelegationTokenProvider] = { - val providers = List(new HadoopFSDelegationTokenProvider(fileSystems), - new HiveDelegationTokenProvider, - new HBaseDelegationTokenProvider) + val providers = Seq(new HadoopFSDelegationTokenProvider(fileSystems)) ++ + safeCreateProvider(new HiveDelegationTokenProvider) ++ + safeCreateProvider(new HBaseDelegationTokenProvider) // Filter out providers for which spark.security.credentials.{service}.enabled is false. providers @@ -75,6 +75,17 @@ private[spark] class HadoopDelegationTokenManager( .toMap } + private def safeCreateProvider( + createFn: => HadoopDelegationTokenProvider): Option[HadoopDelegationTokenProvider] = { + try { + Some(createFn) + } catch { + case t: Throwable => + logDebug(s"Failed to load built in provider.", t) + None + } + } + def isServiceEnabled(serviceName: String): Boolean = { val key = providerEnabledConfig.format(serviceName) diff --git a/core/src/main/scala/org/apache/spark/deploy/security/HiveDelegationTokenProvider.scala b/core/src/main/scala/org/apache/spark/deploy/security/HiveDelegationTokenProvider.scala index ece5ce79c650d..7249eb85ac7c7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/security/HiveDelegationTokenProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/security/HiveDelegationTokenProvider.scala @@ -36,7 +36,7 @@ import org.apache.spark.internal.Logging import org.apache.spark.internal.config.KEYTAB import org.apache.spark.util.Utils -private[security] class HiveDelegationTokenProvider +private[spark] class HiveDelegationTokenProvider extends HadoopDelegationTokenProvider with Logging { override def serviceName: String = "hive" @@ -124,9 +124,9 @@ private[security] class HiveDelegationTokenProvider val currentUser = UserGroupInformation.getCurrentUser() val realUser = Option(currentUser.getRealUser()).getOrElse(currentUser) - // For some reason the Scala-generated anonymous class ends up causing an - // UndeclaredThrowableException, even if you annotate the method with @throws. - try { + // For some reason the Scala-generated anonymous class ends up causing an + // UndeclaredThrowableException, even if you annotate the method with @throws. + try { realUser.doAs(new PrivilegedExceptionAction[T]() { override def run(): T = fn }) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala index 58a181128eb4d..a6d13d12fc28d 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala @@ -225,7 +225,7 @@ private[deploy] class DriverRunner( // check if attempting another run keepTrying = supervise && exitCode != 0 && !killed if (keepTrying) { - if (clock.getTimeMillis() - processStart > successfulRunDuration * 1000) { + if (clock.getTimeMillis() - processStart > successfulRunDuration * 1000L) { waitSeconds = 1 } logInfo(s"Command exited with status $exitCode, re-launching after $waitSeconds s.") diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala index b19c9904d5982..3f71237164a15 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala @@ -79,12 +79,17 @@ object DriverWrapper extends Logging { val secMgr = new SecurityManager(sparkConf) val hadoopConf = SparkHadoopUtil.newConfiguration(sparkConf) - val Seq(packagesExclusions, packages, repositories, ivyRepoPath) = - Seq("spark.jars.excludes", "spark.jars.packages", "spark.jars.repositories", "spark.jars.ivy") - .map(sys.props.get(_).orNull) + val Seq(packagesExclusions, packages, repositories, ivyRepoPath, ivySettingsPath) = + Seq( + "spark.jars.excludes", + "spark.jars.packages", + "spark.jars.repositories", + "spark.jars.ivy", + "spark.jars.ivySettings" + ).map(sys.props.get(_).orNull) val resolvedMavenCoordinates = DependencyUtils.resolveMavenDependencies(packagesExclusions, - packages, repositories, ivyRepoPath) + packages, repositories, ivyRepoPath, Option(ivySettingsPath)) val jars = { val jarsProp = sys.props.get("spark.jars").orNull if (!StringUtils.isBlank(resolvedMavenCoordinates)) { diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index 3962d422f81d3..563b84934f264 100755 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -441,7 +441,7 @@ private[deploy] class Worker( // Spin up a separate thread (in a future) to do the dir cleanup; don't tie up worker // rpcEndpoint. // Copy ids so that it can be used in the cleanup thread. - val appIds = executors.values.map(_.appId).toSet + val appIds = (executors.values.map(_.appId) ++ drivers.values.map(_.driverId)).toSet val cleanupFuture = concurrent.Future { val appDirs = workDir.listFiles() if (appDirs == null) { diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 2c3a8ef74800b..a9c31c741abd3 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -480,6 +480,19 @@ private[spark] class Executor( execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult) } catch { + case t: TaskKilledException => + logInfo(s"Executor killed $taskName (TID $taskId), reason: ${t.reason}") + setTaskFinishedAndClearInterruptStatus() + execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled(t.reason))) + + case _: InterruptedException | NonFatal(_) if + task != null && task.reasonIfKilled.isDefined => + val killReason = task.reasonIfKilled.getOrElse("unknown reason") + logInfo(s"Executor interrupted and killed $taskName (TID $taskId), reason: $killReason") + setTaskFinishedAndClearInterruptStatus() + execBackend.statusUpdate( + taskId, TaskState.KILLED, ser.serialize(TaskKilled(killReason))) + case t: Throwable if hasFetchFailure && !Utils.isFatalError(t) => val reason = task.context.fetchFailed.get.toTaskFailedReason if (!t.isInstanceOf[FetchFailedException]) { @@ -494,19 +507,6 @@ private[spark] class Executor( setTaskFinishedAndClearInterruptStatus() execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) - case t: TaskKilledException => - logInfo(s"Executor killed $taskName (TID $taskId), reason: ${t.reason}") - setTaskFinishedAndClearInterruptStatus() - execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled(t.reason))) - - case _: InterruptedException | NonFatal(_) if - task != null && task.reasonIfKilled.isDefined => - val killReason = task.reasonIfKilled.getOrElse("unknown reason") - logInfo(s"Executor interrupted and killed $taskName (TID $taskId), reason: $killReason") - setTaskFinishedAndClearInterruptStatus() - execBackend.statusUpdate( - taskId, TaskState.KILLED, ser.serialize(TaskKilled(killReason))) - case CausedBy(cDE: CommitDeniedException) => val reason = cDE.toTaskCommitDeniedReason setTaskFinishedAndClearInterruptStatus() diff --git a/core/src/main/scala/org/apache/spark/internal/config/package.scala b/core/src/main/scala/org/apache/spark/internal/config/package.scala index eb12ddf961314..11cb5d39e5647 100644 --- a/core/src/main/scala/org/apache/spark/internal/config/package.scala +++ b/core/src/main/scala/org/apache/spark/internal/config/package.scala @@ -38,10 +38,13 @@ package object config { ConfigBuilder("spark.driver.userClassPathFirst").booleanConf.createWithDefault(false) private[spark] val DRIVER_MEMORY = ConfigBuilder("spark.driver.memory") + .doc("Amount of memory to use for the driver process, in MiB unless otherwise specified.") .bytesConf(ByteUnit.MiB) .createWithDefaultString("1g") private[spark] val DRIVER_MEMORY_OVERHEAD = ConfigBuilder("spark.driver.memoryOverhead") + .doc("The amount of off-heap memory to be allocated per driver in cluster mode, " + + "in MiB unless otherwise specified.") .bytesConf(ByteUnit.MiB) .createOptional @@ -62,6 +65,7 @@ package object config { .createWithDefault(false) private[spark] val EVENT_LOG_OUTPUT_BUFFER_SIZE = ConfigBuilder("spark.eventLog.buffer.kb") + .doc("Buffer size to use when writing to output streams, in KiB unless otherwise specified.") .bytesConf(ByteUnit.KiB) .createWithDefaultString("100k") @@ -81,10 +85,13 @@ package object config { ConfigBuilder("spark.executor.userClassPathFirst").booleanConf.createWithDefault(false) private[spark] val EXECUTOR_MEMORY = ConfigBuilder("spark.executor.memory") + .doc("Amount of memory to use per executor process, in MiB unless otherwise specified.") .bytesConf(ByteUnit.MiB) .createWithDefaultString("1g") private[spark] val EXECUTOR_MEMORY_OVERHEAD = ConfigBuilder("spark.executor.memoryOverhead") + .doc("The amount of off-heap memory to be allocated per executor in cluster mode, " + + "in MiB unless otherwise specified.") .bytesConf(ByteUnit.MiB) .createOptional @@ -325,7 +332,7 @@ package object config { "a property key or value, the value is redacted from the environment UI and various logs " + "like YARN and event logs.") .regexConf - .createWithDefault("(?i)secret|password|url|user|username".r) + .createWithDefault("(?i)secret|password".r) private[spark] val STRING_REDACTION_PATTERN = ConfigBuilder("spark.redaction.string.regex") @@ -335,6 +342,11 @@ package object config { .regexConf .createOptional + private[spark] val AUTH_SECRET_BIT_LENGTH = + ConfigBuilder("spark.authenticate.secretBitLength") + .intConf + .createWithDefault(256) + private[spark] val NETWORK_AUTH_ENABLED = ConfigBuilder("spark.authenticate") .booleanConf @@ -353,7 +365,7 @@ package object config { private[spark] val BUFFER_WRITE_CHUNK_SIZE = ConfigBuilder("spark.buffer.write.chunkSize") .internal() - .doc("The chunk size during writing out the bytes of ChunkedByteBuffer.") + .doc("The chunk size in bytes during writing out the bytes of ChunkedByteBuffer.") .bytesConf(ByteUnit.BYTE) .checkValue(_ <= Int.MaxValue, "The chunk size during writing out the bytes of" + " ChunkedByteBuffer should not larger than Int.MaxValue.") @@ -368,9 +380,9 @@ package object config { private[spark] val SHUFFLE_ACCURATE_BLOCK_THRESHOLD = ConfigBuilder("spark.shuffle.accurateBlockThreshold") - .doc("When we compress the size of shuffle blocks in HighlyCompressedMapStatus, we will " + - "record the size accurately if it's above this config. This helps to prevent OOM by " + - "avoiding underestimating shuffle block size when fetch shuffle blocks.") + .doc("Threshold in bytes above which the size of shuffle blocks in " + + "HighlyCompressedMapStatus is accurately recorded. This helps to prevent OOM " + + "by avoiding underestimating shuffle block size when fetch shuffle blocks.") .bytesConf(ByteUnit.BYTE) .createWithDefault(100 * 1024 * 1024) @@ -389,23 +401,23 @@ package object config { private[spark] val REDUCER_MAX_BLOCKS_IN_FLIGHT_PER_ADDRESS = ConfigBuilder("spark.reducer.maxBlocksInFlightPerAddress") - .doc("This configuration limits the number of remote blocks being fetched per reduce task" + - " from a given host port. When a large number of blocks are being requested from a given" + - " address in a single fetch or simultaneously, this could crash the serving executor or" + - " Node Manager. This is especially useful to reduce the load on the Node Manager when" + - " external shuffle is enabled. You can mitigate the issue by setting it to a lower value.") + .doc("This configuration limits the number of remote blocks being fetched per reduce task " + + "from a given host port. When a large number of blocks are being requested from a given " + + "address in a single fetch or simultaneously, this could crash the serving executor or " + + "Node Manager. This is especially useful to reduce the load on the Node Manager when " + + "external shuffle is enabled. You can mitigate the issue by setting it to a lower value.") .intConf .checkValue(_ > 0, "The max no. of blocks in flight cannot be non-positive.") .createWithDefault(Int.MaxValue) private[spark] val MAX_REMOTE_BLOCK_SIZE_FETCH_TO_MEM = ConfigBuilder("spark.maxRemoteBlockSizeFetchToMem") - .doc("Remote block will be fetched to disk when size of the block is " + - "above this threshold. This is to avoid a giant request takes too much memory. We can " + - "enable this config by setting a specific value(e.g. 200m). Note this configuration will " + - "affect both shuffle fetch and block manager remote block fetch. For users who " + - "enabled external shuffle service, this feature can only be worked when external shuffle" + - " service is newer than Spark 2.2.") + .doc("Remote block will be fetched to disk when size of the block is above this threshold " + + "in bytes. This is to avoid a giant request takes too much memory. We can enable this " + + "config by setting a specific value(e.g. 200m). Note this configuration will affect " + + "both shuffle fetch and block manager remote block fetch. For users who enabled " + + "external shuffle service, this feature can only be worked when external shuffle" + + "service is newer than Spark 2.2.") .bytesConf(ByteUnit.BYTE) .createWithDefault(Long.MaxValue) @@ -419,9 +431,9 @@ package object config { private[spark] val SHUFFLE_FILE_BUFFER_SIZE = ConfigBuilder("spark.shuffle.file.buffer") - .doc("Size of the in-memory buffer for each shuffle file output stream. " + - "These buffers reduce the number of disk seeks and system calls made " + - "in creating intermediate shuffle files.") + .doc("Size of the in-memory buffer for each shuffle file output stream, in KiB unless " + + "otherwise specified. These buffers reduce the number of disk seeks and system calls " + + "made in creating intermediate shuffle files.") .bytesConf(ByteUnit.KiB) .checkValue(v => v > 0 && v <= Int.MaxValue / 1024, s"The file buffer size must be greater than 0 and less than ${Int.MaxValue / 1024}.") @@ -430,7 +442,7 @@ package object config { private[spark] val SHUFFLE_UNSAFE_FILE_OUTPUT_BUFFER_SIZE = ConfigBuilder("spark.shuffle.unsafe.file.output.buffer") .doc("The file system for this buffer size after each partition " + - "is written in unsafe shuffle writer.") + "is written in unsafe shuffle writer. In KiB unless otherwise specified.") .bytesConf(ByteUnit.KiB) .checkValue(v => v > 0 && v <= Int.MaxValue / 1024, s"The buffer size must be greater than 0 and less than ${Int.MaxValue / 1024}.") @@ -438,7 +450,7 @@ package object config { private[spark] val SHUFFLE_DISK_WRITE_BUFFER_SIZE = ConfigBuilder("spark.shuffle.spill.diskWriteBufferSize") - .doc("The buffer size to use when writing the sorted records to an on-disk file.") + .doc("The buffer size, in bytes, to use when writing the sorted records to an on-disk file.") .bytesConf(ByteUnit.BYTE) .checkValue(v => v > 0 && v <= Int.MaxValue, s"The buffer size must be greater than 0 and less than ${Int.MaxValue}.") diff --git a/core/src/main/scala/org/apache/spark/internal/io/FileCommitProtocol.scala b/core/src/main/scala/org/apache/spark/internal/io/FileCommitProtocol.scala index 50f51e1af4530..6d0059b6a0272 100644 --- a/core/src/main/scala/org/apache/spark/internal/io/FileCommitProtocol.scala +++ b/core/src/main/scala/org/apache/spark/internal/io/FileCommitProtocol.scala @@ -28,8 +28,9 @@ import org.apache.spark.util.Utils * * 1. Implementations must be serializable, as the committer instance instantiated on the driver * will be used for tasks on executors. - * 2. Implementations should have a constructor with 2 arguments: - * (jobId: String, path: String) + * 2. Implementations should have a constructor with 2 or 3 arguments: + * (jobId: String, path: String) or + * (jobId: String, path: String, dynamicPartitionOverwrite: Boolean) * 3. A committer should not be reused across multiple Spark jobs. * * The proper call sequence is: @@ -139,10 +140,22 @@ object FileCommitProtocol { /** * Instantiates a FileCommitProtocol using the given className. */ - def instantiate(className: String, jobId: String, outputPath: String) - : FileCommitProtocol = { + def instantiate( + className: String, + jobId: String, + outputPath: String, + dynamicPartitionOverwrite: Boolean = false): FileCommitProtocol = { val clazz = Utils.classForName(className).asInstanceOf[Class[FileCommitProtocol]] - val ctor = clazz.getDeclaredConstructor(classOf[String], classOf[String]) - ctor.newInstance(jobId, outputPath) + // First try the constructor with arguments (jobId: String, outputPath: String, + // dynamicPartitionOverwrite: Boolean). + // If that doesn't exist, try the one with (jobId: string, outputPath: String). + try { + val ctor = clazz.getDeclaredConstructor(classOf[String], classOf[String], classOf[Boolean]) + ctor.newInstance(jobId, outputPath, dynamicPartitionOverwrite.asInstanceOf[java.lang.Boolean]) + } catch { + case _: NoSuchMethodException => + val ctor = clazz.getDeclaredConstructor(classOf[String], classOf[String]) + ctor.newInstance(jobId, outputPath) + } } } diff --git a/core/src/main/scala/org/apache/spark/internal/io/HadoopMapReduceCommitProtocol.scala b/core/src/main/scala/org/apache/spark/internal/io/HadoopMapReduceCommitProtocol.scala index 95c99d29c3a9c..3e60c50ada59b 100644 --- a/core/src/main/scala/org/apache/spark/internal/io/HadoopMapReduceCommitProtocol.scala +++ b/core/src/main/scala/org/apache/spark/internal/io/HadoopMapReduceCommitProtocol.scala @@ -39,8 +39,19 @@ import org.apache.spark.mapred.SparkHadoopMapRedUtil * * @param jobId the job's or stage's id * @param path the job's output path, or null if committer acts as a noop + * @param dynamicPartitionOverwrite If true, Spark will overwrite partition directories at runtime + * dynamically, i.e., we first write files under a staging + * directory with partition path, e.g. + * /path/to/staging/a=1/b=1/xxx.parquet. When committing the job, + * we first clean up the corresponding partition directories at + * destination path, e.g. /path/to/destination/a=1/b=1, and move + * files from staging directory to the corresponding partition + * directories under destination path. */ -class HadoopMapReduceCommitProtocol(jobId: String, path: String) +class HadoopMapReduceCommitProtocol( + jobId: String, + path: String, + dynamicPartitionOverwrite: Boolean = false) extends FileCommitProtocol with Serializable with Logging { import FileCommitProtocol._ @@ -67,9 +78,17 @@ class HadoopMapReduceCommitProtocol(jobId: String, path: String) @transient private var addedAbsPathFiles: mutable.Map[String, String] = null /** - * The staging directory for all files committed with absolute output paths. + * Tracks partitions with default path that have new files written into them by this task, + * e.g. a=1/b=2. Files under these partitions will be saved into staging directory and moved to + * destination directory at the end, if `dynamicPartitionOverwrite` is true. */ - private def absPathStagingDir: Path = new Path(path, "_temporary-" + jobId) + @transient private var partitionPaths: mutable.Set[String] = null + + /** + * The staging directory of this write job. Spark uses it to deal with files with absolute output + * path, or writing data into partitioned directory with dynamicPartitionOverwrite=true. + */ + private def stagingDir = new Path(path, ".spark-staging-" + jobId) protected def setupCommitter(context: TaskAttemptContext): OutputCommitter = { val format = context.getOutputFormatClass.newInstance() @@ -85,11 +104,16 @@ class HadoopMapReduceCommitProtocol(jobId: String, path: String) taskContext: TaskAttemptContext, dir: Option[String], ext: String): String = { val filename = getFilename(taskContext, ext) - val stagingDir: String = committer match { + val stagingDir: Path = committer match { + case _ if dynamicPartitionOverwrite => + assert(dir.isDefined, + "The dataset to be written must be partitioned when dynamicPartitionOverwrite is true.") + partitionPaths += dir.get + this.stagingDir // For FileOutputCommitter it has its own staging path called "work path". case f: FileOutputCommitter => - Option(f.getWorkPath).map(_.toString).getOrElse(path) - case _ => path + new Path(Option(f.getWorkPath).map(_.toString).getOrElse(path)) + case _ => new Path(path) } dir.map { d => @@ -106,8 +130,7 @@ class HadoopMapReduceCommitProtocol(jobId: String, path: String) // Include a UUID here to prevent file collisions for one task writing to different dirs. // In principle we could include hash(absoluteDir) instead but this is simpler. - val tmpOutputPath = new Path( - absPathStagingDir, UUID.randomUUID().toString() + "-" + filename).toString + val tmpOutputPath = new Path(stagingDir, UUID.randomUUID().toString() + "-" + filename).toString addedAbsPathFiles(tmpOutputPath) = absOutputPath tmpOutputPath @@ -141,23 +164,52 @@ class HadoopMapReduceCommitProtocol(jobId: String, path: String) override def commitJob(jobContext: JobContext, taskCommits: Seq[TaskCommitMessage]): Unit = { committer.commitJob(jobContext) - val filesToMove = taskCommits.map(_.obj.asInstanceOf[Map[String, String]]) - .foldLeft(Map[String, String]())(_ ++ _) - logDebug(s"Committing files staged for absolute locations $filesToMove") + if (hasValidPath) { - val fs = absPathStagingDir.getFileSystem(jobContext.getConfiguration) + val (allAbsPathFiles, allPartitionPaths) = + taskCommits.map(_.obj.asInstanceOf[(Map[String, String], Set[String])]).unzip + val fs = stagingDir.getFileSystem(jobContext.getConfiguration) + + val filesToMove = allAbsPathFiles.foldLeft(Map[String, String]())(_ ++ _) + logDebug(s"Committing files staged for absolute locations $filesToMove") + if (dynamicPartitionOverwrite) { + val absPartitionPaths = filesToMove.values.map(new Path(_).getParent).toSet + logDebug(s"Clean up absolute partition directories for overwriting: $absPartitionPaths") + absPartitionPaths.foreach(fs.delete(_, true)) + } for ((src, dst) <- filesToMove) { fs.rename(new Path(src), new Path(dst)) } - fs.delete(absPathStagingDir, true) + + if (dynamicPartitionOverwrite) { + val partitionPaths = allPartitionPaths.foldLeft(Set[String]())(_ ++ _) + logDebug(s"Clean up default partition directories for overwriting: $partitionPaths") + for (part <- partitionPaths) { + val finalPartPath = new Path(path, part) + if (!fs.delete(finalPartPath, true) && !fs.exists(finalPartPath.getParent)) { + // According to the official hadoop FileSystem API spec, delete op should assume + // the destination is no longer present regardless of return value, thus we do not + // need to double check if finalPartPath exists before rename. + // Also in our case, based on the spec, delete returns false only when finalPartPath + // does not exist. When this happens, we need to take action if parent of finalPartPath + // also does not exist(e.g. the scenario described on SPARK-23815), because + // FileSystem API spec on rename op says the rename dest(finalPartPath) must have + // a parent that exists, otherwise we may get unexpected result on the rename. + fs.mkdirs(finalPartPath.getParent) + } + fs.rename(new Path(stagingDir, part), finalPartPath) + } + } + + fs.delete(stagingDir, true) } } override def abortJob(jobContext: JobContext): Unit = { committer.abortJob(jobContext, JobStatus.State.FAILED) if (hasValidPath) { - val fs = absPathStagingDir.getFileSystem(jobContext.getConfiguration) - fs.delete(absPathStagingDir, true) + val fs = stagingDir.getFileSystem(jobContext.getConfiguration) + fs.delete(stagingDir, true) } } @@ -165,13 +217,14 @@ class HadoopMapReduceCommitProtocol(jobId: String, path: String) committer = setupCommitter(taskContext) committer.setupTask(taskContext) addedAbsPathFiles = mutable.Map[String, String]() + partitionPaths = mutable.Set[String]() } override def commitTask(taskContext: TaskAttemptContext): TaskCommitMessage = { val attemptId = taskContext.getTaskAttemptID SparkHadoopMapRedUtil.commitTask( committer, taskContext, attemptId.getJobID.getId, attemptId.getTaskID.getId) - new TaskCommitMessage(addedAbsPathFiles.toMap) + new TaskCommitMessage(addedAbsPathFiles.toMap -> partitionPaths.toSet) } override def abortTask(taskContext: TaskAttemptContext): Unit = { diff --git a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala index 764735dc4eae7..db8aff94ea1e1 100644 --- a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala +++ b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala @@ -69,9 +69,9 @@ object SparkHadoopMapRedUtil extends Logging { if (shouldCoordinateWithDriver) { val outputCommitCoordinator = SparkEnv.get.outputCommitCoordinator - val taskAttemptNumber = TaskContext.get().attemptNumber() - val stageId = TaskContext.get().stageId() - val canCommit = outputCommitCoordinator.canCommit(stageId, splitId, taskAttemptNumber) + val ctx = TaskContext.get() + val canCommit = outputCommitCoordinator.canCommit(ctx.stageId(), ctx.stageAttemptNumber(), + splitId, ctx.attemptNumber()) if (canCommit) { performCommit() @@ -81,7 +81,7 @@ object SparkHadoopMapRedUtil extends Logging { logInfo(message) // We need to abort the task so that the driver can reschedule new attempts, if necessary committer.abortTask(mrTaskContext) - throw new CommitDeniedException(message, stageId, splitId, taskAttemptNumber) + throw new CommitDeniedException(message, ctx.stageId(), splitId, ctx.attemptNumber()) } } else { // Speculation is disabled or a user has chosen to manually bypass the commit coordination diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala index c9ed12f4e1bd4..47669a0aeb478 100644 --- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala @@ -95,7 +95,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi // the left side of max is >=1 whenever partsScanned >= 2 numPartsToTry = Math.max(1, (1.5 * num * partsScanned / results.size).toInt - partsScanned) - numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) + numPartsToTry = Math.min(numPartsToTry, partsScanned * 4L) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 8798dfc925362..0574abdca32ac 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -150,7 +150,7 @@ abstract class RDD[T: ClassTag]( val id: Int = sc.newRddId() /** A friendly name for this RDD */ - @transient var name: String = null + @transient var name: String = _ /** Assign a name to this RDD */ def setName(_name: String): this.type = { @@ -224,8 +224,8 @@ abstract class RDD[T: ClassTag]( // Our dependencies and partitions will be gotten by calling subclass's methods below, and will // be overwritten when we're checkpointed - private var dependencies_ : Seq[Dependency[_]] = null - @transient private var partitions_ : Array[Partition] = null + private var dependencies_ : Seq[Dependency[_]] = _ + @transient private var partitions_ : Array[Partition] = _ /** An Option holding our checkpoint RDD, if we are checkpointed */ private def checkpointRDD: Option[CheckpointRDD[T]] = checkpointData.flatMap(_.checkpointRDD) @@ -297,7 +297,7 @@ abstract class RDD[T: ClassTag]( private[spark] def getNarrowAncestors: Seq[RDD[_]] = { val ancestors = new mutable.HashSet[RDD[_]] - def visit(rdd: RDD[_]) { + def visit(rdd: RDD[_]): Unit = { val narrowDependencies = rdd.dependencies.filter(_.isInstanceOf[NarrowDependency[_]]) val narrowParents = narrowDependencies.map(_.rdd) val narrowParentsNotVisited = narrowParents.filterNot(ancestors.contains) @@ -414,6 +414,8 @@ abstract class RDD[T: ClassTag]( * * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, * which can avoid performing a shuffle. + * + * TODO Fix the Shuffle+Repartition data loss issue described in SPARK-23207. */ def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope { coalesce(numPartitions, shuffle = true) @@ -449,7 +451,7 @@ abstract class RDD[T: ClassTag]( if (shuffle) { /** Distributes elements evenly across output partitions, starting from a random partition. */ val distributePartition = (index: Int, items: Iterator[T]) => { - var position = (new Random(hashing.byteswap32(index))).nextInt(numPartitions) + var position = new Random(hashing.byteswap32(index)).nextInt(numPartitions) items.map { t => // Note that the hash code of the key will just be the key itself. The HashPartitioner // will mod it with the number of total partitions. @@ -951,7 +953,7 @@ abstract class RDD[T: ClassTag]( def collectPartition(p: Int): Array[T] = { sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p)).head } - (0 until partitions.length).iterator.flatMap(i => collectPartition(i)) + partitions.indices.iterator.flatMap(i => collectPartition(i)) } /** @@ -1338,6 +1340,7 @@ abstract class RDD[T: ClassTag]( // The number of partitions to try in this iteration. It is ok for this number to be // greater than totalParts because we actually cap it at totalParts in runJob. var numPartsToTry = 1L + val left = num - buf.size if (partsScanned > 0) { // If we didn't find any rows after the previous iteration, quadruple and retry. // Otherwise, interpolate the number of partitions we need to try, but overestimate @@ -1345,13 +1348,12 @@ abstract class RDD[T: ClassTag]( if (buf.isEmpty) { numPartsToTry = partsScanned * scaleUpFactor } else { - // the left side of max is >=1 whenever partsScanned >= 2 - numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1) + // As left > 0, numPartsToTry is always >= 1 + numPartsToTry = Math.ceil(1.5 * left * partsScanned / buf.size).toInt numPartsToTry = Math.min(numPartsToTry, partsScanned * scaleUpFactor) } } - val left = num - buf.size val p = partsScanned.until(math.min(partsScanned + numPartsToTry, totalParts).toInt) val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p) @@ -1677,8 +1679,7 @@ abstract class RDD[T: ClassTag]( // an RDD and its parent in every batch, in which case the parent may never be checkpointed // and its lineage never truncated, leading to OOMs in the long run (SPARK-6847). private val checkpointAllMarkedAncestors = - Option(sc.getLocalProperty(RDD.CHECKPOINT_ALL_MARKED_ANCESTORS)) - .map(_.toBoolean).getOrElse(false) + Option(sc.getLocalProperty(RDD.CHECKPOINT_ALL_MARKED_ANCESTORS)).exists(_.toBoolean) /** Returns the first parent RDD */ protected[spark] def firstParent[U: ClassTag]: RDD[U] = { @@ -1686,7 +1687,7 @@ abstract class RDD[T: ClassTag]( } /** Returns the jth parent RDD: e.g. rdd.parent[T](0) is equivalent to rdd.firstParent[T] */ - protected[spark] def parent[U: ClassTag](j: Int) = { + protected[spark] def parent[U: ClassTag](j: Int): RDD[U] = { dependencies(j).rdd.asInstanceOf[RDD[U]] } @@ -1754,7 +1755,7 @@ abstract class RDD[T: ClassTag]( * collected. Subclasses of RDD may override this method for implementing their own cleaning * logic. See [[org.apache.spark.rdd.UnionRDD]] for an example. */ - protected def clearDependencies() { + protected def clearDependencies(): Unit = { dependencies_ = null } @@ -1790,7 +1791,7 @@ abstract class RDD[T: ClassTag]( val lastDepStrings = debugString(lastDep.rdd, prefix, lastDep.isInstanceOf[ShuffleDependency[_, _, _]], true) - (frontDepStrings ++ lastDepStrings) + frontDepStrings ++ lastDepStrings } } // The first RDD in the dependency stack has no parents, so no need for a +- diff --git a/core/src/main/scala/org/apache/spark/rpc/netty/NettyRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/netty/NettyRpcEnv.scala index f951591e02a5c..a2936d6ad539c 100644 --- a/core/src/main/scala/org/apache/spark/rpc/netty/NettyRpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/netty/NettyRpcEnv.scala @@ -332,16 +332,14 @@ private[netty] class NettyRpcEnv( val pipe = Pipe.open() val source = new FileDownloadChannel(pipe.source()) - try { + Utils.tryWithSafeFinallyAndFailureCallbacks(block = { val client = downloadClient(parsedUri.getHost(), parsedUri.getPort()) val callback = new FileDownloadCallback(pipe.sink(), source, client) client.stream(parsedUri.getPath(), callback) - } catch { - case e: Exception => - pipe.sink().close() - source.close() - throw e - } + })(catchBlock = { + pipe.sink().close() + source.close() + }) source } @@ -370,24 +368,33 @@ private[netty] class NettyRpcEnv( fileDownloadFactory.createClient(host, port) } - private class FileDownloadChannel(source: ReadableByteChannel) extends ReadableByteChannel { + private class FileDownloadChannel(source: Pipe.SourceChannel) extends ReadableByteChannel { @volatile private var error: Throwable = _ def setError(e: Throwable): Unit = { + // This setError callback is invoked by internal RPC threads in order to propagate remote + // exceptions to application-level threads which are reading from this channel. When an + // RPC error occurs, the RPC system will call setError() and then will close the + // Pipe.SinkChannel corresponding to the other end of the `source` pipe. Closing of the pipe + // sink will cause `source.read()` operations to return EOF, unblocking the application-level + // reading thread. Thus there is no need to actually call `source.close()` here in the + // onError() callback and, in fact, calling it here would be dangerous because the close() + // would be asynchronous with respect to the read() call and could trigger race-conditions + // that lead to data corruption. See the PR for SPARK-22982 for more details on this topic. error = e - source.close() } override def read(dst: ByteBuffer): Int = { Try(source.read(dst)) match { + // See the documentation above in setError(): if an RPC error has occurred then setError() + // will be called to propagate the RPC error and then `source`'s corresponding + // Pipe.SinkChannel will be closed, unblocking this read. In that case, we want to propagate + // the remote RPC exception (and not any exceptions triggered by the pipe close, such as + // ChannelClosedException), hence this `error != null` check: + case _ if error != null => throw error case Success(bytesRead) => bytesRead - case Failure(readErr) => - if (error != null) { - throw error - } else { - throw readErr - } + case Failure(readErr) => throw readErr } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/AsyncEventQueue.scala b/core/src/main/scala/org/apache/spark/scheduler/AsyncEventQueue.scala index 7e14938acd8e0..e2b6df4600590 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/AsyncEventQueue.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/AsyncEventQueue.scala @@ -34,7 +34,11 @@ import org.apache.spark.util.Utils * Delivery will only begin when the `start()` method is called. The `stop()` method should be * called when no more events need to be delivered. */ -private class AsyncEventQueue(val name: String, conf: SparkConf, metrics: LiveListenerBusMetrics) +private class AsyncEventQueue( + val name: String, + conf: SparkConf, + metrics: LiveListenerBusMetrics, + bus: LiveListenerBus) extends SparkListenerBus with Logging { @@ -81,23 +85,18 @@ private class AsyncEventQueue(val name: String, conf: SparkConf, metrics: LiveLi } private def dispatch(): Unit = LiveListenerBus.withinListenerThread.withValue(true) { - try { - var next: SparkListenerEvent = eventQueue.take() - while (next != POISON_PILL) { - val ctx = processingTime.time() - try { - super.postToAll(next) - } finally { - ctx.stop() - } - eventCount.decrementAndGet() - next = eventQueue.take() + var next: SparkListenerEvent = eventQueue.take() + while (next != POISON_PILL) { + val ctx = processingTime.time() + try { + super.postToAll(next) + } finally { + ctx.stop() } eventCount.decrementAndGet() - } catch { - case ie: InterruptedException => - logInfo(s"Stopping listener queue $name.", ie) + next = eventQueue.take() } + eventCount.decrementAndGet() } override protected def getTimer(listener: SparkListenerInterface): Option[Timer] = { @@ -130,7 +129,11 @@ private class AsyncEventQueue(val name: String, conf: SparkConf, metrics: LiveLi eventCount.incrementAndGet() eventQueue.put(POISON_PILL) } - dispatchThread.join() + // this thread might be trying to stop itself as part of error handling -- we can't join + // in that case. + if (Thread.currentThread() != dispatchThread) { + dispatchThread.join() + } } def post(event: SparkListenerEvent): Unit = { @@ -166,7 +169,7 @@ private class AsyncEventQueue(val name: String, conf: SparkConf, metrics: LiveLi val prevLastReportTimestamp = lastReportTimestamp lastReportTimestamp = System.currentTimeMillis() val previous = new java.util.Date(prevLastReportTimestamp) - logWarning(s"Dropped $droppedEvents events from $name since $previous.") + logWarning(s"Dropped $droppedCount events from $name since $previous.") } } } @@ -187,6 +190,12 @@ private class AsyncEventQueue(val name: String, conf: SparkConf, metrics: LiveLi true } + override def removeListenerOnError(listener: SparkListenerInterface): Unit = { + // the listener failed in an unrecoverably way, we want to remove it from the entire + // LiveListenerBus (potentially stopping a queue if it is empty) + bus.removeListener(listener) + } + } private object AsyncEventQueue { diff --git a/core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala b/core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala index cd8e61d6d0208..30cf75d43ee09 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala @@ -152,7 +152,8 @@ private[scheduler] class BlacklistTracker ( case Some(a) => logInfo(s"Killing blacklisted executor id $exec " + s"since ${config.BLACKLIST_KILL_ENABLED.key} is set.") - a.killExecutors(Seq(exec), true, true) + a.killExecutors(Seq(exec), adjustTargetNumExecutors = false, countFailures = false, + force = true) case None => logWarning(s"Not attempting to kill blacklisted executor id $exec " + s"since allocation client is not defined.") @@ -209,7 +210,7 @@ private[scheduler] class BlacklistTracker ( updateNextExpiryTime() killBlacklistedExecutor(exec) - val blacklistedExecsOnNode = nodeToBlacklistedExecs.getOrElseUpdate(exec, HashSet[String]()) + val blacklistedExecsOnNode = nodeToBlacklistedExecs.getOrElseUpdate(host, HashSet[String]()) blacklistedExecsOnNode += exec } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index c2498d4808e91..7029e2237a866 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -39,7 +39,7 @@ import org.apache.spark.internal.Logging import org.apache.spark.internal.config import org.apache.spark.network.util.JavaUtils import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult} -import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.{RDD, RDDCheckpointData} import org.apache.spark.rpc.RpcTimeout import org.apache.spark.storage._ import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat @@ -815,7 +815,8 @@ class DAGScheduler( private[scheduler] def handleBeginEvent(task: Task[_], taskInfo: TaskInfo) { // Note that there is a chance that this task is launched after the stage is cancelled. // In that case, we wouldn't have the stage anymore in stageIdToStage. - val stageAttemptId = stageIdToStage.get(task.stageId).map(_.latestInfo.attemptId).getOrElse(-1) + val stageAttemptId = + stageIdToStage.get(task.stageId).map(_.latestInfo.attemptNumber).getOrElse(-1) listenerBus.post(SparkListenerTaskStart(task.stageId, stageAttemptId, taskInfo)) } @@ -1015,15 +1016,24 @@ class DAGScheduler( // might modify state of objects referenced in their closures. This is necessary in Hadoop // where the JobConf/Configuration object is not thread-safe. var taskBinary: Broadcast[Array[Byte]] = null + var partitions: Array[Partition] = null try { // For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep). // For ResultTask, serialize and broadcast (rdd, func). - val taskBinaryBytes: Array[Byte] = stage match { - case stage: ShuffleMapStage => - JavaUtils.bufferToArray( - closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef)) - case stage: ResultStage => - JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef)) + var taskBinaryBytes: Array[Byte] = null + // taskBinaryBytes and partitions are both effected by the checkpoint status. We need + // this synchronization in case another concurrent job is checkpointing this RDD, so we get a + // consistent view of both variables. + RDDCheckpointData.synchronized { + taskBinaryBytes = stage match { + case stage: ShuffleMapStage => + JavaUtils.bufferToArray( + closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef)) + case stage: ResultStage => + JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef)) + } + + partitions = stage.rdd.partitions } taskBinary = sc.broadcast(taskBinaryBytes) @@ -1048,9 +1058,9 @@ class DAGScheduler( stage.pendingPartitions.clear() partitionsToCompute.map { id => val locs = taskIdToLocations(id) - val part = stage.rdd.partitions(id) + val part = partitions(id) stage.pendingPartitions += id - new ShuffleMapTask(stage.id, stage.latestInfo.attemptId, + new ShuffleMapTask(stage.id, stage.latestInfo.attemptNumber, taskBinary, part, locs, properties, serializedTaskMetrics, Option(jobId), Option(sc.applicationId), sc.applicationAttemptId) } @@ -1058,9 +1068,9 @@ class DAGScheduler( case stage: ResultStage => partitionsToCompute.map { id => val p: Int = stage.partitions(id) - val part = stage.rdd.partitions(p) + val part = partitions(p) val locs = taskIdToLocations(id) - new ResultTask(stage.id, stage.latestInfo.attemptId, + new ResultTask(stage.id, stage.latestInfo.attemptNumber, taskBinary, part, locs, id, properties, serializedTaskMetrics, Option(jobId), Option(sc.applicationId), sc.applicationAttemptId) } @@ -1076,23 +1086,22 @@ class DAGScheduler( logInfo(s"Submitting ${tasks.size} missing tasks from $stage (${stage.rdd}) (first 15 " + s"tasks are for partitions ${tasks.take(15).map(_.partitionId)})") taskScheduler.submitTasks(new TaskSet( - tasks.toArray, stage.id, stage.latestInfo.attemptId, jobId, properties)) + tasks.toArray, stage.id, stage.latestInfo.attemptNumber, jobId, properties)) } else { // Because we posted SparkListenerStageSubmitted earlier, we should mark // the stage as completed here in case there are no tasks to run markStageAsFinished(stage, None) - val debugString = stage match { + stage match { case stage: ShuffleMapStage => - s"Stage ${stage} is actually done; " + - s"(available: ${stage.isAvailable}," + - s"available outputs: ${stage.numAvailableOutputs}," + - s"partitions: ${stage.numPartitions})" + logDebug(s"Stage ${stage} is actually done; " + + s"(available: ${stage.isAvailable}," + + s"available outputs: ${stage.numAvailableOutputs}," + + s"partitions: ${stage.numPartitions})") + markMapStageJobsAsFinished(stage) case stage : ResultStage => - s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})" + logDebug(s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})") } - logDebug(debugString) - submitWaitingChildStages(stage) } } @@ -1164,6 +1173,7 @@ class DAGScheduler( outputCommitCoordinator.taskCompleted( stageId, + task.stageAttemptId, task.partitionId, event.taskInfo.attemptNumber, // this is a task attempt number event.reason) @@ -1245,7 +1255,7 @@ class DAGScheduler( val status = event.result.asInstanceOf[MapStatus] val execId = status.location.executorId logDebug("ShuffleMapTask finished on " + execId) - if (stageIdToStage(task.stageId).latestInfo.attemptId == task.stageAttemptId) { + if (stageIdToStage(task.stageId).latestInfo.attemptNumber == task.stageAttemptId) { // This task was for the currently running attempt of the stage. Since the task // completed successfully from the perspective of the TaskSetManager, mark it as // no longer pending (the TaskSetManager may consider the task complete even @@ -1297,13 +1307,7 @@ class DAGScheduler( shuffleStage.findMissingPartitions().mkString(", ")) submitStage(shuffleStage) } else { - // Mark any map-stage jobs waiting on this stage as finished - if (shuffleStage.mapStageJobs.nonEmpty) { - val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep) - for (job <- shuffleStage.mapStageJobs) { - markMapStageJobAsFinished(job, stats) - } - } + markMapStageJobsAsFinished(shuffleStage) submitWaitingChildStages(shuffleStage) } } @@ -1324,28 +1328,29 @@ class DAGScheduler( val failedStage = stageIdToStage(task.stageId) val mapStage = shuffleIdToMapStage(shuffleId) - if (failedStage.latestInfo.attemptId != task.stageAttemptId) { + if (failedStage.latestInfo.attemptNumber != task.stageAttemptId) { logInfo(s"Ignoring fetch failure from $task as it's from $failedStage attempt" + s" ${task.stageAttemptId} and there is a more recent attempt for that stage " + - s"(attempt ID ${failedStage.latestInfo.attemptId}) running") + s"(attempt ${failedStage.latestInfo.attemptNumber}) running") } else { + failedStage.fetchFailedAttemptIds.add(task.stageAttemptId) + val shouldAbortStage = + failedStage.fetchFailedAttemptIds.size >= maxConsecutiveStageAttempts || + disallowStageRetryForTest + // It is likely that we receive multiple FetchFailed for a single stage (because we have // multiple tasks running concurrently on different executors). In that case, it is // possible the fetch failure has already been handled by the scheduler. if (runningStages.contains(failedStage)) { logInfo(s"Marking $failedStage (${failedStage.name}) as failed " + s"due to a fetch failure from $mapStage (${mapStage.name})") - markStageAsFinished(failedStage, Some(failureMessage)) + markStageAsFinished(failedStage, errorMessage = Some(failureMessage), + willRetry = !shouldAbortStage) } else { logDebug(s"Received fetch failure from $task, but its from $failedStage which is no " + s"longer running") } - failedStage.fetchFailedAttemptIds.add(task.stageAttemptId) - val shouldAbortStage = - failedStage.fetchFailedAttemptIds.size >= maxConsecutiveStageAttempts || - disallowStageRetryForTest - if (shouldAbortStage) { val abortMessage = if (disallowStageRetryForTest) { "Fetch failure will not retry stage due to testing config" @@ -1423,6 +1428,16 @@ class DAGScheduler( } } + private[scheduler] def markMapStageJobsAsFinished(shuffleStage: ShuffleMapStage): Unit = { + // Mark any map-stage jobs waiting on this stage as finished + if (shuffleStage.isAvailable && shuffleStage.mapStageJobs.nonEmpty) { + val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep) + for (job <- shuffleStage.mapStageJobs) { + markMapStageJobAsFinished(job, stats) + } + } + } + /** * Responds to an executor being lost. This is called inside the event loop, so it assumes it can * modify the scheduler's internal state. Use executorLost() to post a loss event from outside. @@ -1534,7 +1549,10 @@ class DAGScheduler( /** * Marks a stage as finished and removes it from the list of running stages. */ - private def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None): Unit = { + private def markStageAsFinished( + stage: Stage, + errorMessage: Option[String] = None, + willRetry: Boolean = false): Unit = { val serviceTime = stage.latestInfo.submissionTime match { case Some(t) => "%.03f".format((clock.getTimeMillis() - t) / 1000.0) case _ => "Unknown" @@ -1553,7 +1571,9 @@ class DAGScheduler( logInfo(s"$stage (${stage.name}) failed in $serviceTime s due to ${errorMessage.get}") } - outputCommitCoordinator.stageEnd(stage.id) + if (!willRetry) { + outputCommitCoordinator.stageEnd(stage.id) + } listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) runningStages -= stage } diff --git a/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala index 23121402b1025..d135190d1e919 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala @@ -62,6 +62,9 @@ private[spark] class LiveListenerBus(conf: SparkConf) { private val queues = new CopyOnWriteArrayList[AsyncEventQueue]() + // Visible for testing. + @volatile private[scheduler] var queuedEvents = new mutable.ListBuffer[SparkListenerEvent]() + /** Add a listener to queue shared by all non-internal listeners. */ def addToSharedQueue(listener: SparkListenerInterface): Unit = { addToQueue(listener, SHARED_QUEUE) @@ -99,7 +102,7 @@ private[spark] class LiveListenerBus(conf: SparkConf) { queue.addListener(listener) case None => - val newQueue = new AsyncEventQueue(queue, conf, metrics) + val newQueue = new AsyncEventQueue(queue, conf, metrics, this) newQueue.addListener(listener) if (started.get()) { newQueue.start(sparkContext) @@ -125,13 +128,39 @@ private[spark] class LiveListenerBus(conf: SparkConf) { /** Post an event to all queues. */ def post(event: SparkListenerEvent): Unit = { - if (!stopped.get()) { - metrics.numEventsPosted.inc() - val it = queues.iterator() - while (it.hasNext()) { - it.next().post(event) + if (stopped.get()) { + return + } + + metrics.numEventsPosted.inc() + + // If the event buffer is null, it means the bus has been started and we can avoid + // synchronization and post events directly to the queues. This should be the most + // common case during the life of the bus. + if (queuedEvents == null) { + postToQueues(event) + return + } + + // Otherwise, need to synchronize to check whether the bus is started, to make sure the thread + // calling start() picks up the new event. + synchronized { + if (!started.get()) { + queuedEvents += event + return } } + + // If the bus was already started when the check above was made, just post directly to the + // queues. + postToQueues(event) + } + + private def postToQueues(event: SparkListenerEvent): Unit = { + val it = queues.iterator() + while (it.hasNext()) { + it.next().post(event) + } } /** @@ -149,7 +178,11 @@ private[spark] class LiveListenerBus(conf: SparkConf) { } this.sparkContext = sc - queues.asScala.foreach(_.start(sc)) + queues.asScala.foreach { q => + q.start(sc) + queuedEvents.foreach(q.post) + } + queuedEvents = null metricsSystem.registerSource(metrics) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index 83d87b548a430..b382d623806e2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -27,7 +27,11 @@ import org.apache.spark.util.{RpcUtils, ThreadUtils} private sealed trait OutputCommitCoordinationMessage extends Serializable private case object StopCoordinator extends OutputCommitCoordinationMessage -private case class AskPermissionToCommitOutput(stage: Int, partition: Int, attemptNumber: Int) +private case class AskPermissionToCommitOutput( + stage: Int, + stageAttempt: Int, + partition: Int, + attemptNumber: Int) /** * Authority that decides whether tasks can commit output to HDFS. Uses a "first committer wins" @@ -45,13 +49,15 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean) // Initialized by SparkEnv var coordinatorRef: Option[RpcEndpointRef] = None - private type StageId = Int - private type PartitionId = Int - private type TaskAttemptNumber = Int - private val NO_AUTHORIZED_COMMITTER: TaskAttemptNumber = -1 + // Class used to identify a committer. The task ID for a committer is implicitly defined by + // the partition being processed, but the coordinator needs to keep track of both the stage + // attempt and the task attempt, because in some situations the same task may be running + // concurrently in two different attempts of the same stage. + private case class TaskIdentifier(stageAttempt: Int, taskAttempt: Int) + private case class StageState(numPartitions: Int) { - val authorizedCommitters = Array.fill[TaskAttemptNumber](numPartitions)(NO_AUTHORIZED_COMMITTER) - val failures = mutable.Map[PartitionId, mutable.Set[TaskAttemptNumber]]() + val authorizedCommitters = Array.fill[TaskIdentifier](numPartitions)(null) + val failures = mutable.Map[Int, mutable.Set[TaskIdentifier]]() } /** @@ -64,7 +70,7 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean) * * Access to this map should be guarded by synchronizing on the OutputCommitCoordinator instance. */ - private val stageStates = mutable.Map[StageId, StageState]() + private val stageStates = mutable.Map[Int, StageState]() /** * Returns whether the OutputCommitCoordinator's internal data structures are all empty. @@ -87,10 +93,11 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean) * @return true if this task is authorized to commit, false otherwise */ def canCommit( - stage: StageId, - partition: PartitionId, - attemptNumber: TaskAttemptNumber): Boolean = { - val msg = AskPermissionToCommitOutput(stage, partition, attemptNumber) + stage: Int, + stageAttempt: Int, + partition: Int, + attemptNumber: Int): Boolean = { + val msg = AskPermissionToCommitOutput(stage, stageAttempt, partition, attemptNumber) coordinatorRef match { case Some(endpointRef) => ThreadUtils.awaitResult(endpointRef.ask[Boolean](msg), @@ -103,26 +110,35 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean) } /** - * Called by the DAGScheduler when a stage starts. + * Called by the DAGScheduler when a stage starts. Initializes the stage's state if it hasn't + * yet been initialized. * * @param stage the stage id. * @param maxPartitionId the maximum partition id that could appear in this stage's tasks (i.e. * the maximum possible value of `context.partitionId`). */ - private[scheduler] def stageStart(stage: StageId, maxPartitionId: Int): Unit = synchronized { - stageStates(stage) = new StageState(maxPartitionId + 1) + private[scheduler] def stageStart(stage: Int, maxPartitionId: Int): Unit = synchronized { + stageStates.get(stage) match { + case Some(state) => + require(state.authorizedCommitters.length == maxPartitionId + 1) + logInfo(s"Reusing state from previous attempt of stage $stage.") + + case _ => + stageStates(stage) = new StageState(maxPartitionId + 1) + } } // Called by DAGScheduler - private[scheduler] def stageEnd(stage: StageId): Unit = synchronized { + private[scheduler] def stageEnd(stage: Int): Unit = synchronized { stageStates.remove(stage) } // Called by DAGScheduler private[scheduler] def taskCompleted( - stage: StageId, - partition: PartitionId, - attemptNumber: TaskAttemptNumber, + stage: Int, + stageAttempt: Int, + partition: Int, + attemptNumber: Int, reason: TaskEndReason): Unit = synchronized { val stageState = stageStates.getOrElse(stage, { logDebug(s"Ignoring task completion for completed stage") @@ -131,16 +147,17 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean) reason match { case Success => // The task output has been committed successfully - case denied: TaskCommitDenied => - logInfo(s"Task was denied committing, stage: $stage, partition: $partition, " + - s"attempt: $attemptNumber") - case otherReason => + case _: TaskCommitDenied => + logInfo(s"Task was denied committing, stage: $stage.$stageAttempt, " + + s"partition: $partition, attempt: $attemptNumber") + case _ => // Mark the attempt as failed to blacklist from future commit protocol - stageState.failures.getOrElseUpdate(partition, mutable.Set()) += attemptNumber - if (stageState.authorizedCommitters(partition) == attemptNumber) { + val taskId = TaskIdentifier(stageAttempt, attemptNumber) + stageState.failures.getOrElseUpdate(partition, mutable.Set()) += taskId + if (stageState.authorizedCommitters(partition) == taskId) { logDebug(s"Authorized committer (attemptNumber=$attemptNumber, stage=$stage, " + s"partition=$partition) failed; clearing lock") - stageState.authorizedCommitters(partition) = NO_AUTHORIZED_COMMITTER + stageState.authorizedCommitters(partition) = null } } } @@ -155,47 +172,41 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean) // Marked private[scheduler] instead of private so this can be mocked in tests private[scheduler] def handleAskPermissionToCommit( - stage: StageId, - partition: PartitionId, - attemptNumber: TaskAttemptNumber): Boolean = synchronized { + stage: Int, + stageAttempt: Int, + partition: Int, + attemptNumber: Int): Boolean = synchronized { stageStates.get(stage) match { - case Some(state) if attemptFailed(state, partition, attemptNumber) => - logInfo(s"Denying attemptNumber=$attemptNumber to commit for stage=$stage," + - s" partition=$partition as task attempt $attemptNumber has already failed.") + case Some(state) if attemptFailed(state, stageAttempt, partition, attemptNumber) => + logInfo(s"Commit denied for stage=$stage.$stageAttempt, partition=$partition: " + + s"task attempt $attemptNumber already marked as failed.") false case Some(state) => - state.authorizedCommitters(partition) match { - case NO_AUTHORIZED_COMMITTER => - logDebug(s"Authorizing attemptNumber=$attemptNumber to commit for stage=$stage, " + - s"partition=$partition") - state.authorizedCommitters(partition) = attemptNumber - true - case existingCommitter => - // Coordinator should be idempotent when receiving AskPermissionToCommit. - if (existingCommitter == attemptNumber) { - logWarning(s"Authorizing duplicate request to commit for " + - s"attemptNumber=$attemptNumber to commit for stage=$stage," + - s" partition=$partition; existingCommitter = $existingCommitter." + - s" This can indicate dropped network traffic.") - true - } else { - logDebug(s"Denying attemptNumber=$attemptNumber to commit for stage=$stage, " + - s"partition=$partition; existingCommitter = $existingCommitter") - false - } + val existing = state.authorizedCommitters(partition) + if (existing == null) { + logDebug(s"Commit allowed for stage=$stage.$stageAttempt, partition=$partition, " + + s"task attempt $attemptNumber") + state.authorizedCommitters(partition) = TaskIdentifier(stageAttempt, attemptNumber) + true + } else { + logDebug(s"Commit denied for stage=$stage.$stageAttempt, partition=$partition: " + + s"already committed by $existing") + false } case None => - logDebug(s"Stage $stage has completed, so not allowing" + - s" attempt number $attemptNumber of partition $partition to commit") + logDebug(s"Commit denied for stage=$stage.$stageAttempt, partition=$partition: " + + "stage already marked as completed.") false } } private def attemptFailed( stageState: StageState, - partition: PartitionId, - attempt: TaskAttemptNumber): Boolean = synchronized { - stageState.failures.get(partition).exists(_.contains(attempt)) + stageAttempt: Int, + partition: Int, + attempt: Int): Boolean = synchronized { + val failInfo = TaskIdentifier(stageAttempt, attempt) + stageState.failures.get(partition).exists(_.contains(failInfo)) } } @@ -215,9 +226,10 @@ private[spark] object OutputCommitCoordinator { } override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { - case AskPermissionToCommitOutput(stage, partition, attemptNumber) => + case AskPermissionToCommitOutput(stage, stageAttempt, partition, attemptNumber) => context.reply( - outputCommitCoordinator.handleAskPermissionToCommit(stage, partition, attemptNumber)) + outputCommitCoordinator.handleAskPermissionToCommit(stage, stageAttempt, partition, + attemptNumber)) } } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala index c513ed36d1680..903e25b7986f2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala @@ -30,7 +30,7 @@ import org.apache.spark.storage.RDDInfo @DeveloperApi class StageInfo( val stageId: Int, - val attemptId: Int, + @deprecated("Use attemptNumber instead", "2.3.0") val attemptId: Int, val name: String, val numTasks: Int, val rddInfos: Seq[RDDInfo], @@ -56,6 +56,8 @@ class StageInfo( completionTime = Some(System.currentTimeMillis) } + def attemptNumber(): Int = attemptId + private[spark] def getStatusString: String = { if (completionTime.isDefined) { if (failureReason.isDefined) { diff --git a/core/src/main/scala/org/apache/spark/scheduler/StatsReportListener.scala b/core/src/main/scala/org/apache/spark/scheduler/StatsReportListener.scala index 3c8cab7504c17..3c7af4f6146fa 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/StatsReportListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/StatsReportListener.scala @@ -79,7 +79,7 @@ class StatsReportListener extends SparkListener with Logging { x => info.completionTime.getOrElse(System.currentTimeMillis()) - x ).getOrElse("-") - s"Stage(${info.stageId}, ${info.attemptId}); Name: '${info.name}'; " + + s"Stage(${info.stageId}, ${info.attemptNumber}); Name: '${info.name}'; " + s"Status: ${info.getStatusString}$failureReason; numTasks: ${info.numTasks}; " + s"Took: $timeTaken msec" } diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index 7767ef1803a06..f536fc2a5f0a1 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -79,6 +79,7 @@ private[spark] abstract class Task[T]( SparkEnv.get.blockManager.registerTask(taskAttemptId) context = new TaskContextImpl( stageId, + stageAttemptId, // stageAttemptId and stageAttemptNumber are semantically equal partitionId, taskAttemptId, attemptNumber, diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 0c11806b3981b..598b62f85a1fa 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -42,7 +42,7 @@ import org.apache.spark.util.{AccumulatorV2, ThreadUtils, Utils} * up to launch speculative tasks, etc. * * Clients should first call initialize() and start(), then submit task sets through the - * runTasks method. + * submitTasks method. * * THREADING: [[SchedulerBackend]]s and task-submitting clients can call this class from multiple * threads, so it needs locks in public API methods to maintain its state. In addition, some @@ -62,7 +62,7 @@ private[spark] class TaskSchedulerImpl( this(sc, sc.conf.get(config.MAX_TASK_FAILURES)) } - // Lazily initializing blackListTrackOpt to avoid getting empty ExecutorAllocationClient, + // Lazily initializing blacklistTrackerOpt to avoid getting empty ExecutorAllocationClient, // because ExecutorAllocationClient is created after this TaskSchedulerImpl. private[scheduler] lazy val blacklistTrackerOpt = maybeCreateBlacklistTracker(sc) @@ -228,7 +228,7 @@ private[spark] class TaskSchedulerImpl( // 1. The task set manager has been created and some tasks have been scheduled. // In this case, send a kill signal to the executors to kill the task and then abort // the stage. - // 2. The task set manager has been created but no tasks has been scheduled. In this case, + // 2. The task set manager has been created but no tasks have been scheduled. In this case, // simply abort the stage. tsm.runningTasksSet.foreach { tid => taskIdToExecutorId.get(tid).foreach(execId => @@ -689,6 +689,20 @@ private[spark] class TaskSchedulerImpl( } } + /** + * Marks the task has completed in all TaskSetManagers for the given stage. + * + * After stage failure and retry, there may be multiple TaskSetManagers for the stage. + * If an earlier attempt of a stage completes a task, we should ensure that the later attempts + * do not also submit those same tasks. That also means that a task completion from an earlier + * attempt can lead to the entire stage getting marked as successful. + */ + private[scheduler] def markPartitionCompletedInAllTaskSets(stageId: Int, partitionId: Int) = { + taskSetsByStageIdAndAttempt.getOrElse(stageId, Map()).values.foreach { tsm => + tsm.markPartitionCompleted(partitionId) + } + } + } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index c3ed11bfe352a..b52e376e7b870 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -74,6 +74,8 @@ private[spark] class TaskSetManager( val ser = env.closureSerializer.newInstance() val tasks = taskSet.tasks + private[scheduler] val partitionToIndex = tasks.zipWithIndex + .map { case (t, idx) => t.partitionId -> idx }.toMap val numTasks = tasks.length val copiesRunning = new Array[Int](numTasks) @@ -154,7 +156,7 @@ private[spark] class TaskSetManager( private[scheduler] val speculatableTasks = new HashSet[Int] // Task index, start and finish time for each task attempt (indexed by task ID) - private val taskInfos = new HashMap[Long, TaskInfo] + private[scheduler] val taskInfos = new HashMap[Long, TaskInfo] // Use a MedianHeap to record durations of successful tasks so we know when to launch // speculative tasks. This is only used when speculation is enabled, to avoid the overhead @@ -755,6 +757,9 @@ private[spark] class TaskSetManager( logInfo("Ignoring task-finished event for " + info.id + " in stage " + taskSet.id + " because task " + index + " has already completed successfully") } + // There may be multiple tasksets for this stage -- we let all of them know that the partition + // was completed. This may result in some of the tasksets getting completed. + sched.markPartitionCompletedInAllTaskSets(stageId, tasks(index).partitionId) // This method is called by "TaskSchedulerImpl.handleSuccessfulTask" which holds the // "TaskSchedulerImpl" lock until exiting. To avoid the SPARK-7655 issue, we should not // "deserialize" the value when holding a lock to avoid blocking other threads. So we call @@ -765,6 +770,19 @@ private[spark] class TaskSetManager( maybeFinishTaskSet() } + private[scheduler] def markPartitionCompleted(partitionId: Int): Unit = { + partitionToIndex.get(partitionId).foreach { index => + if (!successful(index)) { + tasksSuccessful += 1 + successful(index) = true + if (tasksSuccessful == numTasks) { + isZombie = true + } + maybeFinishTaskSet() + } + } + } + /** * Marks the task as failed, re-adds it to the list of pending tasks, and notifies the * DAG Scheduler. diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 4d75063fbf1c5..5627a557a12f3 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -147,7 +147,8 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp case KillExecutorsOnHost(host) => scheduler.getExecutorsAliveOnHost(host).foreach { exec => - killExecutors(exec.toSeq, replace = true, force = true) + killExecutors(exec.toSeq, adjustTargetNumExecutors = false, countFailures = false, + force = true) } case UpdateDelegationTokens(newDelegationTokens) => @@ -584,18 +585,18 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp /** * Request that the cluster manager kill the specified executors. * - * When asking the executor to be replaced, the executor loss is considered a failure, and - * killed tasks that are running on the executor will count towards the failure limits. If no - * replacement is being requested, then the tasks will not count towards the limit. - * * @param executorIds identifiers of executors to kill - * @param replace whether to replace the killed executors with new ones, default false + * @param adjustTargetNumExecutors whether the target number of executors be adjusted down + * after these executors have been killed + * @param countFailures if there are tasks running on the executors when they are killed, whether + * those failures be counted to task failure limits? * @param force whether to force kill busy executors, default false * @return the ids of the executors acknowledged by the cluster manager to be removed. */ final override def killExecutors( executorIds: Seq[String], - replace: Boolean, + adjustTargetNumExecutors: Boolean, + countFailures: Boolean, force: Boolean): Seq[String] = { logInfo(s"Requesting to kill executor(s) ${executorIds.mkString(", ")}") @@ -610,7 +611,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp val executorsToKill = knownExecutors .filter { id => !executorsPendingToRemove.contains(id) } .filter { id => force || !scheduler.isExecutorBusy(id) } - executorsToKill.foreach { id => executorsPendingToRemove(id) = !replace } + executorsToKill.foreach { id => executorsPendingToRemove(id) = !countFailures } logInfo(s"Actual list of executor(s) to be killed is ${executorsToKill.mkString(", ")}") @@ -618,12 +619,13 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp // with the cluster manager to avoid allocating new ones. When computing the new target, // take into account executors that are pending to be added or removed. val adjustTotalExecutors = - if (!replace) { + if (adjustTargetNumExecutors) { requestedTotalExecutors = math.max(requestedTotalExecutors - executorsToKill.size, 0) if (requestedTotalExecutors != (numExistingExecutors + numPendingExecutors - executorsPendingToRemove.size)) { logDebug( - s"""killExecutors($executorIds, $replace, $force): Executor counts do not match: + s"""killExecutors($executorIds, $adjustTargetNumExecutors, $countFailures, $force): + |Executor counts do not match: |requestedTotalExecutors = $requestedTotalExecutors |numExistingExecutors = $numExistingExecutors |numPendingExecutors = $numPendingExecutors diff --git a/core/src/main/scala/org/apache/spark/security/SocketAuthHelper.scala b/core/src/main/scala/org/apache/spark/security/SocketAuthHelper.scala new file mode 100644 index 0000000000000..d15e7937b0523 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/security/SocketAuthHelper.scala @@ -0,0 +1,101 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.security + +import java.io.{DataInputStream, DataOutputStream, InputStream} +import java.net.Socket +import java.nio.charset.StandardCharsets.UTF_8 + +import org.apache.spark.SparkConf +import org.apache.spark.network.util.JavaUtils +import org.apache.spark.util.Utils + +/** + * A class that can be used to add a simple authentication protocol to socket-based communication. + * + * The protocol is simple: an auth secret is written to the socket, and the other side checks the + * secret and writes either "ok" or "err" to the output. If authentication fails, the socket is + * not expected to be valid anymore. + * + * There's no secrecy, so this relies on the sockets being either local or somehow encrypted. + */ +private[spark] class SocketAuthHelper(conf: SparkConf) { + + val secret = Utils.createSecret(conf) + + /** + * Read the auth secret from the socket and compare to the expected value. Write the reply back + * to the socket. + * + * If authentication fails, this method will close the socket. + * + * @param s The client socket. + * @throws IllegalArgumentException If authentication fails. + */ + def authClient(s: Socket): Unit = { + // Set the socket timeout while checking the auth secret. Reset it before returning. + val currentTimeout = s.getSoTimeout() + try { + s.setSoTimeout(10000) + val clientSecret = readUtf8(s) + if (secret == clientSecret) { + writeUtf8("ok", s) + } else { + writeUtf8("err", s) + JavaUtils.closeQuietly(s) + } + } finally { + s.setSoTimeout(currentTimeout) + } + } + + /** + * Authenticate with a server by writing the auth secret and checking the server's reply. + * + * If authentication fails, this method will close the socket. + * + * @param s The socket connected to the server. + * @throws IllegalArgumentException If authentication fails. + */ + def authToServer(s: Socket): Unit = { + writeUtf8(secret, s) + + val reply = readUtf8(s) + if (reply != "ok") { + JavaUtils.closeQuietly(s) + throw new IllegalArgumentException("Authentication failed.") + } + } + + protected def readUtf8(s: Socket): String = { + val din = new DataInputStream(s.getInputStream()) + val len = din.readInt() + val bytes = new Array[Byte](len) + din.readFully(bytes) + new String(bytes, UTF_8) + } + + protected def writeUtf8(str: String, s: Socket): Unit = { + val bytes = str.getBytes(UTF_8) + val dout = new DataOutputStream(s.getOutputStream()) + dout.writeInt(bytes.length) + dout.write(bytes, 0, bytes.length) + dout.flush() + } + +} diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index 538ae05e4eea1..72427dd6ce4d4 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -206,6 +206,7 @@ class KryoSerializer(conf: SparkConf) kryo.register(clazz) } catch { case NonFatal(_) => // do nothing + case _: NoClassDefFoundError if Utils.isTesting => // See SPARK-23422. } } diff --git a/core/src/main/scala/org/apache/spark/shuffle/BlockStoreShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/BlockStoreShuffleReader.scala index 0562d45ff57c5..2a77a1cab976a 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/BlockStoreShuffleReader.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/BlockStoreShuffleReader.scala @@ -95,7 +95,7 @@ private[spark] class BlockStoreShuffleReader[K, C]( } // Sort the output if there is a sort ordering defined. - dep.keyOrdering match { + val resultIter = dep.keyOrdering match { case Some(keyOrd: Ordering[K]) => // Create an ExternalSorter to sort the data. val sorter = @@ -104,9 +104,21 @@ private[spark] class BlockStoreShuffleReader[K, C]( context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled) context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled) context.taskMetrics().incPeakExecutionMemory(sorter.peakMemoryUsedBytes) + // Use completion callback to stop sorter if task was finished/cancelled. + context.addTaskCompletionListener(_ => { + sorter.stop() + }) CompletionIterator[Product2[K, C], Iterator[Product2[K, C]]](sorter.iterator, sorter.stop()) case None => aggregatedIter } + + resultIter match { + case _: InterruptibleIterator[Product2[K, C]] => resultIter + case _ => + // Use another interruptible iterator here to support task cancellation as aggregator + // or(and) sorter may have consumed previous interruptible iterator. + new InterruptibleIterator[Product2[K, C]](context, resultIter) + } } } diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala index 15540485170d0..449f60273b42b 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala @@ -18,8 +18,8 @@ package org.apache.spark.shuffle import java.io._ - -import com.google.common.io.ByteStreams +import java.nio.channels.Channels +import java.nio.file.Files import org.apache.spark.{SparkConf, SparkEnv} import org.apache.spark.internal.Logging @@ -84,7 +84,7 @@ private[spark] class IndexShuffleBlockResolver( */ private def checkIndexAndDataFile(index: File, data: File, blocks: Int): Array[Long] = { // the index file should have `block + 1` longs as offset. - if (index.length() != (blocks + 1) * 8) { + if (index.length() != (blocks + 1) * 8L) { return null } val lengths = new Array[Long](blocks) @@ -196,11 +196,24 @@ private[spark] class IndexShuffleBlockResolver( // find out the consolidated file, then the offset within that from our index val indexFile = getIndexFile(blockId.shuffleId, blockId.mapId) - val in = new DataInputStream(new FileInputStream(indexFile)) + // SPARK-22982: if this FileInputStream's position is seeked forward by another piece of code + // which is incorrectly using our file descriptor then this code will fetch the wrong offsets + // (which may cause a reducer to be sent a different reducer's data). The explicit position + // checks added here were a useful debugging aid during SPARK-22982 and may help prevent this + // class of issue from re-occurring in the future which is why they are left here even though + // SPARK-22982 is fixed. + val channel = Files.newByteChannel(indexFile.toPath) + channel.position(blockId.reduceId * 8L) + val in = new DataInputStream(Channels.newInputStream(channel)) try { - ByteStreams.skipFully(in, blockId.reduceId * 8) val offset = in.readLong() val nextOffset = in.readLong() + val actualPosition = channel.position() + val expectedPosition = blockId.reduceId * 8L + 16 + if (actualPosition != expectedPosition) { + throw new Exception(s"SPARK-22982: Incorrect channel position after index file reads: " + + s"expected $expectedPosition but actual position was $actualPosition.") + } new FileSegmentManagedBuffer( transportConf, getDataFile(blockId.shuffleId, blockId.mapId), diff --git a/core/src/main/scala/org/apache/spark/status/AppStatusListener.scala b/core/src/main/scala/org/apache/spark/status/AppStatusListener.scala index 487a782e865e8..496165c9df0d9 100644 --- a/core/src/main/scala/org/apache/spark/status/AppStatusListener.scala +++ b/core/src/main/scala/org/apache/spark/status/AppStatusListener.scala @@ -377,6 +377,10 @@ private[spark] class AppStatusListener( Option(liveStages.get((event.stageId, event.stageAttemptId))).foreach { stage => stage.activeTasks += 1 stage.firstLaunchTime = math.min(stage.firstLaunchTime, event.taskInfo.launchTime) + + val locality = event.taskInfo.taskLocality.toString() + val count = stage.localitySummary.getOrElse(locality, 0L) + 1L + stage.localitySummary = stage.localitySummary ++ Map(locality -> count) maybeUpdate(stage, now) stage.jobs.foreach { job => @@ -433,7 +437,7 @@ private[spark] class AppStatusListener( } task.errorMessage = errorMessage val delta = task.updateMetrics(event.taskMetrics) - update(task, now) + update(task, now, last = true) delta }.orNull @@ -450,7 +454,7 @@ private[spark] class AppStatusListener( Option(liveStages.get((event.stageId, event.stageAttemptId))).foreach { stage => if (metricsDelta != null) { - stage.metrics.update(metricsDelta) + stage.metrics = LiveEntityHelpers.addMetrics(stage.metrics, metricsDelta) } stage.activeTasks -= 1 stage.completedTasks += completedDelta @@ -486,7 +490,7 @@ private[spark] class AppStatusListener( esummary.failedTasks += failedDelta esummary.killedTasks += killedDelta if (metricsDelta != null) { - esummary.metrics.update(metricsDelta) + esummary.metrics = LiveEntityHelpers.addMetrics(esummary.metrics, metricsDelta) } maybeUpdate(esummary, now) @@ -529,7 +533,8 @@ private[spark] class AppStatusListener( } override def onStageCompleted(event: SparkListenerStageCompleted): Unit = { - val maybeStage = Option(liveStages.remove((event.stageInfo.stageId, event.stageInfo.attemptId))) + val maybeStage = + Option(liveStages.remove((event.stageInfo.stageId, event.stageInfo.attemptNumber))) maybeStage.foreach { stage => val now = System.nanoTime() stage.info = event.stageInfo @@ -603,11 +608,11 @@ private[spark] class AppStatusListener( maybeUpdate(task, now) Option(liveStages.get((sid, sAttempt))).foreach { stage => - stage.metrics.update(delta) + stage.metrics = LiveEntityHelpers.addMetrics(stage.metrics, delta) maybeUpdate(stage, now) val esummary = stage.executorSummary(event.execId) - esummary.metrics.update(delta) + esummary.metrics = LiveEntityHelpers.addMetrics(esummary.metrics, delta) maybeUpdate(esummary, now) } } @@ -689,7 +694,7 @@ private[spark] class AppStatusListener( // can update the executor information too. liveRDDs.get(block.rddId).foreach { rdd => if (updatedStorageLevel.isDefined) { - rdd.storageLevel = updatedStorageLevel.get + rdd.setStorageLevel(updatedStorageLevel.get) } val partition = rdd.partition(block.name) @@ -785,7 +790,7 @@ private[spark] class AppStatusListener( } private def getOrCreateStage(info: StageInfo): LiveStage = { - val stage = liveStages.computeIfAbsent((info.stageId, info.attemptId), + val stage = liveStages.computeIfAbsent((info.stageId, info.attemptNumber), new Function[(Int, Int), LiveStage]() { override def apply(key: (Int, Int)): LiveStage = new LiveStage() }) @@ -813,7 +818,7 @@ private[spark] class AppStatusListener( /** Update a live entity only if it hasn't been updated in the last configured period. */ private def maybeUpdate(entity: LiveEntity, now: Long): Unit = { - if (liveUpdatePeriodNs >= 0 && now - entity.lastWriteTime > liveUpdatePeriodNs) { + if (live && liveUpdatePeriodNs >= 0 && now - entity.lastWriteTime > liveUpdatePeriodNs) { update(entity, now) } } @@ -845,8 +850,8 @@ private[spark] class AppStatusListener( return } - val toDelete = KVUtils.viewToSeq(kvstore.view(classOf[JobDataWrapper]), - countToDelete.toInt) { j => + val view = kvstore.view(classOf[JobDataWrapper]).index("completionTime").first(0L) + val toDelete = KVUtils.viewToSeq(view, countToDelete.toInt) { j => j.info.status != JobExecutionStatus.RUNNING && j.info.status != JobExecutionStatus.UNKNOWN } toDelete.foreach { j => kvstore.delete(j.getClass(), j.info.jobId) } @@ -858,13 +863,16 @@ private[spark] class AppStatusListener( return } - val stages = KVUtils.viewToSeq(kvstore.view(classOf[StageDataWrapper]), - countToDelete.toInt) { s => + // As the completion time of a skipped stage is always -1, we will remove skipped stages first. + // This is safe since the job itself contains enough information to render skipped stages in the + // UI. + val view = kvstore.view(classOf[StageDataWrapper]).index("completionTime") + val stages = KVUtils.viewToSeq(view, countToDelete.toInt) { s => s.info.status != v1.StageStatus.ACTIVE && s.info.status != v1.StageStatus.PENDING } stages.foreach { s => - val key = s.id + val key = Array(s.info.stageId, s.info.attemptId) kvstore.delete(s.getClass(), key) val execSummaries = kvstore.view(classOf[ExecutorStageSummaryWrapper]) @@ -884,15 +892,15 @@ private[spark] class AppStatusListener( .asScala tasks.foreach { t => - kvstore.delete(t.getClass(), t.info.taskId) + kvstore.delete(t.getClass(), t.taskId) } // Check whether there are remaining attempts for the same stage. If there aren't, then // also delete the RDD graph data. val remainingAttempts = kvstore.view(classOf[StageDataWrapper]) .index("stageId") - .first(s.stageId) - .last(s.stageId) + .first(s.info.stageId) + .last(s.info.stageId) .closeableIterator() val hasMoreAttempts = try { @@ -904,23 +912,26 @@ private[spark] class AppStatusListener( } if (!hasMoreAttempts) { - kvstore.delete(classOf[RDDOperationGraphWrapper], s.stageId) + kvstore.delete(classOf[RDDOperationGraphWrapper], s.info.stageId) } + + cleanupCachedQuantiles(key) } } private def cleanupTasks(stage: LiveStage): Unit = { val countToDelete = calculateNumberToRemove(stage.savedTasks.get(), maxTasksPerStage).toInt if (countToDelete > 0) { - val stageKey = Array(stage.info.stageId, stage.info.attemptId) - val view = kvstore.view(classOf[TaskDataWrapper]).index("stage").first(stageKey) - .last(stageKey) + val stageKey = Array(stage.info.stageId, stage.info.attemptNumber) + val view = kvstore.view(classOf[TaskDataWrapper]) + .index(TaskIndexNames.COMPLETION_TIME) + .parent(stageKey) // Try to delete finished tasks only. val toDelete = KVUtils.viewToSeq(view, countToDelete) { t => - !live || t.info.status != TaskState.RUNNING.toString() + !live || t.status != TaskState.RUNNING.toString() } - toDelete.foreach { t => kvstore.delete(t.getClass(), t.info.taskId) } + toDelete.foreach { t => kvstore.delete(t.getClass(), t.taskId) } stage.savedTasks.addAndGet(-toDelete.size) // If there are more running tasks than the configured limit, delete running tasks. This @@ -929,13 +940,34 @@ private[spark] class AppStatusListener( val remaining = countToDelete - toDelete.size if (remaining > 0) { val runningTasksToDelete = view.max(remaining).iterator().asScala.toList - runningTasksToDelete.foreach { t => kvstore.delete(t.getClass(), t.info.taskId) } + runningTasksToDelete.foreach { t => kvstore.delete(t.getClass(), t.taskId) } stage.savedTasks.addAndGet(-remaining) } + + // On live applications, cleanup any cached quantiles for the stage. This makes sure that + // quantiles will be recalculated after tasks are replaced with newer ones. + // + // This is not needed in the SHS since caching only happens after the event logs are + // completely processed. + if (live) { + cleanupCachedQuantiles(stageKey) + } } stage.cleaning = false } + private def cleanupCachedQuantiles(stageKey: Array[Int]): Unit = { + val cachedQuantiles = kvstore.view(classOf[CachedQuantile]) + .index("stage") + .first(stageKey) + .last(stageKey) + .asScala + .toList + cachedQuantiles.foreach { q => + kvstore.delete(q.getClass(), q.id) + } + } + /** * Remove at least (retainedSize / 10) items to reduce friction. Because tracking may be done * asynchronously, this method may return 0 in case enough items have been deleted already. diff --git a/core/src/main/scala/org/apache/spark/status/AppStatusStore.scala b/core/src/main/scala/org/apache/spark/status/AppStatusStore.scala index 5a942f5284018..688f25a9fdea1 100644 --- a/core/src/main/scala/org/apache/spark/status/AppStatusStore.scala +++ b/core/src/main/scala/org/apache/spark/status/AppStatusStore.scala @@ -24,7 +24,7 @@ import scala.collection.JavaConverters._ import org.apache.spark.{JobExecutionStatus, SparkConf} import org.apache.spark.status.api.v1 import org.apache.spark.ui.scope._ -import org.apache.spark.util.Distribution +import org.apache.spark.util.{Distribution, Utils} import org.apache.spark.util.kvstore.{InMemoryStore, KVStore} /** @@ -95,10 +95,18 @@ private[spark] class AppStatusStore( } def lastStageAttempt(stageId: Int): v1.StageData = { - val it = store.view(classOf[StageDataWrapper]).index("stageId").reverse().first(stageId) + val it = store.view(classOf[StageDataWrapper]) + .index("stageId") + .reverse() + .first(stageId) + .last(stageId) .closeableIterator() try { - it.next().info + if (it.hasNext()) { + it.next().info + } else { + throw new NoSuchElementException(s"No stage with id $stageId") + } } finally { it.close() } @@ -110,107 +118,238 @@ private[spark] class AppStatusStore( if (details) stageWithDetails(stage) else stage } + def taskCount(stageId: Int, stageAttemptId: Int): Long = { + store.count(classOf[TaskDataWrapper], "stage", Array(stageId, stageAttemptId)) + } + + def localitySummary(stageId: Int, stageAttemptId: Int): Map[String, Long] = { + store.read(classOf[StageDataWrapper], Array(stageId, stageAttemptId)).locality + } + + /** + * Calculates a summary of the task metrics for the given stage attempt, returning the + * requested quantiles for the recorded metrics. + * + * This method can be expensive if the requested quantiles are not cached; the method + * will only cache certain quantiles (every 0.05 step), so it's recommended to stick to + * those to avoid expensive scans of all task data. + */ def taskSummary( stageId: Int, stageAttemptId: Int, - quantiles: Array[Double]): v1.TaskMetricDistributions = { - - val stage = Array(stageId, stageAttemptId) - - val rawMetrics = store.view(classOf[TaskDataWrapper]) - .index("stage") - .first(stage) - .last(stage) - .asScala - .flatMap(_.info.taskMetrics) - .toList - .view - - def metricQuantiles(f: v1.TaskMetrics => Double): IndexedSeq[Double] = - Distribution(rawMetrics.map { d => f(d) }).get.getQuantiles(quantiles) - - // We need to do a lot of similar munging to nested metrics here. For each one, - // we want (a) extract the values for nested metrics (b) make a distribution for each metric - // (c) shove the distribution into the right field in our return type and (d) only return - // a result if the option is defined for any of the tasks. MetricHelper is a little util - // to make it a little easier to deal w/ all of the nested options. Mostly it lets us just - // implement one "build" method, which just builds the quantiles for each field. - - val inputMetrics = - new MetricHelper[v1.InputMetrics, v1.InputMetricDistributions](rawMetrics, quantiles) { - def getSubmetrics(raw: v1.TaskMetrics): v1.InputMetrics = raw.inputMetrics - - def build: v1.InputMetricDistributions = new v1.InputMetricDistributions( - bytesRead = submetricQuantiles(_.bytesRead), - recordsRead = submetricQuantiles(_.recordsRead) - ) - }.build - - val outputMetrics = - new MetricHelper[v1.OutputMetrics, v1.OutputMetricDistributions](rawMetrics, quantiles) { - def getSubmetrics(raw: v1.TaskMetrics): v1.OutputMetrics = raw.outputMetrics - - def build: v1.OutputMetricDistributions = new v1.OutputMetricDistributions( - bytesWritten = submetricQuantiles(_.bytesWritten), - recordsWritten = submetricQuantiles(_.recordsWritten) - ) - }.build - - val shuffleReadMetrics = - new MetricHelper[v1.ShuffleReadMetrics, v1.ShuffleReadMetricDistributions](rawMetrics, - quantiles) { - def getSubmetrics(raw: v1.TaskMetrics): v1.ShuffleReadMetrics = - raw.shuffleReadMetrics - - def build: v1.ShuffleReadMetricDistributions = new v1.ShuffleReadMetricDistributions( - readBytes = submetricQuantiles { s => s.localBytesRead + s.remoteBytesRead }, - readRecords = submetricQuantiles(_.recordsRead), - remoteBytesRead = submetricQuantiles(_.remoteBytesRead), - remoteBytesReadToDisk = submetricQuantiles(_.remoteBytesReadToDisk), - remoteBlocksFetched = submetricQuantiles(_.remoteBlocksFetched), - localBlocksFetched = submetricQuantiles(_.localBlocksFetched), - totalBlocksFetched = submetricQuantiles { s => - s.localBlocksFetched + s.remoteBlocksFetched - }, - fetchWaitTime = submetricQuantiles(_.fetchWaitTime) - ) - }.build - - val shuffleWriteMetrics = - new MetricHelper[v1.ShuffleWriteMetrics, v1.ShuffleWriteMetricDistributions](rawMetrics, - quantiles) { - def getSubmetrics(raw: v1.TaskMetrics): v1.ShuffleWriteMetrics = - raw.shuffleWriteMetrics - - def build: v1.ShuffleWriteMetricDistributions = new v1.ShuffleWriteMetricDistributions( - writeBytes = submetricQuantiles(_.bytesWritten), - writeRecords = submetricQuantiles(_.recordsWritten), - writeTime = submetricQuantiles(_.writeTime) - ) - }.build - - new v1.TaskMetricDistributions( + unsortedQuantiles: Array[Double]): Option[v1.TaskMetricDistributions] = { + val stageKey = Array(stageId, stageAttemptId) + val quantiles = unsortedQuantiles.sorted + + // We don't know how many tasks remain in the store that actually have metrics. So scan one + // metric and count how many valid tasks there are. Use skip() instead of next() since it's + // cheaper for disk stores (avoids deserialization). + val count = { + Utils.tryWithResource( + store.view(classOf[TaskDataWrapper]) + .parent(stageKey) + .index(TaskIndexNames.EXEC_RUN_TIME) + .first(0L) + .closeableIterator() + ) { it => + var _count = 0L + while (it.hasNext()) { + _count += 1 + it.skip(1) + } + _count + } + } + + if (count <= 0) { + return None + } + + // Find out which quantiles are already cached. The data in the store must match the expected + // task count to be considered, otherwise it will be re-scanned and overwritten. + val cachedQuantiles = quantiles.filter(shouldCacheQuantile).flatMap { q => + val qkey = Array(stageId, stageAttemptId, quantileToString(q)) + asOption(store.read(classOf[CachedQuantile], qkey)).filter(_.taskCount == count) + } + + // If there are no missing quantiles, return the data. Otherwise, just compute everything + // to make the code simpler. + if (cachedQuantiles.size == quantiles.size) { + def toValues(fn: CachedQuantile => Double): IndexedSeq[Double] = cachedQuantiles.map(fn) + + val distributions = new v1.TaskMetricDistributions( + quantiles = quantiles, + executorDeserializeTime = toValues(_.executorDeserializeTime), + executorDeserializeCpuTime = toValues(_.executorDeserializeCpuTime), + executorRunTime = toValues(_.executorRunTime), + executorCpuTime = toValues(_.executorCpuTime), + resultSize = toValues(_.resultSize), + jvmGcTime = toValues(_.jvmGcTime), + resultSerializationTime = toValues(_.resultSerializationTime), + gettingResultTime = toValues(_.gettingResultTime), + schedulerDelay = toValues(_.schedulerDelay), + peakExecutionMemory = toValues(_.peakExecutionMemory), + memoryBytesSpilled = toValues(_.memoryBytesSpilled), + diskBytesSpilled = toValues(_.diskBytesSpilled), + inputMetrics = new v1.InputMetricDistributions( + toValues(_.bytesRead), + toValues(_.recordsRead)), + outputMetrics = new v1.OutputMetricDistributions( + toValues(_.bytesWritten), + toValues(_.recordsWritten)), + shuffleReadMetrics = new v1.ShuffleReadMetricDistributions( + toValues(_.shuffleReadBytes), + toValues(_.shuffleRecordsRead), + toValues(_.shuffleRemoteBlocksFetched), + toValues(_.shuffleLocalBlocksFetched), + toValues(_.shuffleFetchWaitTime), + toValues(_.shuffleRemoteBytesRead), + toValues(_.shuffleRemoteBytesReadToDisk), + toValues(_.shuffleTotalBlocksFetched)), + shuffleWriteMetrics = new v1.ShuffleWriteMetricDistributions( + toValues(_.shuffleWriteBytes), + toValues(_.shuffleWriteRecords), + toValues(_.shuffleWriteTime))) + + return Some(distributions) + } + + // Compute quantiles by scanning the tasks in the store. This is not really stable for live + // stages (e.g. the number of recorded tasks may change while this code is running), but should + // stabilize once the stage finishes. It's also slow, especially with disk stores. + val indices = quantiles.map { q => math.min((q * count).toLong, count - 1) } + + def scanTasks(index: String)(fn: TaskDataWrapper => Long): IndexedSeq[Double] = { + Utils.tryWithResource( + store.view(classOf[TaskDataWrapper]) + .parent(stageKey) + .index(index) + .first(0L) + .closeableIterator() + ) { it => + var last = Double.NaN + var currentIdx = -1L + indices.map { idx => + if (idx == currentIdx) { + last + } else { + val diff = idx - currentIdx + currentIdx = idx + if (it.skip(diff - 1)) { + last = fn(it.next()).toDouble + last + } else { + Double.NaN + } + } + }.toIndexedSeq + } + } + + val computedQuantiles = new v1.TaskMetricDistributions( quantiles = quantiles, - executorDeserializeTime = metricQuantiles(_.executorDeserializeTime), - executorDeserializeCpuTime = metricQuantiles(_.executorDeserializeCpuTime), - executorRunTime = metricQuantiles(_.executorRunTime), - executorCpuTime = metricQuantiles(_.executorCpuTime), - resultSize = metricQuantiles(_.resultSize), - jvmGcTime = metricQuantiles(_.jvmGcTime), - resultSerializationTime = metricQuantiles(_.resultSerializationTime), - memoryBytesSpilled = metricQuantiles(_.memoryBytesSpilled), - diskBytesSpilled = metricQuantiles(_.diskBytesSpilled), - inputMetrics = inputMetrics, - outputMetrics = outputMetrics, - shuffleReadMetrics = shuffleReadMetrics, - shuffleWriteMetrics = shuffleWriteMetrics - ) + executorDeserializeTime = scanTasks(TaskIndexNames.DESER_TIME) { t => + t.executorDeserializeTime + }, + executorDeserializeCpuTime = scanTasks(TaskIndexNames.DESER_CPU_TIME) { t => + t.executorDeserializeCpuTime + }, + executorRunTime = scanTasks(TaskIndexNames.EXEC_RUN_TIME) { t => t.executorRunTime }, + executorCpuTime = scanTasks(TaskIndexNames.EXEC_CPU_TIME) { t => t.executorCpuTime }, + resultSize = scanTasks(TaskIndexNames.RESULT_SIZE) { t => t.resultSize }, + jvmGcTime = scanTasks(TaskIndexNames.GC_TIME) { t => t.jvmGcTime }, + resultSerializationTime = scanTasks(TaskIndexNames.SER_TIME) { t => + t.resultSerializationTime + }, + gettingResultTime = scanTasks(TaskIndexNames.GETTING_RESULT_TIME) { t => + t.gettingResultTime + }, + schedulerDelay = scanTasks(TaskIndexNames.SCHEDULER_DELAY) { t => t.schedulerDelay }, + peakExecutionMemory = scanTasks(TaskIndexNames.PEAK_MEM) { t => t.peakExecutionMemory }, + memoryBytesSpilled = scanTasks(TaskIndexNames.MEM_SPILL) { t => t.memoryBytesSpilled }, + diskBytesSpilled = scanTasks(TaskIndexNames.DISK_SPILL) { t => t.diskBytesSpilled }, + inputMetrics = new v1.InputMetricDistributions( + scanTasks(TaskIndexNames.INPUT_SIZE) { t => t.inputBytesRead }, + scanTasks(TaskIndexNames.INPUT_RECORDS) { t => t.inputRecordsRead }), + outputMetrics = new v1.OutputMetricDistributions( + scanTasks(TaskIndexNames.OUTPUT_SIZE) { t => t.outputBytesWritten }, + scanTasks(TaskIndexNames.OUTPUT_RECORDS) { t => t.outputRecordsWritten }), + shuffleReadMetrics = new v1.ShuffleReadMetricDistributions( + scanTasks(TaskIndexNames.SHUFFLE_TOTAL_READS) { m => + m.shuffleLocalBytesRead + m.shuffleRemoteBytesRead + }, + scanTasks(TaskIndexNames.SHUFFLE_READ_RECORDS) { t => t.shuffleRecordsRead }, + scanTasks(TaskIndexNames.SHUFFLE_REMOTE_BLOCKS) { t => t.shuffleRemoteBlocksFetched }, + scanTasks(TaskIndexNames.SHUFFLE_LOCAL_BLOCKS) { t => t.shuffleLocalBlocksFetched }, + scanTasks(TaskIndexNames.SHUFFLE_READ_TIME) { t => t.shuffleFetchWaitTime }, + scanTasks(TaskIndexNames.SHUFFLE_REMOTE_READS) { t => t.shuffleRemoteBytesRead }, + scanTasks(TaskIndexNames.SHUFFLE_REMOTE_READS_TO_DISK) { t => + t.shuffleRemoteBytesReadToDisk + }, + scanTasks(TaskIndexNames.SHUFFLE_TOTAL_BLOCKS) { m => + m.shuffleLocalBlocksFetched + m.shuffleRemoteBlocksFetched + }), + shuffleWriteMetrics = new v1.ShuffleWriteMetricDistributions( + scanTasks(TaskIndexNames.SHUFFLE_WRITE_SIZE) { t => t.shuffleBytesWritten }, + scanTasks(TaskIndexNames.SHUFFLE_WRITE_RECORDS) { t => t.shuffleRecordsWritten }, + scanTasks(TaskIndexNames.SHUFFLE_WRITE_TIME) { t => t.shuffleWriteTime })) + + // Go through the computed quantiles and cache the values that match the caching criteria. + computedQuantiles.quantiles.zipWithIndex + .filter { case (q, _) => quantiles.contains(q) && shouldCacheQuantile(q) } + .foreach { case (q, idx) => + val cached = new CachedQuantile(stageId, stageAttemptId, quantileToString(q), count, + executorDeserializeTime = computedQuantiles.executorDeserializeTime(idx), + executorDeserializeCpuTime = computedQuantiles.executorDeserializeCpuTime(idx), + executorRunTime = computedQuantiles.executorRunTime(idx), + executorCpuTime = computedQuantiles.executorCpuTime(idx), + resultSize = computedQuantiles.resultSize(idx), + jvmGcTime = computedQuantiles.jvmGcTime(idx), + resultSerializationTime = computedQuantiles.resultSerializationTime(idx), + gettingResultTime = computedQuantiles.gettingResultTime(idx), + schedulerDelay = computedQuantiles.schedulerDelay(idx), + peakExecutionMemory = computedQuantiles.peakExecutionMemory(idx), + memoryBytesSpilled = computedQuantiles.memoryBytesSpilled(idx), + diskBytesSpilled = computedQuantiles.diskBytesSpilled(idx), + + bytesRead = computedQuantiles.inputMetrics.bytesRead(idx), + recordsRead = computedQuantiles.inputMetrics.recordsRead(idx), + + bytesWritten = computedQuantiles.outputMetrics.bytesWritten(idx), + recordsWritten = computedQuantiles.outputMetrics.recordsWritten(idx), + + shuffleReadBytes = computedQuantiles.shuffleReadMetrics.readBytes(idx), + shuffleRecordsRead = computedQuantiles.shuffleReadMetrics.readRecords(idx), + shuffleRemoteBlocksFetched = + computedQuantiles.shuffleReadMetrics.remoteBlocksFetched(idx), + shuffleLocalBlocksFetched = computedQuantiles.shuffleReadMetrics.localBlocksFetched(idx), + shuffleFetchWaitTime = computedQuantiles.shuffleReadMetrics.fetchWaitTime(idx), + shuffleRemoteBytesRead = computedQuantiles.shuffleReadMetrics.remoteBytesRead(idx), + shuffleRemoteBytesReadToDisk = + computedQuantiles.shuffleReadMetrics.remoteBytesReadToDisk(idx), + shuffleTotalBlocksFetched = computedQuantiles.shuffleReadMetrics.totalBlocksFetched(idx), + + shuffleWriteBytes = computedQuantiles.shuffleWriteMetrics.writeBytes(idx), + shuffleWriteRecords = computedQuantiles.shuffleWriteMetrics.writeRecords(idx), + shuffleWriteTime = computedQuantiles.shuffleWriteMetrics.writeTime(idx)) + store.write(cached) + } + + Some(computedQuantiles) } + /** + * Whether to cache information about a specific metric quantile. We cache quantiles at every 0.05 + * step, which covers the default values used both in the API and in the stages page. + */ + private def shouldCacheQuantile(q: Double): Boolean = (math.round(q * 100) % 5) == 0 + + private def quantileToString(q: Double): String = math.round(q * 100).toString + def taskList(stageId: Int, stageAttemptId: Int, maxTasks: Int): Seq[v1.TaskData] = { val stageKey = Array(stageId, stageAttemptId) store.view(classOf[TaskDataWrapper]).index("stage").first(stageKey).last(stageKey).reverse() - .max(maxTasks).asScala.map(_.info).toSeq.reverse + .max(maxTasks).asScala.map(_.toApi).toSeq.reverse } def taskList( @@ -219,18 +358,43 @@ private[spark] class AppStatusStore( offset: Int, length: Int, sortBy: v1.TaskSorting): Seq[v1.TaskData] = { + val (indexName, ascending) = sortBy match { + case v1.TaskSorting.ID => + (None, true) + case v1.TaskSorting.INCREASING_RUNTIME => + (Some(TaskIndexNames.EXEC_RUN_TIME), true) + case v1.TaskSorting.DECREASING_RUNTIME => + (Some(TaskIndexNames.EXEC_RUN_TIME), false) + } + taskList(stageId, stageAttemptId, offset, length, indexName, ascending) + } + + def taskList( + stageId: Int, + stageAttemptId: Int, + offset: Int, + length: Int, + sortBy: Option[String], + ascending: Boolean): Seq[v1.TaskData] = { val stageKey = Array(stageId, stageAttemptId) val base = store.view(classOf[TaskDataWrapper]) val indexed = sortBy match { - case v1.TaskSorting.ID => + case Some(index) => + base.index(index).parent(stageKey) + + case _ => + // Sort by ID, which is the "stage" index. base.index("stage").first(stageKey).last(stageKey) - case v1.TaskSorting.INCREASING_RUNTIME => - base.index("runtime").first(stageKey ++ Array(-1L)).last(stageKey ++ Array(Long.MaxValue)) - case v1.TaskSorting.DECREASING_RUNTIME => - base.index("runtime").first(stageKey ++ Array(Long.MaxValue)).last(stageKey ++ Array(-1L)) - .reverse() } - indexed.skip(offset).max(length).asScala.map(_.info).toSeq + + val ordered = if (ascending) indexed else indexed.reverse() + ordered.skip(offset).max(length).asScala.map(_.toApi).toSeq + } + + def executorSummary(stageId: Int, attemptId: Int): Map[String, v1.ExecutorStageSummary] = { + val stageKey = Array(stageId, attemptId) + store.view(classOf[ExecutorStageSummaryWrapper]).index("stage").first(stageKey).last(stageKey) + .asScala.map { exec => (exec.executorId -> exec.info) }.toMap } def rddList(cachedOnly: Boolean = true): Seq[v1.RDDStorageInfo] = { @@ -256,12 +420,6 @@ private[spark] class AppStatusStore( .map { t => (t.taskId, t) } .toMap - val stageKey = Array(stage.stageId, stage.attemptId) - val execs = store.view(classOf[ExecutorStageSummaryWrapper]).index("stage").first(stageKey) - .last(stageKey).closeableIterator().asScala - .map { exec => (exec.executorId -> exec.info) } - .toMap - new v1.StageData( stage.status, stage.stageId, @@ -295,7 +453,7 @@ private[spark] class AppStatusStore( stage.rddIds, stage.accumulatorUpdates, Some(tasks), - Some(execs), + Some(executorSummary(stage.stageId, stage.attemptId)), stage.killedTasksSummary) } @@ -352,22 +510,3 @@ private[spark] object AppStatusStore { } } - -/** - * Helper for getting distributions from nested metric types. - */ -private abstract class MetricHelper[I, O]( - rawMetrics: Seq[v1.TaskMetrics], - quantiles: Array[Double]) { - - def getSubmetrics(raw: v1.TaskMetrics): I - - def build: O - - val data: Seq[I] = rawMetrics.map(getSubmetrics) - - /** applies the given function to all input metrics, and returns the quantiles */ - def submetricQuantiles(f: I => Double): IndexedSeq[Double] = { - Distribution(data.map { d => f(d) }).get.getQuantiles(quantiles) - } -} diff --git a/core/src/main/scala/org/apache/spark/status/AppStatusUtils.scala b/core/src/main/scala/org/apache/spark/status/AppStatusUtils.scala new file mode 100644 index 0000000000000..87f434daf4870 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/status/AppStatusUtils.scala @@ -0,0 +1,75 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.status + +import org.apache.spark.status.api.v1.TaskData + +private[spark] object AppStatusUtils { + + private val TASK_FINISHED_STATES = Set("FAILED", "KILLED", "SUCCESS") + + private def isTaskFinished(task: TaskData): Boolean = { + TASK_FINISHED_STATES.contains(task.status) + } + + def schedulerDelay(task: TaskData): Long = { + if (isTaskFinished(task) && task.taskMetrics.isDefined && task.duration.isDefined) { + val m = task.taskMetrics.get + schedulerDelay(task.launchTime.getTime(), fetchStart(task), task.duration.get, + m.executorDeserializeTime, m.resultSerializationTime, m.executorRunTime) + } else { + // The task is still running and the metrics like executorRunTime are not available. + 0L + } + } + + def gettingResultTime(task: TaskData): Long = { + gettingResultTime(task.launchTime.getTime(), fetchStart(task), task.duration.getOrElse(-1L)) + } + + def schedulerDelay( + launchTime: Long, + fetchStart: Long, + duration: Long, + deserializeTime: Long, + serializeTime: Long, + runTime: Long): Long = { + math.max(0, duration - runTime - deserializeTime - serializeTime - + gettingResultTime(launchTime, fetchStart, duration)) + } + + def gettingResultTime(launchTime: Long, fetchStart: Long, duration: Long): Long = { + if (fetchStart > 0) { + if (duration > 0) { + launchTime + duration - fetchStart + } else { + System.currentTimeMillis() - fetchStart + } + } else { + 0L + } + } + + private def fetchStart(task: TaskData): Long = { + if (task.resultFetchStart.isDefined) { + task.resultFetchStart.get.getTime() + } else { + -1 + } + } +} diff --git a/core/src/main/scala/org/apache/spark/status/LiveEntity.scala b/core/src/main/scala/org/apache/spark/status/LiveEntity.scala index 52e83f250d34e..4295e664e131c 100644 --- a/core/src/main/scala/org/apache/spark/status/LiveEntity.scala +++ b/core/src/main/scala/org/apache/spark/status/LiveEntity.scala @@ -22,6 +22,8 @@ import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.HashMap +import com.google.common.collect.Interners + import org.apache.spark.JobExecutionStatus import org.apache.spark.executor.TaskMetrics import org.apache.spark.scheduler.{AccumulableInfo, StageInfo, TaskInfo} @@ -119,7 +121,9 @@ private class LiveTask( import LiveEntityHelpers._ - private var recordedMetrics: v1.TaskMetrics = null + // The task metrics use a special value when no metrics have been reported. The special value is + // checked when calculating indexed values when writing to the store (see [[TaskDataWrapper]]). + private var metrics: v1.TaskMetrics = createMetrics(default = -1L) var errorMessage: Option[String] = None @@ -129,8 +133,8 @@ private class LiveTask( */ def updateMetrics(metrics: TaskMetrics): v1.TaskMetrics = { if (metrics != null) { - val old = recordedMetrics - recordedMetrics = new v1.TaskMetrics( + val old = this.metrics + val newMetrics = createMetrics( metrics.executorDeserializeTime, metrics.executorDeserializeCpuTime, metrics.executorRunTime, @@ -141,73 +145,35 @@ private class LiveTask( metrics.memoryBytesSpilled, metrics.diskBytesSpilled, metrics.peakExecutionMemory, - new v1.InputMetrics( - metrics.inputMetrics.bytesRead, - metrics.inputMetrics.recordsRead), - new v1.OutputMetrics( - metrics.outputMetrics.bytesWritten, - metrics.outputMetrics.recordsWritten), - new v1.ShuffleReadMetrics( - metrics.shuffleReadMetrics.remoteBlocksFetched, - metrics.shuffleReadMetrics.localBlocksFetched, - metrics.shuffleReadMetrics.fetchWaitTime, - metrics.shuffleReadMetrics.remoteBytesRead, - metrics.shuffleReadMetrics.remoteBytesReadToDisk, - metrics.shuffleReadMetrics.localBytesRead, - metrics.shuffleReadMetrics.recordsRead), - new v1.ShuffleWriteMetrics( - metrics.shuffleWriteMetrics.bytesWritten, - metrics.shuffleWriteMetrics.writeTime, - metrics.shuffleWriteMetrics.recordsWritten)) - if (old != null) calculateMetricsDelta(recordedMetrics, old) else recordedMetrics + metrics.inputMetrics.bytesRead, + metrics.inputMetrics.recordsRead, + metrics.outputMetrics.bytesWritten, + metrics.outputMetrics.recordsWritten, + metrics.shuffleReadMetrics.remoteBlocksFetched, + metrics.shuffleReadMetrics.localBlocksFetched, + metrics.shuffleReadMetrics.fetchWaitTime, + metrics.shuffleReadMetrics.remoteBytesRead, + metrics.shuffleReadMetrics.remoteBytesReadToDisk, + metrics.shuffleReadMetrics.localBytesRead, + metrics.shuffleReadMetrics.recordsRead, + metrics.shuffleWriteMetrics.bytesWritten, + metrics.shuffleWriteMetrics.writeTime, + metrics.shuffleWriteMetrics.recordsWritten) + + this.metrics = newMetrics + + // Only calculate the delta if the old metrics contain valid information, otherwise + // the new metrics are the delta. + if (old.executorDeserializeTime >= 0L) { + subtractMetrics(newMetrics, old) + } else { + newMetrics + } } else { null } } - /** - * Return a new TaskMetrics object containing the delta of the various fields of the given - * metrics objects. This is currently targeted at updating stage data, so it does not - * necessarily calculate deltas for all the fields. - */ - private def calculateMetricsDelta( - metrics: v1.TaskMetrics, - old: v1.TaskMetrics): v1.TaskMetrics = { - val shuffleWriteDelta = new v1.ShuffleWriteMetrics( - metrics.shuffleWriteMetrics.bytesWritten - old.shuffleWriteMetrics.bytesWritten, - 0L, - metrics.shuffleWriteMetrics.recordsWritten - old.shuffleWriteMetrics.recordsWritten) - - val shuffleReadDelta = new v1.ShuffleReadMetrics( - 0L, 0L, 0L, - metrics.shuffleReadMetrics.remoteBytesRead - old.shuffleReadMetrics.remoteBytesRead, - metrics.shuffleReadMetrics.remoteBytesReadToDisk - - old.shuffleReadMetrics.remoteBytesReadToDisk, - metrics.shuffleReadMetrics.localBytesRead - old.shuffleReadMetrics.localBytesRead, - metrics.shuffleReadMetrics.recordsRead - old.shuffleReadMetrics.recordsRead) - - val inputDelta = new v1.InputMetrics( - metrics.inputMetrics.bytesRead - old.inputMetrics.bytesRead, - metrics.inputMetrics.recordsRead - old.inputMetrics.recordsRead) - - val outputDelta = new v1.OutputMetrics( - metrics.outputMetrics.bytesWritten - old.outputMetrics.bytesWritten, - metrics.outputMetrics.recordsWritten - old.outputMetrics.recordsWritten) - - new v1.TaskMetrics( - 0L, 0L, - metrics.executorRunTime - old.executorRunTime, - metrics.executorCpuTime - old.executorCpuTime, - 0L, 0L, 0L, - metrics.memoryBytesSpilled - old.memoryBytesSpilled, - metrics.diskBytesSpilled - old.diskBytesSpilled, - 0L, - inputDelta, - outputDelta, - shuffleReadDelta, - shuffleWriteDelta) - } - override protected def doUpdate(): Any = { val duration = if (info.finished) { info.duration @@ -215,22 +181,48 @@ private class LiveTask( info.timeRunning(lastUpdateTime.getOrElse(System.currentTimeMillis())) } - val task = new v1.TaskData( + new TaskDataWrapper( info.taskId, info.index, info.attemptNumber, - new Date(info.launchTime), - if (info.gettingResult) Some(new Date(info.gettingResultTime)) else None, - Some(duration), - info.executorId, - info.host, - info.status, - info.taskLocality.toString(), + info.launchTime, + if (info.gettingResult) info.gettingResultTime else -1L, + duration, + weakIntern(info.executorId), + weakIntern(info.host), + weakIntern(info.status), + weakIntern(info.taskLocality.toString()), info.speculative, newAccumulatorInfos(info.accumulables), errorMessage, - Option(recordedMetrics)) - new TaskDataWrapper(task, stageId, stageAttemptId) + + metrics.executorDeserializeTime, + metrics.executorDeserializeCpuTime, + metrics.executorRunTime, + metrics.executorCpuTime, + metrics.resultSize, + metrics.jvmGcTime, + metrics.resultSerializationTime, + metrics.memoryBytesSpilled, + metrics.diskBytesSpilled, + metrics.peakExecutionMemory, + metrics.inputMetrics.bytesRead, + metrics.inputMetrics.recordsRead, + metrics.outputMetrics.bytesWritten, + metrics.outputMetrics.recordsWritten, + metrics.shuffleReadMetrics.remoteBlocksFetched, + metrics.shuffleReadMetrics.localBlocksFetched, + metrics.shuffleReadMetrics.fetchWaitTime, + metrics.shuffleReadMetrics.remoteBytesRead, + metrics.shuffleReadMetrics.remoteBytesReadToDisk, + metrics.shuffleReadMetrics.localBytesRead, + metrics.shuffleReadMetrics.recordsRead, + metrics.shuffleWriteMetrics.bytesWritten, + metrics.shuffleWriteMetrics.writeTime, + metrics.shuffleWriteMetrics.recordsWritten, + + stageId, + stageAttemptId) } } @@ -313,50 +305,19 @@ private class LiveExecutor(val executorId: String, _addTime: Long) extends LiveE } -/** Metrics tracked per stage (both total and per executor). */ -private class MetricsTracker { - var executorRunTime = 0L - var executorCpuTime = 0L - var inputBytes = 0L - var inputRecords = 0L - var outputBytes = 0L - var outputRecords = 0L - var shuffleReadBytes = 0L - var shuffleReadRecords = 0L - var shuffleWriteBytes = 0L - var shuffleWriteRecords = 0L - var memoryBytesSpilled = 0L - var diskBytesSpilled = 0L - - def update(delta: v1.TaskMetrics): Unit = { - executorRunTime += delta.executorRunTime - executorCpuTime += delta.executorCpuTime - inputBytes += delta.inputMetrics.bytesRead - inputRecords += delta.inputMetrics.recordsRead - outputBytes += delta.outputMetrics.bytesWritten - outputRecords += delta.outputMetrics.recordsWritten - shuffleReadBytes += delta.shuffleReadMetrics.localBytesRead + - delta.shuffleReadMetrics.remoteBytesRead - shuffleReadRecords += delta.shuffleReadMetrics.recordsRead - shuffleWriteBytes += delta.shuffleWriteMetrics.bytesWritten - shuffleWriteRecords += delta.shuffleWriteMetrics.recordsWritten - memoryBytesSpilled += delta.memoryBytesSpilled - diskBytesSpilled += delta.diskBytesSpilled - } - -} - private class LiveExecutorStageSummary( stageId: Int, attemptId: Int, executorId: String) extends LiveEntity { + import LiveEntityHelpers._ + var taskTime = 0L var succeededTasks = 0 var failedTasks = 0 var killedTasks = 0 - val metrics = new MetricsTracker() + var metrics = createMetrics(default = 0L) override protected def doUpdate(): Any = { val info = new v1.ExecutorStageSummary( @@ -364,14 +325,14 @@ private class LiveExecutorStageSummary( failedTasks, succeededTasks, killedTasks, - metrics.inputBytes, - metrics.inputRecords, - metrics.outputBytes, - metrics.outputRecords, - metrics.shuffleReadBytes, - metrics.shuffleReadRecords, - metrics.shuffleWriteBytes, - metrics.shuffleWriteRecords, + metrics.inputMetrics.bytesRead, + metrics.inputMetrics.recordsRead, + metrics.outputMetrics.bytesWritten, + metrics.outputMetrics.recordsWritten, + metrics.shuffleReadMetrics.remoteBytesRead + metrics.shuffleReadMetrics.localBytesRead, + metrics.shuffleReadMetrics.recordsRead, + metrics.shuffleWriteMetrics.bytesWritten, + metrics.shuffleWriteMetrics.recordsWritten, metrics.memoryBytesSpilled, metrics.diskBytesSpilled) new ExecutorStageSummaryWrapper(stageId, attemptId, executorId, info) @@ -402,7 +363,9 @@ private class LiveStage extends LiveEntity { var firstLaunchTime = Long.MaxValue - val metrics = new MetricsTracker() + var localitySummary: Map[String, Long] = Map() + + var metrics = createMetrics(default = 0L) val executorSummaries = new HashMap[String, LiveExecutorStageSummary]() @@ -412,14 +375,14 @@ private class LiveStage extends LiveEntity { def executorSummary(executorId: String): LiveExecutorStageSummary = { executorSummaries.getOrElseUpdate(executorId, - new LiveExecutorStageSummary(info.stageId, info.attemptId, executorId)) + new LiveExecutorStageSummary(info.stageId, info.attemptNumber, executorId)) } def toApi(): v1.StageData = { new v1.StageData( status, info.stageId, - info.attemptId, + info.attemptNumber, info.numTasks, activeTasks, @@ -435,14 +398,14 @@ private class LiveStage extends LiveEntity { info.completionTime.map(new Date(_)), info.failureReason, - metrics.inputBytes, - metrics.inputRecords, - metrics.outputBytes, - metrics.outputRecords, - metrics.shuffleReadBytes, - metrics.shuffleReadRecords, - metrics.shuffleWriteBytes, - metrics.shuffleWriteRecords, + metrics.inputMetrics.bytesRead, + metrics.inputMetrics.recordsRead, + metrics.outputMetrics.bytesWritten, + metrics.outputMetrics.recordsWritten, + metrics.shuffleReadMetrics.localBytesRead + metrics.shuffleReadMetrics.remoteBytesRead, + metrics.shuffleReadMetrics.recordsRead, + metrics.shuffleWriteMetrics.bytesWritten, + metrics.shuffleWriteMetrics.recordsWritten, metrics.memoryBytesSpilled, metrics.diskBytesSpilled, @@ -459,13 +422,15 @@ private class LiveStage extends LiveEntity { } override protected def doUpdate(): Any = { - new StageDataWrapper(toApi(), jobIds) + new StageDataWrapper(toApi(), jobIds, localitySummary) } } private class LiveRDDPartition(val blockName: String) { + import LiveEntityHelpers._ + // Pointers used by RDDPartitionSeq. @volatile var prev: LiveRDDPartition = null @volatile var next: LiveRDDPartition = null @@ -485,7 +450,7 @@ private class LiveRDDPartition(val blockName: String) { diskUsed: Long): Unit = { value = new v1.RDDPartitionInfo( blockName, - storageLevel, + weakIntern(storageLevel), memoryUsed, diskUsed, executors) @@ -495,6 +460,8 @@ private class LiveRDDPartition(val blockName: String) { private class LiveRDDDistribution(exec: LiveExecutor) { + import LiveEntityHelpers._ + val executorId = exec.executorId var memoryUsed = 0L var diskUsed = 0L @@ -508,7 +475,7 @@ private class LiveRDDDistribution(exec: LiveExecutor) { def toApi(): v1.RDDDataDistribution = { if (lastUpdate == null) { lastUpdate = new v1.RDDDataDistribution( - exec.hostPort, + weakIntern(exec.hostPort), memoryUsed, exec.maxMemory - exec.memoryUsed, diskUsed, @@ -524,7 +491,9 @@ private class LiveRDDDistribution(exec: LiveExecutor) { private class LiveRDD(val info: RDDInfo) extends LiveEntity { - var storageLevel: String = info.storageLevel.description + import LiveEntityHelpers._ + + var storageLevel: String = weakIntern(info.storageLevel.description) var memoryUsed = 0L var diskUsed = 0L @@ -533,6 +502,10 @@ private class LiveRDD(val info: RDDInfo) extends LiveEntity { private val distributions = new HashMap[String, LiveRDDDistribution]() + def setStorageLevel(level: String): Unit = { + this.storageLevel = weakIntern(level) + } + def partition(blockName: String): LiveRDDPartition = { partitions.getOrElseUpdate(blockName, { val part = new LiveRDDPartition(blockName) @@ -593,6 +566,9 @@ private class SchedulerPool(name: String) extends LiveEntity { private object LiveEntityHelpers { + private val stringInterner = Interners.newWeakInterner[String]() + + def newAccumulatorInfos(accums: Iterable[AccumulableInfo]): Seq[v1.AccumulableInfo] = { accums .filter { acc => @@ -604,13 +580,119 @@ private object LiveEntityHelpers { .map { acc => new v1.AccumulableInfo( acc.id, - acc.name.orNull, + acc.name.map(weakIntern).orNull, acc.update.map(_.toString()), acc.value.map(_.toString()).orNull) } .toSeq } + /** String interning to reduce the memory usage. */ + def weakIntern(s: String): String = { + stringInterner.intern(s) + } + + // scalastyle:off argcount + def createMetrics( + executorDeserializeTime: Long, + executorDeserializeCpuTime: Long, + executorRunTime: Long, + executorCpuTime: Long, + resultSize: Long, + jvmGcTime: Long, + resultSerializationTime: Long, + memoryBytesSpilled: Long, + diskBytesSpilled: Long, + peakExecutionMemory: Long, + inputBytesRead: Long, + inputRecordsRead: Long, + outputBytesWritten: Long, + outputRecordsWritten: Long, + shuffleRemoteBlocksFetched: Long, + shuffleLocalBlocksFetched: Long, + shuffleFetchWaitTime: Long, + shuffleRemoteBytesRead: Long, + shuffleRemoteBytesReadToDisk: Long, + shuffleLocalBytesRead: Long, + shuffleRecordsRead: Long, + shuffleBytesWritten: Long, + shuffleWriteTime: Long, + shuffleRecordsWritten: Long): v1.TaskMetrics = { + new v1.TaskMetrics( + executorDeserializeTime, + executorDeserializeCpuTime, + executorRunTime, + executorCpuTime, + resultSize, + jvmGcTime, + resultSerializationTime, + memoryBytesSpilled, + diskBytesSpilled, + peakExecutionMemory, + new v1.InputMetrics( + inputBytesRead, + inputRecordsRead), + new v1.OutputMetrics( + outputBytesWritten, + outputRecordsWritten), + new v1.ShuffleReadMetrics( + shuffleRemoteBlocksFetched, + shuffleLocalBlocksFetched, + shuffleFetchWaitTime, + shuffleRemoteBytesRead, + shuffleRemoteBytesReadToDisk, + shuffleLocalBytesRead, + shuffleRecordsRead), + new v1.ShuffleWriteMetrics( + shuffleBytesWritten, + shuffleWriteTime, + shuffleRecordsWritten)) + } + // scalastyle:on argcount + + def createMetrics(default: Long): v1.TaskMetrics = { + createMetrics(default, default, default, default, default, default, default, default, + default, default, default, default, default, default, default, default, + default, default, default, default, default, default, default, default) + } + + /** Add m2 values to m1. */ + def addMetrics(m1: v1.TaskMetrics, m2: v1.TaskMetrics): v1.TaskMetrics = addMetrics(m1, m2, 1) + + /** Subtract m2 values from m1. */ + def subtractMetrics(m1: v1.TaskMetrics, m2: v1.TaskMetrics): v1.TaskMetrics = { + addMetrics(m1, m2, -1) + } + + private def addMetrics(m1: v1.TaskMetrics, m2: v1.TaskMetrics, mult: Int): v1.TaskMetrics = { + createMetrics( + m1.executorDeserializeTime + m2.executorDeserializeTime * mult, + m1.executorDeserializeCpuTime + m2.executorDeserializeCpuTime * mult, + m1.executorRunTime + m2.executorRunTime * mult, + m1.executorCpuTime + m2.executorCpuTime * mult, + m1.resultSize + m2.resultSize * mult, + m1.jvmGcTime + m2.jvmGcTime * mult, + m1.resultSerializationTime + m2.resultSerializationTime * mult, + m1.memoryBytesSpilled + m2.memoryBytesSpilled * mult, + m1.diskBytesSpilled + m2.diskBytesSpilled * mult, + m1.peakExecutionMemory + m2.peakExecutionMemory * mult, + m1.inputMetrics.bytesRead + m2.inputMetrics.bytesRead * mult, + m1.inputMetrics.recordsRead + m2.inputMetrics.recordsRead * mult, + m1.outputMetrics.bytesWritten + m2.outputMetrics.bytesWritten * mult, + m1.outputMetrics.recordsWritten + m2.outputMetrics.recordsWritten * mult, + m1.shuffleReadMetrics.remoteBlocksFetched + m2.shuffleReadMetrics.remoteBlocksFetched * mult, + m1.shuffleReadMetrics.localBlocksFetched + m2.shuffleReadMetrics.localBlocksFetched * mult, + m1.shuffleReadMetrics.fetchWaitTime + m2.shuffleReadMetrics.fetchWaitTime * mult, + m1.shuffleReadMetrics.remoteBytesRead + m2.shuffleReadMetrics.remoteBytesRead * mult, + m1.shuffleReadMetrics.remoteBytesReadToDisk + + m2.shuffleReadMetrics.remoteBytesReadToDisk * mult, + m1.shuffleReadMetrics.localBytesRead + m2.shuffleReadMetrics.localBytesRead * mult, + m1.shuffleReadMetrics.recordsRead + m2.shuffleReadMetrics.recordsRead * mult, + m1.shuffleWriteMetrics.bytesWritten + m2.shuffleWriteMetrics.bytesWritten * mult, + m1.shuffleWriteMetrics.writeTime + m2.shuffleWriteMetrics.writeTime * mult, + m1.shuffleWriteMetrics.recordsWritten + m2.shuffleWriteMetrics.recordsWritten * mult) + } + } /** diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/ApiRootResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/ApiRootResource.scala index ed9bdc6e1e3c2..20108ac85c315 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/ApiRootResource.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/ApiRootResource.scala @@ -49,6 +49,7 @@ private[v1] class ApiRootResource extends ApiRequestContext { @Path("applications/{appId}") def application(): Class[OneApplicationResource] = classOf[OneApplicationResource] + @GET @Path("version") def version(): VersionInfo = new VersionInfo(org.apache.spark.SPARK_VERSION) diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/StagesResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/StagesResource.scala index 3b879545b3d2e..96249e4bfd5fa 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/StagesResource.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/StagesResource.scala @@ -87,7 +87,8 @@ private[v1] class StagesResource extends BaseAppResource { } } - ui.store.taskSummary(stageId, stageAttemptId, quantiles) + ui.store.taskSummary(stageId, stageAttemptId, quantiles).getOrElse( + throw new NotFoundException(s"No tasks reported metrics for $stageId / $stageAttemptId yet.")) } @GET diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/api.scala b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala index 45eaf935fb083..7d8e4de3c8efb 100644 --- a/core/src/main/scala/org/apache/spark/status/api/v1/api.scala +++ b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala @@ -261,6 +261,9 @@ class TaskMetricDistributions private[spark]( val resultSize: IndexedSeq[Double], val jvmGcTime: IndexedSeq[Double], val resultSerializationTime: IndexedSeq[Double], + val gettingResultTime: IndexedSeq[Double], + val schedulerDelay: IndexedSeq[Double], + val peakExecutionMemory: IndexedSeq[Double], val memoryBytesSpilled: IndexedSeq[Double], val diskBytesSpilled: IndexedSeq[Double], diff --git a/core/src/main/scala/org/apache/spark/status/storeTypes.scala b/core/src/main/scala/org/apache/spark/status/storeTypes.scala index 1cfd30df49091..646cf25880e37 100644 --- a/core/src/main/scala/org/apache/spark/status/storeTypes.scala +++ b/core/src/main/scala/org/apache/spark/status/storeTypes.scala @@ -17,9 +17,11 @@ package org.apache.spark.status -import java.lang.{Integer => JInteger, Long => JLong} +import java.lang.{Long => JLong} +import java.util.Date import com.fasterxml.jackson.annotation.JsonIgnore +import com.fasterxml.jackson.databind.annotation.JsonDeserialize import org.apache.spark.status.KVUtils._ import org.apache.spark.status.api.v1._ @@ -49,10 +51,10 @@ private[spark] class ApplicationEnvironmentInfoWrapper(val info: ApplicationEnvi private[spark] class ExecutorSummaryWrapper(val info: ExecutorSummary) { @JsonIgnore @KVIndex - private[this] val id: String = info.id + private def id: String = info.id @JsonIgnore @KVIndex("active") - private[this] val active: Boolean = info.isActive + private def active: Boolean = info.isActive @JsonIgnore @KVIndex("host") val host: String = info.hostPort.split(":")(0) @@ -69,52 +71,281 @@ private[spark] class JobDataWrapper( val skippedStages: Set[Int]) { @JsonIgnore @KVIndex - private[this] val id: Int = info.jobId + private def id: Int = info.jobId + @JsonIgnore @KVIndex("completionTime") + private def completionTime: Long = info.completionTime.map(_.getTime).getOrElse(-1L) } private[spark] class StageDataWrapper( val info: StageData, - val jobIds: Set[Int]) { + val jobIds: Set[Int], + @JsonDeserialize(contentAs = classOf[JLong]) + val locality: Map[String, Long]) { @JsonIgnore @KVIndex - def id: Array[Int] = Array(info.stageId, info.attemptId) + private[this] val id: Array[Int] = Array(info.stageId, info.attemptId) @JsonIgnore @KVIndex("stageId") - def stageId: Int = info.stageId + private def stageId: Int = info.stageId + @JsonIgnore @KVIndex("active") + private def active: Boolean = info.status == StageStatus.ACTIVE + + @JsonIgnore @KVIndex("completionTime") + private def completionTime: Long = info.completionTime.map(_.getTime).getOrElse(-1L) +} + +/** + * Tasks have a lot of indices that are used in a few different places. This object keeps logical + * names for these indices, mapped to short strings to save space when using a disk store. + */ +private[spark] object TaskIndexNames { + final val ACCUMULATORS = "acc" + final val ATTEMPT = "att" + final val DESER_CPU_TIME = "dct" + final val DESER_TIME = "des" + final val DISK_SPILL = "dbs" + final val DURATION = "dur" + final val ERROR = "err" + final val EXECUTOR = "exe" + final val HOST = "hst" + final val EXEC_CPU_TIME = "ect" + final val EXEC_RUN_TIME = "ert" + final val GC_TIME = "gc" + final val GETTING_RESULT_TIME = "grt" + final val INPUT_RECORDS = "ir" + final val INPUT_SIZE = "is" + final val LAUNCH_TIME = "lt" + final val LOCALITY = "loc" + final val MEM_SPILL = "mbs" + final val OUTPUT_RECORDS = "or" + final val OUTPUT_SIZE = "os" + final val PEAK_MEM = "pem" + final val RESULT_SIZE = "rs" + final val SCHEDULER_DELAY = "dly" + final val SER_TIME = "rst" + final val SHUFFLE_LOCAL_BLOCKS = "slbl" + final val SHUFFLE_READ_RECORDS = "srr" + final val SHUFFLE_READ_TIME = "srt" + final val SHUFFLE_REMOTE_BLOCKS = "srbl" + final val SHUFFLE_REMOTE_READS = "srby" + final val SHUFFLE_REMOTE_READS_TO_DISK = "srbd" + final val SHUFFLE_TOTAL_READS = "stby" + final val SHUFFLE_TOTAL_BLOCKS = "stbl" + final val SHUFFLE_WRITE_RECORDS = "swr" + final val SHUFFLE_WRITE_SIZE = "sws" + final val SHUFFLE_WRITE_TIME = "swt" + final val STAGE = "stage" + final val STATUS = "sta" + final val TASK_INDEX = "idx" + final val COMPLETION_TIME = "ct" } /** - * The task information is always indexed with the stage ID, since that is how the UI and API - * consume it. That means every indexed value has the stage ID and attempt ID included, aside - * from the actual data being indexed. + * Unlike other data types, the task data wrapper does not keep a reference to the API's TaskData. + * That is to save memory, since for large applications there can be a large number of these + * elements (by default up to 100,000 per stage), and every bit of wasted memory adds up. + * + * It also contains many secondary indices, which are used to sort data efficiently in the UI at the + * expense of storage space (and slower write times). */ private[spark] class TaskDataWrapper( - val info: TaskData, + // Storing this as an object actually saves memory; it's also used as the key in the in-memory + // store, so in that case you'd save the extra copy of the value here. + @KVIndexParam + val taskId: JLong, + @KVIndexParam(value = TaskIndexNames.TASK_INDEX, parent = TaskIndexNames.STAGE) + val index: Int, + @KVIndexParam(value = TaskIndexNames.ATTEMPT, parent = TaskIndexNames.STAGE) + val attempt: Int, + @KVIndexParam(value = TaskIndexNames.LAUNCH_TIME, parent = TaskIndexNames.STAGE) + val launchTime: Long, + val resultFetchStart: Long, + @KVIndexParam(value = TaskIndexNames.DURATION, parent = TaskIndexNames.STAGE) + val duration: Long, + @KVIndexParam(value = TaskIndexNames.EXECUTOR, parent = TaskIndexNames.STAGE) + val executorId: String, + @KVIndexParam(value = TaskIndexNames.HOST, parent = TaskIndexNames.STAGE) + val host: String, + @KVIndexParam(value = TaskIndexNames.STATUS, parent = TaskIndexNames.STAGE) + val status: String, + @KVIndexParam(value = TaskIndexNames.LOCALITY, parent = TaskIndexNames.STAGE) + val taskLocality: String, + val speculative: Boolean, + val accumulatorUpdates: Seq[AccumulableInfo], + val errorMessage: Option[String], + + // The following is an exploded view of a TaskMetrics API object. This saves 5 objects + // (= 80 bytes of Java object overhead) per instance of this wrapper. If the first value + // (executorDeserializeTime) is -1L, it means the metrics for this task have not been + // recorded. + @KVIndexParam(value = TaskIndexNames.DESER_TIME, parent = TaskIndexNames.STAGE) + val executorDeserializeTime: Long, + @KVIndexParam(value = TaskIndexNames.DESER_CPU_TIME, parent = TaskIndexNames.STAGE) + val executorDeserializeCpuTime: Long, + @KVIndexParam(value = TaskIndexNames.EXEC_RUN_TIME, parent = TaskIndexNames.STAGE) + val executorRunTime: Long, + @KVIndexParam(value = TaskIndexNames.EXEC_CPU_TIME, parent = TaskIndexNames.STAGE) + val executorCpuTime: Long, + @KVIndexParam(value = TaskIndexNames.RESULT_SIZE, parent = TaskIndexNames.STAGE) + val resultSize: Long, + @KVIndexParam(value = TaskIndexNames.GC_TIME, parent = TaskIndexNames.STAGE) + val jvmGcTime: Long, + @KVIndexParam(value = TaskIndexNames.SER_TIME, parent = TaskIndexNames.STAGE) + val resultSerializationTime: Long, + @KVIndexParam(value = TaskIndexNames.MEM_SPILL, parent = TaskIndexNames.STAGE) + val memoryBytesSpilled: Long, + @KVIndexParam(value = TaskIndexNames.DISK_SPILL, parent = TaskIndexNames.STAGE) + val diskBytesSpilled: Long, + @KVIndexParam(value = TaskIndexNames.PEAK_MEM, parent = TaskIndexNames.STAGE) + val peakExecutionMemory: Long, + @KVIndexParam(value = TaskIndexNames.INPUT_SIZE, parent = TaskIndexNames.STAGE) + val inputBytesRead: Long, + @KVIndexParam(value = TaskIndexNames.INPUT_RECORDS, parent = TaskIndexNames.STAGE) + val inputRecordsRead: Long, + @KVIndexParam(value = TaskIndexNames.OUTPUT_SIZE, parent = TaskIndexNames.STAGE) + val outputBytesWritten: Long, + @KVIndexParam(value = TaskIndexNames.OUTPUT_RECORDS, parent = TaskIndexNames.STAGE) + val outputRecordsWritten: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_REMOTE_BLOCKS, parent = TaskIndexNames.STAGE) + val shuffleRemoteBlocksFetched: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_LOCAL_BLOCKS, parent = TaskIndexNames.STAGE) + val shuffleLocalBlocksFetched: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_READ_TIME, parent = TaskIndexNames.STAGE) + val shuffleFetchWaitTime: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_REMOTE_READS, parent = TaskIndexNames.STAGE) + val shuffleRemoteBytesRead: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_REMOTE_READS_TO_DISK, + parent = TaskIndexNames.STAGE) + val shuffleRemoteBytesReadToDisk: Long, + val shuffleLocalBytesRead: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_READ_RECORDS, parent = TaskIndexNames.STAGE) + val shuffleRecordsRead: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_WRITE_SIZE, parent = TaskIndexNames.STAGE) + val shuffleBytesWritten: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_WRITE_TIME, parent = TaskIndexNames.STAGE) + val shuffleWriteTime: Long, + @KVIndexParam(value = TaskIndexNames.SHUFFLE_WRITE_RECORDS, parent = TaskIndexNames.STAGE) + val shuffleRecordsWritten: Long, + val stageId: Int, val stageAttemptId: Int) { - @JsonIgnore @KVIndex - def id: Long = info.taskId + def hasMetrics: Boolean = executorDeserializeTime >= 0 + + def toApi: TaskData = { + val metrics = if (hasMetrics) { + Some(new TaskMetrics( + executorDeserializeTime, + executorDeserializeCpuTime, + executorRunTime, + executorCpuTime, + resultSize, + jvmGcTime, + resultSerializationTime, + memoryBytesSpilled, + diskBytesSpilled, + peakExecutionMemory, + new InputMetrics( + inputBytesRead, + inputRecordsRead), + new OutputMetrics( + outputBytesWritten, + outputRecordsWritten), + new ShuffleReadMetrics( + shuffleRemoteBlocksFetched, + shuffleLocalBlocksFetched, + shuffleFetchWaitTime, + shuffleRemoteBytesRead, + shuffleRemoteBytesReadToDisk, + shuffleLocalBytesRead, + shuffleRecordsRead), + new ShuffleWriteMetrics( + shuffleBytesWritten, + shuffleWriteTime, + shuffleRecordsWritten))) + } else { + None + } - @JsonIgnore @KVIndex("stage") - def stage: Array[Int] = Array(stageId, stageAttemptId) + new TaskData( + taskId, + index, + attempt, + new Date(launchTime), + if (resultFetchStart > 0L) Some(new Date(resultFetchStart)) else None, + if (duration > 0L) Some(duration) else None, + executorId, + host, + status, + taskLocality, + speculative, + accumulatorUpdates, + errorMessage, + metrics) + } + + @JsonIgnore @KVIndex(TaskIndexNames.STAGE) + private def stage: Array[Int] = Array(stageId, stageAttemptId) - @JsonIgnore @KVIndex("runtime") - def runtime: Array[AnyRef] = { - val _runtime = info.taskMetrics.map(_.executorRunTime).getOrElse(-1L) - Array(stageId: JInteger, stageAttemptId: JInteger, _runtime: JLong) + @JsonIgnore @KVIndex(value = TaskIndexNames.SCHEDULER_DELAY, parent = TaskIndexNames.STAGE) + def schedulerDelay: Long = { + if (hasMetrics) { + AppStatusUtils.schedulerDelay(launchTime, resultFetchStart, duration, executorDeserializeTime, + resultSerializationTime, executorRunTime) + } else { + -1L + } } - @JsonIgnore @KVIndex("startTime") - def startTime: Array[AnyRef] = { - Array(stageId: JInteger, stageAttemptId: JInteger, info.launchTime.getTime(): JLong) + @JsonIgnore @KVIndex(value = TaskIndexNames.GETTING_RESULT_TIME, parent = TaskIndexNames.STAGE) + def gettingResultTime: Long = { + if (hasMetrics) { + AppStatusUtils.gettingResultTime(launchTime, resultFetchStart, duration) + } else { + -1L + } } - @JsonIgnore @KVIndex("active") - def active: Boolean = info.duration.isEmpty + /** + * Sorting by accumulators is a little weird, and the previous behavior would generate + * insanely long keys in the index. So this implementation just considers the first + * accumulator and its String representation. + */ + @JsonIgnore @KVIndex(value = TaskIndexNames.ACCUMULATORS, parent = TaskIndexNames.STAGE) + private def accumulators: String = { + if (accumulatorUpdates.nonEmpty) { + val acc = accumulatorUpdates.head + s"${acc.name}:${acc.value}" + } else { + "" + } + } + + @JsonIgnore @KVIndex(value = TaskIndexNames.SHUFFLE_TOTAL_READS, parent = TaskIndexNames.STAGE) + private def shuffleTotalReads: Long = { + if (hasMetrics) { + shuffleLocalBytesRead + shuffleRemoteBytesRead + } else { + -1L + } + } + + @JsonIgnore @KVIndex(value = TaskIndexNames.SHUFFLE_TOTAL_BLOCKS, parent = TaskIndexNames.STAGE) + private def shuffleTotalBlocks: Long = { + if (hasMetrics) { + shuffleLocalBlocksFetched + shuffleRemoteBlocksFetched + } else { + -1L + } + } + + @JsonIgnore @KVIndex(value = TaskIndexNames.ERROR, parent = TaskIndexNames.STAGE) + private def error: String = if (errorMessage.isDefined) errorMessage.get else "" + @JsonIgnore @KVIndex(value = TaskIndexNames.COMPLETION_TIME, parent = TaskIndexNames.STAGE) + private def completionTime: Long = launchTime + duration } private[spark] class RDDStorageInfoWrapper(val info: RDDStorageInfo) { @@ -134,10 +365,13 @@ private[spark] class ExecutorStageSummaryWrapper( val info: ExecutorStageSummary) { @JsonIgnore @KVIndex - val id: Array[Any] = Array(stageId, stageAttemptId, executorId) + private val _id: Array[Any] = Array(stageId, stageAttemptId, executorId) @JsonIgnore @KVIndex("stage") - private[this] val stage: Array[Int] = Array(stageId, stageAttemptId) + private def stage: Array[Int] = Array(stageId, stageAttemptId) + + @JsonIgnore + def id: Array[Any] = _id } @@ -203,3 +437,53 @@ private[spark] class AppSummary( def id: String = classOf[AppSummary].getName() } + +/** + * A cached view of a specific quantile for one stage attempt's metrics. + */ +private[spark] class CachedQuantile( + val stageId: Int, + val stageAttemptId: Int, + val quantile: String, + val taskCount: Long, + + // The following fields are an exploded view of a single entry for TaskMetricDistributions. + val executorDeserializeTime: Double, + val executorDeserializeCpuTime: Double, + val executorRunTime: Double, + val executorCpuTime: Double, + val resultSize: Double, + val jvmGcTime: Double, + val resultSerializationTime: Double, + val gettingResultTime: Double, + val schedulerDelay: Double, + val peakExecutionMemory: Double, + val memoryBytesSpilled: Double, + val diskBytesSpilled: Double, + + val bytesRead: Double, + val recordsRead: Double, + + val bytesWritten: Double, + val recordsWritten: Double, + + val shuffleReadBytes: Double, + val shuffleRecordsRead: Double, + val shuffleRemoteBlocksFetched: Double, + val shuffleLocalBlocksFetched: Double, + val shuffleFetchWaitTime: Double, + val shuffleRemoteBytesRead: Double, + val shuffleRemoteBytesReadToDisk: Double, + val shuffleTotalBlocksFetched: Double, + + val shuffleWriteBytes: Double, + val shuffleWriteRecords: Double, + val shuffleWriteTime: Double) { + + @KVIndex @JsonIgnore + def id: Array[Any] = Array(stageId, stageAttemptId, quantile) + + @KVIndex("stage") @JsonIgnore + def stage: Array[Int] = Array(stageId, stageAttemptId) + +} diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index e0276a4dc4224..df1a4bef616b2 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -291,7 +291,7 @@ private[spark] class BlockManager( case e: Exception if i < MAX_ATTEMPTS => logError(s"Failed to connect to external shuffle server, will retry ${MAX_ATTEMPTS - i}" + s" more times after waiting $SLEEP_TIME_SECS seconds...", e) - Thread.sleep(SLEEP_TIME_SECS * 1000) + Thread.sleep(SLEEP_TIME_SECS * 1000L) case NonFatal(e) => throw new SparkException("Unable to register with external shuffle server due to : " + e.getMessage, e) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala index 2c3da0ee85e06..d4a59c33b974c 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala @@ -18,7 +18,8 @@ package org.apache.spark.storage import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput} -import java.util.concurrent.ConcurrentHashMap + +import com.google.common.cache.{CacheBuilder, CacheLoader} import org.apache.spark.SparkContext import org.apache.spark.annotation.DeveloperApi @@ -132,10 +133,17 @@ private[spark] object BlockManagerId { getCachedBlockManagerId(obj) } - val blockManagerIdCache = new ConcurrentHashMap[BlockManagerId, BlockManagerId]() + /** + * The max cache size is hardcoded to 10000, since the size of a BlockManagerId + * object is about 48B, the total memory cost should be below 1MB which is feasible. + */ + val blockManagerIdCache = CacheBuilder.newBuilder() + .maximumSize(10000) + .build(new CacheLoader[BlockManagerId, BlockManagerId]() { + override def load(id: BlockManagerId) = id + }) def getCachedBlockManagerId(id: BlockManagerId): BlockManagerId = { - blockManagerIdCache.putIfAbsent(id, id) blockManagerIdCache.get(id) } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala index 89a6a71a589a1..8e8f7d197c9ef 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala @@ -164,7 +164,8 @@ class BlockManagerMasterEndpoint( val futures = blockManagerInfo.values.map { bm => bm.slaveEndpoint.ask[Int](removeMsg).recover { case e: IOException => - logWarning(s"Error trying to remove RDD $rddId", e) + logWarning(s"Error trying to remove RDD $rddId from block manager ${bm.blockManagerId}", + e) 0 // zero blocks were removed } }.toSeq @@ -192,11 +193,16 @@ class BlockManagerMasterEndpoint( val requiredBlockManagers = blockManagerInfo.values.filter { info => removeFromDriver || !info.blockManagerId.isDriver } - Future.sequence( - requiredBlockManagers.map { bm => - bm.slaveEndpoint.ask[Int](removeMsg) - }.toSeq - ) + val futures = requiredBlockManagers.map { bm => + bm.slaveEndpoint.ask[Int](removeMsg).recover { + case e: IOException => + logWarning(s"Error trying to remove broadcast $broadcastId from block manager " + + s"${bm.blockManagerId}", e) + 0 // zero blocks were removed + } + }.toSeq + + Future.sequence(futures) } private def removeBlockManager(blockManagerId: BlockManagerId) { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockReplicationPolicy.scala b/core/src/main/scala/org/apache/spark/storage/BlockReplicationPolicy.scala index 353eac60df171..0bacc34cdfd90 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockReplicationPolicy.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockReplicationPolicy.scala @@ -54,10 +54,9 @@ trait BlockReplicationPolicy { } object BlockReplicationUtils { - // scalastyle:off line.size.limit /** * Uses sampling algorithm by Robert Floyd. Finds a random sample in O(n) while - * minimizing space usage. Please see + * minimizing space usage. Please see * here. * * @param n total number of indices @@ -65,7 +64,6 @@ object BlockReplicationUtils { * @param r random number generator * @return list of m random unique indices */ - // scalastyle:on line.size.limit private def getSampleIds(n: Int, m: Int, r: Random): List[Int] = { val indices = (n - m + 1 to n).foldLeft(mutable.LinkedHashSet.empty[Int]) {case (set, i) => val t = r.nextInt(i) + 1 diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala index 98b5a735a4529..dd9df74689a13 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala @@ -90,7 +90,7 @@ final class ShuffleBlockFetcherIterator( private[this] val startTime = System.currentTimeMillis /** Local blocks to fetch, excluding zero-sized blocks. */ - private[this] val localBlocks = new ArrayBuffer[BlockId]() + private[this] val localBlocks = scala.collection.mutable.LinkedHashSet[BlockId]() /** Remote blocks to fetch, excluding zero-sized blocks. */ private[this] val remoteBlocks = new HashSet[BlockId]() @@ -316,6 +316,7 @@ final class ShuffleBlockFetcherIterator( * track in-memory are the ManagedBuffer references themselves. */ private[this] def fetchLocalBlocks() { + logDebug(s"Start fetching local blocks: ${localBlocks.mkString(", ")}") val iter = localBlocks.iterator while (iter.hasNext) { val blockId = iter.next() @@ -324,7 +325,8 @@ final class ShuffleBlockFetcherIterator( shuffleMetrics.incLocalBlocksFetched(1) shuffleMetrics.incLocalBytesRead(buf.size) buf.retain() - results.put(new SuccessFetchResult(blockId, blockManager.blockManagerId, 0, buf, false)) + results.put(new SuccessFetchResult(blockId, blockManager.blockManagerId, + buf.size(), buf, false)) } catch { case e: Exception => // If we see an exception, stop immediately. @@ -397,7 +399,9 @@ final class ShuffleBlockFetcherIterator( } shuffleMetrics.incRemoteBlocksFetched(1) } - bytesInFlight -= size + if (!localBlocks.contains(blockId)) { + bytesInFlight -= size + } if (isNetworkReqDone) { reqsInFlight -= 1 logDebug("Number of requests in flight " + reqsInFlight) @@ -583,8 +587,8 @@ object ShuffleBlockFetcherIterator { * Result of a fetch from a remote block successfully. * @param blockId block id * @param address BlockManager that the block was fetched from. - * @param size estimated size of the block, used to calculate bytesInFlight. - * Note that this is NOT the exact bytes. + * @param size estimated size of the block. Note that this is NOT the exact bytes. + * Size of remote block is used to calculate bytesInFlight. * @param buf `ManagedBuffer` for the content. * @param isNetworkReqDone Is this the last network request for this host in this fetch request. */ diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala index 0adeb4058b6e4..ba98fa1548167 100644 --- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala @@ -263,7 +263,7 @@ private[spark] object JettyUtils extends Logging { filters.foreach { case filter : String => if (!filter.isEmpty) { - logInfo("Adding filter: " + filter) + logInfo(s"Adding filter $filter to ${handlers.map(_.getContextPath).mkString(", ")}.") val holder : FilterHolder = new FilterHolder() holder.setClassName(filter) // Get any parameters for each filter @@ -343,12 +343,13 @@ private[spark] object JettyUtils extends Logging { -1, connectionFactories: _*) connector.setPort(port) - connector.start() + connector.setHost(hostName) // Currently we only use "SelectChannelConnector" // Limit the max acceptor number to 8 so that we don't waste a lot of threads connector.setAcceptQueueSize(math.min(connector.getAcceptors, 8)) - connector.setHost(hostName) + + connector.start() // The number of selectors always equals to the number of acceptors minThreads += connector.getAcceptors * 2 @@ -405,7 +406,7 @@ private[spark] object JettyUtils extends Logging { } pool.setMaxThreads(math.max(pool.getMaxThreads, minThreads)) - ServerInfo(server, httpPort, securePort, collection) + ServerInfo(server, httpPort, securePort, conf, collection) } catch { case e: Exception => server.stop() @@ -505,10 +506,12 @@ private[spark] case class ServerInfo( server: Server, boundPort: Int, securePort: Option[Int], + conf: SparkConf, private val rootHandler: ContextHandlerCollection) { - def addHandler(handler: ContextHandler): Unit = { + def addHandler(handler: ServletContextHandler): Unit = { handler.setVirtualHosts(JettyUtils.toVirtualHosts(JettyUtils.SPARK_CONNECTOR_NAME)) + JettyUtils.addFilters(Seq(handler), conf) rootHandler.addHandler(handler) if (!handler.isStarted()) { handler.start() diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala index 37e3b3b304a63..a1bc93e8f6781 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala @@ -36,6 +36,9 @@ import org.apache.spark.util.Utils /** Page showing list of all ongoing and recently finished jobs */ private[ui] class AllJobsPage(parent: JobsTab, store: AppStatusStore) extends WebUIPage("") { + + import ApiHelper._ + private val JOBS_LEGEND =
    val jobId = job.jobId val status = job.status - val displayJobDescription = - if (job.description.isEmpty) { - job.name - } else { - UIUtils.makeDescription(job.description.get, "", plainText = true).text - } + val (_, lastStageDescription) = lastStageNameAndDescription(store, job) + val jobDescription = UIUtils.makeDescription(lastStageDescription, "", plainText = true).text + val submissionTime = job.submissionTime.get.getTime() val completionTime = job.completionTime.map(_.getTime()).getOrElse(System.currentTimeMillis()) val classNameByStatus = status match { @@ -82,7 +82,7 @@ private[ui] class AllJobsPage(parent: JobsTab, store: AppStatusStore) extends We // The timeline library treats contents as HTML, so we have to escape them. We need to add // extra layers of escaping in order to embed this in a Javascript string literal. - val escapedDesc = Utility.escape(displayJobDescription) + val escapedDesc = Utility.escape(jobDescription) val jsEscapedDesc = StringEscapeUtils.escapeEcmaScript(escapedDesc) val jobEventJsonAsStr = s""" @@ -206,7 +206,9 @@ private[ui] class AllJobsPage(parent: JobsTab, store: AppStatusStore) extends We jobs: Seq[v1.JobData], killEnabled: Boolean): Seq[Node] = { // stripXSS is called to remove suspicious characters used in XSS attacks - val allParameters = request.getParameterMap.asScala.toMap.mapValues(_.map(UIUtils.stripXSS)) + val allParameters = request.getParameterMap.asScala.toMap.map { case (k, v) => + UIUtils.stripXSS(k) -> v.map(UIUtils.stripXSS).toSeq + } val parameterOtherTable = allParameters.filterNot(_._1.startsWith(jobTag)) .map(para => para._1 + "=" + para._2(0)) @@ -405,6 +407,8 @@ private[ui] class JobDataSource( sortColumn: String, desc: Boolean) extends PagedDataSource[JobTableRowData](pageSize) { + import ApiHelper._ + // Convert JobUIData to JobTableRowData which contains the final contents to show in the table // so that we can avoid creating duplicate contents during sorting the data private val data = jobs.map(jobRow).sorted(ordering(sortColumn, desc)) @@ -429,15 +433,16 @@ private[ui] class JobDataSource( val formattedDuration = duration.map(d => UIUtils.formatDuration(d)).getOrElse("Unknown") val submissionTime = jobData.submissionTime val formattedSubmissionTime = submissionTime.map(UIUtils.formatDate).getOrElse("Unknown") - val jobDescription = UIUtils.makeDescription(jobData.description.getOrElse(""), - basePath, plainText = false) + val (lastStageName, lastStageDescription) = lastStageNameAndDescription(store, jobData) + + val jobDescription = UIUtils.makeDescription(lastStageDescription, basePath, plainText = false) val detailUrl = "%s/jobs/job?id=%s".format(basePath, jobData.jobId) new JobTableRowData( jobData, - jobData.name, - jobData.description.getOrElse(jobData.name), + lastStageName, + lastStageDescription, duration.getOrElse(-1), formattedDuration, submissionTime.map(_.getTime()).getOrElse(-1L), diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala index b1e343451e28e..9325b903b33a1 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala @@ -36,6 +36,7 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") { val activeStages = allStages.filter(_.status == StageStatus.ACTIVE) val pendingStages = allStages.filter(_.status == StageStatus.PENDING) + val skippedStages = allStages.filter(_.status == StageStatus.SKIPPED) val completedStages = allStages.filter(_.status == StageStatus.COMPLETE) val failedStages = allStages.filter(_.status == StageStatus.FAILED).reverse @@ -51,6 +52,9 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") { val completedStagesTable = new StageTableBase(parent.store, request, completedStages, "completed", "completedStage", parent.basePath, subPath, parent.isFairScheduler, false, false) + val skippedStagesTable = + new StageTableBase(parent.store, request, skippedStages, "skipped", "skippedStage", + parent.basePath, subPath, parent.isFairScheduler, false, false) val failedStagesTable = new StageTableBase(parent.store, request, failedStages, "failed", "failedStage", parent.basePath, subPath, parent.isFairScheduler, false, true) @@ -66,6 +70,7 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") { val shouldShowActiveStages = activeStages.nonEmpty val shouldShowPendingStages = pendingStages.nonEmpty val shouldShowCompletedStages = completedStages.nonEmpty + val shouldShowSkippedStages = skippedStages.nonEmpty val shouldShowFailedStages = failedStages.nonEmpty val appSummary = parent.store.appSummary() @@ -102,6 +107,14 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") { } } + { + if (shouldShowSkippedStages) { +
  • + Skipped Stages: + {skippedStages.size} +
    • + } + } { if (shouldShowFailedStages) {
  • @@ -133,6 +146,20 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") { content ++=

    Completed Stages ({completedStageNumStr})

    ++ completedStagesTable.toNodeSeq } + if (shouldShowSkippedStages) { + content ++= + +

    + + Skipped Stages ({skippedStages.size}) +

    +     ++ +
    + {skippedStagesTable.toNodeSeq} +
    + } if (shouldShowFailedStages) { content ++=

    Failed Stages ({numFailedStages})

    ++ failedStagesTable.toNodeSeq diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala index 41d42b52430a5..95c12b1e73653 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala @@ -87,7 +87,9 @@ private[ui] class ExecutorTable(stage: StageData, store: AppStatusStore) { } private def createExecutorTable(stage: StageData) : Seq[Node] = { - stage.executorSummary.getOrElse(Map.empty).toSeq.sortBy(_._1).map { case (k, v) => + val executorSummary = store.executorSummary(stage.stageId, stage.attemptId) + + executorSummary.toSeq.sortBy(_._1).map { case (k, v) => val executor = store.asOption(store.executorSummary(k)) diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala index 740f12e7d13d4..974e5c5ffd0a0 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala @@ -201,7 +201,7 @@ private[ui] class JobPage(parent: JobsTab, store: AppStatusStore) extends WebUIP val stages = jobData.stageIds.map { stageId => // This could be empty if the listener hasn't received information about the // stage or if the stage information has been garbage collected - store.stageData(stageId).lastOption.getOrElse { + store.asOption(store.lastStageAttempt(stageId)).getOrElse { new v1.StageData( v1.StageStatus.PENDING, stageId, @@ -336,8 +336,14 @@ private[ui] class JobPage(parent: JobsTab, store: AppStatusStore) extends WebUIP content ++= makeTimeline(activeStages ++ completedStages ++ failedStages, store.executorList(false), appStartTime) - content ++= UIUtils.showDagVizForJob( - jobId, store.operationGraphForJob(jobId)) + val operationGraphContent = store.asOption(store.operationGraphForJob(jobId)) match { + case Some(operationGraph) => UIUtils.showDagVizForJob(jobId, operationGraph) + case None => +
    +

    No DAG visualization information to display for job {jobId}

    +
    + } + content ++= operationGraphContent if (shouldShowActiveStages) { content ++=

    Active Stages ({activeStages.size})

    ++ diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala index 99eab1b2a27d8..ff1b75e5c5065 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala @@ -34,10 +34,10 @@ private[ui] class JobsTab(parent: SparkUI, store: AppStatusStore) val killEnabled = parent.killEnabled def isFairScheduler: Boolean = { - store.environmentInfo().sparkProperties.toMap - .get("spark.scheduler.mode") - .map { mode => mode == SchedulingMode.FAIR } - .getOrElse(false) + store + .environmentInfo() + .sparkProperties + .contains(("spark.scheduler.mode", SchedulingMode.FAIR.toString)) } def getSparkUser: String = parent.getSparkUser diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala index 11a6a34344976..7ab433655233e 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala @@ -19,25 +19,23 @@ package org.apache.spark.ui.jobs import java.net.URLEncoder import java.util.Date +import java.util.concurrent.TimeUnit import javax.servlet.http.HttpServletRequest import scala.collection.mutable.{HashMap, HashSet} -import scala.xml.{Elem, Node, Unparsed} +import scala.xml.{Node, Unparsed} import org.apache.commons.lang3.StringEscapeUtils -import org.apache.spark.SparkConf -import org.apache.spark.internal.config._ import org.apache.spark.scheduler.TaskLocality -import org.apache.spark.status.AppStatusStore +import org.apache.spark.status._ import org.apache.spark.status.api.v1._ import org.apache.spark.ui._ -import org.apache.spark.util.{Distribution, Utils} +import org.apache.spark.util.Utils /** Page showing statistics and task list for a given stage */ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends WebUIPage("stage") { import ApiHelper._ - import StagePage._ private val TIMELINE_LEGEND = {
    @@ -67,17 +65,17 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We // if we find that it's okay. private val MAX_TIMELINE_TASKS = parent.conf.getInt("spark.ui.timeline.tasks.maximum", 1000) - private def getLocalitySummaryString(stageData: StageData, taskList: Seq[TaskData]): String = { - val localities = taskList.map(_.taskLocality) - val localityCounts = localities.groupBy(identity).mapValues(_.size) + private def getLocalitySummaryString(localitySummary: Map[String, Long]): String = { val names = Map( TaskLocality.PROCESS_LOCAL.toString() -> "Process local", TaskLocality.NODE_LOCAL.toString() -> "Node local", TaskLocality.RACK_LOCAL.toString() -> "Rack local", TaskLocality.ANY.toString() -> "Any") - val localityNamesAndCounts = localityCounts.toSeq.map { case (locality, count) => - s"${names(locality)}: $count" - } + val localityNamesAndCounts = names.flatMap { case (key, name) => + localitySummary.get(key).map { count => + s"$name: $count" + } + }.toSeq localityNamesAndCounts.sorted.mkString("; ") } @@ -108,7 +106,7 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We val stageHeader = s"Details for Stage $stageId (Attempt $stageAttemptId)" val stageData = parent.store - .asOption(parent.store.stageAttempt(stageId, stageAttemptId, details = true)) + .asOption(parent.store.stageAttempt(stageId, stageAttemptId, details = false)) .getOrElse { val content =
    @@ -117,8 +115,10 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We return UIUtils.headerSparkPage(stageHeader, content, parent) } - val tasks = stageData.tasks.getOrElse(Map.empty).values.toSeq - if (tasks.isEmpty) { + val localitySummary = store.localitySummary(stageData.stageId, stageData.attemptId) + + val totalTasks = taskCount(stageData) + if (totalTasks == 0) { val content =

    Summary Metrics

    No tasks have started yet @@ -127,18 +127,14 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We return UIUtils.headerSparkPage(stageHeader, content, parent) } + val storedTasks = store.taskCount(stageData.stageId, stageData.attemptId) val numCompleted = stageData.numCompleteTasks - val totalTasks = stageData.numActiveTasks + stageData.numCompleteTasks + - stageData.numFailedTasks + stageData.numKilledTasks - val totalTasksNumStr = if (totalTasks == tasks.size) { + val totalTasksNumStr = if (totalTasks == storedTasks) { s"$totalTasks" } else { - s"$totalTasks, showing ${tasks.size}" + s"$storedTasks, showing ${totalTasks}" } - val externalAccumulables = stageData.accumulatorUpdates - val hasAccumulators = externalAccumulables.size > 0 - val summary =
      @@ -148,7 +144,7 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We
    • Locality Level Summary: - {getLocalitySummaryString(stageData, tasks)} + {getLocalitySummaryString(localitySummary)}
      • {if (hasInput(stageData)) {
    • @@ -261,12 +257,16 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We val accumulableHeaders: Seq[String] = Seq("Accumulable", "Value") def accumulableRow(acc: AccumulableInfo): Seq[Node] = { - {acc.name}{acc.value} + if (acc.name != null && acc.value != null) { + {acc.name}{acc.value} + } else { + Nil + } } val accumulableTable = UIUtils.listingTable( accumulableHeaders, accumulableRow, - externalAccumulables.toSeq) + stageData.accumulatorUpdates.toSeq) val page: Int = { // If the user has changed to a larger page size, then go to page 1 in order to avoid @@ -280,16 +280,9 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We val currentTime = System.currentTimeMillis() val (taskTable, taskTableHTML) = try { val _taskTable = new TaskPagedTable( - parent.conf, + stageData, UIUtils.prependBaseUri(parent.basePath) + s"/stages/stage?id=${stageId}&attempt=${stageAttemptId}", - tasks, - hasAccumulators, - hasInput(stageData), - hasOutput(stageData), - hasShuffleRead(stageData), - hasShuffleWrite(stageData), - hasBytesSpilled(stageData), currentTime, pageSize = taskPageSize, sortColumn = taskSortColumn, @@ -320,217 +313,155 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We | } |}); """.stripMargin - } + } } - val taskIdsInPage = if (taskTable == null) Set.empty[Long] - else taskTable.dataSource.slicedTaskIds + val metricsSummary = store.taskSummary(stageData.stageId, stageData.attemptId, + Array(0, 0.25, 0.5, 0.75, 1.0)) - // Excludes tasks which failed and have incomplete metrics - val validTasks = tasks.filter(t => t.status == "SUCCESS" && t.taskMetrics.isDefined) - - val summaryTable: Option[Seq[Node]] = - if (validTasks.size == 0) { - None - } else { - def getDistributionQuantiles(data: Seq[Double]): IndexedSeq[Double] = { - Distribution(data).get.getQuantiles() - } - def getFormattedTimeQuantiles(times: Seq[Double]): Seq[Node] = { - getDistributionQuantiles(times).map { millis => - {UIUtils.formatDuration(millis.toLong)} - } - } - def getFormattedSizeQuantiles(data: Seq[Double]): Seq[Elem] = { - getDistributionQuantiles(data).map(d => {Utils.bytesToString(d.toLong)}) + val summaryTable = metricsSummary.map { metrics => + def timeQuantiles(data: IndexedSeq[Double]): Seq[Node] = { + data.map { millis => + {UIUtils.formatDuration(millis.toLong)} } + } - val deserializationTimes = validTasks.map { task => - task.taskMetrics.get.executorDeserializeTime.toDouble - } - val deserializationQuantiles = - - - Task Deserialization Time - - +: getFormattedTimeQuantiles(deserializationTimes) - - val serviceTimes = validTasks.map(_.taskMetrics.get.executorRunTime.toDouble) - val serviceQuantiles = Duration +: getFormattedTimeQuantiles(serviceTimes) - - val gcTimes = validTasks.map(_.taskMetrics.get.jvmGcTime.toDouble) - val gcQuantiles = - - GC Time - - +: getFormattedTimeQuantiles(gcTimes) - - val serializationTimes = validTasks.map(_.taskMetrics.get.resultSerializationTime.toDouble) - val serializationQuantiles = - - - Result Serialization Time - - +: getFormattedTimeQuantiles(serializationTimes) - - val gettingResultTimes = validTasks.map(getGettingResultTime(_, currentTime).toDouble) - val gettingResultQuantiles = - - - Getting Result Time - - +: - getFormattedTimeQuantiles(gettingResultTimes) - - val peakExecutionMemory = validTasks.map(_.taskMetrics.get.peakExecutionMemory.toDouble) - val peakExecutionMemoryQuantiles = { - - - Peak Execution Memory - - +: getFormattedSizeQuantiles(peakExecutionMemory) + def sizeQuantiles(data: IndexedSeq[Double]): Seq[Node] = { + data.map { size => + {Utils.bytesToString(size.toLong)} } + } - // The scheduler delay includes the network delay to send the task to the worker - // machine and to send back the result (but not the time to fetch the task result, - // if it needed to be fetched from the block manager on the worker). - val schedulerDelays = validTasks.map { task => - getSchedulerDelay(task, task.taskMetrics.get, currentTime).toDouble - } - val schedulerDelayTitle = Scheduler Delay - val schedulerDelayQuantiles = schedulerDelayTitle +: - getFormattedTimeQuantiles(schedulerDelays) - def getFormattedSizeQuantilesWithRecords(data: Seq[Double], records: Seq[Double]) - : Seq[Elem] = { - val recordDist = getDistributionQuantiles(records).iterator - getDistributionQuantiles(data).map(d => - {s"${Utils.bytesToString(d.toLong)} / ${recordDist.next().toLong}"} - ) + def sizeQuantilesWithRecords( + data: IndexedSeq[Double], + records: IndexedSeq[Double]) : Seq[Node] = { + data.zip(records).map { case (d, r) => + {s"${Utils.bytesToString(d.toLong)} / ${r.toLong}"} } + } - val inputSizes = validTasks.map(_.taskMetrics.get.inputMetrics.bytesRead.toDouble) - val inputRecords = validTasks.map(_.taskMetrics.get.inputMetrics.recordsRead.toDouble) - val inputQuantiles = Input Size / Records +: - getFormattedSizeQuantilesWithRecords(inputSizes, inputRecords) + def titleCell(title: String, tooltip: String): Seq[Node] = { + + + {title} + + + } - val outputSizes = validTasks.map(_.taskMetrics.get.outputMetrics.bytesWritten.toDouble) - val outputRecords = validTasks.map(_.taskMetrics.get.outputMetrics.recordsWritten.toDouble) - val outputQuantiles = Output Size / Records +: - getFormattedSizeQuantilesWithRecords(outputSizes, outputRecords) + def simpleTitleCell(title: String): Seq[Node] = {title} - val shuffleReadBlockedTimes = validTasks.map { task => - task.taskMetrics.get.shuffleReadMetrics.fetchWaitTime.toDouble - } - val shuffleReadBlockedQuantiles = - - - Shuffle Read Blocked Time - - +: - getFormattedTimeQuantiles(shuffleReadBlockedTimes) - - val shuffleReadTotalSizes = validTasks.map { task => - totalBytesRead(task.taskMetrics.get.shuffleReadMetrics).toDouble - } - val shuffleReadTotalRecords = validTasks.map { task => - task.taskMetrics.get.shuffleReadMetrics.recordsRead.toDouble - } - val shuffleReadTotalQuantiles = - - - Shuffle Read Size / Records - - +: - getFormattedSizeQuantilesWithRecords(shuffleReadTotalSizes, shuffleReadTotalRecords) - - val shuffleReadRemoteSizes = validTasks.map { task => - task.taskMetrics.get.shuffleReadMetrics.remoteBytesRead.toDouble - } - val shuffleReadRemoteQuantiles = - - - Shuffle Remote Reads - - +: - getFormattedSizeQuantiles(shuffleReadRemoteSizes) - - val shuffleWriteSizes = validTasks.map { task => - task.taskMetrics.get.shuffleWriteMetrics.bytesWritten.toDouble - } + val deserializationQuantiles = titleCell("Task Deserialization Time", + ToolTips.TASK_DESERIALIZATION_TIME) ++ timeQuantiles(metrics.executorDeserializeTime) - val shuffleWriteRecords = validTasks.map { task => - task.taskMetrics.get.shuffleWriteMetrics.recordsWritten.toDouble - } + val serviceQuantiles = simpleTitleCell("Duration") ++ timeQuantiles(metrics.executorRunTime) - val shuffleWriteQuantiles = Shuffle Write Size / Records +: - getFormattedSizeQuantilesWithRecords(shuffleWriteSizes, shuffleWriteRecords) + val gcQuantiles = titleCell("GC Time", ToolTips.GC_TIME) ++ timeQuantiles(metrics.jvmGcTime) - val memoryBytesSpilledSizes = validTasks.map(_.taskMetrics.get.memoryBytesSpilled.toDouble) - val memoryBytesSpilledQuantiles = Shuffle spill (memory) +: - getFormattedSizeQuantiles(memoryBytesSpilledSizes) + val serializationQuantiles = titleCell("Result Serialization Time", + ToolTips.RESULT_SERIALIZATION_TIME) ++ timeQuantiles(metrics.resultSerializationTime) - val diskBytesSpilledSizes = validTasks.map(_.taskMetrics.get.diskBytesSpilled.toDouble) - val diskBytesSpilledQuantiles = Shuffle spill (disk) +: - getFormattedSizeQuantiles(diskBytesSpilledSizes) + val gettingResultQuantiles = titleCell("Getting Result Time", ToolTips.GETTING_RESULT_TIME) ++ + timeQuantiles(metrics.gettingResultTime) - val listings: Seq[Seq[Node]] = Seq( - {serviceQuantiles}, - {schedulerDelayQuantiles}, - - {deserializationQuantiles} - - {gcQuantiles}, - - {serializationQuantiles} - , - {gettingResultQuantiles}, - - {peakExecutionMemoryQuantiles} - , - if (hasInput(stageData)) {inputQuantiles} else Nil, - if (hasOutput(stageData)) {outputQuantiles} else Nil, - if (hasShuffleRead(stageData)) { - - {shuffleReadBlockedQuantiles} - - {shuffleReadTotalQuantiles} - - {shuffleReadRemoteQuantiles} - - } else { - Nil - }, - if (hasShuffleWrite(stageData)) {shuffleWriteQuantiles} else Nil, - if (hasBytesSpilled(stageData)) {memoryBytesSpilledQuantiles} else Nil, - if (hasBytesSpilled(stageData)) {diskBytesSpilledQuantiles} else Nil) - - val quantileHeaders = Seq("Metric", "Min", "25th percentile", - "Median", "75th percentile", "Max") - // The summary table does not use CSS to stripe rows, which doesn't work with hidden - // rows (instead, JavaScript in table.js is used to stripe the non-hidden rows). - Some(UIUtils.listingTable( - quantileHeaders, - identity[Seq[Node]], - listings, - fixedWidth = true, - id = Some("task-summary-table"), - stripeRowsWithCss = false)) + val peakExecutionMemoryQuantiles = titleCell("Peak Execution Memory", + ToolTips.PEAK_EXECUTION_MEMORY) ++ sizeQuantiles(metrics.peakExecutionMemory) + + // The scheduler delay includes the network delay to send the task to the worker + // machine and to send back the result (but not the time to fetch the task result, + // if it needed to be fetched from the block manager on the worker). + val schedulerDelayQuantiles = titleCell("Scheduler Delay", ToolTips.SCHEDULER_DELAY) ++ + timeQuantiles(metrics.schedulerDelay) + + def inputQuantiles: Seq[Node] = { + simpleTitleCell("Input Size / Records") ++ + sizeQuantilesWithRecords(metrics.inputMetrics.bytesRead, metrics.inputMetrics.recordsRead) + } + + def outputQuantiles: Seq[Node] = { + simpleTitleCell("Output Size / Records") ++ + sizeQuantilesWithRecords(metrics.outputMetrics.bytesWritten, + metrics.outputMetrics.recordsWritten) + } + + def shuffleReadBlockedQuantiles: Seq[Node] = { + titleCell("Shuffle Read Blocked Time", ToolTips.SHUFFLE_READ_BLOCKED_TIME) ++ + timeQuantiles(metrics.shuffleReadMetrics.fetchWaitTime) + } + + def shuffleReadTotalQuantiles: Seq[Node] = { + titleCell("Shuffle Read Size / Records", ToolTips.SHUFFLE_READ) ++ + sizeQuantilesWithRecords(metrics.shuffleReadMetrics.readBytes, + metrics.shuffleReadMetrics.readRecords) + } + + def shuffleReadRemoteQuantiles: Seq[Node] = { + titleCell("Shuffle Remote Reads", ToolTips.SHUFFLE_READ_REMOTE_SIZE) ++ + sizeQuantiles(metrics.shuffleReadMetrics.remoteBytesRead) + } + + def shuffleWriteQuantiles: Seq[Node] = { + simpleTitleCell("Shuffle Write Size / Records") ++ + sizeQuantilesWithRecords(metrics.shuffleWriteMetrics.writeBytes, + metrics.shuffleWriteMetrics.writeRecords) + } + + def memoryBytesSpilledQuantiles: Seq[Node] = { + simpleTitleCell("Shuffle spill (memory)") ++ sizeQuantiles(metrics.memoryBytesSpilled) + } + + def diskBytesSpilledQuantiles: Seq[Node] = { + simpleTitleCell("Shuffle spill (disk)") ++ sizeQuantiles(metrics.diskBytesSpilled) } + val listings: Seq[Seq[Node]] = Seq( + {serviceQuantiles}, + {schedulerDelayQuantiles}, + + {deserializationQuantiles} + + {gcQuantiles}, + + {serializationQuantiles} + , + {gettingResultQuantiles}, + + {peakExecutionMemoryQuantiles} + , + if (hasInput(stageData)) {inputQuantiles} else Nil, + if (hasOutput(stageData)) {outputQuantiles} else Nil, + if (hasShuffleRead(stageData)) { + + {shuffleReadBlockedQuantiles} + + {shuffleReadTotalQuantiles} + + {shuffleReadRemoteQuantiles} + + } else { + Nil + }, + if (hasShuffleWrite(stageData)) {shuffleWriteQuantiles} else Nil, + if (hasBytesSpilled(stageData)) {memoryBytesSpilledQuantiles} else Nil, + if (hasBytesSpilled(stageData)) {diskBytesSpilledQuantiles} else Nil) + + val quantileHeaders = Seq("Metric", "Min", "25th percentile", "Median", "75th percentile", + "Max") + // The summary table does not use CSS to stripe rows, which doesn't work with hidden + // rows (instead, JavaScript in table.js is used to stripe the non-hidden rows). + UIUtils.listingTable( + quantileHeaders, + identity[Seq[Node]], + listings, + fixedWidth = true, + id = Some("task-summary-table"), + stripeRowsWithCss = false) + } + val executorTable = new ExecutorTable(stageData, parent.store) val maybeAccumulableTable: Seq[Node] = - if (hasAccumulators) {

      Accumulators

      ++ accumulableTable } else Seq() + if (hasAccumulators(stageData)) {

      Accumulators

      ++ accumulableTable } else Seq() val aggMetrics = taskIdsInPage.contains(t.taskId) }, + Option(taskTable).map(_.dataSource.tasks).getOrElse(Nil), currentTime) ++

      Summary Metrics for {numCompleted} Completed Tasks

      ++
      {summaryTable.getOrElse("No tasks have reported metrics yet.")}
      ++ @@ -593,10 +524,9 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We val serializationTimeProportion = toProportion(serializationTime) val deserializationTime = metricsOpt.map(_.executorDeserializeTime).getOrElse(0L) val deserializationTimeProportion = toProportion(deserializationTime) - val gettingResultTime = getGettingResultTime(taskInfo, currentTime) + val gettingResultTime = AppStatusUtils.gettingResultTime(taskInfo) val gettingResultTimeProportion = toProportion(gettingResultTime) - val schedulerDelay = - metricsOpt.map(getSchedulerDelay(taskInfo, _, currentTime)).getOrElse(0L) + val schedulerDelay = AppStatusUtils.schedulerDelay(taskInfo) val schedulerDelayProportion = toProportion(schedulerDelay) val executorOverhead = serializationTime + deserializationTime @@ -708,7 +638,7 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We { if (MAX_TIMELINE_TASKS < tasks.size) { - This stage has more than the maximum number of tasks that can be shown in the + This page has more than the maximum number of tasks that can be shown in the visualization! Only the most recent {MAX_TIMELINE_TASKS} tasks (of {tasks.size} total) are shown. @@ -733,402 +663,49 @@ private[ui] class StagePage(parent: StagesTab, store: AppStatusStore) extends We } -private[ui] object StagePage { - private[ui] def getGettingResultTime(info: TaskData, currentTime: Long): Long = { - info.resultFetchStart match { - case Some(start) => - info.duration match { - case Some(duration) => - info.launchTime.getTime() + duration - start.getTime() - - case _ => - currentTime - start.getTime() - } - - case _ => - 0L - } - } - - private[ui] def getSchedulerDelay( - info: TaskData, - metrics: TaskMetrics, - currentTime: Long): Long = { - info.duration match { - case Some(duration) => - val executorOverhead = metrics.executorDeserializeTime + metrics.resultSerializationTime - math.max( - 0, - duration - metrics.executorRunTime - executorOverhead - - getGettingResultTime(info, currentTime)) - - case _ => - // The task is still running and the metrics like executorRunTime are not available. - 0L - } - } - -} - -private[ui] case class TaskTableRowInputData(inputSortable: Long, inputReadable: String) - -private[ui] case class TaskTableRowOutputData(outputSortable: Long, outputReadable: String) - -private[ui] case class TaskTableRowShuffleReadData( - shuffleReadBlockedTimeSortable: Long, - shuffleReadBlockedTimeReadable: String, - shuffleReadSortable: Long, - shuffleReadReadable: String, - shuffleReadRemoteSortable: Long, - shuffleReadRemoteReadable: String) - -private[ui] case class TaskTableRowShuffleWriteData( - writeTimeSortable: Long, - writeTimeReadable: String, - shuffleWriteSortable: Long, - shuffleWriteReadable: String) - -private[ui] case class TaskTableRowBytesSpilledData( - memoryBytesSpilledSortable: Long, - memoryBytesSpilledReadable: String, - diskBytesSpilledSortable: Long, - diskBytesSpilledReadable: String) - -/** - * Contains all data that needs for sorting and generating HTML. Using this one rather than - * TaskData to avoid creating duplicate contents during sorting the data. - */ -private[ui] class TaskTableRowData( - val index: Int, - val taskId: Long, - val attempt: Int, - val speculative: Boolean, - val status: String, - val taskLocality: String, - val executorId: String, - val host: String, - val launchTime: Long, - val duration: Long, - val formatDuration: String, - val schedulerDelay: Long, - val taskDeserializationTime: Long, - val gcTime: Long, - val serializationTime: Long, - val gettingResultTime: Long, - val peakExecutionMemoryUsed: Long, - val accumulators: Option[String], // HTML - val input: Option[TaskTableRowInputData], - val output: Option[TaskTableRowOutputData], - val shuffleRead: Option[TaskTableRowShuffleReadData], - val shuffleWrite: Option[TaskTableRowShuffleWriteData], - val bytesSpilled: Option[TaskTableRowBytesSpilledData], - val error: String, - val logs: Map[String, String]) - private[ui] class TaskDataSource( - tasks: Seq[TaskData], - hasAccumulators: Boolean, - hasInput: Boolean, - hasOutput: Boolean, - hasShuffleRead: Boolean, - hasShuffleWrite: Boolean, - hasBytesSpilled: Boolean, + stage: StageData, currentTime: Long, pageSize: Int, sortColumn: String, desc: Boolean, - store: AppStatusStore) extends PagedDataSource[TaskTableRowData](pageSize) { - import StagePage._ + store: AppStatusStore) extends PagedDataSource[TaskData](pageSize) { + import ApiHelper._ // Keep an internal cache of executor log maps so that long task lists render faster. private val executorIdToLogs = new HashMap[String, Map[String, String]]() - // Convert TaskData to TaskTableRowData which contains the final contents to show in the table - // so that we can avoid creating duplicate contents during sorting the data - private val data = tasks.map(taskRow).sorted(ordering(sortColumn, desc)) - - private var _slicedTaskIds: Set[Long] = _ - - override def dataSize: Int = data.size - - override def sliceData(from: Int, to: Int): Seq[TaskTableRowData] = { - val r = data.slice(from, to) - _slicedTaskIds = r.map(_.taskId).toSet - r - } - - def slicedTaskIds: Set[Long] = _slicedTaskIds + private var _tasksToShow: Seq[TaskData] = null - private def taskRow(info: TaskData): TaskTableRowData = { - val metrics = info.taskMetrics - val duration = info.duration.getOrElse(1L) - val formatDuration = info.duration.map(d => UIUtils.formatDuration(d)).getOrElse("") - val schedulerDelay = metrics.map(getSchedulerDelay(info, _, currentTime)).getOrElse(0L) - val gcTime = metrics.map(_.jvmGcTime).getOrElse(0L) - val taskDeserializationTime = metrics.map(_.executorDeserializeTime).getOrElse(0L) - val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L) - val gettingResultTime = getGettingResultTime(info, currentTime) + override def dataSize: Int = taskCount(stage) - val externalAccumulableReadable = info.accumulatorUpdates.map { acc => - StringEscapeUtils.escapeHtml4(s"${acc.name}: ${acc.update}") + override def sliceData(from: Int, to: Int): Seq[TaskData] = { + if (_tasksToShow == null) { + _tasksToShow = store.taskList(stage.stageId, stage.attemptId, from, to - from, + indexName(sortColumn), !desc) } - val peakExecutionMemoryUsed = metrics.map(_.peakExecutionMemory).getOrElse(0L) - - val maybeInput = metrics.map(_.inputMetrics) - val inputSortable = maybeInput.map(_.bytesRead).getOrElse(0L) - val inputReadable = maybeInput - .map(m => s"${Utils.bytesToString(m.bytesRead)}") - .getOrElse("") - val inputRecords = maybeInput.map(_.recordsRead.toString).getOrElse("") - - val maybeOutput = metrics.map(_.outputMetrics) - val outputSortable = maybeOutput.map(_.bytesWritten).getOrElse(0L) - val outputReadable = maybeOutput - .map(m => s"${Utils.bytesToString(m.bytesWritten)}") - .getOrElse("") - val outputRecords = maybeOutput.map(_.recordsWritten.toString).getOrElse("") - - val maybeShuffleRead = metrics.map(_.shuffleReadMetrics) - val shuffleReadBlockedTimeSortable = maybeShuffleRead.map(_.fetchWaitTime).getOrElse(0L) - val shuffleReadBlockedTimeReadable = - maybeShuffleRead.map(ms => UIUtils.formatDuration(ms.fetchWaitTime)).getOrElse("") - - val totalShuffleBytes = maybeShuffleRead.map(ApiHelper.totalBytesRead) - val shuffleReadSortable = totalShuffleBytes.getOrElse(0L) - val shuffleReadReadable = totalShuffleBytes.map(Utils.bytesToString).getOrElse("") - val shuffleReadRecords = maybeShuffleRead.map(_.recordsRead.toString).getOrElse("") - - val remoteShuffleBytes = maybeShuffleRead.map(_.remoteBytesRead) - val shuffleReadRemoteSortable = remoteShuffleBytes.getOrElse(0L) - val shuffleReadRemoteReadable = remoteShuffleBytes.map(Utils.bytesToString).getOrElse("") - - val maybeShuffleWrite = metrics.map(_.shuffleWriteMetrics) - val shuffleWriteSortable = maybeShuffleWrite.map(_.bytesWritten).getOrElse(0L) - val shuffleWriteReadable = maybeShuffleWrite - .map(m => s"${Utils.bytesToString(m.bytesWritten)}").getOrElse("") - val shuffleWriteRecords = maybeShuffleWrite - .map(_.recordsWritten.toString).getOrElse("") - - val maybeWriteTime = metrics.map(_.shuffleWriteMetrics.writeTime) - val writeTimeSortable = maybeWriteTime.getOrElse(0L) - val writeTimeReadable = maybeWriteTime.map(t => t / (1000 * 1000)).map { ms => - if (ms == 0) "" else UIUtils.formatDuration(ms) - }.getOrElse("") - - val maybeMemoryBytesSpilled = metrics.map(_.memoryBytesSpilled) - val memoryBytesSpilledSortable = maybeMemoryBytesSpilled.getOrElse(0L) - val memoryBytesSpilledReadable = - maybeMemoryBytesSpilled.map(Utils.bytesToString).getOrElse("") - - val maybeDiskBytesSpilled = metrics.map(_.diskBytesSpilled) - val diskBytesSpilledSortable = maybeDiskBytesSpilled.getOrElse(0L) - val diskBytesSpilledReadable = maybeDiskBytesSpilled.map(Utils.bytesToString).getOrElse("") - - val input = - if (hasInput) { - Some(TaskTableRowInputData(inputSortable, s"$inputReadable / $inputRecords")) - } else { - None - } - - val output = - if (hasOutput) { - Some(TaskTableRowOutputData(outputSortable, s"$outputReadable / $outputRecords")) - } else { - None - } - - val shuffleRead = - if (hasShuffleRead) { - Some(TaskTableRowShuffleReadData( - shuffleReadBlockedTimeSortable, - shuffleReadBlockedTimeReadable, - shuffleReadSortable, - s"$shuffleReadReadable / $shuffleReadRecords", - shuffleReadRemoteSortable, - shuffleReadRemoteReadable - )) - } else { - None - } - - val shuffleWrite = - if (hasShuffleWrite) { - Some(TaskTableRowShuffleWriteData( - writeTimeSortable, - writeTimeReadable, - shuffleWriteSortable, - s"$shuffleWriteReadable / $shuffleWriteRecords" - )) - } else { - None - } - - val bytesSpilled = - if (hasBytesSpilled) { - Some(TaskTableRowBytesSpilledData( - memoryBytesSpilledSortable, - memoryBytesSpilledReadable, - diskBytesSpilledSortable, - diskBytesSpilledReadable - )) - } else { - None - } - - new TaskTableRowData( - info.index, - info.taskId, - info.attempt, - info.speculative, - info.status, - info.taskLocality.toString, - info.executorId, - info.host, - info.launchTime.getTime(), - duration, - formatDuration, - schedulerDelay, - taskDeserializationTime, - gcTime, - serializationTime, - gettingResultTime, - peakExecutionMemoryUsed, - if (hasAccumulators) Some(externalAccumulableReadable.mkString("
      ")) else None, - input, - output, - shuffleRead, - shuffleWrite, - bytesSpilled, - info.errorMessage.getOrElse(""), - executorLogs(info.executorId)) + _tasksToShow } - private def executorLogs(id: String): Map[String, String] = { + def tasks: Seq[TaskData] = _tasksToShow + + def executorLogs(id: String): Map[String, String] = { executorIdToLogs.getOrElseUpdate(id, store.asOption(store.executorSummary(id)).map(_.executorLogs).getOrElse(Map.empty)) } - /** - * Return Ordering according to sortColumn and desc - */ - private def ordering(sortColumn: String, desc: Boolean): Ordering[TaskTableRowData] = { - val ordering: Ordering[TaskTableRowData] = sortColumn match { - case "Index" => Ordering.by(_.index) - case "ID" => Ordering.by(_.taskId) - case "Attempt" => Ordering.by(_.attempt) - case "Status" => Ordering.by(_.status) - case "Locality Level" => Ordering.by(_.taskLocality) - case "Executor ID" => Ordering.by(_.executorId) - case "Host" => Ordering.by(_.host) - case "Launch Time" => Ordering.by(_.launchTime) - case "Duration" => Ordering.by(_.duration) - case "Scheduler Delay" => Ordering.by(_.schedulerDelay) - case "Task Deserialization Time" => Ordering.by(_.taskDeserializationTime) - case "GC Time" => Ordering.by(_.gcTime) - case "Result Serialization Time" => Ordering.by(_.serializationTime) - case "Getting Result Time" => Ordering.by(_.gettingResultTime) - case "Peak Execution Memory" => Ordering.by(_.peakExecutionMemoryUsed) - case "Accumulators" => - if (hasAccumulators) { - Ordering.by(_.accumulators.get) - } else { - throw new IllegalArgumentException( - "Cannot sort by Accumulators because of no accumulators") - } - case "Input Size / Records" => - if (hasInput) { - Ordering.by(_.input.get.inputSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Input Size / Records because of no inputs") - } - case "Output Size / Records" => - if (hasOutput) { - Ordering.by(_.output.get.outputSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Output Size / Records because of no outputs") - } - // ShuffleRead - case "Shuffle Read Blocked Time" => - if (hasShuffleRead) { - Ordering.by(_.shuffleRead.get.shuffleReadBlockedTimeSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Shuffle Read Blocked Time because of no shuffle reads") - } - case "Shuffle Read Size / Records" => - if (hasShuffleRead) { - Ordering.by(_.shuffleRead.get.shuffleReadSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Shuffle Read Size / Records because of no shuffle reads") - } - case "Shuffle Remote Reads" => - if (hasShuffleRead) { - Ordering.by(_.shuffleRead.get.shuffleReadRemoteSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Shuffle Remote Reads because of no shuffle reads") - } - // ShuffleWrite - case "Write Time" => - if (hasShuffleWrite) { - Ordering.by(_.shuffleWrite.get.writeTimeSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Write Time because of no shuffle writes") - } - case "Shuffle Write Size / Records" => - if (hasShuffleWrite) { - Ordering.by(_.shuffleWrite.get.shuffleWriteSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Shuffle Write Size / Records because of no shuffle writes") - } - // BytesSpilled - case "Shuffle Spill (Memory)" => - if (hasBytesSpilled) { - Ordering.by(_.bytesSpilled.get.memoryBytesSpilledSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Shuffle Spill (Memory) because of no spills") - } - case "Shuffle Spill (Disk)" => - if (hasBytesSpilled) { - Ordering.by(_.bytesSpilled.get.diskBytesSpilledSortable) - } else { - throw new IllegalArgumentException( - "Cannot sort by Shuffle Spill (Disk) because of no spills") - } - case "Errors" => Ordering.by(_.error) - case unknownColumn => throw new IllegalArgumentException(s"Unknown column: $unknownColumn") - } - if (desc) { - ordering.reverse - } else { - ordering - } - } - } private[ui] class TaskPagedTable( - conf: SparkConf, + stage: StageData, basePath: String, - data: Seq[TaskData], - hasAccumulators: Boolean, - hasInput: Boolean, - hasOutput: Boolean, - hasShuffleRead: Boolean, - hasShuffleWrite: Boolean, - hasBytesSpilled: Boolean, currentTime: Long, pageSize: Int, sortColumn: String, desc: Boolean, - store: AppStatusStore) extends PagedTable[TaskTableRowData] { + store: AppStatusStore) extends PagedTable[TaskData] { + + import ApiHelper._ override def tableId: String = "task-table" @@ -1142,13 +719,7 @@ private[ui] class TaskPagedTable( override def pageNumberFormField: String = "task.page" override val dataSource: TaskDataSource = new TaskDataSource( - data, - hasAccumulators, - hasInput, - hasOutput, - hasShuffleRead, - hasShuffleWrite, - hasBytesSpilled, + stage, currentTime, pageSize, sortColumn, @@ -1170,37 +741,39 @@ private[ui] class TaskPagedTable( } def headers: Seq[Node] = { + import ApiHelper._ + val taskHeadersAndCssClasses: Seq[(String, String)] = Seq( - ("Index", ""), ("ID", ""), ("Attempt", ""), ("Status", ""), ("Locality Level", ""), - ("Executor ID", ""), ("Host", ""), ("Launch Time", ""), ("Duration", ""), - ("Scheduler Delay", TaskDetailsClassNames.SCHEDULER_DELAY), - ("Task Deserialization Time", TaskDetailsClassNames.TASK_DESERIALIZATION_TIME), - ("GC Time", ""), - ("Result Serialization Time", TaskDetailsClassNames.RESULT_SERIALIZATION_TIME), - ("Getting Result Time", TaskDetailsClassNames.GETTING_RESULT_TIME), - ("Peak Execution Memory", TaskDetailsClassNames.PEAK_EXECUTION_MEMORY)) ++ - {if (hasAccumulators) Seq(("Accumulators", "")) else Nil} ++ - {if (hasInput) Seq(("Input Size / Records", "")) else Nil} ++ - {if (hasOutput) Seq(("Output Size / Records", "")) else Nil} ++ - {if (hasShuffleRead) { - Seq(("Shuffle Read Blocked Time", TaskDetailsClassNames.SHUFFLE_READ_BLOCKED_TIME), - ("Shuffle Read Size / Records", ""), - ("Shuffle Remote Reads", TaskDetailsClassNames.SHUFFLE_READ_REMOTE_SIZE)) + (HEADER_TASK_INDEX, ""), (HEADER_ID, ""), (HEADER_ATTEMPT, ""), (HEADER_STATUS, ""), + (HEADER_LOCALITY, ""), (HEADER_EXECUTOR, ""), (HEADER_HOST, ""), (HEADER_LAUNCH_TIME, ""), + (HEADER_DURATION, ""), (HEADER_SCHEDULER_DELAY, TaskDetailsClassNames.SCHEDULER_DELAY), + (HEADER_DESER_TIME, TaskDetailsClassNames.TASK_DESERIALIZATION_TIME), + (HEADER_GC_TIME, ""), + (HEADER_SER_TIME, TaskDetailsClassNames.RESULT_SERIALIZATION_TIME), + (HEADER_GETTING_RESULT_TIME, TaskDetailsClassNames.GETTING_RESULT_TIME), + (HEADER_PEAK_MEM, TaskDetailsClassNames.PEAK_EXECUTION_MEMORY)) ++ + {if (hasAccumulators(stage)) Seq((HEADER_ACCUMULATORS, "")) else Nil} ++ + {if (hasInput(stage)) Seq((HEADER_INPUT_SIZE, "")) else Nil} ++ + {if (hasOutput(stage)) Seq((HEADER_OUTPUT_SIZE, "")) else Nil} ++ + {if (hasShuffleRead(stage)) { + Seq((HEADER_SHUFFLE_READ_TIME, TaskDetailsClassNames.SHUFFLE_READ_BLOCKED_TIME), + (HEADER_SHUFFLE_TOTAL_READS, ""), + (HEADER_SHUFFLE_REMOTE_READS, TaskDetailsClassNames.SHUFFLE_READ_REMOTE_SIZE)) } else { Nil }} ++ - {if (hasShuffleWrite) { - Seq(("Write Time", ""), ("Shuffle Write Size / Records", "")) + {if (hasShuffleWrite(stage)) { + Seq((HEADER_SHUFFLE_WRITE_TIME, ""), (HEADER_SHUFFLE_WRITE_SIZE, "")) } else { Nil }} ++ - {if (hasBytesSpilled) { - Seq(("Shuffle Spill (Memory)", ""), ("Shuffle Spill (Disk)", "")) + {if (hasBytesSpilled(stage)) { + Seq((HEADER_MEM_SPILL, ""), (HEADER_DISK_SPILL, "")) } else { Nil }} ++ - Seq(("Errors", "")) + Seq((HEADER_ERROR, "")) if (!taskHeadersAndCssClasses.map(_._1).contains(sortColumn)) { throw new IllegalArgumentException(s"Unknown column: $sortColumn") @@ -1237,7 +810,17 @@ private[ui] class TaskPagedTable( {headerRow} } - def row(task: TaskTableRowData): Seq[Node] = { + def row(task: TaskData): Seq[Node] = { + def formatDuration(value: Option[Long], hideZero: Boolean = false): String = { + value.map { v => + if (v > 0 || !hideZero) UIUtils.formatDuration(v) else "" + }.getOrElse("") + } + + def formatBytes(value: Option[Long]): String = { + Utils.bytesToString(value.getOrElse(0L)) + } + {task.index} {task.taskId} @@ -1249,62 +832,102 @@ private[ui] class TaskPagedTable(
      {task.host}
      { - task.logs.map { + dataSource.executorLogs(task.executorId).map { case (logName, logUrl) => } }
      - {UIUtils.formatDate(new Date(task.launchTime))} - {task.formatDuration} + {UIUtils.formatDate(task.launchTime)} + {formatDuration(task.duration)} - {UIUtils.formatDuration(task.schedulerDelay)} + {UIUtils.formatDuration(AppStatusUtils.schedulerDelay(task))} - {UIUtils.formatDuration(task.taskDeserializationTime)} + {formatDuration(task.taskMetrics.map(_.executorDeserializeTime))} - {if (task.gcTime > 0) UIUtils.formatDuration(task.gcTime) else ""} + {formatDuration(task.taskMetrics.map(_.jvmGcTime), hideZero = true)} - {UIUtils.formatDuration(task.serializationTime)} + {formatDuration(task.taskMetrics.map(_.resultSerializationTime))} - {UIUtils.formatDuration(task.gettingResultTime)} + {UIUtils.formatDuration(AppStatusUtils.gettingResultTime(task))} - {Utils.bytesToString(task.peakExecutionMemoryUsed)} + {formatBytes(task.taskMetrics.map(_.peakExecutionMemory))} - {if (task.accumulators.nonEmpty) { - {Unparsed(task.accumulators.get)} + {if (hasAccumulators(stage)) { + {accumulatorsInfo(task)} }} - {if (task.input.nonEmpty) { - {task.input.get.inputReadable} + {if (hasInput(stage)) { + metricInfo(task) { m => + val bytesRead = Utils.bytesToString(m.inputMetrics.bytesRead) + val records = m.inputMetrics.recordsRead + {bytesRead} / {records} + } }} - {if (task.output.nonEmpty) { - {task.output.get.outputReadable} + {if (hasOutput(stage)) { + metricInfo(task) { m => + val bytesWritten = Utils.bytesToString(m.outputMetrics.bytesWritten) + val records = m.outputMetrics.recordsWritten + {bytesWritten} / {records} + } }} - {if (task.shuffleRead.nonEmpty) { + {if (hasShuffleRead(stage)) { - {task.shuffleRead.get.shuffleReadBlockedTimeReadable} + {formatDuration(task.taskMetrics.map(_.shuffleReadMetrics.fetchWaitTime))} - {task.shuffleRead.get.shuffleReadReadable} + { + metricInfo(task) { m => + val bytesRead = Utils.bytesToString(totalBytesRead(m.shuffleReadMetrics)) + val records = m.shuffleReadMetrics.recordsRead + Unparsed(s"$bytesRead / $records") + } + } - {task.shuffleRead.get.shuffleReadRemoteReadable} + {formatBytes(task.taskMetrics.map(_.shuffleReadMetrics.remoteBytesRead))} }} - {if (task.shuffleWrite.nonEmpty) { - {task.shuffleWrite.get.writeTimeReadable} - {task.shuffleWrite.get.shuffleWriteReadable} + {if (hasShuffleWrite(stage)) { + { + formatDuration( + task.taskMetrics.map { m => + TimeUnit.NANOSECONDS.toMillis(m.shuffleWriteMetrics.writeTime) + }, + hideZero = true) + } + { + metricInfo(task) { m => + val bytesWritten = Utils.bytesToString(m.shuffleWriteMetrics.bytesWritten) + val records = m.shuffleWriteMetrics.recordsWritten + Unparsed(s"$bytesWritten / $records") + } + } }} - {if (task.bytesSpilled.nonEmpty) { - {task.bytesSpilled.get.memoryBytesSpilledReadable} - {task.bytesSpilled.get.diskBytesSpilledReadable} + {if (hasBytesSpilled(stage)) { + {formatBytes(task.taskMetrics.map(_.memoryBytesSpilled))} + {formatBytes(task.taskMetrics.map(_.diskBytesSpilled))} }} - {errorMessageCell(task.error)} + {errorMessageCell(task.errorMessage.getOrElse(""))} } + private def accumulatorsInfo(task: TaskData): Seq[Node] = { + task.accumulatorUpdates.flatMap { acc => + if (acc.name != null && acc.update.isDefined) { + Unparsed(StringEscapeUtils.escapeHtml4(s"${acc.name}: ${acc.update.get}")) ++
      + } else { + Nil + } + } + } + + private def metricInfo(task: TaskData)(fn: TaskMetrics => Seq[Node]): Seq[Node] = { + task.taskMetrics.map(fn).getOrElse(Nil) + } + private def errorMessageCell(error: String): Seq[Node] = { val isMultiline = error.indexOf('\n') >= 0 // Display the first line by default @@ -1331,7 +954,66 @@ private[ui] class TaskPagedTable( } } -private object ApiHelper { +private[ui] object ApiHelper { + + val HEADER_ID = "ID" + val HEADER_TASK_INDEX = "Index" + val HEADER_ATTEMPT = "Attempt" + val HEADER_STATUS = "Status" + val HEADER_LOCALITY = "Locality Level" + val HEADER_EXECUTOR = "Executor ID" + val HEADER_HOST = "Host" + val HEADER_LAUNCH_TIME = "Launch Time" + val HEADER_DURATION = "Duration" + val HEADER_SCHEDULER_DELAY = "Scheduler Delay" + val HEADER_DESER_TIME = "Task Deserialization Time" + val HEADER_GC_TIME = "GC Time" + val HEADER_SER_TIME = "Result Serialization Time" + val HEADER_GETTING_RESULT_TIME = "Getting Result Time" + val HEADER_PEAK_MEM = "Peak Execution Memory" + val HEADER_ACCUMULATORS = "Accumulators" + val HEADER_INPUT_SIZE = "Input Size / Records" + val HEADER_OUTPUT_SIZE = "Output Size / Records" + val HEADER_SHUFFLE_READ_TIME = "Shuffle Read Blocked Time" + val HEADER_SHUFFLE_TOTAL_READS = "Shuffle Read Size / Records" + val HEADER_SHUFFLE_REMOTE_READS = "Shuffle Remote Reads" + val HEADER_SHUFFLE_WRITE_TIME = "Write Time" + val HEADER_SHUFFLE_WRITE_SIZE = "Shuffle Write Size / Records" + val HEADER_MEM_SPILL = "Shuffle Spill (Memory)" + val HEADER_DISK_SPILL = "Shuffle Spill (Disk)" + val HEADER_ERROR = "Errors" + + private[ui] val COLUMN_TO_INDEX = Map( + HEADER_ID -> null.asInstanceOf[String], + HEADER_TASK_INDEX -> TaskIndexNames.TASK_INDEX, + HEADER_ATTEMPT -> TaskIndexNames.ATTEMPT, + HEADER_STATUS -> TaskIndexNames.STATUS, + HEADER_LOCALITY -> TaskIndexNames.LOCALITY, + HEADER_EXECUTOR -> TaskIndexNames.EXECUTOR, + HEADER_HOST -> TaskIndexNames.HOST, + HEADER_LAUNCH_TIME -> TaskIndexNames.LAUNCH_TIME, + HEADER_DURATION -> TaskIndexNames.DURATION, + HEADER_SCHEDULER_DELAY -> TaskIndexNames.SCHEDULER_DELAY, + HEADER_DESER_TIME -> TaskIndexNames.DESER_TIME, + HEADER_GC_TIME -> TaskIndexNames.GC_TIME, + HEADER_SER_TIME -> TaskIndexNames.SER_TIME, + HEADER_GETTING_RESULT_TIME -> TaskIndexNames.GETTING_RESULT_TIME, + HEADER_PEAK_MEM -> TaskIndexNames.PEAK_MEM, + HEADER_ACCUMULATORS -> TaskIndexNames.ACCUMULATORS, + HEADER_INPUT_SIZE -> TaskIndexNames.INPUT_SIZE, + HEADER_OUTPUT_SIZE -> TaskIndexNames.OUTPUT_SIZE, + HEADER_SHUFFLE_READ_TIME -> TaskIndexNames.SHUFFLE_READ_TIME, + HEADER_SHUFFLE_TOTAL_READS -> TaskIndexNames.SHUFFLE_TOTAL_READS, + HEADER_SHUFFLE_REMOTE_READS -> TaskIndexNames.SHUFFLE_REMOTE_READS, + HEADER_SHUFFLE_WRITE_TIME -> TaskIndexNames.SHUFFLE_WRITE_TIME, + HEADER_SHUFFLE_WRITE_SIZE -> TaskIndexNames.SHUFFLE_WRITE_SIZE, + HEADER_MEM_SPILL -> TaskIndexNames.MEM_SPILL, + HEADER_DISK_SPILL -> TaskIndexNames.DISK_SPILL, + HEADER_ERROR -> TaskIndexNames.ERROR) + + def hasAccumulators(stageData: StageData): Boolean = { + stageData.accumulatorUpdates.exists { acc => acc.name != null && acc.value != null } + } def hasInput(stageData: StageData): Boolean = stageData.inputBytes > 0 @@ -1349,4 +1031,21 @@ private object ApiHelper { metrics.localBytesRead + metrics.remoteBytesRead } + def indexName(sortColumn: String): Option[String] = { + COLUMN_TO_INDEX.get(sortColumn) match { + case Some(v) => Option(v) + case _ => throw new IllegalArgumentException(s"Invalid sort column: $sortColumn") + } + } + + def lastStageNameAndDescription(store: AppStatusStore, job: JobData): (String, String) = { + val stage = store.asOption(store.stageAttempt(job.stageIds.max, 0)) + (stage.map(_.name).getOrElse(""), stage.flatMap(_.description).getOrElse(job.name)) + } + + def taskCount(stageData: StageData): Int = { + stageData.numActiveTasks + stageData.numCompleteTasks + stageData.numFailedTasks + + stageData.numKilledTasks + } + } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala index 18a4926f2f6c0..f001a01de3952 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala @@ -43,7 +43,9 @@ private[ui] class StageTableBase( killEnabled: Boolean, isFailedStage: Boolean) { // stripXSS is called to remove suspicious characters used in XSS attacks - val allParameters = request.getParameterMap.asScala.toMap.mapValues(_.map(UIUtils.stripXSS)) + val allParameters = request.getParameterMap.asScala.toMap.map { case (k, v) => + UIUtils.stripXSS(k) -> v.map(UIUtils.stripXSS).toSeq + } val parameterOtherTable = allParameters.filterNot(_._1.startsWith(stageTag)) .map(para => para._1 + "=" + para._2(0)) diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala index be05a963f0e68..10b032084ce4f 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala @@ -37,10 +37,10 @@ private[ui] class StagesTab(val parent: SparkUI, val store: AppStatusStore) attachPage(new PoolPage(this)) def isFairScheduler: Boolean = { - store.environmentInfo().sparkProperties.toMap - .get("spark.scheduler.mode") - .map { mode => mode == SchedulingMode.FAIR } - .getOrElse(false) + store + .environmentInfo() + .sparkProperties + .contains(("spark.scheduler.mode", SchedulingMode.FAIR.toString)) } def handleKillRequest(request: HttpServletRequest): Unit = { diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala index 827a8637b9bd2..948858224d724 100644 --- a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala +++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala @@ -116,7 +116,7 @@ private[spark] object RDDOperationGraph extends Logging { // Use a special prefix here to differentiate this cluster from other operation clusters val stageClusterId = STAGE_CLUSTER_PREFIX + stage.stageId val stageClusterName = s"Stage ${stage.stageId}" + - { if (stage.attemptId == 0) "" else s" (attempt ${stage.attemptId})" } + { if (stage.attemptNumber == 0) "" else s" (attempt ${stage.attemptNumber})" } val rootCluster = new RDDOperationCluster(stageClusterId, stageClusterName) var rootNodeCount = 0 diff --git a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala index 02cee7f8c5b33..2674b9291203a 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala @@ -23,7 +23,7 @@ import javax.servlet.http.HttpServletRequest import scala.xml.{Node, Unparsed} import org.apache.spark.status.AppStatusStore -import org.apache.spark.status.api.v1.{RDDDataDistribution, RDDPartitionInfo} +import org.apache.spark.status.api.v1.{ExecutorSummary, RDDDataDistribution, RDDPartitionInfo} import org.apache.spark.ui._ import org.apache.spark.util.Utils @@ -76,7 +76,8 @@ private[ui] class RDDPage(parent: SparkUITab, store: AppStatusStore) extends Web rddStorageInfo.partitions.get, blockPageSize, blockSortColumn, - blockSortDesc) + blockSortDesc, + store.executorList(true)) _blockTable.table(page) } catch { case e @ (_ : IllegalArgumentException | _ : IndexOutOfBoundsException) => @@ -182,7 +183,8 @@ private[ui] class BlockDataSource( rddPartitions: Seq[RDDPartitionInfo], pageSize: Int, sortColumn: String, - desc: Boolean) extends PagedDataSource[BlockTableRowData](pageSize) { + desc: Boolean, + executorIdToAddress: Map[String, String]) extends PagedDataSource[BlockTableRowData](pageSize) { private val data = rddPartitions.map(blockRow).sorted(ordering(sortColumn, desc)) @@ -198,7 +200,10 @@ private[ui] class BlockDataSource( rddPartition.storageLevel, rddPartition.memoryUsed, rddPartition.diskUsed, - rddPartition.executors.mkString(" ")) + rddPartition.executors + .map { id => executorIdToAddress.get(id).getOrElse(id) } + .sorted + .mkString(" ")) } /** @@ -226,7 +231,8 @@ private[ui] class BlockPagedTable( rddPartitions: Seq[RDDPartitionInfo], pageSize: Int, sortColumn: String, - desc: Boolean) extends PagedTable[BlockTableRowData] { + desc: Boolean, + executorSummaries: Seq[ExecutorSummary]) extends PagedTable[BlockTableRowData] { override def tableId: String = "rdd-storage-by-block-table" @@ -243,7 +249,8 @@ private[ui] class BlockPagedTable( rddPartitions, pageSize, sortColumn, - desc) + desc, + executorSummaries.map { ex => (ex.id, ex.hostPort) }.toMap) override def pageLink(page: Int): String = { val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8") diff --git a/core/src/main/scala/org/apache/spark/util/AccumulatorV2.scala b/core/src/main/scala/org/apache/spark/util/AccumulatorV2.scala index f4a736d6d439a..bf618b4afbce0 100644 --- a/core/src/main/scala/org/apache/spark/util/AccumulatorV2.scala +++ b/core/src/main/scala/org/apache/spark/util/AccumulatorV2.scala @@ -24,6 +24,7 @@ import java.util.concurrent.ConcurrentHashMap import java.util.concurrent.atomic.AtomicLong import org.apache.spark.{InternalAccumulator, SparkContext, TaskContext} +import org.apache.spark.internal.Logging import org.apache.spark.scheduler.AccumulableInfo private[spark] case class AccumulatorMetadata( @@ -199,10 +200,12 @@ abstract class AccumulatorV2[IN, OUT] extends Serializable { } override def toString: String = { + // getClass.getSimpleName can cause Malformed class name error, + // call safer `Utils.getSimpleName` instead if (metadata == null) { - "Un-registered Accumulator: " + getClass.getSimpleName + "Un-registered Accumulator: " + Utils.getSimpleName(getClass) } else { - getClass.getSimpleName + s"(id: $id, name: $name, value: $value)" + Utils.getSimpleName(getClass) + s"(id: $id, name: $name, value: $value)" } } } @@ -211,7 +214,7 @@ abstract class AccumulatorV2[IN, OUT] extends Serializable { /** * An internal class used to track accumulators by Spark itself. */ -private[spark] object AccumulatorContext { +private[spark] object AccumulatorContext extends Logging { /** * This global map holds the original accumulator objects that are created on the driver. @@ -258,13 +261,16 @@ private[spark] object AccumulatorContext { * Returns the [[AccumulatorV2]] registered with the given ID, if any. */ def get(id: Long): Option[AccumulatorV2[_, _]] = { - Option(originals.get(id)).map { ref => - // Since we are storing weak references, we must check whether the underlying data is valid. + val ref = originals.get(id) + if (ref eq null) { + None + } else { + // Since we are storing weak references, warn when the underlying data is not valid. val acc = ref.get if (acc eq null) { - throw new IllegalStateException(s"Attempted to access garbage collected accumulator $id") + logWarning(s"Attempted to access garbage collected accumulator $id") } - acc + Option(acc) } } @@ -290,7 +296,8 @@ class LongAccumulator extends AccumulatorV2[jl.Long, jl.Long] { private var _count = 0L /** - * Adds v to the accumulator, i.e. increment sum by v and count by 1. + * Returns false if this accumulator has had any values added to it or the sum is non-zero. + * * @since 2.0.0 */ override def isZero: Boolean = _sum == 0L && _count == 0 @@ -368,6 +375,9 @@ class DoubleAccumulator extends AccumulatorV2[jl.Double, jl.Double] { private var _sum = 0.0 private var _count = 0L + /** + * Returns false if this accumulator has had any values added to it or the sum is non-zero. + */ override def isZero: Boolean = _sum == 0.0 && _count == 0 override def copy(): DoubleAccumulator = { @@ -441,6 +451,9 @@ class DoubleAccumulator extends AccumulatorV2[jl.Double, jl.Double] { class CollectionAccumulator[T] extends AccumulatorV2[T, java.util.List[T]] { private val _list: java.util.List[T] = Collections.synchronizedList(new ArrayList[T]()) + /** + * Returns false if this accumulator instance has any values in it. + */ override def isZero: Boolean = _list.isEmpty override def copyAndReset(): CollectionAccumulator[T] = new CollectionAccumulator @@ -479,7 +492,9 @@ class LegacyAccumulatorWrapper[R, T]( param: org.apache.spark.AccumulableParam[R, T]) extends AccumulatorV2[T, R] { private[spark] var _value = initialValue // Current value on driver - override def isZero: Boolean = _value == param.zero(initialValue) + @transient private lazy val _zero = param.zero(initialValue) + + override def isZero: Boolean = _value.asInstanceOf[AnyRef].eq(_zero.asInstanceOf[AnyRef]) override def copy(): LegacyAccumulatorWrapper[R, T] = { val acc = new LegacyAccumulatorWrapper(initialValue, param) @@ -488,7 +503,7 @@ class LegacyAccumulatorWrapper[R, T]( } override def reset(): Unit = { - _value = param.zero(initialValue) + _value = _zero } override def add(v: T): Unit = _value = param.addAccumulator(_value, v) diff --git a/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala b/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala index 31d230d0fec8e..21acaa95c5645 100644 --- a/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala +++ b/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala @@ -22,9 +22,7 @@ package org.apache.spark.util * through all the elements. */ private[spark] -// scalastyle:off abstract class CompletionIterator[ +A, +I <: Iterator[A]](sub: I) extends Iterator[A] { -// scalastyle:on private[this] var completed = false def next(): A = sub.next() diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala index 5e60218c5740b..ff83301d631c4 100644 --- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala @@ -263,7 +263,7 @@ private[spark] object JsonProtocol { val completionTime = stageInfo.completionTime.map(JInt(_)).getOrElse(JNothing) val failureReason = stageInfo.failureReason.map(JString(_)).getOrElse(JNothing) ("Stage ID" -> stageInfo.stageId) ~ - ("Stage Attempt ID" -> stageInfo.attemptId) ~ + ("Stage Attempt ID" -> stageInfo.attemptNumber) ~ ("Stage Name" -> stageInfo.name) ~ ("Number of Tasks" -> stageInfo.numTasks) ~ ("RDD Info" -> rddInfo) ~ diff --git a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala index 76a56298aaebc..4a7798434680e 100644 --- a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala @@ -60,6 +60,15 @@ private[spark] trait ListenerBus[L <: AnyRef, E] extends Logging { } } + /** + * This can be overriden by subclasses if there is any extra cleanup to do when removing a + * listener. In particular AsyncEventQueues can clean up queues in the LiveListenerBus. + */ + def removeListenerOnError(listener: L): Unit = { + removeListener(listener) + } + + /** * Post the event to all registered listeners. The `postToAll` caller should guarantee calling * `postToAll` in the same thread for all events. @@ -80,7 +89,16 @@ private[spark] trait ListenerBus[L <: AnyRef, E] extends Logging { } try { doPostEvent(listener, event) + if (Thread.interrupted()) { + // We want to throw the InterruptedException right away so we can associate the interrupt + // with this listener, as opposed to waiting for a queue.take() etc. to detect it. + throw new InterruptedException() + } } catch { + case ie: InterruptedException => + logError(s"Interrupted while posting to ${Utils.getFormattedClassName(listener)}. " + + s"Removing that listener.", ie) + removeListenerOnError(listener) case NonFatal(e) => logError(s"Listener ${Utils.getFormattedClassName(listener)} threw an exception", e) } finally { diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 5853302973140..d4b72e8474626 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -18,6 +18,8 @@ package org.apache.spark.util import java.io._ +import java.lang.{Byte => JByte} +import java.lang.InternalError import java.lang.management.{LockInfo, ManagementFactory, MonitorInfo, ThreadInfo} import java.lang.reflect.InvocationTargetException import java.math.{MathContext, RoundingMode} @@ -26,6 +28,7 @@ import java.nio.ByteBuffer import java.nio.channels.{Channels, FileChannel} import java.nio.charset.StandardCharsets import java.nio.file.{Files, Paths} +import java.security.SecureRandom import java.util.{Locale, Properties, Random, UUID} import java.util.concurrent._ import java.util.concurrent.atomic.AtomicBoolean @@ -44,6 +47,7 @@ import scala.util.matching.Regex import _root_.io.netty.channel.unix.Errors.NativeIoException import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache} +import com.google.common.hash.HashCodes import com.google.common.io.{ByteStreams, Files => GFiles} import com.google.common.net.InetAddresses import org.apache.commons.lang3.SystemUtils @@ -1872,7 +1876,7 @@ private[spark] object Utils extends Logging { /** Return the class name of the given object, removing all dollar signs */ def getFormattedClassName(obj: AnyRef): String = { - obj.getClass.getSimpleName.replace("$", "") + getSimpleName(obj.getClass).replace("$", "") } /** Return an option that translates JNothing to None */ @@ -2805,6 +2809,71 @@ private[spark] object Utils extends Logging { s"k8s://$resolvedURL" } + + def createSecret(conf: SparkConf): String = { + val bits = conf.get(AUTH_SECRET_BIT_LENGTH) + val rnd = new SecureRandom() + val secretBytes = new Array[Byte](bits / JByte.SIZE) + rnd.nextBytes(secretBytes) + HashCodes.fromBytes(secretBytes).toString() + } + + /** + * Safer than Class obj's getSimpleName which may throw Malformed class name error in scala. + * This method mimicks scalatest's getSimpleNameOfAnObjectsClass. + */ + def getSimpleName(cls: Class[_]): String = { + try { + return cls.getSimpleName + } catch { + case err: InternalError => return stripDollars(stripPackages(cls.getName)) + } + } + + /** + * Remove the packages from full qualified class name + */ + private def stripPackages(fullyQualifiedName: String): String = { + fullyQualifiedName.split("\\.").takeRight(1)(0) + } + + /** + * Remove trailing dollar signs from qualified class name, + * and return the trailing part after the last dollar sign in the middle + */ + private def stripDollars(s: String): String = { + val lastDollarIndex = s.lastIndexOf('$') + if (lastDollarIndex < s.length - 1) { + // The last char is not a dollar sign + if (lastDollarIndex == -1 || !s.contains("$iw")) { + // The name does not have dollar sign or is not an intepreter + // generated class, so we should return the full string + s + } else { + // The class name is intepreter generated, + // return the part after the last dollar sign + // This is the same behavior as getClass.getSimpleName + s.substring(lastDollarIndex + 1) + } + } + else { + // The last char is a dollar sign + // Find last non-dollar char + val lastNonDollarChar = s.reverse.find(_ != '$') + lastNonDollarChar match { + case None => s + case Some(c) => + val lastNonDollarIndex = s.lastIndexOf(c) + if (lastNonDollarIndex == -1) { + s + } else { + // Strip the trailing dollar signs + // Invoke stripDollars again to get the simple name + stripDollars(s.substring(0, lastNonDollarIndex + 1)) + } + } + } + } } private[util] object CallerContext extends Logging { diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala index 375f4a6921225..5c6dd45ec58e3 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala @@ -463,7 +463,7 @@ class ExternalAppendOnlyMap[K, V, C]( // An intermediate stream that reads from exactly one batch // This guards against pre-fetching and other arbitrary behavior of higher level streams - private var deserializeStream = nextBatchStream() + private var deserializeStream: DeserializationStream = null private var nextItem: (K, C) = null private var objectsRead = 0 @@ -528,7 +528,11 @@ class ExternalAppendOnlyMap[K, V, C]( override def hasNext: Boolean = { if (nextItem == null) { if (deserializeStream == null) { - return false + // In case of deserializeStream has not been initialized + deserializeStream = nextBatchStream() + if (deserializeStream == null) { + return false + } } nextItem = readNextItem() } @@ -536,19 +540,18 @@ class ExternalAppendOnlyMap[K, V, C]( } override def next(): (K, C) = { - val item = if (nextItem == null) readNextItem() else nextItem - if (item == null) { + if (!hasNext) { throw new NoSuchElementException } + val item = nextItem nextItem = null item } private def cleanup() { batchIndex = batchOffsets.length // Prevent reading any other batch - val ds = deserializeStream - if (ds != null) { - ds.close() + if (deserializeStream != null) { + deserializeStream.close() deserializeStream = null } if (fileStream != null) { diff --git a/core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala b/core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala index 7367af7888bd8..3ae8dfcc1cb66 100644 --- a/core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala +++ b/core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala @@ -63,10 +63,15 @@ private[spark] class ChunkedByteBuffer(var chunks: Array[ByteBuffer]) { */ def writeFully(channel: WritableByteChannel): Unit = { for (bytes <- getChunks()) { - while (bytes.remaining() > 0) { - val ioSize = Math.min(bytes.remaining(), bufferWriteChunkSize) - bytes.limit(bytes.position() + ioSize) - channel.write(bytes) + val curChunkLimit = bytes.limit() + while (bytes.hasRemaining) { + try { + val ioSize = Math.min(bytes.remaining(), bufferWriteChunkSize) + bytes.limit(bytes.position() + ioSize) + channel.write(bytes) + } finally { + bytes.limit(curChunkLimit) + } } } } diff --git a/core/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java b/core/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java index c2261c204cd45..2225591a4ff75 100644 --- a/core/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java +++ b/core/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java @@ -17,6 +17,7 @@ package org.apache.spark.launcher; +import java.time.Duration; import java.util.Arrays; import java.util.ArrayList; import java.util.HashMap; @@ -30,6 +31,7 @@ import static org.mockito.Mockito.*; import org.apache.spark.SparkContext; +import org.apache.spark.SparkContext$; import org.apache.spark.internal.config.package$; import org.apache.spark.util.Utils; @@ -133,6 +135,12 @@ public void testInProcessLauncher() throws Exception { p.put(e.getKey(), e.getValue()); } System.setProperties(p); + // Here DAGScheduler is stopped, while SparkContext.clearActiveContext may not be called yet. + // Wait for a reasonable amount of time to avoid creating two active SparkContext in JVM. + // See SPARK-23019 and SparkContext.stop() for details. + eventually(Duration.ofSeconds(5), Duration.ofMillis(10), () -> { + assertTrue("SparkContext is still alive.", SparkContext$.MODULE$.getActive().isEmpty()); + }); } } @@ -141,26 +149,47 @@ private void inProcessLauncherTestImpl() throws Exception { SparkAppHandle.Listener listener = mock(SparkAppHandle.Listener.class); doAnswer(invocation -> { SparkAppHandle h = (SparkAppHandle) invocation.getArguments()[0]; - transitions.add(h.getState()); + synchronized (transitions) { + transitions.add(h.getState()); + } return null; }).when(listener).stateChanged(any(SparkAppHandle.class)); - SparkAppHandle handle = new InProcessLauncher() - .setMaster("local") - .setAppResource(SparkLauncher.NO_RESOURCE) - .setMainClass(InProcessTestApp.class.getName()) - .addAppArgs("hello") - .startApplication(listener); - - waitFor(handle); - assertEquals(SparkAppHandle.State.FINISHED, handle.getState()); - - // Matches the behavior of LocalSchedulerBackend. - List expected = Arrays.asList( - SparkAppHandle.State.CONNECTED, - SparkAppHandle.State.RUNNING, - SparkAppHandle.State.FINISHED); - assertEquals(expected, transitions); + SparkAppHandle handle = null; + try { + synchronized (InProcessTestApp.LOCK) { + handle = new InProcessLauncher() + .setMaster("local") + .setAppResource(SparkLauncher.NO_RESOURCE) + .setMainClass(InProcessTestApp.class.getName()) + .addAppArgs("hello") + .startApplication(listener); + + // SPARK-23020: see doc for InProcessTestApp.LOCK for a description of the race. Here + // we wait until we know that the connection between the app and the launcher has been + // established before allowing the app to finish. + final SparkAppHandle _handle = handle; + eventually(Duration.ofSeconds(5), Duration.ofMillis(10), () -> { + assertNotEquals(SparkAppHandle.State.UNKNOWN, _handle.getState()); + }); + + InProcessTestApp.LOCK.wait(5000); + } + + waitFor(handle); + assertEquals(SparkAppHandle.State.FINISHED, handle.getState()); + + // Matches the behavior of LocalSchedulerBackend. + List expected = Arrays.asList( + SparkAppHandle.State.CONNECTED, + SparkAppHandle.State.RUNNING, + SparkAppHandle.State.FINISHED); + assertEquals(expected, transitions); + } finally { + if (handle != null) { + handle.kill(); + } + } } public static class SparkLauncherTestApp { @@ -176,10 +205,26 @@ public static void main(String[] args) throws Exception { public static class InProcessTestApp { + /** + * SPARK-23020: there's a race caused by a child app finishing too quickly. This would cause + * the InProcessAppHandle to dispose of itself even before the child connection was properly + * established, so no state changes would be detected for the application and its final + * state would be LOST. + * + * It's not really possible to fix that race safely in the handle code itself without changing + * the way in-process apps talk to the launcher library, so we work around that in the test by + * synchronizing on this object. + */ + public static final Object LOCK = new Object(); + public static void main(String[] args) throws Exception { assertNotEquals(0, args.length); assertEquals(args[0], "hello"); new SparkContext().stop(); + + synchronized (LOCK) { + LOCK.notifyAll(); + } } } diff --git a/core/src/test/java/org/apache/spark/memory/TaskMemoryManagerSuite.java b/core/src/test/java/org/apache/spark/memory/TaskMemoryManagerSuite.java index 46b0516e36141..a0664b30d6cc2 100644 --- a/core/src/test/java/org/apache/spark/memory/TaskMemoryManagerSuite.java +++ b/core/src/test/java/org/apache/spark/memory/TaskMemoryManagerSuite.java @@ -21,6 +21,7 @@ import org.junit.Test; import org.apache.spark.SparkConf; +import org.apache.spark.unsafe.memory.MemoryAllocator; import org.apache.spark.unsafe.memory.MemoryBlock; public class TaskMemoryManagerSuite { @@ -68,6 +69,34 @@ public void encodePageNumberAndOffsetOnHeap() { Assert.assertEquals(64, manager.getOffsetInPage(encodedAddress)); } + @Test + public void freeingPageSetsPageNumberToSpecialConstant() { + final TaskMemoryManager manager = new TaskMemoryManager( + new TestMemoryManager(new SparkConf().set("spark.memory.offHeap.enabled", "false")), 0); + final MemoryConsumer c = new TestMemoryConsumer(manager, MemoryMode.ON_HEAP); + final MemoryBlock dataPage = manager.allocatePage(256, c); + c.freePage(dataPage); + Assert.assertEquals(MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER, dataPage.pageNumber); + } + + @Test(expected = AssertionError.class) + public void freeingPageDirectlyInAllocatorTriggersAssertionError() { + final TaskMemoryManager manager = new TaskMemoryManager( + new TestMemoryManager(new SparkConf().set("spark.memory.offHeap.enabled", "false")), 0); + final MemoryConsumer c = new TestMemoryConsumer(manager, MemoryMode.ON_HEAP); + final MemoryBlock dataPage = manager.allocatePage(256, c); + MemoryAllocator.HEAP.free(dataPage); + } + + @Test(expected = AssertionError.class) + public void callingFreePageOnDirectlyAllocatedPageTriggersAssertionError() { + final TaskMemoryManager manager = new TaskMemoryManager( + new TestMemoryManager(new SparkConf().set("spark.memory.offHeap.enabled", "false")), 0); + final MemoryConsumer c = new TestMemoryConsumer(manager, MemoryMode.ON_HEAP); + final MemoryBlock dataPage = MemoryAllocator.HEAP.allocate(256); + manager.freePage(dataPage, c); + } + @Test public void cooperativeSpilling() { final TestMemoryManager memoryManager = new TestMemoryManager(new SparkConf()); diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java index af4975c888d65..411cd5cb57331 100644 --- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java +++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java @@ -72,8 +72,10 @@ public class UnsafeExternalSorterSuite { public int compare( Object leftBaseObject, long leftBaseOffset, + int leftBaseLength, Object rightBaseObject, - long rightBaseOffset) { + long rightBaseOffset, + int rightBaseLength) { return 0; } }; diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java index 594f07dd780f9..85ffdca436e14 100644 --- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java +++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java @@ -98,8 +98,10 @@ public void testSortingOnlyByIntegerPrefix() throws Exception { public int compare( Object leftBaseObject, long leftBaseOffset, + int leftBaseLength, Object rightBaseObject, - long rightBaseOffset) { + long rightBaseOffset, + int rightBaseLength) { return 0; } }; @@ -127,7 +129,6 @@ public int compare( final UnsafeSorterIterator iter = sorter.getSortedIterator(); int iterLength = 0; long prevPrefix = -1; - Arrays.sort(dataToSort); while (iter.hasNext()) { iter.loadNext(); final String str = @@ -164,8 +165,10 @@ public void freeAfterOOM() { public int compare( Object leftBaseObject, long leftBaseOffset, + int leftBaseLength, Object rightBaseObject, - long rightBaseOffset) { + long rightBaseOffset, + int rightBaseLength) { return 0; } }; diff --git a/core/src/test/java/test/org/apache/spark/JavaTaskContextCompileCheck.java b/core/src/test/java/test/org/apache/spark/JavaTaskContextCompileCheck.java index 94f5805853e1e..f8e233a05a447 100644 --- a/core/src/test/java/test/org/apache/spark/JavaTaskContextCompileCheck.java +++ b/core/src/test/java/test/org/apache/spark/JavaTaskContextCompileCheck.java @@ -38,6 +38,7 @@ public static void test() { tc.attemptNumber(); tc.partitionId(); tc.stageId(); + tc.stageAttemptNumber(); tc.taskAttemptId(); } @@ -51,6 +52,7 @@ public void onTaskCompletion(TaskContext context) { context.isCompleted(); context.isInterrupted(); context.stageId(); + context.stageAttemptNumber(); context.partitionId(); context.addTaskCompletionListener(this); } diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w__custom_quantiles_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w__custom_quantiles_expectation.json index f8e27703c0def..5c42ac1d87f4c 100644 --- a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w__custom_quantiles_expectation.json +++ b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w__custom_quantiles_expectation.json @@ -7,6 +7,9 @@ "resultSize" : [ 2010.0, 2065.0, 2065.0 ], "jvmGcTime" : [ 0.0, 0.0, 7.0 ], "resultSerializationTime" : [ 0.0, 0.0, 2.0 ], + "gettingResultTime" : [ 0.0, 0.0, 0.0 ], + "schedulerDelay" : [ 2.0, 6.0, 53.0 ], + "peakExecutionMemory" : [ 0.0, 0.0, 0.0 ], "memoryBytesSpilled" : [ 0.0, 0.0, 0.0 ], "diskBytesSpilled" : [ 0.0, 0.0, 0.0 ], "inputMetrics" : { diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_read_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_read_expectation.json index a28bda16a956e..e6b705989cc97 100644 --- a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_read_expectation.json +++ b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_read_expectation.json @@ -7,6 +7,9 @@ "resultSize" : [ 1034.0, 1034.0, 1034.0, 1034.0, 1034.0 ], "jvmGcTime" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], "resultSerializationTime" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], + "gettingResultTime" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], + "schedulerDelay" : [ 4.0, 4.0, 6.0, 7.0, 9.0 ], + "peakExecutionMemory" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], "memoryBytesSpilled" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], "diskBytesSpilled" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], "inputMetrics" : { diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_write_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_write_expectation.json index ede3eaed1d1d2..788f28cf7b365 100644 --- a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_write_expectation.json +++ b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_write_expectation.json @@ -7,6 +7,9 @@ "resultSize" : [ 2010.0, 2065.0, 2065.0, 2065.0, 2065.0 ], "jvmGcTime" : [ 0.0, 0.0, 0.0, 5.0, 7.0 ], "resultSerializationTime" : [ 0.0, 0.0, 0.0, 0.0, 1.0 ], + "gettingResultTime" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], + "schedulerDelay" : [ 2.0, 4.0, 6.0, 13.0, 40.0 ], + "peakExecutionMemory" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], "memoryBytesSpilled" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], "diskBytesSpilled" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ], "inputMetrics" : { diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala index 3990ee1ec326d..5d0ffd92647bc 100644 --- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala @@ -209,10 +209,8 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex System.gc() assert(ref.get.isEmpty) - // Getting a garbage collected accum should throw error - intercept[IllegalStateException] { - AccumulatorContext.get(accId) - } + // Getting a garbage collected accum should return None. + assert(AccumulatorContext.get(accId).isEmpty) // Getting a normal accumulator. Note: this has to be separate because referencing an // accumulator above in an `assert` would keep it from being garbage collected. diff --git a/core/src/test/scala/org/apache/spark/DebugFilesystem.scala b/core/src/test/scala/org/apache/spark/DebugFilesystem.scala index 91355f7362900..a5bdc95790722 100644 --- a/core/src/test/scala/org/apache/spark/DebugFilesystem.scala +++ b/core/src/test/scala/org/apache/spark/DebugFilesystem.scala @@ -103,8 +103,11 @@ class DebugFilesystem extends LocalFileSystem { override def markSupported(): Boolean = wrapped.markSupported() override def close(): Unit = { - wrapped.close() - removeOpenStream(wrapped) + try { + wrapped.close() + } finally { + removeOpenStream(wrapped) + } } override def read(): Int = wrapped.read() diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala index a0cae5a9e011c..9807d1269e3d4 100644 --- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala @@ -19,6 +19,8 @@ package org.apache.spark import scala.collection.mutable +import org.mockito.Matchers.{any, eq => meq} +import org.mockito.Mockito.{mock, never, verify, when} import org.scalatest.{BeforeAndAfter, PrivateMethodTester} import org.apache.spark.executor.TaskMetrics @@ -26,6 +28,7 @@ import org.apache.spark.scheduler._ import org.apache.spark.scheduler.ExternalClusterManager import org.apache.spark.scheduler.cluster.ExecutorInfo import org.apache.spark.scheduler.local.LocalSchedulerBackend +import org.apache.spark.storage.BlockManagerMaster import org.apache.spark.util.ManualClock /** @@ -1050,6 +1053,66 @@ class ExecutorAllocationManagerSuite assert(removeTimes(manager) === Map.empty) } + test("SPARK-23365 Don't update target num executors when killing idle executors") { + val minExecutors = 1 + val initialExecutors = 1 + val maxExecutors = 2 + val conf = new SparkConf() + .set("spark.dynamicAllocation.enabled", "true") + .set("spark.shuffle.service.enabled", "true") + .set("spark.dynamicAllocation.minExecutors", minExecutors.toString) + .set("spark.dynamicAllocation.maxExecutors", maxExecutors.toString) + .set("spark.dynamicAllocation.initialExecutors", initialExecutors.toString) + .set("spark.dynamicAllocation.schedulerBacklogTimeout", "1000ms") + .set("spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", "1000ms") + .set("spark.dynamicAllocation.executorIdleTimeout", s"3000ms") + val mockAllocationClient = mock(classOf[ExecutorAllocationClient]) + val mockBMM = mock(classOf[BlockManagerMaster]) + val manager = new ExecutorAllocationManager( + mockAllocationClient, mock(classOf[LiveListenerBus]), conf, mockBMM) + val clock = new ManualClock() + manager.setClock(clock) + + when(mockAllocationClient.requestTotalExecutors(meq(2), any(), any())).thenReturn(true) + // test setup -- job with 2 tasks, scale up to two executors + assert(numExecutorsTarget(manager) === 1) + manager.listener.onExecutorAdded(SparkListenerExecutorAdded( + clock.getTimeMillis(), "executor-1", new ExecutorInfo("host1", 1, Map.empty))) + manager.listener.onStageSubmitted(SparkListenerStageSubmitted(createStageInfo(0, 2))) + clock.advance(1000) + manager invokePrivate _updateAndSyncNumExecutorsTarget(clock.getTimeMillis()) + assert(numExecutorsTarget(manager) === 2) + val taskInfo0 = createTaskInfo(0, 0, "executor-1") + manager.listener.onTaskStart(SparkListenerTaskStart(0, 0, taskInfo0)) + manager.listener.onExecutorAdded(SparkListenerExecutorAdded( + clock.getTimeMillis(), "executor-2", new ExecutorInfo("host1", 1, Map.empty))) + val taskInfo1 = createTaskInfo(1, 1, "executor-2") + manager.listener.onTaskStart(SparkListenerTaskStart(0, 0, taskInfo1)) + assert(numExecutorsTarget(manager) === 2) + + // have one task finish -- we should adjust the target number of executors down + // but we should *not* kill any executors yet + manager.listener.onTaskEnd(SparkListenerTaskEnd(0, 0, null, Success, taskInfo0, null)) + assert(maxNumExecutorsNeeded(manager) === 1) + assert(numExecutorsTarget(manager) === 2) + clock.advance(1000) + manager invokePrivate _updateAndSyncNumExecutorsTarget(clock.getTimeMillis()) + assert(numExecutorsTarget(manager) === 1) + verify(mockAllocationClient, never).killExecutors(any(), any(), any(), any()) + + // now we cross the idle timeout for executor-1, so we kill it. the really important + // thing here is that we do *not* ask the executor allocation client to adjust the target + // number of executors down + when(mockAllocationClient.killExecutors(Seq("executor-1"), false, false, false)) + .thenReturn(Seq("executor-1")) + clock.advance(3000) + schedule(manager) + assert(maxNumExecutorsNeeded(manager) === 1) + assert(numExecutorsTarget(manager) === 1) + // here's the important verify -- we did kill the executors, but did not adjust the target count + verify(mockAllocationClient).killExecutors(Seq("executor-1"), false, false, false) + } + private def createSparkContext( minExecutors: Int = 1, maxExecutors: Int = 5, @@ -1268,7 +1331,8 @@ private class DummyLocalSchedulerBackend (sc: SparkContext, sb: SchedulerBackend override def killExecutors( executorIds: Seq[String], - replace: Boolean, + adjustTargetNumExecutors: Boolean, + countFailures: Boolean, force: Boolean): Seq[String] = executorIds override def start(): Unit = sb.start() diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala index e9539dc73f6fa..55a9122cf9026 100644 --- a/core/src/test/scala/org/apache/spark/FileSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileSuite.scala @@ -244,7 +244,10 @@ class FileSuite extends SparkFunSuite with LocalSparkContext { for (i <- 0 until testOutputCopies) { // Shift values by i so that they're different in the output val alteredOutput = testOutput.map(b => (b + i).toByte) - channel.write(ByteBuffer.wrap(alteredOutput)) + val buffer = ByteBuffer.wrap(alteredOutput) + while (buffer.hasRemaining) { + channel.write(buffer) + } } channel.close() file.close() diff --git a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala index 8a77aea75a992..61da4138896cd 100644 --- a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala +++ b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark import java.util.concurrent.Semaphore +import java.util.concurrent.atomic.AtomicInteger import scala.concurrent.ExecutionContext.Implicits.global import scala.concurrent.Future @@ -26,7 +27,7 @@ import scala.concurrent.duration._ import org.scalatest.BeforeAndAfter import org.scalatest.Matchers -import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskStart} +import org.apache.spark.scheduler.{SparkListener, SparkListenerStageCompleted, SparkListenerTaskEnd, SparkListenerTaskStart} import org.apache.spark.util.ThreadUtils /** @@ -40,6 +41,10 @@ class JobCancellationSuite extends SparkFunSuite with Matchers with BeforeAndAft override def afterEach() { try { resetSparkContext() + JobCancellationSuite.taskStartedSemaphore.drainPermits() + JobCancellationSuite.taskCancelledSemaphore.drainPermits() + JobCancellationSuite.twoJobsSharingStageSemaphore.drainPermits() + JobCancellationSuite.executionOfInterruptibleCounter.set(0) } finally { super.afterEach() } @@ -320,6 +325,67 @@ class JobCancellationSuite extends SparkFunSuite with Matchers with BeforeAndAft f2.get() } + test("interruptible iterator of shuffle reader") { + // In this test case, we create a Spark job of two stages. The second stage is cancelled during + // execution and a counter is used to make sure that the corresponding tasks are indeed + // cancelled. + import JobCancellationSuite._ + sc = new SparkContext("local[2]", "test interruptible iterator") + + // Increase the number of elements to be proceeded to avoid this test being flaky. + val numElements = 10000 + val taskCompletedSem = new Semaphore(0) + + sc.addSparkListener(new SparkListener { + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = { + // release taskCancelledSemaphore when cancelTasks event has been posted + if (stageCompleted.stageInfo.stageId == 1) { + taskCancelledSemaphore.release(numElements) + } + } + + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = { + if (taskEnd.stageId == 1) { // make sure tasks are completed + taskCompletedSem.release() + } + } + }) + + // Explicitly disable interrupt task thread on cancelling tasks, so the task thread can only be + // interrupted by `InterruptibleIterator`. + sc.setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, "false") + + val f = sc.parallelize(1 to numElements).map { i => (i, i) } + .repartitionAndSortWithinPartitions(new HashPartitioner(1)) + .mapPartitions { iter => + taskStartedSemaphore.release() + iter + }.foreachAsync { x => + // Block this code from being executed, until the job get cancelled. In this case, if the + // source iterator is interruptible, the max number of increment should be under + // `numElements`. + taskCancelledSemaphore.acquire() + executionOfInterruptibleCounter.getAndIncrement() + } + + taskStartedSemaphore.acquire() + // Job is cancelled when: + // 1. task in reduce stage has been started, guaranteed by previous line. + // 2. task in reduce stage is blocked as taskCancelledSemaphore is not released until + // JobCancelled event is posted. + // After job being cancelled, task in reduce stage will be cancelled asynchronously, thus + // partial of the inputs should not get processed (It's very unlikely that Spark can process + // 10000 elements between JobCancelled is posted and task is really killed). + f.cancel() + + val e = intercept[SparkException](f.get()).getCause + assert(e.getMessage.contains("cancelled") || e.getMessage.contains("killed")) + + // Make sure tasks are indeed completed. + taskCompletedSem.acquire() + assert(executionOfInterruptibleCounter.get() < numElements) + } + def testCount() { // Cancel before launching any tasks { @@ -381,7 +447,9 @@ class JobCancellationSuite extends SparkFunSuite with Matchers with BeforeAndAft object JobCancellationSuite { + // To avoid any headaches, reset these global variables in the companion class's afterEach block val taskStartedSemaphore = new Semaphore(0) val taskCancelledSemaphore = new Semaphore(0) val twoJobsSharingStageSemaphore = new Semaphore(0) + val executionOfInterruptibleCounter = new AtomicInteger(0) } diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala index 155ca17db726b..9206b5debf4f3 100644 --- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala +++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala @@ -262,14 +262,11 @@ class PartitioningSuite extends SparkFunSuite with SharedSparkContext with Priva test("defaultPartitioner") { val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 150) - val rdd2 = sc - .parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4))) + val rdd2 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4))) .partitionBy(new HashPartitioner(10)) - val rdd3 = sc - .parallelize(Array((1, 6), (7, 8), (3, 10), (5, 12), (13, 14))) + val rdd3 = sc.parallelize(Array((1, 6), (7, 8), (3, 10), (5, 12), (13, 14))) .partitionBy(new HashPartitioner(100)) - val rdd4 = sc - .parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4))) + val rdd4 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4))) .partitionBy(new HashPartitioner(9)) val rdd5 = sc.parallelize((1 to 10).map(x => (x, x)), 11) @@ -284,7 +281,42 @@ class PartitioningSuite extends SparkFunSuite with SharedSparkContext with Priva assert(partitioner3.numPartitions == rdd3.getNumPartitions) assert(partitioner4.numPartitions == rdd3.getNumPartitions) assert(partitioner5.numPartitions == rdd4.getNumPartitions) + } + test("defaultPartitioner when defaultParallelism is set") { + assert(!sc.conf.contains("spark.default.parallelism")) + try { + sc.conf.set("spark.default.parallelism", "4") + + val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 150) + val rdd2 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4))) + .partitionBy(new HashPartitioner(10)) + val rdd3 = sc.parallelize(Array((1, 6), (7, 8), (3, 10), (5, 12), (13, 14))) + .partitionBy(new HashPartitioner(100)) + val rdd4 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4))) + .partitionBy(new HashPartitioner(9)) + val rdd5 = sc.parallelize((1 to 10).map(x => (x, x)), 11) + val rdd6 = sc.parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4))) + .partitionBy(new HashPartitioner(3)) + + val partitioner1 = Partitioner.defaultPartitioner(rdd1, rdd2) + val partitioner2 = Partitioner.defaultPartitioner(rdd2, rdd3) + val partitioner3 = Partitioner.defaultPartitioner(rdd3, rdd1) + val partitioner4 = Partitioner.defaultPartitioner(rdd1, rdd2, rdd3) + val partitioner5 = Partitioner.defaultPartitioner(rdd4, rdd5) + val partitioner6 = Partitioner.defaultPartitioner(rdd5, rdd5) + val partitioner7 = Partitioner.defaultPartitioner(rdd1, rdd6) + + assert(partitioner1.numPartitions == rdd2.getNumPartitions) + assert(partitioner2.numPartitions == rdd3.getNumPartitions) + assert(partitioner3.numPartitions == rdd3.getNumPartitions) + assert(partitioner4.numPartitions == rdd3.getNumPartitions) + assert(partitioner5.numPartitions == rdd4.getNumPartitions) + assert(partitioner6.numPartitions == sc.defaultParallelism) + assert(partitioner7.numPartitions == sc.defaultParallelism) + } finally { + sc.conf.remove("spark.default.parallelism") + } } } diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala index 3931d53b4ae0a..ced5a06516f75 100644 --- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala +++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala @@ -363,14 +363,14 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC // first attempt -- its successful val writer1 = manager.getWriter[Int, Int](shuffleHandle, 0, - new TaskContextImpl(0, 0, 0L, 0, taskMemoryManager, new Properties, metricsSystem)) + new TaskContextImpl(0, 0, 0, 0L, 0, taskMemoryManager, new Properties, metricsSystem)) val data1 = (1 to 10).map { x => x -> x} // second attempt -- also successful. We'll write out different data, // just to simulate the fact that the records may get written differently // depending on what gets spilled, what gets combined, etc. val writer2 = manager.getWriter[Int, Int](shuffleHandle, 0, - new TaskContextImpl(0, 0, 1L, 0, taskMemoryManager, new Properties, metricsSystem)) + new TaskContextImpl(0, 0, 0, 1L, 0, taskMemoryManager, new Properties, metricsSystem)) val data2 = (11 to 20).map { x => x -> x} // interleave writes of both attempts -- we want to test that both attempts can occur @@ -398,7 +398,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC } val reader = manager.getReader[Int, Int](shuffleHandle, 0, 1, - new TaskContextImpl(1, 0, 2L, 0, taskMemoryManager, new Properties, metricsSystem)) + new TaskContextImpl(1, 0, 0, 2L, 0, taskMemoryManager, new Properties, metricsSystem)) val readData = reader.read().toIndexedSeq assert(readData === data1.toIndexedSeq || readData === data2.toIndexedSeq) diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala index b30bd74812b36..ce9f2be1c02dd 100644 --- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark import java.io.File import java.net.{MalformedURLException, URI} import java.nio.charset.StandardCharsets -import java.util.concurrent.{Semaphore, TimeUnit} +import java.util.concurrent.{CountDownLatch, Semaphore, TimeUnit} import scala.concurrent.duration._ @@ -498,45 +498,36 @@ class SparkContextSuite extends SparkFunSuite with LocalSparkContext with Eventu test("Cancelling stages/jobs with custom reasons.") { sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local")) + sc.setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, "true") val REASON = "You shall not pass" - val slices = 10 - val listener = new SparkListener { - override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = { - if (SparkContextSuite.cancelStage) { - eventually(timeout(10.seconds)) { - assert(SparkContextSuite.isTaskStarted) + for (cancelWhat <- Seq("stage", "job")) { + // This countdown latch used to make sure stage or job canceled in listener + val latch = new CountDownLatch(1) + + val listener = cancelWhat match { + case "stage" => + new SparkListener { + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = { + sc.cancelStage(taskStart.stageId, REASON) + latch.countDown() + } } - sc.cancelStage(taskStart.stageId, REASON) - SparkContextSuite.cancelStage = false - SparkContextSuite.semaphore.release(slices) - } - } - - override def onJobStart(jobStart: SparkListenerJobStart): Unit = { - if (SparkContextSuite.cancelJob) { - eventually(timeout(10.seconds)) { - assert(SparkContextSuite.isTaskStarted) + case "job" => + new SparkListener { + override def onJobStart(jobStart: SparkListenerJobStart): Unit = { + sc.cancelJob(jobStart.jobId, REASON) + latch.countDown() + } } - sc.cancelJob(jobStart.jobId, REASON) - SparkContextSuite.cancelJob = false - SparkContextSuite.semaphore.release(slices) - } } - } - sc.addSparkListener(listener) - - for (cancelWhat <- Seq("stage", "job")) { - SparkContextSuite.semaphore.drainPermits() - SparkContextSuite.isTaskStarted = false - SparkContextSuite.cancelStage = (cancelWhat == "stage") - SparkContextSuite.cancelJob = (cancelWhat == "job") + sc.addSparkListener(listener) val ex = intercept[SparkException] { - sc.range(0, 10000L, numSlices = slices).mapPartitions { x => - SparkContextSuite.isTaskStarted = true - // Block waiting for the listener to cancel the stage or job. - SparkContextSuite.semaphore.acquire() + sc.range(0, 10000L, numSlices = 10).mapPartitions { x => + x.synchronized { + x.wait() + } x }.count() } @@ -550,9 +541,11 @@ class SparkContextSuite extends SparkFunSuite with LocalSparkContext with Eventu fail("Expected the cause to be SparkException, got " + cause.toString() + " instead.") } + latch.await(20, TimeUnit.SECONDS) eventually(timeout(20.seconds)) { assert(sc.statusTracker.getExecutorInfos.map(_.numRunningTasks()).sum == 0) } + sc.removeSparkListener(listener) } } @@ -637,8 +630,6 @@ class SparkContextSuite extends SparkFunSuite with LocalSparkContext with Eventu } object SparkContextSuite { - @volatile var cancelJob = false - @volatile var cancelStage = false @volatile var isTaskStarted = false @volatile var taskKilled = false @volatile var taskSucceeded = false diff --git a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala index 159629825c677..9ad2e9a5e74ac 100644 --- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala +++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala @@ -153,6 +153,40 @@ class BroadcastSuite extends SparkFunSuite with LocalSparkContext with Encryptio assert(broadcast.value.sum === 10) } + test("One broadcast value instance per executor") { + val conf = new SparkConf() + .setMaster("local[4]") + .setAppName("test") + + sc = new SparkContext(conf) + val list = List[Int](1, 2, 3, 4) + val broadcast = sc.broadcast(list) + val instances = sc.parallelize(1 to 10) + .map(x => System.identityHashCode(broadcast.value)) + .collect() + .toSet + + assert(instances.size === 1) + } + + test("One broadcast value instance per executor when memory is constrained") { + val conf = new SparkConf() + .setMaster("local[4]") + .setAppName("test") + .set("spark.memory.useLegacyMode", "true") + .set("spark.storage.memoryFraction", "0.0") + + sc = new SparkContext(conf) + val list = List[Int](1, 2, 3, 4) + val broadcast = sc.broadcast(list) + val instances = sc.parallelize(1 to 10) + .map(x => System.identityHashCode(broadcast.value)) + .collect() + .toSet + + assert(instances.size === 1) + } + /** * Verify the persistence of state associated with a TorrentBroadcast in a local-cluster. * diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index 27dd435332348..e5268ca31373e 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.deploy import java.io._ import java.net.URI import java.nio.charset.StandardCharsets -import java.nio.file.Files +import java.nio.file.{Files, Paths} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -105,6 +105,9 @@ class SparkSubmitSuite // Necessary to make ScalaTest 3.x interrupt a thread on the JVM like ScalaTest 2.2.x implicit val defaultSignaler: Signaler = ThreadSignaler + private val emptyIvySettings = File.createTempFile("ivy", ".xml") + FileUtils.write(emptyIvySettings, "", StandardCharsets.UTF_8) + override def beforeEach() { super.beforeEach() System.setProperty("spark.testing", "true") @@ -520,6 +523,7 @@ class SparkSubmitSuite "--repositories", repo, "--conf", "spark.ui.enabled=false", "--conf", "spark.master.rest.enabled=false", + "--conf", s"spark.jars.ivySettings=${emptyIvySettings.getAbsolutePath()}", unusedJar.toString, "my.great.lib.MyLib", "my.great.dep.MyLib") runSparkSubmit(args) @@ -530,7 +534,6 @@ class SparkSubmitSuite val unusedJar = TestUtils.createJarWithClasses(Seq.empty) val main = MavenCoordinate("my.great.lib", "mylib", "0.1") val dep = MavenCoordinate("my.great.dep", "mylib", "0.1") - // Test using "spark.jars.packages" and "spark.jars.repositories" configurations. IvyTestUtils.withRepository(main, Some(dep.toString), None) { repo => val args = Seq( "--class", JarCreationTest.getClass.getName.stripSuffix("$"), @@ -540,6 +543,7 @@ class SparkSubmitSuite "--conf", s"spark.jars.repositories=$repo", "--conf", "spark.ui.enabled=false", "--conf", "spark.master.rest.enabled=false", + "--conf", s"spark.jars.ivySettings=${emptyIvySettings.getAbsolutePath()}", unusedJar.toString, "my.great.lib.MyLib", "my.great.dep.MyLib") runSparkSubmit(args) @@ -550,7 +554,6 @@ class SparkSubmitSuite // See https://gist.github.com/shivaram/3a2fecce60768a603dac for a error log ignore("correctly builds R packages included in a jar with --packages") { assume(RUtils.isRInstalled, "R isn't installed on this machine.") - // Check if the SparkR package is installed assume(RUtils.isSparkRInstalled, "SparkR is not installed in this build.") val main = MavenCoordinate("my.great.lib", "mylib", "0.1") val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!")) @@ -563,6 +566,7 @@ class SparkSubmitSuite "--master", "local-cluster[2,1,1024]", "--packages", main.toString, "--repositories", repo, + "--conf", s"spark.jars.ivySettings=${emptyIvySettings.getAbsolutePath()}", "--verbose", "--conf", "spark.ui.enabled=false", rScriptDir) @@ -573,7 +577,6 @@ class SparkSubmitSuite test("include an external JAR in SparkR") { assume(RUtils.isRInstalled, "R isn't installed on this machine.") val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!")) - // Check if the SparkR package is installed assume(RUtils.isSparkRInstalled, "SparkR is not installed in this build.") val rScriptDir = Seq(sparkHome, "R", "pkg", "tests", "fulltests", "jarTest.R").mkString(File.separator) @@ -606,10 +609,13 @@ class SparkSubmitSuite } test("resolves command line argument paths correctly") { - val jars = "/jar1,/jar2" // --jars - val files = "local:/file1,file2" // --files - val archives = "file:/archive1,archive2" // --archives - val pyFiles = "py-file1,py-file2" // --py-files + val dir = Utils.createTempDir() + val archive = Paths.get(dir.toPath.toString, "single.zip") + Files.createFile(archive) + val jars = "/jar1,/jar2" + val files = "local:/file1,file2" + val archives = s"file:/archive1,${dir.toPath.toAbsolutePath.toString}/*.zip#archive3" + val pyFiles = "py-file1,py-file2" // Test jars and files val clArgs = Seq( @@ -636,9 +642,10 @@ class SparkSubmitSuite val appArgs2 = new SparkSubmitArguments(clArgs2) val (_, _, conf2, _) = SparkSubmit.prepareSubmitEnvironment(appArgs2) appArgs2.files should be (Utils.resolveURIs(files)) - appArgs2.archives should be (Utils.resolveURIs(archives)) + appArgs2.archives should fullyMatch regex ("file:/archive1,file:.*#archive3") conf2.get("spark.yarn.dist.files") should be (Utils.resolveURIs(files)) - conf2.get("spark.yarn.dist.archives") should be (Utils.resolveURIs(archives)) + conf2.get("spark.yarn.dist.archives") should fullyMatch regex + ("file:/archive1,file:.*#archive3") // Test python files val clArgs3 = Seq( @@ -657,6 +664,29 @@ class SparkSubmitSuite conf3.get(PYSPARK_PYTHON.key) should be ("python3.5") } + test("ambiguous archive mapping results in error message") { + val dir = Utils.createTempDir() + val archive1 = Paths.get(dir.toPath.toString, "first.zip") + val archive2 = Paths.get(dir.toPath.toString, "second.zip") + Files.createFile(archive1) + Files.createFile(archive2) + val jars = "/jar1,/jar2" + val files = "local:/file1,file2" + val archives = s"file:/archive1,${dir.toPath.toAbsolutePath.toString}/*.zip#archive3" + val pyFiles = "py-file1,py-file2" + + // Test files and archives (Yarn) + val clArgs2 = Seq( + "--master", "yarn", + "--class", "org.SomeClass", + "--files", files, + "--archives", archives, + "thejar.jar" + ) + + testPrematureExit(clArgs2.toArray, "resolves ambiguously to multiple files") + } + test("resolves config paths correctly") { val jars = "/jar1,/jar2" // spark.jars val files = "local:/file1,file2" // spark.files / spark.yarn.dist.files diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala index eb8c203ae7751..a0f09891787e0 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala @@ -256,4 +256,19 @@ class SparkSubmitUtilsSuite extends SparkFunSuite with BeforeAndAfterAll { assert(jarPath.indexOf("mydep") >= 0, "should find dependency") } } + + test("SPARK-10878: test resolution files cleaned after resolving artifact") { + val main = new MavenCoordinate("my.great.lib", "mylib", "0.1") + + IvyTestUtils.withRepository(main, None, None) { repo => + val ivySettings = SparkSubmitUtils.buildIvySettings(Some(repo), Some(tempIvyPath)) + val jarPath = SparkSubmitUtils.resolveMavenCoordinates( + main.toString, + ivySettings, + isTest = true) + val r = """.*org.apache.spark-spark-submit-parent-.*""".r + assert(!ivySettings.getDefaultCache.listFiles.map(_.getName) + .exists(r.findFirstIn(_).isDefined), "resolution files should be cleaned") + } + } } diff --git a/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala b/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala index bf7480d79f8a1..155564a65c607 100644 --- a/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala @@ -573,7 +573,8 @@ class StandaloneDynamicAllocationSuite syncExecutors(sc) sc.schedulerBackend match { case b: CoarseGrainedSchedulerBackend => - b.killExecutors(Seq(executorId), replace = false, force) + b.killExecutors(Seq(executorId), adjustTargetNumExecutors = true, countFailures = false, + force) case _ => fail("expected coarse grained scheduler") } } diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala index 84ee01c7f5aaf..787de59edf465 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala @@ -31,7 +31,7 @@ import org.apache.hadoop.fs.{FileStatus, Path} import org.apache.hadoop.hdfs.DistributedFileSystem import org.json4s.jackson.JsonMethods._ import org.mockito.Matchers.any -import org.mockito.Mockito.{mock, spy, verify} +import org.mockito.Mockito.{doReturn, mock, spy, verify} import org.scalatest.BeforeAndAfter import org.scalatest.Matchers import org.scalatest.concurrent.Eventually._ @@ -149,8 +149,10 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc class TestFsHistoryProvider extends FsHistoryProvider(createTestConf()) { var mergeApplicationListingCall = 0 - override protected def mergeApplicationListing(fileStatus: FileStatus): Unit = { - super.mergeApplicationListing(fileStatus) + override protected def mergeApplicationListing( + fileStatus: FileStatus, + lastSeen: Long): Unit = { + super.mergeApplicationListing(fileStatus, lastSeen) mergeApplicationListingCall += 1 } } @@ -663,6 +665,115 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc freshUI.get.ui.store.job(0) } + test("clean up stale app information") { + val storeDir = Utils.createTempDir() + val conf = createTestConf().set(LOCAL_STORE_DIR, storeDir.getAbsolutePath()) + val provider = spy(new FsHistoryProvider(conf)) + val appId = "new1" + + // Write logs for two app attempts. + doReturn(1L).when(provider).getNewLastScanTime() + val attempt1 = newLogFile(appId, Some("1"), inProgress = false) + writeFile(attempt1, true, None, + SparkListenerApplicationStart(appId, Some(appId), 1L, "test", Some("1")), + SparkListenerJobStart(0, 1L, Nil, null), + SparkListenerApplicationEnd(5L) + ) + val attempt2 = newLogFile(appId, Some("2"), inProgress = false) + writeFile(attempt2, true, None, + SparkListenerApplicationStart(appId, Some(appId), 1L, "test", Some("2")), + SparkListenerJobStart(0, 1L, Nil, null), + SparkListenerApplicationEnd(5L) + ) + updateAndCheck(provider) { list => + assert(list.size === 1) + assert(list(0).id === appId) + assert(list(0).attempts.size === 2) + } + + // Load the app's UI. + val ui = provider.getAppUI(appId, Some("1")) + assert(ui.isDefined) + + // Delete the underlying log file for attempt 1 and rescan. The UI should go away, but since + // attempt 2 still exists, listing data should be there. + doReturn(2L).when(provider).getNewLastScanTime() + attempt1.delete() + updateAndCheck(provider) { list => + assert(list.size === 1) + assert(list(0).id === appId) + assert(list(0).attempts.size === 1) + } + assert(!ui.get.valid) + assert(provider.getAppUI(appId, None) === None) + + // Delete the second attempt's log file. Now everything should go away. + doReturn(3L).when(provider).getNewLastScanTime() + attempt2.delete() + updateAndCheck(provider) { list => + assert(list.isEmpty) + } + } + + test("SPARK-21571: clean up removes invalid history files") { + // TODO: "maxTime" becoming negative in cleanLogs() causes this test to fail, so avoid that + // until we figure out what's causing the problem. + val clock = new ManualClock(TimeUnit.DAYS.toMillis(120)) + val conf = createTestConf().set(MAX_LOG_AGE_S.key, s"2d") + val provider = new FsHistoryProvider(conf, clock) { + override def getNewLastScanTime(): Long = clock.getTimeMillis() + } + + // Create 0-byte size inprogress and complete files + var logCount = 0 + var validLogCount = 0 + + val emptyInProgress = newLogFile("emptyInprogressLogFile", None, inProgress = true) + emptyInProgress.createNewFile() + emptyInProgress.setLastModified(clock.getTimeMillis()) + logCount += 1 + + val slowApp = newLogFile("slowApp", None, inProgress = true) + slowApp.createNewFile() + slowApp.setLastModified(clock.getTimeMillis()) + logCount += 1 + + val emptyFinished = newLogFile("emptyFinishedLogFile", None, inProgress = false) + emptyFinished.createNewFile() + emptyFinished.setLastModified(clock.getTimeMillis()) + logCount += 1 + + // Create an incomplete log file, has an end record but no start record. + val corrupt = newLogFile("nonEmptyCorruptLogFile", None, inProgress = false) + writeFile(corrupt, true, None, SparkListenerApplicationEnd(0)) + corrupt.setLastModified(clock.getTimeMillis()) + logCount += 1 + + provider.checkForLogs() + provider.cleanLogs() + assert(new File(testDir.toURI).listFiles().size === logCount) + + // Move the clock forward 1 day and scan the files again. They should still be there. + clock.advance(TimeUnit.DAYS.toMillis(1)) + provider.checkForLogs() + provider.cleanLogs() + assert(new File(testDir.toURI).listFiles().size === logCount) + + // Update the slow app to contain valid info. Code should detect the change and not clean + // it up. + writeFile(slowApp, true, None, + SparkListenerApplicationStart(slowApp.getName(), Some(slowApp.getName()), 1L, "test", None)) + slowApp.setLastModified(clock.getTimeMillis()) + validLogCount += 1 + + // Move the clock forward another 2 days and scan the files again. This time the cleaner should + // pick up the invalid files and get rid of them. + clock.advance(TimeUnit.DAYS.toMillis(2)) + provider.checkForLogs() + provider.cleanLogs() + assert(new File(testDir.toURI).listFiles().size === validLogCount) + } + /** * Asks the provider to check for logs and calls a function to perform checks on the updated * app list. Example: diff --git a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala index 3738f85da5831..4c06193225368 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala @@ -48,7 +48,7 @@ import org.apache.spark.deploy.history.config._ import org.apache.spark.status.api.v1.ApplicationInfo import org.apache.spark.status.api.v1.JobData import org.apache.spark.ui.SparkUI -import org.apache.spark.util.{ResetSystemProperties, Utils} +import org.apache.spark.util.{ResetSystemProperties, ShutdownHookManager, Utils} /** * A collection of tests against the historyserver, including comparing responses from the json @@ -294,6 +294,11 @@ class HistoryServerSuite extends SparkFunSuite with BeforeAndAfter with Matchers all (siteRelativeLinks) should startWith (uiRoot) } + test("/version api endpoint") { + val response = getUrl("version") + assert(response.contains(SPARK_VERSION)) + } + test("ajax rendered relative links are prefixed with uiRoot (spark.ui.proxyBase)") { val uiRoot = "/testwebproxybase" System.setProperty("spark.ui.proxyBase", uiRoot) @@ -564,7 +569,7 @@ class HistoryServerSuite extends SparkFunSuite with BeforeAndAfter with Matchers assert(jobcount === getNumJobs("/jobs")) // no need to retain the test dir now the tests complete - logDir.deleteOnExit() + ShutdownHookManager.registerShutdownDeleteDir(logDir) } test("ui and api authorization checks") { diff --git a/core/src/test/scala/org/apache/spark/deploy/security/HadoopDelegationTokenManagerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/security/HadoopDelegationTokenManagerSuite.scala index eeffc36070b44..2849a10a2c81e 100644 --- a/core/src/test/scala/org/apache/spark/deploy/security/HadoopDelegationTokenManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/security/HadoopDelegationTokenManagerSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.deploy.security +import org.apache.commons.io.IOUtils import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.FileSystem import org.apache.hadoop.security.Credentials @@ -110,7 +111,64 @@ class HadoopDelegationTokenManagerSuite extends SparkFunSuite with Matchers { creds.getAllTokens.size should be (0) } + test("SPARK-23209: obtain tokens when Hive classes are not available") { + // This test needs a custom class loader to hide Hive classes which are in the classpath. + // Because the manager code loads the Hive provider directly instead of using reflection, we + // need to drive the test through the custom class loader so a new copy that cannot find + // Hive classes is loaded. + val currentLoader = Thread.currentThread().getContextClassLoader() + val noHive = new ClassLoader() { + override def loadClass(name: String, resolve: Boolean): Class[_] = { + if (name.startsWith("org.apache.hive") || name.startsWith("org.apache.hadoop.hive")) { + throw new ClassNotFoundException(name) + } + + if (name.startsWith("java") || name.startsWith("scala")) { + currentLoader.loadClass(name) + } else { + val classFileName = name.replaceAll("\\.", "/") + ".class" + val in = currentLoader.getResourceAsStream(classFileName) + if (in != null) { + val bytes = IOUtils.toByteArray(in) + defineClass(name, bytes, 0, bytes.length) + } else { + throw new ClassNotFoundException(name) + } + } + } + } + + try { + Thread.currentThread().setContextClassLoader(noHive) + val test = noHive.loadClass(NoHiveTest.getClass.getName().stripSuffix("$")) + test.getMethod("runTest").invoke(null) + } finally { + Thread.currentThread().setContextClassLoader(currentLoader) + } + } + private[spark] def hadoopFSsToAccess(hadoopConf: Configuration): Set[FileSystem] = { Set(FileSystem.get(hadoopConf)) } } + +/** Test code for SPARK-23209 to avoid using too much reflection above. */ +private object NoHiveTest extends Matchers { + + def runTest(): Unit = { + try { + val manager = new HadoopDelegationTokenManager(new SparkConf(), new Configuration(), + _ => Set()) + manager.getServiceDelegationTokenProvider("hive") should be (None) + } catch { + case e: Throwable => + // Throw a better exception in case the test fails, since there may be a lot of nesting. + var cause = e + while (cause.getCause() != null) { + cause = cause.getCause() + } + throw cause + } + } + +} diff --git a/core/src/test/scala/org/apache/spark/executor/ExecutorSuite.scala b/core/src/test/scala/org/apache/spark/executor/ExecutorSuite.scala index 105a178f2d94e..1a7bebe2c53cd 100644 --- a/core/src/test/scala/org/apache/spark/executor/ExecutorSuite.scala +++ b/core/src/test/scala/org/apache/spark/executor/ExecutorSuite.scala @@ -22,6 +22,7 @@ import java.lang.Thread.UncaughtExceptionHandler import java.nio.ByteBuffer import java.util.Properties import java.util.concurrent.{CountDownLatch, TimeUnit} +import java.util.concurrent.atomic.AtomicBoolean import scala.collection.mutable.Map import scala.concurrent.duration._ @@ -139,7 +140,7 @@ class ExecutorSuite extends SparkFunSuite with LocalSparkContext with MockitoSug // the fetch failure. The executor should still tell the driver that the task failed due to a // fetch failure, not a generic exception from user code. val inputRDD = new FetchFailureThrowingRDD(sc) - val secondRDD = new FetchFailureHidingRDD(sc, inputRDD, throwOOM = false) + val secondRDD = new FetchFailureHidingRDD(sc, inputRDD, throwOOM = false, interrupt = false) val taskBinary = sc.broadcast(serializer.serialize((secondRDD, resultFunc)).array()) val serializedTaskMetrics = serializer.serialize(TaskMetrics.registered).array() val task = new ResultTask( @@ -173,17 +174,48 @@ class ExecutorSuite extends SparkFunSuite with LocalSparkContext with MockitoSug } test("SPARK-19276: OOMs correctly handled with a FetchFailure") { + val (failReason, uncaughtExceptionHandler) = testFetchFailureHandling(true) + assert(failReason.isInstanceOf[ExceptionFailure]) + val exceptionCaptor = ArgumentCaptor.forClass(classOf[Throwable]) + verify(uncaughtExceptionHandler).uncaughtException(any(), exceptionCaptor.capture()) + assert(exceptionCaptor.getAllValues.size === 1) + assert(exceptionCaptor.getAllValues().get(0).isInstanceOf[OutOfMemoryError]) + } + + test("SPARK-23816: interrupts are not masked by a FetchFailure") { + // If killing the task causes a fetch failure, we still treat it as a task that was killed, + // as the fetch failure could easily be caused by interrupting the thread. + val (failReason, _) = testFetchFailureHandling(false) + assert(failReason.isInstanceOf[TaskKilled]) + } + + /** + * Helper for testing some cases where a FetchFailure should *not* get sent back, because its + * superceded by another error, either an OOM or intentionally killing a task. + * @param oom if true, throw an OOM after the FetchFailure; else, interrupt the task after the + * FetchFailure + */ + private def testFetchFailureHandling( + oom: Boolean): (TaskFailedReason, UncaughtExceptionHandler) = { // when there is a fatal error like an OOM, we don't do normal fetch failure handling, since it // may be a false positive. And we should call the uncaught exception handler. + // SPARK-23816 also handle interrupts the same way, as killing an obsolete speculative task + // does not represent a real fetch failure. val conf = new SparkConf().setMaster("local").setAppName("executor suite test") sc = new SparkContext(conf) val serializer = SparkEnv.get.closureSerializer.newInstance() val resultFunc = (context: TaskContext, itr: Iterator[Int]) => itr.size - // Submit a job where a fetch failure is thrown, but then there is an OOM. We should treat - // the fetch failure as a false positive, and just do normal OOM handling. + // Submit a job where a fetch failure is thrown, but then there is an OOM or interrupt. We + // should treat the fetch failure as a false positive, and do normal OOM or interrupt handling. val inputRDD = new FetchFailureThrowingRDD(sc) - val secondRDD = new FetchFailureHidingRDD(sc, inputRDD, throwOOM = true) + if (!oom) { + // we are trying to setup a case where a task is killed after a fetch failure -- this + // is just a helper to coordinate between the task thread and this thread that will + // kill the task + ExecutorSuiteHelper.latches = new ExecutorSuiteHelper() + } + val secondRDD = new FetchFailureHidingRDD(sc, inputRDD, throwOOM = oom, interrupt = !oom) val taskBinary = sc.broadcast(serializer.serialize((secondRDD, resultFunc)).array()) val serializedTaskMetrics = serializer.serialize(TaskMetrics.registered).array() val task = new ResultTask( @@ -200,15 +232,8 @@ class ExecutorSuite extends SparkFunSuite with LocalSparkContext with MockitoSug val serTask = serializer.serialize(task) val taskDescription = createFakeTaskDescription(serTask) - val (failReason, uncaughtExceptionHandler) = - runTaskGetFailReasonAndExceptionHandler(taskDescription) - // make sure the task failure just looks like a OOM, not a fetch failure - assert(failReason.isInstanceOf[ExceptionFailure]) - val exceptionCaptor = ArgumentCaptor.forClass(classOf[Throwable]) - verify(uncaughtExceptionHandler).uncaughtException(any(), exceptionCaptor.capture()) - assert(exceptionCaptor.getAllValues.size === 1) - assert(exceptionCaptor.getAllValues.get(0).isInstanceOf[OutOfMemoryError]) - } + runTaskGetFailReasonAndExceptionHandler(taskDescription, killTask = !oom) + } test("Gracefully handle error in task deserialization") { val conf = new SparkConf @@ -257,22 +282,39 @@ class ExecutorSuite extends SparkFunSuite with LocalSparkContext with MockitoSug } private def runTaskAndGetFailReason(taskDescription: TaskDescription): TaskFailedReason = { - runTaskGetFailReasonAndExceptionHandler(taskDescription)._1 + runTaskGetFailReasonAndExceptionHandler(taskDescription, false)._1 } private def runTaskGetFailReasonAndExceptionHandler( - taskDescription: TaskDescription): (TaskFailedReason, UncaughtExceptionHandler) = { + taskDescription: TaskDescription, + killTask: Boolean): (TaskFailedReason, UncaughtExceptionHandler) = { val mockBackend = mock[ExecutorBackend] val mockUncaughtExceptionHandler = mock[UncaughtExceptionHandler] var executor: Executor = null + val timedOut = new AtomicBoolean(false) try { executor = new Executor("id", "localhost", SparkEnv.get, userClassPath = Nil, isLocal = true, uncaughtExceptionHandler = mockUncaughtExceptionHandler) // the task will be launched in a dedicated worker thread executor.launchTask(mockBackend, taskDescription) + if (killTask) { + val killingThread = new Thread("kill-task") { + override def run(): Unit = { + // wait to kill the task until it has thrown a fetch failure + if (ExecutorSuiteHelper.latches.latch1.await(10, TimeUnit.SECONDS)) { + // now we can kill the task + executor.killAllTasks(true, "Killed task, eg. because of speculative execution") + } else { + timedOut.set(true) + } + } + } + killingThread.start() + } eventually(timeout(5.seconds), interval(10.milliseconds)) { assert(executor.numRunningTasks === 0) } + assert(!timedOut.get(), "timed out waiting to be ready to kill tasks") } finally { if (executor != null) { executor.stop() @@ -282,8 +324,9 @@ class ExecutorSuite extends SparkFunSuite with LocalSparkContext with MockitoSug val statusCaptor = ArgumentCaptor.forClass(classOf[ByteBuffer]) orderedMock.verify(mockBackend) .statusUpdate(meq(0L), meq(TaskState.RUNNING), statusCaptor.capture()) + val finalState = if (killTask) TaskState.KILLED else TaskState.FAILED orderedMock.verify(mockBackend) - .statusUpdate(meq(0L), meq(TaskState.FAILED), statusCaptor.capture()) + .statusUpdate(meq(0L), meq(finalState), statusCaptor.capture()) // first statusUpdate for RUNNING has empty data assert(statusCaptor.getAllValues().get(0).remaining() === 0) // second update is more interesting @@ -321,7 +364,8 @@ class SimplePartition extends Partition { class FetchFailureHidingRDD( sc: SparkContext, val input: FetchFailureThrowingRDD, - throwOOM: Boolean) extends RDD[Int](input) { + throwOOM: Boolean, + interrupt: Boolean) extends RDD[Int](input) { override def compute(split: Partition, context: TaskContext): Iterator[Int] = { val inItr = input.compute(split, context) try { @@ -330,6 +374,15 @@ class FetchFailureHidingRDD( case t: Throwable => if (throwOOM) { throw new OutOfMemoryError("OOM while handling another exception") + } else if (interrupt) { + // make sure our test is setup correctly + assert(TaskContext.get().asInstanceOf[TaskContextImpl].fetchFailed.isDefined) + // signal our test is ready for the task to get killed + ExecutorSuiteHelper.latches.latch1.countDown() + // then wait for another thread in the test to kill the task -- this latch + // is never actually decremented, we just wait to get killed. + ExecutorSuiteHelper.latches.latch2.await(10, TimeUnit.SECONDS) + throw new IllegalStateException("timed out waiting to be interrupted") } else { throw new RuntimeException("User Exception that hides the original exception", t) } @@ -352,6 +405,11 @@ private class ExecutorSuiteHelper { @volatile var testFailedReason: TaskFailedReason = _ } +// helper for coordinating killing tasks +private object ExecutorSuiteHelper { + var latches: ExecutorSuiteHelper = null +} + private class NonDeserializableTask extends FakeTask(0, 0) with Externalizable { def writeExternal(out: ObjectOutput): Unit = {} def readExternal(in: ObjectInput): Unit = { diff --git a/core/src/test/scala/org/apache/spark/io/ChunkedByteBufferSuite.scala b/core/src/test/scala/org/apache/spark/io/ChunkedByteBufferSuite.scala index 3b798e36b0499..2107559572d78 100644 --- a/core/src/test/scala/org/apache/spark/io/ChunkedByteBufferSuite.scala +++ b/core/src/test/scala/org/apache/spark/io/ChunkedByteBufferSuite.scala @@ -21,11 +21,12 @@ import java.nio.ByteBuffer import com.google.common.io.ByteStreams -import org.apache.spark.SparkFunSuite +import org.apache.spark.{SharedSparkContext, SparkFunSuite} +import org.apache.spark.internal.config import org.apache.spark.network.util.ByteArrayWritableChannel import org.apache.spark.util.io.ChunkedByteBuffer -class ChunkedByteBufferSuite extends SparkFunSuite { +class ChunkedByteBufferSuite extends SparkFunSuite with SharedSparkContext { test("no chunks") { val emptyChunkedByteBuffer = new ChunkedByteBuffer(Array.empty[ByteBuffer]) @@ -56,6 +57,18 @@ class ChunkedByteBufferSuite extends SparkFunSuite { assert(chunkedByteBuffer.getChunks().head.position() === 0) } + test("SPARK-24107: writeFully() write buffer which is larger than bufferWriteChunkSize") { + try { + sc.conf.set(config.BUFFER_WRITE_CHUNK_SIZE, 32L * 1024L * 1024L) + val chunkedByteBuffer = new ChunkedByteBuffer(Array(ByteBuffer.allocate(40 * 1024 * 1024))) + val byteArrayWritableChannel = new ByteArrayWritableChannel(chunkedByteBuffer.size.toInt) + chunkedByteBuffer.writeFully(byteArrayWritableChannel) + assert(byteArrayWritableChannel.length() === chunkedByteBuffer.size) + } finally { + sc.conf.remove(config.BUFFER_WRITE_CHUNK_SIZE) + } + } + test("toArray()") { val empty = ByteBuffer.wrap(Array.empty[Byte]) val bytes = ByteBuffer.wrap(Array.tabulate(8)(_.toByte)) diff --git a/core/src/test/scala/org/apache/spark/memory/MemoryTestingUtils.scala b/core/src/test/scala/org/apache/spark/memory/MemoryTestingUtils.scala index 362cd861cc248..dcf89e4f75acf 100644 --- a/core/src/test/scala/org/apache/spark/memory/MemoryTestingUtils.scala +++ b/core/src/test/scala/org/apache/spark/memory/MemoryTestingUtils.scala @@ -29,6 +29,7 @@ object MemoryTestingUtils { val taskMemoryManager = new TaskMemoryManager(env.memoryManager, 0) new TaskContextImpl( stageId = 0, + stageAttemptNumber = 0, partitionId = 0, taskAttemptId = 0, attemptNumber = 0, diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala index a39e0469272fe..47af5c3320dd9 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala @@ -322,8 +322,7 @@ class PairRDDFunctionsSuite extends SparkFunSuite with SharedSparkContext { } // See SPARK-22465 - test("cogroup between multiple RDD" + - " with number of partitions similar in order of magnitude") { + test("cogroup between multiple RDD with number of partitions similar in order of magnitude") { val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 20) val rdd2 = sc .parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) @@ -332,6 +331,48 @@ class PairRDDFunctionsSuite extends SparkFunSuite with SharedSparkContext { assert(joined.getNumPartitions == rdd2.getNumPartitions) } + test("cogroup between multiple RDD when defaultParallelism is set without proper partitioner") { + assert(!sc.conf.contains("spark.default.parallelism")) + try { + sc.conf.set("spark.default.parallelism", "4") + val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 20) + val rdd2 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)), 10) + val joined = rdd1.cogroup(rdd2) + assert(joined.getNumPartitions == sc.defaultParallelism) + } finally { + sc.conf.remove("spark.default.parallelism") + } + } + + test("cogroup between multiple RDD when defaultParallelism is set with proper partitioner") { + assert(!sc.conf.contains("spark.default.parallelism")) + try { + sc.conf.set("spark.default.parallelism", "4") + val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 20) + val rdd2 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + .partitionBy(new HashPartitioner(10)) + val joined = rdd1.cogroup(rdd2) + assert(joined.getNumPartitions == rdd2.getNumPartitions) + } finally { + sc.conf.remove("spark.default.parallelism") + } + } + + test("cogroup between multiple RDD when defaultParallelism is set; with huge number of " + + "partitions in upstream RDDs") { + assert(!sc.conf.contains("spark.default.parallelism")) + try { + sc.conf.set("spark.default.parallelism", "4") + val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 1000) + val rdd2 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + .partitionBy(new HashPartitioner(10)) + val joined = rdd1.cogroup(rdd2) + assert(joined.getNumPartitions == rdd2.getNumPartitions) + } finally { + sc.conf.remove("spark.default.parallelism") + } + } + test("rightOuterJoin") { val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) diff --git a/core/src/test/scala/org/apache/spark/scheduler/BlacklistTrackerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/BlacklistTrackerSuite.scala index cd1b7a9e5ab18..00867ef1308a2 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/BlacklistTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/BlacklistTrackerSuite.scala @@ -479,7 +479,7 @@ class BlacklistTrackerSuite extends SparkFunSuite with BeforeAndAfterEach with M test("blacklisting kills executors, configured by BLACKLIST_KILL_ENABLED") { val allocationClientMock = mock[ExecutorAllocationClient] - when(allocationClientMock.killExecutors(any(), any(), any())).thenReturn(Seq("called")) + when(allocationClientMock.killExecutors(any(), any(), any(), any())).thenReturn(Seq("called")) when(allocationClientMock.killExecutorsOnHost("hostA")).thenAnswer(new Answer[Boolean] { // To avoid a race between blacklisting and killing, it is important that the nodeBlacklist // is updated before we ask the executor allocation client to kill all the executors @@ -517,7 +517,7 @@ class BlacklistTrackerSuite extends SparkFunSuite with BeforeAndAfterEach with M } blacklist.updateBlacklistForSuccessfulTaskSet(0, 0, taskSetBlacklist1.execToFailures) - verify(allocationClientMock, never).killExecutors(any(), any(), any()) + verify(allocationClientMock, never).killExecutors(any(), any(), any(), any()) verify(allocationClientMock, never).killExecutorsOnHost(any()) // Enable auto-kill. Blacklist an executor and make sure killExecutors is called. @@ -533,7 +533,7 @@ class BlacklistTrackerSuite extends SparkFunSuite with BeforeAndAfterEach with M } blacklist.updateBlacklistForSuccessfulTaskSet(0, 0, taskSetBlacklist2.execToFailures) - verify(allocationClientMock).killExecutors(Seq("1"), true, true) + verify(allocationClientMock).killExecutors(Seq("1"), false, false, true) val taskSetBlacklist3 = createTaskSetBlacklist(stageId = 1) // Fail 4 tasks in one task set on executor 2, so that executor gets blacklisted for the whole @@ -545,13 +545,13 @@ class BlacklistTrackerSuite extends SparkFunSuite with BeforeAndAfterEach with M } blacklist.updateBlacklistForSuccessfulTaskSet(0, 0, taskSetBlacklist3.execToFailures) - verify(allocationClientMock).killExecutors(Seq("2"), true, true) + verify(allocationClientMock).killExecutors(Seq("2"), false, false, true) verify(allocationClientMock).killExecutorsOnHost("hostA") } test("fetch failure blacklisting kills executors, configured by BLACKLIST_KILL_ENABLED") { val allocationClientMock = mock[ExecutorAllocationClient] - when(allocationClientMock.killExecutors(any(), any(), any())).thenReturn(Seq("called")) + when(allocationClientMock.killExecutors(any(), any(), any(), any())).thenReturn(Seq("called")) when(allocationClientMock.killExecutorsOnHost("hostA")).thenAnswer(new Answer[Boolean] { // To avoid a race between blacklisting and killing, it is important that the nodeBlacklist // is updated before we ask the executor allocation client to kill all the executors @@ -571,16 +571,19 @@ class BlacklistTrackerSuite extends SparkFunSuite with BeforeAndAfterEach with M conf.set(config.BLACKLIST_KILL_ENABLED, false) blacklist.updateBlacklistForFetchFailure("hostA", exec = "1") - verify(allocationClientMock, never).killExecutors(any(), any(), any()) + verify(allocationClientMock, never).killExecutors(any(), any(), any(), any()) verify(allocationClientMock, never).killExecutorsOnHost(any()) + assert(blacklist.nodeToBlacklistedExecs.contains("hostA")) + assert(blacklist.nodeToBlacklistedExecs("hostA").contains("1")) + // Enable auto-kill. Blacklist an executor and make sure killExecutors is called. conf.set(config.BLACKLIST_KILL_ENABLED, true) blacklist = new BlacklistTracker(listenerBusMock, conf, Some(allocationClientMock), clock) clock.advance(1000) blacklist.updateBlacklistForFetchFailure("hostA", exec = "1") - verify(allocationClientMock).killExecutors(Seq("1"), true, true) + verify(allocationClientMock).killExecutors(Seq("1"), false, false, true) verify(allocationClientMock, never).killExecutorsOnHost(any()) assert(blacklist.executorIdToBlacklistStatus.contains("1")) @@ -589,6 +592,8 @@ class BlacklistTrackerSuite extends SparkFunSuite with BeforeAndAfterEach with M 1000 + blacklist.BLACKLIST_TIMEOUT_MILLIS) assert(blacklist.nextExpiryTime === 1000 + blacklist.BLACKLIST_TIMEOUT_MILLIS) assert(blacklist.nodeIdToBlacklistExpiryTime.isEmpty) + assert(blacklist.nodeToBlacklistedExecs.contains("hostA")) + assert(blacklist.nodeToBlacklistedExecs("hostA").contains("1")) // Enable external shuffle service to see if all the executors on this node will be killed. conf.set(config.SHUFFLE_SERVICE_ENABLED, true) diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index d812b5bd92c1b..8b6ec37625eec 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -2146,6 +2146,58 @@ class DAGSchedulerSuite extends SparkFunSuite with LocalSparkContext with TimeLi assertDataStructuresEmpty() } + test("Trigger mapstage's job listener in submitMissingTasks") { + val rdd1 = new MyRDD(sc, 2, Nil) + val dep1 = new ShuffleDependency(rdd1, new HashPartitioner(2)) + val rdd2 = new MyRDD(sc, 2, List(dep1), tracker = mapOutputTracker) + val dep2 = new ShuffleDependency(rdd2, new HashPartitioner(2)) + + val listener1 = new SimpleListener + val listener2 = new SimpleListener + + submitMapStage(dep1, listener1) + submitMapStage(dep2, listener2) + + // Complete the stage0. + assert(taskSets(0).stageId === 0) + complete(taskSets(0), Seq( + (Success, makeMapStatus("hostA", rdd1.partitions.length)), + (Success, makeMapStatus("hostB", rdd1.partitions.length)))) + assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet === + HashSet(makeBlockManagerId("hostA"), makeBlockManagerId("hostB"))) + assert(listener1.results.size === 1) + + // When attempting stage1, trigger a fetch failure. + assert(taskSets(1).stageId === 1) + complete(taskSets(1), Seq( + (Success, makeMapStatus("hostC", rdd2.partitions.length)), + (FetchFailed(makeBlockManagerId("hostA"), dep1.shuffleId, 0, 0, "ignored"), null))) + scheduler.resubmitFailedStages() + // Stage1 listener should not have a result yet + assert(listener2.results.size === 0) + + // Speculative task succeeded in stage1. + runEvent(makeCompletionEvent( + taskSets(1).tasks(1), + Success, + makeMapStatus("hostD", rdd2.partitions.length))) + // stage1 listener still should not have a result, though there's no missing partitions + // in it. Because stage1 has been failed and is not inside `runningStages` at this moment. + assert(listener2.results.size === 0) + + // Stage0 should now be running as task set 2; make its task succeed + assert(taskSets(2).stageId === 0) + complete(taskSets(2), Seq( + (Success, makeMapStatus("hostC", rdd2.partitions.length)))) + assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet === + Set(makeBlockManagerId("hostC"), makeBlockManagerId("hostB"))) + + // After stage0 is finished, stage1 will be submitted and found there is no missing + // partitions in it. Then listener got triggered. + assert(listener2.results.size === 1) + assertDataStructuresEmpty() + } + /** * In this test, we run a map stage where one of the executors fails but we still receive a * "zombie" complete message from that executor. We want to make sure the stage is not reported diff --git a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala index 03b1903902491..158c9eb75f2b6 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala @@ -35,6 +35,7 @@ import org.scalatest.BeforeAndAfter import org.apache.spark._ import org.apache.spark.internal.io.{FileCommitProtocol, HadoopMapRedCommitProtocol, SparkHadoopWriterUtils} import org.apache.spark.rdd.{FakeOutputCommitter, RDD} +import org.apache.spark.shuffle.FetchFailedException import org.apache.spark.util.{ThreadUtils, Utils} /** @@ -153,7 +154,7 @@ class OutputCommitCoordinatorSuite extends SparkFunSuite with BeforeAndAfter { test("Job should not complete if all commits are denied") { // Create a mock OutputCommitCoordinator that denies all attempts to commit doReturn(false).when(outputCommitCoordinator).handleAskPermissionToCommit( - Matchers.any(), Matchers.any(), Matchers.any()) + Matchers.any(), Matchers.any(), Matchers.any(), Matchers.any()) val rdd: RDD[Int] = sc.parallelize(Seq(1), 1) def resultHandler(x: Int, y: Unit): Unit = {} val futureAction: SimpleFutureAction[Unit] = sc.submitJob[Int, Unit, Unit](rdd, @@ -169,45 +170,106 @@ class OutputCommitCoordinatorSuite extends SparkFunSuite with BeforeAndAfter { test("Only authorized committer failures can clear the authorized committer lock (SPARK-6614)") { val stage: Int = 1 + val stageAttempt: Int = 1 val partition: Int = 2 val authorizedCommitter: Int = 3 val nonAuthorizedCommitter: Int = 100 outputCommitCoordinator.stageStart(stage, maxPartitionId = 2) - assert(outputCommitCoordinator.canCommit(stage, partition, authorizedCommitter)) - assert(!outputCommitCoordinator.canCommit(stage, partition, nonAuthorizedCommitter)) + assert(outputCommitCoordinator.canCommit(stage, stageAttempt, partition, authorizedCommitter)) + assert(!outputCommitCoordinator.canCommit(stage, stageAttempt, partition, + nonAuthorizedCommitter)) // The non-authorized committer fails - outputCommitCoordinator.taskCompleted( - stage, partition, attemptNumber = nonAuthorizedCommitter, reason = TaskKilled("test")) + outputCommitCoordinator.taskCompleted(stage, stageAttempt, partition, + attemptNumber = nonAuthorizedCommitter, reason = TaskKilled("test")) // New tasks should still not be able to commit because the authorized committer has not failed - assert( - !outputCommitCoordinator.canCommit(stage, partition, nonAuthorizedCommitter + 1)) + assert(!outputCommitCoordinator.canCommit(stage, stageAttempt, partition, + nonAuthorizedCommitter + 1)) // The authorized committer now fails, clearing the lock - outputCommitCoordinator.taskCompleted( - stage, partition, attemptNumber = authorizedCommitter, reason = TaskKilled("test")) + outputCommitCoordinator.taskCompleted(stage, stageAttempt, partition, + attemptNumber = authorizedCommitter, reason = TaskKilled("test")) // A new task should now be allowed to become the authorized committer - assert( - outputCommitCoordinator.canCommit(stage, partition, nonAuthorizedCommitter + 2)) + assert(outputCommitCoordinator.canCommit(stage, stageAttempt, partition, + nonAuthorizedCommitter + 2)) // There can only be one authorized committer - assert( - !outputCommitCoordinator.canCommit(stage, partition, nonAuthorizedCommitter + 3)) - } - - test("Duplicate calls to canCommit from the authorized committer gets idempotent responses.") { - val rdd = sc.parallelize(Seq(1), 1) - sc.runJob(rdd, OutputCommitFunctions(tempDir.getAbsolutePath).callCanCommitMultipleTimes _, - 0 until rdd.partitions.size) + assert(!outputCommitCoordinator.canCommit(stage, stageAttempt, partition, + nonAuthorizedCommitter + 3)) } test("SPARK-19631: Do not allow failed attempts to be authorized for committing") { val stage: Int = 1 + val stageAttempt: Int = 1 val partition: Int = 1 val failedAttempt: Int = 0 outputCommitCoordinator.stageStart(stage, maxPartitionId = 1) - outputCommitCoordinator.taskCompleted(stage, partition, attemptNumber = failedAttempt, + outputCommitCoordinator.taskCompleted(stage, stageAttempt, partition, + attemptNumber = failedAttempt, reason = ExecutorLostFailure("0", exitCausedByApp = true, None)) - assert(!outputCommitCoordinator.canCommit(stage, partition, failedAttempt)) - assert(outputCommitCoordinator.canCommit(stage, partition, failedAttempt + 1)) + assert(!outputCommitCoordinator.canCommit(stage, stageAttempt, partition, failedAttempt)) + assert(outputCommitCoordinator.canCommit(stage, stageAttempt, partition, failedAttempt + 1)) + } + + test("SPARK-24589: Differentiate tasks from different stage attempts") { + var stage = 1 + val taskAttempt = 1 + val partition = 1 + + outputCommitCoordinator.stageStart(stage, maxPartitionId = 1) + assert(outputCommitCoordinator.canCommit(stage, 1, partition, taskAttempt)) + assert(!outputCommitCoordinator.canCommit(stage, 2, partition, taskAttempt)) + + // Fail the task in the first attempt, the task in the second attempt should succeed. + stage += 1 + outputCommitCoordinator.stageStart(stage, maxPartitionId = 1) + outputCommitCoordinator.taskCompleted(stage, 1, partition, taskAttempt, + ExecutorLostFailure("0", exitCausedByApp = true, None)) + assert(!outputCommitCoordinator.canCommit(stage, 1, partition, taskAttempt)) + assert(outputCommitCoordinator.canCommit(stage, 2, partition, taskAttempt)) + + // Commit the 1st attempt, fail the 2nd attempt, make sure 3rd attempt cannot commit, + // then fail the 1st attempt and make sure the 4th one can commit again. + stage += 1 + outputCommitCoordinator.stageStart(stage, maxPartitionId = 1) + assert(outputCommitCoordinator.canCommit(stage, 1, partition, taskAttempt)) + outputCommitCoordinator.taskCompleted(stage, 2, partition, taskAttempt, + ExecutorLostFailure("0", exitCausedByApp = true, None)) + assert(!outputCommitCoordinator.canCommit(stage, 3, partition, taskAttempt)) + outputCommitCoordinator.taskCompleted(stage, 1, partition, taskAttempt, + ExecutorLostFailure("0", exitCausedByApp = true, None)) + assert(outputCommitCoordinator.canCommit(stage, 4, partition, taskAttempt)) + } + + test("SPARK-24589: Make sure stage state is cleaned up") { + // Normal application without stage failures. + sc.parallelize(1 to 100, 100) + .map { i => (i % 10, i) } + .reduceByKey(_ + _) + .collect() + + assert(sc.dagScheduler.outputCommitCoordinator.isEmpty) + + // Force failures in a few tasks so that a stage is retried. Collect the ID of the failing + // stage so that we can check the state of the output committer. + val retriedStage = sc.parallelize(1 to 100, 10) + .map { i => (i % 10, i) } + .reduceByKey { case (_, _) => + val ctx = TaskContext.get() + if (ctx.stageAttemptNumber() == 0) { + throw new FetchFailedException(SparkEnv.get.blockManager.blockManagerId, 1, 1, 1, + new Exception("Failure for test.")) + } else { + ctx.stageId() + } + } + .collect() + .map { case (k, v) => v } + .toSet + + assert(retriedStage.size === 1) + assert(sc.dagScheduler.outputCommitCoordinator.isEmpty) + verify(sc.env.outputCommitCoordinator, times(2)) + .stageStart(Matchers.eq(retriedStage.head), Matchers.any()) + verify(sc.env.outputCommitCoordinator).stageEnd(Matchers.eq(retriedStage.head)) } } @@ -243,16 +305,6 @@ private case class OutputCommitFunctions(tempDirPath: String) { if (ctx.attemptNumber == 0) failingOutputCommitter else successfulOutputCommitter) } - // Receiver should be idempotent for AskPermissionToCommitOutput - def callCanCommitMultipleTimes(iter: Iterator[Int]): Unit = { - val ctx = TaskContext.get() - val canCommit1 = SparkEnv.get.outputCommitCoordinator - .canCommit(ctx.stageId(), ctx.partitionId(), ctx.attemptNumber()) - val canCommit2 = SparkEnv.get.outputCommitCoordinator - .canCommit(ctx.stageId(), ctx.partitionId(), ctx.attemptNumber()) - assert(canCommit1 && canCommit2) - } - private def runCommitWithProvidedCommitter( ctx: TaskContext, iter: Iterator[Int], diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala index 1beb36afa95f0..6ffd1e84f7adb 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.scheduler +import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput} import java.util.concurrent.Semaphore import scala.collection.JavaConverters._ @@ -48,7 +49,7 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match bus.metrics.metricRegistry.counter(s"queue.$SHARED_QUEUE.numDroppedEvents").getCount } - private def queueSize(bus: LiveListenerBus): Int = { + private def sharedQueueSize(bus: LiveListenerBus): Int = { bus.metrics.metricRegistry.getGauges().get(s"queue.$SHARED_QUEUE.size").getValue() .asInstanceOf[Int] } @@ -73,12 +74,11 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match val conf = new SparkConf() val counter = new BasicJobCounter val bus = new LiveListenerBus(conf) - bus.addToSharedQueue(counter) // Metrics are initially empty. assert(bus.metrics.numEventsPosted.getCount === 0) assert(numDroppedEvents(bus) === 0) - assert(queueSize(bus) === 0) + assert(bus.queuedEvents.size === 0) assert(eventProcessingTimeCount(bus) === 0) // Post five events: @@ -87,7 +87,10 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match // Five messages should be marked as received and queued, but no messages should be posted to // listeners yet because the the listener bus hasn't been started. assert(bus.metrics.numEventsPosted.getCount === 5) - assert(queueSize(bus) === 5) + assert(bus.queuedEvents.size === 5) + + // Add the counter to the bus after messages have been queued for later delivery. + bus.addToSharedQueue(counter) assert(counter.count === 0) // Starting listener bus should flush all buffered events @@ -95,9 +98,12 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match Mockito.verify(mockMetricsSystem).registerSource(bus.metrics) bus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS) assert(counter.count === 5) - assert(queueSize(bus) === 0) + assert(sharedQueueSize(bus) === 0) assert(eventProcessingTimeCount(bus) === 5) + // After the bus is started, there should be no more queued events. + assert(bus.queuedEvents === null) + // After listener bus has stopped, posting events should not increment counter bus.stop() (1 to 5).foreach { _ => bus.post(SparkListenerJobEnd(0, jobCompletionTime, JobSucceeded)) } @@ -188,18 +194,18 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match // Post a message to the listener bus and wait for processing to begin: bus.post(SparkListenerJobEnd(0, jobCompletionTime, JobSucceeded)) listenerStarted.acquire() - assert(queueSize(bus) === 0) + assert(sharedQueueSize(bus) === 0) assert(numDroppedEvents(bus) === 0) // If we post an additional message then it should remain in the queue because the listener is // busy processing the first event: bus.post(SparkListenerJobEnd(0, jobCompletionTime, JobSucceeded)) - assert(queueSize(bus) === 1) + assert(sharedQueueSize(bus) === 1) assert(numDroppedEvents(bus) === 0) // The queue is now full, so any additional events posted to the listener will be dropped: bus.post(SparkListenerJobEnd(0, jobCompletionTime, JobSucceeded)) - assert(queueSize(bus) === 1) + assert(sharedQueueSize(bus) === 1) assert(numDroppedEvents(bus) === 1) // Allow the the remaining events to be processed so we can stop the listener bus: @@ -289,10 +295,13 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match val listener = new SaveStageAndTaskInfo sc.addSparkListener(listener) sc.addSparkListener(new StatsReportListener) - // just to make sure some of the tasks take a noticeable amount of time + // just to make sure some of the tasks and their deserialization take a noticeable + // amount of time + val slowDeserializable = new SlowDeserializable val w = { i: Int => if (i == 0) { Thread.sleep(100) + slowDeserializable.use() } i } @@ -480,6 +489,48 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match assert(bus.findListenersByClass[BasicJobCounter]().isEmpty) } + Seq(true, false).foreach { throwInterruptedException => + val suffix = if (throwInterruptedException) "throw interrupt" else "set Thread interrupted" + test(s"interrupt within listener is handled correctly: $suffix") { + val conf = new SparkConf(false) + .set(LISTENER_BUS_EVENT_QUEUE_CAPACITY, 5) + val bus = new LiveListenerBus(conf) + val counter1 = new BasicJobCounter() + val counter2 = new BasicJobCounter() + val interruptingListener1 = new InterruptingListener(throwInterruptedException) + val interruptingListener2 = new InterruptingListener(throwInterruptedException) + bus.addToSharedQueue(counter1) + bus.addToSharedQueue(interruptingListener1) + bus.addToStatusQueue(counter2) + bus.addToEventLogQueue(interruptingListener2) + assert(bus.activeQueues() === Set(SHARED_QUEUE, APP_STATUS_QUEUE, EVENT_LOG_QUEUE)) + assert(bus.findListenersByClass[BasicJobCounter]().size === 2) + assert(bus.findListenersByClass[InterruptingListener]().size === 2) + + bus.start(mockSparkContext, mockMetricsSystem) + + // after we post one event, both interrupting listeners should get removed, and the + // event log queue should be removed + bus.post(SparkListenerJobEnd(0, jobCompletionTime, JobSucceeded)) + bus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS) + assert(bus.activeQueues() === Set(SHARED_QUEUE, APP_STATUS_QUEUE)) + assert(bus.findListenersByClass[BasicJobCounter]().size === 2) + assert(bus.findListenersByClass[InterruptingListener]().size === 0) + assert(counter1.count === 1) + assert(counter2.count === 1) + + // posting more events should be fine, they'll just get processed from the OK queue. + (0 until 5).foreach { _ => bus.post(SparkListenerJobEnd(0, jobCompletionTime, JobSucceeded)) } + bus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS) + assert(counter1.count === 6) + assert(counter2.count === 6) + + // Make sure stopping works -- this requires putting a poison pill in all active queues, which + // would fail if our interrupted queue was still active, as its queue would be full. + bus.stop() + } + } + /** * Assert that the given list of numbers has an average that is greater than zero. */ @@ -538,6 +589,18 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { throw new Exception } } + /** + * A simple listener that interrupts on job end. + */ + private class InterruptingListener(val throwInterruptedException: Boolean) extends SparkListener { + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { + if (throwInterruptedException) { + throw new InterruptedException("got interrupted") + } else { + Thread.currentThread().interrupt() + } + } + } } // These classes can't be declared inside of the SparkListenerSuite class because we don't want @@ -578,3 +641,12 @@ private class FirehoseListenerThatAcceptsSparkConf(conf: SparkConf) extends Spar case _ => } } + +private class SlowDeserializable extends Externalizable { + + override def writeExternal(out: ObjectOutput): Unit = { } + + override def readExternal(in: ObjectInput): Unit = Thread.sleep(1) + + def use(): Unit = { } +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala index a1d9085fa085d..aa9c36c0aaacb 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala @@ -29,6 +29,7 @@ import org.apache.spark.memory.TaskMemoryManager import org.apache.spark.metrics.source.JvmSource import org.apache.spark.network.util.JavaUtils import org.apache.spark.rdd.RDD +import org.apache.spark.shuffle.FetchFailedException import org.apache.spark.util._ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSparkContext { @@ -158,6 +159,30 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark assert(attemptIdsWithFailedTask.toSet === Set(0, 1)) } + test("TaskContext.stageAttemptNumber getter") { + sc = new SparkContext("local[1,2]", "test") + + // Check stageAttemptNumbers are 0 for initial stage + val stageAttemptNumbers = sc.parallelize(Seq(1, 2), 2).mapPartitions { _ => + Seq(TaskContext.get().stageAttemptNumber()).iterator + }.collect() + assert(stageAttemptNumbers.toSet === Set(0)) + + // Check stageAttemptNumbers that are resubmitted when tasks have FetchFailedException + val stageAttemptNumbersWithFailedStage = + sc.parallelize(Seq(1, 2, 3, 4), 4).repartition(1).mapPartitions { _ => + val stageAttemptNumber = TaskContext.get().stageAttemptNumber() + if (stageAttemptNumber < 2) { + // Throw FetchFailedException to explicitly trigger stage resubmission. A normal exception + // will only trigger task resubmission in the same stage. + throw new FetchFailedException(null, 0, 0, 0, "Fake") + } + Seq(stageAttemptNumber).iterator + }.collect() + + assert(stageAttemptNumbersWithFailedStage.toSet === Set(2)) + } + test("accumulators are updated on exception failures") { // This means use 1 core and 4 max task failures sc = new SparkContext("local[1,4]", "test") @@ -190,7 +215,7 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark // accumulator updates from it. val taskMetrics = TaskMetrics.empty val task = new Task[Int](0, 0, 0) { - context = new TaskContextImpl(0, 0, 0L, 0, + context = new TaskContextImpl(0, 0, 0, 0L, 0, new TaskMemoryManager(SparkEnv.get.memoryManager, 0L), new Properties, SparkEnv.get.metricsSystem, @@ -213,7 +238,7 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark // accumulator updates from it. val taskMetrics = TaskMetrics.registered val task = new Task[Int](0, 0, 0) { - context = new TaskContextImpl(0, 0, 0L, 0, + context = new TaskContextImpl(0, 0, 0, 0L, 0, new TaskMemoryManager(SparkEnv.get.memoryManager, 0L), new Properties, SparkEnv.get.metricsSystem, diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala index 6003899bb7bef..33f2ea1c94e75 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala @@ -917,4 +917,108 @@ class TaskSchedulerImplSuite extends SparkFunSuite with LocalSparkContext with B taskScheduler.initialize(new FakeSchedulerBackend) } } + + test("Completions in zombie tasksets update status of non-zombie taskset") { + val taskScheduler = setupSchedulerWithMockTaskSetBlacklist() + val valueSer = SparkEnv.get.serializer.newInstance() + + def completeTaskSuccessfully(tsm: TaskSetManager, partition: Int): Unit = { + val indexInTsm = tsm.partitionToIndex(partition) + val matchingTaskInfo = tsm.taskAttempts.flatten.filter(_.index == indexInTsm).head + val result = new DirectTaskResult[Int](valueSer.serialize(1), Seq()) + tsm.handleSuccessfulTask(matchingTaskInfo.taskId, result) + } + + // Submit a task set, have it fail with a fetch failed, and then re-submit the task attempt, + // two times, so we have three active task sets for one stage. (For this to really happen, + // you'd need the previous stage to also get restarted, and then succeed, in between each + // attempt, but that happens outside what we're mocking here.) + val zombieAttempts = (0 until 2).map { stageAttempt => + val attempt = FakeTask.createTaskSet(10, stageAttemptId = stageAttempt) + taskScheduler.submitTasks(attempt) + val tsm = taskScheduler.taskSetManagerForAttempt(0, stageAttempt).get + val offers = (0 until 10).map{ idx => WorkerOffer(s"exec-$idx", s"host-$idx", 1) } + taskScheduler.resourceOffers(offers) + assert(tsm.runningTasks === 10) + // fail attempt + tsm.handleFailedTask(tsm.taskAttempts.head.head.taskId, TaskState.FAILED, + FetchFailed(null, 0, 0, 0, "fetch failed")) + // the attempt is a zombie, but the tasks are still running (this could be true even if + // we actively killed those tasks, as killing is best-effort) + assert(tsm.isZombie) + assert(tsm.runningTasks === 9) + tsm + } + + // we've now got 2 zombie attempts, each with 9 tasks still active. Submit the 3rd attempt for + // the stage, but this time with insufficient resources so not all tasks are active. + + val finalAttempt = FakeTask.createTaskSet(10, stageAttemptId = 2) + taskScheduler.submitTasks(finalAttempt) + val finalTsm = taskScheduler.taskSetManagerForAttempt(0, 2).get + val offers = (0 until 5).map{ idx => WorkerOffer(s"exec-$idx", s"host-$idx", 1) } + val finalAttemptLaunchedPartitions = taskScheduler.resourceOffers(offers).flatten.map { task => + finalAttempt.tasks(task.index).partitionId + }.toSet + assert(finalTsm.runningTasks === 5) + assert(!finalTsm.isZombie) + + // We simulate late completions from our zombie tasksets, corresponding to all the pending + // partitions in our final attempt. This means we're only waiting on the tasks we've already + // launched. + val finalAttemptPendingPartitions = (0 until 10).toSet.diff(finalAttemptLaunchedPartitions) + finalAttemptPendingPartitions.foreach { partition => + completeTaskSuccessfully(zombieAttempts(0), partition) + } + + // If there is another resource offer, we shouldn't run anything. Though our final attempt + // used to have pending tasks, now those tasks have been completed by zombie attempts. The + // remaining tasks to compute are already active in the non-zombie attempt. + assert( + taskScheduler.resourceOffers(IndexedSeq(WorkerOffer("exec-1", "host-1", 1))).flatten.isEmpty) + + val remainingTasks = finalAttemptLaunchedPartitions.toIndexedSeq.sorted + + // finally, if we finish the remaining partitions from a mix of tasksets, all attempts should be + // marked as zombie. + // for each of the remaining tasks, find the tasksets with an active copy of the task, and + // finish the task. + remainingTasks.foreach { partition => + val tsm = if (partition == 0) { + // we failed this task on both zombie attempts, this one is only present in the latest + // taskset + finalTsm + } else { + // should be active in every taskset. We choose a zombie taskset just to make sure that + // we transition the active taskset correctly even if the final completion comes + // from a zombie. + zombieAttempts(partition % 2) + } + completeTaskSuccessfully(tsm, partition) + } + + assert(finalTsm.isZombie) + + // no taskset has completed all of its tasks, so no updates to the blacklist tracker yet + verify(blacklist, never).updateBlacklistForSuccessfulTaskSet(anyInt(), anyInt(), anyObject()) + + // finally, lets complete all the tasks. We simulate failures in attempt 1, but everything + // else succeeds, to make sure we get the right updates to the blacklist in all cases. + (zombieAttempts ++ Seq(finalTsm)).foreach { tsm => + val stageAttempt = tsm.taskSet.stageAttemptId + tsm.runningTasksSet.foreach { index => + if (stageAttempt == 1) { + tsm.handleFailedTask(tsm.taskInfos(index).taskId, TaskState.FAILED, TaskResultLost) + } else { + val result = new DirectTaskResult[Int](valueSer.serialize(1), Seq()) + tsm.handleSuccessfulTask(tsm.taskInfos(index).taskId, result) + } + } + + // we update the blacklist for the stage attempts with all successful tasks. Even though + // some tasksets had failures, we still consider them all successful from a blacklisting + // perspective, as the failures weren't from a problem w/ the tasks themselves. + verify(blacklist).updateBlacklistForSuccessfulTaskSet(meq(0), meq(stageAttempt), anyObject()) + } + } } diff --git a/core/src/test/scala/org/apache/spark/security/SocketAuthHelperSuite.scala b/core/src/test/scala/org/apache/spark/security/SocketAuthHelperSuite.scala new file mode 100644 index 0000000000000..e57cb701b6284 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/security/SocketAuthHelperSuite.scala @@ -0,0 +1,97 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.security + +import java.io.Closeable +import java.net._ + +import org.apache.spark.{SparkConf, SparkFunSuite} +import org.apache.spark.internal.config._ +import org.apache.spark.util.Utils + +class SocketAuthHelperSuite extends SparkFunSuite { + + private val conf = new SparkConf() + private val authHelper = new SocketAuthHelper(conf) + + test("successful auth") { + Utils.tryWithResource(new ServerThread()) { server => + Utils.tryWithResource(server.createClient()) { client => + authHelper.authToServer(client) + server.close() + server.join() + assert(server.error == null) + assert(server.authenticated) + } + } + } + + test("failed auth") { + Utils.tryWithResource(new ServerThread()) { server => + Utils.tryWithResource(server.createClient()) { client => + val badHelper = new SocketAuthHelper(new SparkConf().set(AUTH_SECRET_BIT_LENGTH, 128)) + intercept[IllegalArgumentException] { + badHelper.authToServer(client) + } + server.close() + server.join() + assert(server.error != null) + assert(!server.authenticated) + } + } + } + + private class ServerThread extends Thread with Closeable { + + private val ss = new ServerSocket() + ss.bind(new InetSocketAddress(InetAddress.getLoopbackAddress(), 0)) + + @volatile var error: Exception = _ + @volatile var authenticated = false + + setDaemon(true) + start() + + def createClient(): Socket = { + new Socket(InetAddress.getLoopbackAddress(), ss.getLocalPort()) + } + + override def run(): Unit = { + var clientConn: Socket = null + try { + clientConn = ss.accept() + authHelper.authClient(clientConn) + authenticated = true + } catch { + case e: Exception => + error = e + } finally { + Option(clientConn).foreach(_.close()) + } + } + + override def close(): Unit = { + try { + ss.close() + } finally { + interrupt() + } + } + + } + +} diff --git a/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala b/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala index 997c7de8dd02b..eb03ef3b3b5e3 100644 --- a/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala @@ -195,7 +195,9 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { val s1Tasks = createTasks(4, execIds) s1Tasks.foreach { task => - listener.onTaskStart(SparkListenerTaskStart(stages.head.stageId, stages.head.attemptId, task)) + listener.onTaskStart(SparkListenerTaskStart(stages.head.stageId, + stages.head.attemptNumber, + task)) } assert(store.count(classOf[TaskDataWrapper]) === s1Tasks.size) @@ -211,55 +213,53 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { s1Tasks.foreach { task => check[TaskDataWrapper](task.taskId) { wrapper => - assert(wrapper.info.taskId === task.taskId) + assert(wrapper.taskId === task.taskId) assert(wrapper.stageId === stages.head.stageId) assert(wrapper.stageAttemptId === stages.head.attemptId) - assert(Arrays.equals(wrapper.stage, Array(stages.head.stageId, stages.head.attemptId))) - - val runtime = Array[AnyRef](stages.head.stageId: JInteger, stages.head.attemptId: JInteger, - -1L: JLong) - assert(Arrays.equals(wrapper.runtime, runtime)) - - assert(wrapper.info.index === task.index) - assert(wrapper.info.attempt === task.attemptNumber) - assert(wrapper.info.launchTime === new Date(task.launchTime)) - assert(wrapper.info.executorId === task.executorId) - assert(wrapper.info.host === task.host) - assert(wrapper.info.status === task.status) - assert(wrapper.info.taskLocality === task.taskLocality.toString()) - assert(wrapper.info.speculative === task.speculative) + assert(wrapper.index === task.index) + assert(wrapper.attempt === task.attemptNumber) + assert(wrapper.launchTime === task.launchTime) + assert(wrapper.executorId === task.executorId) + assert(wrapper.host === task.host) + assert(wrapper.status === task.status) + assert(wrapper.taskLocality === task.taskLocality.toString()) + assert(wrapper.speculative === task.speculative) } } - // Send executor metrics update. Only update one metric to avoid a lot of boilerplate code. - s1Tasks.foreach { task => - val accum = new AccumulableInfo(1L, Some(InternalAccumulator.MEMORY_BYTES_SPILLED), - Some(1L), None, true, false, None) - listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate( - task.executorId, - Seq((task.taskId, stages.head.stageId, stages.head.attemptId, Seq(accum))))) - } + // Send two executor metrics update. Only update one metric to avoid a lot of boilerplate code. + // The tasks are distributed among the two executors, so the executor-level metrics should + // hold half of the cummulative value of the metric being updated. + Seq(1L, 2L).foreach { value => + s1Tasks.foreach { task => + val accum = new AccumulableInfo(1L, Some(InternalAccumulator.MEMORY_BYTES_SPILLED), + Some(value), None, true, false, None) + listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate( + task.executorId, + Seq((task.taskId, stages.head.stageId, stages.head.attemptNumber, Seq(accum))))) + } - check[StageDataWrapper](key(stages.head)) { stage => - assert(stage.info.memoryBytesSpilled === s1Tasks.size) - } + check[StageDataWrapper](key(stages.head)) { stage => + assert(stage.info.memoryBytesSpilled === s1Tasks.size * value) + } - val execs = store.view(classOf[ExecutorStageSummaryWrapper]).index("stage") - .first(key(stages.head)).last(key(stages.head)).asScala.toSeq - assert(execs.size > 0) - execs.foreach { exec => - assert(exec.info.memoryBytesSpilled === s1Tasks.size / 2) + val execs = store.view(classOf[ExecutorStageSummaryWrapper]).index("stage") + .first(key(stages.head)).last(key(stages.head)).asScala.toSeq + assert(execs.size > 0) + execs.foreach { exec => + assert(exec.info.memoryBytesSpilled === s1Tasks.size * value / 2) + } } // Fail one of the tasks, re-start it. time += 1 s1Tasks.head.markFinished(TaskState.FAILED, time) - listener.onTaskEnd(SparkListenerTaskEnd(stages.head.stageId, stages.head.attemptId, + listener.onTaskEnd(SparkListenerTaskEnd(stages.head.stageId, stages.head.attemptNumber, "taskType", TaskResultLost, s1Tasks.head, null)) time += 1 val reattempt = newAttempt(s1Tasks.head, nextTaskId()) - listener.onTaskStart(SparkListenerTaskStart(stages.head.stageId, stages.head.attemptId, + listener.onTaskStart(SparkListenerTaskStart(stages.head.stageId, stages.head.attemptNumber, reattempt)) assert(store.count(classOf[TaskDataWrapper]) === s1Tasks.size + 1) @@ -275,13 +275,13 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { } check[TaskDataWrapper](s1Tasks.head.taskId) { task => - assert(task.info.status === s1Tasks.head.status) - assert(task.info.errorMessage == Some(TaskResultLost.toErrorString)) + assert(task.status === s1Tasks.head.status) + assert(task.errorMessage == Some(TaskResultLost.toErrorString)) } check[TaskDataWrapper](reattempt.taskId) { task => - assert(task.info.index === s1Tasks.head.index) - assert(task.info.attempt === reattempt.attemptNumber) + assert(task.index === s1Tasks.head.index) + assert(task.attempt === reattempt.attemptNumber) } // Kill one task, restart it. @@ -289,7 +289,7 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { val killed = s1Tasks.drop(1).head killed.finishTime = time killed.failed = true - listener.onTaskEnd(SparkListenerTaskEnd(stages.head.stageId, stages.head.attemptId, + listener.onTaskEnd(SparkListenerTaskEnd(stages.head.stageId, stages.head.attemptNumber, "taskType", TaskKilled("killed"), killed, null)) check[JobDataWrapper](1) { job => @@ -303,21 +303,21 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { } check[TaskDataWrapper](killed.taskId) { task => - assert(task.info.index === killed.index) - assert(task.info.errorMessage === Some("killed")) + assert(task.index === killed.index) + assert(task.errorMessage === Some("killed")) } // Start a new attempt and finish it with TaskCommitDenied, make sure it's handled like a kill. time += 1 val denied = newAttempt(killed, nextTaskId()) val denyReason = TaskCommitDenied(1, 1, 1) - listener.onTaskStart(SparkListenerTaskStart(stages.head.stageId, stages.head.attemptId, + listener.onTaskStart(SparkListenerTaskStart(stages.head.stageId, stages.head.attemptNumber, denied)) time += 1 denied.finishTime = time denied.failed = true - listener.onTaskEnd(SparkListenerTaskEnd(stages.head.stageId, stages.head.attemptId, + listener.onTaskEnd(SparkListenerTaskEnd(stages.head.stageId, stages.head.attemptNumber, "taskType", denyReason, denied, null)) check[JobDataWrapper](1) { job => @@ -331,13 +331,13 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { } check[TaskDataWrapper](denied.taskId) { task => - assert(task.info.index === killed.index) - assert(task.info.errorMessage === Some(denyReason.toErrorString)) + assert(task.index === killed.index) + assert(task.errorMessage === Some(denyReason.toErrorString)) } // Start a new attempt. val reattempt2 = newAttempt(denied, nextTaskId()) - listener.onTaskStart(SparkListenerTaskStart(stages.head.stageId, stages.head.attemptId, + listener.onTaskStart(SparkListenerTaskStart(stages.head.stageId, stages.head.attemptNumber, reattempt2)) // Succeed all tasks in stage 1. @@ -350,7 +350,7 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { time += 1 pending.foreach { task => task.markFinished(TaskState.FINISHED, time) - listener.onTaskEnd(SparkListenerTaskEnd(stages.head.stageId, stages.head.attemptId, + listener.onTaskEnd(SparkListenerTaskEnd(stages.head.stageId, stages.head.attemptNumber, "taskType", Success, task, s1Metrics)) } @@ -370,10 +370,10 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { pending.foreach { task => check[TaskDataWrapper](task.taskId) { wrapper => - assert(wrapper.info.errorMessage === None) - assert(wrapper.info.taskMetrics.get.executorCpuTime === 2L) - assert(wrapper.info.taskMetrics.get.executorRunTime === 4L) - assert(wrapper.info.duration === Some(task.duration)) + assert(wrapper.errorMessage === None) + assert(wrapper.executorCpuTime === 2L) + assert(wrapper.executorRunTime === 4L) + assert(wrapper.duration === task.duration) } } @@ -414,13 +414,15 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { time += 1 val s2Tasks = createTasks(4, execIds) s2Tasks.foreach { task => - listener.onTaskStart(SparkListenerTaskStart(stages.last.stageId, stages.last.attemptId, task)) + listener.onTaskStart(SparkListenerTaskStart(stages.last.stageId, + stages.last.attemptNumber, + task)) } time += 1 s2Tasks.foreach { task => task.markFinished(TaskState.FAILED, time) - listener.onTaskEnd(SparkListenerTaskEnd(stages.last.stageId, stages.last.attemptId, + listener.onTaskEnd(SparkListenerTaskEnd(stages.last.stageId, stages.last.attemptNumber, "taskType", TaskResultLost, task, null)) } @@ -455,7 +457,7 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { // - Re-submit stage 2, all tasks, and succeed them and the stage. val oldS2 = stages.last - val newS2 = new StageInfo(oldS2.stageId, oldS2.attemptId + 1, oldS2.name, oldS2.numTasks, + val newS2 = new StageInfo(oldS2.stageId, oldS2.attemptNumber + 1, oldS2.name, oldS2.numTasks, oldS2.rddInfos, oldS2.parentIds, oldS2.details, oldS2.taskMetrics) time += 1 @@ -466,14 +468,14 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { val newS2Tasks = createTasks(4, execIds) newS2Tasks.foreach { task => - listener.onTaskStart(SparkListenerTaskStart(newS2.stageId, newS2.attemptId, task)) + listener.onTaskStart(SparkListenerTaskStart(newS2.stageId, newS2.attemptNumber, task)) } time += 1 newS2Tasks.foreach { task => task.markFinished(TaskState.FINISHED, time) - listener.onTaskEnd(SparkListenerTaskEnd(newS2.stageId, newS2.attemptId, "taskType", Success, - task, null)) + listener.onTaskEnd(SparkListenerTaskEnd(newS2.stageId, newS2.attemptNumber, "taskType", + Success, task, null)) } time += 1 @@ -522,14 +524,15 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { val j2s2Tasks = createTasks(4, execIds) j2s2Tasks.foreach { task => - listener.onTaskStart(SparkListenerTaskStart(j2Stages.last.stageId, j2Stages.last.attemptId, + listener.onTaskStart(SparkListenerTaskStart(j2Stages.last.stageId, + j2Stages.last.attemptNumber, task)) } time += 1 j2s2Tasks.foreach { task => task.markFinished(TaskState.FINISHED, time) - listener.onTaskEnd(SparkListenerTaskEnd(j2Stages.last.stageId, j2Stages.last.attemptId, + listener.onTaskEnd(SparkListenerTaskEnd(j2Stages.last.stageId, j2Stages.last.attemptNumber, "taskType", Success, task, null)) } @@ -888,6 +891,27 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { assert(store.count(classOf[StageDataWrapper]) === 3) assert(store.count(classOf[RDDOperationGraphWrapper]) === 3) + val dropped = stages.drop(1).head + + // Cache some quantiles by calling AppStatusStore.taskSummary(). For quantiles to be + // calculated, we need at least one finished task. The code in AppStatusStore uses + // `executorRunTime` to detect valid tasks, so that metric needs to be updated in the + // task end event. + time += 1 + val task = createTasks(1, Array("1")).head + listener.onTaskStart(SparkListenerTaskStart(dropped.stageId, dropped.attemptId, task)) + + time += 1 + task.markFinished(TaskState.FINISHED, time) + val metrics = TaskMetrics.empty + metrics.setExecutorRunTime(42L) + listener.onTaskEnd(SparkListenerTaskEnd(dropped.stageId, dropped.attemptId, + "taskType", Success, task, metrics)) + + new AppStatusStore(store) + .taskSummary(dropped.stageId, dropped.attemptId, Array(0.25d, 0.50d, 0.75d)) + assert(store.count(classOf[CachedQuantile], "stage", key(dropped)) === 3) + stages.drop(1).foreach { s => time += 1 s.completionTime = Some(time) @@ -899,6 +923,7 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { intercept[NoSuchElementException] { store.read(classOf[StageDataWrapper], Array(2, 0)) } + assert(store.count(classOf[CachedQuantile], "stage", key(dropped)) === 0) val attempt2 = new StageInfo(3, 1, "stage3", 4, Nil, Nil, "details3") time += 1 @@ -919,13 +944,13 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { time += 1 val tasks = createTasks(2, Array("1")) tasks.foreach { task => - listener.onTaskStart(SparkListenerTaskStart(attempt2.stageId, attempt2.attemptId, task)) + listener.onTaskStart(SparkListenerTaskStart(attempt2.stageId, attempt2.attemptNumber, task)) } assert(store.count(classOf[TaskDataWrapper]) === 2) // Start a 3rd task. The finished tasks should be deleted. createTasks(1, Array("1")).foreach { task => - listener.onTaskStart(SparkListenerTaskStart(attempt2.stageId, attempt2.attemptId, task)) + listener.onTaskStart(SparkListenerTaskStart(attempt2.stageId, attempt2.attemptNumber, task)) } assert(store.count(classOf[TaskDataWrapper]) === 2) intercept[NoSuchElementException] { @@ -934,7 +959,7 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { // Start a 4th task. The first task should be deleted, even if it's still running. createTasks(1, Array("1")).foreach { task => - listener.onTaskStart(SparkListenerTaskStart(attempt2.stageId, attempt2.attemptId, task)) + listener.onTaskStart(SparkListenerTaskStart(attempt2.stageId, attempt2.attemptNumber, task)) } assert(store.count(classOf[TaskDataWrapper]) === 2) intercept[NoSuchElementException] { @@ -942,6 +967,165 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { } } + test("eviction should respect job completion time") { + val testConf = conf.clone().set(MAX_RETAINED_JOBS, 2) + val listener = new AppStatusListener(store, testConf, true) + + // Start job 1 and job 2 + time += 1 + listener.onJobStart(SparkListenerJobStart(1, time, Nil, null)) + time += 1 + listener.onJobStart(SparkListenerJobStart(2, time, Nil, null)) + + // Stop job 2 before job 1 + time += 1 + listener.onJobEnd(SparkListenerJobEnd(2, time, JobSucceeded)) + time += 1 + listener.onJobEnd(SparkListenerJobEnd(1, time, JobSucceeded)) + + // Start job 3 and job 2 should be evicted. + time += 1 + listener.onJobStart(SparkListenerJobStart(3, time, Nil, null)) + assert(store.count(classOf[JobDataWrapper]) === 2) + intercept[NoSuchElementException] { + store.read(classOf[JobDataWrapper], 2) + } + } + + test("eviction should respect stage completion time") { + val testConf = conf.clone().set(MAX_RETAINED_STAGES, 2) + val listener = new AppStatusListener(store, testConf, true) + + val stage1 = new StageInfo(1, 0, "stage1", 4, Nil, Nil, "details1") + val stage2 = new StageInfo(2, 0, "stage2", 4, Nil, Nil, "details2") + val stage3 = new StageInfo(3, 0, "stage3", 4, Nil, Nil, "details3") + + // Start stage 1 and stage 2 + time += 1 + stage1.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage1, new Properties())) + time += 1 + stage2.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage2, new Properties())) + + // Stop stage 2 before stage 1 + time += 1 + stage2.completionTime = Some(time) + listener.onStageCompleted(SparkListenerStageCompleted(stage2)) + time += 1 + stage1.completionTime = Some(time) + listener.onStageCompleted(SparkListenerStageCompleted(stage1)) + + // Start stage 3 and stage 2 should be evicted. + stage3.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage3, new Properties())) + assert(store.count(classOf[StageDataWrapper]) === 2) + intercept[NoSuchElementException] { + store.read(classOf[StageDataWrapper], Array(2, 0)) + } + } + + test("skipped stages should be evicted before completed stages") { + val testConf = conf.clone().set(MAX_RETAINED_STAGES, 2) + val listener = new AppStatusListener(store, testConf, true) + + val stage1 = new StageInfo(1, 0, "stage1", 4, Nil, Nil, "details1") + val stage2 = new StageInfo(2, 0, "stage2", 4, Nil, Nil, "details2") + + // Sart job 1 + time += 1 + listener.onJobStart(SparkListenerJobStart(1, time, Seq(stage1, stage2), null)) + + // Start and stop stage 1 + time += 1 + stage1.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage1, new Properties())) + + time += 1 + stage1.completionTime = Some(time) + listener.onStageCompleted(SparkListenerStageCompleted(stage1)) + + // Stop job 1 and stage 2 will become SKIPPED + time += 1 + listener.onJobEnd(SparkListenerJobEnd(1, time, JobSucceeded)) + + // Submit stage 3 and verify stage 2 is evicted + val stage3 = new StageInfo(3, 0, "stage3", 4, Nil, Nil, "details3") + time += 1 + stage3.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage3, new Properties())) + + assert(store.count(classOf[StageDataWrapper]) === 2) + intercept[NoSuchElementException] { + store.read(classOf[StageDataWrapper], Array(2, 0)) + } + } + + test("eviction should respect task completion time") { + val testConf = conf.clone().set(MAX_RETAINED_TASKS_PER_STAGE, 2) + val listener = new AppStatusListener(store, testConf, true) + + val stage1 = new StageInfo(1, 0, "stage1", 4, Nil, Nil, "details1") + stage1.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage1, new Properties())) + + // Start task 1 and task 2 + val tasks = createTasks(3, Array("1")) + tasks.take(2).foreach { task => + listener.onTaskStart(SparkListenerTaskStart(stage1.stageId, stage1.attemptNumber, task)) + } + + // Stop task 2 before task 1 + time += 1 + tasks(1).markFinished(TaskState.FINISHED, time) + listener.onTaskEnd( + SparkListenerTaskEnd(stage1.stageId, stage1.attemptId, "taskType", Success, tasks(1), null)) + time += 1 + tasks(0).markFinished(TaskState.FINISHED, time) + listener.onTaskEnd( + SparkListenerTaskEnd(stage1.stageId, stage1.attemptId, "taskType", Success, tasks(0), null)) + + // Start task 3 and task 2 should be evicted. + listener.onTaskStart(SparkListenerTaskStart(stage1.stageId, stage1.attemptNumber, tasks(2))) + assert(store.count(classOf[TaskDataWrapper]) === 2) + intercept[NoSuchElementException] { + store.read(classOf[TaskDataWrapper], tasks(1).id) + } + } + + test("lastStageAttempt should fail when the stage doesn't exist") { + val testConf = conf.clone().set(MAX_RETAINED_STAGES, 1) + val listener = new AppStatusListener(store, testConf, true) + val appStore = new AppStatusStore(store) + + val stage1 = new StageInfo(1, 0, "stage1", 4, Nil, Nil, "details1") + val stage2 = new StageInfo(2, 0, "stage2", 4, Nil, Nil, "details2") + val stage3 = new StageInfo(3, 0, "stage3", 4, Nil, Nil, "details3") + + time += 1 + stage1.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage1, new Properties())) + stage1.completionTime = Some(time) + listener.onStageCompleted(SparkListenerStageCompleted(stage1)) + + // Make stage 3 complete before stage 2 so that stage 3 will be evicted + time += 1 + stage3.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage3, new Properties())) + stage3.completionTime = Some(time) + listener.onStageCompleted(SparkListenerStageCompleted(stage3)) + + time += 1 + stage2.submissionTime = Some(time) + listener.onStageSubmitted(SparkListenerStageSubmitted(stage2, new Properties())) + stage2.completionTime = Some(time) + listener.onStageCompleted(SparkListenerStageCompleted(stage2)) + + assert(appStore.asOption(appStore.lastStageAttempt(1)) === None) + assert(appStore.asOption(appStore.lastStageAttempt(2)).map(_.stageId) === Some(2)) + assert(appStore.asOption(appStore.lastStageAttempt(3)) === None) + } + test("driver logs") { val listener = new AppStatusListener(store, conf, true) @@ -960,7 +1144,7 @@ class AppStatusListenerSuite extends SparkFunSuite with BeforeAndAfter { } } - private def key(stage: StageInfo): Array[Int] = Array(stage.stageId, stage.attemptId) + private def key(stage: StageInfo): Array[Int] = Array(stage.stageId, stage.attemptNumber) private def check[T: ClassTag](key: Any)(fn: T => Unit): Unit = { val value = store.read(classTag[T].runtimeClass, key).asInstanceOf[T] diff --git a/core/src/test/scala/org/apache/spark/status/AppStatusStoreSuite.scala b/core/src/test/scala/org/apache/spark/status/AppStatusStoreSuite.scala new file mode 100644 index 0000000000000..92f90f3d96ddf --- /dev/null +++ b/core/src/test/scala/org/apache/spark/status/AppStatusStoreSuite.scala @@ -0,0 +1,104 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.status + +import org.apache.spark.SparkFunSuite +import org.apache.spark.status.api.v1.TaskMetricDistributions +import org.apache.spark.util.Distribution +import org.apache.spark.util.kvstore._ + +class AppStatusStoreSuite extends SparkFunSuite { + + private val uiQuantiles = Array(0.0, 0.25, 0.5, 0.75, 1.0) + private val stageId = 1 + private val attemptId = 1 + + test("quantile calculation: 1 task") { + compareQuantiles(1, uiQuantiles) + } + + test("quantile calculation: few tasks") { + compareQuantiles(4, uiQuantiles) + } + + test("quantile calculation: more tasks") { + compareQuantiles(100, uiQuantiles) + } + + test("quantile calculation: lots of tasks") { + compareQuantiles(4096, uiQuantiles) + } + + test("quantile calculation: custom quantiles") { + compareQuantiles(4096, Array(0.01, 0.33, 0.5, 0.42, 0.69, 0.99)) + } + + test("quantile cache") { + val store = new InMemoryStore() + (0 until 4096).foreach { i => store.write(newTaskData(i)) } + + val appStore = new AppStatusStore(store) + + appStore.taskSummary(stageId, attemptId, Array(0.13d)) + intercept[NoSuchElementException] { + store.read(classOf[CachedQuantile], Array(stageId, attemptId, "13")) + } + + appStore.taskSummary(stageId, attemptId, Array(0.25d)) + val d1 = store.read(classOf[CachedQuantile], Array(stageId, attemptId, "25")) + + // Add a new task to force the cached quantile to be evicted, and make sure it's updated. + store.write(newTaskData(4096)) + appStore.taskSummary(stageId, attemptId, Array(0.25d, 0.50d, 0.73d)) + + val d2 = store.read(classOf[CachedQuantile], Array(stageId, attemptId, "25")) + assert(d1.taskCount != d2.taskCount) + + store.read(classOf[CachedQuantile], Array(stageId, attemptId, "50")) + intercept[NoSuchElementException] { + store.read(classOf[CachedQuantile], Array(stageId, attemptId, "73")) + } + + assert(store.count(classOf[CachedQuantile]) === 2) + } + + private def compareQuantiles(count: Int, quantiles: Array[Double]): Unit = { + val store = new InMemoryStore() + val values = (0 until count).map { i => + val task = newTaskData(i) + store.write(task) + i.toDouble + }.toArray + + val summary = new AppStatusStore(store).taskSummary(stageId, attemptId, quantiles).get + val dist = new Distribution(values, 0, values.length).getQuantiles(quantiles.sorted) + + dist.zip(summary.executorRunTime).foreach { case (expected, actual) => + assert(expected === actual) + } + } + + private def newTaskData(i: Int): TaskDataWrapper = { + new TaskDataWrapper( + i, i, i, i, i, i, i.toString, i.toString, i.toString, i.toString, false, Nil, None, + i, i, i, i, i, i, i, i, i, i, + i, i, i, i, i, i, i, i, i, i, + i, i, i, i, stageId, attemptId) + } + +} diff --git a/core/src/test/scala/org/apache/spark/status/AppStatusUtilsSuite.scala b/core/src/test/scala/org/apache/spark/status/AppStatusUtilsSuite.scala new file mode 100644 index 0000000000000..9e74e86ad54b9 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/status/AppStatusUtilsSuite.scala @@ -0,0 +1,89 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.status + +import java.util.Date + +import org.apache.spark.SparkFunSuite +import org.apache.spark.status.api.v1.{TaskData, TaskMetrics} + +class AppStatusUtilsSuite extends SparkFunSuite { + + test("schedulerDelay") { + val runningTask = new TaskData( + taskId = 0, + index = 0, + attempt = 0, + launchTime = new Date(1L), + resultFetchStart = None, + duration = Some(100L), + executorId = "1", + host = "localhost", + status = "RUNNING", + taskLocality = "PROCESS_LOCAL", + speculative = false, + accumulatorUpdates = Nil, + errorMessage = None, + taskMetrics = Some(new TaskMetrics( + executorDeserializeTime = 0L, + executorDeserializeCpuTime = 0L, + executorRunTime = 0L, + executorCpuTime = 0L, + resultSize = 0L, + jvmGcTime = 0L, + resultSerializationTime = 0L, + memoryBytesSpilled = 0L, + diskBytesSpilled = 0L, + peakExecutionMemory = 0L, + inputMetrics = null, + outputMetrics = null, + shuffleReadMetrics = null, + shuffleWriteMetrics = null))) + assert(AppStatusUtils.schedulerDelay(runningTask) === 0L) + + val finishedTask = new TaskData( + taskId = 0, + index = 0, + attempt = 0, + launchTime = new Date(1L), + resultFetchStart = None, + duration = Some(100L), + executorId = "1", + host = "localhost", + status = "SUCCESS", + taskLocality = "PROCESS_LOCAL", + speculative = false, + accumulatorUpdates = Nil, + errorMessage = None, + taskMetrics = Some(new TaskMetrics( + executorDeserializeTime = 5L, + executorDeserializeCpuTime = 3L, + executorRunTime = 90L, + executorCpuTime = 10L, + resultSize = 100L, + jvmGcTime = 10L, + resultSerializationTime = 2L, + memoryBytesSpilled = 0L, + diskBytesSpilled = 0L, + peakExecutionMemory = 100L, + inputMetrics = null, + outputMetrics = null, + shuffleReadMetrics = null, + shuffleWriteMetrics = null))) + assert(AppStatusUtils.schedulerDelay(finishedTask) === 3L) + } +} diff --git a/core/src/test/scala/org/apache/spark/storage/BlockInfoManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockInfoManagerSuite.scala index 917db766f7f11..9c0699bc981f8 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockInfoManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockInfoManagerSuite.scala @@ -62,7 +62,7 @@ class BlockInfoManagerSuite extends SparkFunSuite with BeforeAndAfterEach { private def withTaskId[T](taskAttemptId: Long)(block: => T): T = { try { TaskContext.setTaskContext( - new TaskContextImpl(0, 0, taskAttemptId, 0, null, new Properties, null)) + new TaskContextImpl(0, 0, 0, taskAttemptId, 0, null, new Properties, null)) block } finally { TaskContext.unset() diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala index 5bfe9905ff17b..692ae3bf597e0 100644 --- a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala @@ -352,6 +352,51 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT intercept[FetchFailedException] { iterator.next() } } + test("big blocks are not checked for corruption") { + val corruptStream = mock(classOf[InputStream]) + when(corruptStream.read(any(), any(), any())).thenThrow(new IOException("corrupt")) + val corruptBuffer = mock(classOf[ManagedBuffer]) + when(corruptBuffer.createInputStream()).thenReturn(corruptStream) + doReturn(10000L).when(corruptBuffer).size() + + val blockManager = mock(classOf[BlockManager]) + val localBmId = BlockManagerId("test-client", "test-client", 1) + doReturn(localBmId).when(blockManager).blockManagerId + doReturn(corruptBuffer).when(blockManager).getBlockData(ShuffleBlockId(0, 0, 0)) + val localBlockLengths = Seq[Tuple2[BlockId, Long]]( + ShuffleBlockId(0, 0, 0) -> corruptBuffer.size() + ) + + val remoteBmId = BlockManagerId("test-client-1", "test-client-1", 2) + val remoteBlockLengths = Seq[Tuple2[BlockId, Long]]( + ShuffleBlockId(0, 1, 0) -> corruptBuffer.size() + ) + + val transfer = createMockTransfer( + Map(ShuffleBlockId(0, 0, 0) -> corruptBuffer, ShuffleBlockId(0, 1, 0) -> corruptBuffer)) + + val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])]( + (localBmId, localBlockLengths), + (remoteBmId, remoteBlockLengths) + ) + + val taskContext = TaskContext.empty() + val iterator = new ShuffleBlockFetcherIterator( + taskContext, + transfer, + blockManager, + blocksByAddress, + (_, in) => new LimitedInputStream(in, 10000), + 2048, + Int.MaxValue, + Int.MaxValue, + Int.MaxValue, + true) + // Blocks should be returned without exceptions. + assert(Set(iterator.next()._1, iterator.next()._1) === + Set(ShuffleBlockId(0, 0, 0), ShuffleBlockId(0, 1, 0))) + } + test("retry corrupt blocks (disabled)") { val blockManager = mock(classOf[BlockManager]) val localBmId = BlockManagerId("test-client", "test-client", 1) diff --git a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala index 661d0d48d2f37..6044563f7dde7 100644 --- a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala @@ -28,22 +28,82 @@ import org.apache.spark._ import org.apache.spark.executor.TaskMetrics import org.apache.spark.scheduler._ import org.apache.spark.status.AppStatusStore -import org.apache.spark.ui.jobs.{StagePage, StagesTab} +import org.apache.spark.status.api.v1.{AccumulableInfo => UIAccumulableInfo, StageData, StageStatus} +import org.apache.spark.status.config._ +import org.apache.spark.ui.jobs.{ApiHelper, StagePage, StagesTab, TaskPagedTable} class StagePageSuite extends SparkFunSuite with LocalSparkContext { private val peakExecutionMemory = 10 + test("ApiHelper.COLUMN_TO_INDEX should match headers of the task table") { + val conf = new SparkConf(false).set(LIVE_ENTITY_UPDATE_PERIOD, 0L) + val statusStore = AppStatusStore.createLiveStore(conf) + try { + val stageData = new StageData( + status = StageStatus.ACTIVE, + stageId = 1, + attemptId = 1, + numTasks = 1, + numActiveTasks = 1, + numCompleteTasks = 1, + numFailedTasks = 1, + numKilledTasks = 1, + numCompletedIndices = 1, + + executorRunTime = 1L, + executorCpuTime = 1L, + submissionTime = None, + firstTaskLaunchedTime = None, + completionTime = None, + failureReason = None, + + inputBytes = 1L, + inputRecords = 1L, + outputBytes = 1L, + outputRecords = 1L, + shuffleReadBytes = 1L, + shuffleReadRecords = 1L, + shuffleWriteBytes = 1L, + shuffleWriteRecords = 1L, + memoryBytesSpilled = 1L, + diskBytesSpilled = 1L, + + name = "stage1", + description = Some("description"), + details = "detail", + schedulingPool = "pool1", + + rddIds = Seq(1), + accumulatorUpdates = Seq(new UIAccumulableInfo(0L, "acc", None, "value")), + tasks = None, + executorSummary = None, + killedTasksSummary = Map.empty + ) + val taskTable = new TaskPagedTable( + stageData, + basePath = "/a/b/c", + currentTime = 0, + pageSize = 10, + sortColumn = "Index", + desc = false, + store = statusStore + ) + val columnNames = (taskTable.headers \ "th" \ "a").map(_.child(1).text).toSet + assert(columnNames === ApiHelper.COLUMN_TO_INDEX.keySet) + } finally { + statusStore.close() + } + } + test("peak execution memory should displayed") { - val conf = new SparkConf(false) - val html = renderStagePage(conf).toString().toLowerCase(Locale.ROOT) + val html = renderStagePage().toString().toLowerCase(Locale.ROOT) val targetString = "peak execution memory" assert(html.contains(targetString)) } test("SPARK-10543: peak execution memory should be per-task rather than cumulative") { - val conf = new SparkConf(false) - val html = renderStagePage(conf).toString().toLowerCase(Locale.ROOT) + val html = renderStagePage().toString().toLowerCase(Locale.ROOT) // verify min/25/50/75/max show task value not cumulative values assert(html.contains(s"$peakExecutionMemory.0 b" * 5)) } @@ -52,7 +112,8 @@ class StagePageSuite extends SparkFunSuite with LocalSparkContext { * Render a stage page started with the given conf and return the HTML. * This also runs a dummy stage to populate the page with useful content. */ - private def renderStagePage(conf: SparkConf): Seq[Node] = { + private def renderStagePage(): Seq[Node] = { + val conf = new SparkConf(false).set(LIVE_ENTITY_UPDATE_PERIOD, 0L) val statusStore = AppStatusStore.createLiveStore(conf) val listener = statusStore.listener.get diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala index 326546787ab6c..0f20eea735044 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala @@ -706,6 +706,23 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B } } + test("stages page should show skipped stages") { + withSpark(newSparkContext()) { sc => + val rdd = sc.parallelize(0 to 100, 100).repartition(10).cache() + rdd.count() + rdd.count() + + eventually(timeout(5 seconds), interval(50 milliseconds)) { + goToUi(sc, "/stages") + find(id("skipped")).get.text should be("Skipped Stages (1)") + } + val stagesJson = getJson(sc.ui.get, "stages") + stagesJson.children.size should be (4) + val stagesStatus = stagesJson.children.map(_ \ "status") + stagesStatus.count(_ == JString(StageStatus.SKIPPED.name())) should be (1) + } + } + def goToUi(sc: SparkContext, path: String): Unit = { goToUi(sc.ui.get, path) } diff --git a/core/src/test/scala/org/apache/spark/util/AccumulatorV2Suite.scala b/core/src/test/scala/org/apache/spark/util/AccumulatorV2Suite.scala index a04644d57ed88..fe0a9a471a651 100644 --- a/core/src/test/scala/org/apache/spark/util/AccumulatorV2Suite.scala +++ b/core/src/test/scala/org/apache/spark/util/AccumulatorV2Suite.scala @@ -18,6 +18,7 @@ package org.apache.spark.util import org.apache.spark._ +import org.apache.spark.serializer.JavaSerializer class AccumulatorV2Suite extends SparkFunSuite { @@ -162,4 +163,22 @@ class AccumulatorV2Suite extends SparkFunSuite { assert(acc3.isZero) assert(acc3.value === "") } + + test("LegacyAccumulatorWrapper with AccumulatorParam that has no equals/hashCode") { + class MyData(val i: Int) extends Serializable + val param = new AccumulatorParam[MyData] { + override def zero(initialValue: MyData): MyData = new MyData(0) + override def addInPlace(r1: MyData, r2: MyData): MyData = new MyData(r1.i + r2.i) + } + + val acc = new LegacyAccumulatorWrapper(new MyData(0), param) + acc.metadata = AccumulatorMetadata( + AccumulatorContext.newId(), + Some("test"), + countFailedValues = false) + AccumulatorContext.register(acc) + + val ser = new JavaSerializer(new SparkConf).newInstance() + ser.serialize(acc) + } } diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index eaea6b030c154..cde250ca65660 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -1167,6 +1167,22 @@ class UtilsSuite extends SparkFunSuite with ResetSystemProperties with Logging { Utils.checkAndGetK8sMasterUrl("k8s://foo://host:port") } } + + object MalformedClassObject { + class MalformedClass + } + + test("Safe getSimpleName") { + // getSimpleName on class of MalformedClass will result in error: Malformed class name + // Utils.getSimpleName works + val err = intercept[java.lang.InternalError] { + classOf[MalformedClassObject.MalformedClass].getSimpleName + } + assert(err.getMessage === "Malformed class name") + + assert(Utils.getSimpleName(classOf[MalformedClassObject.MalformedClass]) === + "UtilsSuite$MalformedClassObject$MalformedClass") + } } private class SimpleExtension diff --git a/data/mllib/images/multi-channel/BGRA_alpha_60.png b/data/mllib/images/multi-channel/BGRA_alpha_60.png new file mode 100644 index 0000000000000..913637cd2828a Binary files /dev/null and b/data/mllib/images/multi-channel/BGRA_alpha_60.png differ diff --git a/dev/create-release/release-build.sh b/dev/create-release/release-build.sh index c71137468054f..5faa3d3260a56 100755 --- a/dev/create-release/release-build.sh +++ b/dev/create-release/release-build.sh @@ -92,9 +92,9 @@ MVN="build/mvn --force" # Hive-specific profiles for some builds HIVE_PROFILES="-Phive -Phive-thriftserver" # Profiles for publishing snapshots and release to Maven Central -PUBLISH_PROFILES="-Pmesos -Pyarn -Pflume $HIVE_PROFILES -Pspark-ganglia-lgpl -Pkinesis-asl" +PUBLISH_PROFILES="-Pmesos -Pyarn -Pkubernetes -Pflume $HIVE_PROFILES -Pspark-ganglia-lgpl -Pkinesis-asl" # Profiles for building binary releases -BASE_RELEASE_PROFILES="-Pmesos -Pyarn -Pflume -Psparkr" +BASE_RELEASE_PROFILES="-Pmesos -Pyarn -Pkubernetes -Pflume -Psparkr" # Scala 2.11 only profiles for some builds SCALA_2_11_PROFILES="-Pkafka-0-8" # Scala 2.12 only profiles for some builds @@ -164,8 +164,6 @@ if [[ "$1" == "package" ]]; then tar cvzf spark-$SPARK_VERSION.tgz spark-$SPARK_VERSION echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --armour --output spark-$SPARK_VERSION.tgz.asc \ --detach-sig spark-$SPARK_VERSION.tgz - echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md MD5 spark-$SPARK_VERSION.tgz > \ - spark-$SPARK_VERSION.tgz.md5 echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \ SHA512 spark-$SPARK_VERSION.tgz > spark-$SPARK_VERSION.tgz.sha512 rm -rf spark-$SPARK_VERSION @@ -215,9 +213,6 @@ if [[ "$1" == "package" ]]; then echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --armour \ --output $R_DIST_NAME.asc \ --detach-sig $R_DIST_NAME - echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \ - MD5 $R_DIST_NAME > \ - $R_DIST_NAME.md5 echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \ SHA512 $R_DIST_NAME > \ $R_DIST_NAME.sha512 @@ -234,9 +229,6 @@ if [[ "$1" == "package" ]]; then echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --armour \ --output $PYTHON_DIST_NAME.asc \ --detach-sig $PYTHON_DIST_NAME - echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \ - MD5 $PYTHON_DIST_NAME > \ - $PYTHON_DIST_NAME.md5 echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \ SHA512 $PYTHON_DIST_NAME > \ $PYTHON_DIST_NAME.sha512 @@ -247,9 +239,6 @@ if [[ "$1" == "package" ]]; then echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --armour \ --output spark-$SPARK_VERSION-bin-$NAME.tgz.asc \ --detach-sig spark-$SPARK_VERSION-bin-$NAME.tgz - echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \ - MD5 spark-$SPARK_VERSION-bin-$NAME.tgz > \ - spark-$SPARK_VERSION-bin-$NAME.tgz.md5 echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \ SHA512 spark-$SPARK_VERSION-bin-$NAME.tgz > \ spark-$SPARK_VERSION-bin-$NAME.tgz.sha512 diff --git a/dev/create-release/releaseutils.py b/dev/create-release/releaseutils.py index 730138195e5fe..32f6cbb29f0be 100755 --- a/dev/create-release/releaseutils.py +++ b/dev/create-release/releaseutils.py @@ -185,6 +185,8 @@ def get_commits(tag): "graphx": "GraphX", "input/output": CORE_COMPONENT, "java api": "Java API", + "k8s": "Kubernetes", + "kubernetes": "Kubernetes", "mesos": "Mesos", "ml": "MLlib", "mllib": "MLlib", diff --git a/dev/deps/spark-deps-hadoop-2.6 b/dev/deps/spark-deps-hadoop-2.6 index a7fce2ede0ea5..4f0794d6f1a11 100644 --- a/dev/deps/spark-deps-hadoop-2.6 +++ b/dev/deps/spark-deps-hadoop-2.6 @@ -17,6 +17,7 @@ arpack_combined_all-0.1.jar arrow-format-0.8.0.jar arrow-memory-0.8.0.jar arrow-vector-0.8.0.jar +automaton-1.11-8.jar avro-1.7.7.jar avro-ipc-1.7.7.jar avro-mapred-1.7.7-hadoop2.jar @@ -60,6 +61,7 @@ datanucleus-rdbms-3.2.9.jar derby-10.12.1.1.jar eigenbase-properties-1.1.5.jar flatbuffers-1.2.0-3f79e055.jar +generex-1.0.1.jar gson-2.2.4.jar guava-14.0.1.jar guice-3.0.jar @@ -91,8 +93,10 @@ jackson-annotations-2.6.7.jar jackson-core-2.6.7.jar jackson-core-asl-1.9.13.jar jackson-databind-2.6.7.1.jar +jackson-dataformat-yaml-2.6.7.jar jackson-jaxrs-1.9.13.jar jackson-mapper-asl-1.9.13.jar +jackson-module-jaxb-annotations-2.6.7.jar jackson-module-paranamer-2.7.9.jar jackson-module-scala_2.11-2.6.7.1.jar jackson-xc-1.9.13.jar @@ -130,10 +134,13 @@ jta-1.1.jar jtransforms-2.4.0.jar jul-to-slf4j-1.7.16.jar kryo-shaded-3.0.3.jar +kubernetes-client-3.0.0.jar +kubernetes-model-2.0.0.jar leveldbjni-all-1.8.jar libfb303-0.9.3.jar libthrift-0.9.3.jar log4j-1.2.17.jar +logging-interceptor-3.8.1.jar lz4-java-1.4.0.jar machinist_2.11-0.6.1.jar macro-compat_2.11-1.1.1.jar @@ -146,21 +153,23 @@ minlog-1.3.0.jar netty-3.9.9.Final.jar netty-all-4.1.17.Final.jar objenesis-2.1.jar +okhttp-3.8.1.jar +okio-1.13.0.jar opencsv-2.3.jar -orc-core-1.4.1-nohive.jar -orc-mapreduce-1.4.1-nohive.jar +orc-core-1.4.4-nohive.jar +orc-mapreduce-1.4.4-nohive.jar oro-2.0.8.jar osgi-resource-locator-1.0.1.jar paranamer-2.8.jar -parquet-column-1.8.2.jar -parquet-common-1.8.2.jar -parquet-encoding-1.8.2.jar +parquet-column-1.8.3.jar +parquet-common-1.8.3.jar +parquet-encoding-1.8.3.jar parquet-format-2.3.1.jar -parquet-hadoop-1.8.2.jar +parquet-hadoop-1.8.3.jar parquet-hadoop-bundle-1.6.0.jar -parquet-jackson-1.8.2.jar +parquet-jackson-1.8.3.jar protobuf-java-2.5.0.jar -py4j-0.10.6.jar +py4j-0.10.7.jar pyrolite-4.13.jar scala-compiler-2.11.8.jar scala-library-2.11.8.jar @@ -171,6 +180,7 @@ scalap-2.11.8.jar shapeless_2.11-2.3.2.jar slf4j-api-1.7.16.jar slf4j-log4j12-1.7.16.jar +snakeyaml-1.15.jar snappy-0.2.jar snappy-java-1.1.2.6.jar spire-macros_2.11-0.13.0.jar @@ -186,5 +196,6 @@ xbean-asm5-shaded-4.4.jar xercesImpl-2.9.1.jar xmlenc-0.52.jar xz-1.0.jar +zjsonpatch-0.3.0.jar zookeeper-3.4.6.jar zstd-jni-1.3.2-2.jar diff --git a/dev/deps/spark-deps-hadoop-2.7 b/dev/deps/spark-deps-hadoop-2.7 index 94b2e98d85e74..df2be777ff5ac 100644 --- a/dev/deps/spark-deps-hadoop-2.7 +++ b/dev/deps/spark-deps-hadoop-2.7 @@ -17,6 +17,7 @@ arpack_combined_all-0.1.jar arrow-format-0.8.0.jar arrow-memory-0.8.0.jar arrow-vector-0.8.0.jar +automaton-1.11-8.jar avro-1.7.7.jar avro-ipc-1.7.7.jar avro-mapred-1.7.7-hadoop2.jar @@ -60,6 +61,7 @@ datanucleus-rdbms-3.2.9.jar derby-10.12.1.1.jar eigenbase-properties-1.1.5.jar flatbuffers-1.2.0-3f79e055.jar +generex-1.0.1.jar gson-2.2.4.jar guava-14.0.1.jar guice-3.0.jar @@ -91,8 +93,10 @@ jackson-annotations-2.6.7.jar jackson-core-2.6.7.jar jackson-core-asl-1.9.13.jar jackson-databind-2.6.7.1.jar +jackson-dataformat-yaml-2.6.7.jar jackson-jaxrs-1.9.13.jar jackson-mapper-asl-1.9.13.jar +jackson-module-jaxb-annotations-2.6.7.jar jackson-module-paranamer-2.7.9.jar jackson-module-scala_2.11-2.6.7.1.jar jackson-xc-1.9.13.jar @@ -131,10 +135,13 @@ jta-1.1.jar jtransforms-2.4.0.jar jul-to-slf4j-1.7.16.jar kryo-shaded-3.0.3.jar +kubernetes-client-3.0.0.jar +kubernetes-model-2.0.0.jar leveldbjni-all-1.8.jar libfb303-0.9.3.jar libthrift-0.9.3.jar log4j-1.2.17.jar +logging-interceptor-3.8.1.jar lz4-java-1.4.0.jar machinist_2.11-0.6.1.jar macro-compat_2.11-1.1.1.jar @@ -147,21 +154,23 @@ minlog-1.3.0.jar netty-3.9.9.Final.jar netty-all-4.1.17.Final.jar objenesis-2.1.jar +okhttp-3.8.1.jar +okio-1.13.0.jar opencsv-2.3.jar -orc-core-1.4.1-nohive.jar -orc-mapreduce-1.4.1-nohive.jar +orc-core-1.4.4-nohive.jar +orc-mapreduce-1.4.4-nohive.jar oro-2.0.8.jar osgi-resource-locator-1.0.1.jar paranamer-2.8.jar -parquet-column-1.8.2.jar -parquet-common-1.8.2.jar -parquet-encoding-1.8.2.jar +parquet-column-1.8.3.jar +parquet-common-1.8.3.jar +parquet-encoding-1.8.3.jar parquet-format-2.3.1.jar -parquet-hadoop-1.8.2.jar +parquet-hadoop-1.8.3.jar parquet-hadoop-bundle-1.6.0.jar -parquet-jackson-1.8.2.jar +parquet-jackson-1.8.3.jar protobuf-java-2.5.0.jar -py4j-0.10.6.jar +py4j-0.10.7.jar pyrolite-4.13.jar scala-compiler-2.11.8.jar scala-library-2.11.8.jar @@ -172,6 +181,7 @@ scalap-2.11.8.jar shapeless_2.11-2.3.2.jar slf4j-api-1.7.16.jar slf4j-log4j12-1.7.16.jar +snakeyaml-1.15.jar snappy-0.2.jar snappy-java-1.1.2.6.jar spire-macros_2.11-0.13.0.jar @@ -187,5 +197,6 @@ xbean-asm5-shaded-4.4.jar xercesImpl-2.9.1.jar xmlenc-0.52.jar xz-1.0.jar +zjsonpatch-0.3.0.jar zookeeper-3.4.6.jar zstd-jni-1.3.2-2.jar diff --git a/dev/lint-java b/dev/lint-java index c2e80538ef2a5..1f0b0c8379ed0 100755 --- a/dev/lint-java +++ b/dev/lint-java @@ -20,7 +20,7 @@ SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )" SPARK_ROOT_DIR="$(dirname $SCRIPT_DIR)" -ERRORS=$($SCRIPT_DIR/../build/mvn -Pkinesis-asl -Pmesos -Pyarn -Phive -Phive-thriftserver checkstyle:check | grep ERROR) +ERRORS=$($SCRIPT_DIR/../build/mvn -Pkinesis-asl -Pmesos -Pkubernetes -Pyarn -Phive -Phive-thriftserver checkstyle:check | grep ERROR) if test ! -z "$ERRORS"; then echo -e "Checkstyle checks failed at following occurrences:\n$ERRORS" diff --git a/dev/mima b/dev/mima index 1e3ca9700bc07..cd2694ff4d3de 100755 --- a/dev/mima +++ b/dev/mima @@ -24,7 +24,7 @@ set -e FWDIR="$(cd "`dirname "$0"`"/..; pwd)" cd "$FWDIR" -SPARK_PROFILES="-Pmesos -Pkafka-0-8 -Pyarn -Pflume -Pspark-ganglia-lgpl -Pkinesis-asl -Phive-thriftserver -Phive" +SPARK_PROFILES="-Pmesos -Pkafka-0-8 -Pkubernetes -Pyarn -Pflume -Pspark-ganglia-lgpl -Pkinesis-asl -Phive-thriftserver -Phive" TOOLS_CLASSPATH="$(build/sbt -DcopyDependencies=false "export tools/fullClasspath" | tail -n1)" OLD_DEPS_CLASSPATH="$(build/sbt -DcopyDependencies=false $SPARK_PROFILES "export oldDeps/fullClasspath" | tail -n1)" diff --git a/dev/run-pip-tests b/dev/run-pip-tests index d51dde12a03c5..03fc83298dc2f 100755 --- a/dev/run-pip-tests +++ b/dev/run-pip-tests @@ -89,7 +89,7 @@ for python in "${PYTHON_EXECS[@]}"; do source "$VIRTUALENV_PATH"/bin/activate fi # Upgrade pip & friends if using virutal env - if [ ! -n "USE_CONDA" ]; then + if [ ! -n "$USE_CONDA" ]; then pip install --upgrade pip pypandoc wheel numpy fi diff --git a/dev/scalastyle b/dev/scalastyle index 89ecc8abd6f8c..b8053df05fa2b 100755 --- a/dev/scalastyle +++ b/dev/scalastyle @@ -24,6 +24,7 @@ ERRORS=$(echo -e "q\n" \ -Pkinesis-asl \ -Pmesos \ -Pkafka-0-8 \ + -Pkubernetes \ -Pyarn \ -Pflume \ -Phive \ diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py index f834563da9dda..b900f0bd913c3 100644 --- a/dev/sparktestsupport/modules.py +++ b/dev/sparktestsupport/modules.py @@ -400,6 +400,7 @@ def __hash__(self): "pyspark.sql.functions", "pyspark.sql.readwriter", "pyspark.sql.streaming", + "pyspark.sql.udf", "pyspark.sql.window", "pyspark.sql.tests", ] @@ -539,7 +540,7 @@ def __hash__(self): kubernetes = Module( name="kubernetes", dependencies=[], - source_file_regexes=["resource-managers/kubernetes/core"], + source_file_regexes=["resource-managers/kubernetes"], build_profile_flags=["-Pkubernetes"], sbt_test_goals=["kubernetes/test"] ) diff --git a/dev/test-dependencies.sh b/dev/test-dependencies.sh index 58b295d4f6e00..3bf7618e1ea96 100755 --- a/dev/test-dependencies.sh +++ b/dev/test-dependencies.sh @@ -29,7 +29,7 @@ export LC_ALL=C # TODO: This would be much nicer to do in SBT, once SBT supports Maven-style resolution. # NOTE: These should match those in the release publishing script -HADOOP2_MODULE_PROFILES="-Phive-thriftserver -Pmesos -Pkafka-0-8 -Pyarn -Pflume -Phive" +HADOOP2_MODULE_PROFILES="-Phive-thriftserver -Pmesos -Pkafka-0-8 -Pkubernetes -Pyarn -Pflume -Phive" MVN="build/mvn" HADOOP_PROFILES=( hadoop-2.6 diff --git a/docs/README.md b/docs/README.md index 225bb1b2040de..166a7e572982d 100644 --- a/docs/README.md +++ b/docs/README.md @@ -22,10 +22,13 @@ $ sudo gem install jekyll jekyll-redirect-from pygments.rb $ sudo pip install Pygments # Following is needed only for generating API docs $ sudo pip install sphinx pypandoc mkdocs -$ sudo Rscript -e 'install.packages(c("knitr", "devtools", "roxygen2", "testthat", "rmarkdown"), repos="http://cran.stat.ucla.edu/")' +$ sudo Rscript -e 'install.packages(c("knitr", "devtools", "testthat", "rmarkdown"), repos="http://cran.stat.ucla.edu/")' +$ sudo Rscript -e 'devtools::install_version("roxygen2", version = "5.0.1", repos="http://cran.stat.ucla.edu/")' ``` -(Note: If you are on a system with both Ruby 1.9 and Ruby 2.0 you may need to replace gem with gem2.0) +Note: If you are on a system with both Ruby 1.9 and Ruby 2.0 you may need to replace gem with gem2.0. + +Note: Other versions of roxygen2 might work in SparkR documentation generation but `RoxygenNote` field in `$SPARK_HOME/R/pkg/DESCRIPTION` is 5.0.1, which is updated if the version is mismatched. ## Generating the Documentation HTML @@ -62,12 +65,12 @@ $ PRODUCTION=1 jekyll build ## API Docs (Scaladoc, Javadoc, Sphinx, roxygen2, MkDocs) -You can build just the Spark scaladoc and javadoc by running `build/sbt unidoc` from the `SPARK_HOME` directory. +You can build just the Spark scaladoc and javadoc by running `build/sbt unidoc` from the `$SPARK_HOME` directory. Similarly, you can build just the PySpark docs by running `make html` from the -`SPARK_HOME/python/docs` directory. Documentation is only generated for classes that are listed as -public in `__init__.py`. The SparkR docs can be built by running `SPARK_HOME/R/create-docs.sh`, and -the SQL docs can be built by running `SPARK_HOME/sql/create-docs.sh` +`$SPARK_HOME/python/docs` directory. Documentation is only generated for classes that are listed as +public in `__init__.py`. The SparkR docs can be built by running `$SPARK_HOME/R/create-docs.sh`, and +the SQL docs can be built by running `$SPARK_HOME/sql/create-docs.sh` after [building Spark](https://github.com/apache/spark#building-spark) first. When you run `jekyll build` in the `docs` directory, it will also copy over the scaladoc and javadoc for the various diff --git a/docs/_config.yml b/docs/_config.yml index dcc211204d766..8579166c2e635 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -14,8 +14,8 @@ include: # These allow the documentation to be updated with newer releases # of Spark, Scala, and Mesos. -SPARK_VERSION: 2.3.0-SNAPSHOT -SPARK_VERSION_SHORT: 2.3.0 +SPARK_VERSION: 2.3.2-SNAPSHOT +SPARK_VERSION_SHORT: 2.3.2 SCALA_BINARY_VERSION: "2.11" SCALA_VERSION: "2.11.8" MESOS_VERSION: 1.0.0 diff --git a/docs/cloud-integration.md b/docs/cloud-integration.md index 751a192da4ffd..c150d9efc06ff 100644 --- a/docs/cloud-integration.md +++ b/docs/cloud-integration.md @@ -180,10 +180,10 @@ under the path, not the number of *new* files, so it can become a slow operation The size of the window needs to be set to handle this. 1. Files only appear in an object store once they are completely written; there -is no need for a worklow of write-then-rename to ensure that files aren't picked up +is no need for a workflow of write-then-rename to ensure that files aren't picked up while they are still being written. Applications can write straight to the monitored directory. -1. Streams should only be checkpointed to an store implementing a fast and +1. Streams should only be checkpointed to a store implementing a fast and atomic `rename()` operation Otherwise the checkpointing may be slow and potentially unreliable. ## Further Reading diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index 658e67f99dd71..7277e2fb2731d 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -52,8 +52,8 @@ The system currently supports three cluster managers: * [Apache Mesos](running-on-mesos.html) -- a general cluster manager that can also run Hadoop MapReduce and service applications. * [Hadoop YARN](running-on-yarn.html) -- the resource manager in Hadoop 2. -* [Kubernetes](running-on-kubernetes.html) -- [Kubernetes](https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/) -is an open-source platform that provides container-centric infrastructure. +* [Kubernetes](running-on-kubernetes.html) -- an open-source system for automating deployment, scaling, + and management of containerized applications. A third-party project (not supported by the Spark project) exists to add support for [Nomad](https://github.com/hashicorp/nomad-spark) as a cluster manager. diff --git a/docs/configuration.md b/docs/configuration.md index 1189aea2aa71f..ec4f5d41c2d9d 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -58,6 +58,10 @@ The following format is accepted: 1t or 1tb (tebibytes = 1024 gibibytes) 1p or 1pb (pebibytes = 1024 tebibytes) +While numbers without units are generally interpreted as bytes, a few are interpreted as KiB or MiB. +See documentation of individual configuration properties. Specifying units is desirable where +possible. + ## Dynamically Loading Spark Properties In some cases, you may want to avoid hard-coding certain configurations in a `SparkConf`. For @@ -75,7 +79,7 @@ Then, you can supply configuration values at runtime: {% endhighlight %} The Spark shell and [`spark-submit`](submitting-applications.html) -tool support two ways to load configurations dynamically. The first are command line options, +tool support two ways to load configurations dynamically. The first is command line options, such as `--master`, as shown above. `spark-submit` can accept any Spark property using the `--conf` flag, but uses special flags for properties that play a part in launching the Spark application. Running `./bin/spark-submit --help` will show the entire list of these options. @@ -136,9 +140,9 @@ of the most common options to set are: spark.driver.maxResultSize 1g - Limit of total size of serialized results of all partitions for each Spark action (e.g. collect). - Should be at least 1M, or 0 for unlimited. Jobs will be aborted if the total size - is above this limit. + Limit of total size of serialized results of all partitions for each Spark action (e.g. + collect) in bytes. Should be at least 1M, or 0 for unlimited. Jobs will be aborted if the total + size is above this limit. Having a high limit may cause out-of-memory errors in driver (depends on spark.driver.memory and memory overhead of objects in JVM). Setting a proper limit can protect the driver from out-of-memory errors. @@ -148,10 +152,10 @@ of the most common options to set are: spark.driver.memory 1g - Amount of memory to use for the driver process, i.e. where SparkContext is initialized. - (e.g. 1g, 2g). - -
      Note: In client mode, this config must not be set through the SparkConf + Amount of memory to use for the driver process, i.e. where SparkContext is initialized, in MiB + unless otherwise specified (e.g. 1g, 2g). +
      + Note: In client mode, this config must not be set through the SparkConf directly in your application, because the driver JVM has already started at that point. Instead, please set this through the --driver-memory command line option or in your default properties file. @@ -161,27 +165,28 @@ of the most common options to set are: spark.driver.memoryOverhead driverMemory * 0.10, with minimum of 384 - The amount of off-heap memory (in megabytes) to be allocated per driver in cluster mode. This is - memory that accounts for things like VM overheads, interned strings, other native overheads, etc. - This tends to grow with the container size (typically 6-10%). This option is currently supported - on YARN and Kubernetes. + The amount of off-heap memory to be allocated per driver in cluster mode, in MiB unless + otherwise specified. This is memory that accounts for things like VM overheads, interned strings, + other native overheads, etc. This tends to grow with the container size (typically 6-10%). + This option is currently supported on YARN and Kubernetes. spark.executor.memory 1g - Amount of memory to use per executor process (e.g. 2g, 8g). + Amount of memory to use per executor process, in MiB unless otherwise specified. + (e.g. 2g, 8g). spark.executor.memoryOverhead executorMemory * 0.10, with minimum of 384 - The amount of off-heap memory (in megabytes) to be allocated per executor. This is memory that - accounts for things like VM overheads, interned strings, other native overheads, etc. This tends - to grow with the executor size (typically 6-10%). This option is currently supported on YARN and - Kubernetes. + The amount of off-heap memory to be allocated per executor, in MiB unless otherwise specified. + This is memory that accounts for things like VM overheads, interned strings, other native + overheads, etc. This tends to grow with the executor size (typically 6-10%). + This option is currently supported on YARN and Kubernetes. @@ -408,7 +413,7 @@ Apart from these, the following properties are also available, and may be useful false Enable profiling in Python worker, the profile result will show up by sc.show_profiles(), - or it will be displayed before the driver exiting. It also can be dumped into disk by + or it will be displayed before the driver exits. It also can be dumped into disk by sc.dump_profiles(path). If some of the profile results had been displayed manually, they will not be displayed automatically before driver exiting. @@ -431,8 +436,9 @@ Apart from these, the following properties are also available, and may be useful 512m Amount of memory to use per python worker process during aggregation, in the same - format as JVM memory strings (e.g. 512m, 2g). If the memory - used during aggregation goes above this amount, it will spill the data into disks. + format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") + (e.g. 512m, 2g). + If the memory used during aggregation goes above this amount, it will spill the data into disks. @@ -440,7 +446,7 @@ Apart from these, the following properties are also available, and may be useful true Reuse Python worker or not. If yes, it will use a fixed number of Python workers, - does not need to fork() a Python process for every tasks. It will be very useful + does not need to fork() a Python process for every task. It will be very useful if there is large broadcast, then the broadcast will not be needed to transferred from JVM to Python worker for every task. @@ -540,9 +546,10 @@ Apart from these, the following properties are also available, and may be useful spark.reducer.maxSizeInFlight 48m - Maximum size of map outputs to fetch simultaneously from each reduce task. Since - each output requires us to create a buffer to receive it, this represents a fixed memory - overhead per reduce task, so keep it small unless you have a large amount of memory. + Maximum size of map outputs to fetch simultaneously from each reduce task, in MiB unless + otherwise specified. Since each output requires us to create a buffer to receive it, this + represents a fixed memory overhead per reduce task, so keep it small unless you have a + large amount of memory. @@ -570,9 +577,9 @@ Apart from these, the following properties are also available, and may be useful spark.maxRemoteBlockSizeFetchToMem Long.MaxValue - The remote block will be fetched to disk when size of the block is above this threshold. + The remote block will be fetched to disk when size of the block is above this threshold in bytes. This is to avoid a giant request takes too much memory. We can enable this config by setting - a specific value(e.g. 200m). Note this configuration will affect both shuffle fetch + a specific value(e.g. 200m). Note this configuration will affect both shuffle fetch and block manager remote block fetch. For users who enabled external shuffle service, this feature can only be worked when external shuffle service is newer than Spark 2.2. @@ -589,8 +596,9 @@ Apart from these, the following properties are also available, and may be useful spark.shuffle.file.buffer 32k - Size of the in-memory buffer for each shuffle file output stream. These buffers - reduce the number of disk seeks and system calls made in creating intermediate shuffle files. + Size of the in-memory buffer for each shuffle file output stream, in KiB unless otherwise + specified. These buffers reduce the number of disk seeks and system calls made in creating + intermediate shuffle files. @@ -651,7 +659,7 @@ Apart from these, the following properties are also available, and may be useful spark.shuffle.service.index.cache.size 100m - Cache entries limited to the specified memory footprint. + Cache entries limited to the specified memory footprint in bytes. @@ -685,9 +693,9 @@ Apart from these, the following properties are also available, and may be useful spark.shuffle.accurateBlockThreshold 100 * 1024 * 1024 - When we compress the size of shuffle blocks in HighlyCompressedMapStatus, we will record the - size accurately if it's above this config. This helps to prevent OOM by avoiding - underestimating shuffle block size when fetch shuffle blocks. + Threshold in bytes above which the size of shuffle blocks in HighlyCompressedMapStatus is + accurately recorded. This helps to prevent OOM by avoiding underestimating shuffle + block size when fetch shuffle blocks. @@ -779,7 +787,7 @@ Apart from these, the following properties are also available, and may be useful spark.eventLog.buffer.kb 100k - Buffer size in KB to use when writing to output streams. + Buffer size to use when writing to output streams, in KiB unless otherwise specified. @@ -904,8 +912,8 @@ Apart from these, the following properties are also available, and may be useful lz4 The codec used to compress internal data such as RDD partitions, event log, broadcast variables - and shuffle outputs. By default, Spark provides three codecs: lz4, lzf, - and snappy. You can also use fully qualified class names to specify the codec, + and shuffle outputs. By default, Spark provides four codecs: lz4, lzf, + snappy, and zstd. You can also use fully qualified class names to specify the codec, e.g. org.apache.spark.io.LZ4CompressionCodec, org.apache.spark.io.LZFCompressionCodec, @@ -917,7 +925,7 @@ Apart from these, the following properties are also available, and may be useful spark.io.compression.lz4.blockSize 32k - Block size used in LZ4 compression, in the case when LZ4 compression codec + Block size in bytes used in LZ4 compression, in the case when LZ4 compression codec is used. Lowering this block size will also lower shuffle memory usage when LZ4 is used. @@ -925,7 +933,7 @@ Apart from these, the following properties are also available, and may be useful spark.io.compression.snappy.blockSize 32k - Block size used in Snappy compression, in the case when Snappy compression codec + Block size in bytes used in Snappy compression, in the case when Snappy compression codec is used. Lowering this block size will also lower shuffle memory usage when Snappy is used. @@ -941,7 +949,7 @@ Apart from these, the following properties are also available, and may be useful spark.io.compression.zstd.bufferSize 32k - Buffer size used in Zstd compression, in the case when Zstd compression codec + Buffer size in bytes used in Zstd compression, in the case when Zstd compression codec is used. Lowering this size will lower the shuffle memory usage when Zstd is used, but it might increase the compression cost because of excessive JNI call overhead. @@ -1001,8 +1009,8 @@ Apart from these, the following properties are also available, and may be useful spark.kryoserializer.buffer.max 64m - Maximum allowable size of Kryo serialization buffer. This must be larger than any - object you attempt to serialize and must be less than 2048m. + Maximum allowable size of Kryo serialization buffer, in MiB unless otherwise specified. + This must be larger than any object you attempt to serialize and must be less than 2048m. Increase this if you get a "buffer limit exceeded" exception inside Kryo. @@ -1010,9 +1018,9 @@ Apart from these, the following properties are also available, and may be useful spark.kryoserializer.buffer 64k - Initial size of Kryo's serialization buffer. Note that there will be one buffer - per core on each worker. This buffer will grow up to - spark.kryoserializer.buffer.max if needed. + Initial size of Kryo's serialization buffer, in KiB unless otherwise specified. + Note that there will be one buffer per core on each worker. This buffer will grow up to + spark.kryoserializer.buffer.max if needed. @@ -1086,7 +1094,8 @@ Apart from these, the following properties are also available, and may be useful spark.memory.offHeap.enabled false - If true, Spark will attempt to use off-heap memory for certain operations. If off-heap memory use is enabled, then spark.memory.offHeap.size must be positive. + If true, Spark will attempt to use off-heap memory for certain operations. If off-heap memory + use is enabled, then spark.memory.offHeap.size must be positive. @@ -1094,7 +1103,8 @@ Apart from these, the following properties are also available, and may be useful 0 The absolute amount of memory in bytes which can be used for off-heap allocation. - This setting has no impact on heap memory usage, so if your executors' total memory consumption must fit within some hard limit then be sure to shrink your JVM heap size accordingly. + This setting has no impact on heap memory usage, so if your executors' total memory consumption + must fit within some hard limit then be sure to shrink your JVM heap size accordingly. This must be set to a positive value when spark.memory.offHeap.enabled=true. @@ -1202,9 +1212,9 @@ Apart from these, the following properties are also available, and may be useful spark.broadcast.blockSize 4m - Size of each piece of a block for TorrentBroadcastFactory. - Too large a value decreases parallelism during broadcast (makes it slower); however, if it is - too small, BlockManager might take a performance hit. + Size of each piece of a block for TorrentBroadcastFactory, in KiB unless otherwise + specified. Too large a value decreases parallelism during broadcast (makes it slower); however, + if it is too small, BlockManager might take a performance hit. @@ -1284,7 +1294,7 @@ Apart from these, the following properties are also available, and may be useful spark.files.openCostInBytes 4194304 (4 MB) - The estimated cost to open a file, measured by the number of bytes could be scanned in the same + The estimated cost to open a file, measured by the number of bytes could be scanned at the same time. This is used when putting multiple files into a partition. It is better to over estimate, then the partitions with small files will be faster than partitions with bigger files. @@ -1312,7 +1322,7 @@ Apart from these, the following properties are also available, and may be useful spark.storage.memoryMapThreshold 2m - Size of a block above which Spark memory maps when reading a block from disk. + Size in bytes of a block above which Spark memory maps when reading a block from disk. This prevents Spark from memory mapping very small blocks. In general, memory mapping has high overhead for blocks close to or below the page size of the operating system. @@ -1845,8 +1855,8 @@ Apart from these, the following properties are also available, and may be useful spark.user.groups.mapping org.apache.spark.security.ShellBasedGroupsMappingProvider - The list of groups for a user are determined by a group mapping service defined by the trait - org.apache.spark.security.GroupMappingServiceProvider which can configured by this property. + The list of groups for a user is determined by a group mapping service defined by the trait + org.apache.spark.security.GroupMappingServiceProvider which can be configured by this property. A default unix shell based implementation is provided org.apache.spark.security.ShellBasedGroupsMappingProvider which can be specified to resolve a list of groups for a user. Note: This implementation supports only a Unix/Linux based environment. Windows environment is @@ -2455,7 +2465,7 @@ should be included on Spark's classpath: The location of these configuration files varies across Hadoop versions, but a common location is inside of `/etc/hadoop/conf`. Some tools create -configurations on-the-fly, but offer a mechanisms to download copies of them. +configurations on-the-fly, but offer a mechanism to download copies of them. To make these files visible to Spark, set `HADOOP_CONF_DIR` in `$SPARK_HOME/conf/spark-env.sh` to a location containing the configuration files. @@ -2490,4 +2500,4 @@ Also, you can modify or add configurations at runtime: --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" \ --conf spark.hadoop.abc.def=xyz \ myApp.jar -{% endhighlight %} \ No newline at end of file +{% endhighlight %} diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md index 46225dc598da8..5c97a248df4bc 100644 --- a/docs/graphx-programming-guide.md +++ b/docs/graphx-programming-guide.md @@ -708,7 +708,7 @@ messages remaining. > messaging function. These constraints allow additional optimization within GraphX. The following is the type signature of the [Pregel operator][GraphOps.pregel] as well as a *sketch* -of its implementation (note: to avoid stackOverflowError due to long lineage chains, pregel support periodcally +of its implementation (note: to avoid stackOverflowError due to long lineage chains, pregel support periodically checkpoint graph and messages by setting "spark.graphx.pregel.checkpointInterval" to a positive number, say 10. And set checkpoint directory as well using SparkContext.setCheckpointDir(directory: String)): @@ -928,7 +928,7 @@ switch to 2D-partitioning or other heuristics included in GraphX.

      -Once the edges have be partitioned the key challenge to efficient graph-parallel computation is +Once the edges have been partitioned the key challenge to efficient graph-parallel computation is efficiently joining vertex attributes with the edges. Because real-world graphs typically have more edges than vertices, we move vertex attributes to the edges. Because not all partitions will contain edges adjacent to all vertices we internally maintain a routing table which identifies where diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md index bf979f3c73a52..ddd2f4b49ca07 100644 --- a/docs/ml-classification-regression.md +++ b/docs/ml-classification-regression.md @@ -87,7 +87,7 @@ More details on parameters can be found in the [R API documentation](api/R/spark The `spark.ml` implementation of logistic regression also supports extracting a summary of the model over the training set. Note that the predictions and metrics which are stored as `DataFrame` in -`BinaryLogisticRegressionSummary` are annotated `@transient` and hence +`LogisticRegressionSummary` are annotated `@transient` and hence only available on the driver.
      @@ -97,10 +97,9 @@ only available on the driver. [`LogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionTrainingSummary) provides a summary for a [`LogisticRegressionModel`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionModel). -Currently, only binary classification is supported and the -summary must be explicitly cast to -[`BinaryLogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary). -This will likely change when multiclass classification is supported. +In the case of binary classification, certain additional metrics are +available, e.g. ROC curve. The binary summary can be accessed via the +`binarySummary` method. See [`BinaryLogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary). Continuing the earlier example: @@ -111,10 +110,9 @@ Continuing the earlier example: [`LogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/LogisticRegressionTrainingSummary.html) provides a summary for a [`LogisticRegressionModel`](api/java/org/apache/spark/ml/classification/LogisticRegressionModel.html). -Currently, only binary classification is supported and the -summary must be explicitly cast to -[`BinaryLogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/BinaryLogisticRegressionTrainingSummary.html). -Support for multiclass model summaries will be added in the future. +In the case of binary classification, certain additional metrics are +available, e.g. ROC curve. The binary summary can be accessed via the +`binarySummary` method. See [`BinaryLogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/BinaryLogisticRegressionTrainingSummary.html). Continuing the earlier example: @@ -125,7 +123,8 @@ Continuing the earlier example: [`LogisticRegressionTrainingSummary`](api/python/pyspark.ml.html#pyspark.ml.classification.LogisticRegressionSummary) provides a summary for a [`LogisticRegressionModel`](api/python/pyspark.ml.html#pyspark.ml.classification.LogisticRegressionModel). -Currently, only binary classification is supported. Support for multiclass model summaries will be added in the future. +In the case of binary classification, certain additional metrics are +available, e.g. ROC curve. See [`BinaryLogisticRegressionTrainingSummary`](api/python/pyspark.ml.html#pyspark.ml.classification.BinaryLogisticRegressionTrainingSummary). Continuing the earlier example: @@ -162,7 +161,8 @@ For a detailed derivation please see [here](https://en.wikipedia.org/wiki/Multin **Examples** The following example shows how to train a multiclass logistic regression -model with elastic net regularization. +model with elastic net regularization, as well as extract the multiclass +training summary for evaluating the model.
      diff --git a/docs/ml-features.md b/docs/ml-features.md index 72643137d96b1..3370eb3893272 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -222,9 +222,9 @@ The `FeatureHasher` transformer operates on multiple columns. Each column may co numeric or categorical features. Behavior and handling of column data types is as follows: - Numeric columns: For numeric features, the hash value of the column name is used to map the -feature value to its index in the feature vector. Numeric features are never treated as -categorical, even when they are integers. You must explicitly convert numeric columns containing -categorical features to strings first. +feature value to its index in the feature vector. By default, numeric features are not treated +as categorical (even when they are integers). To treat them as categorical, specify the relevant +columns using the `categoricalCols` parameter. - String columns: For categorical features, the hash value of the string "column_name=value" is used to map to the vector index, with an indicator value of `1.0`. Thus, categorical features are "one-hot" encoded (similarly to using [OneHotEncoder](ml-features.html#onehotencoder) with @@ -775,35 +775,43 @@ for more details on the API.
      -## OneHotEncoder +## OneHotEncoder (Deprecated since 2.3.0) -[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a column of label indices to a column of binary vectors, with at most a single one-value. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features. +Because this existing `OneHotEncoder` is a stateless transformer, it is not usable on new data where the number of categories may differ from the training data. In order to fix this, a new `OneHotEncoderEstimator` was created that produces an `OneHotEncoderModel` when fitting. For more detail, please see [SPARK-13030](https://issues.apache.org/jira/browse/SPARK-13030). + +`OneHotEncoder` has been deprecated in 2.3.0 and will be removed in 3.0.0. Please use [OneHotEncoderEstimator](ml-features.html#onehotencoderestimator) instead. + +## OneHotEncoderEstimator + +[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a categorical feature, represented as a label index, to a binary vector with at most a single one-value indicating the presence of a specific feature value from among the set of all feature values. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features. For string type input data, it is common to encode categorical features using [StringIndexer](ml-features.html#stringindexer) first. + +`OneHotEncoderEstimator` can transform multiple columns, returning an one-hot-encoded output vector column for each input column. It is common to merge these vectors into a single feature vector using [VectorAssembler](ml-features.html#vectorassembler). + +`OneHotEncoderEstimator` supports the `handleInvalid` parameter to choose how to handle invalid input during transforming data. Available options include 'keep' (any invalid inputs are assigned to an extra categorical index) and 'error' (throw an error). **Examples**
      -Refer to the [OneHotEncoder Scala docs](api/scala/index.html#org.apache.spark.ml.feature.OneHotEncoder) -for more details on the API. +Refer to the [OneHotEncoderEstimator Scala docs](api/scala/index.html#org.apache.spark.ml.feature.OneHotEncoderEstimator) for more details on the API. -{% include_example scala/org/apache/spark/examples/ml/OneHotEncoderExample.scala %} +{% include_example scala/org/apache/spark/examples/ml/OneHotEncoderEstimatorExample.scala %}
      -Refer to the [OneHotEncoder Java docs](api/java/org/apache/spark/ml/feature/OneHotEncoder.html) +Refer to the [OneHotEncoderEstimator Java docs](api/java/org/apache/spark/ml/feature/OneHotEncoderEstimator.html) for more details on the API. -{% include_example java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java %} +{% include_example java/org/apache/spark/examples/ml/JavaOneHotEncoderEstimatorExample.java %}
      -Refer to the [OneHotEncoder Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.OneHotEncoder) -for more details on the API. +Refer to the [OneHotEncoderEstimator Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.OneHotEncoderEstimator) for more details on the API. -{% include_example python/ml/onehot_encoder_example.py %} +{% include_example python/ml/onehot_encoder_estimator_example.py %}
      @@ -1283,6 +1291,57 @@ for more details on the API.
    +## VectorSizeHint + +It can sometimes be useful to explicitly specify the size of the vectors for a column of +`VectorType`. For example, `VectorAssembler` uses size information from its input columns to +produce size information and metadata for its output column. While in some cases this information +can be obtained by inspecting the contents of the column, in a streaming dataframe the contents are +not available until the stream is started. `VectorSizeHint` allows a user to explicitly specify the +vector size for a column so that `VectorAssembler`, or other transformers that might +need to know vector size, can use that column as an input. + +To use `VectorSizeHint` a user must set the `inputCol` and `size` parameters. Applying this +transformer to a dataframe produces a new dataframe with updated metadata for `inputCol` specifying +the vector size. Downstream operations on the resulting dataframe can get this size using the +meatadata. + +`VectorSizeHint` can also take an optional `handleInvalid` parameter which controls its +behaviour when the vector column contains nulls or vectors of the wrong size. By default +`handleInvalid` is set to "error", indicating an exception should be thrown. This parameter can +also be set to "skip", indicating that rows containing invalid values should be filtered out from +the resulting dataframe, or "optimistic", indicating that the column should not be checked for +invalid values and all rows should be kept. Note that the use of "optimistic" can cause the +resulting dataframe to be in an inconsistent state, me:aning the metadata for the column +`VectorSizeHint` was applied to does not match the contents of that column. Users should take care +to avoid this kind of inconsistent state. + +
    +
    + +Refer to the [VectorSizeHint Scala docs](api/scala/index.html#org.apache.spark.ml.feature.VectorSizeHint) +for more details on the API. + +{% include_example scala/org/apache/spark/examples/ml/VectorSizeHintExample.scala %} +
    + +
    + +Refer to the [VectorSizeHint Java docs](api/java/org/apache/spark/ml/feature/VectorSizeHint.html) +for more details on the API. + +{% include_example java/org/apache/spark/examples/ml/JavaVectorSizeHintExample.java %} +
    + +
    + +Refer to the [VectorSizeHint Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.VectorSizeHint) +for more details on the API. + +{% include_example python/ml/vector_size_hint_example.py %} +
    +
    + ## QuantileDiscretizer `QuantileDiscretizer` takes a column with continuous features and outputs a column with binned diff --git a/docs/ml-guide.md b/docs/ml-guide.md index f6288e7c32d97..aea07be34cb86 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -72,32 +72,31 @@ To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 [^1]: To learn more about the benefits and background of system optimised natives, you may wish to watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/). -# Highlights in 2.2 +# Highlights in 2.3 -The list below highlights some of the new features and enhancements added to MLlib in the `2.2` +The list below highlights some of the new features and enhancements added to MLlib in the `2.3` release of Spark: -* [`ALS`](ml-collaborative-filtering.html) methods for _top-k_ recommendations for all - users or items, matching the functionality in `mllib` - ([SPARK-19535](https://issues.apache.org/jira/browse/SPARK-19535)). - Performance was also improved for both `ml` and `mllib` - ([SPARK-11968](https://issues.apache.org/jira/browse/SPARK-11968) and - [SPARK-20587](https://issues.apache.org/jira/browse/SPARK-20587)) -* [`Correlation`](ml-statistics.html#correlation) and - [`ChiSquareTest`](ml-statistics.html#hypothesis-testing) stats functions for `DataFrames` - ([SPARK-19636](https://issues.apache.org/jira/browse/SPARK-19636) and - [SPARK-19635](https://issues.apache.org/jira/browse/SPARK-19635)) -* [`FPGrowth`](ml-frequent-pattern-mining.html#fp-growth) algorithm for frequent pattern mining - ([SPARK-14503](https://issues.apache.org/jira/browse/SPARK-14503)) -* `GLM` now supports the full `Tweedie` family - ([SPARK-18929](https://issues.apache.org/jira/browse/SPARK-18929)) -* [`Imputer`](ml-features.html#imputer) feature transformer to impute missing values in a dataset - ([SPARK-13568](https://issues.apache.org/jira/browse/SPARK-13568)) -* [`LinearSVC`](ml-classification-regression.html#linear-support-vector-machine) - for linear Support Vector Machine classification - ([SPARK-14709](https://issues.apache.org/jira/browse/SPARK-14709)) -* Logistic regression now supports constraints on the coefficients during training - ([SPARK-20047](https://issues.apache.org/jira/browse/SPARK-20047)) +* Built-in support for reading images into a `DataFrame` was added +([SPARK-21866](https://issues.apache.org/jira/browse/SPARK-21866)). +* [`OneHotEncoderEstimator`](ml-features.html#onehotencoderestimator) was added, and should be +used instead of the existing `OneHotEncoder` transformer. The new estimator supports +transforming multiple columns. +* Multiple column support was also added to `QuantileDiscretizer` and `Bucketizer` +([SPARK-22397](https://issues.apache.org/jira/browse/SPARK-22397) and +[SPARK-20542](https://issues.apache.org/jira/browse/SPARK-20542)) +* A new [`FeatureHasher`](ml-features.html#featurehasher) transformer was added + ([SPARK-13969](https://issues.apache.org/jira/browse/SPARK-13969)). +* Added support for evaluating multiple models in parallel when performing cross-validation using +[`TrainValidationSplit` or `CrossValidator`](ml-tuning.html) +([SPARK-19357](https://issues.apache.org/jira/browse/SPARK-19357)). +* Improved support for custom pipeline components in Python (see +[SPARK-21633](https://issues.apache.org/jira/browse/SPARK-21633) and +[SPARK-21542](https://issues.apache.org/jira/browse/SPARK-21542)). +* `DataFrame` functions for descriptive summary statistics over vector columns +([SPARK-19634](https://issues.apache.org/jira/browse/SPARK-19634)). +* Robust linear regression with Huber loss +([SPARK-3181](https://issues.apache.org/jira/browse/SPARK-3181)). # Migration guide @@ -109,42 +108,40 @@ and the migration guide below will explain all changes between releases. ### Breaking changes -There are no breaking changes. +* The class and trait hierarchy for logistic regression model summaries was changed to be cleaner +and better accommodate the addition of the multi-class summary. This is a breaking change for user +code that casts a `LogisticRegressionTrainingSummary` to a +`BinaryLogisticRegressionTrainingSummary`. Users should instead use the `model.binarySummary` +method. See [SPARK-17139](https://issues.apache.org/jira/browse/SPARK-17139) for more detail +(_note_ this is an `Experimental` API). This _does not_ affect the Python `summary` method, which +will still work correctly for both multinomial and binary cases. ### Deprecations and changes of behavior **Deprecations** -There are no deprecations. +* `OneHotEncoder` has been deprecated and will be removed in `3.0`. It has been replaced by the +new [`OneHotEncoderEstimator`](ml-features.html#onehotencoderestimator) +(see [SPARK-13030](https://issues.apache.org/jira/browse/SPARK-13030)). **Note** that +`OneHotEncoderEstimator` will be renamed to `OneHotEncoder` in `3.0` (but +`OneHotEncoderEstimator` will be kept as an alias). **Changes of behavior** * [SPARK-21027](https://issues.apache.org/jira/browse/SPARK-21027): - We are now setting the default parallelism used in `OneVsRest` to be 1 (i.e. serial), in 2.2 and earlier version, - the `OneVsRest` parallelism would be parallelism of the default threadpool in scala. - -## From 2.1 to 2.2 - -### Breaking changes - -There are no breaking changes. - -### Deprecations and changes of behavior - -**Deprecations** - -There are no deprecations. - -**Changes of behavior** - -* [SPARK-19787](https://issues.apache.org/jira/browse/SPARK-19787): - Default value of `regParam` changed from `1.0` to `0.1` for `ALS.train` method (marked `DeveloperApi`). - **Note** this does _not affect_ the `ALS` Estimator or Model, nor MLlib's `ALS` class. -* [SPARK-14772](https://issues.apache.org/jira/browse/SPARK-14772): - Fixed inconsistency between Python and Scala APIs for `Param.copy` method. -* [SPARK-11569](https://issues.apache.org/jira/browse/SPARK-11569): - `StringIndexer` now handles `NULL` values in the same way as unseen values. Previously an exception - would always be thrown regardless of the setting of the `handleInvalid` parameter. + The default parallelism used in `OneVsRest` is now set to 1 (i.e. serial). In `2.2` and + earlier versions, the level of parallelism was set to the default threadpool size in Scala. +* [SPARK-22156](https://issues.apache.org/jira/browse/SPARK-22156): + The learning rate update for `Word2Vec` was incorrect when `numIterations` was set greater than + `1`. This will cause training results to be different between `2.3` and earlier versions. +* [SPARK-21681](https://issues.apache.org/jira/browse/SPARK-21681): + Fixed an edge case bug in multinomial logistic regression that resulted in incorrect coefficients + when some features had zero variance. +* [SPARK-16957](https://issues.apache.org/jira/browse/SPARK-16957): + Tree algorithms now use mid-points for split values. This may change results from model training. +* [SPARK-14657](https://issues.apache.org/jira/browse/SPARK-14657): + Fixed an issue where the features generated by `RFormula` without an intercept were inconsistent + with the output in R. This may change results from model training in this scenario. ## Previous Spark versions diff --git a/docs/ml-migration-guides.md b/docs/ml-migration-guides.md index 687d7c8930362..f4b0df58cf63b 100644 --- a/docs/ml-migration-guides.md +++ b/docs/ml-migration-guides.md @@ -7,6 +7,29 @@ description: MLlib migration guides from before Spark SPARK_VERSION_SHORT The migration guide for the current Spark version is kept on the [MLlib Guide main page](ml-guide.html#migration-guide). +## From 2.1 to 2.2 + +### Breaking changes + +There are no breaking changes. + +### Deprecations and changes of behavior + +**Deprecations** + +There are no deprecations. + +**Changes of behavior** + +* [SPARK-19787](https://issues.apache.org/jira/browse/SPARK-19787): + Default value of `regParam` changed from `1.0` to `0.1` for `ALS.train` method (marked `DeveloperApi`). + **Note** this does _not affect_ the `ALS` Estimator or Model, nor MLlib's `ALS` class. +* [SPARK-14772](https://issues.apache.org/jira/browse/SPARK-14772): + Fixed inconsistency between Python and Scala APIs for `Param.copy` method. +* [SPARK-11569](https://issues.apache.org/jira/browse/SPARK-11569): + `StringIndexer` now handles `NULL` values in the same way as unseen values. Previously an exception + would always be thrown regardless of the setting of the `handleInvalid` parameter. + ## From 2.0 to 2.1 ### Breaking changes diff --git a/docs/ml-pipeline.md b/docs/ml-pipeline.md index aa92c0a37c0f4..e22e9003c30f6 100644 --- a/docs/ml-pipeline.md +++ b/docs/ml-pipeline.md @@ -188,9 +188,36 @@ Parameters belong to specific instances of `Estimator`s and `Transformer`s. For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`. This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`. -## Saving and Loading Pipelines +## ML persistence: Saving and Loading Pipelines -Often times it is worth it to save a model or a pipeline to disk for later use. In Spark 1.6, a model import/export functionality was added to the Pipeline API. Most basic transformers are supported as well as some of the more basic ML models. Please refer to the algorithm's API documentation to see if saving and loading is supported. +Often times it is worth it to save a model or a pipeline to disk for later use. In Spark 1.6, a model import/export functionality was added to the Pipeline API. +As of Spark 2.3, the DataFrame-based API in `spark.ml` and `pyspark.ml` has complete coverage. + +ML persistence works across Scala, Java and Python. However, R currently uses a modified format, +so models saved in R can only be loaded back in R; this should be fixed in the future and is +tracked in [SPARK-15572](https://issues.apache.org/jira/browse/SPARK-15572). + +### Backwards compatibility for ML persistence + +In general, MLlib maintains backwards compatibility for ML persistence. I.e., if you save an ML +model or Pipeline in one version of Spark, then you should be able to load it back and use it in a +future version of Spark. However, there are rare exceptions, described below. + +Model persistence: Is a model or Pipeline saved using Apache Spark ML persistence in Spark +version X loadable by Spark version Y? + +* Major versions: No guarantees, but best-effort. +* Minor and patch versions: Yes; these are backwards compatible. +* Note about the format: There are no guarantees for a stable persistence format, but model loading itself is designed to be backwards compatible. + +Model behavior: Does a model or Pipeline in Spark version X behave identically in Spark version Y? + +* Major versions: No guarantees, but best-effort. +* Minor and patch versions: Identical behavior, except for bug fixes. + +For both model persistence and model behavior, any breaking changes across a minor version or patch +version are reported in the Spark version release notes. If a breakage is not reported in release +notes, then it should be treated as a bug to be fixed. # Code examples diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index 75aea70601875..8b89296b14cdd 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -278,8 +278,8 @@ for details on the API. multiplication. In other words, it scales each column of the dataset by a scalar multiplier. This represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) between the input vector, `v` and transforming vector, `scalingVec`, to yield a result vector. -Qu8T948*1# -Denoting the `scalingVec` as "`w`," this transformation may be written as: + +Denoting the `scalingVec` as "`w`", this transformation may be written as: `\[ \begin{pmatrix} v_1 \\ diff --git a/docs/mllib-pmml-model-export.md b/docs/mllib-pmml-model-export.md index d3530908706d0..f567565437927 100644 --- a/docs/mllib-pmml-model-export.md +++ b/docs/mllib-pmml-model-export.md @@ -7,7 +7,7 @@ displayTitle: PMML model export - RDD-based API * Table of contents {:toc} -## `spark.mllib` supported models +## spark.mllib supported models `spark.mllib` supports model export to Predictive Model Markup Language ([PMML](http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language)). @@ -15,7 +15,7 @@ The table below outlines the `spark.mllib` models that can be exported to PMML a - + diff --git a/docs/monitoring.md b/docs/monitoring.md index f8d3ce91a0691..6f6cfc1288d73 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -118,7 +118,7 @@ The history server can be configured as follows: @@ -407,7 +407,7 @@ can be identified by their `[attempt-id]`. In the API listed below, when running
    `spark.mllib` modelPMML model
    spark.mllib modelPMML model
    The number of applications to retain UI data for in the cache. If this cap is exceeded, then the oldest applications will be removed from the cache. If an application is not in the cache, - it will have to be loaded from disk if its accessed from the UI. + it will have to be loaded from disk if it is accessed from the UI.
    -The number of jobs and stages which can retrieved is constrained by the same retention +The number of jobs and stages which can be retrieved is constrained by the same retention mechanism of the standalone Spark UI; `"spark.ui.retainedJobs"` defines the threshold value triggering garbage collection on jobs, and `spark.ui.retainedStages` that for stages. Note that the garbage collection takes place on playback: it is possible to retrieve @@ -422,10 +422,10 @@ These endpoints have been strongly versioned to make it easier to develop applic * Individual fields will never be removed for any given endpoint * New endpoints may be added * New fields may be added to existing endpoints -* New versions of the api may be added in the future at a separate endpoint (eg., `api/v2`). New versions are *not* required to be backwards compatible. +* New versions of the api may be added in the future as a separate endpoint (eg., `api/v2`). New versions are *not* required to be backwards compatible. * Api versions may be dropped, but only after at least one minor release of co-existing with a new api version. -Note that even when examining the UI of a running applications, the `applications/[app-id]` portion is +Note that even when examining the UI of running applications, the `applications/[app-id]` portion is still required, though there is only one application available. Eg. to see the list of jobs for the running app, you would go to `http://localhost:4040/api/v1/applications/[app-id]/jobs`. This is to keep the paths consistent in both modes. diff --git a/docs/quick-start.md b/docs/quick-start.md index 200b97230e866..07c520cbee6be 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -67,7 +67,7 @@ res3: Long = 15 ./bin/pyspark -Or if PySpark is installed with pip in your current enviroment: +Or if PySpark is installed with pip in your current environment: pyspark @@ -156,7 +156,7 @@ One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can i >>> wordCounts = textFile.select(explode(split(textFile.value, "\s+")).alias("word")).groupBy("word").count() {% endhighlight %} -Here, we use the `explode` function in `select`, to transfrom a Dataset of lines to a Dataset of words, and then combine `groupBy` and `count` to compute the per-word counts in the file as a DataFrame of 2 columns: "word" and "count". To collect the word counts in our shell, we can call `collect`: +Here, we use the `explode` function in `select`, to transform a Dataset of lines to a Dataset of words, and then combine `groupBy` and `count` to compute the per-word counts in the file as a DataFrame of 2 columns: "word" and "count". To collect the word counts in our shell, we can call `collect`: {% highlight python %} >>> wordCounts.collect() @@ -422,7 +422,7 @@ $ YOUR_SPARK_HOME/bin/spark-submit \ Lines with a: 46, Lines with b: 23 {% endhighlight %} -If you have PySpark pip installed into your enviroment (e.g., `pip install pyspark`), you can run your application with the regular Python interpreter or use the provided 'spark-submit' as you prefer. +If you have PySpark pip installed into your environment (e.g., `pip install pyspark`), you can run your application with the regular Python interpreter or use the provided 'spark-submit' as you prefer. {% highlight bash %} # Use the Python interpreter to run your application diff --git a/docs/running-on-kubernetes.md b/docs/running-on-kubernetes.md index e491329136a3c..3c7586e8544ba 100644 --- a/docs/running-on-kubernetes.md +++ b/docs/running-on-kubernetes.md @@ -8,6 +8,10 @@ title: Running Spark on Kubernetes Spark can run on clusters managed by [Kubernetes](https://kubernetes.io). This feature makes use of native Kubernetes scheduler that has been added to Spark. +**The Kubernetes scheduler is currently experimental. +In future versions, there may be behavioral changes around configuration, +container images and entrypoints.** + # Prerequisites * A runnable distribution of Spark 2.3 or above. @@ -16,6 +20,9 @@ Kubernetes scheduler that has been added to Spark. you may setup a test cluster on your local machine using [minikube](https://kubernetes.io/docs/getting-started-guides/minikube/). * We recommend using the latest release of minikube with the DNS addon enabled. + * Be aware that the default minikube configuration is not enough for running Spark applications. + We recommend 3 CPUs and 4g of memory to be able to start a simple Spark application with a single + executor. * You must have appropriate permissions to list, create, edit and delete [pods](https://kubernetes.io/docs/user-guide/pods/) in your cluster. You can verify that you can list these resources by running `kubectl auth can-i pods`. @@ -38,11 +45,10 @@ logs and remains in "completed" state in the Kubernetes API until it's eventuall Note that in the completed state, the driver pod does *not* use any computational or memory resources. -The driver and executor pod scheduling is handled by Kubernetes. It will be possible to affect Kubernetes scheduling -decisions for driver and executor pods using advanced primitives like -[node selectors](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#nodeselector) -and [node/pod affinities](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity) -in a future release. +The driver and executor pod scheduling is handled by Kubernetes. It is possible to schedule the +driver and executor pods on a subset of available nodes through a [node selector](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#nodeselector) +using the configuration property for it. It will be possible to use more advanced +scheduling hints like [node/pod affinities](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity) in a future release. # Submitting Applications to Kubernetes @@ -50,20 +56,19 @@ in a future release. Kubernetes requires users to supply images that can be deployed into containers within pods. The images are built to be run in a container runtime environment that Kubernetes supports. Docker is a container runtime environment that is -frequently used with Kubernetes. With Spark 2.3, there are Dockerfiles provided in the runnable distribution that can be customized -and built for your usage. +frequently used with Kubernetes. Spark (starting with version 2.3) ships with a Dockerfile that can be used for this +purpose, or customized to match an individual application's needs. It can be found in the `kubernetes/dockerfiles/` +directory. -You may build these docker images from sources. -There is a script, `sbin/build-push-docker-images.sh` that you can use to build and push -customized Spark distribution images consisting of all the above components. +Spark also ships with a `bin/docker-image-tool.sh` script that can be used to build and publish the Docker images to +use with the Kubernetes backend. Example usage is: - ./sbin/build-push-docker-images.sh -r -t my-tag build - ./sbin/build-push-docker-images.sh -r -t my-tag push - -Docker files are under the `kubernetes/dockerfiles/` directory and can be customized further before -building using the supplied script, or manually. +```bash +$ ./bin/docker-image-tool.sh -r -t my-tag build +$ ./bin/docker-image-tool.sh -r -t my-tag push +``` ## Cluster Mode @@ -76,8 +81,7 @@ $ bin/spark-submit \ --name spark-pi \ --class org.apache.spark.examples.SparkPi \ --conf spark.executor.instances=5 \ - --conf spark.kubernetes.driver.container.image= \ - --conf spark.kubernetes.executor.container.image= \ + --conf spark.kubernetes.container.image= \ local:///path/to/examples.jar ``` @@ -95,7 +99,7 @@ must consist of lower case alphanumeric characters, `-`, and `.` and must start If you have a Kubernetes cluster setup, one way to discover the apiserver URL is by executing `kubectl cluster-info`. ```bash -kubectl cluster-info +$ kubectl cluster-info Kubernetes master is running at http://127.0.0.1:6443 ``` @@ -106,7 +110,7 @@ authenticating proxy, `kubectl proxy` to communicate to the Kubernetes API. The local proxy can be started by: ```bash -kubectl proxy +$ kubectl proxy ``` If the local proxy is running at localhost:8001, `--master k8s://http://127.0.0.1:8001` can be used as the argument to @@ -118,18 +122,15 @@ This URI is the location of the example jar that is already in the Docker image. If your application's dependencies are all hosted in remote locations like HDFS or HTTP servers, they may be referred to by their appropriate remote URIs. Also, application dependencies can be pre-mounted into custom-built Docker images. Those dependencies can be added to the classpath by referencing them with `local://` URIs and/or setting the -`SPARK_EXTRA_CLASSPATH` environment variable in your Dockerfiles. +`SPARK_EXTRA_CLASSPATH` environment variable in your Dockerfiles. The `local://` scheme is also required when referring to +dependencies in custom-built Docker images in `spark-submit`. Note that using application dependencies from the submission +client's local file system is currently not yet supported. + ### Using Remote Dependencies When there are application dependencies hosted in remote locations like HDFS or HTTP servers, the driver and executor pods need a Kubernetes [init-container](https://kubernetes.io/docs/concepts/workloads/pods/init-containers/) for downloading -the dependencies so the driver and executor containers can use them locally. This requires users to specify the container -image for the init-container using the configuration property `spark.kubernetes.initContainer.image`. For example, users -simply add the following option to the `spark-submit` command to specify the init-container image: - -``` ---conf spark.kubernetes.initContainer.image= -``` +the dependencies so the driver and executor containers can use them locally. The init-container handles remote dependencies specified in `spark.jars` (or the `--jars` option of `spark-submit`) and `spark.files` (or the `--files` option of `spark-submit`). It also handles remotely hosted main application resources, e.g., @@ -144,9 +145,7 @@ $ bin/spark-submit \ --jars https://path/to/dependency1.jar,https://path/to/dependency2.jar --files hdfs://host:port/path/to/file1,hdfs://host:port/path/to/file2 --conf spark.executor.instances=5 \ - --conf spark.kubernetes.driver.container.image= \ - --conf spark.kubernetes.executor.container.image= \ - --conf spark.kubernetes.initContainer.image= + --conf spark.kubernetes.container.image= \ https://path/to/examples.jar ``` @@ -179,7 +178,7 @@ Logs can be accessed using the Kubernetes API and the `kubectl` CLI. When a Spar to stream logs from the application using: ```bash -kubectl -n= logs -f +$ kubectl -n= logs -f ``` The same logs can also be accessed through the @@ -192,12 +191,12 @@ The UI associated with any application can be accessed locally using [`kubectl port-forward`](https://kubernetes.io/docs/tasks/access-application-cluster/port-forward-access-application-cluster/#forward-a-local-port-to-a-port-on-the-pod). ```bash -kubectl port-forward 4040:4040 +$ kubectl port-forward 4040:4040 ``` Then, the Spark driver UI can be accessed on `http://localhost:4040`. -### Debugging +### Debugging There may be several kinds of failures. If the Kubernetes API server rejects the request made from spark-submit, or the connection is refused for a different reason, the submission logic should indicate the error encountered. However, if there @@ -206,17 +205,17 @@ are errors during the running of the application, often, the best way to investi To get some basic information about the scheduling decisions made around the driver pod, you can run: ```bash -kubectl describe pod +$ kubectl describe pod ``` If the pod has encountered a runtime error, the status can be probed further using: ```bash -kubectl logs +$ kubectl logs ``` -Status and logs of failed executor pods can be checked in similar ways. Finally, deleting the driver pod will clean up the entire spark -application, includling all executors, associated service, etc. The driver pod can be thought of as the Kubernetes representation of +Status and logs of failed executor pods can be checked in similar ways. Finally, deleting the driver pod will clean up the entire spark +application, including all executors, associated service, etc. The driver pod can be thought of as the Kubernetes representation of the Spark application. ## Kubernetes Features @@ -260,7 +259,7 @@ To create a custom service account, a user can use the `kubectl create serviceac following command creates a service account named `spark`: ```bash -kubectl create serviceaccount spark +$ kubectl create serviceaccount spark ``` To grant a service account a `Role` or `ClusterRole`, a `RoleBinding` or `ClusterRoleBinding` is needed. To create @@ -269,7 +268,7 @@ for `ClusterRoleBinding`) command. For example, the following command creates an namespace and grants it to the `spark` service account created above: ```bash -kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=default:spark --namespace=default +$ kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=default:spark --namespace=default ``` Note that a `Role` can only be used to grant access to resources (like pods) within a single namespace, whereas a @@ -319,21 +318,27 @@ specific to Spark on Kubernetes. - spark.kubernetes.driver.container.image + spark.kubernetes.container.image (none) - Container image to use for the driver. - This is usually of the form example.com/repo/spark-driver:v1.0.0. - This configuration is required and must be provided by the user. + Container image to use for the Spark application. + This is usually of the form example.com/repo/spark:v1.0.0. + This configuration is required and must be provided by the user, unless explicit + images are provided for each different container type. + + + + spark.kubernetes.driver.container.image + (value of spark.kubernetes.container.image) + + Custom container image to use for the driver. spark.kubernetes.executor.container.image - (none) + (value of spark.kubernetes.container.image) - Container image to use for the executors. - This is usually of the form example.com/repo/spark-executor:v1.0.0. - This configuration is required and must be provided by the user. + Custom container image to use for executors. @@ -543,14 +548,6 @@ specific to Spark on Kubernetes. to avoid name conflicts. - - spark.kubernetes.executor.podNamePrefix - (none) - - Prefix for naming the executor pods. - If not set, the executor pod name is set to driver pod name suffixed by an integer. - - spark.kubernetes.executor.lostCheck.maxAttempts 10 @@ -640,9 +637,9 @@ specific to Spark on Kubernetes. spark.kubernetes.initContainer.image - (none) + (value of spark.kubernetes.container.image) - Container image for the init-container of the driver and executors for downloading dependencies. This is usually of the form example.com/repo/spark-init:v1.0.0. This configuration is optional and must be provided by the user if any non-container local dependency is used and must be downloaded remotely. + Custom container image for the init container of both driver and executors. diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 382cbfd5301b0..2bb5ecf1b8509 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -154,7 +154,7 @@ can find the results of the driver from the Mesos Web UI. To use cluster mode, you must start the `MesosClusterDispatcher` in your cluster via the `sbin/start-mesos-dispatcher.sh` script, passing in the Mesos master URL (e.g: mesos://host:5050). This starts the `MesosClusterDispatcher` as a daemon running on the host. -By setting the Mesos proxy config property (requires mesos version >= 1.4), `--conf spark.mesos.proxy.baseURL=http://localhost:5050` when launching the dispacther, the mesos sandbox URI for each driver is added to the mesos dispatcher UI. +By setting the Mesos proxy config property (requires mesos version >= 1.4), `--conf spark.mesos.proxy.baseURL=http://localhost:5050` when launching the dispatcher, the mesos sandbox URI for each driver is added to the mesos dispatcher UI. If you like to run the `MesosClusterDispatcher` with Marathon, you need to run the `MesosClusterDispatcher` in the foreground (i.e: `bin/spark-class org.apache.spark.deploy.mesos.MesosClusterDispatcher`). Note that the `MesosClusterDispatcher` not yet supports multiple instances for HA. diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index e7edec5990363..c010af35f8d2e 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -35,7 +35,7 @@ For example: --executor-memory 2g \ --executor-cores 1 \ --queue thequeue \ - lib/spark-examples*.jar \ + examples/jars/spark-examples*.jar \ 10 The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running. Refer to the "Debugging your Application" section below for how to see driver and executor logs. @@ -445,7 +445,7 @@ To use a custom metrics.properties for the application master and executors, upd yarn.nodemanager.log-aggregation.roll-monitoring-interval-seconds should be configured in yarn-site.xml. This feature can only be used with Hadoop 2.6.4+. The Spark log4j appender needs be changed to use - FileAppender or another appender that can handle the files being removed while its running. Based + FileAppender or another appender that can handle the files being removed while it is running. Based on the file name configured in the log4j configuration (like spark.log), the user should set the regex (spark*) to include all the log files that need to be aggregated. diff --git a/docs/security.md b/docs/security.md index 15aadf07cf873..bebc28ddbfb0e 100644 --- a/docs/security.md +++ b/docs/security.md @@ -62,7 +62,7 @@ component-specific configuration namespaces used to override the default setting -The full breakdown of available SSL options can be found on the [configuration page](configuration.html). +The full breakdown of available SSL options can be found on the [configuration page](configuration.html). SSL must be configured on each node and configured for each component involved in communication using the particular protocol. ### YARN mode diff --git a/docs/sparkr.md b/docs/sparkr.md index 997ea60fb6cf0..73f9424ebc1ac 100644 --- a/docs/sparkr.md +++ b/docs/sparkr.md @@ -596,7 +596,7 @@ The following example shows how to save/load a MLlib model by SparkR. # Structured Streaming -SparkR supports the Structured Streaming API (experimental). Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. For more information see the R API on the [Structured Streaming Programming Guide](structured-streaming-programming-guide.html) +SparkR supports the Structured Streaming API. Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. For more information see the R API on the [Structured Streaming Programming Guide](structured-streaming-programming-guide.html) # R Function Name Conflicts @@ -663,3 +663,7 @@ You can inspect the search path in R with [`search()`](https://stat.ethz.ch/R-ma - The `stringsAsFactors` parameter was previously ignored with `collect`, for example, in `collect(createDataFrame(iris), stringsAsFactors = TRUE))`. It has been corrected. - For `summary`, option for statistics to compute has been added. Its output is changed from that from `describe`. - A warning can be raised if versions of SparkR package and the Spark JVM do not match. + +## Upgrading to SparkR 2.3.1 and above + + - In SparkR 2.3.0 and earlier, the `start` parameter of `substr` method was wrongly subtracted by one and considered as 0-based. This can lead to inconsistent substring results and also does not match with the behaviour with `substr` in R. In version 2.3.1 and later, it has been fixed so the `start` parameter of `substr` method is now 1-base. As an example, `substr(lit('abcdef'), 2, 4))` would result to `abc` in SparkR 2.3.0, and the result would be `bcd` in SparkR 2.3.1. diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index dc3e384008d27..461806a659965 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -915,6 +915,14 @@ spark.catalog.refreshTable("my_table")
    +
    + +{% highlight r %} +refreshTable("my_table") +{% endhighlight %} + +
    +
    {% highlight sql %} @@ -953,8 +961,10 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession spark.sql.parquet.compression.codec snappy - Sets the compression codec use when writing Parquet files. Acceptable values include: - uncompressed, snappy, gzip, lzo. + Sets the compression codec used when writing Parquet files. If either `compression` or + `parquet.compression` is specified in the table-specific options/properties, the precedence would be + `compression`, `parquet.compression`, `spark.sql.parquet.compression.codec`. Acceptable values include: + none, uncompressed, snappy, gzip, lzo. @@ -994,6 +1004,29 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession +## ORC Files + +Since Spark 2.3, Spark supports a vectorized ORC reader with a new ORC file format for ORC files. +To do that, the following configurations are newly added. The vectorized reader is used for the +native ORC tables (e.g., the ones created using the clause `USING ORC`) when `spark.sql.orc.impl` +is set to `native` and `spark.sql.orc.enableVectorizedReader` is set to `true`. For the Hive ORC +serde tables (e.g., the ones created using the clause `USING HIVE OPTIONS (fileFormat 'ORC')`), +the vectorized reader is used when `spark.sql.hive.convertMetastoreOrc` is also set to `true`. + + + + + + + + + + + + + +
    Property NameDefaultMeaning
    spark.sql.orc.implhiveThe name of ORC implementation. It can be one of native and hive. native means the native ORC support that is built on Apache ORC 1.4.1. `hive` means the ORC library in Hive 1.2.1.
    spark.sql.orc.enableVectorizedReadertrueEnables vectorized orc decoding in native implementation. If false, a new non-vectorized ORC reader is used in native implementation. For hive implementation, this is ignored.
    + ## JSON Datasets
    @@ -1243,7 +1276,7 @@ provide a ClassTag. (Note that this is different than the Spark SQL JDBC server, which allows other applications to run queries using Spark SQL). -To get started you will need to include the JDBC driver for you particular database on the +To get started you will need to include the JDBC driver for your particular database on the spark classpath. For example, to connect to postgres from the Spark Shell you would run the following command: @@ -1496,10 +1529,10 @@ that these options will be deprecated in future release as more optimizations ar ## Broadcast Hint for SQL Queries The `BROADCAST` hint guides Spark to broadcast each specified table when joining them with another table or view. -When Spark deciding the join methods, the broadcast hash join (i.e., BHJ) is preferred, +When Spark deciding the join methods, the broadcast hash join (i.e., BHJ) is preferred, even if the statistics is above the configuration `spark.sql.autoBroadcastJoinThreshold`. When both sides of a join are specified, Spark broadcasts the one having the lower statistics. -Note Spark does not guarantee BHJ is always chosen, since not all cases (e.g. full outer join) +Note Spark does not guarantee BHJ is always chosen, since not all cases (e.g. full outer join) support BHJ. When the broadcast nested loop join is selected, we still respect the hint.
    @@ -1630,8 +1663,153 @@ Configuration of Hive is done by placing your `hive-site.xml`, `core-site.xml` a You may run `./bin/spark-sql --help` for a complete list of all available options. +# PySpark Usage Guide for Pandas with Apache Arrow + +## Apache Arrow in Spark + +Apache Arrow is an in-memory columnar data format that is used in Spark to efficiently transfer +data between JVM and Python processes. This currently is most beneficial to Python users that +work with Pandas/NumPy data. Its usage is not automatic and might require some minor +changes to configuration or code to take full advantage and ensure compatibility. This guide will +give a high-level description of how to use Arrow in Spark and highlight any differences when +working with Arrow-enabled data. + +### Ensure PyArrow Installed + +If you install PySpark using pip, then PyArrow can be brought in as an extra dependency of the +SQL module with the command `pip install pyspark[sql]`. Otherwise, you must ensure that PyArrow +is installed and available on all cluster nodes. The current supported version is 0.8.0. +You can install using pip or conda from the conda-forge channel. See PyArrow +[installation](https://arrow.apache.org/docs/python/install.html) for details. + +## Enabling for Conversion to/from Pandas + +Arrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame +using the call `toPandas()` and when creating a Spark DataFrame from a Pandas DataFrame with +`createDataFrame(pandas_df)`. To use Arrow when executing these calls, users need to first set +the Spark configuration 'spark.sql.execution.arrow.enabled' to 'true'. This is disabled by default. + +
    +
    +{% include_example dataframe_with_arrow python/sql/arrow.py %} +
    +
    + +Using the above optimizations with Arrow will produce the same results as when Arrow is not +enabled. Note that even with Arrow, `toPandas()` results in the collection of all records in the +DataFrame to the driver program and should be done on a small subset of the data. Not all Spark +data types are currently supported and an error can be raised if a column has an unsupported type, +see [Supported SQL Types](#supported-sql-types). If an error occurs during `createDataFrame()`, +Spark will fall back to create the DataFrame without Arrow. + +## Pandas UDFs (a.k.a. Vectorized UDFs) + +Pandas UDFs are user defined functions that are executed by Spark using Arrow to transfer data and +Pandas to work with the data. A Pandas UDF is defined using the keyword `pandas_udf` as a decorator +or to wrap the function, no additional configuration is required. Currently, there are two types of +Pandas UDF: Scalar and Grouped Map. + +### Scalar + +Scalar Pandas UDFs are used for vectorizing scalar operations. They can be used with functions such +as `select` and `withColumn`. The Python function should take `pandas.Series` as inputs and return +a `pandas.Series` of the same length. Internally, Spark will execute a Pandas UDF by splitting +columns into batches and calling the function for each batch as a subset of the data, then +concatenating the results together. + +The following example shows how to create a scalar Pandas UDF that computes the product of 2 columns. + +
    +
    +{% include_example scalar_pandas_udf python/sql/arrow.py %} +
    +
    + +### Grouped Map +Grouped map Pandas UDFs are used with `groupBy().apply()` which implements the "split-apply-combine" pattern. +Split-apply-combine consists of three steps: +* Split the data into groups by using `DataFrame.groupBy`. +* Apply a function on each group. The input and output of the function are both `pandas.DataFrame`. The + input data contains all the rows and columns for each group. +* Combine the results into a new `DataFrame`. + +To use `groupBy().apply()`, the user needs to define the following: +* A Python function that defines the computation for each group. +* A `StructType` object or a string that defines the schema of the output `DataFrame`. + +The output schema will be applied to the columns of the returned `pandas.DataFrame` in order by position, +not by name. This means that the columns in the `pandas.DataFrame` must be indexed so that their +position matches the corresponding field in the schema. + +Note that when creating a new `pandas.DataFrame` using a dictionary, the actual position of the column +can differ from the order that it was placed in the dictionary. It is recommended in this case to +explicitly define the column order using the `columns` keyword, e.g. +`pandas.DataFrame({'id': ids, 'a': data}, columns=['id', 'a'])`, or alternatively use an `OrderedDict`. + +Note that all data for a group will be loaded into memory before the function is applied. This can +lead to out of memory exceptons, especially if the group sizes are skewed. The configuration for +[maxRecordsPerBatch](#setting-arrow-batch-size) is not applied on groups and it is up to the user +to ensure that the grouped data will fit into the available memory. + +The following example shows how to use `groupby().apply()` to subtract the mean from each value in the group. + +
    +
    +{% include_example grouped_map_pandas_udf python/sql/arrow.py %} +
    +
    + +For detailed usage, please see [`pyspark.sql.functions.pandas_udf`](api/python/pyspark.sql.html#pyspark.sql.functions.pandas_udf) and +[`pyspark.sql.GroupedData.apply`](api/python/pyspark.sql.html#pyspark.sql.GroupedData.apply). + +## Usage Notes + +### Supported SQL Types + +Currently, all Spark SQL data types are supported by Arrow-based conversion except `BinaryType`, `MapType`, +`ArrayType` of `TimestampType`, and nested `StructType`. + +### Setting Arrow Batch Size + +Data partitions in Spark are converted into Arrow record batches, which can temporarily lead to +high memory usage in the JVM. To avoid possible out of memory exceptions, the size of the Arrow +record batches can be adjusted by setting the conf "spark.sql.execution.arrow.maxRecordsPerBatch" +to an integer that will determine the maximum number of rows for each batch. The default value is +10,000 records per batch. If the number of columns is large, the value should be adjusted +accordingly. Using this limit, each data partition will be made into 1 or more record batches for +processing. + +### Timestamp with Time Zone Semantics + +Spark internally stores timestamps as UTC values, and timestamp data that is brought in without +a specified time zone is converted as local time to UTC with microsecond resolution. When timestamp +data is exported or displayed in Spark, the session time zone is used to localize the timestamp +values. The session time zone is set with the configuration 'spark.sql.session.timeZone' and will +default to the JVM system local time zone if not set. Pandas uses a `datetime64` type with nanosecond +resolution, `datetime64[ns]`, with optional time zone on a per-column basis. + +When timestamp data is transferred from Spark to Pandas it will be converted to nanoseconds +and each column will be converted to the Spark session time zone then localized to that time +zone, which removes the time zone and displays values as local time. This will occur +when calling `toPandas()` or `pandas_udf` with timestamp columns. + +When timestamp data is transferred from Pandas to Spark, it will be converted to UTC microseconds. This +occurs when calling `createDataFrame` with a Pandas DataFrame or when returning a timestamp from a +`pandas_udf`. These conversions are done automatically to ensure Spark will have data in the +expected format, so it is not necessary to do any of these conversions yourself. Any nanosecond +values will be truncated. + +Note that a standard UDF (non-Pandas) will load timestamp data as Python datetime objects, which is +different than a Pandas timestamp. It is recommended to use Pandas time series functionality when +working with timestamps in `pandas_udf`s to get the best performance, see +[here](https://pandas.pydata.org/pandas-docs/stable/timeseries.html) for details. + # Migration Guide +## Upgrading From Spark SQL 2.3.0 to 2.3.1 and above + + - As of version 2.3.1 Arrow functionality, including `pandas_udf` and `toPandas()`/`createDataFrame()` with `spark.sql.execution.arrow.enabled` set to `True`, has been marked as experimental. These are still evolving and not currently recommended for use in production. + ## Upgrading From Spark SQL 2.2 to 2.3 - Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column (named `_corrupt_record` by default). For example, `spark.read.schema(schema).json(file).filter($"_corrupt_record".isNotNull).count()` and `spark.read.schema(schema).json(file).select("_corrupt_record").show()`. Instead, you can cache or save the parsed results and then send the same query. For example, `val df = spark.read.schema(schema).json(file).cache()` and then `df.filter($"_corrupt_record".isNotNull).count()`. @@ -1778,14 +1956,22 @@ options. Note that, for DecimalType(38,0)*, the table above intentionally does not cover all other combinations of scales and precisions because currently we only infer decimal type like `BigInteger`/`BigInt`. For example, 1.1 is inferred as double type. - In PySpark, now we need Pandas 0.19.2 or upper if you want to use Pandas related functionalities, such as `toPandas`, `createDataFrame` from Pandas DataFrame, etc. - In PySpark, the behavior of timestamp values for Pandas related functionalities was changed to respect session timezone. If you want to use the old behavior, you need to set a configuration `spark.sql.execution.pandas.respectSessionTimeZone` to `False`. See [SPARK-22395](https://issues.apache.org/jira/browse/SPARK-22395) for details. - - - Since Spark 2.3, when either broadcast hash join or broadcast nested loop join is applicable, we prefer to broadcasting the table that is explicitly specified in a broadcast hint. For details, see the section [Broadcast Hint](#broadcast-hint-for-sql-queries) and [SPARK-22489](https://issues.apache.org/jira/browse/SPARK-22489). + - In PySpark, `na.fill()` or `fillna` also accepts boolean and replaces nulls with booleans. In prior Spark versions, PySpark just ignores it and returns the original Dataset/DataFrame. + - Since Spark 2.3, when either broadcast hash join or broadcast nested loop join is applicable, we prefer to broadcasting the table that is explicitly specified in a broadcast hint. For details, see the section [Broadcast Hint](#broadcast-hint-for-sql-queries) and [SPARK-22489](https://issues.apache.org/jira/browse/SPARK-22489). + - Since Spark 2.3, when all inputs are binary, `functions.concat()` returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set `spark.sql.function.concatBinaryAsString` to `true`. + - Since Spark 2.3, when all inputs are binary, SQL `elt()` returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set `spark.sql.function.eltOutputAsString` to `true`. - - Since Spark 2.3, when all inputs are binary, `functions.concat()` returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set `spark.sql.function.concatBinaryAsString` to `true`. + - Since Spark 2.3, by default arithmetic operations between decimals return a rounded value if an exact representation is not possible (instead of returning NULL). This is compliant with SQL ANSI 2011 specification and Hive's new behavior introduced in Hive 2.2 (HIVE-15331). This involves the following changes + - The rules to determine the result type of an arithmetic operation have been updated. In particular, if the precision / scale needed are out of the range of available values, the scale is reduced up to 6, in order to prevent the truncation of the integer part of the decimals. All the arithmetic operations are affected by the change, ie. addition (`+`), subtraction (`-`), multiplication (`*`), division (`/`), remainder (`%`) and positive module (`pmod`). + - Literal values used in SQL operations are converted to DECIMAL with the exact precision and scale needed by them. + - The configuration `spark.sql.decimalOperations.allowPrecisionLoss` has been introduced. It defaults to `true`, which means the new behavior described here; if set to `false`, Spark uses previous rules, ie. it doesn't adjust the needed scale to represent the values and it returns NULL if an exact representation of the value is not possible. + - In PySpark, `df.replace` does not allow to omit `value` when `to_replace` is not a dictionary. Previously, `value` could be omitted in the other cases and had `None` by default, which is counterintuitive and error prone. ## Upgrading From Spark SQL 2.1 to 2.2 - Spark 2.1.1 introduced a new configuration key: `spark.sql.hive.caseSensitiveInferenceMode`. It had a default setting of `NEVER_INFER`, which kept behavior identical to 2.1.0. However, Spark 2.2.0 changes this setting's default value to `INFER_AND_SAVE` to restore compatibility with reading Hive metastore tables whose underlying file schema have mixed-case column names. With the `INFER_AND_SAVE` configuration value, on first access Spark will perform schema inference on any Hive metastore table for which it has not already saved an inferred schema. Note that schema inference can be a very time consuming operation for tables with thousands of partitions. If compatibility with mixed-case column names is not a concern, you can safely set `spark.sql.hive.caseSensitiveInferenceMode` to `NEVER_INFER` to avoid the initial overhead of schema inference. Note that with the new default `INFER_AND_SAVE` setting, the results of the schema inference are saved as a metastore key for future use. Therefore, the initial schema inference occurs only at a table's first access. + + - Since Spark 2.2.1 and 2.3.0, the schema is always inferred at runtime when the data source tables have the columns that exist in both partition schema and data schema. The inferred schema does not have the partitioned columns. When reading the table, Spark respects the partition values of these overlapping columns instead of the values stored in the data source files. In 2.2.0 and 2.1.x release, the inferred schema is partitioned but the data of the table is invisible to users (i.e., the result set is empty). ## Upgrading From Spark SQL 2.0 to 2.1 @@ -1806,7 +1992,7 @@ options. transformations (e.g., `map`, `filter`, and `groupByKey`) and untyped transformations (e.g., `select` and `groupBy`) are available on the Dataset class. Since compile-time type-safety in Python and R is not a language feature, the concept of Dataset does not apply to these languages’ - APIs. Instead, `DataFrame` remains the primary programing abstraction, which is analogous to the + APIs. Instead, `DataFrame` remains the primary programming abstraction, which is analogous to the single-node data frame notion in these languages. - Dataset and DataFrame API `unionAll` has been deprecated and replaced by `union` @@ -1982,7 +2168,7 @@ Java and Python users will need to update their code. Prior to Spark 1.3 there were separate Java compatible classes (`JavaSQLContext` and `JavaSchemaRDD`) that mirrored the Scala API. In Spark 1.3 the Java API and Scala API have been unified. Users -of either language should use `SQLContext` and `DataFrame`. In general theses classes try to +of either language should use `SQLContext` and `DataFrame`. In general these classes try to use types that are usable from both languages (i.e. `Array` instead of language specific collections). In some cases where no common type exists (e.g., for passing in closures or Maps) function overloading is used instead. @@ -2046,7 +2232,7 @@ referencing a singleton. Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs. Currently Hive SerDes and UDFs are based on Hive 1.2.1, and Spark SQL can be connected to different versions of Hive Metastore -(from 0.12.0 to 2.1.1. Also see [Interacting with Different Versions of Hive Metastore] (#interacting-with-different-versions-of-hive-metastore)). +(from 0.12.0 to 2.1.1. Also see [Interacting with Different Versions of Hive Metastore](#interacting-with-different-versions-of-hive-metastore)). #### Deploying in Existing Hive Warehouses @@ -2163,7 +2349,7 @@ Not all the APIs of the Hive UDF/UDTF/UDAF are supported by Spark SQL. Below are Spark SQL currently does not support the reuse of aggregation. * `getWindowingEvaluator` (`GenericUDAFEvaluator`) is a function to optimize aggregation by evaluating an aggregate over a fixed window. - + ### Incompatible Hive UDF Below are the scenarios in which Hive and Spark generate different results: diff --git a/docs/storage-openstack-swift.md b/docs/storage-openstack-swift.md index f4bb2353e3c49..1dd54719b21aa 100644 --- a/docs/storage-openstack-swift.md +++ b/docs/storage-openstack-swift.md @@ -42,7 +42,7 @@ Create core-site.xml and place it inside Spark's conf The main category of parameters that should be configured are the authentication parameters required by Keystone. -The following table contains a list of Keystone mandatory parameters. PROVIDER can be +The following table contains a list of Keystone mandatory parameters. PROVIDER can be any (alphanumeric) name. diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 868acc41226dc..ffda36d64a770 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -74,7 +74,7 @@ import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext._ // not necessary since Spark 1.3 // Create a local StreamingContext with two working thread and batch interval of 1 second. -// The master requires 2 cores to prevent from a starvation scenario. +// The master requires 2 cores to prevent a starvation scenario. val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount") val ssc = new StreamingContext(conf, Seconds(1)) @@ -172,7 +172,7 @@ each line will be split into multiple words and the stream of words is represent `words` DStream. Note that we defined the transformation using a [FlatMapFunction](api/scala/index.html#org.apache.spark.api.java.function.FlatMapFunction) object. As we will discover along the way, there are a number of such convenience classes in the Java API -that help define DStream transformations. +that help defines DStream transformations. Next, we want to count these words. diff --git a/docs/structured-streaming-kafka-integration.md b/docs/structured-streaming-kafka-integration.md index bab0be8ddeb9f..5647ec6bc5797 100644 --- a/docs/structured-streaming-kafka-integration.md +++ b/docs/structured-streaming-kafka-integration.md @@ -61,7 +61,7 @@ df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") {% highlight java %} // Subscribe to 1 topic -DataFrame df = spark +Dataset df = spark .readStream() .format("kafka") .option("kafka.bootstrap.servers", "host1:port1,host2:port2") @@ -70,7 +70,7 @@ DataFrame df = spark df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") // Subscribe to multiple topics -DataFrame df = spark +Dataset df = spark .readStream() .format("kafka") .option("kafka.bootstrap.servers", "host1:port1,host2:port2") @@ -79,7 +79,7 @@ DataFrame df = spark df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") // Subscribe to a pattern -DataFrame df = spark +Dataset df = spark .readStream() .format("kafka") .option("kafka.bootstrap.servers", "host1:port1,host2:port2") @@ -125,7 +125,7 @@ df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") ### Creating a Kafka Source for Batch Queries If you have a use case that is better suited to batch processing, -you can create an Dataset/DataFrame for a defined range of offsets. +you can create a Dataset/DataFrame for a defined range of offsets.
    @@ -171,7 +171,7 @@ df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") {% highlight java %} // Subscribe to 1 topic defaults to the earliest and latest offsets -DataFrame df = spark +Dataset df = spark .read() .format("kafka") .option("kafka.bootstrap.servers", "host1:port1,host2:port2") @@ -180,7 +180,7 @@ DataFrame df = spark df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)"); // Subscribe to multiple topics, specifying explicit Kafka offsets -DataFrame df = spark +Dataset df = spark .read() .format("kafka") .option("kafka.bootstrap.servers", "host1:port1,host2:port2") @@ -191,7 +191,7 @@ DataFrame df = spark df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)"); // Subscribe to a pattern, at the earliest and latest offsets -DataFrame df = spark +Dataset df = spark .read() .format("kafka") .option("kafka.bootstrap.servers", "host1:port1,host2:port2") @@ -597,7 +597,7 @@ Note that the following Kafka params cannot be set and the Kafka source or sink - **key.serializer**: Keys are always serialized with ByteArraySerializer or StringSerializer. Use DataFrame operations to explicitly serialize the keys into either strings or byte arrays. - **value.serializer**: values are always serialized with ByteArraySerializer or StringSerializer. Use -DataFrame oeprations to explicitly serialize the values into either strings or byte arrays. +DataFrame operations to explicitly serialize the values into either strings or byte arrays. - **enable.auto.commit**: Kafka source doesn't commit any offset. - **interceptor.classes**: Kafka source always read keys and values as byte arrays. It's not safe to use ConsumerInterceptor as it may break the query. diff --git a/docs/structured-streaming-programming-guide.md b/docs/structured-streaming-programming-guide.md index 31fcfabb9cacc..9a83f157452ad 100644 --- a/docs/structured-streaming-programming-guide.md +++ b/docs/structured-streaming-programming-guide.md @@ -10,7 +10,9 @@ title: Structured Streaming Programming Guide # Overview Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive. You can use the [Dataset/DataFrame API](sql-programming-guide.html) in Scala, Java, Python or R to express streaming aggregations, event-time windows, stream-to-batch joins, etc. The computation is executed on the same optimized Spark SQL engine. Finally, the system ensures end-to-end exactly-once fault-tolerance guarantees through checkpointing and Write Ahead Logs. In short, *Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing without the user having to reason about streaming.* -In this guide, we are going to walk you through the programming model and the APIs. First, let's start with a simple example - a streaming word count. +Internally, by default, Structured Streaming queries are processed using a *micro-batch processing* engine, which processes data streams as a series of small batch jobs thereby achieving end-to-end latencies as low as 100 milliseconds and exactly-once fault-tolerance guarantees. However, since Spark 2.3, we have introduced a new low-latency processing mode called **Continuous Processing**, which can achieve end-to-end latencies as low as 1 millisecond with at-least-once guarantees. Without changing the Dataset/DataFrame operations in your queries, you will be able to choose the mode based on your application requirements. + +In this guide, we are going to walk you through the programming model and the APIs. We are going to explain the concepts mostly using the default micro-batch processing model, and then [later](#continuous-processing-experimental) discuss Continuous Processing model. First, let's start with a simple example of a Structured Streaming query - a streaming word count. # Quick Example Let’s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let’s see how you can express this using Structured Streaming. You can see the full code in @@ -827,8 +829,8 @@ df.isStreaming() {% endhighlight %}
    -{% highlight bash %} -Not available. +{% highlight r %} +isStreaming(df) {% endhighlight %}
    @@ -885,11 +887,24 @@ windowedCounts = words.groupBy( ).count() {% endhighlight %} + +
    +{% highlight r %} +words <- ... # streaming DataFrame of schema { timestamp: Timestamp, word: String } + +# Group the data by window and word and compute the count of each group +windowedCounts <- count( + groupBy( + words, + window(words$timestamp, "10 minutes", "5 minutes"), + words$word)) +{% endhighlight %} +
    -### Handling Late Data and Watermarking +#### Handling Late Data and Watermarking Now consider what happens if one of the events arrives late to the application. For example, say, a word generated at 12:04 (i.e. event time) could be received by the application at 12:11. The application should use the time 12:04 instead of 12:11 @@ -910,7 +925,9 @@ specifying the event time column and the threshold on how late the data is expec event time. For a specific window starting at time `T`, the engine will maintain state and allow late data to update the state until `(max event time seen by the engine - late threshold > T)`. In other words, late data within the threshold will be aggregated, -but data later than the threshold will be dropped. Let's understand this with an example. We can +but data later than the threshold will start getting dropped +(see [later]((#semantic-guarantees-of-aggregation-with-watermarking)) +in the section for the exact guarantees). Let's understand this with an example. We can easily define watermarking on the previous example using `withWatermark()` as shown below.
    @@ -959,6 +976,21 @@ windowedCounts = words \ .count() {% endhighlight %} +
    +
    +{% highlight r %} +words <- ... # streaming DataFrame of schema { timestamp: Timestamp, word: String } + +# Group the data by window and word and compute the count of each group + +words <- withWatermark(words, "timestamp", "10 minutes") +windowedCounts <- count( + groupBy( + words, + window(words$timestamp, "10 minutes", "5 minutes"), + words$word)) +{% endhighlight %} +
    @@ -1001,7 +1033,9 @@ then drops intermediate state of a window < watermark, and appends the final counts to the Result Table/sink. For example, the final counts of window `12:00 - 12:10` is appended to the Result Table only after the watermark is updated to `12:11`. -**Conditions for watermarking to clean aggregation state** +##### Conditions for watermarking to clean aggregation state +{:.no_toc} + It is important to note that the following conditions must be satisfied for the watermarking to clean the state in aggregation queries *(as of Spark 2.1.1, subject to change in the future)*. @@ -1021,9 +1055,31 @@ from the aggregation column. For example, `df.groupBy("time").count().withWatermark("time", "1 min")` is invalid in Append output mode. +##### Semantic Guarantees of Aggregation with Watermarking +{:.no_toc} + +- A watermark delay (set with `withWatermark`) of "2 hours" guarantees that the engine will never +drop any data that is less than 2 hours delayed. In other words, any data less than 2 hours behind +(in terms of event-time) the latest data processed till then is guaranteed to be aggregated. + +- However, the guarantee is strict only in one direction. Data delayed by more than 2 hours is +not guaranteed to be dropped; it may or may not get aggregated. More delayed is the data, less +likely is the engine going to process it. ### Join Operations -Streaming DataFrames can be joined with static DataFrames to create new streaming DataFrames. Here are a few examples. +Structured Streaming supports joining a streaming Dataset/DataFrame with a static Dataset/DataFrame +as well as another streaming Dataset/DataFrame. The result of the streaming join is generated +incrementally, similar to the results of streaming aggregations in the previous section. In this +section we will explore what type of joins (i.e. inner, outer, etc.) are supported in the above +cases. Note that in all the supported join types, the result of the join with a streaming +Dataset/DataFrame will be the exactly the same as if it was with a static Dataset/DataFrame +containing the same data in the stream. + + +#### Stream-static Joins + +Since the introduction in Spark 2.0, Structured Streaming has supported joins (inner join and some +type of outer joins) between a streaming and a static DataFrame/Dataset. Here is a simple example.
    @@ -1058,9 +1114,382 @@ streamingDf.join(staticDf, "type") # inner equi-join with a static DF streamingDf.join(staticDf, "type", "right_join") # right outer join with a static DF {% endhighlight %} +
    + +
    + +{% highlight r %} +staticDf <- read.df(...) +streamingDf <- read.stream(...) +joined <- merge(streamingDf, staticDf, sort = FALSE) # inner equi-join with a static DF +joined <- join( + staticDf, + streamingDf, + streamingDf$value == staticDf$value, + "right_outer") # right outer join with a static DF +{% endhighlight %} + +
    +
    + +Note that stream-static joins are not stateful, so no state management is necessary. +However, a few types of stream-static outer joins are not yet supported. +These are listed at the [end of this Join section](#support-matrix-for-joins-in-streaming-queries). + +#### Stream-stream Joins +In Spark 2.3, we have added support for stream-stream joins, that is, you can join two streaming +Datasets/DataFrames. The challenge of generating join results between two data streams is that, +at any point of time, the view of the dataset is incomplete for both sides of the join making +it much harder to find matches between inputs. Any row received from one input stream can match +with any future, yet-to-be-received row from the other input stream. Hence, for both the input +streams, we buffer past input as streaming state, so that we can match every future input with +past input and accordingly generate joined results. Furthermore, similar to streaming aggregations, +we automatically handle late, out-of-order data and can limit the state using watermarks. +Let’s discuss the different types of supported stream-stream joins and how to use them. + +##### Inner Joins with optional Watermarking +Inner joins on any kind of columns along with any kind of join conditions are supported. +However, as the stream runs, the size of streaming state will keep growing indefinitely as +*all* past input must be saved as any new input can match with any input from the past. +To avoid unbounded state, you have to define additional join conditions such that indefinitely +old inputs cannot match with future inputs and therefore can be cleared from the state. +In other words, you will have to do the following additional steps in the join. + +1. Define watermark delays on both inputs such that the engine knows how delayed the input can be +(similar to streaming aggregations) + +1. Define a constraint on event-time across the two inputs such that the engine can figure out when +old rows of one input is not going to be required (i.e. will not satisfy the time constraint) for +matches with the other input. This constraint can be defined in one of the two ways. + + 1. Time range join conditions (e.g. `...JOIN ON leftTime BETWEN rightTime AND rightTime + INTERVAL 1 HOUR`), + + 1. Join on event-time windows (e.g. `...JOIN ON leftTimeWindow = rightTimeWindow`). + +Let’s understand this with an example. + +Let’s say we want to join a stream of advertisement impressions (when an ad was shown) with +another stream of user clicks on advertisements to correlate when impressions led to +monetizable clicks. To allow the state cleanup in this stream-stream join, you will have to +specify the watermarking delays and the time constraints as follows. + +1. Watermark delays: Say, the impressions and the corresponding clicks can be late/out-of-order +in event-time by at most 2 and 3 hours, respectively. + +1. Event-time range condition: Say, a click can occur within a time range of 0 seconds to 1 hour +after the corresponding impression. + +The code would look like this. + +
    +
    + +{% highlight scala %} +import org.apache.spark.sql.functions.expr + +val impressions = spark.readStream. ... +val clicks = spark.readStream. ... + +// Apply watermarks on event-time columns +val impressionsWithWatermark = impressions.withWatermark("impressionTime", "2 hours") +val clicksWithWatermark = clicks.withWatermark("clickTime", "3 hours") + +// Join with event-time constraints +impressionsWithWatermark.join( + clicksWithWatermark, + expr(""" + clickAdId = impressionAdId AND + clickTime >= impressionTime AND + clickTime <= impressionTime + interval 1 hour + """) +) + +{% endhighlight %} + +
    +
    + +{% highlight java %} +import static org.apache.spark.sql.functions.expr + +Dataset impressions = spark.readStream(). ... +Dataset clicks = spark.readStream(). ... + +// Apply watermarks on event-time columns +Dataset impressionsWithWatermark = impressions.withWatermark("impressionTime", "2 hours"); +Dataset clicksWithWatermark = clicks.withWatermark("clickTime", "3 hours"); + +// Join with event-time constraints +impressionsWithWatermark.join( + clicksWithWatermark, + expr( + "clickAdId = impressionAdId AND " + + "clickTime >= impressionTime AND " + + "clickTime <= impressionTime + interval 1 hour ") +); + +{% endhighlight %} + + +
    +
    + +{% highlight python %} +from pyspark.sql.functions import expr + +impressions = spark.readStream. ... +clicks = spark.readStream. ... + +# Apply watermarks on event-time columns +impressionsWithWatermark = impressions.withWatermark("impressionTime", "2 hours") +clicksWithWatermark = clicks.withWatermark("clickTime", "3 hours") + +# Join with event-time constraints +impressionsWithWatermark.join( + clicksWithWatermark, + expr(""" + clickAdId = impressionAdId AND + clickTime >= impressionTime AND + clickTime <= impressionTime + interval 1 hour + """) +) + +{% endhighlight %} + +
    +
    + +{% highlight r %} +impressions <- read.stream(...) +clicks <- read.stream(...) + +# Apply watermarks on event-time columns +impressionsWithWatermark <- withWatermark(impressions, "impressionTime", "2 hours") +clicksWithWatermark <- withWatermark(clicks, "clickTime", "3 hours") + +# Join with event-time constraints +joined <- join( + impressionsWithWatermark, + clicksWithWatermark, + expr( + paste( + "clickAdId = impressionAdId AND", + "clickTime >= impressionTime AND", + "clickTime <= impressionTime + interval 1 hour" +))) + +{% endhighlight %} + +
    +
    + +###### Semantic Guarantees of Stream-stream Inner Joins with Watermarking +{:.no_toc} +This is similar to the [guarantees provided by watermarking on aggregations](#semantic-guarantees-of-aggregation-with-watermarking). +A watermark delay of "2 hours" guarantees that the engine will never drop any data that is less than + 2 hours delayed. But data delayed by more than 2 hours may or may not get processed. + +##### Outer Joins with Watermarking +While the watermark + event-time constraints is optional for inner joins, for left and right outer +joins they must be specified. This is because for generating the NULL results in outer join, the +engine must know when an input row is not going to match with anything in future. Hence, the +watermark + event-time constraints must be specified for generating correct results. Therefore, +a query with outer-join will look quite like the ad-monetization example earlier, except that +there will be an additional parameter specifying it to be an outer-join. + +
    +
    + +{% highlight scala %} + +impressionsWithWatermark.join( + clicksWithWatermark, + expr(""" + clickAdId = impressionAdId AND + clickTime >= impressionTime AND + clickTime <= impressionTime + interval 1 hour + """), + joinType = "leftOuter" // can be "inner", "leftOuter", "rightOuter" + ) + +{% endhighlight %} + +
    +
    + +{% highlight java %} +impressionsWithWatermark.join( + clicksWithWatermark, + expr( + "clickAdId = impressionAdId AND " + + "clickTime >= impressionTime AND " + + "clickTime <= impressionTime + interval 1 hour "), + "leftOuter" // can be "inner", "leftOuter", "rightOuter" +); + +{% endhighlight %} + + +
    +
    + +{% highlight python %} +impressionsWithWatermark.join( + clicksWithWatermark, + expr(""" + clickAdId = impressionAdId AND + clickTime >= impressionTime AND + clickTime <= impressionTime + interval 1 hour + """), + "leftOuter" # can be "inner", "leftOuter", "rightOuter" +) + +{% endhighlight %} + +
    +
    + +{% highlight r %} +joined <- join( + impressionsWithWatermark, + clicksWithWatermark, + expr( + paste( + "clickAdId = impressionAdId AND", + "clickTime >= impressionTime AND", + "clickTime <= impressionTime + interval 1 hour"), + "left_outer" # can be "inner", "left_outer", "right_outer" +)) + +{% endhighlight %} +
    + +###### Semantic Guarantees of Stream-stream Outer Joins with Watermarking +{:.no_toc} +Outer joins have the same guarantees as [inner joins](#semantic-guarantees-of-stream-stream-inner-joins-with-watermarking) +regarding watermark delays and whether data will be dropped or not. + +###### Caveats +{:.no_toc} +There are a few important characteristics to note regarding how the outer results are generated. + +- *The outer NULL results will be generated with a delay that depends on the specified watermark +delay and the time range condition.* This is because the engine has to wait for that long to ensure +there were no matches and there will be no more matches in future. + +- In the current implementation in the micro-batch engine, watermarks are advanced at the end of a +micro-batch, and the next micro-batch uses the updated watermark to clean up state and output +outer results. Since we trigger a micro-batch only when there is new data to be processed, the +generation of the outer result may get delayed if there no new data being received in the stream. +*In short, if any of the two input streams being joined does not receive data for a while, the +outer (both cases, left or right) output may get delayed.* + +##### Support matrix for joins in streaming queries + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Left InputRight InputJoin Type
    StaticStaticAll types + Supported, since its not on streaming data even though it + can be present in a streaming query +
    StreamStaticInnerSupported, not stateful
    Left OuterSupported, not stateful
    Right OuterNot supported
    Full OuterNot supported
    StaticStreamInnerSupported, not stateful
    Left OuterNot supported
    Right OuterSupported, not stateful
    Full OuterNot supported
    StreamStreamInner + Supported, optionally specify watermark on both sides + + time constraints for state cleanup +
    Left Outer + Conditionally supported, must specify watermark on right + time constraints for correct + results, optionally specify watermark on left for all state cleanup +
    Right Outer + Conditionally supported, must specify watermark on left + time constraints for correct + results, optionally specify watermark on right for all state cleanup +
    Full OuterNot supported
    + +Additional details on supported joins: + +- Joins can be cascaded, that is, you can do `df1.join(df2, ...).join(df3, ...).join(df4, ....)`. + +- As of Spark 2.3, you can use joins only when the query is in Append output mode. Other output modes are not yet supported. + +- As of Spark 2.3, you cannot use other non-map-like operations before joins. Here are a few examples of + what cannot be used. + + - Cannot use streaming aggregations before joins. + + - Cannot use mapGroupsWithState and flatMapGroupsWithState in Update mode before joins. + + ### Streaming Deduplication You can deduplicate records in data streams using a unique identifier in the events. This is exactly same as deduplication on static using a unique identifier column. The query will store the necessary amount of data from previous records such that it can filter duplicate records. Similar to aggregations, you can use deduplication with or without watermarking. @@ -1105,15 +1534,29 @@ streamingDf {% highlight python %} streamingDf = spark.readStream. ... -// Without watermark using guid column +# Without watermark using guid column streamingDf.dropDuplicates("guid") -// With watermark using guid and eventTime columns +# With watermark using guid and eventTime columns streamingDf \ .withWatermark("eventTime", "10 seconds") \ .dropDuplicates("guid", "eventTime") {% endhighlight %} +
    +
    + +{% highlight r %} +streamingDf <- read.stream(...) + +# Without watermark using guid column +streamingDf <- dropDuplicates(streamingDf, "guid") + +# With watermark using guid and eventTime columns +streamingDf <- withWatermark(streamingDf, "eventTime", "10 seconds") +streamingDf <- dropDuplicates(streamingDf, "guid", "eventTime") +{% endhighlight %} +
    @@ -1132,15 +1575,9 @@ Some of them are as follows. - Sorting operations are supported on streaming Datasets only after an aggregation and in Complete Output Mode. -- Outer joins between a streaming and a static Datasets are conditionally supported. - - + Full outer join with a streaming Dataset is not supported - - + Left outer join with a streaming Dataset on the right is not supported - - + Right outer join with a streaming Dataset on the left is not supported - -- Any kind of joins between two streaming Datasets is not yet supported. +- Few types of outer joins on streaming Datasets are not supported. See the + support matrix in the Join Operations section + for more details. In addition, there are some Dataset methods that will not work on streaming Datasets. They are actions that will immediately run queries and return results, which does not make sense on a streaming Dataset. Rather, those functionalities can be done by explicitly starting a streaming query (see the next section regarding that). @@ -1248,6 +1685,15 @@ Here is the compatibility matrix. Aggregations not allowed after flatMapGroupsWithState. + + Queries with joins + Append + + Update and Complete mode not supported yet. See the + support matrix in the Join Operations section + for more details on what types of joins are supported. + + Other queries Append, Update @@ -1500,7 +1946,7 @@ aggDF \ .format("console") \ .start() -# Have all the aggregates in an in memory table. The query name will be the table name +# Have all the aggregates in an in-memory table. The query name will be the table name aggDF \ .writeStream \ .queryName("aggregates") \ @@ -1543,7 +1989,7 @@ head(sql("select * from aggregates"))
    -#### Using Foreach +##### Using Foreach The `foreach` operation allows arbitrary operations to be computed on the output data. As of Spark 2.1, this is available only for Scala and Java. To use this, you will have to implement the interface `ForeachWriter` ([Scala](api/scala/index.html#org.apache.spark.sql.ForeachWriter)/[Java](api/java/org/apache/spark/sql/ForeachWriter.html) docs), which has methods that get called whenever there is a sequence of rows generated as output after a trigger. Note the following important points. @@ -1560,6 +2006,172 @@ which has methods that get called whenever there is a sequence of rows generated - Whenever `open` is called, `close` will also be called (unless the JVM exits due to some error). This is true even if `open` returns false. If there is any error in processing and writing the data, `close` will be called with the error. It is your responsibility to clean up state (e.g. connections, transactions, etc.) that have been created in `open` such that there are no resource leaks. +#### Triggers +The trigger settings of a streaming query defines the timing of streaming data processing, whether +the query is going to executed as micro-batch query with a fixed batch interval or as a continuous processing query. +Here are the different kinds of triggers that are supported. + + + + + + + + + + + + + + + + + + + + + + +
    Trigger TypeDescription
    unspecified (default) + If no trigger setting is explicitly specified, then by default, the query will be + executed in micro-batch mode, where micro-batches will be generated as soon as + the previous micro-batch has completed processing. +
    Fixed interval micro-batches + The query will be executed with micro-batches mode, where micro-batches will be kicked off + at the user-specified intervals. +
      +
    • If the previous micro-batch completes within the interval, then the engine will wait until + the interval is over before kicking off the next micro-batch.
      • + +
    • If the previous micro-batch takes longer than the interval to complete (i.e. if an + interval boundary is missed), then the next micro-batch will start as soon as the + previous one completes (i.e., it will not wait for the next interval boundary).
      • + +
    • If no new data is available, then no micro-batch will be kicked off.
      • +
    +
    One-time micro-batch + The query will execute *only one* micro-batch to process all the available data and then + stop on its own. This is useful in scenarios you want to periodically spin up a cluster, + process everything that is available since the last period, and then shutdown the + cluster. In some case, this may lead to significant cost savings. +
    Continuous with fixed checkpoint interval
    (experimental)    		 + The query will be executed in the new low-latency, continuous processing mode. Read more + about this in the Continuous Processing section below. +
    + +Here are a few code examples. + +
    +
    + +{% highlight scala %} +import org.apache.spark.sql.streaming.Trigger + +// Default trigger (runs micro-batch as soon as it can) +df.writeStream + .format("console") + .start() + +// ProcessingTime trigger with two-seconds micro-batch interval +df.writeStream + .format("console") + .trigger(Trigger.ProcessingTime("2 seconds")) + .start() + +// One-time trigger +df.writeStream + .format("console") + .trigger(Trigger.Once()) + .start() + +// Continuous trigger with one-second checkpointing interval +df.writeStream + .format("console") + .trigger(Trigger.Continuous("1 second")) + .start() + +{% endhighlight %} + + +
    +
    + +{% highlight java %} +import org.apache.spark.sql.streaming.Trigger + +// Default trigger (runs micro-batch as soon as it can) +df.writeStream + .format("console") + .start(); + +// ProcessingTime trigger with two-seconds micro-batch interval +df.writeStream + .format("console") + .trigger(Trigger.ProcessingTime("2 seconds")) + .start(); + +// One-time trigger +df.writeStream + .format("console") + .trigger(Trigger.Once()) + .start(); + +// Continuous trigger with one-second checkpointing interval +df.writeStream + .format("console") + .trigger(Trigger.Continuous("1 second")) + .start(); + +{% endhighlight %} + +
    +
    + +{% highlight python %} + +# Default trigger (runs micro-batch as soon as it can) +df.writeStream \ + .format("console") \ + .start() + +# ProcessingTime trigger with two-seconds micro-batch interval +df.writeStream \ + .format("console") \ + .trigger(processingTime='2 seconds') \ + .start() + +# One-time trigger +df.writeStream \ + .format("console") \ + .trigger(once=True) \ + .start() + +# Continuous trigger with one-second checkpointing interval +df.writeStream + .format("console") + .trigger(continuous='1 second') + .start() + +{% endhighlight %} +
    +
    + +{% highlight r %} +# Default trigger (runs micro-batch as soon as it can) +write.stream(df, "console") + +# ProcessingTime trigger with two-seconds micro-batch interval +write.stream(df, "console", trigger.processingTime = "2 seconds") + +# One-time trigger +write.stream(df, "console", trigger.once = TRUE) + +# Continuous trigger is not yet supported +{% endhighlight %} +
    +
    + + ## Managing Streaming Queries The `StreamingQuery` object created when a query is started can be used to monitor and manage the query. @@ -2097,6 +2709,107 @@ write.stream(aggDF, "memory", outputMode = "complete", checkpointLocation = "pat
+# Continuous Processing +## [Experimental] +{:.no_toc} + +**Continuous processing** is a new, experimental streaming execution mode introduced in Spark 2.3 that enables low (~1 ms) end-to-end latency with at-least-once fault-tolerance guarantees. Compare this with the default *micro-batch processing* engine which can achieve exactly-once guarantees but achieve latencies of ~100ms at best. For some types of queries (discussed below), you can choose which mode to execute them in without modifying the application logic (i.e. without changing the DataFrame/Dataset operations). + +To run a supported query in continuous processing mode, all you need to do is specify a **continuous trigger** with the desired checkpoint interval as a parameter. For example, + +
+
+{% highlight scala %} +import org.apache.spark.sql.streaming.Trigger + +spark + .readStream + .format("rate") + .option("rowsPerSecond", "10") + .option("") + +spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", "host1:port1,host2:port2") + .option("subscribe", "topic1") + .load() + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") + .writeStream + .format("kafka") + .option("kafka.bootstrap.servers", "host1:port1,host2:port2") + .option("topic", "topic1") + .trigger(Trigger.Continuous("1 second")) // only change in query + .start() +{% endhighlight %} +
+
+{% highlight java %} +import org.apache.spark.sql.streaming.Trigger; + +spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", "host1:port1,host2:port2") + .option("subscribe", "topic1") + .load() + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") + .writeStream + .format("kafka") + .option("kafka.bootstrap.servers", "host1:port1,host2:port2") + .option("topic", "topic1") + .trigger(Trigger.Continuous("1 second")) // only change in query + .start(); +{% endhighlight %} +
+
+{% highlight python %} +spark \ + .readStream \ + .format("kafka") \ + .option("kafka.bootstrap.servers", "host1:port1,host2:port2") \ + .option("subscribe", "topic1") \ + .load() \ + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") \ + .writeStream \ + .format("kafka") \ + .option("kafka.bootstrap.servers", "host1:port1,host2:port2") \ + .option("topic", "topic1") \ + .trigger(continuous="1 second") \ # only change in query + .start() + +{% endhighlight %} +
+
+ +A checkpoint interval of 1 second means that the continuous processing engine will records the progress of the query every second. The resulting checkpoints are in a format compatible with the micro-batch engine, hence any query can be restarted with any trigger. For example, a supported query started with the micro-batch mode can be restarted in continuous mode, and vice versa. Note that any time you switch to continuous mode, you will get at-least-once fault-tolerance guarantees. + +## Supported Queries +{:.no_toc} + +As of Spark 2.3, only the following type of queries are supported in the continuous processing mode. + +- *Operations*: Only map-like Dataset/DataFrame operations are supported in continuous mode, that is, only projections (`select`, `map`, `flatMap`, `mapPartitions`, etc.) and selections (`where`, `filter`, etc.). + + All SQL functions are supported except aggregation functions (since aggregations are not yet supported), `current_timestamp()` and `current_date()` (deterministic computations using time is challenging). + +- *Sources*: + + Kafka source: All options are supported. + + Rate source: Good for testing. Only options that are supported in the continuous mode are `numPartitions` and `rowsPerSecond`. + +- *Sinks*: + + Kafka sink: All options are supported. + + Memory sink: Good for debugging. + + Console sink: Good for debugging. All options are supported. Note that the console will print every checkpoint interval that you have specified in the continuous trigger. + +See [Input Sources](#input-sources) and [Output Sinks](#output-sinks) sections for more details on them. While the console sink is good for testing, the end-to-end low-latency processing can be best observed with Kafka as the source and sink, as this allows the engine to process the data and make the results available in the output topic within milliseconds of the input data being available in the input topic. + +## Caveats +{:.no_toc} + +- Continuous processing engine launches multiple long-running tasks that continuously read data from sources, process it and continuously write to sinks. The number of tasks required by the query depends on how many partitions the query can read from the sources in parallel. Therefore, before starting a continuous processing query, you must ensure there are enough cores in the cluster to all the tasks in parallel. For example, if you are reading from a Kafka topic that has 10 partitions, then the cluster must have at least 10 cores for the query to make progress. +- Stopping a continuous processing stream may produce spurious task termination warnings. These can be safely ignored. +- There are currently no automatic retries of failed tasks. Any failure will lead to the query being stopped and it needs to be manually restarted from the checkpoint. + # Additional Information **Further Reading** @@ -2114,6 +2827,11 @@ write.stream(aggDF, "memory", outputMode = "complete", checkpointLocation = "pat **Talks** -- Spark Summit 2017 Talk - [Easy, Scalable, Fault-tolerant Stream Processing with Structured Streaming in Apache Spark](https://spark-summit.org/2017/events/easy-scalable-fault-tolerant-stream-processing-with-structured-streaming-in-apache-spark/) -- Spark Summit 2016 Talk - [A Deep Dive into Structured Streaming](https://spark-summit.org/2016/events/a-deep-dive-into-structured-streaming/) +- Spark Summit Europe 2017 + - Easy, Scalable, Fault-tolerant Stream Processing with Structured Streaming in Apache Spark - + [Part 1 slides/video](https://databricks.com/session/easy-scalable-fault-tolerant-stream-processing-with-structured-streaming-in-apache-spark), [Part 2 slides/video](https://databricks.com/session/easy-scalable-fault-tolerant-stream-processing-with-structured-streaming-in-apache-spark-continues) + - Deep Dive into Stateful Stream Processing in Structured Streaming - [slides/video](https://databricks.com/session/deep-dive-into-stateful-stream-processing-in-structured-streaming) +- Spark Summit 2016 + - A Deep Dive into Structured Streaming - [slides/video](https://spark-summit.org/2016/events/a-deep-dive-into-structured-streaming/) + diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md index 0473ab73a5e6c..a3643bf0838a1 100644 --- a/docs/submitting-applications.md +++ b/docs/submitting-applications.md @@ -5,7 +5,7 @@ title: Submitting Applications The `spark-submit` script in Spark's `bin` directory is used to launch applications on a cluster. It can use all of Spark's supported [cluster managers](cluster-overview.html#cluster-manager-types) -through a uniform interface so you don't have to configure your application specially for each one. +through a uniform interface so you don't have to configure your application especially for each one. # Bundling Your Application's Dependencies If your code depends on other projects, you will need to package them alongside @@ -58,7 +58,7 @@ for applications that involve the REPL (e.g. Spark shell). Alternatively, if your application is submitted from a machine far from the worker machines (e.g. locally on your laptop), it is common to use `cluster` mode to minimize network latency between -the drivers and the executors. Currently, standalone mode does not support cluster mode for Python +the drivers and the executors. Currently, the standalone mode does not support cluster mode for Python applications. For Python applications, simply pass a `.py` file in the place of `` instead of a JAR, @@ -68,7 +68,7 @@ There are a few options available that are specific to the [cluster manager](cluster-overview.html#cluster-manager-types) that is being used. For example, with a [Spark standalone cluster](spark-standalone.html) with `cluster` deploy mode, you can also specify `--supervise` to make sure that the driver is automatically restarted if it -fails with non-zero exit code. To enumerate all such options available to `spark-submit`, +fails with a non-zero exit code. To enumerate all such options available to `spark-submit`, run it with `--help`. Here are a few examples of common options: {% highlight bash %} @@ -192,7 +192,7 @@ debugging information by running `spark-submit` with the `--verbose` option. # Advanced Dependency Management When using `spark-submit`, the application jar along with any jars included with the `--jars` option -will be automatically transferred to the cluster. URLs supplied after `--jars` must be separated by commas. That list is included on the driver and executor classpaths. Directory expansion does not work with `--jars`. +will be automatically transferred to the cluster. URLs supplied after `--jars` must be separated by commas. That list is included in the driver and executor classpaths. Directory expansion does not work with `--jars`. Spark uses the following URL scheme to allow different strategies for disseminating jars: diff --git a/examples/pom.xml b/examples/pom.xml index 1791dbaad775e..b873bc9b20322 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java index dee56799d8aee..1529da16f051f 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java @@ -18,10 +18,9 @@ package org.apache.spark.examples.ml; // $example on$ -import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary; +import org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary; import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.LogisticRegressionModel; -import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession; @@ -50,7 +49,7 @@ public static void main(String[] args) { // $example on$ // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier // example - LogisticRegressionTrainingSummary trainingSummary = lrModel.summary(); + BinaryLogisticRegressionTrainingSummary trainingSummary = lrModel.binarySummary(); // Obtain the loss per iteration. double[] objectiveHistory = trainingSummary.objectiveHistory(); @@ -58,21 +57,15 @@ public static void main(String[] args) { System.out.println(lossPerIteration); } - // Obtain the metrics useful to judge performance on test data. - // We cast the summary to a BinaryLogisticRegressionSummary since the problem is a binary - // classification problem. - BinaryLogisticRegressionSummary binarySummary = - (BinaryLogisticRegressionSummary) trainingSummary; - // Obtain the receiver-operating characteristic as a dataframe and areaUnderROC. - Dataset roc = binarySummary.roc(); + Dataset roc = trainingSummary.roc(); roc.show(); roc.select("FPR").show(); - System.out.println(binarySummary.areaUnderROC()); + System.out.println(trainingSummary.areaUnderROC()); // Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with // this selected threshold. - Dataset fMeasure = binarySummary.fMeasureByThreshold(); + Dataset fMeasure = trainingSummary.fMeasureByThreshold(); double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0); double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure)) .select("threshold").head().getDouble(0); diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java index da410cba2b3f1..801a82cd2f24f 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java @@ -20,6 +20,7 @@ // $example on$ import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.LogisticRegressionModel; +import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession; @@ -48,6 +49,67 @@ public static void main(String[] args) { // Print the coefficients and intercept for multinomial logistic regression System.out.println("Coefficients: \n" + lrModel.coefficientMatrix() + " \nIntercept: " + lrModel.interceptVector()); + LogisticRegressionTrainingSummary trainingSummary = lrModel.summary(); + + // Obtain the loss per iteration. + double[] objectiveHistory = trainingSummary.objectiveHistory(); + for (double lossPerIteration : objectiveHistory) { + System.out.println(lossPerIteration); + } + + // for multiclass, we can inspect metrics on a per-label basis + System.out.println("False positive rate by label:"); + int i = 0; + double[] fprLabel = trainingSummary.falsePositiveRateByLabel(); + for (double fpr : fprLabel) { + System.out.println("label " + i + ": " + fpr); + i++; + } + + System.out.println("True positive rate by label:"); + i = 0; + double[] tprLabel = trainingSummary.truePositiveRateByLabel(); + for (double tpr : tprLabel) { + System.out.println("label " + i + ": " + tpr); + i++; + } + + System.out.println("Precision by label:"); + i = 0; + double[] precLabel = trainingSummary.precisionByLabel(); + for (double prec : precLabel) { + System.out.println("label " + i + ": " + prec); + i++; + } + + System.out.println("Recall by label:"); + i = 0; + double[] recLabel = trainingSummary.recallByLabel(); + for (double rec : recLabel) { + System.out.println("label " + i + ": " + rec); + i++; + } + + System.out.println("F-measure by label:"); + i = 0; + double[] fLabel = trainingSummary.fMeasureByLabel(); + for (double f : fLabel) { + System.out.println("label " + i + ": " + f); + i++; + } + + double accuracy = trainingSummary.accuracy(); + double falsePositiveRate = trainingSummary.weightedFalsePositiveRate(); + double truePositiveRate = trainingSummary.weightedTruePositiveRate(); + double fMeasure = trainingSummary.weightedFMeasure(); + double precision = trainingSummary.weightedPrecision(); + double recall = trainingSummary.weightedRecall(); + System.out.println("Accuracy: " + accuracy); + System.out.println("FPR: " + falsePositiveRate); + System.out.println("TPR: " + truePositiveRate); + System.out.println("F-measure: " + fMeasure); + System.out.println("Precision: " + precision); + System.out.println("Recall: " + recall); // $example off$ spark.stop(); diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderEstimatorExample.java similarity index 62% rename from examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java rename to examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderEstimatorExample.java index 99af37676ba98..6f93cff94b725 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderEstimatorExample.java @@ -23,9 +23,8 @@ import java.util.Arrays; import java.util.List; -import org.apache.spark.ml.feature.OneHotEncoder; -import org.apache.spark.ml.feature.StringIndexer; -import org.apache.spark.ml.feature.StringIndexerModel; +import org.apache.spark.ml.feature.OneHotEncoderEstimator; +import org.apache.spark.ml.feature.OneHotEncoderModel; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; @@ -35,41 +34,37 @@ import org.apache.spark.sql.types.StructType; // $example off$ -public class JavaOneHotEncoderExample { +public class JavaOneHotEncoderEstimatorExample { public static void main(String[] args) { SparkSession spark = SparkSession .builder() - .appName("JavaOneHotEncoderExample") + .appName("JavaOneHotEncoderEstimatorExample") .getOrCreate(); + // Note: categorical features are usually first encoded with StringIndexer // $example on$ List data = Arrays.asList( - RowFactory.create(0, "a"), - RowFactory.create(1, "b"), - RowFactory.create(2, "c"), - RowFactory.create(3, "a"), - RowFactory.create(4, "a"), - RowFactory.create(5, "c") + RowFactory.create(0.0, 1.0), + RowFactory.create(1.0, 0.0), + RowFactory.create(2.0, 1.0), + RowFactory.create(0.0, 2.0), + RowFactory.create(0.0, 1.0), + RowFactory.create(2.0, 0.0) ); StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("category", DataTypes.StringType, false, Metadata.empty()) + new StructField("categoryIndex1", DataTypes.DoubleType, false, Metadata.empty()), + new StructField("categoryIndex2", DataTypes.DoubleType, false, Metadata.empty()) }); Dataset df = spark.createDataFrame(data, schema); - StringIndexerModel indexer = new StringIndexer() - .setInputCol("category") - .setOutputCol("categoryIndex") - .fit(df); - Dataset indexed = indexer.transform(df); + OneHotEncoderEstimator encoder = new OneHotEncoderEstimator() + .setInputCols(new String[] {"categoryIndex1", "categoryIndex2"}) + .setOutputCols(new String[] {"categoryVec1", "categoryVec2"}); - OneHotEncoder encoder = new OneHotEncoder() - .setInputCol("categoryIndex") - .setOutputCol("categoryVec"); - - Dataset encoded = encoder.transform(indexed); + OneHotEncoderModel model = encoder.fit(df); + Dataset encoded = model.transform(df); encoded.show(); // $example off$ diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java index dd20cac621102..43cc30c1a899b 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java @@ -66,7 +66,7 @@ public static void main(String[] args) { .setNumBuckets(3); Dataset result = discretizer.fit(df).transform(df); - result.show(); + result.show(false); // $example off$ spark.stop(); } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSizeHintExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSizeHintExample.java new file mode 100644 index 0000000000000..d649a2ccbaa72 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSizeHintExample.java @@ -0,0 +1,79 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml; + +import org.apache.spark.sql.SparkSession; + +// $example on$ +import java.util.Arrays; + +import org.apache.spark.ml.feature.VectorAssembler; +import org.apache.spark.ml.feature.VectorSizeHint; +import org.apache.spark.ml.linalg.VectorUDT; +import org.apache.spark.ml.linalg.Vectors; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import static org.apache.spark.sql.types.DataTypes.*; +// $example off$ + +public class JavaVectorSizeHintExample { + public static void main(String[] args) { + SparkSession spark = SparkSession + .builder() + .appName("JavaVectorSizeHintExample") + .getOrCreate(); + + // $example on$ + StructType schema = createStructType(new StructField[]{ + createStructField("id", IntegerType, false), + createStructField("hour", IntegerType, false), + createStructField("mobile", DoubleType, false), + createStructField("userFeatures", new VectorUDT(), false), + createStructField("clicked", DoubleType, false) + }); + Row row0 = RowFactory.create(0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0); + Row row1 = RowFactory.create(0, 18, 1.0, Vectors.dense(0.0, 10.0), 0.0); + Dataset dataset = spark.createDataFrame(Arrays.asList(row0, row1), schema); + + VectorSizeHint sizeHint = new VectorSizeHint() + .setInputCol("userFeatures") + .setHandleInvalid("skip") + .setSize(3); + + Dataset datasetWithSize = sizeHint.transform(dataset); + System.out.println("Rows where 'userFeatures' is not the right size are filtered out"); + datasetWithSize.show(false); + + VectorAssembler assembler = new VectorAssembler() + .setInputCols(new String[]{"hour", "mobile", "userFeatures"}) + .setOutputCol("features"); + + // This dataframe can be used by downstream transformers as before + Dataset output = assembler.transform(datasetWithSize); + System.out.println("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column " + + "'features'"); + output.select("features", "clicked").show(false); + // $example off$ + + spark.stop(); + } +} + diff --git a/examples/src/main/python/avro_inputformat.py b/examples/src/main/python/avro_inputformat.py index 4422f9e7a9589..6286ba6541fbd 100644 --- a/examples/src/main/python/avro_inputformat.py +++ b/examples/src/main/python/avro_inputformat.py @@ -15,13 +15,6 @@ # limitations under the License. # -from __future__ import print_function - -import sys - -from functools import reduce -from pyspark.sql import SparkSession - """ Read data file users.avro in local Spark distro: @@ -50,6 +43,13 @@ {u'favorite_color': None, u'name': u'Alyssa'} {u'favorite_color': u'red', u'name': u'Ben'} """ +from __future__ import print_function + +import sys + +from functools import reduce +from pyspark.sql import SparkSession + if __name__ == "__main__": if len(sys.argv) != 2 and len(sys.argv) != 3: print(""" diff --git a/examples/src/main/python/ml/aft_survival_regression.py b/examples/src/main/python/ml/aft_survival_regression.py index 2f0ca995e55c7..0a71f76418ea6 100644 --- a/examples/src/main/python/ml/aft_survival_regression.py +++ b/examples/src/main/python/ml/aft_survival_regression.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +An example demonstrating aft survival regression. +Run with: + bin/spark-submit examples/src/main/python/ml/aft_survival_regression.py +""" from __future__ import print_function # $example on$ @@ -23,12 +28,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example demonstrating aft survival regression. -Run with: - bin/spark-submit examples/src/main/python/ml/aft_survival_regression.py -""" - if __name__ == "__main__": spark = SparkSession \ .builder \ diff --git a/examples/src/main/python/ml/bisecting_k_means_example.py b/examples/src/main/python/ml/bisecting_k_means_example.py index 1263cb5d177a8..7842d2009e238 100644 --- a/examples/src/main/python/ml/bisecting_k_means_example.py +++ b/examples/src/main/python/ml/bisecting_k_means_example.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +An example demonstrating bisecting k-means clustering. +Run with: + bin/spark-submit examples/src/main/python/ml/bisecting_k_means_example.py +""" from __future__ import print_function # $example on$ @@ -22,12 +27,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example demonstrating bisecting k-means clustering. -Run with: - bin/spark-submit examples/src/main/python/ml/bisecting_k_means_example.py -""" - if __name__ == "__main__": spark = SparkSession\ .builder\ diff --git a/examples/src/main/python/ml/bucketed_random_projection_lsh_example.py b/examples/src/main/python/ml/bucketed_random_projection_lsh_example.py index 1b7a458125cef..610176ea596ca 100644 --- a/examples/src/main/python/ml/bucketed_random_projection_lsh_example.py +++ b/examples/src/main/python/ml/bucketed_random_projection_lsh_example.py @@ -15,7 +15,11 @@ # limitations under the License. # - +""" +An example demonstrating BucketedRandomProjectionLSH. +Run with: + bin/spark-submit examples/src/main/python/ml/bucketed_random_projection_lsh_example.py +""" from __future__ import print_function # $example on$ @@ -25,12 +29,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example demonstrating BucketedRandomProjectionLSH. -Run with: - bin/spark-submit examples/src/main/python/ml/bucketed_random_projection_lsh_example.py -""" - if __name__ == "__main__": spark = SparkSession \ .builder \ diff --git a/examples/src/main/python/ml/chi_square_test_example.py b/examples/src/main/python/ml/chi_square_test_example.py index 8f25318ded00a..2af7e683cdb72 100644 --- a/examples/src/main/python/ml/chi_square_test_example.py +++ b/examples/src/main/python/ml/chi_square_test_example.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +An example for Chi-square hypothesis testing. +Run with: + bin/spark-submit examples/src/main/python/ml/chi_square_test_example.py +""" from __future__ import print_function from pyspark.sql import SparkSession @@ -23,11 +28,6 @@ from pyspark.ml.stat import ChiSquareTest # $example off$ -""" -An example for Chi-square hypothesis testing. -Run with: - bin/spark-submit examples/src/main/python/ml/chi_square_test_example.py -""" if __name__ == "__main__": spark = SparkSession \ .builder \ diff --git a/examples/src/main/python/ml/correlation_example.py b/examples/src/main/python/ml/correlation_example.py index 0a9d30da5a42e..1f4e402ac1a51 100644 --- a/examples/src/main/python/ml/correlation_example.py +++ b/examples/src/main/python/ml/correlation_example.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +An example for computing correlation matrix. +Run with: + bin/spark-submit examples/src/main/python/ml/correlation_example.py +""" from __future__ import print_function # $example on$ @@ -23,11 +28,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example for computing correlation matrix. -Run with: - bin/spark-submit examples/src/main/python/ml/correlation_example.py -""" if __name__ == "__main__": spark = SparkSession \ .builder \ diff --git a/examples/src/main/python/ml/cross_validator.py b/examples/src/main/python/ml/cross_validator.py index db7054307c2e3..6256d11504afb 100644 --- a/examples/src/main/python/ml/cross_validator.py +++ b/examples/src/main/python/ml/cross_validator.py @@ -15,6 +15,13 @@ # limitations under the License. # +""" +A simple example demonstrating model selection using CrossValidator. +This example also demonstrates how Pipelines are Estimators. +Run with: + + bin/spark-submit examples/src/main/python/ml/cross_validator.py +""" from __future__ import print_function # $example on$ @@ -26,14 +33,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -A simple example demonstrating model selection using CrossValidator. -This example also demonstrates how Pipelines are Estimators. -Run with: - - bin/spark-submit examples/src/main/python/ml/cross_validator.py -""" - if __name__ == "__main__": spark = SparkSession\ .builder\ diff --git a/examples/src/main/python/ml/dataframe_example.py b/examples/src/main/python/ml/dataframe_example.py index 109f901012c9c..ee162fa582b10 100644 --- a/examples/src/main/python/ml/dataframe_example.py +++ b/examples/src/main/python/ml/dataframe_example.py @@ -17,7 +17,7 @@ """ An example of how to use DataFrame for ML. Run with:: - bin/spark-submit examples/src/main/python/ml/dataframe_example.py + bin/spark-submit examples/src/main/python/ml/dataframe_example.py """ from __future__ import print_function @@ -35,18 +35,18 @@ print("Usage: dataframe_example.py ", file=sys.stderr) exit(-1) elif len(sys.argv) == 2: - input = sys.argv[1] + input_path = sys.argv[1] else: - input = "data/mllib/sample_libsvm_data.txt" + input_path = "data/mllib/sample_libsvm_data.txt" spark = SparkSession \ .builder \ .appName("DataFrameExample") \ .getOrCreate() - # Load input data - print("Loading LIBSVM file with UDT from " + input + ".") - df = spark.read.format("libsvm").load(input).cache() + # Load an input file + print("Loading LIBSVM file with UDT from " + input_path + ".") + df = spark.read.format("libsvm").load(input_path).cache() print("Schema from LIBSVM:") df.printSchema() print("Loaded training data as a DataFrame with " + diff --git a/examples/src/main/python/ml/fpgrowth_example.py b/examples/src/main/python/ml/fpgrowth_example.py index c92c3c27abb21..39092e616d429 100644 --- a/examples/src/main/python/ml/fpgrowth_example.py +++ b/examples/src/main/python/ml/fpgrowth_example.py @@ -15,16 +15,15 @@ # limitations under the License. # -# $example on$ -from pyspark.ml.fpm import FPGrowth -# $example off$ -from pyspark.sql import SparkSession - """ An example demonstrating FPGrowth. Run with: bin/spark-submit examples/src/main/python/ml/fpgrowth_example.py """ +# $example on$ +from pyspark.ml.fpm import FPGrowth +# $example off$ +from pyspark.sql import SparkSession if __name__ == "__main__": spark = SparkSession\ diff --git a/examples/src/main/python/ml/gaussian_mixture_example.py b/examples/src/main/python/ml/gaussian_mixture_example.py index e4a0d314e9d91..4938a904189f9 100644 --- a/examples/src/main/python/ml/gaussian_mixture_example.py +++ b/examples/src/main/python/ml/gaussian_mixture_example.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +A simple example demonstrating Gaussian Mixture Model (GMM). +Run with: + bin/spark-submit examples/src/main/python/ml/gaussian_mixture_example.py +""" from __future__ import print_function # $example on$ @@ -22,12 +27,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -A simple example demonstrating Gaussian Mixture Model (GMM). -Run with: - bin/spark-submit examples/src/main/python/ml/gaussian_mixture_example.py -""" - if __name__ == "__main__": spark = SparkSession\ .builder\ diff --git a/examples/src/main/python/ml/generalized_linear_regression_example.py b/examples/src/main/python/ml/generalized_linear_regression_example.py index 796752a60f3ab..a52f4650c1c6f 100644 --- a/examples/src/main/python/ml/generalized_linear_regression_example.py +++ b/examples/src/main/python/ml/generalized_linear_regression_example.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +An example demonstrating generalized linear regression. +Run with: + bin/spark-submit examples/src/main/python/ml/generalized_linear_regression_example.py +""" from __future__ import print_function from pyspark.sql import SparkSession @@ -22,12 +27,6 @@ from pyspark.ml.regression import GeneralizedLinearRegression # $example off$ -""" -An example demonstrating generalized linear regression. -Run with: - bin/spark-submit examples/src/main/python/ml/generalized_linear_regression_example.py -""" - if __name__ == "__main__": spark = SparkSession\ .builder\ diff --git a/examples/src/main/python/ml/imputer_example.py b/examples/src/main/python/ml/imputer_example.py index b8437f827e56d..9ba0147763618 100644 --- a/examples/src/main/python/ml/imputer_example.py +++ b/examples/src/main/python/ml/imputer_example.py @@ -15,16 +15,15 @@ # limitations under the License. # -# $example on$ -from pyspark.ml.feature import Imputer -# $example off$ -from pyspark.sql import SparkSession - """ An example demonstrating Imputer. Run with: bin/spark-submit examples/src/main/python/ml/imputer_example.py """ +# $example on$ +from pyspark.ml.feature import Imputer +# $example off$ +from pyspark.sql import SparkSession if __name__ == "__main__": spark = SparkSession\ diff --git a/examples/src/main/python/ml/isotonic_regression_example.py b/examples/src/main/python/ml/isotonic_regression_example.py index 6ae15f1b4b0dd..89cba9dfc7e8f 100644 --- a/examples/src/main/python/ml/isotonic_regression_example.py +++ b/examples/src/main/python/ml/isotonic_regression_example.py @@ -17,6 +17,9 @@ """ Isotonic Regression Example. + +Run with: + bin/spark-submit examples/src/main/python/ml/isotonic_regression_example.py """ from __future__ import print_function @@ -25,12 +28,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example demonstrating isotonic regression. -Run with: - bin/spark-submit examples/src/main/python/ml/isotonic_regression_example.py -""" - if __name__ == "__main__": spark = SparkSession\ .builder\ diff --git a/examples/src/main/python/ml/kmeans_example.py b/examples/src/main/python/ml/kmeans_example.py index 5f77843e3743a..80a878af679f4 100644 --- a/examples/src/main/python/ml/kmeans_example.py +++ b/examples/src/main/python/ml/kmeans_example.py @@ -15,6 +15,13 @@ # limitations under the License. # +""" +An example demonstrating k-means clustering. +Run with: + bin/spark-submit examples/src/main/python/ml/kmeans_example.py + +This example requires NumPy (http://www.numpy.org/). +""" from __future__ import print_function # $example on$ @@ -24,14 +31,6 @@ from pyspark.sql import SparkSession -""" -An example demonstrating k-means clustering. -Run with: - bin/spark-submit examples/src/main/python/ml/kmeans_example.py - -This example requires NumPy (http://www.numpy.org/). -""" - if __name__ == "__main__": spark = SparkSession\ .builder\ diff --git a/examples/src/main/python/ml/lda_example.py b/examples/src/main/python/ml/lda_example.py index a8b346f72cd6f..97d1a042d1479 100644 --- a/examples/src/main/python/ml/lda_example.py +++ b/examples/src/main/python/ml/lda_example.py @@ -15,7 +15,11 @@ # limitations under the License. # - +""" +An example demonstrating LDA. +Run with: + bin/spark-submit examples/src/main/python/ml/lda_example.py +""" from __future__ import print_function # $example on$ @@ -23,12 +27,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example demonstrating LDA. -Run with: - bin/spark-submit examples/src/main/python/ml/lda_example.py -""" - if __name__ == "__main__": spark = SparkSession \ .builder \ diff --git a/examples/src/main/python/ml/logistic_regression_summary_example.py b/examples/src/main/python/ml/logistic_regression_summary_example.py index bd440a1fbe8df..2274ff707b2a3 100644 --- a/examples/src/main/python/ml/logistic_regression_summary_example.py +++ b/examples/src/main/python/ml/logistic_regression_summary_example.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +An example demonstrating Logistic Regression Summary. +Run with: + bin/spark-submit examples/src/main/python/ml/logistic_regression_summary_example.py +""" from __future__ import print_function # $example on$ @@ -22,12 +27,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example demonstrating Logistic Regression Summary. -Run with: - bin/spark-submit examples/src/main/python/ml/logistic_regression_summary_example.py -""" - if __name__ == "__main__": spark = SparkSession \ .builder \ diff --git a/examples/src/main/python/ml/min_hash_lsh_example.py b/examples/src/main/python/ml/min_hash_lsh_example.py index 7b1dd611a865b..93136e6ae3cae 100644 --- a/examples/src/main/python/ml/min_hash_lsh_example.py +++ b/examples/src/main/python/ml/min_hash_lsh_example.py @@ -15,7 +15,11 @@ # limitations under the License. # - +""" +An example demonstrating MinHashLSH. +Run with: + bin/spark-submit examples/src/main/python/ml/min_hash_lsh_example.py +""" from __future__ import print_function # $example on$ @@ -25,12 +29,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example demonstrating MinHashLSH. -Run with: - bin/spark-submit examples/src/main/python/ml/min_hash_lsh_example.py -""" - if __name__ == "__main__": spark = SparkSession \ .builder \ diff --git a/examples/src/main/python/ml/multiclass_logistic_regression_with_elastic_net.py b/examples/src/main/python/ml/multiclass_logistic_regression_with_elastic_net.py index bb9cd82d6ba27..bec9860c79a2d 100644 --- a/examples/src/main/python/ml/multiclass_logistic_regression_with_elastic_net.py +++ b/examples/src/main/python/ml/multiclass_logistic_regression_with_elastic_net.py @@ -43,6 +43,44 @@ # Print the coefficients and intercept for multinomial logistic regression print("Coefficients: \n" + str(lrModel.coefficientMatrix)) print("Intercept: " + str(lrModel.interceptVector)) + + trainingSummary = lrModel.summary + + # Obtain the objective per iteration + objectiveHistory = trainingSummary.objectiveHistory + print("objectiveHistory:") + for objective in objectiveHistory: + print(objective) + + # for multiclass, we can inspect metrics on a per-label basis + print("False positive rate by label:") + for i, rate in enumerate(trainingSummary.falsePositiveRateByLabel): + print("label %d: %s" % (i, rate)) + + print("True positive rate by label:") + for i, rate in enumerate(trainingSummary.truePositiveRateByLabel): + print("label %d: %s" % (i, rate)) + + print("Precision by label:") + for i, prec in enumerate(trainingSummary.precisionByLabel): + print("label %d: %s" % (i, prec)) + + print("Recall by label:") + for i, rec in enumerate(trainingSummary.recallByLabel): + print("label %d: %s" % (i, rec)) + + print("F-measure by label:") + for i, f in enumerate(trainingSummary.fMeasureByLabel()): + print("label %d: %s" % (i, f)) + + accuracy = trainingSummary.accuracy + falsePositiveRate = trainingSummary.weightedFalsePositiveRate + truePositiveRate = trainingSummary.weightedTruePositiveRate + fMeasure = trainingSummary.weightedFMeasure() + precision = trainingSummary.weightedPrecision + recall = trainingSummary.weightedRecall + print("Accuracy: %s\nFPR: %s\nTPR: %s\nF-measure: %s\nPrecision: %s\nRecall: %s" + % (accuracy, falsePositiveRate, truePositiveRate, fMeasure, precision, recall)) # $example off$ spark.stop() diff --git a/examples/src/main/python/ml/one_vs_rest_example.py b/examples/src/main/python/ml/one_vs_rest_example.py index 8e00c25d9342e..956e94ae4ab62 100644 --- a/examples/src/main/python/ml/one_vs_rest_example.py +++ b/examples/src/main/python/ml/one_vs_rest_example.py @@ -15,6 +15,12 @@ # limitations under the License. # +""" +An example of Multiclass to Binary Reduction with One Vs Rest, +using Logistic Regression as the base classifier. +Run with: + bin/spark-submit examples/src/main/python/ml/one_vs_rest_example.py +""" from __future__ import print_function # $example on$ @@ -23,13 +29,6 @@ # $example off$ from pyspark.sql import SparkSession -""" -An example of Multiclass to Binary Reduction with One Vs Rest, -using Logistic Regression as the base classifier. -Run with: - bin/spark-submit examples/src/main/python/ml/one_vs_rest_example.py -""" - if __name__ == "__main__": spark = SparkSession \ .builder \ diff --git a/examples/src/main/python/ml/onehot_encoder_example.py b/examples/src/main/python/ml/onehot_encoder_estimator_example.py similarity index 65% rename from examples/src/main/python/ml/onehot_encoder_example.py rename to examples/src/main/python/ml/onehot_encoder_estimator_example.py index e1996c7f0a55b..2723e681cea7c 100644 --- a/examples/src/main/python/ml/onehot_encoder_example.py +++ b/examples/src/main/python/ml/onehot_encoder_estimator_example.py @@ -18,32 +18,31 @@ from __future__ import print_function # $example on$ -from pyspark.ml.feature import OneHotEncoder, StringIndexer +from pyspark.ml.feature import OneHotEncoderEstimator # $example off$ from pyspark.sql import SparkSession if __name__ == "__main__": spark = SparkSession\ .builder\ - .appName("OneHotEncoderExample")\ + .appName("OneHotEncoderEstimatorExample")\ .getOrCreate() + # Note: categorical features are usually first encoded with StringIndexer # $example on$ df = spark.createDataFrame([ - (0, "a"), - (1, "b"), - (2, "c"), - (3, "a"), - (4, "a"), - (5, "c") - ], ["id", "category"]) + (0.0, 1.0), + (1.0, 0.0), + (2.0, 1.0), + (0.0, 2.0), + (0.0, 1.0), + (2.0, 0.0) + ], ["categoryIndex1", "categoryIndex2"]) - stringIndexer = StringIndexer(inputCol="category", outputCol="categoryIndex") - model = stringIndexer.fit(df) - indexed = model.transform(df) - - encoder = OneHotEncoder(inputCol="categoryIndex", outputCol="categoryVec") - encoded = encoder.transform(indexed) + encoder = OneHotEncoderEstimator(inputCols=["categoryIndex1", "categoryIndex2"], + outputCols=["categoryVec1", "categoryVec2"]) + model = encoder.fit(df) + encoded = model.transform(df) encoded.show() # $example off$ diff --git a/examples/src/main/python/ml/train_validation_split.py b/examples/src/main/python/ml/train_validation_split.py index d104f7d30a1bf..d4f9184bf576e 100644 --- a/examples/src/main/python/ml/train_validation_split.py +++ b/examples/src/main/python/ml/train_validation_split.py @@ -15,13 +15,6 @@ # limitations under the License. # -# $example on$ -from pyspark.ml.evaluation import RegressionEvaluator -from pyspark.ml.regression import LinearRegression -from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit -# $example off$ -from pyspark.sql import SparkSession - """ This example demonstrates applying TrainValidationSplit to split data and preform model selection. @@ -29,6 +22,12 @@ bin/spark-submit examples/src/main/python/ml/train_validation_split.py """ +# $example on$ +from pyspark.ml.evaluation import RegressionEvaluator +from pyspark.ml.regression import LinearRegression +from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit +# $example off$ +from pyspark.sql import SparkSession if __name__ == "__main__": spark = SparkSession\ diff --git a/examples/src/main/python/ml/vector_size_hint_example.py b/examples/src/main/python/ml/vector_size_hint_example.py new file mode 100644 index 0000000000000..fb77dacec629d --- /dev/null +++ b/examples/src/main/python/ml/vector_size_hint_example.py @@ -0,0 +1,57 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import print_function + +# $example on$ +from pyspark.ml.linalg import Vectors +from pyspark.ml.feature import (VectorSizeHint, VectorAssembler) +# $example off$ +from pyspark.sql import SparkSession + +if __name__ == "__main__": + spark = SparkSession\ + .builder\ + .appName("VectorSizeHintExample")\ + .getOrCreate() + + # $example on$ + dataset = spark.createDataFrame( + [(0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0), + (0, 18, 1.0, Vectors.dense([0.0, 10.0]), 0.0)], + ["id", "hour", "mobile", "userFeatures", "clicked"]) + + sizeHint = VectorSizeHint( + inputCol="userFeatures", + handleInvalid="skip", + size=3) + + datasetWithSize = sizeHint.transform(dataset) + print("Rows where 'userFeatures' is not the right size are filtered out") + datasetWithSize.show(truncate=False) + + assembler = VectorAssembler( + inputCols=["hour", "mobile", "userFeatures"], + outputCol="features") + + # This dataframe can be used by downstream transformers as before + output = assembler.transform(datasetWithSize) + print("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'") + output.select("features", "clicked").show(truncate=False) + # $example off$ + + spark.stop() diff --git a/examples/src/main/python/parquet_inputformat.py b/examples/src/main/python/parquet_inputformat.py index 52e9662d528d8..a3f86cf8999cf 100644 --- a/examples/src/main/python/parquet_inputformat.py +++ b/examples/src/main/python/parquet_inputformat.py @@ -15,12 +15,6 @@ # limitations under the License. # -from __future__ import print_function - -import sys - -from pyspark.sql import SparkSession - """ Read data file users.parquet in local Spark distro: @@ -35,6 +29,12 @@ {u'favorite_color': u'red', u'name': u'Ben', u'favorite_numbers': []} <...more log output...> """ +from __future__ import print_function + +import sys + +from pyspark.sql import SparkSession + if __name__ == "__main__": if len(sys.argv) != 2: print(""" diff --git a/examples/src/main/python/sql/arrow.py b/examples/src/main/python/sql/arrow.py new file mode 100644 index 0000000000000..4c5aefb6ff4a6 --- /dev/null +++ b/examples/src/main/python/sql/arrow.py @@ -0,0 +1,129 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +A simple example demonstrating Arrow in Spark. +Run with: + ./bin/spark-submit examples/src/main/python/sql/arrow.py +""" + +from __future__ import print_function + +from pyspark.sql import SparkSession +from pyspark.sql.utils import require_minimum_pandas_version, require_minimum_pyarrow_version + +require_minimum_pandas_version() +require_minimum_pyarrow_version() + + +def dataframe_with_arrow_example(spark): + # $example on:dataframe_with_arrow$ + import numpy as np + import pandas as pd + + # Enable Arrow-based columnar data transfers + spark.conf.set("spark.sql.execution.arrow.enabled", "true") + + # Generate a Pandas DataFrame + pdf = pd.DataFrame(np.random.rand(100, 3)) + + # Create a Spark DataFrame from a Pandas DataFrame using Arrow + df = spark.createDataFrame(pdf) + + # Convert the Spark DataFrame back to a Pandas DataFrame using Arrow + result_pdf = df.select("*").toPandas() + # $example off:dataframe_with_arrow$ + print("Pandas DataFrame result statistics:\n%s\n" % str(result_pdf.describe())) + + +def scalar_pandas_udf_example(spark): + # $example on:scalar_pandas_udf$ + import pandas as pd + + from pyspark.sql.functions import col, pandas_udf + from pyspark.sql.types import LongType + + # Declare the function and create the UDF + def multiply_func(a, b): + return a * b + + multiply = pandas_udf(multiply_func, returnType=LongType()) + + # The function for a pandas_udf should be able to execute with local Pandas data + x = pd.Series([1, 2, 3]) + print(multiply_func(x, x)) + # 0 1 + # 1 4 + # 2 9 + # dtype: int64 + + # Create a Spark DataFrame, 'spark' is an existing SparkSession + df = spark.createDataFrame(pd.DataFrame(x, columns=["x"])) + + # Execute function as a Spark vectorized UDF + df.select(multiply(col("x"), col("x"))).show() + # +-------------------+ + # |multiply_func(x, x)| + # +-------------------+ + # | 1| + # | 4| + # | 9| + # +-------------------+ + # $example off:scalar_pandas_udf$ + + +def grouped_map_pandas_udf_example(spark): + # $example on:grouped_map_pandas_udf$ + from pyspark.sql.functions import pandas_udf, PandasUDFType + + df = spark.createDataFrame( + [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], + ("id", "v")) + + @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP) + def substract_mean(pdf): + # pdf is a pandas.DataFrame + v = pdf.v + return pdf.assign(v=v - v.mean()) + + df.groupby("id").apply(substract_mean).show() + # +---+----+ + # | id| v| + # +---+----+ + # | 1|-0.5| + # | 1| 0.5| + # | 2|-3.0| + # | 2|-1.0| + # | 2| 4.0| + # +---+----+ + # $example off:grouped_map_pandas_udf$ + + +if __name__ == "__main__": + spark = SparkSession \ + .builder \ + .appName("Python Arrow-in-Spark example") \ + .getOrCreate() + + print("Running Pandas to/from conversion example") + dataframe_with_arrow_example(spark) + print("Running pandas_udf scalar example") + scalar_pandas_udf_example(spark) + print("Running pandas_udf grouped map example") + grouped_map_pandas_udf_example(spark) + + spark.stop() diff --git a/examples/src/main/python/sql/basic.py b/examples/src/main/python/sql/basic.py index c07fa8f2752b3..c8fb25d0533b5 100644 --- a/examples/src/main/python/sql/basic.py +++ b/examples/src/main/python/sql/basic.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +A simple example demonstrating basic Spark SQL features. +Run with: + ./bin/spark-submit examples/src/main/python/sql/basic.py +""" from __future__ import print_function # $example on:init_session$ @@ -30,12 +35,6 @@ from pyspark.sql.types import * # $example off:programmatic_schema$ -""" -A simple example demonstrating basic Spark SQL features. -Run with: - ./bin/spark-submit examples/src/main/python/sql/basic.py -""" - def basic_df_example(spark): # $example on:create_df$ diff --git a/examples/src/main/python/sql/datasource.py b/examples/src/main/python/sql/datasource.py index b375fa775de39..d8c879dfe02ed 100644 --- a/examples/src/main/python/sql/datasource.py +++ b/examples/src/main/python/sql/datasource.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +A simple example demonstrating Spark SQL data sources. +Run with: + ./bin/spark-submit examples/src/main/python/sql/datasource.py +""" from __future__ import print_function from pyspark.sql import SparkSession @@ -22,12 +27,6 @@ from pyspark.sql import Row # $example off:schema_merging$ -""" -A simple example demonstrating Spark SQL data sources. -Run with: - ./bin/spark-submit examples/src/main/python/sql/datasource.py -""" - def basic_datasource_example(spark): # $example on:generic_load_save_functions$ diff --git a/examples/src/main/python/sql/hive.py b/examples/src/main/python/sql/hive.py index 1f83a6fb48b97..33fc2dfbeefa2 100644 --- a/examples/src/main/python/sql/hive.py +++ b/examples/src/main/python/sql/hive.py @@ -15,6 +15,11 @@ # limitations under the License. # +""" +A simple example demonstrating Spark SQL Hive integration. +Run with: + ./bin/spark-submit examples/src/main/python/sql/hive.py +""" from __future__ import print_function # $example on:spark_hive$ @@ -24,12 +29,6 @@ from pyspark.sql import Row # $example off:spark_hive$ -""" -A simple example demonstrating Spark SQL Hive integration. -Run with: - ./bin/spark-submit examples/src/main/python/sql/hive.py -""" - if __name__ == "__main__": # $example on:spark_hive$ diff --git a/examples/src/main/python/sql/streaming/structured_network_wordcount.py b/examples/src/main/python/sql/streaming/structured_network_wordcount.py index afde2550587ca..c3284c1d01017 100644 --- a/examples/src/main/python/sql/streaming/structured_network_wordcount.py +++ b/examples/src/main/python/sql/streaming/structured_network_wordcount.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Counts words in UTF8 encoded, '\n' delimited text received from the network. Usage: structured_network_wordcount.py and describe the TCP server that Structured Streaming diff --git a/examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py b/examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py index 02a7d3363d780..db672551504b5 100644 --- a/examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py +++ b/examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Counts words in UTF8 encoded, '\n' delimited text received from the network over a sliding window of configurable duration. Each line from the network is tagged with a timestamp that is used to determine the windows into which it falls. diff --git a/examples/src/main/python/streaming/direct_kafka_wordcount.py b/examples/src/main/python/streaming/direct_kafka_wordcount.py index 7097f7f4502bd..425df309011a0 100644 --- a/examples/src/main/python/streaming/direct_kafka_wordcount.py +++ b/examples/src/main/python/streaming/direct_kafka_wordcount.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Counts words in UTF8 encoded, '\n' delimited text directly received from Kafka in every 2 seconds. Usage: direct_kafka_wordcount.py diff --git a/examples/src/main/python/streaming/flume_wordcount.py b/examples/src/main/python/streaming/flume_wordcount.py index d75bc6daac138..5d6e6dc36d6f9 100644 --- a/examples/src/main/python/streaming/flume_wordcount.py +++ b/examples/src/main/python/streaming/flume_wordcount.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Counts words in UTF8 encoded, '\n' delimited text received from the network every second. Usage: flume_wordcount.py diff --git a/examples/src/main/python/streaming/kafka_wordcount.py b/examples/src/main/python/streaming/kafka_wordcount.py index 8d697f620f467..704f6602e2297 100644 --- a/examples/src/main/python/streaming/kafka_wordcount.py +++ b/examples/src/main/python/streaming/kafka_wordcount.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Counts words in UTF8 encoded, '\n' delimited text received from the network every second. Usage: kafka_wordcount.py diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py index 2b48bcfd55db0..9010fafb425e6 100644 --- a/examples/src/main/python/streaming/network_wordcount.py +++ b/examples/src/main/python/streaming/network_wordcount.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Counts words in UTF8 encoded, '\n' delimited text received from the network every second. Usage: network_wordcount.py and describe the TCP server that Spark Streaming would connect to receive data. diff --git a/examples/src/main/python/streaming/network_wordjoinsentiments.py b/examples/src/main/python/streaming/network_wordjoinsentiments.py index b309d9fad33f5..d51a380a5d5f9 100644 --- a/examples/src/main/python/streaming/network_wordjoinsentiments.py +++ b/examples/src/main/python/streaming/network_wordjoinsentiments.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Shows the most positive words in UTF8 encoded, '\n' delimited text directly received the network every 5 seconds. The streaming data is joined with a static RDD of the AFINN word list (http://neuro.imm.dtu.dk/wiki/AFINN) diff --git a/examples/src/main/python/streaming/sql_network_wordcount.py b/examples/src/main/python/streaming/sql_network_wordcount.py index 398ac8d2d8f5e..7f12281c0e3fe 100644 --- a/examples/src/main/python/streaming/sql_network_wordcount.py +++ b/examples/src/main/python/streaming/sql_network_wordcount.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Use DataFrames and SQL to count words in UTF8 encoded, '\n' delimited text received from the network every second. diff --git a/examples/src/main/python/streaming/stateful_network_wordcount.py b/examples/src/main/python/streaming/stateful_network_wordcount.py index f8bbc659c2ea7..d7bb61e729f18 100644 --- a/examples/src/main/python/streaming/stateful_network_wordcount.py +++ b/examples/src/main/python/streaming/stateful_network_wordcount.py @@ -15,7 +15,7 @@ # limitations under the License. # -""" +r""" Counts words in UTF8 encoded, '\n' delimited text received from the network every second. diff --git a/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala index e64dcbd182d94..2332a661f26a0 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala @@ -60,10 +60,6 @@ object SimpleSkewedGroupByTest { pairs1.count println(s"RESULT: ${pairs1.groupByKey(numReducers).count}") - // Print how many keys each reducer got (for debugging) - // println("RESULT: " + pairs1.groupByKey(numReducers) - // .map{case (k,v) => (k, v.size)} - // .collectAsMap) spark.stop() } diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala index 92936bd30dbc0..815404d1218b7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala +++ b/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala @@ -145,9 +145,11 @@ object Analytics extends Logging { // TriangleCount requires the graph to be partitioned .partitionBy(partitionStrategy.getOrElse(RandomVertexCut)).cache() val triangles = TriangleCount.run(graph) - println("Triangles: " + triangles.vertices.map { + val triangleTypes = triangles.vertices.map { case (vid, data) => data.toLong - }.reduce(_ + _) / 3) + }.reduce(_ + _) / 3 + + println(s"Triangles: ${triangleTypes}") sc.stop() case _ => diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala index 6d2228c8742aa..57b2edf992208 100644 --- a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala +++ b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala @@ -52,7 +52,7 @@ object SynthBenchmark { arg => arg.dropWhile(_ == '-').split('=') match { case Array(opt, v) => (opt -> v) - case _ => throw new IllegalArgumentException("Invalid argument: " + arg) + case _ => throw new IllegalArgumentException(s"Invalid argument: $arg") } } @@ -76,7 +76,7 @@ object SynthBenchmark { case ("sigma", v) => sigma = v.toDouble case ("degFile", v) => degFile = v case ("seed", v) => seed = v.toInt - case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt) + case (opt, _) => throw new IllegalArgumentException(s"Invalid option: $opt") } val conf = new SparkConf() @@ -86,7 +86,7 @@ object SynthBenchmark { val sc = new SparkContext(conf) // Create the graph - println(s"Creating graph...") + println("Creating graph...") val unpartitionedGraph = GraphGenerators.logNormalGraph(sc, numVertices, numEPart.getOrElse(sc.defaultParallelism), mu, sigma, seed) // Repartition the graph diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ChiSquareTestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ChiSquareTestExample.scala index dcee1e427ce58..5146fd0316467 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/ChiSquareTestExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/ChiSquareTestExample.scala @@ -52,9 +52,9 @@ object ChiSquareTestExample { val df = data.toDF("label", "features") val chi = ChiSquareTest.test(df, "features", "label").head - println("pValues = " + chi.getAs[Vector](0)) - println("degreesOfFreedom = " + chi.getSeq[Int](1).mkString("[", ",", "]")) - println("statistics = " + chi.getAs[Vector](2)) + println(s"pValues = ${chi.getAs[Vector](0)}") + println(s"degreesOfFreedom ${chi.getSeq[Int](1).mkString("[", ",", "]")}") + println(s"statistics ${chi.getAs[Vector](2)}") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/CorrelationExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/CorrelationExample.scala index 3f57dc342eb00..d7f1fc8ed74d7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/CorrelationExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/CorrelationExample.scala @@ -51,10 +51,10 @@ object CorrelationExample { val df = data.map(Tuple1.apply).toDF("features") val Row(coeff1: Matrix) = Correlation.corr(df, "features").head - println("Pearson correlation matrix:\n" + coeff1.toString) + println(s"Pearson correlation matrix:\n $coeff1") val Row(coeff2: Matrix) = Correlation.corr(df, "features", "spearman").head - println("Spearman correlation matrix:\n" + coeff2.toString) + println(s"Spearman correlation matrix:\n $coeff2") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala index 0658bddf16961..ee4469faab3a0 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala @@ -47,7 +47,7 @@ object DataFrameExample { val parser = new OptionParser[Params]("DataFrameExample") { head("DataFrameExample: an example app using DataFrame for ML.") opt[String]("input") - .text(s"input path to dataframe") + .text("input path to dataframe") .action((x, c) => c.copy(input = x)) checkConfig { params => success @@ -93,7 +93,7 @@ object DataFrameExample { // Load the records back. println(s"Loading Parquet file with UDT from $outputDir.") val newDF = spark.read.parquet(outputDir) - println(s"Schema from Parquet:") + println("Schema from Parquet:") newDF.printSchema() spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala index bc6d3275933ea..276cedab11abc 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala @@ -83,10 +83,10 @@ object DecisionTreeClassificationExample { .setPredictionCol("prediction") .setMetricName("accuracy") val accuracy = evaluator.evaluate(predictions) - println("Test Error = " + (1.0 - accuracy)) + println(s"Test Error = ${(1.0 - accuracy)}") val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel] - println("Learned classification tree model:\n" + treeModel.toDebugString) + println(s"Learned classification tree model:\n ${treeModel.toDebugString}") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala index ee61200ad1d0c..aaaecaea47081 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala @@ -73,10 +73,10 @@ object DecisionTreeRegressionExample { .setPredictionCol("prediction") .setMetricName("rmse") val rmse = evaluator.evaluate(predictions) - println("Root Mean Squared Error (RMSE) on test data = " + rmse) + println(s"Root Mean Squared Error (RMSE) on test data = $rmse") val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel] - println("Learned regression tree model:\n" + treeModel.toDebugString) + println(s"Learned regression tree model:\n ${treeModel.toDebugString}") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala index d94d837d10e96..2dc11b07d88ef 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala @@ -53,7 +53,7 @@ object DeveloperApiExample { // Create a LogisticRegression instance. This instance is an Estimator. val lr = new MyLogisticRegression() // Print out the parameters, documentation, and any default values. - println("MyLogisticRegression parameters:\n" + lr.explainParams() + "\n") + println(s"MyLogisticRegression parameters:\n ${lr.explainParams()}") // We may set parameters using setter methods. lr.setMaxIter(10) @@ -169,10 +169,10 @@ private class MyLogisticRegressionModel( Vectors.dense(-margin, margin) } - /** Number of classes the label can take. 2 indicates binary classification. */ + // Number of classes the label can take. 2 indicates binary classification. override val numClasses: Int = 2 - /** Number of features the model was trained on. */ + // Number of features the model was trained on. override val numFeatures: Int = coefficients.size /** diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala index f18d86e1a6921..e5d91f132a3f2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala @@ -46,7 +46,7 @@ object EstimatorTransformerParamExample { // Create a LogisticRegression instance. This instance is an Estimator. val lr = new LogisticRegression() // Print out the parameters, documentation, and any default values. - println("LogisticRegression parameters:\n" + lr.explainParams() + "\n") + println(s"LogisticRegression parameters:\n ${lr.explainParams()}\n") // We may set parameters using setter methods. lr.setMaxIter(10) @@ -58,7 +58,7 @@ object EstimatorTransformerParamExample { // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this // LogisticRegression instance. - println("Model 1 was fit using parameters: " + model1.parent.extractParamMap) + println(s"Model 1 was fit using parameters: ${model1.parent.extractParamMap}") // We may alternatively specify parameters using a ParamMap, // which supports several methods for specifying parameters. @@ -73,7 +73,7 @@ object EstimatorTransformerParamExample { // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. val model2 = lr.fit(training, paramMapCombined) - println("Model 2 was fit using parameters: " + model2.parent.extractParamMap) + println(s"Model 2 was fit using parameters: ${model2.parent.extractParamMap}") // Prepare test data. val test = spark.createDataFrame(Seq( diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala index 3656773c8b817..ef78c0a1145ef 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala @@ -86,10 +86,10 @@ object GradientBoostedTreeClassifierExample { .setPredictionCol("prediction") .setMetricName("accuracy") val accuracy = evaluator.evaluate(predictions) - println("Test Error = " + (1.0 - accuracy)) + println(s"Test Error = ${1.0 - accuracy}") val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel] - println("Learned classification GBT model:\n" + gbtModel.toDebugString) + println(s"Learned classification GBT model:\n ${gbtModel.toDebugString}") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala index e53aab7f326d3..3feb2343f6a85 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala @@ -73,10 +73,10 @@ object GradientBoostedTreeRegressorExample { .setPredictionCol("prediction") .setMetricName("rmse") val rmse = evaluator.evaluate(predictions) - println("Root Mean Squared Error (RMSE) on test data = " + rmse) + println(s"Root Mean Squared Error (RMSE) on test data = $rmse") val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel] - println("Learned regression GBT model:\n" + gbtModel.toDebugString) + println(s"Learned regression GBT model:\n ${gbtModel.toDebugString}") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionSummaryExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionSummaryExample.scala index 1740a0d3f9d12..0368dcba460b5 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionSummaryExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionSummaryExample.scala @@ -19,7 +19,7 @@ package org.apache.spark.examples.ml // $example on$ -import org.apache.spark.ml.classification.{BinaryLogisticRegressionSummary, LogisticRegression} +import org.apache.spark.ml.classification.LogisticRegression // $example off$ import org.apache.spark.sql.SparkSession import org.apache.spark.sql.functions.max @@ -47,25 +47,20 @@ object LogisticRegressionSummaryExample { // $example on$ // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier // example - val trainingSummary = lrModel.summary + val trainingSummary = lrModel.binarySummary // Obtain the objective per iteration. val objectiveHistory = trainingSummary.objectiveHistory println("objectiveHistory:") objectiveHistory.foreach(loss => println(loss)) - // Obtain the metrics useful to judge performance on test data. - // We cast the summary to a BinaryLogisticRegressionSummary since the problem is a - // binary classification problem. - val binarySummary = trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary] - // Obtain the receiver-operating characteristic as a dataframe and areaUnderROC. - val roc = binarySummary.roc + val roc = trainingSummary.roc roc.show() - println(s"areaUnderROC: ${binarySummary.areaUnderROC}") + println(s"areaUnderROC: ${trainingSummary.areaUnderROC}") // Set the model threshold to maximize F-Measure - val fMeasure = binarySummary.fMeasureByThreshold + val fMeasure = trainingSummary.fMeasureByThreshold val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0) val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure) .select("threshold").head().getDouble(0) diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MulticlassLogisticRegressionWithElasticNetExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MulticlassLogisticRegressionWithElasticNetExample.scala index 42f0ace7a353d..1f7dbddd454e8 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/MulticlassLogisticRegressionWithElasticNetExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/MulticlassLogisticRegressionWithElasticNetExample.scala @@ -48,7 +48,50 @@ object MulticlassLogisticRegressionWithElasticNetExample { // Print the coefficients and intercept for multinomial logistic regression println(s"Coefficients: \n${lrModel.coefficientMatrix}") - println(s"Intercepts: ${lrModel.interceptVector}") + println(s"Intercepts: \n${lrModel.interceptVector}") + + val trainingSummary = lrModel.summary + + // Obtain the objective per iteration + val objectiveHistory = trainingSummary.objectiveHistory + println("objectiveHistory:") + objectiveHistory.foreach(println) + + // for multiclass, we can inspect metrics on a per-label basis + println("False positive rate by label:") + trainingSummary.falsePositiveRateByLabel.zipWithIndex.foreach { case (rate, label) => + println(s"label $label: $rate") + } + + println("True positive rate by label:") + trainingSummary.truePositiveRateByLabel.zipWithIndex.foreach { case (rate, label) => + println(s"label $label: $rate") + } + + println("Precision by label:") + trainingSummary.precisionByLabel.zipWithIndex.foreach { case (prec, label) => + println(s"label $label: $prec") + } + + println("Recall by label:") + trainingSummary.recallByLabel.zipWithIndex.foreach { case (rec, label) => + println(s"label $label: $rec") + } + + + println("F-measure by label:") + trainingSummary.fMeasureByLabel.zipWithIndex.foreach { case (f, label) => + println(s"label $label: $f") + } + + val accuracy = trainingSummary.accuracy + val falsePositiveRate = trainingSummary.weightedFalsePositiveRate + val truePositiveRate = trainingSummary.weightedTruePositiveRate + val fMeasure = trainingSummary.weightedFMeasure + val precision = trainingSummary.weightedPrecision + val recall = trainingSummary.weightedRecall + println(s"Accuracy: $accuracy\nFPR: $falsePositiveRate\nTPR: $truePositiveRate\n" + + s"F-measure: $fMeasure\nPrecision: $precision\nRecall: $recall") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala index 6fce82d294f8d..646f46a925062 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala @@ -66,7 +66,7 @@ object MultilayerPerceptronClassifierExample { val evaluator = new MulticlassClassificationEvaluator() .setMetricName("accuracy") - println("Test set accuracy = " + evaluator.evaluate(predictionAndLabels)) + println(s"Test set accuracy = ${evaluator.evaluate(predictionAndLabels)}") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala index bd9fcc420a66c..50c70c626b128 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala @@ -52,7 +52,7 @@ object NaiveBayesExample { .setPredictionCol("prediction") .setMetricName("accuracy") val accuracy = evaluator.evaluate(predictions) - println("Test set accuracy = " + accuracy) + println(s"Test set accuracy = $accuracy") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/OneHotEncoderExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/OneHotEncoderEstimatorExample.scala similarity index 65% rename from examples/src/main/scala/org/apache/spark/examples/ml/OneHotEncoderExample.scala rename to examples/src/main/scala/org/apache/spark/examples/ml/OneHotEncoderEstimatorExample.scala index 274cc1268f4d1..45d816808ed8e 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/OneHotEncoderExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/OneHotEncoderEstimatorExample.scala @@ -19,38 +19,34 @@ package org.apache.spark.examples.ml // $example on$ -import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer} +import org.apache.spark.ml.feature.OneHotEncoderEstimator // $example off$ import org.apache.spark.sql.SparkSession -object OneHotEncoderExample { +object OneHotEncoderEstimatorExample { def main(args: Array[String]): Unit = { val spark = SparkSession .builder - .appName("OneHotEncoderExample") + .appName("OneHotEncoderEstimatorExample") .getOrCreate() + // Note: categorical features are usually first encoded with StringIndexer // $example on$ val df = spark.createDataFrame(Seq( - (0, "a"), - (1, "b"), - (2, "c"), - (3, "a"), - (4, "a"), - (5, "c") - )).toDF("id", "category") - - val indexer = new StringIndexer() - .setInputCol("category") - .setOutputCol("categoryIndex") - .fit(df) - val indexed = indexer.transform(df) - - val encoder = new OneHotEncoder() - .setInputCol("categoryIndex") - .setOutputCol("categoryVec") - - val encoded = encoder.transform(indexed) + (0.0, 1.0), + (1.0, 0.0), + (2.0, 1.0), + (0.0, 2.0), + (0.0, 1.0), + (2.0, 0.0) + )).toDF("categoryIndex1", "categoryIndex2") + + val encoder = new OneHotEncoderEstimator() + .setInputCols(Array("categoryIndex1", "categoryIndex2")) + .setOutputCols(Array("categoryVec1", "categoryVec2")) + val model = encoder.fit(df) + + val encoded = model.transform(df) encoded.show() // $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala index aedb9e7d3bb70..0fe16fb6dfa9f 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala @@ -36,7 +36,7 @@ object QuantileDiscretizerExample { // Output of QuantileDiscretizer for such small datasets can depend on the number of // partitions. Here we force a single partition to ensure consistent results. // Note this is not necessary for normal use cases - .repartition(1) + .repartition(1) // $example on$ val discretizer = new QuantileDiscretizer() @@ -45,7 +45,7 @@ object QuantileDiscretizerExample { .setNumBuckets(3) val result = discretizer.fit(df).transform(df) - result.show() + result.show(false) // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala index 5eafda8ce4285..6265f83902528 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala @@ -85,10 +85,10 @@ object RandomForestClassifierExample { .setPredictionCol("prediction") .setMetricName("accuracy") val accuracy = evaluator.evaluate(predictions) - println("Test Error = " + (1.0 - accuracy)) + println(s"Test Error = ${(1.0 - accuracy)}") val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel] - println("Learned classification forest model:\n" + rfModel.toDebugString) + println(s"Learned classification forest model:\n ${rfModel.toDebugString}") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala index 9a0a001c26ef5..2679fcb353a8a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala @@ -72,10 +72,10 @@ object RandomForestRegressorExample { .setPredictionCol("prediction") .setMetricName("rmse") val rmse = evaluator.evaluate(predictions) - println("Root Mean Squared Error (RMSE) on test data = " + rmse) + println(s"Root Mean Squared Error (RMSE) on test data = $rmse") val rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel] - println("Learned regression forest model:\n" + rfModel.toDebugString) + println(s"Learned regression forest model:\n ${rfModel.toDebugString}") // $example off$ spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/VectorIndexerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/VectorIndexerExample.scala index afa761aee0b98..96bb8ea2338af 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/VectorIndexerExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/VectorIndexerExample.scala @@ -41,8 +41,8 @@ object VectorIndexerExample { val indexerModel = indexer.fit(data) val categoricalFeatures: Set[Int] = indexerModel.categoryMaps.keys.toSet - println(s"Chose ${categoricalFeatures.size} categorical features: " + - categoricalFeatures.mkString(", ")) + println(s"Chose ${categoricalFeatures.size} " + + s"categorical features: ${categoricalFeatures.mkString(", ")}") // Create new column "indexed" with categorical values transformed to indices val indexedData = indexerModel.transform(data) diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/VectorSizeHintExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/VectorSizeHintExample.scala new file mode 100644 index 0000000000000..688731a791f35 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/VectorSizeHintExample.scala @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// scalastyle:off println +package org.apache.spark.examples.ml + +// $example on$ +import org.apache.spark.ml.feature.{VectorAssembler, VectorSizeHint} +import org.apache.spark.ml.linalg.Vectors +// $example off$ +import org.apache.spark.sql.SparkSession + +object VectorSizeHintExample { + def main(args: Array[String]): Unit = { + val spark = SparkSession + .builder + .appName("VectorSizeHintExample") + .getOrCreate() + + // $example on$ + val dataset = spark.createDataFrame( + Seq( + (0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0), + (0, 18, 1.0, Vectors.dense(0.0, 10.0), 0.0)) + ).toDF("id", "hour", "mobile", "userFeatures", "clicked") + + val sizeHint = new VectorSizeHint() + .setInputCol("userFeatures") + .setHandleInvalid("skip") + .setSize(3) + + val datasetWithSize = sizeHint.transform(dataset) + println("Rows where 'userFeatures' is not the right size are filtered out") + datasetWithSize.show(false) + + val assembler = new VectorAssembler() + .setInputCols(Array("hour", "mobile", "userFeatures")) + .setOutputCol("features") + + // This dataframe can be used by downstream transformers as before + val output = assembler.transform(datasetWithSize) + println("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'") + output.select("features", "clicked").show(false) + // $example off$ + + spark.stop() + } +} +// scalastyle:on println diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/AssociationRulesExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/AssociationRulesExample.scala index ff44de56839e5..a07535bb5a38d 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/AssociationRulesExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/AssociationRulesExample.scala @@ -42,9 +42,8 @@ object AssociationRulesExample { val results = ar.run(freqItemsets) results.collect().foreach { rule => - println("[" + rule.antecedent.mkString(",") - + "=>" - + rule.consequent.mkString(",") + "]," + rule.confidence) + println(s"[${rule.antecedent.mkString(",")}=>${rule.consequent.mkString(",")} ]" + + s" ${rule.confidence}") } // $example off$ @@ -53,3 +52,4 @@ object AssociationRulesExample { } // scalastyle:on println + diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassificationMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassificationMetricsExample.scala index b9263ac6fcff6..c6312d71cc912 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassificationMetricsExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassificationMetricsExample.scala @@ -86,7 +86,7 @@ object BinaryClassificationMetricsExample { // AUPRC val auPRC = metrics.areaUnderPR - println("Area under precision-recall curve = " + auPRC) + println(s"Area under precision-recall curve = $auPRC") // Compute thresholds used in ROC and PR curves val thresholds = precision.map(_._1) @@ -96,7 +96,7 @@ object BinaryClassificationMetricsExample { // AUROC val auROC = metrics.areaUnderROC - println("Area under ROC = " + auROC) + println(s"Area under ROC = $auROC") // $example off$ sc.stop() } diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeClassificationExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeClassificationExample.scala index b50b4592777ce..c2f89b72c9a2e 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeClassificationExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeClassificationExample.scala @@ -55,8 +55,8 @@ object DecisionTreeClassificationExample { (point.label, prediction) } val testErr = labelAndPreds.filter(r => r._1 != r._2).count().toDouble / testData.count() - println("Test Error = " + testErr) - println("Learned classification tree model:\n" + model.toDebugString) + println(s"Test Error = $testErr") + println(s"Learned classification tree model:\n ${model.toDebugString}") // Save and load model model.save(sc, "target/tmp/myDecisionTreeClassificationModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRegressionExample.scala index 2af45afae3d5b..1ecf6426e1f95 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRegressionExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRegressionExample.scala @@ -54,8 +54,8 @@ object DecisionTreeRegressionExample { (point.label, prediction) } val testMSE = labelsAndPredictions.map{ case (v, p) => math.pow(v - p, 2) }.mean() - println("Test Mean Squared Error = " + testMSE) - println("Learned regression tree model:\n" + model.toDebugString) + println(s"Test Mean Squared Error = $testMSE") + println(s"Learned regression tree model:\n ${model.toDebugString}") // Save and load model model.save(sc, "target/tmp/myDecisionTreeRegressionModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala index 6435abc127752..f724ee1030f04 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala @@ -74,7 +74,7 @@ object FPGrowthExample { println(s"Number of frequent itemsets: ${model.freqItemsets.count()}") model.freqItemsets.collect().foreach { itemset => - println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq) + println(s"${itemset.items.mkString("[", ",", "]")}, ${itemset.freq}") } sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingClassificationExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingClassificationExample.scala index 00bb3348d2a36..3c56e1941aeca 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingClassificationExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingClassificationExample.scala @@ -54,8 +54,8 @@ object GradientBoostingClassificationExample { (point.label, prediction) } val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count() - println("Test Error = " + testErr) - println("Learned classification GBT model:\n" + model.toDebugString) + println(s"Test Error = $testErr") + println(s"Learned classification GBT model:\n ${model.toDebugString}") // Save and load model model.save(sc, "target/tmp/myGradientBoostingClassificationModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingRegressionExample.scala index d8c263460839b..c288bf29bf255 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingRegressionExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingRegressionExample.scala @@ -53,8 +53,8 @@ object GradientBoostingRegressionExample { (point.label, prediction) } val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean() - println("Test Mean Squared Error = " + testMSE) - println("Learned regression GBT model:\n" + model.toDebugString) + println(s"Test Mean Squared Error = $testMSE") + println(s"Learned regression GBT model:\n ${model.toDebugString}") // Save and load model model.save(sc, "target/tmp/myGradientBoostingRegressionModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/HypothesisTestingExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/HypothesisTestingExample.scala index 0d391a3637c07..add1719739539 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/HypothesisTestingExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/HypothesisTestingExample.scala @@ -68,7 +68,7 @@ object HypothesisTestingExample { // against the label. val featureTestResults: Array[ChiSqTestResult] = Statistics.chiSqTest(obs) featureTestResults.zipWithIndex.foreach { case (k, v) => - println("Column " + (v + 1).toString + ":") + println(s"Column ${(v + 1)} :") println(k) } // summary of the test // $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala index 4aee951f5b04c..a10d6f0dda880 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala @@ -56,7 +56,7 @@ object IsotonicRegressionExample { // Calculate mean squared error between predicted and real labels. val meanSquaredError = predictionAndLabel.map { case (p, l) => math.pow((p - l), 2) }.mean() - println("Mean Squared Error = " + meanSquaredError) + println(s"Mean Squared Error = $meanSquaredError") // Save and load model model.save(sc, "target/tmp/myIsotonicRegressionModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/KMeansExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/KMeansExample.scala index c4d71d862f375..b0a6f1671a898 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/KMeansExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/KMeansExample.scala @@ -43,7 +43,7 @@ object KMeansExample { // Evaluate clustering by computing Within Set Sum of Squared Errors val WSSSE = clusters.computeCost(parsedData) - println("Within Set Sum of Squared Errors = " + WSSSE) + println(s"Within Set Sum of Squared Errors = $WSSSE") // Save and load model clusters.save(sc, "target/org/apache/spark/KMeansExample/KMeansModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LBFGSExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LBFGSExample.scala index fedcefa098381..123782fa6b9cf 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LBFGSExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LBFGSExample.scala @@ -82,7 +82,7 @@ object LBFGSExample { println("Loss of each step in training process") loss.foreach(println) - println("Area under ROC = " + auROC) + println(s"Area under ROC = $auROC") // $example off$ sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LatentDirichletAllocationExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LatentDirichletAllocationExample.scala index f2c8ec01439f1..d25962c5500ed 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LatentDirichletAllocationExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LatentDirichletAllocationExample.scala @@ -42,11 +42,13 @@ object LatentDirichletAllocationExample { val ldaModel = new LDA().setK(3).run(corpus) // Output topics. Each is a distribution over words (matching word count vectors) - println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize + " words):") + println(s"Learned topics (as distributions over vocab of ${ldaModel.vocabSize} words):") val topics = ldaModel.topicsMatrix for (topic <- Range(0, 3)) { - print("Topic " + topic + ":") - for (word <- Range(0, ldaModel.vocabSize)) { print(" " + topics(word, topic)); } + print(s"Topic $topic :") + for (word <- Range(0, ldaModel.vocabSize)) { + print(s"${topics(word, topic)}") + } println() } diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala index d399618094487..449b725d1d173 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala @@ -52,7 +52,7 @@ object LinearRegressionWithSGDExample { (point.label, prediction) } val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2) }.mean() - println("training Mean Squared Error = " + MSE) + println(s"training Mean Squared Error $MSE") // Save and load model model.save(sc, "target/tmp/scalaLinearRegressionWithSGDModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala index eb36697d94ba1..eff2393cc3abe 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala @@ -65,8 +65,8 @@ object PCAExample { val MSE = valuesAndPreds.map { case (v, p) => math.pow((v - p), 2) }.mean() val MSE_pca = valuesAndPreds_pca.map { case (v, p) => math.pow((v - p), 2) }.mean() - println("Mean Squared Error = " + MSE) - println("PCA Mean Squared Error = " + MSE_pca) + println(s"Mean Squared Error = $MSE") + println(s"PCA Mean Squared Error = $MSE_pca") // $example off$ sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala index d74d74a37fb11..96deafd469bc7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala @@ -41,7 +41,7 @@ object PMMLModelExportExample { val clusters = KMeans.train(parsedData, numClusters, numIterations) // Export to PMML to a String in PMML format - println("PMML Model:\n" + clusters.toPMML) + println(s"PMML Model:\n ${clusters.toPMML}") // Export the model to a local file in PMML format clusters.toPMML("/tmp/kmeans.xml") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PrefixSpanExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PrefixSpanExample.scala index 69c72c4336576..8b789277774af 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/PrefixSpanExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PrefixSpanExample.scala @@ -42,8 +42,8 @@ object PrefixSpanExample { val model = prefixSpan.run(sequences) model.freqSequences.collect().foreach { freqSequence => println( - freqSequence.sequence.map(_.mkString("[", ", ", "]")).mkString("[", ", ", "]") + - ", " + freqSequence.freq) + s"${freqSequence.sequence.map(_.mkString("[", ", ", "]")).mkString("[", ", ", "]")}," + + s" ${freqSequence.freq}") } // $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RandomForestClassificationExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RandomForestClassificationExample.scala index f1ebdf1a733ed..246e71de25615 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/RandomForestClassificationExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RandomForestClassificationExample.scala @@ -55,8 +55,8 @@ object RandomForestClassificationExample { (point.label, prediction) } val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count() - println("Test Error = " + testErr) - println("Learned classification forest model:\n" + model.toDebugString) + println(s"Test Error = $testErr") + println(s"Learned classification forest model:\n ${model.toDebugString}") // Save and load model model.save(sc, "target/tmp/myRandomForestClassificationModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RandomForestRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RandomForestRegressionExample.scala index 11d612e651b4b..770e30276bc30 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/RandomForestRegressionExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RandomForestRegressionExample.scala @@ -55,8 +55,8 @@ object RandomForestRegressionExample { (point.label, prediction) } val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean() - println("Test Mean Squared Error = " + testMSE) - println("Learned regression forest model:\n" + model.toDebugString) + println(s"Test Mean Squared Error = $testMSE") + println(s"Learned regression forest model:\n ${model.toDebugString}") // Save and load model model.save(sc, "target/tmp/myRandomForestRegressionModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RecommendationExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RecommendationExample.scala index 6df742d737e70..0bb2b8c8c2b43 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/RecommendationExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RecommendationExample.scala @@ -56,7 +56,7 @@ object RecommendationExample { val err = (r1 - r2) err * err }.mean() - println("Mean Squared Error = " + MSE) + println(s"Mean Squared Error = $MSE") // Save and load model model.save(sc, "target/tmp/myCollaborativeFilter") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SVMWithSGDExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SVMWithSGDExample.scala index b73fe9b2b3faa..285e2ce512639 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/SVMWithSGDExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SVMWithSGDExample.scala @@ -57,7 +57,7 @@ object SVMWithSGDExample { val metrics = new BinaryClassificationMetrics(scoreAndLabels) val auROC = metrics.areaUnderROC() - println("Area under ROC = " + auROC) + println(s"Area under ROC = $auROC") // Save and load model model.save(sc, "target/tmp/scalaSVMWithSGDModel") diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SimpleFPGrowth.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SimpleFPGrowth.scala index b5c3033bcba09..694c3bb18b045 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/SimpleFPGrowth.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SimpleFPGrowth.scala @@ -42,15 +42,13 @@ object SimpleFPGrowth { val model = fpg.run(transactions) model.freqItemsets.collect().foreach { itemset => - println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq) + println(s"${itemset.items.mkString("[", ",", "]")},${itemset.freq}") } val minConfidence = 0.8 model.generateAssociationRules(minConfidence).collect().foreach { rule => - println( - rule.antecedent.mkString("[", ",", "]") - + " => " + rule.consequent .mkString("[", ",", "]") - + ", " + rule.confidence) + println(s"${rule.antecedent.mkString("[", ",", "]")}=> " + + s"${rule.consequent .mkString("[", ",", "]")},${rule.confidence}") } // $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StratifiedSamplingExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StratifiedSamplingExample.scala index 16b074ef60699..3d41bef0af88c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/StratifiedSamplingExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StratifiedSamplingExample.scala @@ -41,10 +41,10 @@ object StratifiedSamplingExample { val exactSample = data.sampleByKeyExact(withReplacement = false, fractions = fractions) // $example off$ - println("approxSample size is " + approxSample.collect().size.toString) + println(s"approxSample size is ${approxSample.collect().size}") approxSample.collect().foreach(println) - println("exactSample its size is " + exactSample.collect().size.toString) + println(s"exactSample its size is ${exactSample.collect().size}") exactSample.collect().foreach(println) sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala index 03bc675299c5a..071d341b81614 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala @@ -54,7 +54,7 @@ object TallSkinnyPCA { // Compute principal components. val pc = mat.computePrincipalComponents(mat.numCols().toInt) - println("Principal components are:\n" + pc) + println(s"Principal components are:\n $pc") sc.stop() } diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala index 067e49b9599e7..8ae6de16d80e7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala @@ -54,7 +54,7 @@ object TallSkinnySVD { // Compute SVD. val svd = mat.computeSVD(mat.numCols().toInt) - println("Singular values are " + svd.s) + println(s"Singular values are ${svd.s}") sc.stop() } diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala index 43044d01b1204..25c7bf2871972 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala @@ -82,9 +82,9 @@ class CustomReceiver(host: String, port: Int) var socket: Socket = null var userInput: String = null try { - logInfo("Connecting to " + host + ":" + port) + logInfo(s"Connecting to $host : $port") socket = new Socket(host, port) - logInfo("Connected to " + host + ":" + port) + logInfo(s"Connected to $host : $port") val reader = new BufferedReader( new InputStreamReader(socket.getInputStream(), StandardCharsets.UTF_8)) userInput = reader.readLine() @@ -98,7 +98,7 @@ class CustomReceiver(host: String, port: Int) restart("Trying to connect again") } catch { case e: java.net.ConnectException => - restart("Error connecting to " + host + ":" + port, e) + restart(s"Error connecting to $host : $port", e) case t: Throwable => restart("Error receiving data", t) } diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala index 5322929d177b4..437ccf0898d7c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala @@ -54,7 +54,7 @@ object RawNetworkGrep { ssc.rawSocketStream[String](host, port, StorageLevel.MEMORY_ONLY_SER_2)).toArray val union = ssc.union(rawStreams) union.filter(_.contains("the")).count().foreachRDD(r => - println("Grep count: " + r.collect().mkString)) + println(s"Grep count: ${r.collect().mkString}")) ssc.start() ssc.awaitTermination() } diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala index 49c0427321133..f018f3a26d2e9 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala @@ -130,10 +130,10 @@ object RecoverableNetworkWordCount { true } }.collect().mkString("[", ", ", "]") - val output = "Counts at time " + time + " " + counts + val output = s"Counts at time $time $counts" println(output) - println("Dropped " + droppedWordsCounter.value + " word(s) totally") - println("Appending to " + outputFile.getAbsolutePath) + println(s"Dropped ${droppedWordsCounter.value} word(s) totally") + println(s"Appending to ${outputFile.getAbsolutePath}") Files.append(output + "\n", outputFile, Charset.defaultCharset()) } ssc @@ -141,7 +141,7 @@ object RecoverableNetworkWordCount { def main(args: Array[String]) { if (args.length != 4) { - System.err.println("Your arguments were " + args.mkString("[", ", ", "]")) + System.err.println(s"Your arguments were ${args.mkString("[", ", ", "]")}") System.err.println( """ |Usage: RecoverableNetworkWordCount diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala index 0ddd065f0db2b..2108bc63edea2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala @@ -90,13 +90,13 @@ object PageViewGenerator { val viewsPerSecond = args(1).toFloat val sleepDelayMs = (1000.0 / viewsPerSecond).toInt val listener = new ServerSocket(port) - println("Listening on port: " + port) + println(s"Listening on port: $port") while (true) { val socket = listener.accept() new Thread() { override def run(): Unit = { - println("Got client connected from: " + socket.getInetAddress) + println(s"Got client connected from: ${socket.getInetAddress}") val out = new PrintWriter(socket.getOutputStream(), true) while (true) { diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala index 1ba093f57b32c..b8e7c7e9e9152 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala @@ -104,8 +104,8 @@ object PageViewStream { .foreachRDD((rdd, time) => rdd.join(userList) .map(_._2._2) .take(10) - .foreach(u => println("Saw user %s at time %s".format(u, time)))) - case _ => println("Invalid metric entered: " + metric) + .foreach(u => println(s"Saw user $u at time $time"))) + case _ => println(s"Invalid metric entered: $metric") } ssc.start() diff --git a/external/docker-integration-tests/pom.xml b/external/docker-integration-tests/pom.xml index 485b562dce990..b7dc48e4a7001 100644 --- a/external/docker-integration-tests/pom.xml +++ b/external/docker-integration-tests/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/docker/spark-test/base/Dockerfile b/external/docker/spark-test/base/Dockerfile index 5a95a9387c310..c70cd71367679 100644 --- a/external/docker/spark-test/base/Dockerfile +++ b/external/docker/spark-test/base/Dockerfile @@ -15,14 +15,14 @@ # limitations under the License. # -FROM ubuntu:precise +FROM ubuntu:xenial # Upgrade package index -# install a few other useful packages plus Open Jdk 7 +# install a few other useful packages plus Open Jdk 8 # Remove unneeded /var/lib/apt/lists/* after install to reduce the # docker image size (by ~30MB) RUN apt-get update && \ - apt-get install -y less openjdk-7-jre-headless net-tools vim-tiny sudo openssh-server && \ + apt-get install -y less openjdk-8-jre-headless iproute2 vim-tiny sudo openssh-server && \ rm -rf /var/lib/apt/lists/* ENV SCALA_VERSION 2.11.8 diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml index 71016bc645ca7..161fd0faac82a 100644 --- a/external/flume-assembly/pom.xml +++ b/external/flume-assembly/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml index 12630840e79dc..26abc31f9620d 100644 --- a/external/flume-sink/pom.xml +++ b/external/flume-sink/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/flume/pom.xml b/external/flume/pom.xml index 87a09642405a7..bcc13ebb359b3 100644 --- a/external/flume/pom.xml +++ b/external/flume/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/kafka-0-10-assembly/pom.xml b/external/kafka-0-10-assembly/pom.xml index d6f97316b326a..020e0ba7d568b 100644 --- a/external/kafka-0-10-assembly/pom.xml +++ b/external/kafka-0-10-assembly/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/kafka-0-10-sql/pom.xml b/external/kafka-0-10-sql/pom.xml index 0c9f0aa765a39..e12115ee6e20c 100644 --- a/external/kafka-0-10-sql/pom.xml +++ b/external/kafka-0-10-sql/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaContinuousReader.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaContinuousReader.scala new file mode 100644 index 0000000000000..a2a4c8349d968 --- /dev/null +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaContinuousReader.scala @@ -0,0 +1,260 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.kafka010 + +import java.{util => ju} +import java.util.concurrent.TimeoutException + +import org.apache.kafka.clients.consumer.{ConsumerRecord, OffsetOutOfRangeException} +import org.apache.kafka.common.TopicPartition + +import org.apache.spark.TaskContext +import org.apache.spark.internal.Logging +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.catalyst.expressions.UnsafeRow +import org.apache.spark.sql.catalyst.expressions.codegen.{BufferHolder, UnsafeRowWriter} +import org.apache.spark.sql.catalyst.util.DateTimeUtils +import org.apache.spark.sql.kafka010.KafkaSource.{INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_FALSE, INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_TRUE} +import org.apache.spark.sql.sources.v2.reader._ +import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousDataReader, ContinuousReader, Offset, PartitionOffset} +import org.apache.spark.sql.types.StructType +import org.apache.spark.unsafe.types.UTF8String + +/** + * A [[ContinuousReader]] for data from kafka. + * + * @param offsetReader a reader used to get kafka offsets. Note that the actual data will be + * read by per-task consumers generated later. + * @param kafkaParams String params for per-task Kafka consumers. + * @param sourceOptions The [[org.apache.spark.sql.sources.v2.DataSourceOptions]] params which + * are not Kafka consumer params. + * @param metadataPath Path to a directory this reader can use for writing metadata. + * @param initialOffsets The Kafka offsets to start reading data at. + * @param failOnDataLoss Flag indicating whether reading should fail in data loss + * scenarios, where some offsets after the specified initial ones can't be + * properly read. + */ +class KafkaContinuousReader( + offsetReader: KafkaOffsetReader, + kafkaParams: ju.Map[String, Object], + sourceOptions: Map[String, String], + metadataPath: String, + initialOffsets: KafkaOffsetRangeLimit, + failOnDataLoss: Boolean) + extends ContinuousReader with SupportsScanUnsafeRow with Logging { + + private lazy val session = SparkSession.getActiveSession.get + private lazy val sc = session.sparkContext + + private val pollTimeoutMs = sourceOptions.getOrElse("kafkaConsumer.pollTimeoutMs", "512").toLong + + // Initialized when creating reader factories. If this diverges from the partitions at the latest + // offsets, we need to reconfigure. + // Exposed outside this object only for unit tests. + @volatile private[sql] var knownPartitions: Set[TopicPartition] = _ + + override def readSchema: StructType = KafkaOffsetReader.kafkaSchema + + private var offset: Offset = _ + override def setStartOffset(start: ju.Optional[Offset]): Unit = { + offset = start.orElse { + val offsets = initialOffsets match { + case EarliestOffsetRangeLimit => KafkaSourceOffset(offsetReader.fetchEarliestOffsets()) + case LatestOffsetRangeLimit => KafkaSourceOffset(offsetReader.fetchLatestOffsets()) + case SpecificOffsetRangeLimit(p) => offsetReader.fetchSpecificOffsets(p, reportDataLoss) + } + logInfo(s"Initial offsets: $offsets") + offsets + } + } + + override def getStartOffset(): Offset = offset + + override def deserializeOffset(json: String): Offset = { + KafkaSourceOffset(JsonUtils.partitionOffsets(json)) + } + + override def createUnsafeRowReaderFactories(): ju.List[DataReaderFactory[UnsafeRow]] = { + import scala.collection.JavaConverters._ + + val oldStartPartitionOffsets = KafkaSourceOffset.getPartitionOffsets(offset) + + val currentPartitionSet = offsetReader.fetchEarliestOffsets().keySet + val newPartitions = currentPartitionSet.diff(oldStartPartitionOffsets.keySet) + val newPartitionOffsets = offsetReader.fetchEarliestOffsets(newPartitions.toSeq) + + val deletedPartitions = oldStartPartitionOffsets.keySet.diff(currentPartitionSet) + if (deletedPartitions.nonEmpty) { + reportDataLoss(s"Some partitions were deleted: $deletedPartitions") + } + + val startOffsets = newPartitionOffsets ++ + oldStartPartitionOffsets.filterKeys(!deletedPartitions.contains(_)) + knownPartitions = startOffsets.keySet + + startOffsets.toSeq.map { + case (topicPartition, start) => + KafkaContinuousDataReaderFactory( + topicPartition, start, kafkaParams, pollTimeoutMs, failOnDataLoss) + .asInstanceOf[DataReaderFactory[UnsafeRow]] + }.asJava + } + + /** Stop this source and free any resources it has allocated. */ + def stop(): Unit = synchronized { + offsetReader.close() + } + + override def commit(end: Offset): Unit = {} + + override def mergeOffsets(offsets: Array[PartitionOffset]): Offset = { + val mergedMap = offsets.map { + case KafkaSourcePartitionOffset(p, o) => Map(p -> o) + }.reduce(_ ++ _) + KafkaSourceOffset(mergedMap) + } + + override def needsReconfiguration(): Boolean = { + knownPartitions != null && offsetReader.fetchLatestOffsets().keySet != knownPartitions + } + + override def toString(): String = s"KafkaSource[$offsetReader]" + + /** + * If `failOnDataLoss` is true, this method will throw an `IllegalStateException`. + * Otherwise, just log a warning. + */ + private def reportDataLoss(message: String): Unit = { + if (failOnDataLoss) { + throw new IllegalStateException(message + s". $INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_TRUE") + } else { + logWarning(message + s". $INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_FALSE") + } + } +} + +/** + * A data reader factory for continuous Kafka processing. This will be serialized and transformed + * into a full reader on executors. + * + * @param topicPartition The (topic, partition) pair this task is responsible for. + * @param startOffset The offset to start reading from within the partition. + * @param kafkaParams Kafka consumer params to use. + * @param pollTimeoutMs The timeout for Kafka consumer polling. + * @param failOnDataLoss Flag indicating whether data reader should fail if some offsets + * are skipped. + */ +case class KafkaContinuousDataReaderFactory( + topicPartition: TopicPartition, + startOffset: Long, + kafkaParams: ju.Map[String, Object], + pollTimeoutMs: Long, + failOnDataLoss: Boolean) extends DataReaderFactory[UnsafeRow] { + override def createDataReader(): KafkaContinuousDataReader = { + new KafkaContinuousDataReader( + topicPartition, startOffset, kafkaParams, pollTimeoutMs, failOnDataLoss) + } +} + +/** + * A per-task data reader for continuous Kafka processing. + * + * @param topicPartition The (topic, partition) pair this data reader is responsible for. + * @param startOffset The offset to start reading from within the partition. + * @param kafkaParams Kafka consumer params to use. + * @param pollTimeoutMs The timeout for Kafka consumer polling. + * @param failOnDataLoss Flag indicating whether data reader should fail if some offsets + * are skipped. + */ +class KafkaContinuousDataReader( + topicPartition: TopicPartition, + startOffset: Long, + kafkaParams: ju.Map[String, Object], + pollTimeoutMs: Long, + failOnDataLoss: Boolean) extends ContinuousDataReader[UnsafeRow] { + private val topic = topicPartition.topic + private val kafkaPartition = topicPartition.partition + private val consumer = KafkaDataConsumer.acquire(topicPartition, kafkaParams, useCache = false) + + private val sharedRow = new UnsafeRow(7) + private val bufferHolder = new BufferHolder(sharedRow) + private val rowWriter = new UnsafeRowWriter(bufferHolder, 7) + + private var nextKafkaOffset = startOffset + private var currentRecord: ConsumerRecord[Array[Byte], Array[Byte]] = _ + + override def next(): Boolean = { + var r: ConsumerRecord[Array[Byte], Array[Byte]] = null + while (r == null) { + if (TaskContext.get().isInterrupted() || TaskContext.get().isCompleted()) return false + // Our consumer.get is not interruptible, so we have to set a low poll timeout, leaving + // interrupt points to end the query rather than waiting for new data that might never come. + try { + r = consumer.get( + nextKafkaOffset, + untilOffset = Long.MaxValue, + pollTimeoutMs, + failOnDataLoss) + } catch { + // We didn't read within the timeout. We're supposed to block indefinitely for new data, so + // swallow and ignore this. + case _: TimeoutException => + + // This is a failOnDataLoss exception. Retry if nextKafkaOffset is within the data range, + // or if it's the endpoint of the data range (i.e. the "true" next offset). + case e: IllegalStateException if e.getCause.isInstanceOf[OffsetOutOfRangeException] => + val range = consumer.getAvailableOffsetRange() + if (range.latest >= nextKafkaOffset && range.earliest <= nextKafkaOffset) { + // retry + } else { + throw e + } + } + } + nextKafkaOffset = r.offset + 1 + currentRecord = r + true + } + + override def get(): UnsafeRow = { + bufferHolder.reset() + + if (currentRecord.key == null) { + rowWriter.setNullAt(0) + } else { + rowWriter.write(0, currentRecord.key) + } + rowWriter.write(1, currentRecord.value) + rowWriter.write(2, UTF8String.fromString(currentRecord.topic)) + rowWriter.write(3, currentRecord.partition) + rowWriter.write(4, currentRecord.offset) + rowWriter.write(5, + DateTimeUtils.fromJavaTimestamp(new java.sql.Timestamp(currentRecord.timestamp))) + rowWriter.write(6, currentRecord.timestampType.id) + sharedRow.setTotalSize(bufferHolder.totalSize) + sharedRow + } + + override def getOffset(): KafkaSourcePartitionOffset = { + KafkaSourcePartitionOffset(topicPartition, nextKafkaOffset) + } + + override def close(): Unit = { + consumer.release() + } +} diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/CachedKafkaConsumer.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaDataConsumer.scala similarity index 66% rename from external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/CachedKafkaConsumer.scala rename to external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaDataConsumer.scala index 90ed7b1fba2f8..dcf2f6359896c 100644 --- a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/CachedKafkaConsumer.scala +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaDataConsumer.scala @@ -27,30 +27,73 @@ import org.apache.kafka.common.TopicPartition import org.apache.spark.{SparkEnv, SparkException, TaskContext} import org.apache.spark.internal.Logging +import org.apache.spark.sql.kafka010.KafkaDataConsumer.AvailableOffsetRange import org.apache.spark.sql.kafka010.KafkaSource._ import org.apache.spark.util.UninterruptibleThread +private[kafka010] sealed trait KafkaDataConsumer { + /** + * Get the record for the given offset if available. Otherwise it will either throw error + * (if failOnDataLoss = true), or return the next available offset within [offset, untilOffset), + * or null. + * + * @param offset the offset to fetch. + * @param untilOffset the max offset to fetch. Exclusive. + * @param pollTimeoutMs timeout in milliseconds to poll data from Kafka. + * @param failOnDataLoss When `failOnDataLoss` is `true`, this method will either return record at + * offset if available, or throw exception.when `failOnDataLoss` is `false`, + * this method will either return record at offset if available, or return + * the next earliest available record less than untilOffset, or null. It + * will not throw any exception. + */ + def get( + offset: Long, + untilOffset: Long, + pollTimeoutMs: Long, + failOnDataLoss: Boolean): ConsumerRecord[Array[Byte], Array[Byte]] = { + internalConsumer.get(offset, untilOffset, pollTimeoutMs, failOnDataLoss) + } + + /** + * Return the available offset range of the current partition. It's a pair of the earliest offset + * and the latest offset. + */ + def getAvailableOffsetRange(): AvailableOffsetRange = internalConsumer.getAvailableOffsetRange() + + /** + * Release this consumer from being further used. Depending on its implementation, + * this consumer will be either finalized, or reset for reuse later. + */ + def release(): Unit + + /** Reference to the internal implementation that this wrapper delegates to */ + protected def internalConsumer: InternalKafkaConsumer +} + /** - * Consumer of single topicpartition, intended for cached reuse. - * Underlying consumer is not threadsafe, so neither is this, - * but processing the same topicpartition and group id in multiple threads is usually bad anyway. + * A wrapper around Kafka's KafkaConsumer that throws error when data loss is detected. + * This is not for direct use outside this file. */ -private[kafka010] case class CachedKafkaConsumer private( +private[kafka010] case class InternalKafkaConsumer( topicPartition: TopicPartition, kafkaParams: ju.Map[String, Object]) extends Logging { - import CachedKafkaConsumer._ + import InternalKafkaConsumer._ private val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String] - private var consumer = createConsumer + @volatile private var consumer = createConsumer /** indicates whether this consumer is in use or not */ - private var inuse = true + @volatile var inUse = true + + /** indicate whether this consumer is going to be stopped in the next release */ + @volatile var markedForClose = false /** Iterator to the already fetch data */ - private var fetchedData = ju.Collections.emptyIterator[ConsumerRecord[Array[Byte], Array[Byte]]] - private var nextOffsetInFetchedData = UNKNOWN_OFFSET + @volatile private var fetchedData = + ju.Collections.emptyIterator[ConsumerRecord[Array[Byte], Array[Byte]]] + @volatile private var nextOffsetInFetchedData = UNKNOWN_OFFSET /** Create a KafkaConsumer to fetch records for `topicPartition` */ private def createConsumer: KafkaConsumer[Array[Byte], Array[Byte]] = { @@ -61,8 +104,6 @@ private[kafka010] case class CachedKafkaConsumer private( c } - case class AvailableOffsetRange(earliest: Long, latest: Long) - private def runUninterruptiblyIfPossible[T](body: => T): T = Thread.currentThread match { case ut: UninterruptibleThread => ut.runUninterruptibly(body) @@ -313,21 +354,51 @@ private[kafka010] case class CachedKafkaConsumer private( } } -private[kafka010] object CachedKafkaConsumer extends Logging { - private val UNKNOWN_OFFSET = -2L +private[kafka010] object KafkaDataConsumer extends Logging { + + case class AvailableOffsetRange(earliest: Long, latest: Long) + + private case class CachedKafkaDataConsumer(internalConsumer: InternalKafkaConsumer) + extends KafkaDataConsumer { + assert(internalConsumer.inUse) // make sure this has been set to true + override def release(): Unit = { KafkaDataConsumer.release(internalConsumer) } + } + + private case class NonCachedKafkaDataConsumer(internalConsumer: InternalKafkaConsumer) + extends KafkaDataConsumer { + override def release(): Unit = { internalConsumer.close() } + } - private case class CacheKey(groupId: String, topicPartition: TopicPartition) + private case class CacheKey(groupId: String, topicPartition: TopicPartition) { + def this(topicPartition: TopicPartition, kafkaParams: ju.Map[String, Object]) = + this(kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String], topicPartition) + } + // This cache has the following important properties. + // - We make a best-effort attempt to maintain the max size of the cache as configured capacity. + // The capacity is not guaranteed to be maintained, especially when there are more active + // tasks simultaneously using consumers than the capacity. private lazy val cache = { val conf = SparkEnv.get.conf val capacity = conf.getInt("spark.sql.kafkaConsumerCache.capacity", 64) - new ju.LinkedHashMap[CacheKey, CachedKafkaConsumer](capacity, 0.75f, true) { + new ju.LinkedHashMap[CacheKey, InternalKafkaConsumer](capacity, 0.75f, true) { override def removeEldestEntry( - entry: ju.Map.Entry[CacheKey, CachedKafkaConsumer]): Boolean = { - if (entry.getValue.inuse == false && this.size > capacity) { - logWarning(s"KafkaConsumer cache hitting max capacity of $capacity, " + - s"removing consumer for ${entry.getKey}") + entry: ju.Map.Entry[CacheKey, InternalKafkaConsumer]): Boolean = { + + // Try to remove the least-used entry if its currently not in use. + // + // If you cannot remove it, then the cache will keep growing. In the worst case, + // the cache will grow to the max number of concurrent tasks that can run in the executor, + // (that is, number of tasks slots) after which it will never reduce. This is unlikely to + // be a serious problem because an executor with more than 64 (default) tasks slots is + // likely running on a beefy machine that can handle a large number of simultaneously + // active consumers. + + if (entry.getValue.inUse == false && this.size > capacity) { + logWarning( + s"KafkaConsumer cache hitting max capacity of $capacity, " + + s"removing consumer for ${entry.getKey}") try { entry.getValue.close() } catch { @@ -342,80 +413,87 @@ private[kafka010] object CachedKafkaConsumer extends Logging { } } - def releaseKafkaConsumer( - topic: String, - partition: Int, - kafkaParams: ju.Map[String, Object]): Unit = { - val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String] - val topicPartition = new TopicPartition(topic, partition) - val key = CacheKey(groupId, topicPartition) - - synchronized { - val consumer = cache.get(key) - if (consumer != null) { - consumer.inuse = false - } else { - logWarning(s"Attempting to release consumer that does not exist") - } - } - } - /** - * Removes (and closes) the Kafka Consumer for the given topic, partition and group id. + * Get a cached consumer for groupId, assigned to topic and partition. + * If matching consumer doesn't already exist, will be created using kafkaParams. + * The returned consumer must be released explicitly using [[KafkaDataConsumer.release()]]. + * + * Note: This method guarantees that the consumer returned is not currently in use by any one + * else. Within this guarantee, this method will make a best effort attempt to re-use consumers by + * caching them and tracking when they are in use. */ - def removeKafkaConsumer( - topic: String, - partition: Int, - kafkaParams: ju.Map[String, Object]): Unit = { - val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String] - val topicPartition = new TopicPartition(topic, partition) - val key = CacheKey(groupId, topicPartition) + def acquire( + topicPartition: TopicPartition, + kafkaParams: ju.Map[String, Object], + useCache: Boolean): KafkaDataConsumer = synchronized { + val key = new CacheKey(topicPartition, kafkaParams) + val existingInternalConsumer = cache.get(key) - synchronized { - val removedConsumer = cache.remove(key) - if (removedConsumer != null) { - removedConsumer.close() + lazy val newInternalConsumer = new InternalKafkaConsumer(topicPartition, kafkaParams) + + if (TaskContext.get != null && TaskContext.get.attemptNumber >= 1) { + // If this is reattempt at running the task, then invalidate cached consumer if any and + // start with a new one. + if (existingInternalConsumer != null) { + // Consumer exists in cache. If its in use, mark it for closing later, or close it now. + if (existingInternalConsumer.inUse) { + existingInternalConsumer.markedForClose = true + } else { + existingInternalConsumer.close() + } } + cache.remove(key) // Invalidate the cache in any case + NonCachedKafkaDataConsumer(newInternalConsumer) + + } else if (!useCache) { + // If planner asks to not reuse consumers, then do not use it, return a new consumer + NonCachedKafkaDataConsumer(newInternalConsumer) + + } else if (existingInternalConsumer == null) { + // If consumer is not already cached, then put a new in the cache and return it + cache.put(key, newInternalConsumer) + newInternalConsumer.inUse = true + CachedKafkaDataConsumer(newInternalConsumer) + + } else if (existingInternalConsumer.inUse) { + // If consumer is already cached but is currently in use, then return a new consumer + NonCachedKafkaDataConsumer(newInternalConsumer) + + } else { + // If consumer is already cached and is currently not in use, then return that consumer + existingInternalConsumer.inUse = true + CachedKafkaDataConsumer(existingInternalConsumer) } } - /** - * Get a cached consumer for groupId, assigned to topic and partition. - * If matching consumer doesn't already exist, will be created using kafkaParams. - */ - def getOrCreate( - topic: String, - partition: Int, - kafkaParams: ju.Map[String, Object]): CachedKafkaConsumer = synchronized { - val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String] - val topicPartition = new TopicPartition(topic, partition) - val key = CacheKey(groupId, topicPartition) - - // If this is reattempt at running the task, then invalidate cache and start with - // a new consumer - if (TaskContext.get != null && TaskContext.get.attemptNumber >= 1) { - removeKafkaConsumer(topic, partition, kafkaParams) - val consumer = new CachedKafkaConsumer(topicPartition, kafkaParams) - consumer.inuse = true - cache.put(key, consumer) - consumer - } else { - if (!cache.containsKey(key)) { - cache.put(key, new CachedKafkaConsumer(topicPartition, kafkaParams)) + private def release(intConsumer: InternalKafkaConsumer): Unit = { + synchronized { + + // Clear the consumer from the cache if this is indeed the consumer present in the cache + val key = new CacheKey(intConsumer.topicPartition, intConsumer.kafkaParams) + val cachedIntConsumer = cache.get(key) + if (intConsumer.eq(cachedIntConsumer)) { + // The released consumer is the same object as the cached one. + if (intConsumer.markedForClose) { + intConsumer.close() + cache.remove(key) + } else { + intConsumer.inUse = false + } + } else { + // The released consumer is either not the same one as in the cache, or not in the cache + // at all. This may happen if the cache was invalidate while this consumer was being used. + // Just close this consumer. + intConsumer.close() + logInfo(s"Released a supposedly cached consumer that was not found in the cache") } - val consumer = cache.get(key) - consumer.inuse = true - consumer } } +} - /** Create an [[CachedKafkaConsumer]] but don't put it into cache. */ - def createUncached( - topic: String, - partition: Int, - kafkaParams: ju.Map[String, Object]): CachedKafkaConsumer = { - new CachedKafkaConsumer(new TopicPartition(topic, partition), kafkaParams) - } +private[kafka010] object InternalKafkaConsumer extends Logging { + + private val UNKNOWN_OFFSET = -2L private def reportDataLoss0( failOnDataLoss: Boolean, diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaOffsetReader.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaOffsetReader.scala index 3e65949a6fd1b..551641cfdbca8 100644 --- a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaOffsetReader.scala +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaOffsetReader.scala @@ -117,10 +117,14 @@ private[kafka010] class KafkaOffsetReader( * Resolves the specific offsets based on Kafka seek positions. * This method resolves offset value -1 to the latest and -2 to the * earliest Kafka seek position. + * + * @param partitionOffsets the specific offsets to resolve + * @param reportDataLoss callback to either report or log data loss depending on setting */ def fetchSpecificOffsets( - partitionOffsets: Map[TopicPartition, Long]): Map[TopicPartition, Long] = - runUninterruptibly { + partitionOffsets: Map[TopicPartition, Long], + reportDataLoss: String => Unit): KafkaSourceOffset = { + val fetched = runUninterruptibly { withRetriesWithoutInterrupt { // Poll to get the latest assigned partitions consumer.poll(0) @@ -145,6 +149,19 @@ private[kafka010] class KafkaOffsetReader( } } + partitionOffsets.foreach { + case (tp, off) if off != KafkaOffsetRangeLimit.LATEST && + off != KafkaOffsetRangeLimit.EARLIEST => + if (fetched(tp) != off) { + reportDataLoss( + s"startingOffsets for $tp was $off but consumer reset to ${fetched(tp)}") + } + case _ => + // no real way to check that beginning or end is reasonable + } + KafkaSourceOffset(fetched) + } + /** * Fetch the earliest offsets for the topic partitions that are indicated * in the [[ConsumerStrategy]]. diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala index e9cff04ba5f2e..169a5d006fb04 100644 --- a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala @@ -130,7 +130,7 @@ private[kafka010] class KafkaSource( val offsets = startingOffsets match { case EarliestOffsetRangeLimit => KafkaSourceOffset(kafkaReader.fetchEarliestOffsets()) case LatestOffsetRangeLimit => KafkaSourceOffset(kafkaReader.fetchLatestOffsets()) - case SpecificOffsetRangeLimit(p) => fetchAndVerify(p) + case SpecificOffsetRangeLimit(p) => kafkaReader.fetchSpecificOffsets(p, reportDataLoss) } metadataLog.add(0, offsets) logInfo(s"Initial offsets: $offsets") @@ -138,21 +138,6 @@ private[kafka010] class KafkaSource( }.partitionToOffsets } - private def fetchAndVerify(specificOffsets: Map[TopicPartition, Long]) = { - val result = kafkaReader.fetchSpecificOffsets(specificOffsets) - specificOffsets.foreach { - case (tp, off) if off != KafkaOffsetRangeLimit.LATEST && - off != KafkaOffsetRangeLimit.EARLIEST => - if (result(tp) != off) { - reportDataLoss( - s"startingOffsets for $tp was $off but consumer reset to ${result(tp)}") - } - case _ => - // no real way to check that beginning or end is reasonable - } - KafkaSourceOffset(result) - } - private var currentPartitionOffsets: Option[Map[TopicPartition, Long]] = None override def schema: StructType = KafkaOffsetReader.kafkaSchema @@ -223,6 +208,14 @@ private[kafka010] class KafkaSource( logInfo(s"GetBatch called with start = $start, end = $end") val untilPartitionOffsets = KafkaSourceOffset.getPartitionOffsets(end) + // On recovery, getBatch will get called before getOffset + if (currentPartitionOffsets.isEmpty) { + currentPartitionOffsets = Some(untilPartitionOffsets) + } + if (start.isDefined && start.get == end) { + return sqlContext.internalCreateDataFrame( + sqlContext.sparkContext.emptyRDD, schema, isStreaming = true) + } val fromPartitionOffsets = start match { case Some(prevBatchEndOffset) => KafkaSourceOffset.getPartitionOffsets(prevBatchEndOffset) @@ -305,11 +298,6 @@ private[kafka010] class KafkaSource( logInfo("GetBatch generating RDD of offset range: " + offsetRanges.sortBy(_.topicPartition.toString).mkString(", ")) - // On recovery, getBatch will get called before getOffset - if (currentPartitionOffsets.isEmpty) { - currentPartitionOffsets = Some(untilPartitionOffsets) - } - sqlContext.internalCreateDataFrame(rdd, schema, isStreaming = true) } diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceOffset.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceOffset.scala index b5da415b3097e..8d41c0da2b133 100644 --- a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceOffset.scala +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceOffset.scala @@ -20,17 +20,22 @@ package org.apache.spark.sql.kafka010 import org.apache.kafka.common.TopicPartition import org.apache.spark.sql.execution.streaming.{Offset, SerializedOffset} +import org.apache.spark.sql.sources.v2.reader.streaming.{Offset => OffsetV2, PartitionOffset} /** * An [[Offset]] for the [[KafkaSource]]. This one tracks all partitions of subscribed topics and * their offsets. */ private[kafka010] -case class KafkaSourceOffset(partitionToOffsets: Map[TopicPartition, Long]) extends Offset { +case class KafkaSourceOffset(partitionToOffsets: Map[TopicPartition, Long]) extends OffsetV2 { override val json = JsonUtils.partitionOffsets(partitionToOffsets) } +private[kafka010] +case class KafkaSourcePartitionOffset(topicPartition: TopicPartition, partitionOffset: Long) + extends PartitionOffset + /** Companion object of the [[KafkaSourceOffset]] */ private[kafka010] object KafkaSourceOffset { diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceProvider.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceProvider.scala index 3cb4d8cad12cc..d4fa0359c12d6 100644 --- a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceProvider.scala +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceProvider.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.kafka010 import java.{util => ju} -import java.util.{Locale, UUID} +import java.util.{Locale, Optional, UUID} import scala.collection.JavaConverters._ @@ -27,9 +27,11 @@ import org.apache.kafka.clients.producer.ProducerConfig import org.apache.kafka.common.serialization.{ByteArrayDeserializer, ByteArraySerializer} import org.apache.spark.internal.Logging -import org.apache.spark.sql.{AnalysisException, DataFrame, SaveMode, SQLContext} +import org.apache.spark.sql.{AnalysisException, DataFrame, SaveMode, SparkSession, SQLContext} import org.apache.spark.sql.execution.streaming.{Sink, Source} import org.apache.spark.sql.sources._ +import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceOptions, StreamWriteSupport} +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter import org.apache.spark.sql.streaming.OutputMode import org.apache.spark.sql.types.StructType @@ -43,6 +45,8 @@ private[kafka010] class KafkaSourceProvider extends DataSourceRegister with StreamSinkProvider with RelationProvider with CreatableRelationProvider + with StreamWriteSupport + with ContinuousReadSupport with Logging { import KafkaSourceProvider._ @@ -101,6 +105,43 @@ private[kafka010] class KafkaSourceProvider extends DataSourceRegister failOnDataLoss(caseInsensitiveParams)) } + override def createContinuousReader( + schema: Optional[StructType], + metadataPath: String, + options: DataSourceOptions): KafkaContinuousReader = { + val parameters = options.asMap().asScala.toMap + validateStreamOptions(parameters) + // Each running query should use its own group id. Otherwise, the query may be only assigned + // partial data since Kafka will assign partitions to multiple consumers having the same group + // id. Hence, we should generate a unique id for each query. + val uniqueGroupId = s"spark-kafka-source-${UUID.randomUUID}-${metadataPath.hashCode}" + + val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) } + val specifiedKafkaParams = + parameters + .keySet + .filter(_.toLowerCase(Locale.ROOT).startsWith("kafka.")) + .map { k => k.drop(6).toString -> parameters(k) } + .toMap + + val startingStreamOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(caseInsensitiveParams, + STARTING_OFFSETS_OPTION_KEY, LatestOffsetRangeLimit) + + val kafkaOffsetReader = new KafkaOffsetReader( + strategy(caseInsensitiveParams), + kafkaParamsForDriver(specifiedKafkaParams), + parameters, + driverGroupIdPrefix = s"$uniqueGroupId-driver") + + new KafkaContinuousReader( + kafkaOffsetReader, + kafkaParamsForExecutors(specifiedKafkaParams, uniqueGroupId), + parameters, + metadataPath, + startingStreamOffsets, + failOnDataLoss(caseInsensitiveParams)) + } + /** * Returns a new base relation with the given parameters. * @@ -181,26 +222,22 @@ private[kafka010] class KafkaSourceProvider extends DataSourceRegister } } - private def kafkaParamsForProducer(parameters: Map[String, String]): Map[String, String] = { - val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) } - if (caseInsensitiveParams.contains(s"kafka.${ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG}")) { - throw new IllegalArgumentException( - s"Kafka option '${ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG}' is not supported as keys " - + "are serialized with ByteArraySerializer.") - } + override def createStreamWriter( + queryId: String, + schema: StructType, + mode: OutputMode, + options: DataSourceOptions): StreamWriter = { + import scala.collection.JavaConverters._ - if (caseInsensitiveParams.contains(s"kafka.${ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG}")) - { - throw new IllegalArgumentException( - s"Kafka option '${ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG}' is not supported as " - + "value are serialized with ByteArraySerializer.") - } - parameters - .keySet - .filter(_.toLowerCase(Locale.ROOT).startsWith("kafka.")) - .map { k => k.drop(6).toString -> parameters(k) } - .toMap + (ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG -> classOf[ByteArraySerializer].getName, - ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG -> classOf[ByteArraySerializer].getName) + val spark = SparkSession.getActiveSession.get + val topic = Option(options.get(TOPIC_OPTION_KEY).orElse(null)).map(_.trim) + // We convert the options argument from V2 -> Java map -> scala mutable -> scala immutable. + val producerParams = kafkaParamsForProducer(options.asMap.asScala.toMap) + + KafkaWriter.validateQuery( + schema.toAttributes, new java.util.HashMap[String, Object](producerParams.asJava), topic) + + new KafkaStreamWriter(topic, producerParams, schema) } private def strategy(caseInsensitiveParams: Map[String, String]) = @@ -269,7 +306,7 @@ private[kafka010] class KafkaSourceProvider extends DataSourceRegister if (caseInsensitiveParams.contains(s"kafka.${ConsumerConfig.GROUP_ID_CONFIG}")) { throw new IllegalArgumentException( s"Kafka option '${ConsumerConfig.GROUP_ID_CONFIG}' is not supported as " + - s"user-specified consumer groups is not used to track offsets.") + s"user-specified consumer groups are not used to track offsets.") } if (caseInsensitiveParams.contains(s"kafka.${ConsumerConfig.AUTO_OFFSET_RESET_CONFIG}")) { @@ -297,7 +334,7 @@ private[kafka010] class KafkaSourceProvider extends DataSourceRegister { throw new IllegalArgumentException( s"Kafka option '${ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG}' is not supported as " - + "value are deserialized as byte arrays with ByteArrayDeserializer. Use DataFrame " + + "values are deserialized as byte arrays with ByteArrayDeserializer. Use DataFrame " + "operations to explicitly deserialize the values.") } @@ -450,4 +487,27 @@ private[kafka010] object KafkaSourceProvider extends Logging { def build(): ju.Map[String, Object] = map } + + private[kafka010] def kafkaParamsForProducer( + parameters: Map[String, String]): Map[String, String] = { + val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) } + if (caseInsensitiveParams.contains(s"kafka.${ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG}")) { + throw new IllegalArgumentException( + s"Kafka option '${ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG}' is not supported as keys " + + "are serialized with ByteArraySerializer.") + } + + if (caseInsensitiveParams.contains(s"kafka.${ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG}")) + { + throw new IllegalArgumentException( + s"Kafka option '${ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG}' is not supported as " + + "value are serialized with ByteArraySerializer.") + } + parameters + .keySet + .filter(_.toLowerCase(Locale.ROOT).startsWith("kafka.")) + .map { k => k.drop(6).toString -> parameters(k) } + .toMap + (ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG -> classOf[ByteArraySerializer].getName, + ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG -> classOf[ByteArraySerializer].getName) + } } diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceRDD.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceRDD.scala index 66b3409c0cd04..498e344ea39f4 100644 --- a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceRDD.scala +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceRDD.scala @@ -52,7 +52,7 @@ private[kafka010] case class KafkaSourceRDDPartition( * An RDD that reads data from Kafka based on offset ranges across multiple partitions. * Additionally, it allows preferred locations to be set for each topic + partition, so that * the [[KafkaSource]] can ensure the same executor always reads the same topic + partition - * and cached KafkaConsumers (see [[CachedKafkaConsumer]] can be used read data efficiently. + * and cached KafkaConsumers (see [[KafkaDataConsumer]] can be used read data efficiently. * * @param sc the [[SparkContext]] * @param executorKafkaParams Kafka configuration for creating KafkaConsumer on the executors @@ -126,14 +126,9 @@ private[kafka010] class KafkaSourceRDD( val sourcePartition = thePart.asInstanceOf[KafkaSourceRDDPartition] val topic = sourcePartition.offsetRange.topic val kafkaPartition = sourcePartition.offsetRange.partition - val consumer = - if (!reuseKafkaConsumer) { - // If we can't reuse CachedKafkaConsumers, creating a new CachedKafkaConsumer. As here we - // uses `assign`, we don't need to worry about the "group.id" conflicts. - CachedKafkaConsumer.createUncached(topic, kafkaPartition, executorKafkaParams) - } else { - CachedKafkaConsumer.getOrCreate(topic, kafkaPartition, executorKafkaParams) - } + val consumer = KafkaDataConsumer.acquire( + sourcePartition.offsetRange.topicPartition, executorKafkaParams, reuseKafkaConsumer) + val range = resolveRange(consumer, sourcePartition.offsetRange) assert( range.fromOffset <= range.untilOffset, @@ -167,13 +162,7 @@ private[kafka010] class KafkaSourceRDD( } override protected def close(): Unit = { - if (!reuseKafkaConsumer) { - // Don't forget to close non-reuse KafkaConsumers. You may take down your cluster! - consumer.close() - } else { - // Indicate that we're no longer using this consumer - CachedKafkaConsumer.releaseKafkaConsumer(topic, kafkaPartition, executorKafkaParams) - } + consumer.release() } } // Release consumer, either by removing it or indicating we're no longer using it @@ -184,7 +173,7 @@ private[kafka010] class KafkaSourceRDD( } } - private def resolveRange(consumer: CachedKafkaConsumer, range: KafkaSourceRDDOffsetRange) = { + private def resolveRange(consumer: KafkaDataConsumer, range: KafkaSourceRDDOffsetRange) = { if (range.fromOffset < 0 || range.untilOffset < 0) { // Late bind the offset range val availableOffsetRange = consumer.getAvailableOffsetRange() diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaStreamWriter.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaStreamWriter.scala new file mode 100644 index 0000000000000..9307bfc001c03 --- /dev/null +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaStreamWriter.scala @@ -0,0 +1,115 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.kafka010 + +import scala.collection.JavaConverters._ + +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.Attribute +import org.apache.spark.sql.kafka010.KafkaWriter.validateQuery +import org.apache.spark.sql.sources.v2.writer._ +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter +import org.apache.spark.sql.types.StructType + +/** + * Dummy commit message. The DataSourceV2 framework requires a commit message implementation but we + * don't need to really send one. + */ +case object KafkaWriterCommitMessage extends WriterCommitMessage + +/** + * A [[StreamWriter]] for Kafka writing. Responsible for generating the writer factory. + * + * @param topic The topic this writer is responsible for. If None, topic will be inferred from + * a `topic` field in the incoming data. + * @param producerParams Parameters for Kafka producers in each task. + * @param schema The schema of the input data. + */ +class KafkaStreamWriter( + topic: Option[String], producerParams: Map[String, String], schema: StructType) + extends StreamWriter with SupportsWriteInternalRow { + + validateQuery(schema.toAttributes, producerParams.toMap[String, Object].asJava, topic) + + override def createInternalRowWriterFactory(): KafkaStreamWriterFactory = + KafkaStreamWriterFactory(topic, producerParams, schema) + + override def commit(epochId: Long, messages: Array[WriterCommitMessage]): Unit = {} + override def abort(epochId: Long, messages: Array[WriterCommitMessage]): Unit = {} +} + +/** + * A [[DataWriterFactory]] for Kafka writing. Will be serialized and sent to executors to generate + * the per-task data writers. + * @param topic The topic that should be written to. If None, topic will be inferred from + * a `topic` field in the incoming data. + * @param producerParams Parameters for Kafka producers in each task. + * @param schema The schema of the input data. + */ +case class KafkaStreamWriterFactory( + topic: Option[String], producerParams: Map[String, String], schema: StructType) + extends DataWriterFactory[InternalRow] { + + override def createDataWriter(partitionId: Int, attemptNumber: Int): DataWriter[InternalRow] = { + new KafkaStreamDataWriter(topic, producerParams, schema.toAttributes) + } +} + +/** + * A [[DataWriter]] for Kafka writing. One data writer will be created in each partition to + * process incoming rows. + * + * @param targetTopic The topic that this data writer is targeting. If None, topic will be inferred + * from a `topic` field in the incoming data. + * @param producerParams Parameters to use for the Kafka producer. + * @param inputSchema The attributes in the input data. + */ +class KafkaStreamDataWriter( + targetTopic: Option[String], producerParams: Map[String, String], inputSchema: Seq[Attribute]) + extends KafkaRowWriter(inputSchema, targetTopic) with DataWriter[InternalRow] { + import scala.collection.JavaConverters._ + + private lazy val producer = CachedKafkaProducer.getOrCreate( + new java.util.HashMap[String, Object](producerParams.asJava)) + + def write(row: InternalRow): Unit = { + checkForErrors() + sendRow(row, producer) + } + + def commit(): WriterCommitMessage = { + // Send is asynchronous, but we can't commit until all rows are actually in Kafka. + // This requires flushing and then checking that no callbacks produced errors. + // We also check for errors before to fail as soon as possible - the check is cheap. + checkForErrors() + producer.flush() + checkForErrors() + KafkaWriterCommitMessage + } + + def abort(): Unit = {} + + def close(): Unit = { + checkForErrors() + if (producer != null) { + producer.flush() + checkForErrors() + CachedKafkaProducer.close(new java.util.HashMap[String, Object](producerParams.asJava)) + } + } +} diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriteTask.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriteTask.scala index 6fd333e2f43ba..d90630a8adc93 100644 --- a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriteTask.scala +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriteTask.scala @@ -26,17 +26,15 @@ import org.apache.spark.sql.catalyst.expressions.{Attribute, Cast, Literal, Unsa import org.apache.spark.sql.types.{BinaryType, StringType} /** - * A simple trait for writing out data in a single Spark task, without any concerns about how + * Writes out data in a single Spark task, without any concerns about how * to commit or abort tasks. Exceptions thrown by the implementation of this class will * automatically trigger task aborts. */ private[kafka010] class KafkaWriteTask( producerConfiguration: ju.Map[String, Object], inputSchema: Seq[Attribute], - topic: Option[String]) { + topic: Option[String]) extends KafkaRowWriter(inputSchema, topic) { // used to synchronize with Kafka callbacks - @volatile private var failedWrite: Exception = null - private val projection = createProjection private var producer: KafkaProducer[Array[Byte], Array[Byte]] = _ /** @@ -46,23 +44,7 @@ private[kafka010] class KafkaWriteTask( producer = CachedKafkaProducer.getOrCreate(producerConfiguration) while (iterator.hasNext && failedWrite == null) { val currentRow = iterator.next() - val projectedRow = projection(currentRow) - val topic = projectedRow.getUTF8String(0) - val key = projectedRow.getBinary(1) - val value = projectedRow.getBinary(2) - if (topic == null) { - throw new NullPointerException(s"null topic present in the data. Use the " + - s"${KafkaSourceProvider.TOPIC_OPTION_KEY} option for setting a default topic.") - } - val record = new ProducerRecord[Array[Byte], Array[Byte]](topic.toString, key, value) - val callback = new Callback() { - override def onCompletion(recordMetadata: RecordMetadata, e: Exception): Unit = { - if (failedWrite == null && e != null) { - failedWrite = e - } - } - } - producer.send(record, callback) + sendRow(currentRow, producer) } } @@ -74,8 +56,49 @@ private[kafka010] class KafkaWriteTask( producer = null } } +} + +private[kafka010] abstract class KafkaRowWriter( + inputSchema: Seq[Attribute], topic: Option[String]) { + + // used to synchronize with Kafka callbacks + @volatile protected var failedWrite: Exception = _ + protected val projection = createProjection + + private val callback = new Callback() { + override def onCompletion(recordMetadata: RecordMetadata, e: Exception): Unit = { + if (failedWrite == null && e != null) { + failedWrite = e + } + } + } - private def createProjection: UnsafeProjection = { + /** + * Send the specified row to the producer, with a callback that will save any exception + * to failedWrite. Note that send is asynchronous; subclasses must flush() their producer before + * assuming the row is in Kafka. + */ + protected def sendRow( + row: InternalRow, producer: KafkaProducer[Array[Byte], Array[Byte]]): Unit = { + val projectedRow = projection(row) + val topic = projectedRow.getUTF8String(0) + val key = projectedRow.getBinary(1) + val value = projectedRow.getBinary(2) + if (topic == null) { + throw new NullPointerException(s"null topic present in the data. Use the " + + s"${KafkaSourceProvider.TOPIC_OPTION_KEY} option for setting a default topic.") + } + val record = new ProducerRecord[Array[Byte], Array[Byte]](topic.toString, key, value) + producer.send(record, callback) + } + + protected def checkForErrors(): Unit = { + if (failedWrite != null) { + throw failedWrite + } + } + + private def createProjection = { val topicExpression = topic.map(Literal(_)).orElse { inputSchema.find(_.name == KafkaWriter.TOPIC_ATTRIBUTE_NAME) }.getOrElse { @@ -112,11 +135,5 @@ private[kafka010] class KafkaWriteTask( Seq(topicExpression, Cast(keyExpression, BinaryType), Cast(valueExpression, BinaryType)), inputSchema) } - - private def checkForErrors(): Unit = { - if (failedWrite != null) { - throw failedWrite - } - } } diff --git a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriter.scala b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriter.scala index 5e9ae35b3f008..15cd44812cb0c 100644 --- a/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriter.scala +++ b/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriter.scala @@ -43,10 +43,9 @@ private[kafka010] object KafkaWriter extends Logging { override def toString: String = "KafkaWriter" def validateQuery( - queryExecution: QueryExecution, + schema: Seq[Attribute], kafkaParameters: ju.Map[String, Object], topic: Option[String] = None): Unit = { - val schema = queryExecution.analyzed.output schema.find(_.name == TOPIC_ATTRIBUTE_NAME).getOrElse( if (topic.isEmpty) { throw new AnalysisException(s"topic option required when no " + @@ -84,7 +83,7 @@ private[kafka010] object KafkaWriter extends Logging { kafkaParameters: ju.Map[String, Object], topic: Option[String] = None): Unit = { val schema = queryExecution.analyzed.output - validateQuery(queryExecution, kafkaParameters, topic) + validateQuery(schema, kafkaParameters, topic) queryExecution.toRdd.foreachPartition { iter => val writeTask = new KafkaWriteTask(kafkaParameters, schema, topic) Utils.tryWithSafeFinally(block = writeTask.execute(iter))( diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSinkSuite.scala b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSinkSuite.scala new file mode 100644 index 0000000000000..ddfc0c1a4be2d --- /dev/null +++ b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSinkSuite.scala @@ -0,0 +1,474 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.kafka010 + +import java.util.Locale + +import org.apache.kafka.clients.producer.ProducerConfig +import org.apache.kafka.common.serialization.ByteArraySerializer +import org.scalatest.time.SpanSugar._ +import scala.collection.JavaConverters._ + +import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, SpecificInternalRow, UnsafeProjection} +import org.apache.spark.sql.streaming._ +import org.apache.spark.sql.types.{BinaryType, DataType} +import org.apache.spark.util.Utils + +/** + * This is a temporary port of KafkaSinkSuite, since we do not yet have a V2 memory stream. + * Once we have one, this will be changed to a specialization of KafkaSinkSuite and we won't have + * to duplicate all the code. + */ +class KafkaContinuousSinkSuite extends KafkaContinuousTest { + import testImplicits._ + + override val streamingTimeout = 30.seconds + + override def beforeAll(): Unit = { + super.beforeAll() + testUtils = new KafkaTestUtils( + withBrokerProps = Map("auto.create.topics.enable" -> "false")) + testUtils.setup() + } + + override def afterAll(): Unit = { + if (testUtils != null) { + testUtils.teardown() + testUtils = null + } + super.afterAll() + } + + test("streaming - write to kafka with topic field") { + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 1) + + val input = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .option("startingOffsets", "earliest") + .load() + + val topic = newTopic() + testUtils.createTopic(topic) + + val writer = createKafkaWriter( + input.toDF(), + withTopic = None, + withOutputMode = Some(OutputMode.Append))( + withSelectExpr = s"'$topic' as topic", "value") + + val reader = createKafkaReader(topic) + .selectExpr("CAST(key as STRING) key", "CAST(value as STRING) value") + .selectExpr("CAST(key as INT) key", "CAST(value as INT) value") + .as[(Option[Int], Int)] + .map(_._2) + + try { + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + checkDatasetUnorderly(reader, 1, 2, 3, 4, 5) + } + testUtils.sendMessages(inputTopic, Array("6", "7", "8", "9", "10")) + eventually(timeout(streamingTimeout)) { + checkDatasetUnorderly(reader, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + } + } finally { + writer.stop() + } + } + + test("streaming - write w/o topic field, with topic option") { + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 1) + + val input = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .option("startingOffsets", "earliest") + .load() + + val topic = newTopic() + testUtils.createTopic(topic) + + val writer = createKafkaWriter( + input.toDF(), + withTopic = Some(topic), + withOutputMode = Some(OutputMode.Append()))() + + val reader = createKafkaReader(topic) + .selectExpr("CAST(key as STRING) key", "CAST(value as STRING) value") + .selectExpr("CAST(key as INT) key", "CAST(value as INT) value") + .as[(Option[Int], Int)] + .map(_._2) + + try { + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + checkDatasetUnorderly(reader, 1, 2, 3, 4, 5) + } + testUtils.sendMessages(inputTopic, Array("6", "7", "8", "9", "10")) + eventually(timeout(streamingTimeout)) { + checkDatasetUnorderly(reader, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + } + } finally { + writer.stop() + } + } + + test("streaming - topic field and topic option") { + /* The purpose of this test is to ensure that the topic option + * overrides the topic field. We begin by writing some data that + * includes a topic field and value (e.g., 'foo') along with a topic + * option. Then when we read from the topic specified in the option + * we should see the data i.e., the data was written to the topic + * option, and not to the topic in the data e.g., foo + */ + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 1) + + val input = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .option("startingOffsets", "earliest") + .load() + + val topic = newTopic() + testUtils.createTopic(topic) + + val writer = createKafkaWriter( + input.toDF(), + withTopic = Some(topic), + withOutputMode = Some(OutputMode.Append()))( + withSelectExpr = "'foo' as topic", "CAST(value as STRING) value") + + val reader = createKafkaReader(topic) + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") + .selectExpr("CAST(key AS INT)", "CAST(value AS INT)") + .as[(Option[Int], Int)] + .map(_._2) + + try { + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + checkDatasetUnorderly(reader, 1, 2, 3, 4, 5) + } + testUtils.sendMessages(inputTopic, Array("6", "7", "8", "9", "10")) + eventually(timeout(streamingTimeout)) { + checkDatasetUnorderly(reader, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + } + } finally { + writer.stop() + } + } + + test("null topic attribute") { + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 1) + + val input = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .option("startingOffsets", "earliest") + .load() + val topic = newTopic() + testUtils.createTopic(topic) + + /* No topic field or topic option */ + var writer: StreamingQuery = null + var ex: Exception = null + try { + writer = createKafkaWriter(input.toDF())( + withSelectExpr = "CAST(null as STRING) as topic", "value" + ) + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + ex = writer.exception.get + } + } finally { + writer.stop() + } + assert(ex.getCause.getCause.getMessage + .toLowerCase(Locale.ROOT) + .contains("null topic present in the data.")) + } + + test("streaming - write data with bad schema") { + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 1) + + val input = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .option("startingOffsets", "earliest") + .load() + val topic = newTopic() + testUtils.createTopic(topic) + + /* No topic field or topic option */ + var writer: StreamingQuery = null + var ex: Exception = null + try { + writer = createKafkaWriter(input.toDF())( + withSelectExpr = "value as key", "value" + ) + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + ex = writer.exception.get + } + } finally { + writer.stop() + } + assert(ex.getMessage + .toLowerCase(Locale.ROOT) + .contains("topic option required when no 'topic' attribute is present")) + + try { + /* No value field */ + writer = createKafkaWriter(input.toDF())( + withSelectExpr = s"'$topic' as topic", "value as key" + ) + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + ex = writer.exception.get + } + } finally { + writer.stop() + } + assert(ex.getMessage.toLowerCase(Locale.ROOT).contains( + "required attribute 'value' not found")) + } + + test("streaming - write data with valid schema but wrong types") { + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 1) + + val input = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .option("startingOffsets", "earliest") + .load() + .selectExpr("CAST(value as STRING) value") + val topic = newTopic() + testUtils.createTopic(topic) + + var writer: StreamingQuery = null + var ex: Exception = null + try { + /* topic field wrong type */ + writer = createKafkaWriter(input.toDF())( + withSelectExpr = s"CAST('1' as INT) as topic", "value" + ) + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + ex = writer.exception.get + } + } finally { + writer.stop() + } + assert(ex.getMessage.toLowerCase(Locale.ROOT).contains("topic type must be a string")) + + try { + /* value field wrong type */ + writer = createKafkaWriter(input.toDF())( + withSelectExpr = s"'$topic' as topic", "CAST(value as INT) as value" + ) + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + ex = writer.exception.get + } + } finally { + writer.stop() + } + assert(ex.getMessage.toLowerCase(Locale.ROOT).contains( + "value attribute type must be a string or binarytype")) + + try { + /* key field wrong type */ + writer = createKafkaWriter(input.toDF())( + withSelectExpr = s"'$topic' as topic", "CAST(value as INT) as key", "value" + ) + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + ex = writer.exception.get + } + } finally { + writer.stop() + } + assert(ex.getMessage.toLowerCase(Locale.ROOT).contains( + "key attribute type must be a string or binarytype")) + } + + test("streaming - write to non-existing topic") { + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 1) + + val input = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .option("startingOffsets", "earliest") + .load() + val topic = newTopic() + + var writer: StreamingQuery = null + var ex: Exception = null + try { + ex = intercept[StreamingQueryException] { + writer = createKafkaWriter(input.toDF(), withTopic = Some(topic))() + testUtils.sendMessages(inputTopic, Array("1", "2", "3", "4", "5")) + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + } + throw writer.exception.get + } + } finally { + writer.stop() + } + assert(ex.getCause.getCause.getMessage.toLowerCase(Locale.ROOT).contains("job aborted")) + } + + test("streaming - exception on config serializer") { + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 1) + testUtils.sendMessages(inputTopic, Array("0")) + + val input = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .load() + var writer: StreamingQuery = null + var ex: Exception = null + try { + writer = createKafkaWriter( + input.toDF(), + withOptions = Map("kafka.key.serializer" -> "foo"))() + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + ex = writer.exception.get + } + assert(ex.getMessage.toLowerCase(Locale.ROOT).contains( + "kafka option 'key.serializer' is not supported")) + } finally { + writer.stop() + } + + try { + writer = createKafkaWriter( + input.toDF(), + withOptions = Map("kafka.value.serializer" -> "foo"))() + eventually(timeout(streamingTimeout)) { + assert(writer.exception.isDefined) + ex = writer.exception.get + } + assert(ex.getMessage.toLowerCase(Locale.ROOT).contains( + "kafka option 'value.serializer' is not supported")) + } finally { + writer.stop() + } + } + + test("generic - write big data with small producer buffer") { + /* This test ensures that we understand the semantics of Kafka when + * is comes to blocking on a call to send when the send buffer is full. + * This test will configure the smallest possible producer buffer and + * indicate that we should block when it is full. Thus, no exception should + * be thrown in the case of a full buffer. + */ + val topic = newTopic() + testUtils.createTopic(topic, 1) + val options = new java.util.HashMap[String, String] + options.put("bootstrap.servers", testUtils.brokerAddress) + options.put("buffer.memory", "16384") // min buffer size + options.put("block.on.buffer.full", "true") + options.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, classOf[ByteArraySerializer].getName) + options.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, classOf[ByteArraySerializer].getName) + val inputSchema = Seq(AttributeReference("value", BinaryType)()) + val data = new Array[Byte](15000) // large value + val writeTask = new KafkaStreamDataWriter(Some(topic), options.asScala.toMap, inputSchema) + try { + val fieldTypes: Array[DataType] = Array(BinaryType) + val converter = UnsafeProjection.create(fieldTypes) + val row = new SpecificInternalRow(fieldTypes) + row.update(0, data) + val iter = Seq.fill(1000)(converter.apply(row)).iterator + iter.foreach(writeTask.write(_)) + writeTask.commit() + } finally { + writeTask.close() + } + } + + private def createKafkaReader(topic: String): DataFrame = { + spark.read + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("startingOffsets", "earliest") + .option("endingOffsets", "latest") + .option("subscribe", topic) + .load() + } + + private def createKafkaWriter( + input: DataFrame, + withTopic: Option[String] = None, + withOutputMode: Option[OutputMode] = None, + withOptions: Map[String, String] = Map[String, String]()) + (withSelectExpr: String*): StreamingQuery = { + var stream: DataStreamWriter[Row] = null + val checkpointDir = Utils.createTempDir() + var df = input.toDF() + if (withSelectExpr.length > 0) { + df = df.selectExpr(withSelectExpr: _*) + } + stream = df.writeStream + .format("kafka") + .option("checkpointLocation", checkpointDir.getCanonicalPath) + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + // We need to reduce blocking time to efficiently test non-existent partition behavior. + .option("kafka.max.block.ms", "1000") + .trigger(Trigger.Continuous(1000)) + .queryName("kafkaStream") + withTopic.foreach(stream.option("topic", _)) + withOutputMode.foreach(stream.outputMode(_)) + withOptions.foreach(opt => stream.option(opt._1, opt._2)) + stream.start() + } +} diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSourceSuite.scala b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSourceSuite.scala new file mode 100644 index 0000000000000..a7083fa4e3417 --- /dev/null +++ b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSourceSuite.scala @@ -0,0 +1,97 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.kafka010 + +import java.util.Properties +import java.util.concurrent.atomic.AtomicInteger + +import org.scalatest.time.SpanSugar._ +import scala.collection.mutable +import scala.util.Random + +import org.apache.spark.SparkContext +import org.apache.spark.sql.{DataFrame, Dataset, ForeachWriter, Row} +import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation +import org.apache.spark.sql.execution.streaming.StreamExecution +import org.apache.spark.sql.execution.streaming.continuous.ContinuousExecution +import org.apache.spark.sql.streaming.{StreamTest, Trigger} +import org.apache.spark.sql.test.{SharedSQLContext, TestSparkSession} + +// Run tests in KafkaSourceSuiteBase in continuous execution mode. +class KafkaContinuousSourceSuite extends KafkaSourceSuiteBase with KafkaContinuousTest + +class KafkaContinuousSourceTopicDeletionSuite extends KafkaContinuousTest { + import testImplicits._ + + override val brokerProps = Map("auto.create.topics.enable" -> "false") + + test("subscribing topic by pattern with topic deletions") { + val topicPrefix = newTopic() + val topic = topicPrefix + "-seems" + val topic2 = topicPrefix + "-bad" + testUtils.createTopic(topic, partitions = 5) + testUtils.sendMessages(topic, Array("-1")) + require(testUtils.getLatestOffsets(Set(topic)).size === 5) + + val reader = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("kafka.metadata.max.age.ms", "1") + .option("subscribePattern", s"$topicPrefix-.*") + .option("failOnDataLoss", "false") + + val kafka = reader.load() + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") + .as[(String, String)] + val mapped = kafka.map(kv => kv._2.toInt + 1) + + testStream(mapped)( + makeSureGetOffsetCalled, + AddKafkaData(Set(topic), 1, 2, 3), + CheckAnswer(2, 3, 4), + Execute { query => + testUtils.deleteTopic(topic) + testUtils.createTopic(topic2, partitions = 5) + eventually(timeout(streamingTimeout)) { + assert( + query.lastExecution.logical.collectFirst { + case DataSourceV2Relation(_, r: KafkaContinuousReader) => r + }.exists { r => + // Ensure the new topic is present and the old topic is gone. + r.knownPartitions.exists(_.topic == topic2) + }, + s"query never reconfigured to new topic $topic2") + } + }, + AddKafkaData(Set(topic2), 4, 5, 6), + CheckAnswer(2, 3, 4, 5, 6, 7) + ) + } +} + +class KafkaContinuousSourceStressForDontFailOnDataLossSuite + extends KafkaSourceStressForDontFailOnDataLossSuite { + override protected def startStream(ds: Dataset[Int]) = { + ds.writeStream + .format("memory") + .queryName("memory") + .trigger(Trigger.Continuous("1 second")) + .start() + } +} diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousTest.scala b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousTest.scala new file mode 100644 index 0000000000000..5a1a14f7a307a --- /dev/null +++ b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousTest.scala @@ -0,0 +1,94 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.kafka010 + +import java.util.concurrent.atomic.AtomicInteger + +import org.apache.spark.SparkContext +import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskEnd, SparkListenerTaskStart} +import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation +import org.apache.spark.sql.execution.streaming.StreamExecution +import org.apache.spark.sql.execution.streaming.continuous.ContinuousExecution +import org.apache.spark.sql.streaming.Trigger +import org.apache.spark.sql.test.TestSparkSession + +// Trait to configure StreamTest for kafka continuous execution tests. +trait KafkaContinuousTest extends KafkaSourceTest { + override val defaultTrigger = Trigger.Continuous(1000) + override val defaultUseV2Sink = true + + // We need more than the default local[2] to be able to schedule all partitions simultaneously. + override protected def createSparkSession = new TestSparkSession( + new SparkContext( + "local[10]", + "continuous-stream-test-sql-context", + sparkConf.set("spark.sql.testkey", "true"))) + + // In addition to setting the partitions in Kafka, we have to wait until the query has + // reconfigured to the new count so the test framework can hook in properly. + override protected def setTopicPartitions( + topic: String, newCount: Int, query: StreamExecution) = { + testUtils.addPartitions(topic, newCount) + eventually(timeout(streamingTimeout)) { + assert( + query.lastExecution.logical.collectFirst { + case DataSourceV2Relation(_, r: KafkaContinuousReader) => r + }.exists(_.knownPartitions.size == newCount), + s"query never reconfigured to $newCount partitions") + } + } + + // Continuous processing tasks end asynchronously, so test that they actually end. + private val tasksEndedListener = new SparkListener() { + val activeTaskIdCount = new AtomicInteger(0) + + override def onTaskStart(start: SparkListenerTaskStart): Unit = { + activeTaskIdCount.incrementAndGet() + } + + override def onTaskEnd(end: SparkListenerTaskEnd): Unit = { + activeTaskIdCount.decrementAndGet() + } + } + + override def beforeEach(): Unit = { + super.beforeEach() + spark.sparkContext.addSparkListener(tasksEndedListener) + } + + override def afterEach(): Unit = { + eventually(timeout(streamingTimeout)) { + assert(tasksEndedListener.activeTaskIdCount.get() == 0) + } + spark.sparkContext.removeSparkListener(tasksEndedListener) + super.afterEach() + } + + + test("ensure continuous stream is being used") { + val query = spark.readStream + .format("rate") + .option("numPartitions", "1") + .option("rowsPerSecond", "1") + .load() + + testStream(query)( + Execute(q => assert(q.isInstanceOf[ContinuousExecution])) + ) + } +} diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaDataConsumerSuite.scala b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaDataConsumerSuite.scala new file mode 100644 index 0000000000000..0d0fb9c3ab5af --- /dev/null +++ b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaDataConsumerSuite.scala @@ -0,0 +1,124 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.kafka010 + +import java.util.concurrent.{Executors, TimeUnit} + +import scala.collection.JavaConverters._ +import scala.concurrent.{ExecutionContext, Future} +import scala.concurrent.duration.Duration +import scala.util.Random + +import org.apache.kafka.clients.consumer.ConsumerConfig +import org.apache.kafka.common.TopicPartition +import org.apache.kafka.common.serialization.ByteArrayDeserializer +import org.scalatest.PrivateMethodTester + +import org.apache.spark.{TaskContext, TaskContextImpl} +import org.apache.spark.sql.test.SharedSQLContext +import org.apache.spark.util.ThreadUtils + +class KafkaDataConsumerSuite extends SharedSQLContext with PrivateMethodTester { + + protected var testUtils: KafkaTestUtils = _ + + override def beforeAll(): Unit = { + super.beforeAll() + testUtils = new KafkaTestUtils(Map[String, Object]()) + testUtils.setup() + } + + override def afterAll(): Unit = { + if (testUtils != null) { + testUtils.teardown() + testUtils = null + } + super.afterAll() + } + + test("SPARK-19886: Report error cause correctly in reportDataLoss") { + val cause = new Exception("D'oh!") + val reportDataLoss = PrivateMethod[Unit]('reportDataLoss0) + val e = intercept[IllegalStateException] { + InternalKafkaConsumer.invokePrivate(reportDataLoss(true, "message", cause)) + } + assert(e.getCause === cause) + } + + test("SPARK-23623: concurrent use of KafkaDataConsumer") { + val topic = "topic" + Random.nextInt() + val data = (1 to 1000).map(_.toString) + testUtils.createTopic(topic, 1) + testUtils.sendMessages(topic, data.toArray) + val topicPartition = new TopicPartition(topic, 0) + + import ConsumerConfig._ + val kafkaParams = Map[String, Object]( + GROUP_ID_CONFIG -> "groupId", + BOOTSTRAP_SERVERS_CONFIG -> testUtils.brokerAddress, + KEY_DESERIALIZER_CLASS_CONFIG -> classOf[ByteArrayDeserializer].getName, + VALUE_DESERIALIZER_CLASS_CONFIG -> classOf[ByteArrayDeserializer].getName, + AUTO_OFFSET_RESET_CONFIG -> "earliest", + ENABLE_AUTO_COMMIT_CONFIG -> "false" + ) + + val numThreads = 100 + val numConsumerUsages = 500 + + @volatile var error: Throwable = null + + def consume(i: Int): Unit = { + val useCache = Random.nextBoolean + val taskContext = if (Random.nextBoolean) { + new TaskContextImpl(0, 0, 0, 0, attemptNumber = Random.nextInt(2), null, null, null) + } else { + null + } + TaskContext.setTaskContext(taskContext) + val consumer = KafkaDataConsumer.acquire( + topicPartition, kafkaParams.asJava, useCache) + try { + val range = consumer.getAvailableOffsetRange() + val rcvd = range.earliest until range.latest map { offset => + val bytes = consumer.get(offset, Long.MaxValue, 10000, failOnDataLoss = false).value() + new String(bytes) + } + assert(rcvd == data) + } catch { + case e: Throwable => + error = e + throw e + } finally { + consumer.release() + } + } + + val threadpool = Executors.newFixedThreadPool(numThreads) + try { + val futures = (1 to numConsumerUsages).map { i => + threadpool.submit(new Runnable { + override def run(): Unit = { consume(i) } + }) + } + futures.foreach(_.get(1, TimeUnit.MINUTES)) + assert(error == null) + } finally { + threadpool.shutdown() + } + } +} diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSinkSuite.scala b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSinkSuite.scala index 2ab336c7ac476..7079ac6453ffc 100644 --- a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSinkSuite.scala +++ b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSinkSuite.scala @@ -138,7 +138,7 @@ class KafkaSinkSuite extends StreamTest with SharedSQLContext { val reader = createKafkaReader(topic) .selectExpr("CAST(key as STRING) key", "CAST(value as STRING) value") .selectExpr("CAST(key as INT) key", "CAST(value as INT) value") - .as[(Int, Int)] + .as[(Option[Int], Int)] .map(_._2) try { @@ -336,27 +336,31 @@ class KafkaSinkSuite extends StreamTest with SharedSQLContext { } finally { writer.stop() } - assert(ex.getMessage.toLowerCase(Locale.ROOT).contains("job aborted")) + assert(ex.getCause.getCause.getMessage.toLowerCase(Locale.ROOT).contains("job aborted")) } test("streaming - exception on config serializer") { val input = MemoryStream[String] var writer: StreamingQuery = null var ex: Exception = null - ex = intercept[IllegalArgumentException] { + ex = intercept[StreamingQueryException] { writer = createKafkaWriter( input.toDF(), withOptions = Map("kafka.key.serializer" -> "foo"))() + input.addData("1") + writer.processAllAvailable() } - assert(ex.getMessage.toLowerCase(Locale.ROOT).contains( + assert(ex.getCause.getMessage.toLowerCase(Locale.ROOT).contains( "kafka option 'key.serializer' is not supported")) - ex = intercept[IllegalArgumentException] { + ex = intercept[StreamingQueryException] { writer = createKafkaWriter( input.toDF(), withOptions = Map("kafka.value.serializer" -> "foo"))() + input.addData("1") + writer.processAllAvailable() } - assert(ex.getMessage.toLowerCase(Locale.ROOT).contains( + assert(ex.getCause.getMessage.toLowerCase(Locale.ROOT).contains( "kafka option 'value.serializer' is not supported")) } diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSourceSuite.scala b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSourceSuite.scala index 2034b9be07f24..02c87643568bd 100644 --- a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSourceSuite.scala +++ b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSourceSuite.scala @@ -29,13 +29,14 @@ import scala.util.Random import org.apache.kafka.clients.producer.RecordMetadata import org.apache.kafka.common.TopicPartition -import org.scalatest.concurrent.Eventually._ import org.scalatest.concurrent.PatienceConfiguration.Timeout import org.scalatest.time.SpanSugar._ import org.apache.spark.SparkContext -import org.apache.spark.sql.ForeachWriter +import org.apache.spark.sql.{Dataset, ForeachWriter} +import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation import org.apache.spark.sql.execution.streaming._ +import org.apache.spark.sql.execution.streaming.continuous.ContinuousExecution import org.apache.spark.sql.functions.{count, window} import org.apache.spark.sql.kafka010.KafkaSourceProvider._ import org.apache.spark.sql.streaming.{ProcessingTime, StreamTest} @@ -49,9 +50,11 @@ abstract class KafkaSourceTest extends StreamTest with SharedSQLContext { override val streamingTimeout = 30.seconds + protected val brokerProps = Map[String, Object]() + override def beforeAll(): Unit = { super.beforeAll() - testUtils = new KafkaTestUtils + testUtils = new KafkaTestUtils(brokerProps) testUtils.setup() } @@ -59,18 +62,25 @@ abstract class KafkaSourceTest extends StreamTest with SharedSQLContext { if (testUtils != null) { testUtils.teardown() testUtils = null - super.afterAll() } + super.afterAll() } protected def makeSureGetOffsetCalled = AssertOnQuery { q => // Because KafkaSource's initialPartitionOffsets is set lazily, we need to make sure - // its "getOffset" is called before pushing any data. Otherwise, because of the race contion, + // its "getOffset" is called before pushing any data. Otherwise, because of the race condition, // we don't know which data should be fetched when `startingOffsets` is latest. - q.processAllAvailable() + q match { + case c: ContinuousExecution => c.awaitEpoch(0) + case m: MicroBatchExecution => m.processAllAvailable() + } true } + protected def setTopicPartitions(topic: String, newCount: Int, query: StreamExecution) : Unit = { + testUtils.addPartitions(topic, newCount) + } + /** * Add data to Kafka. * @@ -82,10 +92,11 @@ abstract class KafkaSourceTest extends StreamTest with SharedSQLContext { message: String = "", topicAction: (String, Option[Int]) => Unit = (_, _) => {}) extends AddData { - override def addData(query: Option[StreamExecution]): (Source, Offset) = { - if (query.get.isActive) { + override def addData(query: Option[StreamExecution]): (BaseStreamingSource, Offset) = { + query match { // Make sure no Spark job is running when deleting a topic - query.get.processAllAvailable() + case Some(m: MicroBatchExecution) => m.processAllAvailable() + case _ => } val existingTopics = testUtils.getAllTopicsAndPartitionSize().toMap @@ -97,16 +108,18 @@ abstract class KafkaSourceTest extends StreamTest with SharedSQLContext { topicAction(existingTopicPartitions._1, Some(existingTopicPartitions._2)) } - // Read all topics again in case some topics are delete. - val allTopics = testUtils.getAllTopicsAndPartitionSize().toMap.keys require( query.nonEmpty, "Cannot add data when there is no query for finding the active kafka source") val sources = query.get.logicalPlan.collect { - case StreamingExecutionRelation(source, _) if source.isInstanceOf[KafkaSource] => - source.asInstanceOf[KafkaSource] - } + case StreamingExecutionRelation(source: KafkaSource, _) => source + } ++ (query.get.lastExecution match { + case null => Seq() + case e => e.logical.collect { + case DataSourceV2Relation(_, reader: KafkaContinuousReader) => reader + } + }) if (sources.isEmpty) { throw new Exception( "Could not find Kafka source in the StreamExecution logical plan to add data to") @@ -137,14 +150,158 @@ abstract class KafkaSourceTest extends StreamTest with SharedSQLContext { override def toString: String = s"AddKafkaData(topics = $topics, data = $data, message = $message)" } -} + private val topicId = new AtomicInteger(0) + protected def newTopic(): String = s"topic-${topicId.getAndIncrement()}" +} -class KafkaSourceSuite extends KafkaSourceTest { +class KafkaMicroBatchSourceSuite extends KafkaSourceSuiteBase { import testImplicits._ - private val topicId = new AtomicInteger(0) + test("(de)serialization of initial offsets") { + val topic = newTopic() + testUtils.createTopic(topic, partitions = 5) + + val reader = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", topic) + + testStream(reader.load)( + makeSureGetOffsetCalled, + StopStream, + StartStream(), + StopStream) + } + + test("maxOffsetsPerTrigger") { + val topic = newTopic() + testUtils.createTopic(topic, partitions = 3) + testUtils.sendMessages(topic, (100 to 200).map(_.toString).toArray, Some(0)) + testUtils.sendMessages(topic, (10 to 20).map(_.toString).toArray, Some(1)) + testUtils.sendMessages(topic, Array("1"), Some(2)) + + val reader = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("kafka.metadata.max.age.ms", "1") + .option("maxOffsetsPerTrigger", 10) + .option("subscribe", topic) + .option("startingOffsets", "earliest") + val kafka = reader.load() + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") + .as[(String, String)] + val mapped: org.apache.spark.sql.Dataset[_] = kafka.map(kv => kv._2.toInt) + + val clock = new StreamManualClock + + val waitUntilBatchProcessed = AssertOnQuery { q => + eventually(Timeout(streamingTimeout)) { + if (!q.exception.isDefined) { + assert(clock.isStreamWaitingAt(clock.getTimeMillis())) + } + } + if (q.exception.isDefined) { + throw q.exception.get + } + true + } + + testStream(mapped)( + StartStream(ProcessingTime(100), clock), + waitUntilBatchProcessed, + // 1 from smallest, 1 from middle, 8 from biggest + CheckAnswer(1, 10, 100, 101, 102, 103, 104, 105, 106, 107), + AdvanceManualClock(100), + waitUntilBatchProcessed, + // smallest now empty, 1 more from middle, 9 more from biggest + CheckAnswer(1, 10, 100, 101, 102, 103, 104, 105, 106, 107, + 11, 108, 109, 110, 111, 112, 113, 114, 115, 116 + ), + StopStream, + StartStream(ProcessingTime(100), clock), + waitUntilBatchProcessed, + // smallest now empty, 1 more from middle, 9 more from biggest + CheckAnswer(1, 10, 100, 101, 102, 103, 104, 105, 106, 107, + 11, 108, 109, 110, 111, 112, 113, 114, 115, 116, + 12, 117, 118, 119, 120, 121, 122, 123, 124, 125 + ), + AdvanceManualClock(100), + waitUntilBatchProcessed, + // smallest now empty, 1 more from middle, 9 more from biggest + CheckAnswer(1, 10, 100, 101, 102, 103, 104, 105, 106, 107, + 11, 108, 109, 110, 111, 112, 113, 114, 115, 116, + 12, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 13, 126, 127, 128, 129, 130, 131, 132, 133, 134 + ) + ) + } + + test("input row metrics") { + val topic = newTopic() + testUtils.createTopic(topic, partitions = 5) + testUtils.sendMessages(topic, Array("-1")) + require(testUtils.getLatestOffsets(Set(topic)).size === 5) + + val kafka = spark + .readStream + .format("kafka") + .option("subscribe", topic) + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .load() + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") + .as[(String, String)] + + val mapped = kafka.map(kv => kv._2.toInt + 1) + testStream(mapped)( + StartStream(trigger = ProcessingTime(1)), + makeSureGetOffsetCalled, + AddKafkaData(Set(topic), 1, 2, 3), + CheckAnswer(2, 3, 4), + AssertOnQuery { query => + val recordsRead = query.recentProgress.map(_.numInputRows).sum + recordsRead == 3 + } + ) + } + + test("subscribing topic by pattern with topic deletions") { + val topicPrefix = newTopic() + val topic = topicPrefix + "-seems" + val topic2 = topicPrefix + "-bad" + testUtils.createTopic(topic, partitions = 5) + testUtils.sendMessages(topic, Array("-1")) + require(testUtils.getLatestOffsets(Set(topic)).size === 5) + + val reader = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("kafka.metadata.max.age.ms", "1") + .option("subscribePattern", s"$topicPrefix-.*") + .option("failOnDataLoss", "false") + + val kafka = reader.load() + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") + .as[(String, String)] + val mapped = kafka.map(kv => kv._2.toInt + 1) + + testStream(mapped)( + makeSureGetOffsetCalled, + AddKafkaData(Set(topic), 1, 2, 3), + CheckAnswer(2, 3, 4), + Assert { + testUtils.deleteTopic(topic) + testUtils.createTopic(topic2, partitions = 5) + true + }, + AddKafkaData(Set(topic2), 4, 5, 6), + CheckAnswer(2, 3, 4, 5, 6, 7) + ) + } testWithUninterruptibleThread( "deserialization of initial offset with Spark 2.1.0") { @@ -237,42 +394,115 @@ class KafkaSourceSuite extends KafkaSourceTest { } } - test("(de)serialization of initial offsets") { + test("KafkaSource with watermark") { + val now = System.currentTimeMillis() val topic = newTopic() - testUtils.createTopic(topic, partitions = 64) + testUtils.createTopic(newTopic(), partitions = 1) + testUtils.sendMessages(topic, Array(1).map(_.toString)) - val reader = spark + val kafka = spark .readStream .format("kafka") .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("kafka.metadata.max.age.ms", "1") + .option("startingOffsets", s"earliest") .option("subscribe", topic) + .load() - testStream(reader.load)( - makeSureGetOffsetCalled, - StopStream, - StartStream(), - StopStream) + val windowedAggregation = kafka + .withWatermark("timestamp", "10 seconds") + .groupBy(window($"timestamp", "5 seconds") as 'window) + .agg(count("*") as 'count) + .select($"window".getField("start") as 'window, $"count") + + val query = windowedAggregation + .writeStream + .format("memory") + .outputMode("complete") + .queryName("kafkaWatermark") + .start() + query.processAllAvailable() + val rows = spark.table("kafkaWatermark").collect() + assert(rows.length === 1, s"Unexpected results: ${rows.toList}") + val row = rows(0) + // We cannot check the exact window start time as it depands on the time that messages were + // inserted by the producer. So here we just use a low bound to make sure the internal + // conversion works. + assert( + row.getAs[java.sql.Timestamp]("window").getTime >= now - 5 * 1000, + s"Unexpected results: $row") + assert(row.getAs[Int]("count") === 1, s"Unexpected results: $row") + query.stop() } - test("maxOffsetsPerTrigger") { + test("delete a topic when a Spark job is running") { + KafkaSourceSuite.collectedData.clear() + val topic = newTopic() - testUtils.createTopic(topic, partitions = 3) - testUtils.sendMessages(topic, (100 to 200).map(_.toString).toArray, Some(0)) - testUtils.sendMessages(topic, (10 to 20).map(_.toString).toArray, Some(1)) - testUtils.sendMessages(topic, Array("1"), Some(2)) + testUtils.createTopic(topic, partitions = 1) + testUtils.sendMessages(topic, (1 to 10).map(_.toString).toArray) val reader = spark .readStream .format("kafka") .option("kafka.bootstrap.servers", testUtils.brokerAddress) .option("kafka.metadata.max.age.ms", "1") - .option("maxOffsetsPerTrigger", 10) .option("subscribe", topic) + // If a topic is deleted and we try to poll data starting from offset 0, + // the Kafka consumer will just block until timeout and return an empty result. + // So set the timeout to 1 second to make this test fast. + .option("kafkaConsumer.pollTimeoutMs", "1000") .option("startingOffsets", "earliest") + .option("failOnDataLoss", "false") val kafka = reader.load() .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") .as[(String, String)] - val mapped: org.apache.spark.sql.Dataset[_] = kafka.map(kv => kv._2.toInt) + KafkaSourceSuite.globalTestUtils = testUtils + // The following ForeachWriter will delete the topic before fetching data from Kafka + // in executors. + val query = kafka.map(kv => kv._2.toInt).writeStream.foreach(new ForeachWriter[Int] { + override def open(partitionId: Long, version: Long): Boolean = { + KafkaSourceSuite.globalTestUtils.deleteTopic(topic) + true + } + + override def process(value: Int): Unit = { + KafkaSourceSuite.collectedData.add(value) + } + + override def close(errorOrNull: Throwable): Unit = {} + }).start() + query.processAllAvailable() + query.stop() + // `failOnDataLoss` is `false`, we should not fail the query + assert(query.exception.isEmpty) + } + + test("SPARK-22956: currentPartitionOffsets should be set when no new data comes in") { + def getSpecificDF(range: Range.Inclusive): org.apache.spark.sql.Dataset[Int] = { + val topic = newTopic() + testUtils.createTopic(topic, partitions = 1) + testUtils.sendMessages(topic, range.map(_.toString).toArray, Some(0)) + + val reader = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("kafka.metadata.max.age.ms", "1") + .option("maxOffsetsPerTrigger", 5) + .option("subscribe", topic) + .option("startingOffsets", "earliest") + + reader.load() + .selectExpr("CAST(value AS STRING)") + .as[String] + .map(k => k.toInt) + } + + val df1 = getSpecificDF(0 to 9) + val df2 = getSpecificDF(100 to 199) + + val kafka = df1.union(df2) val clock = new StreamManualClock @@ -288,35 +518,35 @@ class KafkaSourceSuite extends KafkaSourceTest { true } - testStream(mapped)( + testStream(kafka)( StartStream(ProcessingTime(100), clock), waitUntilBatchProcessed, - // 1 from smallest, 1 from middle, 8 from biggest - CheckAnswer(1, 10, 100, 101, 102, 103, 104, 105, 106, 107), + // 5 from smaller topic, 5 from bigger one + CheckLastBatch((0 to 4) ++ (100 to 104): _*), AdvanceManualClock(100), waitUntilBatchProcessed, - // smallest now empty, 1 more from middle, 9 more from biggest - CheckAnswer(1, 10, 100, 101, 102, 103, 104, 105, 106, 107, - 11, 108, 109, 110, 111, 112, 113, 114, 115, 116 - ), + // 5 from smaller topic, 5 from bigger one + CheckLastBatch((5 to 9) ++ (105 to 109): _*), + AdvanceManualClock(100), + waitUntilBatchProcessed, + // smaller topic empty, 5 from bigger one + CheckLastBatch(110 to 114: _*), StopStream, StartStream(ProcessingTime(100), clock), waitUntilBatchProcessed, - // smallest now empty, 1 more from middle, 9 more from biggest - CheckAnswer(1, 10, 100, 101, 102, 103, 104, 105, 106, 107, - 11, 108, 109, 110, 111, 112, 113, 114, 115, 116, - 12, 117, 118, 119, 120, 121, 122, 123, 124, 125 - ), + // smallest now empty, 5 from bigger one + CheckLastBatch(115 to 119: _*), AdvanceManualClock(100), waitUntilBatchProcessed, - // smallest now empty, 1 more from middle, 9 more from biggest - CheckAnswer(1, 10, 100, 101, 102, 103, 104, 105, 106, 107, - 11, 108, 109, 110, 111, 112, 113, 114, 115, 116, - 12, 117, 118, 119, 120, 121, 122, 123, 124, 125, - 13, 126, 127, 128, 129, 130, 131, 132, 133, 134 - ) + // smallest now empty, 5 from bigger one + CheckLastBatch(120 to 124: _*) ) } +} + +abstract class KafkaSourceSuiteBase extends KafkaSourceTest { + + import testImplicits._ test("cannot stop Kafka stream") { val topic = newTopic() @@ -328,7 +558,7 @@ class KafkaSourceSuite extends KafkaSourceTest { .format("kafka") .option("kafka.bootstrap.servers", testUtils.brokerAddress) .option("kafka.metadata.max.age.ms", "1") - .option("subscribePattern", s"topic-.*") + .option("subscribePattern", s"$topic.*") val kafka = reader.load() .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") @@ -422,65 +652,6 @@ class KafkaSourceSuite extends KafkaSourceTest { } } - test("subscribing topic by pattern with topic deletions") { - val topicPrefix = newTopic() - val topic = topicPrefix + "-seems" - val topic2 = topicPrefix + "-bad" - testUtils.createTopic(topic, partitions = 5) - testUtils.sendMessages(topic, Array("-1")) - require(testUtils.getLatestOffsets(Set(topic)).size === 5) - - val reader = spark - .readStream - .format("kafka") - .option("kafka.bootstrap.servers", testUtils.brokerAddress) - .option("kafka.metadata.max.age.ms", "1") - .option("subscribePattern", s"$topicPrefix-.*") - .option("failOnDataLoss", "false") - - val kafka = reader.load() - .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") - .as[(String, String)] - val mapped = kafka.map(kv => kv._2.toInt + 1) - - testStream(mapped)( - makeSureGetOffsetCalled, - AddKafkaData(Set(topic), 1, 2, 3), - CheckAnswer(2, 3, 4), - Assert { - testUtils.deleteTopic(topic) - testUtils.createTopic(topic2, partitions = 5) - true - }, - AddKafkaData(Set(topic2), 4, 5, 6), - CheckAnswer(2, 3, 4, 5, 6, 7) - ) - } - - test("starting offset is latest by default") { - val topic = newTopic() - testUtils.createTopic(topic, partitions = 5) - testUtils.sendMessages(topic, Array("0")) - require(testUtils.getLatestOffsets(Set(topic)).size === 5) - - val reader = spark - .readStream - .format("kafka") - .option("kafka.bootstrap.servers", testUtils.brokerAddress) - .option("subscribe", topic) - - val kafka = reader.load() - .selectExpr("CAST(value AS STRING)") - .as[String] - val mapped = kafka.map(_.toInt) - - testStream(mapped)( - makeSureGetOffsetCalled, - AddKafkaData(Set(topic), 1, 2, 3), - CheckAnswer(1, 2, 3) // should not have 0 - ) - } - test("bad source options") { def testBadOptions(options: (String, String)*)(expectedMsgs: String*): Unit = { val ex = intercept[IllegalArgumentException] { @@ -540,77 +711,6 @@ class KafkaSourceSuite extends KafkaSourceTest { testUnsupportedConfig("kafka.auto.offset.reset", "latest") } - test("input row metrics") { - val topic = newTopic() - testUtils.createTopic(topic, partitions = 5) - testUtils.sendMessages(topic, Array("-1")) - require(testUtils.getLatestOffsets(Set(topic)).size === 5) - - val kafka = spark - .readStream - .format("kafka") - .option("subscribe", topic) - .option("kafka.bootstrap.servers", testUtils.brokerAddress) - .load() - .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") - .as[(String, String)] - - val mapped = kafka.map(kv => kv._2.toInt + 1) - testStream(mapped)( - StartStream(trigger = ProcessingTime(1)), - makeSureGetOffsetCalled, - AddKafkaData(Set(topic), 1, 2, 3), - CheckAnswer(2, 3, 4), - AssertOnQuery { query => - val recordsRead = query.recentProgress.map(_.numInputRows).sum - recordsRead == 3 - } - ) - } - - test("delete a topic when a Spark job is running") { - KafkaSourceSuite.collectedData.clear() - - val topic = newTopic() - testUtils.createTopic(topic, partitions = 1) - testUtils.sendMessages(topic, (1 to 10).map(_.toString).toArray) - - val reader = spark - .readStream - .format("kafka") - .option("kafka.bootstrap.servers", testUtils.brokerAddress) - .option("kafka.metadata.max.age.ms", "1") - .option("subscribe", topic) - // If a topic is deleted and we try to poll data starting from offset 0, - // the Kafka consumer will just block until timeout and return an empty result. - // So set the timeout to 1 second to make this test fast. - .option("kafkaConsumer.pollTimeoutMs", "1000") - .option("startingOffsets", "earliest") - .option("failOnDataLoss", "false") - val kafka = reader.load() - .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") - .as[(String, String)] - KafkaSourceSuite.globalTestUtils = testUtils - // The following ForeachWriter will delete the topic before fetching data from Kafka - // in executors. - val query = kafka.map(kv => kv._2.toInt).writeStream.foreach(new ForeachWriter[Int] { - override def open(partitionId: Long, version: Long): Boolean = { - KafkaSourceSuite.globalTestUtils.deleteTopic(topic) - true - } - - override def process(value: Int): Unit = { - KafkaSourceSuite.collectedData.add(value) - } - - override def close(errorOrNull: Throwable): Unit = {} - }).start() - query.processAllAvailable() - query.stop() - // `failOnDataLoss` is `false`, we should not fail the query - assert(query.exception.isEmpty) - } - test("get offsets from case insensitive parameters") { for ((optionKey, optionValue, answer) <- Seq( (STARTING_OFFSETS_OPTION_KEY, "earLiEst", EarliestOffsetRangeLimit), @@ -629,8 +729,6 @@ class KafkaSourceSuite extends KafkaSourceTest { } } - private def newTopic(): String = s"topic-${topicId.getAndIncrement()}" - private def assignString(topic: String, partitions: Iterable[Int]): String = { JsonUtils.partitions(partitions.map(p => new TopicPartition(topic, p))) } @@ -676,6 +774,10 @@ class KafkaSourceSuite extends KafkaSourceTest { testStream(mapped)( makeSureGetOffsetCalled, + Execute { q => + // wait to reach the last offset in every partition + q.awaitOffset(0, KafkaSourceOffset(partitionOffsets.mapValues(_ => 3L))) + }, CheckAnswer(-20, -21, -22, 0, 1, 2, 11, 12, 22), StopStream, StartStream(), @@ -704,13 +806,14 @@ class KafkaSourceSuite extends KafkaSourceTest { val query = kafka .writeStream .format("memory") - .outputMode("append") .queryName("kafkaColumnTypes") + .trigger(defaultTrigger) .start() - query.processAllAvailable() - val rows = spark.table("kafkaColumnTypes").collect() - assert(rows.length === 1, s"Unexpected results: ${rows.toList}") - val row = rows(0) + eventually(timeout(streamingTimeout)) { + assert(spark.table("kafkaColumnTypes").count == 1, + s"Unexpected results: ${spark.table("kafkaColumnTypes").collectAsList()}") + } + val row = spark.table("kafkaColumnTypes").head() assert(row.getAs[Array[Byte]]("key") === null, s"Unexpected results: $row") assert(row.getAs[Array[Byte]]("value") === "1".getBytes(UTF_8), s"Unexpected results: $row") assert(row.getAs[String]("topic") === topic, s"Unexpected results: $row") @@ -723,47 +826,6 @@ class KafkaSourceSuite extends KafkaSourceTest { query.stop() } - test("KafkaSource with watermark") { - val now = System.currentTimeMillis() - val topic = newTopic() - testUtils.createTopic(newTopic(), partitions = 1) - testUtils.sendMessages(topic, Array(1).map(_.toString)) - - val kafka = spark - .readStream - .format("kafka") - .option("kafka.bootstrap.servers", testUtils.brokerAddress) - .option("kafka.metadata.max.age.ms", "1") - .option("startingOffsets", s"earliest") - .option("subscribe", topic) - .load() - - val windowedAggregation = kafka - .withWatermark("timestamp", "10 seconds") - .groupBy(window($"timestamp", "5 seconds") as 'window) - .agg(count("*") as 'count) - .select($"window".getField("start") as 'window, $"count") - - val query = windowedAggregation - .writeStream - .format("memory") - .outputMode("complete") - .queryName("kafkaWatermark") - .start() - query.processAllAvailable() - val rows = spark.table("kafkaWatermark").collect() - assert(rows.length === 1, s"Unexpected results: ${rows.toList}") - val row = rows(0) - // We cannot check the exact window start time as it depands on the time that messages were - // inserted by the producer. So here we just use a low bound to make sure the internal - // conversion works. - assert( - row.getAs[java.sql.Timestamp]("window").getTime >= now - 5 * 1000, - s"Unexpected results: $row") - assert(row.getAs[Int]("count") === 1, s"Unexpected results: $row") - query.stop() - } - private def testFromLatestOffsets( topic: String, addPartitions: Boolean, @@ -800,9 +862,7 @@ class KafkaSourceSuite extends KafkaSourceTest { AddKafkaData(Set(topic), 7, 8), CheckAnswer(2, 3, 4, 5, 6, 7, 8, 9), AssertOnQuery("Add partitions") { query: StreamExecution => - if (addPartitions) { - testUtils.addPartitions(topic, 10) - } + if (addPartitions) setTopicPartitions(topic, 10, query) true }, AddKafkaData(Set(topic), 9, 10, 11, 12, 13, 14, 15, 16), @@ -843,9 +903,7 @@ class KafkaSourceSuite extends KafkaSourceTest { StartStream(), CheckAnswer(2, 3, 4, 5, 6, 7, 8, 9), AssertOnQuery("Add partitions") { query: StreamExecution => - if (addPartitions) { - testUtils.addPartitions(topic, 10) - } + if (addPartitions) setTopicPartitions(topic, 10, query) true }, AddKafkaData(Set(topic), 9, 10, 11, 12, 13, 14, 15, 16), @@ -977,20 +1035,8 @@ class KafkaSourceStressForDontFailOnDataLossSuite extends StreamTest with Shared } } - test("stress test for failOnDataLoss=false") { - val reader = spark - .readStream - .format("kafka") - .option("kafka.bootstrap.servers", testUtils.brokerAddress) - .option("kafka.metadata.max.age.ms", "1") - .option("subscribePattern", "failOnDataLoss.*") - .option("startingOffsets", "earliest") - .option("failOnDataLoss", "false") - .option("fetchOffset.retryIntervalMs", "3000") - val kafka = reader.load() - .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") - .as[(String, String)] - val query = kafka.map(kv => kv._2.toInt).writeStream.foreach(new ForeachWriter[Int] { + protected def startStream(ds: Dataset[Int]) = { + ds.writeStream.foreach(new ForeachWriter[Int] { override def open(partitionId: Long, version: Long): Boolean = { true @@ -1004,6 +1050,22 @@ class KafkaSourceStressForDontFailOnDataLossSuite extends StreamTest with Shared override def close(errorOrNull: Throwable): Unit = { } }).start() + } + + test("stress test for failOnDataLoss=false") { + val reader = spark + .readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("kafka.metadata.max.age.ms", "1") + .option("subscribePattern", "failOnDataLoss.*") + .option("startingOffsets", "earliest") + .option("failOnDataLoss", "false") + .option("fetchOffset.retryIntervalMs", "3000") + val kafka = reader.load() + .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") + .as[(String, String)] + val query = startStream(kafka.map(kv => kv._2.toInt)) val testTime = 1.minutes val startTime = System.currentTimeMillis() diff --git a/external/kafka-0-10/pom.xml b/external/kafka-0-10/pom.xml index 6eb7ba5f0092d..07d205b01dfde 100644 --- a/external/kafka-0-10/pom.xml +++ b/external/kafka-0-10/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/CachedKafkaConsumer.scala b/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/CachedKafkaConsumer.scala index fa3ea6131a507..aeb8c1dc342b3 100644 --- a/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/CachedKafkaConsumer.scala +++ b/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/CachedKafkaConsumer.scala @@ -22,10 +22,8 @@ import java.{ util => ju } import org.apache.kafka.clients.consumer.{ ConsumerConfig, ConsumerRecord, KafkaConsumer } import org.apache.kafka.common.{ KafkaException, TopicPartition } -import org.apache.spark.SparkConf import org.apache.spark.internal.Logging - /** * Consumer of single topicpartition, intended for cached reuse. * Underlying consumer is not threadsafe, so neither is this, @@ -38,7 +36,7 @@ class CachedKafkaConsumer[K, V] private( val partition: Int, val kafkaParams: ju.Map[String, Object]) extends Logging { - assert(groupId == kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG), + require(groupId == kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG), "groupId used for cache key must match the groupId in kafkaParams") val topicPartition = new TopicPartition(topic, partition) @@ -53,7 +51,7 @@ class CachedKafkaConsumer[K, V] private( // TODO if the buffer was kept around as a random-access structure, // could possibly optimize re-calculating of an RDD in the same batch - protected var buffer = ju.Collections.emptyList[ConsumerRecord[K, V]]().iterator + protected var buffer = ju.Collections.emptyListIterator[ConsumerRecord[K, V]]() protected var nextOffset = -2L def close(): Unit = consumer.close() @@ -71,7 +69,7 @@ class CachedKafkaConsumer[K, V] private( } if (!buffer.hasNext()) { poll(timeout) } - assert(buffer.hasNext(), + require(buffer.hasNext(), s"Failed to get records for $groupId $topic $partition $offset after polling for $timeout") var record = buffer.next() @@ -79,17 +77,56 @@ class CachedKafkaConsumer[K, V] private( logInfo(s"Buffer miss for $groupId $topic $partition $offset") seek(offset) poll(timeout) - assert(buffer.hasNext(), + require(buffer.hasNext(), s"Failed to get records for $groupId $topic $partition $offset after polling for $timeout") record = buffer.next() - assert(record.offset == offset, - s"Got wrong record for $groupId $topic $partition even after seeking to offset $offset") + require(record.offset == offset, + s"Got wrong record for $groupId $topic $partition even after seeking to offset $offset " + + s"got offset ${record.offset} instead. If this is a compacted topic, consider enabling " + + "spark.streaming.kafka.allowNonConsecutiveOffsets" + ) } nextOffset = offset + 1 record } + /** + * Start a batch on a compacted topic + */ + def compactedStart(offset: Long, timeout: Long): Unit = { + logDebug(s"compacted start $groupId $topic $partition starting $offset") + // This seek may not be necessary, but it's hard to tell due to gaps in compacted topics + if (offset != nextOffset) { + logInfo(s"Initial fetch for compacted $groupId $topic $partition $offset") + seek(offset) + poll(timeout) + } + } + + /** + * Get the next record in the batch from a compacted topic. + * Assumes compactedStart has been called first, and ignores gaps. + */ + def compactedNext(timeout: Long): ConsumerRecord[K, V] = { + if (!buffer.hasNext()) { + poll(timeout) + } + require(buffer.hasNext(), + s"Failed to get records for compacted $groupId $topic $partition after polling for $timeout") + val record = buffer.next() + nextOffset = record.offset + 1 + record + } + + /** + * Rewind to previous record in the batch from a compacted topic. + * @throws NoSuchElementException if no previous element + */ + def compactedPrevious(): ConsumerRecord[K, V] = { + buffer.previous() + } + private def seek(offset: Long): Unit = { logDebug(s"Seeking to $topicPartition $offset") consumer.seek(topicPartition, offset) @@ -99,7 +136,7 @@ class CachedKafkaConsumer[K, V] private( val p = consumer.poll(timeout) val r = p.records(topicPartition) logDebug(s"Polled ${p.partitions()} ${r.size}") - buffer = r.iterator + buffer = r.listIterator } } diff --git a/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/KafkaRDD.scala b/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/KafkaRDD.scala index d9fc9cc206647..07239eda64d2e 100644 --- a/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/KafkaRDD.scala +++ b/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/KafkaRDD.scala @@ -55,12 +55,12 @@ private[spark] class KafkaRDD[K, V]( useConsumerCache: Boolean ) extends RDD[ConsumerRecord[K, V]](sc, Nil) with Logging with HasOffsetRanges { - assert("none" == + require("none" == kafkaParams.get(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG).asInstanceOf[String], ConsumerConfig.AUTO_OFFSET_RESET_CONFIG + " must be set to none for executor kafka params, else messages may not match offsetRange") - assert(false == + require(false == kafkaParams.get(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG).asInstanceOf[Boolean], ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG + " must be set to false for executor kafka params, else offsets may commit before processing") @@ -74,6 +74,8 @@ private[spark] class KafkaRDD[K, V]( conf.getInt("spark.streaming.kafka.consumer.cache.maxCapacity", 64) private val cacheLoadFactor = conf.getDouble("spark.streaming.kafka.consumer.cache.loadFactor", 0.75).toFloat + private val compacted = + conf.getBoolean("spark.streaming.kafka.allowNonConsecutiveOffsets", false) override def persist(newLevel: StorageLevel): this.type = { logError("Kafka ConsumerRecord is not serializable. " + @@ -87,48 +89,63 @@ private[spark] class KafkaRDD[K, V]( }.toArray } - override def count(): Long = offsetRanges.map(_.count).sum + override def count(): Long = + if (compacted) { + super.count() + } else { + offsetRanges.map(_.count).sum + } override def countApprox( timeout: Long, confidence: Double = 0.95 - ): PartialResult[BoundedDouble] = { - val c = count - new PartialResult(new BoundedDouble(c, 1.0, c, c), true) - } - - override def isEmpty(): Boolean = count == 0L - - override def take(num: Int): Array[ConsumerRecord[K, V]] = { - val nonEmptyPartitions = this.partitions - .map(_.asInstanceOf[KafkaRDDPartition]) - .filter(_.count > 0) + ): PartialResult[BoundedDouble] = + if (compacted) { + super.countApprox(timeout, confidence) + } else { + val c = count + new PartialResult(new BoundedDouble(c, 1.0, c, c), true) + } - if (num < 1 || nonEmptyPartitions.isEmpty) { - return new Array[ConsumerRecord[K, V]](0) + override def isEmpty(): Boolean = + if (compacted) { + super.isEmpty() + } else { + count == 0L } - // Determine in advance how many messages need to be taken from each partition - val parts = nonEmptyPartitions.foldLeft(Map[Int, Int]()) { (result, part) => - val remain = num - result.values.sum - if (remain > 0) { - val taken = Math.min(remain, part.count) - result + (part.index -> taken.toInt) + override def take(num: Int): Array[ConsumerRecord[K, V]] = + if (compacted) { + super.take(num) + } else if (num < 1) { + Array.empty[ConsumerRecord[K, V]] + } else { + val nonEmptyPartitions = this.partitions + .map(_.asInstanceOf[KafkaRDDPartition]) + .filter(_.count > 0) + + if (nonEmptyPartitions.isEmpty) { + Array.empty[ConsumerRecord[K, V]] } else { - result + // Determine in advance how many messages need to be taken from each partition + val parts = nonEmptyPartitions.foldLeft(Map[Int, Int]()) { (result, part) => + val remain = num - result.values.sum + if (remain > 0) { + val taken = Math.min(remain, part.count) + result + (part.index -> taken.toInt) + } else { + result + } + } + + context.runJob( + this, + (tc: TaskContext, it: Iterator[ConsumerRecord[K, V]]) => + it.take(parts(tc.partitionId)).toArray, parts.keys.toArray + ).flatten } } - val buf = new ArrayBuffer[ConsumerRecord[K, V]] - val res = context.runJob( - this, - (tc: TaskContext, it: Iterator[ConsumerRecord[K, V]]) => - it.take(parts(tc.partitionId)).toArray, parts.keys.toArray - ) - res.foreach(buf ++= _) - buf.toArray - } - private def executors(): Array[ExecutorCacheTaskLocation] = { val bm = sparkContext.env.blockManager bm.master.getPeers(bm.blockManagerId).toArray @@ -172,57 +189,138 @@ private[spark] class KafkaRDD[K, V]( override def compute(thePart: Partition, context: TaskContext): Iterator[ConsumerRecord[K, V]] = { val part = thePart.asInstanceOf[KafkaRDDPartition] - assert(part.fromOffset <= part.untilOffset, errBeginAfterEnd(part)) + require(part.fromOffset <= part.untilOffset, errBeginAfterEnd(part)) if (part.fromOffset == part.untilOffset) { logInfo(s"Beginning offset ${part.fromOffset} is the same as ending offset " + s"skipping ${part.topic} ${part.partition}") Iterator.empty } else { - new KafkaRDDIterator(part, context) + logInfo(s"Computing topic ${part.topic}, partition ${part.partition} " + + s"offsets ${part.fromOffset} -> ${part.untilOffset}") + if (compacted) { + new CompactedKafkaRDDIterator[K, V]( + part, + context, + kafkaParams, + useConsumerCache, + pollTimeout, + cacheInitialCapacity, + cacheMaxCapacity, + cacheLoadFactor + ) + } else { + new KafkaRDDIterator[K, V]( + part, + context, + kafkaParams, + useConsumerCache, + pollTimeout, + cacheInitialCapacity, + cacheMaxCapacity, + cacheLoadFactor + ) + } } } +} - /** - * An iterator that fetches messages directly from Kafka for the offsets in partition. - * Uses a cached consumer where possible to take advantage of prefetching - */ - private class KafkaRDDIterator( - part: KafkaRDDPartition, - context: TaskContext) extends Iterator[ConsumerRecord[K, V]] { - - logInfo(s"Computing topic ${part.topic}, partition ${part.partition} " + - s"offsets ${part.fromOffset} -> ${part.untilOffset}") - - val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String] +/** + * An iterator that fetches messages directly from Kafka for the offsets in partition. + * Uses a cached consumer where possible to take advantage of prefetching + */ +private class KafkaRDDIterator[K, V]( + part: KafkaRDDPartition, + context: TaskContext, + kafkaParams: ju.Map[String, Object], + useConsumerCache: Boolean, + pollTimeout: Long, + cacheInitialCapacity: Int, + cacheMaxCapacity: Int, + cacheLoadFactor: Float +) extends Iterator[ConsumerRecord[K, V]] { + + val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String] + + context.addTaskCompletionListener(_ => closeIfNeeded()) + + val consumer = if (useConsumerCache) { + CachedKafkaConsumer.init(cacheInitialCapacity, cacheMaxCapacity, cacheLoadFactor) + if (context.attemptNumber >= 1) { + // just in case the prior attempt failures were cache related + CachedKafkaConsumer.remove(groupId, part.topic, part.partition) + } + CachedKafkaConsumer.get[K, V](groupId, part.topic, part.partition, kafkaParams) + } else { + CachedKafkaConsumer.getUncached[K, V](groupId, part.topic, part.partition, kafkaParams) + } - context.addTaskCompletionListener{ context => closeIfNeeded() } + var requestOffset = part.fromOffset - val consumer = if (useConsumerCache) { - CachedKafkaConsumer.init(cacheInitialCapacity, cacheMaxCapacity, cacheLoadFactor) - if (context.attemptNumber >= 1) { - // just in case the prior attempt failures were cache related - CachedKafkaConsumer.remove(groupId, part.topic, part.partition) - } - CachedKafkaConsumer.get[K, V](groupId, part.topic, part.partition, kafkaParams) - } else { - CachedKafkaConsumer.getUncached[K, V](groupId, part.topic, part.partition, kafkaParams) + def closeIfNeeded(): Unit = { + if (!useConsumerCache && consumer != null) { + consumer.close() } + } - var requestOffset = part.fromOffset + override def hasNext(): Boolean = requestOffset < part.untilOffset - def closeIfNeeded(): Unit = { - if (!useConsumerCache && consumer != null) { - consumer.close - } + override def next(): ConsumerRecord[K, V] = { + if (!hasNext) { + throw new ju.NoSuchElementException("Can't call getNext() once untilOffset has been reached") } + val r = consumer.get(requestOffset, pollTimeout) + requestOffset += 1 + r + } +} - override def hasNext(): Boolean = requestOffset < part.untilOffset - - override def next(): ConsumerRecord[K, V] = { - assert(hasNext(), "Can't call getNext() once untilOffset has been reached") - val r = consumer.get(requestOffset, pollTimeout) - requestOffset += 1 - r +/** + * An iterator that fetches messages directly from Kafka for the offsets in partition. + * Uses a cached consumer where possible to take advantage of prefetching. + * Intended for compacted topics, or other cases when non-consecutive offsets are ok. + */ +private class CompactedKafkaRDDIterator[K, V]( + part: KafkaRDDPartition, + context: TaskContext, + kafkaParams: ju.Map[String, Object], + useConsumerCache: Boolean, + pollTimeout: Long, + cacheInitialCapacity: Int, + cacheMaxCapacity: Int, + cacheLoadFactor: Float + ) extends KafkaRDDIterator[K, V]( + part, + context, + kafkaParams, + useConsumerCache, + pollTimeout, + cacheInitialCapacity, + cacheMaxCapacity, + cacheLoadFactor + ) { + + consumer.compactedStart(part.fromOffset, pollTimeout) + + private var nextRecord = consumer.compactedNext(pollTimeout) + + private var okNext: Boolean = true + + override def hasNext(): Boolean = okNext + + override def next(): ConsumerRecord[K, V] = { + if (!hasNext) { + throw new ju.NoSuchElementException("Can't call getNext() once untilOffset has been reached") + } + val r = nextRecord + if (r.offset + 1 >= part.untilOffset) { + okNext = false + } else { + nextRecord = consumer.compactedNext(pollTimeout) + if (nextRecord.offset >= part.untilOffset) { + okNext = false + consumer.compactedPrevious() + } } + r } } diff --git a/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaRDDSuite.scala b/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaRDDSuite.scala index be373af0599cc..271adea1df731 100644 --- a/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaRDDSuite.scala +++ b/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaRDDSuite.scala @@ -18,16 +18,22 @@ package org.apache.spark.streaming.kafka010 import java.{ util => ju } +import java.io.File import scala.collection.JavaConverters._ import scala.util.Random +import kafka.common.TopicAndPartition +import kafka.log._ +import kafka.message._ +import kafka.utils.Pool import org.apache.kafka.common.TopicPartition import org.apache.kafka.common.serialization.StringDeserializer import org.scalatest.BeforeAndAfterAll import org.apache.spark._ import org.apache.spark.scheduler.ExecutorCacheTaskLocation +import org.apache.spark.streaming.kafka010.mocks.MockTime class KafkaRDDSuite extends SparkFunSuite with BeforeAndAfterAll { @@ -64,6 +70,41 @@ class KafkaRDDSuite extends SparkFunSuite with BeforeAndAfterAll { private val preferredHosts = LocationStrategies.PreferConsistent + private def compactLogs(topic: String, partition: Int, messages: Array[(String, String)]) { + val mockTime = new MockTime() + // LogCleaner in 0.10 version of Kafka is still expecting the old TopicAndPartition api + val logs = new Pool[TopicAndPartition, Log]() + val logDir = kafkaTestUtils.brokerLogDir + val dir = new File(logDir, topic + "-" + partition) + dir.mkdirs() + val logProps = new ju.Properties() + logProps.put(LogConfig.CleanupPolicyProp, LogConfig.Compact) + logProps.put(LogConfig.MinCleanableDirtyRatioProp, java.lang.Float.valueOf(0.1f)) + val log = new Log( + dir, + LogConfig(logProps), + 0L, + mockTime.scheduler, + mockTime + ) + messages.foreach { case (k, v) => + val msg = new ByteBufferMessageSet( + NoCompressionCodec, + new Message(v.getBytes, k.getBytes, Message.NoTimestamp, Message.CurrentMagicValue)) + log.append(msg) + } + log.roll() + logs.put(TopicAndPartition(topic, partition), log) + + val cleaner = new LogCleaner(CleanerConfig(), logDirs = Array(dir), logs = logs) + cleaner.startup() + cleaner.awaitCleaned(topic, partition, log.activeSegment.baseOffset, 1000) + + cleaner.shutdown() + mockTime.scheduler.shutdown() + } + + test("basic usage") { val topic = s"topicbasic-${Random.nextInt}-${System.currentTimeMillis}" kafkaTestUtils.createTopic(topic) @@ -102,6 +143,71 @@ class KafkaRDDSuite extends SparkFunSuite with BeforeAndAfterAll { } } + test("compacted topic") { + val compactConf = sparkConf.clone() + compactConf.set("spark.streaming.kafka.allowNonConsecutiveOffsets", "true") + sc.stop() + sc = new SparkContext(compactConf) + val topic = s"topiccompacted-${Random.nextInt}-${System.currentTimeMillis}" + + val messages = Array( + ("a", "1"), + ("a", "2"), + ("b", "1"), + ("c", "1"), + ("c", "2"), + ("b", "2"), + ("b", "3") + ) + val compactedMessages = Array( + ("a", "2"), + ("b", "3"), + ("c", "2") + ) + + compactLogs(topic, 0, messages) + + val props = new ju.Properties() + props.put("cleanup.policy", "compact") + props.put("flush.messages", "1") + props.put("segment.ms", "1") + props.put("segment.bytes", "256") + kafkaTestUtils.createTopic(topic, 1, props) + + + val kafkaParams = getKafkaParams() + + val offsetRanges = Array(OffsetRange(topic, 0, 0, messages.size)) + + val rdd = KafkaUtils.createRDD[String, String]( + sc, kafkaParams, offsetRanges, preferredHosts + ).map(m => m.key -> m.value) + + val received = rdd.collect.toSet + assert(received === compactedMessages.toSet) + + // size-related method optimizations return sane results + assert(rdd.count === compactedMessages.size) + assert(rdd.countApprox(0).getFinalValue.mean === compactedMessages.size) + assert(!rdd.isEmpty) + assert(rdd.take(1).size === 1) + assert(rdd.take(1).head === compactedMessages.head) + assert(rdd.take(messages.size + 10).size === compactedMessages.size) + + val emptyRdd = KafkaUtils.createRDD[String, String]( + sc, kafkaParams, Array(OffsetRange(topic, 0, 0, 0)), preferredHosts) + + assert(emptyRdd.isEmpty) + + // invalid offset ranges throw exceptions + val badRanges = Array(OffsetRange(topic, 0, 0, messages.size + 1)) + intercept[SparkException] { + val result = KafkaUtils.createRDD[String, String](sc, kafkaParams, badRanges, preferredHosts) + .map(_.value) + .collect() + } + } + test("iterator boundary conditions") { // the idea is to find e.g. off-by-one errors between what kafka has available and the rdd val topic = s"topicboundary-${Random.nextInt}-${System.currentTimeMillis}" diff --git a/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaTestUtils.scala b/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaTestUtils.scala index 6c7024ea4b5a5..70b579d96d692 100644 --- a/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaTestUtils.scala +++ b/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/KafkaTestUtils.scala @@ -162,17 +162,22 @@ private[kafka010] class KafkaTestUtils extends Logging { } /** Create a Kafka topic and wait until it is propagated to the whole cluster */ - def createTopic(topic: String, partitions: Int): Unit = { - AdminUtils.createTopic(zkUtils, topic, partitions, 1) + def createTopic(topic: String, partitions: Int, config: Properties): Unit = { + AdminUtils.createTopic(zkUtils, topic, partitions, 1, config) // wait until metadata is propagated (0 until partitions).foreach { p => waitUntilMetadataIsPropagated(topic, p) } } + /** Create a Kafka topic and wait until it is propagated to the whole cluster */ + def createTopic(topic: String, partitions: Int): Unit = { + createTopic(topic, partitions, new Properties()) + } + /** Create a Kafka topic and wait until it is propagated to the whole cluster */ def createTopic(topic: String): Unit = { - createTopic(topic, 1) + createTopic(topic, 1, new Properties()) } /** Java-friendly function for sending messages to the Kafka broker */ @@ -196,12 +201,24 @@ private[kafka010] class KafkaTestUtils extends Logging { producer = null } + /** Send the array of (key, value) messages to the Kafka broker */ + def sendMessages(topic: String, messages: Array[(String, String)]): Unit = { + producer = new KafkaProducer[String, String](producerConfiguration) + messages.foreach { message => + producer.send(new ProducerRecord[String, String](topic, message._1, message._2)) + } + producer.close() + producer = null + } + + val brokerLogDir = Utils.createTempDir().getAbsolutePath + private def brokerConfiguration: Properties = { val props = new Properties() props.put("broker.id", "0") props.put("host.name", "localhost") props.put("port", brokerPort.toString) - props.put("log.dir", Utils.createTempDir().getAbsolutePath) + props.put("log.dir", brokerLogDir) props.put("zookeeper.connect", zkAddress) props.put("log.flush.interval.messages", "1") props.put("replica.socket.timeout.ms", "1500") diff --git a/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/mocks/MockScheduler.scala b/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/mocks/MockScheduler.scala new file mode 100644 index 0000000000000..928e1a6ef54b9 --- /dev/null +++ b/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/mocks/MockScheduler.scala @@ -0,0 +1,96 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.kafka010.mocks + +import java.util.concurrent.TimeUnit + +import scala.collection.mutable.PriorityQueue + +import kafka.utils.{Scheduler, Time} + +/** + * A mock scheduler that executes tasks synchronously using a mock time instance. + * Tasks are executed synchronously when the time is advanced. + * This class is meant to be used in conjunction with MockTime. + * + * Example usage + * + * val time = new MockTime + * time.scheduler.schedule("a task", println("hello world: " + time.milliseconds), delay = 1000) + * time.sleep(1001) // this should cause our scheduled task to fire + * + * + * Incrementing the time to the exact next execution time of a task will result in that task + * executing (it as if execution itself takes no time). + */ +private[kafka010] class MockScheduler(val time: Time) extends Scheduler { + + /* a priority queue of tasks ordered by next execution time */ + var tasks = new PriorityQueue[MockTask]() + + def isStarted: Boolean = true + + def startup(): Unit = {} + + def shutdown(): Unit = synchronized { + tasks.foreach(_.fun()) + tasks.clear() + } + + /** + * Check for any tasks that need to execute. Since this is a mock scheduler this check only occurs + * when this method is called and the execution happens synchronously in the calling thread. + * If you are using the scheduler associated with a MockTime instance this call + * will be triggered automatically. + */ + def tick(): Unit = synchronized { + val now = time.milliseconds + while(!tasks.isEmpty && tasks.head.nextExecution <= now) { + /* pop and execute the task with the lowest next execution time */ + val curr = tasks.dequeue + curr.fun() + /* if the task is periodic, reschedule it and re-enqueue */ + if(curr.periodic) { + curr.nextExecution += curr.period + this.tasks += curr + } + } + } + + def schedule( + name: String, + fun: () => Unit, + delay: Long = 0, + period: Long = -1, + unit: TimeUnit = TimeUnit.MILLISECONDS): Unit = synchronized { + tasks += MockTask(name, fun, time.milliseconds + delay, period = period) + tick() + } + +} + +case class MockTask( + val name: String, + val fun: () => Unit, + var nextExecution: Long, + val period: Long) extends Ordered[MockTask] { + def periodic: Boolean = period >= 0 + def compare(t: MockTask): Int = { + java.lang.Long.compare(t.nextExecution, nextExecution) + } +} diff --git a/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/mocks/MockTime.scala b/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/mocks/MockTime.scala new file mode 100644 index 0000000000000..a68f94db1f689 --- /dev/null +++ b/external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/mocks/MockTime.scala @@ -0,0 +1,51 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.kafka010.mocks + +import java.util.concurrent._ + +import kafka.utils.Time + +/** + * A class used for unit testing things which depend on the Time interface. + * + * This class never manually advances the clock, it only does so when you call + * sleep(ms) + * + * It also comes with an associated scheduler instance for managing background tasks in + * a deterministic way. + */ +private[kafka010] class MockTime(@volatile private var currentMs: Long) extends Time { + + val scheduler = new MockScheduler(this) + + def this() = this(System.currentTimeMillis) + + def milliseconds: Long = currentMs + + def nanoseconds: Long = + TimeUnit.NANOSECONDS.convert(currentMs, TimeUnit.MILLISECONDS) + + def sleep(ms: Long) { + this.currentMs += ms + scheduler.tick() + } + + override def toString(): String = s"MockTime($milliseconds)" + +} diff --git a/external/kafka-0-8-assembly/pom.xml b/external/kafka-0-8-assembly/pom.xml index 786349474389b..11d971a0de046 100644 --- a/external/kafka-0-8-assembly/pom.xml +++ b/external/kafka-0-8-assembly/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/kafka-0-8/pom.xml b/external/kafka-0-8/pom.xml index 849c8b465f99e..70eb580d474af 100644 --- a/external/kafka-0-8/pom.xml +++ b/external/kafka-0-8/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/kinesis-asl-assembly/pom.xml b/external/kinesis-asl-assembly/pom.xml index 48783d65826aa..0728baede26f0 100644 --- a/external/kinesis-asl-assembly/pom.xml +++ b/external/kinesis-asl-assembly/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/kinesis-asl/pom.xml b/external/kinesis-asl/pom.xml index 40a751a652fa9..c6925851ebc23 100644 --- a/external/kinesis-asl/pom.xml +++ b/external/kinesis-asl/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/external/spark-ganglia-lgpl/pom.xml b/external/spark-ganglia-lgpl/pom.xml index 36d555066b181..8b95f1be216c7 100644 --- a/external/spark-ganglia-lgpl/pom.xml +++ b/external/spark-ganglia-lgpl/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/graphx/pom.xml b/graphx/pom.xml index cb30e4a4af4bc..b5253fd8954f3 100644 --- a/graphx/pom.xml +++ b/graphx/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/hadoop-cloud/pom.xml b/hadoop-cloud/pom.xml index aa36dd4774d86..dacd42126393b 100644 --- a/hadoop-cloud/pom.xml +++ b/hadoop-cloud/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/launcher/pom.xml b/launcher/pom.xml index e9b46c4cf0ffa..2fcb367cf301c 100644 --- a/launcher/pom.xml +++ b/launcher/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractAppHandle.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractAppHandle.java index df1e7316861d4..9cbebdaeb33d3 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/AbstractAppHandle.java +++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractAppHandle.java @@ -18,39 +18,40 @@ package org.apache.spark.launcher; import java.io.IOException; -import java.util.ArrayList; import java.util.List; +import java.util.concurrent.CopyOnWriteArrayList; +import java.util.concurrent.atomic.AtomicReference; import java.util.logging.Level; import java.util.logging.Logger; abstract class AbstractAppHandle implements SparkAppHandle { - private static final Logger LOG = Logger.getLogger(ChildProcAppHandle.class.getName()); + private static final Logger LOG = Logger.getLogger(AbstractAppHandle.class.getName()); private final LauncherServer server; - private LauncherConnection connection; + private LauncherServer.ServerConnection connection; private List listeners; - private State state; - private String appId; - private boolean disposed; + private AtomicReference state; + private volatile String appId; + private volatile boolean disposed; protected AbstractAppHandle(LauncherServer server) { this.server = server; - this.state = State.UNKNOWN; + this.state = new AtomicReference<>(State.UNKNOWN); } @Override public synchronized void addListener(Listener l) { if (listeners == null) { - listeners = new ArrayList<>(); + listeners = new CopyOnWriteArrayList<>(); } listeners.add(l); } @Override public State getState() { - return state; + return state.get(); } @Override @@ -70,20 +71,17 @@ public void stop() { @Override public synchronized void disconnect() { - if (!disposed) { - disposed = true; - if (connection != null) { - try { - connection.close(); - } catch (IOException ioe) { - // no-op. - } + if (connection != null && connection.isOpen()) { + try { + connection.close(); + } catch (IOException ioe) { + // no-op. } - server.unregister(this); } + dispose(); } - void setConnection(LauncherConnection connection) { + void setConnection(LauncherServer.ServerConnection connection) { this.connection = connection; } @@ -95,21 +93,60 @@ boolean isDisposed() { return disposed; } + /** + * Mark the handle as disposed, and set it as LOST in case the current state is not final. + * + * This method should be called only when there's a reasonable expectation that the communication + * with the child application is not needed anymore, either because the code managing the handle + * has said so, or because the child application is finished. + */ + synchronized void dispose() { + if (!isDisposed()) { + // First wait for all data from the connection to be read. Then unregister the handle. + // Otherwise, unregistering might cause the server to be stopped and all child connections + // to be closed. + if (connection != null) { + try { + connection.waitForClose(); + } catch (IOException ioe) { + // no-op. + } + } + server.unregister(this); + + // Set state to LOST if not yet final. + setState(State.LOST, false); + this.disposed = true; + } + } + void setState(State s) { setState(s, false); } - synchronized void setState(State s, boolean force) { - if (force || !state.isFinal()) { - state = s; + void setState(State s, boolean force) { + if (force) { + state.set(s); fireEvent(false); - } else { + return; + } + + State current = state.get(); + while (!current.isFinal()) { + if (state.compareAndSet(current, s)) { + fireEvent(false); + return; + } + current = state.get(); + } + + if (s != State.LOST) { LOG.log(Level.WARNING, "Backend requested transition from final state {0} to {1}.", - new Object[] { state, s }); + new Object[] { current, s }); } } - synchronized void setAppId(String appId) { + void setAppId(String appId) { this.appId = appId; fireEvent(true); } diff --git a/launcher/src/main/java/org/apache/spark/launcher/ChildProcAppHandle.java b/launcher/src/main/java/org/apache/spark/launcher/ChildProcAppHandle.java index 8b3f427b7750e..5609f8492f4f4 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/ChildProcAppHandle.java +++ b/launcher/src/main/java/org/apache/spark/launcher/ChildProcAppHandle.java @@ -48,14 +48,16 @@ public synchronized void disconnect() { @Override public synchronized void kill() { - disconnect(); - if (childProc != null) { - if (childProc.isAlive()) { - childProc.destroyForcibly(); + if (!isDisposed()) { + setState(State.KILLED); + disconnect(); + if (childProc != null) { + if (childProc.isAlive()) { + childProc.destroyForcibly(); + } + childProc = null; } - childProc = null; } - setState(State.KILLED); } void setChildProc(Process childProc, String loggerName, InputStream logStream) { @@ -94,8 +96,6 @@ void monitorChild() { return; } - disconnect(); - int ec; try { ec = proc.exitValue(); @@ -104,20 +104,15 @@ void monitorChild() { ec = 1; } - State currState = getState(); - State newState = null; if (ec != 0) { + State currState = getState(); // Override state with failure if the current state is not final, or is success. if (!currState.isFinal() || currState == State.FINISHED) { - newState = State.FAILED; + setState(State.FAILED, true); } - } else if (!currState.isFinal()) { - newState = State.LOST; } - if (newState != null) { - setState(newState, true); - } + dispose(); } } diff --git a/launcher/src/main/java/org/apache/spark/launcher/InProcessAppHandle.java b/launcher/src/main/java/org/apache/spark/launcher/InProcessAppHandle.java index 0d6a73a3da3ed..15fbca0facef2 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/InProcessAppHandle.java +++ b/launcher/src/main/java/org/apache/spark/launcher/InProcessAppHandle.java @@ -17,7 +17,6 @@ package org.apache.spark.launcher; -import java.io.IOException; import java.lang.reflect.Method; import java.util.concurrent.atomic.AtomicLong; import java.util.logging.Level; @@ -26,7 +25,7 @@ class InProcessAppHandle extends AbstractAppHandle { private static final String THREAD_NAME_FMT = "spark-app-%d: '%s'"; - private static final Logger LOG = Logger.getLogger(ChildProcAppHandle.class.getName()); + private static final Logger LOG = Logger.getLogger(InProcessAppHandle.class.getName()); private static final AtomicLong THREAD_IDS = new AtomicLong(); // Avoid really long thread names. @@ -40,15 +39,16 @@ class InProcessAppHandle extends AbstractAppHandle { @Override public synchronized void kill() { - LOG.warning("kill() may leave the underlying app running in in-process mode."); - disconnect(); - - // Interrupt the thread. This is not guaranteed to kill the app, though. - if (app != null) { - app.interrupt(); + if (!isDisposed()) { + LOG.warning("kill() may leave the underlying app running in in-process mode."); + setState(State.KILLED); + disconnect(); + + // Interrupt the thread. This is not guaranteed to kill the app, though. + if (app != null) { + app.interrupt(); + } } - - setState(State.KILLED); } synchronized void start(String appName, Method main, String[] args) { @@ -66,14 +66,7 @@ synchronized void start(String appName, Method main, String[] args) { setState(State.FAILED); } - synchronized (InProcessAppHandle.this) { - if (!isDisposed()) { - disconnect(); - if (!getState().isFinal()) { - setState(State.LOST, true); - } - } - } + dispose(); }); app.setName(String.format(THREAD_NAME_FMT, THREAD_IDS.incrementAndGet(), appName)); diff --git a/launcher/src/main/java/org/apache/spark/launcher/LauncherConnection.java b/launcher/src/main/java/org/apache/spark/launcher/LauncherConnection.java index b4a8719e26053..e8ab3f5e369ab 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/LauncherConnection.java +++ b/launcher/src/main/java/org/apache/spark/launcher/LauncherConnection.java @@ -53,7 +53,7 @@ abstract class LauncherConnection implements Closeable, Runnable { public void run() { try { FilteredObjectInputStream in = new FilteredObjectInputStream(socket.getInputStream()); - while (!closed) { + while (isOpen()) { Message msg = (Message) in.readObject(); handle(msg); } @@ -95,15 +95,15 @@ protected synchronized void send(Message msg) throws IOException { } @Override - public void close() throws IOException { - if (!closed) { - synchronized (this) { - if (!closed) { - closed = true; - socket.close(); - } - } + public synchronized void close() throws IOException { + if (isOpen()) { + closed = true; + socket.close(); } } + boolean isOpen() { + return !closed; + } + } diff --git a/launcher/src/main/java/org/apache/spark/launcher/LauncherServer.java b/launcher/src/main/java/org/apache/spark/launcher/LauncherServer.java index b8999a1d7a4f4..607879fd02ea9 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/LauncherServer.java +++ b/launcher/src/main/java/org/apache/spark/launcher/LauncherServer.java @@ -217,6 +217,7 @@ void unregister(AbstractAppHandle handle) { break; } } + unref(); } @@ -237,6 +238,7 @@ public void run() { }; ServerConnection clientConnection = new ServerConnection(client, timeout); Thread clientThread = factory.newThread(clientConnection); + clientConnection.setConnectionThread(clientThread); synchronized (clients) { clients.add(clientConnection); } @@ -285,16 +287,21 @@ private String createSecret() { } } - private class ServerConnection extends LauncherConnection { + class ServerConnection extends LauncherConnection { private TimerTask timeout; - private AbstractAppHandle handle; + private volatile Thread connectionThread; + private volatile AbstractAppHandle handle; ServerConnection(Socket socket, TimerTask timeout) throws IOException { super(socket); this.timeout = timeout; } + void setConnectionThread(Thread t) { + this.connectionThread = t; + } + @Override protected void handle(Message msg) throws IOException { try { @@ -313,7 +320,7 @@ protected void handle(Message msg) throws IOException { } else { if (handle == null) { throw new IllegalArgumentException("Expected hello, got: " + - msg != null ? msg.getClass().getName() : null); + msg != null ? msg.getClass().getName() : null); } if (msg instanceof SetAppId) { SetAppId set = (SetAppId) msg; @@ -331,6 +338,9 @@ protected void handle(Message msg) throws IOException { timeout.cancel(); } close(); + if (handle != null) { + handle.dispose(); + } } finally { timeoutTimer.purge(); } @@ -338,16 +348,42 @@ protected void handle(Message msg) throws IOException { @Override public void close() throws IOException { + if (!isOpen()) { + return; + } + synchronized (clients) { clients.remove(this); } + super.close(); - if (handle != null) { - if (!handle.getState().isFinal()) { - LOG.log(Level.WARNING, "Lost connection to spark application."); - handle.setState(SparkAppHandle.State.LOST); + } + + /** + * Wait for the remote side to close the connection so that any pending data is processed. + * This ensures any changes reported by the child application take effect. + * + * This method allows a short period for the above to happen (same amount of time as the + * connection timeout, which is configurable). This should be fine for well-behaved + * applications, where they close the connection arond the same time the app handle detects the + * app has finished. + * + * In case the connection is not closed within the grace period, this method forcefully closes + * it and any subsequent data that may arrive will be ignored. + */ + public void waitForClose() throws IOException { + Thread connThread = this.connectionThread; + if (Thread.currentThread() != connThread) { + try { + connThread.join(getConnectionTimeout()); + } catch (InterruptedException ie) { + // Ignore. + } + + if (connThread.isAlive()) { + LOG.log(Level.WARNING, "Timed out waiting for child connection to close."); + close(); } - handle.disconnect(); } } diff --git a/launcher/src/test/java/org/apache/spark/launcher/BaseSuite.java b/launcher/src/test/java/org/apache/spark/launcher/BaseSuite.java index 3e1a90eae98d4..438349e027a24 100644 --- a/launcher/src/test/java/org/apache/spark/launcher/BaseSuite.java +++ b/launcher/src/test/java/org/apache/spark/launcher/BaseSuite.java @@ -17,7 +17,7 @@ package org.apache.spark.launcher; -import java.util.concurrent.TimeUnit; +import java.time.Duration; import org.junit.After; import org.slf4j.bridge.SLF4JBridgeHandler; @@ -47,19 +47,46 @@ public void postChecks() { assertNull(server); } - protected void waitFor(SparkAppHandle handle) throws Exception { - long deadline = System.nanoTime() + TimeUnit.SECONDS.toNanos(10); + protected void waitFor(final SparkAppHandle handle) throws Exception { try { - while (!handle.getState().isFinal()) { - assertTrue("Timed out waiting for handle to transition to final state.", - System.nanoTime() < deadline); - TimeUnit.MILLISECONDS.sleep(10); - } + eventually(Duration.ofSeconds(10), Duration.ofMillis(10), () -> { + assertTrue("Handle is not in final state.", handle.getState().isFinal()); + }); } finally { if (!handle.getState().isFinal()) { handle.kill(); } } + + // Wait until the handle has been marked as disposed, to make sure all cleanup tasks + // have been performed. + AbstractAppHandle ahandle = (AbstractAppHandle) handle; + eventually(Duration.ofSeconds(10), Duration.ofMillis(10), () -> { + assertTrue("Handle is still not marked as disposed.", ahandle.isDisposed()); + }); + } + + /** + * Call a closure that performs a check every "period" until it succeeds, or the timeout + * elapses. + */ + protected void eventually(Duration timeout, Duration period, Runnable check) throws Exception { + assertTrue("Timeout needs to be larger than period.", timeout.compareTo(period) > 0); + long deadline = System.nanoTime() + timeout.toNanos(); + int count = 0; + while (true) { + try { + count++; + check.run(); + return; + } catch (Throwable t) { + if (System.nanoTime() >= deadline) { + String msg = String.format("Failed check after %d tries: %s.", count, t.getMessage()); + throw new IllegalStateException(msg, t); + } + Thread.sleep(period.toMillis()); + } + } } } diff --git a/launcher/src/test/java/org/apache/spark/launcher/LauncherServerSuite.java b/launcher/src/test/java/org/apache/spark/launcher/LauncherServerSuite.java index 7e2b09ce25c9b..d16337a319be3 100644 --- a/launcher/src/test/java/org/apache/spark/launcher/LauncherServerSuite.java +++ b/launcher/src/test/java/org/apache/spark/launcher/LauncherServerSuite.java @@ -23,12 +23,14 @@ import java.net.InetAddress; import java.net.Socket; import java.net.SocketException; +import java.time.Duration; import java.util.Arrays; import java.util.List; import java.util.concurrent.BlockingQueue; import java.util.concurrent.LinkedBlockingQueue; import java.util.concurrent.Semaphore; import java.util.concurrent.TimeUnit; +import java.util.concurrent.atomic.AtomicBoolean; import org.junit.Test; import static org.junit.Assert.*; @@ -92,8 +94,8 @@ public void infoChanged(SparkAppHandle handle) { Message stopMsg = client.inbound.poll(30, TimeUnit.SECONDS); assertTrue(stopMsg instanceof Stop); } finally { - handle.kill(); close(client); + handle.kill(); client.clientThread.join(); } } @@ -143,7 +145,8 @@ public void infoChanged(SparkAppHandle handle) { assertTrue(semaphore.tryAcquire(30, TimeUnit.SECONDS)); // Make sure the server matched the client to the handle. assertNotNull(handle.getConnection()); - close(client); + client.close(); + handle.dispose(); assertTrue(semaphore.tryAcquire(30, TimeUnit.SECONDS)); assertEquals(SparkAppHandle.State.LOST, handle.getState()); } finally { @@ -197,28 +200,20 @@ private void close(Closeable c) { * server-side close immediately. */ private void waitForError(TestClient client, String secret) throws Exception { - boolean helloSent = false; - int maxTries = 10; - for (int i = 0; i < maxTries; i++) { + final AtomicBoolean helloSent = new AtomicBoolean(); + eventually(Duration.ofSeconds(1), Duration.ofMillis(10), () -> { try { - if (!helloSent) { + if (!helloSent.get()) { client.send(new Hello(secret, "1.4.0")); - helloSent = true; + helloSent.set(true); } else { client.send(new SetAppId("appId")); } fail("Expected error but message went through."); } catch (IllegalStateException | IOException e) { // Expected. - break; - } catch (AssertionError e) { - if (i < maxTries - 1) { - Thread.sleep(100); - } else { - throw new AssertionError("Test failed after " + maxTries + " attempts.", e); - } } - } + }); } private static class TestClient extends LauncherConnection { diff --git a/mllib-local/pom.xml b/mllib-local/pom.xml index 043d13609fd26..8ecd126ebb9b2 100644 --- a/mllib-local/pom.xml +++ b/mllib-local/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/mllib/pom.xml b/mllib/pom.xml index a906c9e02cd4c..f52b83475c515 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala index 8299a3e95d822..f49c410cbcfe2 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala @@ -19,6 +19,10 @@ package org.apache.spark.ml.feature import java.{util => ju} +import org.json4s.JsonDSL._ +import org.json4s.JValue +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.SparkException import org.apache.spark.annotation.Since import org.apache.spark.ml.Model @@ -32,11 +36,13 @@ import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DoubleType, StructField, StructType} /** - * `Bucketizer` maps a column of continuous features to a column of feature buckets. Since 2.3.0, + * `Bucketizer` maps a column of continuous features to a column of feature buckets. + * + * Since 2.3.0, * `Bucketizer` can map multiple columns at once by setting the `inputCols` parameter. Note that - * when both the `inputCol` and `inputCols` parameters are set, a log warning will be printed and - * only `inputCol` will take effect, while `inputCols` will be ignored. The `splits` parameter is - * only used for single column usage, and `splitsArray` is for multiple columns. + * when both the `inputCol` and `inputCols` parameters are set, an Exception will be thrown. The + * `splits` parameter is only used for single column usage, and `splitsArray` is for multiple + * columns. */ @Since("1.4.0") final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String) @@ -134,28 +140,11 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String @Since("2.3.0") def setOutputCols(value: Array[String]): this.type = set(outputCols, value) - /** - * Determines whether this `Bucketizer` is going to map multiple columns. If and only if - * `inputCols` is set, it will map multiple columns. Otherwise, it just maps a column specified - * by `inputCol`. A warning will be printed if both are set. - */ - private[feature] def isBucketizeMultipleColumns(): Boolean = { - if (isSet(inputCols) && isSet(inputCol)) { - logWarning("Both `inputCol` and `inputCols` are set, we ignore `inputCols` and this " + - "`Bucketizer` only map one column specified by `inputCol`") - false - } else if (isSet(inputCols)) { - true - } else { - false - } - } - @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { val transformedSchema = transformSchema(dataset.schema) - val (inputColumns, outputColumns) = if (isBucketizeMultipleColumns()) { + val (inputColumns, outputColumns) = if (isSet(inputCols)) { ($(inputCols).toSeq, $(outputCols).toSeq) } else { (Seq($(inputCol)), Seq($(outputCol))) @@ -170,7 +159,7 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String } } - val seqOfSplits = if (isBucketizeMultipleColumns()) { + val seqOfSplits = if (isSet(inputCols)) { $(splitsArray).toSeq } else { Seq($(splits)) @@ -201,9 +190,18 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String @Since("1.4.0") override def transformSchema(schema: StructType): StructType = { - if (isBucketizeMultipleColumns()) { + ParamValidators.checkSingleVsMultiColumnParams(this, Seq(outputCol, splits), + Seq(outputCols, splitsArray)) + + if (isSet(inputCols)) { + require(getInputCols.length == getOutputCols.length && + getInputCols.length == getSplitsArray.length, s"Bucketizer $this has mismatched Params " + + s"for multi-column transform. Params (inputCols, outputCols, splitsArray) should have " + + s"equal lengths, but they have different lengths: " + + s"(${getInputCols.length}, ${getOutputCols.length}, ${getSplitsArray.length}).") + var transformedSchema = schema - $(inputCols).zip($(outputCols)).zipWithIndex.map { case ((inputCol, outputCol), idx) => + $(inputCols).zip($(outputCols)).zipWithIndex.foreach { case ((inputCol, outputCol), idx) => SchemaUtils.checkNumericType(transformedSchema, inputCol) transformedSchema = SchemaUtils.appendColumn(transformedSchema, prepOutputField($(splitsArray)(idx), outputCol)) @@ -219,6 +217,8 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String override def copy(extra: ParamMap): Bucketizer = { defaultCopy[Bucketizer](extra).setParent(parent) } + + override def write: MLWriter = new Bucketizer.BucketizerWriter(this) } @Since("1.6.0") @@ -296,6 +296,28 @@ object Bucketizer extends DefaultParamsReadable[Bucketizer] { } } + + private[Bucketizer] class BucketizerWriter(instance: Bucketizer) extends MLWriter { + + override protected def saveImpl(path: String): Unit = { + // SPARK-23377: The default params will be saved and loaded as user-supplied params. + // Once `inputCols` is set, the default value of `outputCol` param causes the error + // when checking exclusive params. As a temporary to fix it, we skip the default value + // of `outputCol` if `inputCols` is set when saving the metadata. + // TODO: If we modify the persistence mechanism later to better handle default params, + // we can get rid of this. + var paramWithoutOutputCol: Option[JValue] = None + if (instance.isSet(instance.inputCols)) { + val params = instance.extractParamMap().toSeq + val jsonParams = params.filter(_.param != instance.outputCol).map { case ParamPair(p, v) => + p.name -> parse(p.jsonEncode(v)) + }.toList + paramWithoutOutputCol = Some(render(jsonParams)) + } + DefaultParamsWriter.saveMetadata(instance, path, sc, paramMap = paramWithoutOutputCol) + } + } + @Since("1.6.0") override def load(path: String): Bucketizer = super.load(path) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/FeatureHasher.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/FeatureHasher.scala index a918dd4c075da..c78f61ac3ef71 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/FeatureHasher.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/FeatureHasher.scala @@ -17,6 +17,7 @@ package org.apache.spark.ml.feature +import org.apache.spark.SparkException import org.apache.spark.annotation.{Experimental, Since} import org.apache.spark.ml.Transformer import org.apache.spark.ml.attribute.AttributeGroup @@ -28,6 +29,8 @@ import org.apache.spark.mllib.feature.{HashingTF => OldHashingTF} import org.apache.spark.sql.{DataFrame, Dataset, Row} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.hash.Murmur3_x86_32.{hashInt, hashLong, hashUnsafeBytes2} +import org.apache.spark.unsafe.types.UTF8String import org.apache.spark.util.Utils import org.apache.spark.util.collection.OpenHashMap @@ -138,7 +141,7 @@ class FeatureHasher(@Since("2.3.0") override val uid: String) extends Transforme @Since("2.3.0") override def transform(dataset: Dataset[_]): DataFrame = { - val hashFunc: Any => Int = OldHashingTF.murmur3Hash + val hashFunc: Any => Int = FeatureHasher.murmur3Hash val n = $(numFeatures) val localInputCols = $(inputCols) val catCols = if (isSet(categoricalCols)) { @@ -218,4 +221,32 @@ object FeatureHasher extends DefaultParamsReadable[FeatureHasher] { @Since("2.3.0") override def load(path: String): FeatureHasher = super.load(path) + + private val seed = OldHashingTF.seed + + /** + * Calculate a hash code value for the term object using + * Austin Appleby's MurmurHash 3 algorithm (MurmurHash3_x86_32). + * This is the default hash algorithm used from Spark 2.0 onwards. + * Use hashUnsafeBytes2 to match the original algorithm with the value. + * See SPARK-23381. + */ + @Since("2.3.0") + private[feature] def murmur3Hash(term: Any): Int = { + term match { + case null => seed + case b: Boolean => hashInt(if (b) 1 else 0, seed) + case b: Byte => hashInt(b, seed) + case s: Short => hashInt(s, seed) + case i: Int => hashInt(i, seed) + case l: Long => hashLong(l, seed) + case f: Float => hashInt(java.lang.Float.floatToIntBits(f), seed) + case d: Double => hashLong(java.lang.Double.doubleToLongBits(d), seed) + case s: String => + val utf8 = UTF8String.fromString(s) + hashUnsafeBytes2(utf8.getBaseObject, utf8.getBaseOffset, utf8.numBytes(), seed) + case _ => throw new SparkException("FeatureHasher with murmur3 algorithm does not " + + s"support type ${term.getClass.getCanonicalName} of input data.") + } + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoderEstimator.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoderEstimator.scala index 074622d41e28d..bd1e3426c8780 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoderEstimator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoderEstimator.scala @@ -30,24 +30,27 @@ import org.apache.spark.ml.util._ import org.apache.spark.sql.{DataFrame, Dataset} import org.apache.spark.sql.expressions.UserDefinedFunction import org.apache.spark.sql.functions.{col, lit, udf} -import org.apache.spark.sql.types.{DoubleType, NumericType, StructField, StructType} +import org.apache.spark.sql.types.{DoubleType, StructField, StructType} /** Private trait for params and common methods for OneHotEncoderEstimator and OneHotEncoderModel */ private[ml] trait OneHotEncoderBase extends Params with HasHandleInvalid with HasInputCols with HasOutputCols { /** - * Param for how to handle invalid data. + * Param for how to handle invalid data during transform(). * Options are 'keep' (invalid data presented as an extra categorical feature) or * 'error' (throw an error). + * Note that this Param is only used during transform; during fitting, invalid data + * will result in an error. * Default: "error" * @group param */ @Since("2.3.0") override val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", - "How to handle invalid data " + + "How to handle invalid data during transform(). " + "Options are 'keep' (invalid data presented as an extra categorical feature) " + - "or error (throw an error).", + "or error (throw an error). Note that this Param is only used during transform; " + + "during fitting, invalid data will result in an error.", ParamValidators.inArray(OneHotEncoderEstimator.supportedHandleInvalids)) setDefault(handleInvalid, OneHotEncoderEstimator.ERROR_INVALID) @@ -66,10 +69,11 @@ private[ml] trait OneHotEncoderBase extends Params with HasHandleInvalid def getDropLast: Boolean = $(dropLast) protected def validateAndTransformSchema( - schema: StructType, dropLast: Boolean, keepInvalid: Boolean): StructType = { + schema: StructType, + dropLast: Boolean, + keepInvalid: Boolean): StructType = { val inputColNames = $(inputCols) val outputColNames = $(outputCols) - val existingFields = schema.fields require(inputColNames.length == outputColNames.length, s"The number of input columns ${inputColNames.length} must be the same as the number of " + @@ -197,6 +201,10 @@ object OneHotEncoderEstimator extends DefaultParamsReadable[OneHotEncoderEstimat override def load(path: String): OneHotEncoderEstimator = super.load(path) } +/** + * @param categorySizes Original number of categories for each feature being encoded. + * The array contains one value for each input column, in order. + */ @Since("2.3.0") class OneHotEncoderModel private[ml] ( @Since("2.3.0") override val uid: String, @@ -205,60 +213,58 @@ class OneHotEncoderModel private[ml] ( import OneHotEncoderModel._ - // Returns the category size for a given index with `dropLast` and `handleInvalid` + // Returns the category size for each index with `dropLast` and `handleInvalid` // taken into account. - private def configedCategorySize(orgCategorySize: Int, idx: Int): Int = { + private def getConfigedCategorySizes: Array[Int] = { val dropLast = getDropLast val keepInvalid = getHandleInvalid == OneHotEncoderEstimator.KEEP_INVALID if (!dropLast && keepInvalid) { // When `handleInvalid` is "keep", an extra category is added as last category // for invalid data. - orgCategorySize + 1 + categorySizes.map(_ + 1) } else if (dropLast && !keepInvalid) { // When `dropLast` is true, the last category is removed. - orgCategorySize - 1 + categorySizes.map(_ - 1) } else { // When `dropLast` is true and `handleInvalid` is "keep", the extra category for invalid // data is removed. Thus, it is the same as the plain number of categories. - orgCategorySize + categorySizes } } private def encoder: UserDefinedFunction = { - val oneValue = Array(1.0) - val emptyValues = Array.empty[Double] - val emptyIndices = Array.empty[Int] - val dropLast = getDropLast - val handleInvalid = getHandleInvalid - val keepInvalid = handleInvalid == OneHotEncoderEstimator.KEEP_INVALID + val keepInvalid = getHandleInvalid == OneHotEncoderEstimator.KEEP_INVALID + val configedSizes = getConfigedCategorySizes + val localCategorySizes = categorySizes // The udf performed on input data. The first parameter is the input value. The second - // parameter is the index of input. - udf { (label: Double, idx: Int) => - val plainNumCategories = categorySizes(idx) - val size = configedCategorySize(plainNumCategories, idx) - - if (label < 0) { - throw new SparkException(s"Negative value: $label. Input can't be negative.") - } else if (label == size && dropLast && !keepInvalid) { - // When `dropLast` is true and `handleInvalid` is not "keep", - // the last category is removed. - Vectors.sparse(size, emptyIndices, emptyValues) - } else if (label >= plainNumCategories && keepInvalid) { - // When `handleInvalid` is "keep", encodes invalid data to last category (and removed - // if `dropLast` is true) - if (dropLast) { - Vectors.sparse(size, emptyIndices, emptyValues) + // parameter is the index in inputCols of the column being encoded. + udf { (label: Double, colIdx: Int) => + val origCategorySize = localCategorySizes(colIdx) + // idx: index in vector of the single 1-valued element + val idx = if (label >= 0 && label < origCategorySize) { + label + } else { + if (keepInvalid) { + origCategorySize } else { - Vectors.sparse(size, Array(size - 1), oneValue) + if (label < 0) { + throw new SparkException(s"Negative value: $label. Input can't be negative. " + + s"To handle invalid values, set Param handleInvalid to " + + s"${OneHotEncoderEstimator.KEEP_INVALID}") + } else { + throw new SparkException(s"Unseen value: $label. To handle unseen values, " + + s"set Param handleInvalid to ${OneHotEncoderEstimator.KEEP_INVALID}.") + } } - } else if (label < plainNumCategories) { - Vectors.sparse(size, Array(label.toInt), oneValue) + } + + val size = configedSizes(colIdx) + if (idx < size) { + Vectors.sparse(size, Array(idx.toInt), Array(1.0)) } else { - assert(handleInvalid == OneHotEncoderEstimator.ERROR_INVALID) - throw new SparkException(s"Unseen value: $label. To handle unseen values, " + - s"set Param handleInvalid to ${OneHotEncoderEstimator.KEEP_INVALID}.") + Vectors.sparse(size, Array.empty[Int], Array.empty[Double]) } } } @@ -282,7 +288,6 @@ class OneHotEncoderModel private[ml] ( @Since("2.3.0") override def transformSchema(schema: StructType): StructType = { val inputColNames = $(inputCols) - val outputColNames = $(outputCols) require(inputColNames.length == categorySizes.length, s"The number of input columns ${inputColNames.length} must be the same as the number of " + @@ -300,6 +305,7 @@ class OneHotEncoderModel private[ml] ( * account. Mismatched numbers will cause exception. */ private def verifyNumOfValues(schema: StructType): StructType = { + val configedSizes = getConfigedCategorySizes $(outputCols).zipWithIndex.foreach { case (outputColName, idx) => val inputColName = $(inputCols)(idx) val attrGroup = AttributeGroup.fromStructField(schema(outputColName)) @@ -308,9 +314,9 @@ class OneHotEncoderModel private[ml] ( // comparing with expected category number with `handleInvalid` and // `dropLast` taken into account. if (attrGroup.attributes.nonEmpty) { - val numCategories = configedCategorySize(categorySizes(idx), idx) + val numCategories = configedSizes(idx) require(attrGroup.size == numCategories, "OneHotEncoderModel expected " + - s"$numCategories categorical values for input column ${inputColName}, " + + s"$numCategories categorical values for input column $inputColName, " + s"but the input column had metadata specifying ${attrGroup.size} values.") } } @@ -322,7 +328,7 @@ class OneHotEncoderModel private[ml] ( val transformedSchema = transformSchema(dataset.schema, logging = true) val keepInvalid = $(handleInvalid) == OneHotEncoderEstimator.KEEP_INVALID - val encodedColumns = (0 until $(inputCols).length).map { idx => + val encodedColumns = $(inputCols).indices.map { idx => val inputColName = $(inputCols)(idx) val outputColName = $(outputCols)(idx) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala index 1ec5f8cb6139b..3b4c25478fb1d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala @@ -17,6 +17,10 @@ package org.apache.spark.ml.feature +import org.json4s.JsonDSL._ +import org.json4s.JValue +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.annotation.Since import org.apache.spark.internal.Logging import org.apache.spark.ml._ @@ -249,11 +253,35 @@ final class QuantileDiscretizer @Since("1.6.0") (@Since("1.6.0") override val ui @Since("1.6.0") override def copy(extra: ParamMap): QuantileDiscretizer = defaultCopy(extra) + + override def write: MLWriter = new QuantileDiscretizer.QuantileDiscretizerWriter(this) } @Since("1.6.0") object QuantileDiscretizer extends DefaultParamsReadable[QuantileDiscretizer] with Logging { + private[QuantileDiscretizer] + class QuantileDiscretizerWriter(instance: QuantileDiscretizer) extends MLWriter { + + override protected def saveImpl(path: String): Unit = { + // SPARK-23377: The default params will be saved and loaded as user-supplied params. + // Once `inputCols` is set, the default value of `outputCol` param causes the error + // when checking exclusive params. As a temporary to fix it, we skip the default value + // of `outputCol` if `inputCols` is set when saving the metadata. + // TODO: If we modify the persistence mechanism later to better handle default params, + // we can get rid of this. + var paramWithoutOutputCol: Option[JValue] = None + if (instance.isSet(instance.inputCols)) { + val params = instance.extractParamMap().toSeq + val jsonParams = params.filter(_.param != instance.outputCol).map { case ParamPair(p, v) => + p.name -> parse(p.jsonEncode(v)) + }.toList + paramWithoutOutputCol = Some(render(jsonParams)) + } + DefaultParamsWriter.saveMetadata(instance, path, sc, paramMap = paramWithoutOutputCol) + } + } + @Since("1.6.0") override def load(path: String): QuantileDiscretizer = super.load(path) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala index 7da3339f8b487..22e7b8bbf1ff5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala @@ -25,7 +25,7 @@ import org.apache.hadoop.fs.Path import org.apache.spark.annotation.{Experimental, Since} import org.apache.spark.ml.{Estimator, Model, Pipeline, PipelineModel, PipelineStage, Transformer} import org.apache.spark.ml.attribute.AttributeGroup -import org.apache.spark.ml.linalg.VectorUDT +import org.apache.spark.ml.linalg.{Vector, VectorUDT} import org.apache.spark.ml.param.{BooleanParam, Param, ParamMap, ParamValidators} import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasHandleInvalid, HasLabelCol} import org.apache.spark.ml.util._ @@ -74,7 +74,7 @@ private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol with * @group param */ @Since("2.3.0") - final override val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", + override val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", "How to handle invalid data (unseen or NULL values) in features and label column of string " + "type. Options are 'skip' (filter out rows with invalid data), error (throw an error), " + "or 'keep' (put invalid data in a special additional bucket, at index numLabels).", @@ -199,6 +199,7 @@ class RFormula @Since("1.5.0") (@Since("1.5.0") override val uid: String) val parsedFormula = RFormulaParser.parse($(formula)) val resolvedFormula = parsedFormula.resolve(dataset.schema) val encoderStages = ArrayBuffer[PipelineStage]() + val oneHotEncodeColumns = ArrayBuffer[(String, String)]() val prefixesToRewrite = mutable.Map[String, String]() val tempColumns = ArrayBuffer[String]() @@ -210,8 +211,8 @@ class RFormula @Since("1.5.0") (@Since("1.5.0") override val uid: String) // First we index each string column referenced by the input terms. val indexed: Map[String, String] = resolvedFormula.terms.flatten.distinct.map { term => - dataset.schema(term) match { - case column if column.dataType == StringType => + dataset.schema(term).dataType match { + case _: StringType => val indexCol = tmpColumn("stridx") encoderStages += new StringIndexer() .setInputCol(term) @@ -220,6 +221,18 @@ class RFormula @Since("1.5.0") (@Since("1.5.0") override val uid: String) .setHandleInvalid($(handleInvalid)) prefixesToRewrite(indexCol + "_") = term + "_" (term, indexCol) + case _: VectorUDT => + val group = AttributeGroup.fromStructField(dataset.schema(term)) + val size = if (group.size < 0) { + dataset.select(term).first().getAs[Vector](0).size + } else { + group.size + } + encoderStages += new VectorSizeHint(uid) + .setHandleInvalid("optimistic") + .setInputCol(term) + .setSize(size) + (term, term) case _ => (term, term) } @@ -230,16 +243,17 @@ class RFormula @Since("1.5.0") (@Since("1.5.0") override val uid: String) val encodedTerms = resolvedFormula.terms.map { case Seq(term) if dataset.schema(term).dataType == StringType => val encodedCol = tmpColumn("onehot") - var encoder = new OneHotEncoder() - .setInputCol(indexed(term)) - .setOutputCol(encodedCol) // Formula w/o intercept, one of the categories in the first category feature is // being used as reference category, we will not drop any category for that feature. if (!hasIntercept && !keepReferenceCategory) { - encoder = encoder.setDropLast(false) + encoderStages += new OneHotEncoderEstimator(uid) + .setInputCols(Array(indexed(term))) + .setOutputCols(Array(encodedCol)) + .setDropLast(false) keepReferenceCategory = true + } else { + oneHotEncodeColumns += indexed(term) -> encodedCol } - encoderStages += encoder prefixesToRewrite(encodedCol + "_") = term + "_" encodedCol case Seq(term) => @@ -253,6 +267,14 @@ class RFormula @Since("1.5.0") (@Since("1.5.0") override val uid: String) interactionCol } + if (oneHotEncodeColumns.nonEmpty) { + val (inputCols, outputCols) = oneHotEncodeColumns.toArray.unzip + encoderStages += new OneHotEncoderEstimator(uid) + .setInputCols(inputCols) + .setOutputCols(outputCols) + .setDropLast(true) + } + encoderStages += new VectorAssembler(uid) .setInputCols(encodedTerms.toArray) .setOutputCol($(featuresCol)) diff --git a/mllib/src/main/scala/org/apache/spark/ml/image/ImageSchema.scala b/mllib/src/main/scala/org/apache/spark/ml/image/ImageSchema.scala index f7850b238465b..dcc40b6668c7a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/image/ImageSchema.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/image/ImageSchema.scala @@ -169,12 +169,11 @@ object ImageSchema { var offset = 0 for (h <- 0 until height) { for (w <- 0 until width) { - val color = new Color(img.getRGB(w, h)) - + val color = new Color(img.getRGB(w, h), hasAlpha) decoded(offset) = color.getBlue.toByte decoded(offset + 1) = color.getGreen.toByte decoded(offset + 2) = color.getRed.toByte - if (nChannels == 4) { + if (hasAlpha) { decoded(offset + 3) = color.getAlpha.toByte } offset += nChannels diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 1b4b401ac4aa0..9a83a5882ce29 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -249,6 +249,75 @@ object ParamValidators { def arrayLengthGt[T](lowerBound: Double): Array[T] => Boolean = { (value: Array[T]) => value.length > lowerBound } + + /** + * Utility for Param validity checks for Transformers which have both single- and multi-column + * support. This utility assumes that `inputCol` indicates single-column usage and + * that `inputCols` indicates multi-column usage. + * + * This checks to ensure that exactly one set of Params has been set, and it + * raises an `IllegalArgumentException` if not. + * + * @param singleColumnParams Params which should be set (or have defaults) if `inputCol` has been + * set. This does not need to include `inputCol`. + * @param multiColumnParams Params which should be set (or have defaults) if `inputCols` has been + * set. This does not need to include `inputCols`. + */ + def checkSingleVsMultiColumnParams( + model: Params, + singleColumnParams: Seq[Param[_]], + multiColumnParams: Seq[Param[_]]): Unit = { + val name = s"${model.getClass.getSimpleName} $model" + + def checkExclusiveParams( + isSingleCol: Boolean, + requiredParams: Seq[Param[_]], + excludedParams: Seq[Param[_]]): Unit = { + val badParamsMsgBuilder = new mutable.StringBuilder() + + val mustUnsetParams = excludedParams.filter(p => model.isSet(p)) + .map(_.name).mkString(", ") + if (mustUnsetParams.nonEmpty) { + badParamsMsgBuilder ++= + s"The following Params are not applicable and should not be set: $mustUnsetParams." + } + + val mustSetParams = requiredParams.filter(p => !model.isDefined(p)) + .map(_.name).mkString(", ") + if (mustSetParams.nonEmpty) { + badParamsMsgBuilder ++= + s"The following Params must be defined but are not set: $mustSetParams." + } + + val badParamsMsg = badParamsMsgBuilder.toString() + + if (badParamsMsg.nonEmpty) { + val errPrefix = if (isSingleCol) { + s"$name has the inputCol Param set for single-column transform." + } else { + s"$name has the inputCols Param set for multi-column transform." + } + throw new IllegalArgumentException(s"$errPrefix $badParamsMsg") + } + } + + val inputCol = model.getParam("inputCol") + val inputCols = model.getParam("inputCols") + + if (model.isSet(inputCol)) { + require(!model.isSet(inputCols), s"$name requires " + + s"exactly one of inputCol, inputCols Params to be set, but both are set.") + + checkExclusiveParams(isSingleCol = true, requiredParams = singleColumnParams, + excludedParams = multiColumnParams) + } else if (model.isSet(inputCols)) { + checkExclusiveParams(isSingleCol = false, requiredParams = multiColumnParams, + excludedParams = singleColumnParams) + } else { + throw new IllegalArgumentException(s"$name requires " + + s"exactly one of inputCol, inputCols Params to be set, but neither is set.") + } + } } // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ... diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index a5d57a15317e6..6ad44af9ef7eb 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -63,7 +63,9 @@ private[shared] object SharedParamsCodeGen { ParamDesc[Array[String]]("outputCols", "output column names"), ParamDesc[Int]("checkpointInterval", "set checkpoint interval (>= 1) or " + "disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed " + - "every 10 iterations", isValid = "(interval: Int) => interval == -1 || interval >= 1"), + "every 10 iterations. Note: this setting will be ignored if the checkpoint directory " + + "is not set in the SparkContext", + isValid = "(interval: Int) => interval == -1 || interval >= 1"), ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")), ParamDesc[String]("handleInvalid", "how to handle invalid entries. Options are skip (which " + "will filter out rows with bad values), or error (which will throw an error). More " + diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index 13425dacc9f18..be8b2f273164b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -282,10 +282,10 @@ trait HasOutputCols extends Params { trait HasCheckpointInterval extends Params { /** - * Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. + * Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext. * @group param */ - final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations", (interval: Int) => interval == -1 || interval >= 1) + final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext", (interval: Int) => interval == -1 || interval >= 1) /** @group getParam */ final def getCheckpointInterval: Int = $(checkpointInterval) diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index a5873d03b4161..6d3fe7a6c748c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -645,7 +645,7 @@ class LinearRegressionModel private[ml] ( extends RegressionModel[Vector, LinearRegressionModel] with LinearRegressionParams with MLWritable { - def this(uid: String, coefficients: Vector, intercept: Double) = + private[ml] def this(uid: String, coefficients: Vector, intercept: Double) = this(uid, coefficients, intercept, 1.0) private var trainingSummary: Option[LinearRegressionTrainingSummary] = None diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index 095b54c0fe83f..a0b507d2e718c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -160,8 +160,10 @@ class CrossValidator @Since("1.2.0") (@Since("1.4.0") override val uid: String) } (executionContext) } - // Wait for metrics to be calculated before unpersisting validation dataset + // Wait for metrics to be calculated val foldMetrics = foldMetricFutures.map(ThreadUtils.awaitResult(_, Duration.Inf)) + + // Unpersist training & validation set once all metrics have been produced trainingDataset.unpersist() validationDataset.unpersist() foldMetrics diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala index c73bd18475475..8826ef3271bc1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala @@ -143,24 +143,13 @@ class TrainValidationSplit @Since("1.5.0") (@Since("1.5.0") override val uid: St // Fit models in a Future for training in parallel logDebug(s"Train split with multiple sets of parameters.") - val modelFutures = epm.zipWithIndex.map { case (paramMap, paramIndex) => - Future[Model[_]] { + val metricFutures = epm.zipWithIndex.map { case (paramMap, paramIndex) => + Future[Double] { val model = est.fit(trainingDataset, paramMap).asInstanceOf[Model[_]] if (collectSubModelsParam) { subModels.get(paramIndex) = model } - model - } (executionContext) - } - - // Unpersist training data only when all models have trained - Future.sequence[Model[_], Iterable](modelFutures)(implicitly, executionContext) - .onComplete { _ => trainingDataset.unpersist() } (executionContext) - - // Evaluate models in a Future that will calulate a metric and allow model to be cleaned up - val metricFutures = modelFutures.zip(epm).map { case (modelFuture, paramMap) => - modelFuture.map { model => // TODO: duplicate evaluator to take extra params from input val metric = eval.evaluate(model.transform(validationDataset, paramMap)) logDebug(s"Got metric $metric for model trained with $paramMap.") @@ -171,7 +160,8 @@ class TrainValidationSplit @Since("1.5.0") (@Since("1.5.0") override val uid: St // Wait for all metrics to be calculated val metrics = metricFutures.map(ThreadUtils.awaitResult(_, Duration.Inf)) - // Unpersist validation set once all metrics have been produced + // Unpersist training & validation set once all metrics have been produced + trainingDataset.unpersist() validationDataset.unpersist() logInfo(s"Train validation split metrics: ${metrics.toSeq}") diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala b/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala index 7c46f45c59717..8920e615c8d72 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala @@ -28,6 +28,7 @@ import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param.Param import org.apache.spark.rdd.RDD import org.apache.spark.sql.Dataset +import org.apache.spark.util.Utils /** * A small wrapper that defines a training session for an estimator, and some methods to log @@ -44,7 +45,9 @@ private[spark] class Instrumentation[E <: Estimator[_]] private ( private val id = Instrumentation.counter.incrementAndGet() private val prefix = { - val className = estimator.getClass.getSimpleName + // estimator.getClass.getSimpleName can cause Malformed class name error, + // call safer `Utils.getSimpleName` instead + val className = Utils.getSimpleName(estimator.getClass) s"$className-${estimator.uid}-${dataset.hashCode()}-$id: " } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala index 9abdd44a635d1..8935c8496cdbb 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala @@ -135,7 +135,7 @@ object HashingTF { private[HashingTF] val Murmur3: String = "murmur3" - private val seed = 42 + private[spark] val seed = 42 /** * Calculate a hash code value for the term object using the native Scala implementation. diff --git a/mllib/src/test/java/org/apache/spark/SharedSparkSession.java b/mllib/src/test/java/org/apache/spark/SharedSparkSession.java index 43779878890db..35a250955b282 100644 --- a/mllib/src/test/java/org/apache/spark/SharedSparkSession.java +++ b/mllib/src/test/java/org/apache/spark/SharedSparkSession.java @@ -42,7 +42,12 @@ public void setUp() throws IOException { @After public void tearDown() { - spark.stop(); - spark = null; + try { + spark.stop(); + spark = null; + } finally { + SparkSession.clearDefaultSession(); + SparkSession.clearActiveSession(); + } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala index 98c879ece62d6..1968041aaf161 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -23,15 +23,14 @@ import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.ml.tree.{CategoricalSplit, InternalNode, LeafNode} import org.apache.spark.ml.tree.impl.TreeTests -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint} -import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, DecisionTreeSuite => OldDecisionTreeSuite} -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, + DecisionTreeSuite => OldDecisionTreeSuite} import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row} -class DecisionTreeClassifierSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class DecisionTreeClassifierSuite extends MLTest with DefaultReadWriteTest { import DecisionTreeClassifierSuite.compareAPIs import testImplicits._ @@ -251,20 +250,18 @@ class DecisionTreeClassifierSuite MLTestingUtils.checkCopyAndUids(dt, newTree) - val predictions = newTree.transform(newData) - .select(newTree.getPredictionCol, newTree.getRawPredictionCol, newTree.getProbabilityCol) - .collect() - - predictions.foreach { case Row(pred: Double, rawPred: Vector, probPred: Vector) => - assert(pred === rawPred.argmax, - s"Expected prediction $pred but calculated ${rawPred.argmax} from rawPrediction.") - val sum = rawPred.toArray.sum - assert(Vectors.dense(rawPred.toArray.map(_ / sum)) === probPred, - "probability prediction mismatch") + testTransformer[(Vector, Double)](newData, newTree, + "prediction", "rawPrediction", "probability") { + case Row(pred: Double, rawPred: Vector, probPred: Vector) => + assert(pred === rawPred.argmax, + s"Expected prediction $pred but calculated ${rawPred.argmax} from rawPrediction.") + val sum = rawPred.toArray.sum + assert(Vectors.dense(rawPred.toArray.map(_ / sum)) === probPred, + "probability prediction mismatch") } ProbabilisticClassifierSuite.testPredictMethods[ - Vector, DecisionTreeClassificationModel](newTree, newData) + Vector, DecisionTreeClassificationModel](this, newTree, newData) } test("training with 1-category categorical feature") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala index 978f89c459f0a..092b4a01d5b0d 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala @@ -26,13 +26,12 @@ import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.ml.regression.DecisionTreeRegressionModel import org.apache.spark.ml.tree.LeafNode import org.apache.spark.ml.tree.impl.TreeTests -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint} import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} import org.apache.spark.mllib.tree.loss.LogLoss -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.util.Utils @@ -40,8 +39,7 @@ import org.apache.spark.util.Utils /** * Test suite for [[GBTClassifier]]. */ -class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext - with DefaultReadWriteTest { +class GBTClassifierSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ import GBTClassifierSuite.compareAPIs @@ -126,14 +124,15 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext // should predict all zeros binaryModel.setThresholds(Array(0.0, 1.0)) - val binaryZeroPredictions = binaryModel.transform(df).select("prediction").collect() - assert(binaryZeroPredictions.forall(_.getDouble(0) === 0.0)) + testTransformer[(Double, Vector)](df, binaryModel, "prediction") { + case Row(prediction: Double) => prediction === 0.0 + } // should predict all ones binaryModel.setThresholds(Array(1.0, 0.0)) - val binaryOnePredictions = binaryModel.transform(df).select("prediction").collect() - assert(binaryOnePredictions.forall(_.getDouble(0) === 1.0)) - + testTransformer[(Double, Vector)](df, binaryModel, "prediction") { + case Row(prediction: Double) => prediction === 1.0 + } val gbtBase = new GBTClassifier val model = gbtBase.fit(df) @@ -141,15 +140,18 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext // constant threshold scaling is the same as no thresholds binaryModel.setThresholds(Array(1.0, 1.0)) - val scaledPredictions = binaryModel.transform(df).select("prediction").collect() - assert(scaledPredictions.zip(basePredictions).forall { case (scaled, base) => - scaled.getDouble(0) === base.getDouble(0) - }) + testTransformerByGlobalCheckFunc[(Double, Vector)](df, binaryModel, "prediction") { + scaledPredictions: Seq[Row] => + assert(scaledPredictions.zip(basePredictions).forall { case (scaled, base) => + scaled.getDouble(0) === base.getDouble(0) + }) + } // force it to use the predict method model.setRawPredictionCol("").setProbabilityCol("").setThresholds(Array(0, 1)) - val predictionsWithPredict = model.transform(df).select("prediction").collect() - assert(predictionsWithPredict.forall(_.getDouble(0) === 0.0)) + testTransformer[(Double, Vector)](df, model, "prediction") { + case Row(prediction: Double) => prediction === 0.0 + } } test("GBTClassifier: Predictor, Classifier methods") { @@ -169,61 +171,30 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext val blas = BLAS.getInstance() val validationDataset = validationData.toDF(labelCol, featuresCol) - val results = gbtModel.transform(validationDataset) - // check that raw prediction is tree predictions dot tree weights - results.select(rawPredictionCol, featuresCol).collect().foreach { - case Row(raw: Vector, features: Vector) => + testTransformer[(Double, Vector)](validationDataset, gbtModel, + "rawPrediction", "features", "probability", "prediction") { + case Row(raw: Vector, features: Vector, prob: Vector, pred: Double) => assert(raw.size === 2) + // check that raw prediction is tree predictions dot tree weights val treePredictions = gbtModel.trees.map(_.rootNode.predictImpl(features).prediction) val prediction = blas.ddot(gbtModel.numTrees, treePredictions, 1, gbtModel.treeWeights, 1) assert(raw ~== Vectors.dense(-prediction, prediction) relTol eps) - } - // Compare rawPrediction with probability - results.select(rawPredictionCol, probabilityCol).collect().foreach { - case Row(raw: Vector, prob: Vector) => - assert(raw.size === 2) + // Compare rawPrediction with probability assert(prob.size === 2) // Note: we should check other loss types for classification if they are added val predFromRaw = raw.toDense.values.map(value => LogLoss.computeProbability(value)) assert(prob(0) ~== predFromRaw(0) relTol eps) assert(prob(1) ~== predFromRaw(1) relTol eps) assert(prob(0) + prob(1) ~== 1.0 absTol absEps) - } - // Compare prediction with probability - results.select(predictionCol, probabilityCol).collect().foreach { - case Row(pred: Double, prob: Vector) => + // Compare prediction with probability val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2 assert(pred == predFromProb) } - // force it to use raw2prediction - gbtModel.setRawPredictionCol(rawPredictionCol).setProbabilityCol("") - val resultsUsingRaw2Predict = - gbtModel.transform(validationDataset).select(predictionCol).as[Double].collect() - resultsUsingRaw2Predict.zip(results.select(predictionCol).as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - - // force it to use probability2prediction - gbtModel.setRawPredictionCol("").setProbabilityCol(probabilityCol) - val resultsUsingProb2Predict = - gbtModel.transform(validationDataset).select(predictionCol).as[Double].collect() - resultsUsingProb2Predict.zip(results.select(predictionCol).as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - - // force it to use predict - gbtModel.setRawPredictionCol("").setProbabilityCol("") - val resultsUsingPredict = - gbtModel.transform(validationDataset).select(predictionCol).as[Double].collect() - resultsUsingPredict.zip(results.select(predictionCol).as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - ProbabilisticClassifierSuite.testPredictMethods[ - Vector, GBTClassificationModel](gbtModel, validationDataset) + Vector, GBTClassificationModel](this, gbtModel, validationDataset) } test("GBT parameter stepSize should be in interval (0, 1]") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala index 41a5d22dd6283..a93825b8a812d 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala @@ -21,20 +21,18 @@ import scala.util.Random import breeze.linalg.{DenseVector => BDV} -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.classification.LinearSVCSuite._ import org.apache.spark.ml.feature.{Instance, LabeledPoint} import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.ml.optim.aggregator.HingeAggregator import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{Dataset, Row} import org.apache.spark.sql.functions.udf -class LinearSVCSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class LinearSVCSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -141,10 +139,11 @@ class LinearSVCSuite extends SparkFunSuite with MLlibTestSparkContext with Defau threshold: Double, expected: Set[(Int, Double)]): Unit = { model.setThreshold(threshold) - val results = model.transform(df).select("id", "prediction").collect() - .map(r => (r.getInt(0), r.getDouble(1))) - .toSet - assert(results === expected, s"Failed for threshold = $threshold") + testTransformerByGlobalCheckFunc[(Int, Vector)](df, model, "id", "prediction") { + rows: Seq[Row] => + val results = rows.map(r => (r.getInt(0), r.getDouble(1))).toSet + assert(results === expected, s"Failed for threshold = $threshold") + } } def checkResults(threshold: Double, expected: Set[(Int, Double)]): Unit = { diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index a5f81a38face9..9987cbf6ba116 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -22,22 +22,20 @@ import scala.language.existentials import scala.util.Random import scala.util.control.Breaks._ -import org.apache.spark.{SparkException, SparkFunSuite} +import org.apache.spark.SparkException import org.apache.spark.ml.attribute.NominalAttribute import org.apache.spark.ml.classification.LogisticRegressionSuite._ import org.apache.spark.ml.feature.{Instance, LabeledPoint} import org.apache.spark.ml.linalg.{DenseMatrix, Matrices, Matrix, SparseMatrix, Vector, Vectors} import org.apache.spark.ml.optim.aggregator.LogisticAggregator import org.apache.spark.ml.param.{ParamMap, ParamsSuite} -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{Dataset, Row} import org.apache.spark.sql.functions.{col, lit, rand} import org.apache.spark.sql.types.LongType -class LogisticRegressionSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -332,15 +330,14 @@ class LogisticRegressionSuite val binaryModel = blr.fit(smallBinaryDataset) binaryModel.setThreshold(1.0) - val binaryZeroPredictions = - binaryModel.transform(smallBinaryDataset).select("prediction").collect() - assert(binaryZeroPredictions.forall(_.getDouble(0) === 0.0)) + testTransformer[(Double, Vector)](smallBinaryDataset.toDF(), binaryModel, "prediction") { + row => assert(row.getDouble(0) === 0.0) + } binaryModel.setThreshold(0.0) - val binaryOnePredictions = - binaryModel.transform(smallBinaryDataset).select("prediction").collect() - assert(binaryOnePredictions.forall(_.getDouble(0) === 1.0)) - + testTransformer[(Double, Vector)](smallBinaryDataset.toDF(), binaryModel, "prediction") { + row => assert(row.getDouble(0) === 1.0) + } val mlr = new LogisticRegression().setFamily("multinomial") val model = mlr.fit(smallMultinomialDataset) @@ -348,31 +345,36 @@ class LogisticRegressionSuite // should predict all zeros model.setThresholds(Array(1, 1000, 1000)) - val zeroPredictions = model.transform(smallMultinomialDataset).select("prediction").collect() - assert(zeroPredictions.forall(_.getDouble(0) === 0.0)) + testTransformer[(Double, Vector)](smallMultinomialDataset.toDF(), model, "prediction") { + row => assert(row.getDouble(0) === 0.0) + } // should predict all ones model.setThresholds(Array(1000, 1, 1000)) - val onePredictions = model.transform(smallMultinomialDataset).select("prediction").collect() - assert(onePredictions.forall(_.getDouble(0) === 1.0)) + testTransformer[(Double, Vector)](smallMultinomialDataset.toDF(), model, "prediction") { + row => assert(row.getDouble(0) === 1.0) + } // should predict all twos model.setThresholds(Array(1000, 1000, 1)) - val twoPredictions = model.transform(smallMultinomialDataset).select("prediction").collect() - assert(twoPredictions.forall(_.getDouble(0) === 2.0)) + testTransformer[(Double, Vector)](smallMultinomialDataset.toDF(), model, "prediction") { + row => assert(row.getDouble(0) === 2.0) + } // constant threshold scaling is the same as no thresholds model.setThresholds(Array(1000, 1000, 1000)) - val scaledPredictions = model.transform(smallMultinomialDataset).select("prediction").collect() - assert(scaledPredictions.zip(basePredictions).forall { case (scaled, base) => - scaled.getDouble(0) === base.getDouble(0) - }) + testTransformerByGlobalCheckFunc[(Double, Vector)](smallMultinomialDataset.toDF(), model, + "prediction") { scaledPredictions: Seq[Row] => + assert(scaledPredictions.zip(basePredictions).forall { case (scaled, base) => + scaled.getDouble(0) === base.getDouble(0) + }) + } // force it to use the predict method model.setRawPredictionCol("").setProbabilityCol("").setThresholds(Array(0, 1, 1)) - val predictionsWithPredict = - model.transform(smallMultinomialDataset).select("prediction").collect() - assert(predictionsWithPredict.forall(_.getDouble(0) === 0.0)) + testTransformer[(Double, Vector)](smallMultinomialDataset.toDF(), model, "prediction") { + row => assert(row.getDouble(0) === 0.0) + } } test("logistic regression doesn't fit intercept when fitIntercept is off") { @@ -403,21 +405,19 @@ class LogisticRegressionSuite // Modify model params, and check that the params worked. model.setThreshold(1.0) - val predAllZero = model.transform(smallBinaryDataset) - .select("prediction", "myProbability") - .collect() - .map { case Row(pred: Double, prob: Vector) => pred } - assert(predAllZero.forall(_ === 0), - s"With threshold=1.0, expected predictions to be all 0, but only" + - s" ${predAllZero.count(_ === 0)} of ${smallBinaryDataset.count()} were 0.") + testTransformerByGlobalCheckFunc[(Double, Vector)](smallBinaryDataset.toDF(), + model, "prediction", "myProbability") { rows => + val predAllZero = rows.map(_.getDouble(0)) + assert(predAllZero.forall(_ === 0), + s"With threshold=1.0, expected predictions to be all 0, but only" + + s" ${predAllZero.count(_ === 0)} of ${smallBinaryDataset.count()} were 0.") + } // Call transform with params, and check that the params worked. - val predNotAllZero = - model.transform(smallBinaryDataset, model.threshold -> 0.0, - model.probabilityCol -> "myProb") - .select("prediction", "myProb") - .collect() - .map { case Row(pred: Double, prob: Vector) => pred } - assert(predNotAllZero.exists(_ !== 0.0)) + testTransformerByGlobalCheckFunc[(Double, Vector)](smallBinaryDataset.toDF(), + model.copy(ParamMap(model.threshold -> 0.0, + model.probabilityCol -> "myProb")), "prediction", "myProb") { + rows => assert(rows.map(_.getDouble(0)).exists(_ !== 0.0)) + } // Call fit() with new params, and check as many params as we can. lr.setThresholds(Array(0.6, 0.4)) @@ -441,10 +441,10 @@ class LogisticRegressionSuite val numFeatures = smallMultinomialDataset.select("features").first().getAs[Vector](0).size assert(model.numFeatures === numFeatures) - val results = model.transform(smallMultinomialDataset) - // check that raw prediction is coefficients dot features + intercept - results.select("rawPrediction", "features").collect().foreach { - case Row(raw: Vector, features: Vector) => + testTransformer[(Double, Vector)](smallMultinomialDataset.toDF(), + model, "rawPrediction", "features", "probability") { + case Row(raw: Vector, features: Vector, prob: Vector) => + // check that raw prediction is coefficients dot features + intercept assert(raw.size === 3) val margins = Array.tabulate(3) { k => var margin = 0.0 @@ -455,12 +455,7 @@ class LogisticRegressionSuite margin } assert(raw ~== Vectors.dense(margins) relTol eps) - } - - // Compare rawPrediction with probability - results.select("rawPrediction", "probability").collect().foreach { - case Row(raw: Vector, prob: Vector) => - assert(raw.size === 3) + // Compare rawPrediction with probability assert(prob.size === 3) val max = raw.toArray.max val subtract = if (max > 0) max else 0.0 @@ -472,39 +467,8 @@ class LogisticRegressionSuite assert(prob(2) ~== 1.0 - probFromRaw1 - probFromRaw0 relTol eps) } - // Compare prediction with probability - results.select("prediction", "probability").collect().foreach { - case Row(pred: Double, prob: Vector) => - val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2 - assert(pred == predFromProb) - } - - // force it to use raw2prediction - model.setRawPredictionCol("rawPrediction").setProbabilityCol("") - val resultsUsingRaw2Predict = - model.transform(smallMultinomialDataset).select("prediction").as[Double].collect() - resultsUsingRaw2Predict.zip(results.select("prediction").as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - - // force it to use probability2prediction - model.setRawPredictionCol("").setProbabilityCol("probability") - val resultsUsingProb2Predict = - model.transform(smallMultinomialDataset).select("prediction").as[Double].collect() - resultsUsingProb2Predict.zip(results.select("prediction").as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - - // force it to use predict - model.setRawPredictionCol("").setProbabilityCol("") - val resultsUsingPredict = - model.transform(smallMultinomialDataset).select("prediction").as[Double].collect() - resultsUsingPredict.zip(results.select("prediction").as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - ProbabilisticClassifierSuite.testPredictMethods[ - Vector, LogisticRegressionModel](model, smallMultinomialDataset) + Vector, LogisticRegressionModel](this, model, smallMultinomialDataset) } test("binary logistic regression: Predictor, Classifier methods") { @@ -517,51 +481,22 @@ class LogisticRegressionSuite val numFeatures = smallBinaryDataset.select("features").first().getAs[Vector](0).size assert(model.numFeatures === numFeatures) - val results = model.transform(smallBinaryDataset) - - // Compare rawPrediction with probability - results.select("rawPrediction", "probability").collect().foreach { - case Row(raw: Vector, prob: Vector) => + testTransformer[(Double, Vector)](smallBinaryDataset.toDF(), + model, "rawPrediction", "probability", "prediction") { + case Row(raw: Vector, prob: Vector, pred: Double) => + // Compare rawPrediction with probability assert(raw.size === 2) assert(prob.size === 2) val probFromRaw1 = 1.0 / (1.0 + math.exp(-raw(1))) assert(prob(1) ~== probFromRaw1 relTol eps) assert(prob(0) ~== 1.0 - probFromRaw1 relTol eps) - } - - // Compare prediction with probability - results.select("prediction", "probability").collect().foreach { - case Row(pred: Double, prob: Vector) => + // Compare prediction with probability val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2 assert(pred == predFromProb) } - // force it to use raw2prediction - model.setRawPredictionCol("rawPrediction").setProbabilityCol("") - val resultsUsingRaw2Predict = - model.transform(smallBinaryDataset).select("prediction").as[Double].collect() - resultsUsingRaw2Predict.zip(results.select("prediction").as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - - // force it to use probability2prediction - model.setRawPredictionCol("").setProbabilityCol("probability") - val resultsUsingProb2Predict = - model.transform(smallBinaryDataset).select("prediction").as[Double].collect() - resultsUsingProb2Predict.zip(results.select("prediction").as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - - // force it to use predict - model.setRawPredictionCol("").setProbabilityCol("") - val resultsUsingPredict = - model.transform(smallBinaryDataset).select("prediction").as[Double].collect() - resultsUsingPredict.zip(results.select("prediction").as[Double].collect()).foreach { - case (pred1, pred2) => assert(pred1 === pred2) - } - ProbabilisticClassifierSuite.testPredictMethods[ - Vector, LogisticRegressionModel](model, smallBinaryDataset) + Vector, LogisticRegressionModel](this, model, smallBinaryDataset) } test("coefficients and intercept methods") { @@ -616,19 +551,21 @@ class LogisticRegressionSuite LabeledPoint(1.0, Vectors.dense(0.0, 1000.0)), LabeledPoint(1.0, Vectors.dense(0.0, -1.0)) ).toDF() - val results = model.transform(overFlowData).select("rawPrediction", "probability").collect() - - // probabilities are correct when margins have to be adjusted - val raw1 = results(0).getAs[Vector](0) - val prob1 = results(0).getAs[Vector](1) - assert(raw1 === Vectors.dense(1000.0, 2000.0, 3000.0)) - assert(prob1 ~== Vectors.dense(0.0, 0.0, 1.0) absTol eps) - - // probabilities are correct when margins don't have to be adjusted - val raw2 = results(1).getAs[Vector](0) - val prob2 = results(1).getAs[Vector](1) - assert(raw2 === Vectors.dense(-1.0, -2.0, -3.0)) - assert(prob2 ~== Vectors.dense(0.66524096, 0.24472847, 0.09003057) relTol eps) + + testTransformerByGlobalCheckFunc[(Double, Vector)](overFlowData.toDF(), + model, "rawPrediction", "probability") { results: Seq[Row] => + // probabilities are correct when margins have to be adjusted + val raw1 = results(0).getAs[Vector](0) + val prob1 = results(0).getAs[Vector](1) + assert(raw1 === Vectors.dense(1000.0, 2000.0, 3000.0)) + assert(prob1 ~== Vectors.dense(0.0, 0.0, 1.0) absTol eps) + + // probabilities are correct when margins don't have to be adjusted + val raw2 = results(1).getAs[Vector](0) + val prob2 = results(1).getAs[Vector](1) + assert(raw2 === Vectors.dense(-1.0, -2.0, -3.0)) + assert(prob2 ~== Vectors.dense(0.66524096, 0.24472847, 0.09003057) relTol eps) + } } test("MultiClassSummarizer") { @@ -2567,10 +2504,13 @@ class LogisticRegressionSuite val model1 = lr.fit(smallBinaryDataset) val lr2 = new LogisticRegression().setInitialModel(model1).setMaxIter(5).setFamily("binomial") val model2 = lr2.fit(smallBinaryDataset) - val predictions1 = model1.transform(smallBinaryDataset).select("prediction").collect() - val predictions2 = model2.transform(smallBinaryDataset).select("prediction").collect() - predictions1.zip(predictions2).foreach { case (Row(p1: Double), Row(p2: Double)) => - assert(p1 === p2) + val binaryExpected = model1.transform(smallBinaryDataset).select("prediction").collect() + .map(_.getDouble(0)) + for (model <- Seq(model1, model2)) { + testTransformerByGlobalCheckFunc[(Double, Vector)](smallBinaryDataset.toDF(), model, + "prediction") { rows: Seq[Row] => + rows.map(_.getDouble(0)).toArray === binaryExpected + } } assert(model2.summary.totalIterations === 1) @@ -2579,10 +2519,13 @@ class LogisticRegressionSuite val lr4 = new LogisticRegression() .setInitialModel(model3).setMaxIter(5).setFamily("multinomial") val model4 = lr4.fit(smallMultinomialDataset) - val predictions3 = model3.transform(smallMultinomialDataset).select("prediction").collect() - val predictions4 = model4.transform(smallMultinomialDataset).select("prediction").collect() - predictions3.zip(predictions4).foreach { case (Row(p1: Double), Row(p2: Double)) => - assert(p1 === p2) + val multinomialExpected = model3.transform(smallMultinomialDataset).select("prediction") + .collect().map(_.getDouble(0)) + for (model <- Seq(model3, model4)) { + testTransformerByGlobalCheckFunc[(Double, Vector)](smallMultinomialDataset.toDF(), model, + "prediction") { rows: Seq[Row] => + rows.map(_.getDouble(0)).toArray === multinomialExpected + } } assert(model4.summary.totalIterations === 1) } @@ -2638,8 +2581,8 @@ class LogisticRegressionSuite LabeledPoint(4.0, Vectors.dense(2.0))).toDF() val mlr = new LogisticRegression().setFamily("multinomial") val model = mlr.fit(constantData) - val results = model.transform(constantData) - results.select("rawPrediction", "probability", "prediction").collect().foreach { + testTransformer[(Double, Vector)](constantData, model, + "rawPrediction", "probability", "prediction") { case Row(raw: Vector, prob: Vector, pred: Double) => assert(raw === Vectors.dense(Array(0.0, 0.0, 0.0, 0.0, Double.PositiveInfinity))) assert(prob === Vectors.dense(Array(0.0, 0.0, 0.0, 0.0, 1.0))) @@ -2653,8 +2596,8 @@ class LogisticRegressionSuite LabeledPoint(0.0, Vectors.dense(1.0)), LabeledPoint(0.0, Vectors.dense(2.0))).toDF() val modelZeroLabel = mlr.setFitIntercept(false).fit(constantZeroData) - val resultsZero = modelZeroLabel.transform(constantZeroData) - resultsZero.select("rawPrediction", "probability", "prediction").collect().foreach { + testTransformer[(Double, Vector)](constantZeroData, modelZeroLabel, + "rawPrediction", "probability", "prediction") { case Row(raw: Vector, prob: Vector, pred: Double) => assert(prob === Vectors.dense(Array(1.0))) assert(pred === 0.0) @@ -2666,8 +2609,8 @@ class LogisticRegressionSuite val constantDataWithMetadata = constantData .select(constantData("label").as("label", labelMeta), constantData("features")) val modelWithMetadata = mlr.setFitIntercept(true).fit(constantDataWithMetadata) - val resultsWithMetadata = modelWithMetadata.transform(constantDataWithMetadata) - resultsWithMetadata.select("rawPrediction", "probability", "prediction").collect().foreach { + testTransformer[(Double, Vector)](constantDataWithMetadata, modelWithMetadata, + "rawPrediction", "probability", "prediction") { case Row(raw: Vector, prob: Vector, pred: Double) => assert(raw === Vectors.dense(Array(0.0, 0.0, 0.0, 0.0, Double.PositiveInfinity, 0.0))) assert(prob === Vectors.dense(Array(0.0, 0.0, 0.0, 0.0, 1.0, 0.0))) diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala index d3141ec708560..daa58a56896d7 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala @@ -17,22 +17,17 @@ package org.apache.spark.ml.classification -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.classification.LogisticRegressionSuite._ import org.apache.spark.ml.linalg.{Vector, Vectors} -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.ml.util.MLTestingUtils +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS import org.apache.spark.mllib.evaluation.MulticlassMetrics import org.apache.spark.mllib.linalg.{Vectors => OldVectors} import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint} -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{Dataset, Row} -import org.apache.spark.sql.functions._ -class MultilayerPerceptronClassifierSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class MultilayerPerceptronClassifierSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -75,11 +70,9 @@ class MultilayerPerceptronClassifierSuite .setMaxIter(100) .setSolver("l-bfgs") val model = trainer.fit(dataset) - val result = model.transform(dataset) MLTestingUtils.checkCopyAndUids(trainer, model) - val predictionAndLabels = result.select("prediction", "label").collect() - predictionAndLabels.foreach { case Row(p: Double, l: Double) => - assert(p == l) + testTransformer[(Vector, Double)](dataset.toDF(), model, "prediction", "label") { + case Row(p: Double, l: Double) => assert(p == l) } } @@ -99,13 +92,12 @@ class MultilayerPerceptronClassifierSuite .setMaxIter(100) .setSolver("l-bfgs") val model = trainer.fit(strongDataset) - val result = model.transform(strongDataset) - result.select("probability", "expectedProbability").collect().foreach { - case Row(p: Vector, e: Vector) => - assert(p ~== e absTol 1e-3) + testTransformer[(Vector, Double, Vector)](strongDataset.toDF(), model, + "probability", "expectedProbability") { + case Row(p: Vector, e: Vector) => assert(p ~== e absTol 1e-3) } ProbabilisticClassifierSuite.testPredictMethods[ - Vector, MultilayerPerceptronClassificationModel](model, strongDataset) + Vector, MultilayerPerceptronClassificationModel](this, model, strongDataset) } test("test model probability") { @@ -118,11 +110,10 @@ class MultilayerPerceptronClassifierSuite .setSolver("l-bfgs") val model = trainer.fit(dataset) model.setProbabilityCol("probability") - val result = model.transform(dataset) - val features2prob = udf { features: Vector => model.mlpModel.predict(features) } - result.select(features2prob(col("features")), col("probability")).collect().foreach { - case Row(p1: Vector, p2: Vector) => - assert(p1 ~== p2 absTol 1e-3) + testTransformer[(Vector, Double)](dataset.toDF(), model, "features", "probability") { + case Row(features: Vector, prob: Vector) => + val prob2 = model.mlpModel.predict(features) + assert(prob ~== prob2 absTol 1e-3) } } @@ -175,9 +166,6 @@ class MultilayerPerceptronClassifierSuite val model = trainer.fit(dataFrame) val numFeatures = dataFrame.select("features").first().getAs[Vector](0).size assert(model.numFeatures === numFeatures) - val mlpPredictionAndLabels = model.transform(dataFrame).select("prediction", "label").rdd.map { - case Row(p: Double, l: Double) => (p, l) - } // train multinomial logistic regression val lr = new LogisticRegressionWithLBFGS() .setIntercept(true) @@ -189,8 +177,12 @@ class MultilayerPerceptronClassifierSuite lrModel.predict(data.rdd.map(p => OldVectors.fromML(p.features))).zip(data.rdd.map(_.label)) // MLP's predictions should not differ a lot from LR's. val lrMetrics = new MulticlassMetrics(lrPredictionAndLabels) - val mlpMetrics = new MulticlassMetrics(mlpPredictionAndLabels) - assert(mlpMetrics.confusionMatrix.asML ~== lrMetrics.confusionMatrix.asML absTol 100) + testTransformerByGlobalCheckFunc[(Double, Vector)](dataFrame, model, "prediction", "label") { + rows: Seq[Row] => + val mlpPredictionAndLabels = rows.map(x => (x.getDouble(0), x.getDouble(1))) + val mlpMetrics = new MulticlassMetrics(sc.makeRDD(mlpPredictionAndLabels)) + assert(mlpMetrics.confusionMatrix.asML ~== lrMetrics.confusionMatrix.asML absTol 100) + } } test("read/write: MultilayerPerceptronClassifier") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala index 0d3adf993383f..49115c8a4db30 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala @@ -28,12 +28,11 @@ import org.apache.spark.ml.classification.NaiveBayesSuite._ import org.apache.spark.ml.feature.LabeledPoint import org.apache.spark.ml.linalg._ import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{DataFrame, Dataset, Row} -class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class NaiveBayesSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -56,13 +55,13 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa bernoulliDataset = generateNaiveBayesInput(pi, theta, 100, seed, "bernoulli").toDF() } - def validatePrediction(predictionAndLabels: DataFrame): Unit = { - val numOfErrorPredictions = predictionAndLabels.collect().count { + def validatePrediction(predictionAndLabels: Seq[Row]): Unit = { + val numOfErrorPredictions = predictionAndLabels.filter { case Row(prediction: Double, label: Double) => prediction != label - } + }.length // At least 80% of the predictions should be on. - assert(numOfErrorPredictions < predictionAndLabels.count() / 5) + assert(numOfErrorPredictions < predictionAndLabels.length / 5) } def validateModelFit( @@ -92,10 +91,10 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa } def validateProbabilities( - featureAndProbabilities: DataFrame, + featureAndProbabilities: Seq[Row], model: NaiveBayesModel, modelType: String): Unit = { - featureAndProbabilities.collect().foreach { + featureAndProbabilities.foreach { case Row(features: Vector, probability: Vector) => assert(probability.toArray.sum ~== 1.0 relTol 1.0e-10) val expected = modelType match { @@ -154,15 +153,18 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa val validationDataset = generateNaiveBayesInput(piArray, thetaArray, nPoints, 17, "multinomial").toDF() - val predictionAndLabels = model.transform(validationDataset).select("prediction", "label") - validatePrediction(predictionAndLabels) + testTransformerByGlobalCheckFunc[(Double, Vector)](validationDataset, model, + "prediction", "label") { predictionAndLabels: Seq[Row] => + validatePrediction(predictionAndLabels) + } - val featureAndProbabilities = model.transform(validationDataset) - .select("features", "probability") - validateProbabilities(featureAndProbabilities, model, "multinomial") + testTransformerByGlobalCheckFunc[(Double, Vector)](validationDataset, model, + "features", "probability") { featureAndProbabilities: Seq[Row] => + validateProbabilities(featureAndProbabilities, model, "multinomial") + } ProbabilisticClassifierSuite.testPredictMethods[ - Vector, NaiveBayesModel](model, testDataset) + Vector, NaiveBayesModel](this, model, testDataset) } test("Naive Bayes with weighted samples") { @@ -210,15 +212,18 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa val validationDataset = generateNaiveBayesInput(piArray, thetaArray, nPoints, 20, "bernoulli").toDF() - val predictionAndLabels = model.transform(validationDataset).select("prediction", "label") - validatePrediction(predictionAndLabels) + testTransformerByGlobalCheckFunc[(Double, Vector)](validationDataset, model, + "prediction", "label") { predictionAndLabels: Seq[Row] => + validatePrediction(predictionAndLabels) + } - val featureAndProbabilities = model.transform(validationDataset) - .select("features", "probability") - validateProbabilities(featureAndProbabilities, model, "bernoulli") + testTransformerByGlobalCheckFunc[(Double, Vector)](validationDataset, model, + "features", "probability") { featureAndProbabilities: Seq[Row] => + validateProbabilities(featureAndProbabilities, model, "bernoulli") + } ProbabilisticClassifierSuite.testPredictMethods[ - Vector, NaiveBayesModel](model, testDataset) + Vector, NaiveBayesModel](this, model, testDataset) } test("detect negative values") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala index 25bad59b9c9cf..11e88367108b4 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala @@ -17,26 +17,24 @@ package org.apache.spark.ml.classification -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.attribute.NominalAttribute import org.apache.spark.ml.classification.LogisticRegressionSuite._ import org.apache.spark.ml.feature.LabeledPoint import org.apache.spark.ml.feature.StringIndexer -import org.apache.spark.ml.linalg.Vectors +import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.{ParamMap, ParamsSuite} -import org.apache.spark.ml.util.{DefaultReadWriteTest, MetadataUtils, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MetadataUtils, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS import org.apache.spark.mllib.evaluation.MulticlassMetrics import org.apache.spark.mllib.linalg.{Vectors => OldVectors} import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint} -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD import org.apache.spark.sql.Dataset import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.Metadata -class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class OneVsRestSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -85,10 +83,6 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext with Defau val predictionColSchema = transformedDataset.schema(ovaModel.getPredictionCol) assert(MetadataUtils.getNumClasses(predictionColSchema) === Some(3)) - val ovaResults = transformedDataset.select("prediction", "label").rdd.map { - row => (row.getDouble(0), row.getDouble(1)) - } - val lr = new LogisticRegressionWithLBFGS().setIntercept(true).setNumClasses(numClasses) lr.optimizer.setRegParam(0.1).setNumIterations(100) @@ -97,8 +91,13 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext with Defau // determine the #confusion matrix in each class. // bound how much error we allow compared to multinomial logistic regression. val expectedMetrics = new MulticlassMetrics(results) - val ovaMetrics = new MulticlassMetrics(ovaResults) - assert(expectedMetrics.confusionMatrix.asML ~== ovaMetrics.confusionMatrix.asML absTol 400) + + testTransformerByGlobalCheckFunc[(Double, Vector)](dataset.toDF(), ovaModel, + "prediction", "label") { rows => + val ovaResults = rows.map { row => (row.getDouble(0), row.getDouble(1)) } + val ovaMetrics = new MulticlassMetrics(sc.makeRDD(ovaResults)) + assert(expectedMetrics.confusionMatrix.asML ~== ovaMetrics.confusionMatrix.asML absTol 400) + } } test("one-vs-rest: tuning parallelism does not change output") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala index d649ceac949c4..1c8c9829f18d1 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala @@ -20,6 +20,7 @@ package org.apache.spark.ml.classification import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.util.MLTest import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.sql.{Dataset, Row} @@ -122,13 +123,15 @@ object ProbabilisticClassifierSuite { def testPredictMethods[ FeaturesType, M <: ProbabilisticClassificationModel[FeaturesType, M]]( - model: M, testData: Dataset[_]): Unit = { + mlTest: MLTest, model: M, testData: Dataset[_]): Unit = { val allColModel = model.copy(ParamMap.empty) .setRawPredictionCol("rawPredictionAll") .setProbabilityCol("probabilityAll") .setPredictionCol("predictionAll") - val allColResult = allColModel.transform(testData) + + val allColResult = allColModel.transform(testData.select(allColModel.getFeaturesCol)) + .select(allColModel.getFeaturesCol, "rawPredictionAll", "probabilityAll", "predictionAll") for (rawPredictionCol <- Seq("", "rawPredictionSingle")) { for (probabilityCol <- Seq("", "probabilitySingle")) { @@ -138,22 +141,14 @@ object ProbabilisticClassifierSuite { .setProbabilityCol(probabilityCol) .setPredictionCol(predictionCol) - val result = newModel.transform(allColResult) - - import org.apache.spark.sql.functions._ - - val resultRawPredictionCol = - if (rawPredictionCol.isEmpty) col("rawPredictionAll") else col(rawPredictionCol) - val resultProbabilityCol = - if (probabilityCol.isEmpty) col("probabilityAll") else col(probabilityCol) - val resultPredictionCol = - if (predictionCol.isEmpty) col("predictionAll") else col(predictionCol) + import allColResult.sparkSession.implicits._ - result.select( - resultRawPredictionCol, col("rawPredictionAll"), - resultProbabilityCol, col("probabilityAll"), - resultPredictionCol, col("predictionAll") - ).collect().foreach { + mlTest.testTransformer[(Vector, Vector, Vector, Double)](allColResult, newModel, + if (rawPredictionCol.isEmpty) "rawPredictionAll" else rawPredictionCol, + "rawPredictionAll", + if (probabilityCol.isEmpty) "probabilityAll" else probabilityCol, "probabilityAll", + if (predictionCol.isEmpty) "predictionAll" else predictionCol, "predictionAll" + ) { case Row( rawPredictionSingle: Vector, rawPredictionAll: Vector, probabilitySingle: Vector, probabilityAll: Vector, diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala index 2cca2e6c04698..02a9d5c2a18c0 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala @@ -23,11 +23,10 @@ import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.ml.tree.LeafNode import org.apache.spark.ml.tree.impl.TreeTests -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint} import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row} @@ -35,8 +34,7 @@ import org.apache.spark.sql.{DataFrame, Row} /** * Test suite for [[RandomForestClassifier]]. */ -class RandomForestClassifierSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class RandomForestClassifierSuite extends MLTest with DefaultReadWriteTest { import RandomForestClassifierSuite.compareAPIs import testImplicits._ @@ -143,11 +141,8 @@ class RandomForestClassifierSuite MLTestingUtils.checkCopyAndUids(rf, model) - val predictions = model.transform(df) - .select(rf.getPredictionCol, rf.getRawPredictionCol, rf.getProbabilityCol) - .collect() - - predictions.foreach { case Row(pred: Double, rawPred: Vector, probPred: Vector) => + testTransformer[(Vector, Double)](df, model, "prediction", "rawPrediction", + "probability") { case Row(pred: Double, rawPred: Vector, probPred: Vector) => assert(pred === rawPred.argmax, s"Expected prediction $pred but calculated ${rawPred.argmax} from rawPrediction.") val sum = rawPred.toArray.sum @@ -155,8 +150,9 @@ class RandomForestClassifierSuite "probability prediction mismatch") assert(probPred.toArray.sum ~== 1.0 relTol 1E-5) } + ProbabilisticClassifierSuite.testPredictMethods[ - Vector, RandomForestClassificationModel](model, df) + Vector, RandomForestClassificationModel](this, model, df) } test("Fitting without numClasses in metadata") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala index 4455d35210878..05d4a6ee2dabf 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala @@ -17,14 +17,12 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.sql.{DataFrame, Row} -class BinarizerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class BinarizerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -47,7 +45,7 @@ class BinarizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defau .setInputCol("feature") .setOutputCol("binarized_feature") - binarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach { + testTransformer[(Double, Double)](dataFrame, binarizer, "binarized_feature", "expected") { case Row(x: Double, y: Double) => assert(x === y, "The feature value is not correct after binarization.") } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketedRandomProjectionLSHSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketedRandomProjectionLSHSuite.scala index 7175c721bff36..ed9a39d8d1512 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketedRandomProjectionLSHSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketedRandomProjectionLSHSuite.scala @@ -20,16 +20,15 @@ package org.apache.spark.ml.feature import breeze.numerics.{cos, sin} import breeze.numerics.constants.Pi -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.Dataset +import org.apache.spark.sql.{Dataset, Row} -class BucketedRandomProjectionLSHSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class BucketedRandomProjectionLSHSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ @transient var dataset: Dataset[_] = _ @@ -98,6 +97,21 @@ class BucketedRandomProjectionLSHSuite MLTestingUtils.checkCopyAndUids(brp, brpModel) } + test("BucketedRandomProjectionLSH: streaming transform") { + val brp = new BucketedRandomProjectionLSH() + .setNumHashTables(2) + .setInputCol("keys") + .setOutputCol("values") + .setBucketLength(1.0) + .setSeed(12345) + val brpModel = brp.fit(dataset) + + testTransformer[Tuple1[Vector]](dataset.toDF(), brpModel, "values") { + case Row(values: Seq[_]) => + assert(values.length === brp.getNumHashTables) + } + } + test("BucketedRandomProjectionLSH: test of LSH property") { // Project from 2 dimensional Euclidean Space to 1 dimensions val brp = new BucketedRandomProjectionLSH() diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala index d9c97ae8067d3..9ea15e1918532 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala @@ -23,14 +23,13 @@ import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.ml.Pipeline import org.apache.spark.ml.linalg.Vectors import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ -class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class BucketizerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -50,7 +49,7 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setOutputCol("result") .setSplits(splits) - bucketizer.transform(dataFrame).select("result", "expected").collect().foreach { + testTransformer[(Double, Double)](dataFrame, bucketizer, "result", "expected") { case Row(x: Double, y: Double) => assert(x === y, s"The feature value is not correct after bucketing. Expected $y but found $x") @@ -84,7 +83,7 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setOutputCol("result") .setSplits(splits) - bucketizer.transform(dataFrame).select("result", "expected").collect().foreach { + testTransformer[(Double, Double)](dataFrame, bucketizer, "result", "expected") { case Row(x: Double, y: Double) => assert(x === y, s"The feature value is not correct after bucketing. Expected $y but found $x") @@ -103,7 +102,7 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setSplits(splits) bucketizer.setHandleInvalid("keep") - bucketizer.transform(dataFrame).select("result", "expected").collect().foreach { + testTransformer[(Double, Double)](dataFrame, bucketizer, "result", "expected") { case Row(x: Double, y: Double) => assert(x === y, s"The feature value is not correct after bucketing. Expected $y but found $x") @@ -172,7 +171,10 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setInputCol("myInputCol") .setOutputCol("myOutputCol") .setSplits(Array(0.1, 0.8, 0.9)) - testDefaultReadWrite(t) + + val bucketizer = testDefaultReadWrite(t) + val data = Seq((1.0, 2.0), (10.0, 100.0), (101.0, -1.0)).toDF("myInputCol", "myInputCol2") + bucketizer.transform(data) } test("Bucket numeric features") { @@ -216,8 +218,6 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setOutputCols(Array("result1", "result2")) .setSplitsArray(splits) - assert(bucketizer1.isBucketizeMultipleColumns()) - bucketizer1.transform(dataFrame).select("result1", "expected1", "result2", "expected2") BucketizerSuite.checkBucketResults(bucketizer1.transform(dataFrame), Seq("result1", "result2"), @@ -233,8 +233,6 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setOutputCols(Array("result")) .setSplitsArray(Array(splits(0))) - assert(bucketizer2.isBucketizeMultipleColumns()) - withClue("Invalid feature value -0.9 was not caught as an invalid feature!") { intercept[SparkException] { bucketizer2.transform(badDF1).collect() @@ -268,8 +266,6 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setOutputCols(Array("result1", "result2")) .setSplitsArray(splits) - assert(bucketizer.isBucketizeMultipleColumns()) - BucketizerSuite.checkBucketResults(bucketizer.transform(dataFrame), Seq("result1", "result2"), Seq("expected1", "expected2")) @@ -295,8 +291,6 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setOutputCols(Array("result1", "result2")) .setSplitsArray(splits) - assert(bucketizer.isBucketizeMultipleColumns()) - bucketizer.setHandleInvalid("keep") BucketizerSuite.checkBucketResults(bucketizer.transform(dataFrame), Seq("result1", "result2"), @@ -335,8 +329,12 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setInputCols(Array("myInputCol")) .setOutputCols(Array("myOutputCol")) .setSplitsArray(Array(Array(0.1, 0.8, 0.9))) - assert(t.isBucketizeMultipleColumns()) - testDefaultReadWrite(t) + + val bucketizer = testDefaultReadWrite(t) + val data = Seq((1.0, 2.0), (10.0, 100.0), (101.0, -1.0)).toDF("myInputCol", "myInputCol2") + bucketizer.transform(data) + assert(t.hasDefault(t.outputCol)) + assert(bucketizer.hasDefault(bucketizer.outputCol)) } test("Bucketizer in a pipeline") { @@ -348,8 +346,6 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa .setOutputCols(Array("result1", "result2")) .setSplitsArray(Array(Array(-0.5, 0.0, 0.5), Array(-0.5, 0.0, 0.5))) - assert(bucket.isBucketizeMultipleColumns()) - val pl = new Pipeline() .setStages(Array(bucket)) .fit(df) @@ -401,15 +397,27 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa } } - test("Both inputCol and inputCols are set") { - val bucket = new Bucketizer() - .setInputCol("feature1") - .setOutputCol("result") - .setSplits(Array(-0.5, 0.0, 0.5)) - .setInputCols(Array("feature1", "feature2")) - - // When both are set, we ignore `inputCols` and just map the column specified by `inputCol`. - assert(bucket.isBucketizeMultipleColumns() == false) + test("assert exception is thrown if both multi-column and single-column params are set") { + val df = Seq((0.5, 0.3), (0.5, -0.4)).toDF("feature1", "feature2") + ParamsSuite.testExclusiveParams(new Bucketizer, df, ("inputCol", "feature1"), + ("inputCols", Array("feature1", "feature2"))) + ParamsSuite.testExclusiveParams(new Bucketizer, df, ("inputCol", "feature1"), + ("outputCol", "result1"), ("splits", Array(-0.5, 0.0, 0.5)), + ("outputCols", Array("result1", "result2"))) + ParamsSuite.testExclusiveParams(new Bucketizer, df, ("inputCol", "feature1"), + ("outputCol", "result1"), ("splits", Array(-0.5, 0.0, 0.5)), + ("splitsArray", Array(Array(-0.5, 0.0, 0.5), Array(-0.5, 0.0, 0.5)))) + + // this should fail because at least one of inputCol and inputCols must be set + ParamsSuite.testExclusiveParams(new Bucketizer, df, ("outputCol", "feature1"), + ("splits", Array(-0.5, 0.0, 0.5))) + + // the following should fail because not all the params are set + ParamsSuite.testExclusiveParams(new Bucketizer, df, ("inputCol", "feature1"), + ("outputCol", "result1")) + ParamsSuite.testExclusiveParams(new Bucketizer, df, + ("inputCols", Array("feature1", "feature2")), + ("outputCols", Array("result1", "result2"))) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/ChiSqSelectorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/ChiSqSelectorSuite.scala index c83909c4498f2..c843df9f33e3e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/ChiSqSelectorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/ChiSqSelectorSuite.scala @@ -17,16 +17,15 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{Dataset, Row} -class ChiSqSelectorSuite extends SparkFunSuite with MLlibTestSparkContext - with DefaultReadWriteTest { +class ChiSqSelectorSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ @transient var dataset: Dataset[_] = _ @@ -119,32 +118,32 @@ class ChiSqSelectorSuite extends SparkFunSuite with MLlibTestSparkContext test("Test Chi-Square selector: numTopFeatures") { val selector = new ChiSqSelector() .setOutputCol("filtered").setSelectorType("numTopFeatures").setNumTopFeatures(1) - val model = ChiSqSelectorSuite.testSelector(selector, dataset) + val model = testSelector(selector, dataset) MLTestingUtils.checkCopyAndUids(selector, model) } test("Test Chi-Square selector: percentile") { val selector = new ChiSqSelector() .setOutputCol("filtered").setSelectorType("percentile").setPercentile(0.17) - ChiSqSelectorSuite.testSelector(selector, dataset) + testSelector(selector, dataset) } test("Test Chi-Square selector: fpr") { val selector = new ChiSqSelector() .setOutputCol("filtered").setSelectorType("fpr").setFpr(0.02) - ChiSqSelectorSuite.testSelector(selector, dataset) + testSelector(selector, dataset) } test("Test Chi-Square selector: fdr") { val selector = new ChiSqSelector() .setOutputCol("filtered").setSelectorType("fdr").setFdr(0.12) - ChiSqSelectorSuite.testSelector(selector, dataset) + testSelector(selector, dataset) } test("Test Chi-Square selector: fwe") { val selector = new ChiSqSelector() .setOutputCol("filtered").setSelectorType("fwe").setFwe(0.12) - ChiSqSelectorSuite.testSelector(selector, dataset) + testSelector(selector, dataset) } test("read/write") { @@ -163,18 +162,19 @@ class ChiSqSelectorSuite extends SparkFunSuite with MLlibTestSparkContext assert(expected.selectedFeatures === actual.selectedFeatures) } } -} -object ChiSqSelectorSuite { - - private def testSelector(selector: ChiSqSelector, dataset: Dataset[_]): ChiSqSelectorModel = { - val selectorModel = selector.fit(dataset) - selectorModel.transform(dataset).select("filtered", "topFeature").collect() - .foreach { case Row(vec1: Vector, vec2: Vector) => + private def testSelector(selector: ChiSqSelector, data: Dataset[_]): ChiSqSelectorModel = { + val selectorModel = selector.fit(data) + testTransformer[(Double, Vector, Vector)](data.toDF(), selectorModel, + "filtered", "topFeature") { + case Row(vec1: Vector, vec2: Vector) => assert(vec1 ~== vec2 absTol 1e-1) - } + } selectorModel } +} + +object ChiSqSelectorSuite { /** * Mapping from all Params to valid settings which differ from the defaults. diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala index f213145f1ba0a..b4cabff0ecacb 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala @@ -16,16 +16,13 @@ */ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.Row -class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext - with DefaultReadWriteTest { +class CountVectorizerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -50,7 +47,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext val cv = new CountVectorizerModel(Array("a", "b", "c", "d")) .setInputCol("words") .setOutputCol("features") - cv.transform(df).select("features", "expected").collect().foreach { + testTransformer[(Int, Seq[String], Vector)](df, cv, "features", "expected") { case Row(features: Vector, expected: Vector) => assert(features ~== expected absTol 1e-14) } @@ -72,7 +69,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext MLTestingUtils.checkCopyAndUids(cv, cvm) assert(cvm.vocabulary.toSet === Set("a", "b", "c", "d", "e")) - cvm.transform(df).select("features", "expected").collect().foreach { + testTransformer[(Int, Seq[String], Vector)](df, cvm, "features", "expected") { case Row(features: Vector, expected: Vector) => assert(features ~== expected absTol 1e-14) } @@ -100,7 +97,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext .fit(df) assert(cvModel2.vocabulary === Array("a", "b")) - cvModel2.transform(df).select("features", "expected").collect().foreach { + testTransformer[(Int, Seq[String], Vector)](df, cvModel2, "features", "expected") { case Row(features: Vector, expected: Vector) => assert(features ~== expected absTol 1e-14) } @@ -113,7 +110,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext .fit(df) assert(cvModel3.vocabulary === Array("a", "b")) - cvModel3.transform(df).select("features", "expected").collect().foreach { + testTransformer[(Int, Seq[String], Vector)](df, cvModel3, "features", "expected") { case Row(features: Vector, expected: Vector) => assert(features ~== expected absTol 1e-14) } @@ -147,7 +144,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext .setInputCol("words") .setOutputCol("features") .setMinTF(3) - cv.transform(df).select("features", "expected").collect().foreach { + testTransformer[(Int, Seq[String], Vector)](df, cv, "features", "expected") { case Row(features: Vector, expected: Vector) => assert(features ~== expected absTol 1e-14) } @@ -166,7 +163,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext .setInputCol("words") .setOutputCol("features") .setMinTF(0.3) - cv.transform(df).select("features", "expected").collect().foreach { + testTransformer[(Int, Seq[String], Vector)](df, cv, "features", "expected") { case Row(features: Vector, expected: Vector) => assert(features ~== expected absTol 1e-14) } @@ -186,7 +183,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext .setOutputCol("features") .setBinary(true) .fit(df) - cv.transform(df).select("features", "expected").collect().foreach { + testTransformer[(Int, Seq[String], Vector)](df, cv, "features", "expected") { case Row(features: Vector, expected: Vector) => assert(features ~== expected absTol 1e-14) } @@ -196,7 +193,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext .setInputCol("words") .setOutputCol("features") .setBinary(true) - cv2.transform(df).select("features", "expected").collect().foreach { + testTransformer[(Int, Seq[String], Vector)](df, cv2, "features", "expected") { case Row(features: Vector, expected: Vector) => assert(features ~== expected absTol 1e-14) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala index 8dd3dd75e1be5..6734336aac39c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala @@ -21,16 +21,14 @@ import scala.beans.BeanInfo import edu.emory.mathcs.jtransforms.dct.DoubleDCT_1D -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.sql.Row @BeanInfo case class DCTTestData(vec: Vector, wantedVec: Vector) -class DCTSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class DCTSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -72,11 +70,9 @@ class DCTSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead .setOutputCol("resultVec") .setInverse(inverse) - transformer.transform(dataset) - .select("resultVec", "wantedVec") - .collect() - .foreach { case Row(resultVec: Vector, wantedVec: Vector) => - assert(Vectors.sqdist(resultVec, wantedVec) < 1e-6) + testTransformer[(Vector, Vector)](dataset, transformer, "resultVec", "wantedVec") { + case Row(resultVec: Vector, wantedVec: Vector) => + assert(Vectors.sqdist(resultVec, wantedVec) < 1e-6) } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/ElementwiseProductSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/ElementwiseProductSuite.scala index a4cca27be7815..3a8d0762e2ab7 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/ElementwiseProductSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/ElementwiseProductSuite.scala @@ -17,13 +17,31 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite -import org.apache.spark.ml.linalg.Vectors -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.linalg.{Vector, Vectors} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.ml.util.TestingUtils._ +import org.apache.spark.sql.Row -class ElementwiseProductSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class ElementwiseProductSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ + + test("streaming transform") { + val scalingVec = Vectors.dense(0.1, 10.0) + val data = Seq( + (Vectors.dense(0.1, 1.0), Vectors.dense(0.01, 10.0)), + (Vectors.dense(0.0, -1.1), Vectors.dense(0.0, -11.0)) + ) + val df = spark.createDataFrame(data).toDF("features", "expected") + val ep = new ElementwiseProduct() + .setInputCol("features") + .setOutputCol("actual") + .setScalingVec(scalingVec) + testTransformer[(Vector, Vector)](df, ep, "actual", "expected") { + case Row(actual: Vector, expected: Vector) => + assert(actual ~== expected relTol 1e-14) + } + } test("read/write") { val ep = new ElementwiseProduct() diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/FeatureHasherSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/FeatureHasherSuite.scala index 3fc3cbb62d5b5..d799ba6011fa8 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/FeatureHasherSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/FeatureHasherSuite.scala @@ -17,26 +17,24 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.attribute.AttributeGroup import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder import org.apache.spark.sql.functions.col import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils -class FeatureHasherSuite extends SparkFunSuite - with MLlibTestSparkContext - with DefaultReadWriteTest { +class FeatureHasherSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ - import HashingTFSuite.murmur3FeatureIdx + import FeatureHasherSuite.murmur3FeatureIdx - implicit private val vectorEncoder = ExpressionEncoder[Vector]() + implicit private val vectorEncoder: ExpressionEncoder[Vector] = ExpressionEncoder[Vector]() test("params") { ParamsSuite.checkParams(new FeatureHasher) @@ -51,31 +49,31 @@ class FeatureHasherSuite extends SparkFunSuite } test("feature hashing") { + val numFeatures = 100 + // Assume perfect hash on field names in computing expected results + def idx: Any => Int = murmur3FeatureIdx(numFeatures) + val df = Seq( - (2.0, true, "1", "foo"), - (3.0, false, "2", "bar") - ).toDF("real", "bool", "stringNum", "string") + (2.0, true, "1", "foo", + Vectors.sparse(numFeatures, Seq((idx("real"), 2.0), (idx("bool=true"), 1.0), + (idx("stringNum=1"), 1.0), (idx("string=foo"), 1.0)))), + (3.0, false, "2", "bar", + Vectors.sparse(numFeatures, Seq((idx("real"), 3.0), (idx("bool=false"), 1.0), + (idx("stringNum=2"), 1.0), (idx("string=bar"), 1.0)))) + ).toDF("real", "bool", "stringNum", "string", "expected") - val n = 100 val hasher = new FeatureHasher() .setInputCols("real", "bool", "stringNum", "string") .setOutputCol("features") - .setNumFeatures(n) + .setNumFeatures(numFeatures) val output = hasher.transform(df) val attrGroup = AttributeGroup.fromStructField(output.schema("features")) - assert(attrGroup.numAttributes === Some(n)) + assert(attrGroup.numAttributes === Some(numFeatures)) - val features = output.select("features").as[Vector].collect() - // Assume perfect hash on field names - def idx: Any => Int = murmur3FeatureIdx(n) - // check expected indices - val expected = Seq( - Vectors.sparse(n, Seq((idx("real"), 2.0), (idx("bool=true"), 1.0), - (idx("stringNum=1"), 1.0), (idx("string=foo"), 1.0))), - Vectors.sparse(n, Seq((idx("real"), 3.0), (idx("bool=false"), 1.0), - (idx("stringNum=2"), 1.0), (idx("string=bar"), 1.0))) - ) - assert(features.zip(expected).forall { case (e, a) => e ~== a absTol 1e-14 }) + testTransformer[(Double, Boolean, String, String, Vector)](df, hasher, "features", "expected") { + case Row(features: Vector, expected: Vector) => + assert(features ~== expected absTol 1e-14 ) + } } test("setting explicit numerical columns to treat as categorical") { @@ -216,3 +214,11 @@ class FeatureHasherSuite extends SparkFunSuite testDefaultReadWrite(t) } } + +object FeatureHasherSuite { + + private[feature] def murmur3FeatureIdx(numFeatures: Int)(term: Any): Int = { + Utils.nonNegativeMod(FeatureHasher.murmur3Hash(term), numFeatures) + } + +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala index a46272fdce1fb..c5183ecfef7d7 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala @@ -17,17 +17,16 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.attribute.AttributeGroup import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.mllib.feature.{HashingTF => MLlibHashingTF} -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.Row import org.apache.spark.util.Utils -class HashingTFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class HashingTFSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ import HashingTFSuite.murmur3FeatureIdx @@ -37,21 +36,28 @@ class HashingTFSuite extends SparkFunSuite with MLlibTestSparkContext with Defau } test("hashingTF") { - val df = Seq((0, "a a b b c d".split(" ").toSeq)).toDF("id", "words") - val n = 100 + val numFeatures = 100 + // Assume perfect hash when computing expected features. + def idx: Any => Int = murmur3FeatureIdx(numFeatures) + val data = Seq( + ("a a b b c d".split(" ").toSeq, + Vectors.sparse(numFeatures, + Seq((idx("a"), 2.0), (idx("b"), 2.0), (idx("c"), 1.0), (idx("d"), 1.0)))) + ) + + val df = data.toDF("words", "expected") val hashingTF = new HashingTF() .setInputCol("words") .setOutputCol("features") - .setNumFeatures(n) + .setNumFeatures(numFeatures) val output = hashingTF.transform(df) val attrGroup = AttributeGroup.fromStructField(output.schema("features")) - require(attrGroup.numAttributes === Some(n)) - val features = output.select("features").first().getAs[Vector](0) - // Assume perfect hash on "a", "b", "c", and "d". - def idx: Any => Int = murmur3FeatureIdx(n) - val expected = Vectors.sparse(n, - Seq((idx("a"), 2.0), (idx("b"), 2.0), (idx("c"), 1.0), (idx("d"), 1.0))) - assert(features ~== expected absTol 1e-14) + require(attrGroup.numAttributes === Some(numFeatures)) + + testTransformer[(Seq[String], Vector)](df, hashingTF, "features", "expected") { + case Row(features: Vector, expected: Vector) => + assert(features ~== expected absTol 1e-14) + } } test("applying binary term freqs") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala index 005edf73d29be..cdd62be43b54c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala @@ -17,17 +17,15 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.mllib.feature.{IDFModel => OldIDFModel} import org.apache.spark.mllib.linalg.VectorImplicits._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.Row -class IDFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class IDFSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -57,7 +55,7 @@ class IDFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead Vectors.dense(0.0, 1.0, 2.0, 3.0), Vectors.sparse(numOfFeatures, Array(1), Array(1.0)) ) - val numOfData = data.size + val numOfData = data.length val idf = Vectors.dense(Array(0, 3, 1, 2).map { x => math.log((numOfData + 1.0) / (x + 1.0)) }) @@ -72,7 +70,7 @@ class IDFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead MLTestingUtils.checkCopyAndUids(idfEst, idfModel) - idfModel.transform(df).select("idfValue", "expected").collect().foreach { + testTransformer[(Vector, Vector)](df, idfModel, "idfValue", "expected") { case Row(x: Vector, y: Vector) => assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.") } @@ -85,7 +83,7 @@ class IDFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead Vectors.dense(0.0, 1.0, 2.0, 3.0), Vectors.sparse(numOfFeatures, Array(1), Array(1.0)) ) - val numOfData = data.size + val numOfData = data.length val idf = Vectors.dense(Array(0, 3, 1, 2).map { x => if (x > 0) math.log((numOfData + 1.0) / (x + 1.0)) else 0 }) @@ -99,7 +97,7 @@ class IDFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead .setMinDocFreq(1) .fit(df) - idfModel.transform(df).select("idfValue", "expected").collect().foreach { + testTransformer[(Vector, Vector)](df, idfModel, "idfValue", "expected") { case Row(x: Vector, y: Vector) => assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.") } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/ImputerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/ImputerSuite.scala index c08b35b419266..75f63a623e6d8 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/ImputerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/ImputerSuite.scala @@ -16,13 +16,12 @@ */ package org.apache.spark.ml.feature -import org.apache.spark.{SparkException, SparkFunSuite} -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.SparkException +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.sql.{DataFrame, Row} -class ImputerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class ImputerSuite extends MLTest with DefaultReadWriteTest { test("Imputer for Double with default missing Value NaN") { val df = spark.createDataFrame( Seq( @@ -76,6 +75,28 @@ class ImputerSuite extends SparkFunSuite with MLlibTestSparkContext with Default ImputerSuite.iterateStrategyTest(imputer, df) } + test("Imputer should work with Structured Streaming") { + val localSpark = spark + import localSpark.implicits._ + val df = Seq[(java.lang.Double, Double)]( + (4.0, 4.0), + (10.0, 10.0), + (10.0, 10.0), + (Double.NaN, 8.0), + (null, 8.0) + ).toDF("value", "expected_mean_value") + val imputer = new Imputer() + .setInputCols(Array("value")) + .setOutputCols(Array("out")) + .setStrategy("mean") + val model = imputer.fit(df) + testTransformer[(java.lang.Double, Double)](df, model, "expected_mean_value", "out") { + case Row(exp: java.lang.Double, out: Double) => + assert((exp.isNaN && out.isNaN) || (exp == out), + s"Imputed values differ. Expected: $exp, actual: $out") + } + } + test("Imputer throws exception when surrogate cannot be computed") { val df = spark.createDataFrame( Seq( (0, Double.NaN, 1.0, 1.0), @@ -164,8 +185,6 @@ object ImputerSuite { * @param df DataFrame with columns "id", "value", "expected_mean", "expected_median" */ def iterateStrategyTest(imputer: Imputer, df: DataFrame): Unit = { - val inputCols = imputer.getInputCols - Seq("mean", "median").foreach { strategy => imputer.setStrategy(strategy) val model = imputer.fit(df) diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/InteractionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/InteractionSuite.scala index 54f059e5f143e..eea31fc7ae3f2 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/InteractionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/InteractionSuite.scala @@ -19,15 +19,15 @@ package org.apache.spark.ml.feature import scala.collection.mutable.ArrayBuilder -import org.apache.spark.{SparkException, SparkFunSuite} +import org.apache.spark.SparkException import org.apache.spark.ml.attribute._ import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.Row import org.apache.spark.sql.functions.col -class InteractionSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class InteractionSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -63,9 +63,9 @@ class InteractionSuite extends SparkFunSuite with MLlibTestSparkContext with Def test("numeric interaction") { val data = Seq( - (2, Vectors.dense(3.0, 4.0)), - (1, Vectors.dense(1.0, 5.0)) - ).toDF("a", "b") + (2, Vectors.dense(3.0, 4.0), Vectors.dense(6.0, 8.0)), + (1, Vectors.dense(1.0, 5.0), Vectors.dense(1.0, 5.0)) + ).toDF("a", "b", "expected") val groupAttr = new AttributeGroup( "b", Array[Attribute]( @@ -73,14 +73,15 @@ class InteractionSuite extends SparkFunSuite with MLlibTestSparkContext with Def NumericAttribute.defaultAttr.withName("bar"))) val df = data.select( col("a").as("a", NumericAttribute.defaultAttr.toMetadata()), - col("b").as("b", groupAttr.toMetadata())) + col("b").as("b", groupAttr.toMetadata()), + col("expected")) val trans = new Interaction().setInputCols(Array("a", "b")).setOutputCol("features") + testTransformer[(Int, Vector, Vector)](df, trans, "features", "expected") { + case Row(features: Vector, expected: Vector) => + assert(features === expected) + } + val res = trans.transform(df) - val expected = Seq( - (2, Vectors.dense(3.0, 4.0), Vectors.dense(6.0, 8.0)), - (1, Vectors.dense(1.0, 5.0), Vectors.dense(1.0, 5.0)) - ).toDF("a", "b", "features") - assert(res.collect() === expected.collect()) val attrs = AttributeGroup.fromStructField(res.schema("features")) val expectedAttrs = new AttributeGroup( "features", @@ -92,9 +93,9 @@ class InteractionSuite extends SparkFunSuite with MLlibTestSparkContext with Def test("nominal interaction") { val data = Seq( - (2, Vectors.dense(3.0, 4.0)), - (1, Vectors.dense(1.0, 5.0)) - ).toDF("a", "b") + (2, Vectors.dense(3.0, 4.0), Vectors.dense(0, 0, 0, 0, 3, 4)), + (1, Vectors.dense(1.0, 5.0), Vectors.dense(0, 0, 1, 5, 0, 0)) + ).toDF("a", "b", "expected") val groupAttr = new AttributeGroup( "b", Array[Attribute]( @@ -103,14 +104,15 @@ class InteractionSuite extends SparkFunSuite with MLlibTestSparkContext with Def val df = data.select( col("a").as( "a", NominalAttribute.defaultAttr.withValues(Array("up", "down", "left")).toMetadata()), - col("b").as("b", groupAttr.toMetadata())) + col("b").as("b", groupAttr.toMetadata()), + col("expected")) val trans = new Interaction().setInputCols(Array("a", "b")).setOutputCol("features") + testTransformer[(Int, Vector, Vector)](df, trans, "features", "expected") { + case Row(features: Vector, expected: Vector) => + assert(features === expected) + } + val res = trans.transform(df) - val expected = Seq( - (2, Vectors.dense(3.0, 4.0), Vectors.dense(0, 0, 0, 0, 3, 4)), - (1, Vectors.dense(1.0, 5.0), Vectors.dense(0, 0, 1, 5, 0, 0)) - ).toDF("a", "b", "features") - assert(res.collect() === expected.collect()) val attrs = AttributeGroup.fromStructField(res.schema("features")) val expectedAttrs = new AttributeGroup( "features", diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MaxAbsScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MaxAbsScalerSuite.scala index 918da4f9388d4..8dd0f0cb91e37 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/MaxAbsScalerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MaxAbsScalerSuite.scala @@ -14,15 +14,14 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.sql.Row -class MaxAbsScalerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class MaxAbsScalerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -45,9 +44,10 @@ class MaxAbsScalerSuite extends SparkFunSuite with MLlibTestSparkContext with De .setOutputCol("scaled") val model = scaler.fit(df) - model.transform(df).select("expected", "scaled").collect() - .foreach { case Row(vector1: Vector, vector2: Vector) => - assert(vector1.equals(vector2), s"MaxAbsScaler ut error: $vector2 should be $vector1") + testTransformer[(Vector, Vector)](df, model, "expected", "scaled") { + case Row(expectedVec: Vector, actualVec: Vector) => + assert(expectedVec === actualVec, + s"MaxAbsScaler error: Expected $expectedVec but computed $actualVec") } MLTestingUtils.checkCopyAndUids(scaler, model) diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MinHashLSHSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MinHashLSHSuite.scala index 96df68dbdf053..085070a1098d7 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/MinHashLSHSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MinHashLSHSuite.scala @@ -17,14 +17,13 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.Dataset +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} +import org.apache.spark.sql.{Dataset, Row} -class MinHashLSHSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { + +class MinHashLSHSuite extends MLTest with DefaultReadWriteTest { @transient var dataset: Dataset[_] = _ @@ -167,4 +166,20 @@ class MinHashLSHSuite extends SparkFunSuite with MLlibTestSparkContext with Defa assert(precision == 1.0) assert(recall >= 0.7) } + + test("MinHashLSHModel.transform should work with Structured Streaming") { + val localSpark = spark + import localSpark.implicits._ + + val model = new MinHashLSHModel("mh", randCoefficients = Array((1, 0))) + model.set(model.inputCol, "keys") + testTransformer[Tuple1[Vector]](dataset.toDF(), model, "keys", model.getOutputCol) { + case Row(_: Vector, output: Seq[_]) => + assert(output.length === model.randCoefficients.length) + // no AND-amplification yet: SPARK-18450, so each hash output is of length 1 + output.foreach { + case hashOutput: Vector => assert(hashOutput.size === 1) + } + } + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala index 51db74eb739ca..2d965f2ca2c54 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala @@ -17,13 +17,11 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.sql.Row -class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class MinMaxScalerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -48,9 +46,9 @@ class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext with De .setMax(5) val model = scaler.fit(df) - model.transform(df).select("expected", "scaled").collect() - .foreach { case Row(vector1: Vector, vector2: Vector) => - assert(vector1.equals(vector2), "Transformed vector is different with expected.") + testTransformer[(Vector, Vector)](df, model, "expected", "scaled") { + case Row(vector1: Vector, vector2: Vector) => + assert(vector1 === vector2, "Transformed vector is different with expected.") } MLTestingUtils.checkCopyAndUids(scaler, model) @@ -114,7 +112,7 @@ class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext with De val model = scaler.fit(df) model.transform(df).select("expected", "scaled").collect() .foreach { case Row(vector1: Vector, vector2: Vector) => - assert(vector1.equals(vector2), "Transformed vector is different with expected.") + assert(vector1 === vector2, "Transformed vector is different with expected.") } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala index d4975c0b4e20e..201a335e0d7be 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala @@ -19,17 +19,15 @@ package org.apache.spark.ml.feature import scala.beans.BeanInfo -import org.apache.spark.SparkFunSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{Dataset, Row} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.{DataFrame, Row} + @BeanInfo case class NGramTestData(inputTokens: Array[String], wantedNGrams: Array[String]) -class NGramSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class NGramSuite extends MLTest with DefaultReadWriteTest { - import org.apache.spark.ml.feature.NGramSuite._ import testImplicits._ test("default behavior yields bigram features") { @@ -83,16 +81,11 @@ class NGramSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRe .setN(3) testDefaultReadWrite(t) } -} - -object NGramSuite extends SparkFunSuite { - def testNGram(t: NGram, dataset: Dataset[_]): Unit = { - t.transform(dataset) - .select("nGrams", "wantedNGrams") - .collect() - .foreach { case Row(actualNGrams, wantedNGrams) => + def testNGram(t: NGram, dataFrame: DataFrame): Unit = { + testTransformer[(Seq[String], Seq[String])](dataFrame, t, "nGrams", "wantedNGrams") { + case Row(actualNGrams : Seq[_], wantedNGrams: Seq[_]) => assert(actualNGrams === wantedNGrams) - } + } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala index c75027fb4553d..eff57f1223af4 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala @@ -17,21 +17,17 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors} -import org.apache.spark.ml.util.DefaultReadWriteTest +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{DataFrame, Row} -class NormalizerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class NormalizerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @transient var data: Array[Vector] = _ - @transient var dataFrame: DataFrame = _ - @transient var normalizer: Normalizer = _ @transient var l1Normalized: Array[Vector] = _ @transient var l2Normalized: Array[Vector] = _ @@ -62,49 +58,40 @@ class NormalizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa Vectors.dense(0.897906166, 0.113419726, 0.42532397), Vectors.sparse(3, Seq()) ) - - dataFrame = data.map(NormalizerSuite.FeatureData).toSeq.toDF() - normalizer = new Normalizer() - .setInputCol("features") - .setOutputCol("normalized_features") - } - - def collectResult(result: DataFrame): Array[Vector] = { - result.select("normalized_features").collect().map { - case Row(features: Vector) => features - } } - def assertTypeOfVector(lhs: Array[Vector], rhs: Array[Vector]): Unit = { - assert((lhs, rhs).zipped.forall { + def assertTypeOfVector(lhs: Vector, rhs: Vector): Unit = { + assert((lhs, rhs) match { case (v1: DenseVector, v2: DenseVector) => true case (v1: SparseVector, v2: SparseVector) => true case _ => false }, "The vector type should be preserved after normalization.") } - def assertValues(lhs: Array[Vector], rhs: Array[Vector]): Unit = { - assert((lhs, rhs).zipped.forall { (vector1, vector2) => - vector1 ~== vector2 absTol 1E-5 - }, "The vector value is not correct after normalization.") + def assertValues(lhs: Vector, rhs: Vector): Unit = { + assert(lhs ~== rhs absTol 1E-5, "The vector value is not correct after normalization.") } test("Normalization with default parameter") { - val result = collectResult(normalizer.transform(dataFrame)) - - assertTypeOfVector(data, result) + val normalizer = new Normalizer().setInputCol("features").setOutputCol("normalized") + val dataFrame: DataFrame = data.zip(l2Normalized).seq.toDF("features", "expected") - assertValues(result, l2Normalized) + testTransformer[(Vector, Vector)](dataFrame, normalizer, "features", "normalized", "expected") { + case Row(features: Vector, normalized: Vector, expected: Vector) => + assertTypeOfVector(normalized, features) + assertValues(normalized, expected) + } } test("Normalization with setter") { - normalizer.setP(1) + val dataFrame: DataFrame = data.zip(l1Normalized).seq.toDF("features", "expected") + val normalizer = new Normalizer().setInputCol("features").setOutputCol("normalized").setP(1) - val result = collectResult(normalizer.transform(dataFrame)) - - assertTypeOfVector(data, result) - - assertValues(result, l1Normalized) + testTransformer[(Vector, Vector)](dataFrame, normalizer, "features", "normalized", "expected") { + case Row(features: Vector, normalized: Vector, expected: Vector) => + assertTypeOfVector(normalized, features) + assertValues(normalized, expected) + } } test("read/write") { @@ -115,7 +102,3 @@ class NormalizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa testDefaultReadWrite(t) } } - -private object NormalizerSuite { - case class FeatureData(features: Vector) -} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderEstimatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderEstimatorSuite.scala index 1d3f845586426..d549e13262273 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderEstimatorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderEstimatorSuite.scala @@ -17,18 +17,16 @@ package org.apache.spark.ml.feature -import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.ml.attribute.{AttributeGroup, BinaryAttribute, NominalAttribute} import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.{Encoder, Row} +import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder import org.apache.spark.sql.functions.col import org.apache.spark.sql.types._ -class OneHotEncoderEstimatorSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class OneHotEncoderEstimatorSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -57,13 +55,10 @@ class OneHotEncoderEstimatorSuite assert(encoder.getDropLast === true) encoder.setDropLast(false) assert(encoder.getDropLast === false) - val model = encoder.fit(df) - val encoded = model.transform(df) - encoded.select("output", "expected").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1)) - }.collect().foreach { case (vec1, vec2) => - assert(vec1 === vec2) + testTransformer[(Double, Vector)](df, model, "output", "expected") { + case Row(output: Vector, expected: Vector) => + assert(output === expected) } } @@ -87,11 +82,9 @@ class OneHotEncoderEstimatorSuite .setOutputCols(Array("output")) val model = encoder.fit(df) - val encoded = model.transform(df) - encoded.select("output", "expected").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1)) - }.collect().foreach { case (vec1, vec2) => - assert(vec1 === vec2) + testTransformer[(Double, Vector)](df, model, "output", "expected") { + case Row(output: Vector, expected: Vector) => + assert(output === expected) } } @@ -103,11 +96,12 @@ class OneHotEncoderEstimatorSuite .setInputCols(Array("size")) .setOutputCols(Array("encoded")) val model = encoder.fit(df) - val output = model.transform(df) - val group = AttributeGroup.fromStructField(output.schema("encoded")) - assert(group.size === 2) - assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("small").withIndex(0)) - assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("medium").withIndex(1)) + testTransformerByGlobalCheckFunc[(Double)](df, model, "encoded") { rows => + val group = AttributeGroup.fromStructField(rows.head.schema("encoded")) + assert(group.size === 2) + assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("small").withIndex(0)) + assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("medium").withIndex(1)) + } } test("input column without ML attribute") { @@ -116,11 +110,12 @@ class OneHotEncoderEstimatorSuite .setInputCols(Array("index")) .setOutputCols(Array("encoded")) val model = encoder.fit(df) - val output = model.transform(df) - val group = AttributeGroup.fromStructField(output.schema("encoded")) - assert(group.size === 2) - assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("0").withIndex(0)) - assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("1").withIndex(1)) + testTransformerByGlobalCheckFunc[(Double)](df, model, "encoded") { rows => + val group = AttributeGroup.fromStructField(rows.head.schema("encoded")) + assert(group.size === 2) + assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("0").withIndex(0)) + assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("1").withIndex(1)) + } } test("read/write") { @@ -151,29 +146,30 @@ class OneHotEncoderEstimatorSuite val df = spark.createDataFrame(sc.parallelize(data), schema) - val dfWithTypes = df - .withColumn("shortInput", df("input").cast(ShortType)) - .withColumn("longInput", df("input").cast(LongType)) - .withColumn("intInput", df("input").cast(IntegerType)) - .withColumn("floatInput", df("input").cast(FloatType)) - .withColumn("decimalInput", df("input").cast(DecimalType(10, 0))) - - val cols = Array("input", "shortInput", "longInput", "intInput", - "floatInput", "decimalInput") - for (col <- cols) { - val encoder = new OneHotEncoderEstimator() - .setInputCols(Array(col)) + class NumericTypeWithEncoder[A](val numericType: NumericType) + (implicit val encoder: Encoder[(A, Vector)]) + + val types = Seq( + new NumericTypeWithEncoder[Short](ShortType), + new NumericTypeWithEncoder[Long](LongType), + new NumericTypeWithEncoder[Int](IntegerType), + new NumericTypeWithEncoder[Float](FloatType), + new NumericTypeWithEncoder[Byte](ByteType), + new NumericTypeWithEncoder[Double](DoubleType), + new NumericTypeWithEncoder[Decimal](DecimalType(10, 0))(ExpressionEncoder())) + + for (t <- types) { + val dfWithTypes = df.select(col("input").cast(t.numericType), col("expected")) + val estimator = new OneHotEncoderEstimator() + .setInputCols(Array("input")) .setOutputCols(Array("output")) .setDropLast(false) - val model = encoder.fit(dfWithTypes) - val encoded = model.transform(dfWithTypes) - - encoded.select("output", "expected").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1)) - }.collect().foreach { case (vec1, vec2) => - assert(vec1 === vec2) - } + val model = estimator.fit(dfWithTypes) + testTransformer(dfWithTypes, model, "output", "expected") { + case Row(output: Vector, expected: Vector) => + assert(output === expected) + }(t.encoder) } } @@ -202,12 +198,16 @@ class OneHotEncoderEstimatorSuite assert(encoder.getDropLast === false) val model = encoder.fit(df) - val encoded = model.transform(df) - encoded.select("output1", "expected1", "output2", "expected2").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1), r.getAs[Vector](2), r.getAs[Vector](3)) - }.collect().foreach { case (vec1, vec2, vec3, vec4) => - assert(vec1 === vec2) - assert(vec3 === vec4) + testTransformer[(Double, Vector, Double, Vector)]( + df, + model, + "output1", + "output2", + "expected1", + "expected2") { + case Row(output1: Vector, output2: Vector, expected1: Vector, expected2: Vector) => + assert(output1 === expected1) + assert(output2 === expected2) } } @@ -233,12 +233,16 @@ class OneHotEncoderEstimatorSuite .setOutputCols(Array("output1", "output2")) val model = encoder.fit(df) - val encoded = model.transform(df) - encoded.select("output1", "expected1", "output2", "expected2").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1), r.getAs[Vector](2), r.getAs[Vector](3)) - }.collect().foreach { case (vec1, vec2, vec3, vec4) => - assert(vec1 === vec2) - assert(vec3 === vec4) + testTransformer[(Double, Vector, Double, Vector)]( + df, + model, + "output1", + "output2", + "expected1", + "expected2") { + case Row(output1: Vector, output2: Vector, expected1: Vector, expected2: Vector) => + assert(output1 === expected1) + assert(output2 === expected2) } } @@ -253,10 +257,12 @@ class OneHotEncoderEstimatorSuite .setOutputCols(Array("encoded")) val model = encoder.fit(trainingDF) - val err = intercept[SparkException] { - model.transform(testDF).show - } - err.getMessage.contains("Unseen value: 3.0. To handle unseen values") + testTransformerByInterceptingException[(Int, Int)]( + testDF, + model, + expectedMessagePart = "Unseen value: 3.0. To handle unseen values", + firstResultCol = "encoded") + } test("Can't transform on negative input") { @@ -268,10 +274,11 @@ class OneHotEncoderEstimatorSuite .setOutputCols(Array("encoded")) val model = encoder.fit(trainingDF) - val err = intercept[SparkException] { - model.transform(testDF).collect() - } - err.getMessage.contains("Negative value: -1.0. Input can't be negative") + testTransformerByInterceptingException[(Int, Int)]( + testDF, + model, + expectedMessagePart = "Negative value: -1.0. Input can't be negative", + firstResultCol = "encoded") } test("Keep on invalid values: dropLast = false") { @@ -295,11 +302,9 @@ class OneHotEncoderEstimatorSuite .setDropLast(false) val model = encoder.fit(trainingDF) - val encoded = model.transform(testDF) - encoded.select("output", "expected").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1)) - }.collect().foreach { case (vec1, vec2) => - assert(vec1 === vec2) + testTransformer[(Double, Vector)](testDF, model, "output", "expected") { + case Row(output: Vector, expected: Vector) => + assert(output === expected) } } @@ -324,11 +329,9 @@ class OneHotEncoderEstimatorSuite .setDropLast(true) val model = encoder.fit(trainingDF) - val encoded = model.transform(testDF) - encoded.select("output", "expected").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1)) - }.collect().foreach { case (vec1, vec2) => - assert(vec1 === vec2) + testTransformer[(Double, Vector)](testDF, model, "output", "expected") { + case Row(output: Vector, expected: Vector) => + assert(output === expected) } } @@ -355,19 +358,15 @@ class OneHotEncoderEstimatorSuite val model = encoder.fit(df) model.setDropLast(false) - val encoded1 = model.transform(df) - encoded1.select("output", "expected1").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1)) - }.collect().foreach { case (vec1, vec2) => - assert(vec1 === vec2) + testTransformer[(Double, Vector, Vector)](df, model, "output", "expected1") { + case Row(output: Vector, expected1: Vector) => + assert(output === expected1) } model.setDropLast(true) - val encoded2 = model.transform(df) - encoded2.select("output", "expected2").rdd.map { r => - (r.getAs[Vector](0), r.getAs[Vector](1)) - }.collect().foreach { case (vec1, vec2) => - assert(vec1 === vec2) + testTransformer[(Double, Vector, Vector)](df, model, "output", "expected2") { + case Row(output: Vector, expected2: Vector) => + assert(output === expected2) } } @@ -392,13 +391,14 @@ class OneHotEncoderEstimatorSuite val model = encoder.fit(trainingDF) model.setHandleInvalid("error") - val err = intercept[SparkException] { - model.transform(testDF).collect() - } - err.getMessage.contains("Unseen value: 3.0. To handle unseen values") + testTransformerByInterceptingException[(Double, Vector)]( + testDF, + model, + expectedMessagePart = "Unseen value: 3.0. To handle unseen values", + firstResultCol = "output") model.setHandleInvalid("keep") - model.transform(testDF).collect() + testTransformerByGlobalCheckFunc[(Double, Vector)](testDF, model, "output") { _ => } } test("Transforming on mismatched attributes") { @@ -413,9 +413,10 @@ class OneHotEncoderEstimatorSuite val testAttr = NominalAttribute.defaultAttr.withValues("tiny", "small", "medium", "large") val testDF = Seq(0.0, 1.0, 2.0, 3.0).map(Tuple1.apply).toDF("size") .select(col("size").as("size", testAttr.toMetadata())) - val err = intercept[Exception] { - model.transform(testDF).collect() - } - err.getMessage.contains("OneHotEncoderModel expected 2 categorical values") + testTransformerByInterceptingException[(Double)]( + testDF, + model, + expectedMessagePart = "OneHotEncoderModel expected 2 categorical values", + firstResultCol = "encoded") } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala index c44c6813a94be..41b32b2ffa096 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala @@ -17,18 +17,18 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.attribute.{AttributeGroup, BinaryAttribute, NominalAttribute} import org.apache.spark.ml.linalg.Vector +import org.apache.spark.ml.linalg.Vectors import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.DataFrame +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.{DataFrame, Encoder, Row} +import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder import org.apache.spark.sql.functions.col import org.apache.spark.sql.types._ class OneHotEncoderSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { + extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -54,16 +54,19 @@ class OneHotEncoderSuite assert(encoder.getDropLast === true) encoder.setDropLast(false) assert(encoder.getDropLast === false) - val encoded = encoder.transform(transformed) - - val output = encoded.select("id", "labelVec").rdd.map { r => - val vec = r.getAs[Vector](1) - (r.getInt(0), vec(0), vec(1), vec(2)) - }.collect().toSet - // a -> 0, b -> 2, c -> 1 - val expected = Set((0, 1.0, 0.0, 0.0), (1, 0.0, 0.0, 1.0), (2, 0.0, 1.0, 0.0), - (3, 1.0, 0.0, 0.0), (4, 1.0, 0.0, 0.0), (5, 0.0, 1.0, 0.0)) - assert(output === expected) + val expected = Seq( + (0, Vectors.sparse(3, Seq((0, 1.0)))), + (1, Vectors.sparse(3, Seq((2, 1.0)))), + (2, Vectors.sparse(3, Seq((1, 1.0)))), + (3, Vectors.sparse(3, Seq((0, 1.0)))), + (4, Vectors.sparse(3, Seq((0, 1.0)))), + (5, Vectors.sparse(3, Seq((1, 1.0))))).toDF("id", "expected") + + val withExpected = transformed.join(expected, "id") + testTransformer[(Int, String, Double, Vector)](withExpected, encoder, "labelVec", "expected") { + case Row(output: Vector, expected: Vector) => + assert(output === expected) + } } test("OneHotEncoder dropLast = true") { @@ -71,16 +74,19 @@ class OneHotEncoderSuite val encoder = new OneHotEncoder() .setInputCol("labelIndex") .setOutputCol("labelVec") - val encoded = encoder.transform(transformed) - - val output = encoded.select("id", "labelVec").rdd.map { r => - val vec = r.getAs[Vector](1) - (r.getInt(0), vec(0), vec(1)) - }.collect().toSet - // a -> 0, b -> 2, c -> 1 - val expected = Set((0, 1.0, 0.0), (1, 0.0, 0.0), (2, 0.0, 1.0), - (3, 1.0, 0.0), (4, 1.0, 0.0), (5, 0.0, 1.0)) - assert(output === expected) + val expected = Seq( + (0, Vectors.sparse(2, Seq((0, 1.0)))), + (1, Vectors.sparse(2, Seq())), + (2, Vectors.sparse(2, Seq((1, 1.0)))), + (3, Vectors.sparse(2, Seq((0, 1.0)))), + (4, Vectors.sparse(2, Seq((0, 1.0)))), + (5, Vectors.sparse(2, Seq((1, 1.0))))).toDF("id", "expected") + + val withExpected = transformed.join(expected, "id") + testTransformer[(Int, String, Double, Vector)](withExpected, encoder, "labelVec", "expected") { + case Row(output: Vector, expected: Vector) => + assert(output === expected) + } } test("input column with ML attribute") { @@ -90,20 +96,22 @@ class OneHotEncoderSuite val encoder = new OneHotEncoder() .setInputCol("size") .setOutputCol("encoded") - val output = encoder.transform(df) - val group = AttributeGroup.fromStructField(output.schema("encoded")) - assert(group.size === 2) - assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("small").withIndex(0)) - assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("medium").withIndex(1)) + testTransformerByGlobalCheckFunc[(Double)](df, encoder, "encoded") { rows => + val group = AttributeGroup.fromStructField(rows.head.schema("encoded")) + assert(group.size === 2) + assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("small").withIndex(0)) + assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("medium").withIndex(1)) + } } + test("input column without ML attribute") { val df = Seq(0.0, 1.0, 2.0, 1.0).map(Tuple1.apply).toDF("index") val encoder = new OneHotEncoder() .setInputCol("index") .setOutputCol("encoded") - val output = encoder.transform(df) - val group = AttributeGroup.fromStructField(output.schema("encoded")) + val rows = encoder.transform(df).select("encoded").collect() + val group = AttributeGroup.fromStructField(rows.head.schema("encoded")) assert(group.size === 2) assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("0").withIndex(0)) assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("1").withIndex(1)) @@ -119,29 +127,41 @@ class OneHotEncoderSuite test("OneHotEncoder with varying types") { val df = stringIndexed() - val dfWithTypes = df - .withColumn("shortLabel", df("labelIndex").cast(ShortType)) - .withColumn("longLabel", df("labelIndex").cast(LongType)) - .withColumn("intLabel", df("labelIndex").cast(IntegerType)) - .withColumn("floatLabel", df("labelIndex").cast(FloatType)) - .withColumn("decimalLabel", df("labelIndex").cast(DecimalType(10, 0))) - val cols = Array("labelIndex", "shortLabel", "longLabel", "intLabel", - "floatLabel", "decimalLabel") - for (col <- cols) { + val attr = NominalAttribute.defaultAttr.withValues("small", "medium", "large") + val expected = Seq( + (0, Vectors.sparse(3, Seq((0, 1.0)))), + (1, Vectors.sparse(3, Seq((2, 1.0)))), + (2, Vectors.sparse(3, Seq((1, 1.0)))), + (3, Vectors.sparse(3, Seq((0, 1.0)))), + (4, Vectors.sparse(3, Seq((0, 1.0)))), + (5, Vectors.sparse(3, Seq((1, 1.0))))).toDF("id", "expected") + + val withExpected = df.join(expected, "id") + + class NumericTypeWithEncoder[A](val numericType: NumericType) + (implicit val encoder: Encoder[(A, Vector)]) + + val types = Seq( + new NumericTypeWithEncoder[Short](ShortType), + new NumericTypeWithEncoder[Long](LongType), + new NumericTypeWithEncoder[Int](IntegerType), + new NumericTypeWithEncoder[Float](FloatType), + new NumericTypeWithEncoder[Byte](ByteType), + new NumericTypeWithEncoder[Double](DoubleType), + new NumericTypeWithEncoder[Decimal](DecimalType(10, 0))(ExpressionEncoder())) + + for (t <- types) { + val dfWithTypes = withExpected.select(col("labelIndex") + .cast(t.numericType).as("labelIndex", attr.toMetadata()), col("expected")) val encoder = new OneHotEncoder() - .setInputCol(col) + .setInputCol("labelIndex") .setOutputCol("labelVec") .setDropLast(false) - val encoded = encoder.transform(dfWithTypes) - - val output = encoded.select("id", "labelVec").rdd.map { r => - val vec = r.getAs[Vector](1) - (r.getInt(0), vec(0), vec(1), vec(2)) - }.collect().toSet - // a -> 0, b -> 2, c -> 1 - val expected = Set((0, 1.0, 0.0, 0.0), (1, 0.0, 0.0, 1.0), (2, 0.0, 1.0, 0.0), - (3, 1.0, 0.0, 0.0), (4, 1.0, 0.0, 0.0), (5, 0.0, 1.0, 0.0)) - assert(output === expected) + + testTransformer(dfWithTypes, encoder, "labelVec", "expected") { + case Row(output: Vector, expected: Vector) => + assert(output === expected) + }(t.encoder) } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala index 3067a52a4df76..531b1d7c4d9f7 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala @@ -17,17 +17,15 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg._ import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.mllib.linalg.{Vectors => OldVectors} import org.apache.spark.mllib.linalg.distributed.RowMatrix -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.Row -class PCASuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class PCASuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -62,10 +60,10 @@ class PCASuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead val pcaModel = pca.fit(df) MLTestingUtils.checkCopyAndUids(pca, pcaModel) - - pcaModel.transform(df).select("pca_features", "expected").collect().foreach { - case Row(x: Vector, y: Vector) => - assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.") + testTransformer[(Vector, Vector)](df, pcaModel, "pca_features", "expected") { + case Row(result: Vector, expected: Vector) => + assert(result ~== expected absTol 1e-5, + "Transformed vector is different with expected vector.") } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala index e4b0ddf98bfad..0be7aa6c83f29 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala @@ -17,18 +17,13 @@ package org.apache.spark.ml.feature -import org.scalatest.exceptions.TestFailedException - -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.Row -class PolynomialExpansionSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class PolynomialExpansionSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -60,6 +55,18 @@ class PolynomialExpansionSuite -1.08, 3.3, 1.98, -3.63, 9.0, 5.4, -9.9, -27.0), Vectors.sparse(19, Array.empty, Array.empty)) + def assertTypeOfVector(lhs: Vector, rhs: Vector): Unit = { + assert((lhs, rhs) match { + case (v1: DenseVector, v2: DenseVector) => true + case (v1: SparseVector, v2: SparseVector) => true + case _ => false + }, "The vector type should be preserved after polynomial expansion.") + } + + def assertValues(lhs: Vector, rhs: Vector): Unit = { + assert(lhs ~== rhs absTol 1e-1, "The vector value is not correct after polynomial expansion.") + } + test("Polynomial expansion with default parameter") { val df = data.zip(twoDegreeExpansion).toSeq.toDF("features", "expected") @@ -67,13 +74,10 @@ class PolynomialExpansionSuite .setInputCol("features") .setOutputCol("polyFeatures") - polynomialExpansion.transform(df).select("polyFeatures", "expected").collect().foreach { - case Row(expanded: DenseVector, expected: DenseVector) => - assert(expanded ~== expected absTol 1e-1) - case Row(expanded: SparseVector, expected: SparseVector) => - assert(expanded ~== expected absTol 1e-1) - case _ => - throw new TestFailedException("Unmatched data types after polynomial expansion", 0) + testTransformer[(Vector, Vector)](df, polynomialExpansion, "polyFeatures", "expected") { + case Row(expanded: Vector, expected: Vector) => + assertTypeOfVector(expanded, expected) + assertValues(expanded, expected) } } @@ -85,13 +89,10 @@ class PolynomialExpansionSuite .setOutputCol("polyFeatures") .setDegree(3) - polynomialExpansion.transform(df).select("polyFeatures", "expected").collect().foreach { - case Row(expanded: DenseVector, expected: DenseVector) => - assert(expanded ~== expected absTol 1e-1) - case Row(expanded: SparseVector, expected: SparseVector) => - assert(expanded ~== expected absTol 1e-1) - case _ => - throw new TestFailedException("Unmatched data types after polynomial expansion", 0) + testTransformer[(Vector, Vector)](df, polynomialExpansion, "polyFeatures", "expected") { + case Row(expanded: Vector, expected: Vector) => + assertTypeOfVector(expanded, expected) + assertValues(expanded, expected) } } @@ -103,11 +104,9 @@ class PolynomialExpansionSuite .setOutputCol("polyFeatures") .setDegree(1) - polynomialExpansion.transform(df).select("polyFeatures", "expected").collect().foreach { + testTransformer[(Vector, Vector)](df, polynomialExpansion, "polyFeatures", "expected") { case Row(expanded: Vector, expected: Vector) => - assert(expanded ~== expected absTol 1e-1) - case _ => - throw new TestFailedException("Unmatched data types after polynomial expansion", 0) + assertValues(expanded, expected) } } @@ -133,12 +132,13 @@ class PolynomialExpansionSuite .setOutputCol("polyFeatures") for (i <- Seq(10, 11)) { - val transformed = t.setDegree(i) - .transform(df) - .select(s"expectedPoly${i}size", "polyFeatures") - .rdd.map { case Row(expected: Int, v: Vector) => expected == v.size } - - assert(transformed.collect.forall(identity)) + testTransformer[(Vector, Int, Int)]( + df, + t.setDegree(i), + s"expectedPoly${i}size", + "polyFeatures") { case Row(size: Int, expected: Vector) => + assert(size === expected.size) + } } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/QuantileDiscretizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/QuantileDiscretizerSuite.scala index e9a75e931e6a8..b009038bbd833 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/QuantileDiscretizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/QuantileDiscretizerSuite.scala @@ -17,15 +17,13 @@ package org.apache.spark.ml.feature -import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.sql._ -import org.apache.spark.sql.functions.udf -class QuantileDiscretizerSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class QuantileDiscretizerSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ test("Test observed number of buckets and their sizes match expected values") { val spark = this.spark @@ -38,19 +36,19 @@ class QuantileDiscretizerSuite .setInputCol("input") .setOutputCol("result") .setNumBuckets(numBuckets) - val result = discretizer.fit(df).transform(df) - - val observedNumBuckets = result.select("result").distinct.count - assert(observedNumBuckets === numBuckets, - "Observed number of buckets does not equal expected number of buckets.") + val model = discretizer.fit(df) - val relativeError = discretizer.getRelativeError - val isGoodBucket = udf { - (size: Int) => math.abs( size - (datasetSize / numBuckets)) <= (relativeError * datasetSize) + testTransformerByGlobalCheckFunc[(Double)](df, model, "result") { rows => + val result = rows.map { r => Tuple1(r.getDouble(0)) }.toDF("result") + val observedNumBuckets = result.select("result").distinct.count + assert(observedNumBuckets === numBuckets, + "Observed number of buckets does not equal expected number of buckets.") + val relativeError = discretizer.getRelativeError + val numGoodBuckets = result.groupBy("result").count + .filter(s"abs(count - ${datasetSize / numBuckets}) <= ${relativeError * datasetSize}").count + assert(numGoodBuckets === numBuckets, + "Bucket sizes are not within expected relative error tolerance.") } - val numGoodBuckets = result.groupBy("result").count.filter(isGoodBucket($"count")).count - assert(numGoodBuckets === numBuckets, - "Bucket sizes are not within expected relative error tolerance.") } test("Test on data with high proportion of duplicated values") { @@ -65,11 +63,14 @@ class QuantileDiscretizerSuite .setInputCol("input") .setOutputCol("result") .setNumBuckets(numBuckets) - val result = discretizer.fit(df).transform(df) - val observedNumBuckets = result.select("result").distinct.count - assert(observedNumBuckets == expectedNumBuckets, - s"Observed number of buckets are not correct." + - s" Expected $expectedNumBuckets but found $observedNumBuckets") + val model = discretizer.fit(df) + testTransformerByGlobalCheckFunc[(Double)](df, model, "result") { rows => + val result = rows.map { r => Tuple1(r.getDouble(0)) }.toDF("result") + val observedNumBuckets = result.select("result").distinct.count + assert(observedNumBuckets == expectedNumBuckets, + s"Observed number of buckets are not correct." + + s" Expected $expectedNumBuckets but found $observedNumBuckets") + } } test("Test transform on data with NaN value") { @@ -88,17 +89,20 @@ class QuantileDiscretizerSuite withClue("QuantileDiscretizer with handleInvalid=error should throw exception for NaN values") { val dataFrame: DataFrame = validData.toSeq.toDF("input") - intercept[SparkException] { - discretizer.fit(dataFrame).transform(dataFrame).collect() - } + val model = discretizer.fit(dataFrame) + testTransformerByInterceptingException[(Double)]( + dataFrame, + model, + expectedMessagePart = "Bucketizer encountered NaN value.", + firstResultCol = "result") } List(("keep", expectedKeep), ("skip", expectedSkip)).foreach{ case(u, v) => discretizer.setHandleInvalid(u) val dataFrame: DataFrame = validData.zip(v).toSeq.toDF("input", "expected") - val result = discretizer.fit(dataFrame).transform(dataFrame) - result.select("result", "expected").collect().foreach { + val model = discretizer.fit(dataFrame) + testTransformer[(Double, Double)](dataFrame, model, "result", "expected") { case Row(x: Double, y: Double) => assert(x === y, s"The feature value is not correct after bucketing. Expected $y but found $x") @@ -117,14 +121,17 @@ class QuantileDiscretizerSuite .setOutputCol("result") .setNumBuckets(5) - val result = discretizer.fit(trainDF).transform(testDF) - val firstBucketSize = result.filter(result("result") === 0.0).count - val lastBucketSize = result.filter(result("result") === 4.0).count + val model = discretizer.fit(trainDF) + testTransformerByGlobalCheckFunc[(Double)](testDF, model, "result") { rows => + val result = rows.map { r => Tuple1(r.getDouble(0)) }.toDF("result") + val firstBucketSize = result.filter(result("result") === 0.0).count + val lastBucketSize = result.filter(result("result") === 4.0).count - assert(firstBucketSize === 30L, - s"Size of first bucket ${firstBucketSize} did not equal expected value of 30.") - assert(lastBucketSize === 31L, - s"Size of last bucket ${lastBucketSize} did not equal expected value of 31.") + assert(firstBucketSize === 30L, + s"Size of first bucket ${firstBucketSize} did not equal expected value of 30.") + assert(lastBucketSize === 31L, + s"Size of last bucket ${lastBucketSize} did not equal expected value of 31.") + } } test("read/write") { @@ -132,7 +139,10 @@ class QuantileDiscretizerSuite .setInputCol("myInputCol") .setOutputCol("myOutputCol") .setNumBuckets(6) - testDefaultReadWrite(t) + + val readDiscretizer = testDefaultReadWrite(t) + val data = sc.parallelize(1 to 100).map(Tuple1.apply).toDF("myInputCol") + readDiscretizer.fit(data) } test("Verify resulting model has parent") { @@ -162,21 +172,24 @@ class QuantileDiscretizerSuite .setInputCols(Array("input1", "input2")) .setOutputCols(Array("result1", "result2")) .setNumBuckets(numBuckets) - val result = discretizer.fit(df).transform(df) - - val relativeError = discretizer.getRelativeError - val isGoodBucket = udf { - (size: Int) => math.abs( size - (datasetSize / numBuckets)) <= (relativeError * datasetSize) - } - - for (i <- 1 to 2) { - val observedNumBuckets = result.select("result" + i).distinct.count - assert(observedNumBuckets === numBuckets, - "Observed number of buckets does not equal expected number of buckets.") - - val numGoodBuckets = result.groupBy("result" + i).count.filter(isGoodBucket($"count")).count - assert(numGoodBuckets === numBuckets, - "Bucket sizes are not within expected relative error tolerance.") + val model = discretizer.fit(df) + testTransformerByGlobalCheckFunc[(Double, Double)](df, model, "result1", "result2") { rows => + val result = + rows.map { r => Tuple2(r.getDouble(0), r.getDouble(1)) }.toDF("result1", "result2") + val relativeError = discretizer.getRelativeError + for (i <- 1 to 2) { + val observedNumBuckets = result.select("result" + i).distinct.count + assert(observedNumBuckets === numBuckets, + "Observed number of buckets does not equal expected number of buckets.") + + val numGoodBuckets = result + .groupBy("result" + i) + .count + .filter(s"abs(count - ${datasetSize / numBuckets}) <= ${relativeError * datasetSize}") + .count + assert(numGoodBuckets === numBuckets, + "Bucket sizes are not within expected relative error tolerance.") + } } } @@ -193,12 +206,16 @@ class QuantileDiscretizerSuite .setInputCols(Array("input1", "input2")) .setOutputCols(Array("result1", "result2")) .setNumBuckets(numBuckets) - val result = discretizer.fit(df).transform(df) - for (i <- 1 to 2) { - val observedNumBuckets = result.select("result" + i).distinct.count - assert(observedNumBuckets == expectedNumBucket, - s"Observed number of buckets are not correct." + - s" Expected $expectedNumBucket but found ($observedNumBuckets") + val model = discretizer.fit(df) + testTransformerByGlobalCheckFunc[(Double, Double)](df, model, "result1", "result2") { rows => + val result = + rows.map { r => Tuple2(r.getDouble(0), r.getDouble(1)) }.toDF("result1", "result2") + for (i <- 1 to 2) { + val observedNumBuckets = result.select("result" + i).distinct.count + assert(observedNumBuckets == expectedNumBucket, + s"Observed number of buckets are not correct." + + s" Expected $expectedNumBucket but found ($observedNumBuckets") + } } } @@ -221,9 +238,12 @@ class QuantileDiscretizerSuite withClue("QuantileDiscretizer with handleInvalid=error should throw exception for NaN values") { val dataFrame: DataFrame = validData1.zip(validData2).toSeq.toDF("input1", "input2") - intercept[SparkException] { - discretizer.fit(dataFrame).transform(dataFrame).collect() - } + val model = discretizer.fit(dataFrame) + testTransformerByInterceptingException[(Double, Double)]( + dataFrame, + model, + expectedMessagePart = "Bucketizer encountered NaN value.", + firstResultCol = "result1") } List(("keep", expectedKeep1, expectedKeep2), ("skip", expectedSkip1, expectedSkip2)).foreach { @@ -232,8 +252,14 @@ class QuantileDiscretizerSuite val dataFrame: DataFrame = validData1.zip(validData2).zip(v).zip(w).map { case (((a, b), c), d) => (a, b, c, d) }.toSeq.toDF("input1", "input2", "expected1", "expected2") - val result = discretizer.fit(dataFrame).transform(dataFrame) - result.select("result1", "expected1", "result2", "expected2").collect().foreach { + val model = discretizer.fit(dataFrame) + testTransformer[(Double, Double, Double, Double)]( + dataFrame, + model, + "result1", + "expected1", + "result2", + "expected2") { case Row(x: Double, y: Double, z: Double, w: Double) => assert(x === y && w === z) } @@ -265,9 +291,16 @@ class QuantileDiscretizerSuite .setOutputCols(Array("result1", "result2", "result3")) .setNumBucketsArray(numBucketsArray) - discretizer.fit(df).transform(df). - select("result1", "expected1", "result2", "expected2", "result3", "expected3") - .collect().foreach { + val model = discretizer.fit(df) + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df, + model, + "result1", + "expected1", + "result2", + "expected2", + "result3", + "expected3") { case Row(r1: Double, e1: Double, r2: Double, e2: Double, r3: Double, e3: Double) => assert(r1 === e1, s"The result value is not correct after bucketing. Expected $e1 but found $r1") @@ -319,20 +352,16 @@ class QuantileDiscretizerSuite .setStages(Array(discretizerForCol1, discretizerForCol2, discretizerForCol3)) .fit(df) - val resultForMultiCols = plForMultiCols.transform(df) - .select("result1", "result2", "result3") - .collect() - - val resultForSingleCol = plForSingleCol.transform(df) - .select("result1", "result2", "result3") - .collect() + val expected = plForSingleCol.transform(df).select("result1", "result2", "result3").collect() - resultForSingleCol.zip(resultForMultiCols).foreach { - case (rowForSingle, rowForMultiCols) => - assert(rowForSingle.getDouble(0) == rowForMultiCols.getDouble(0) && - rowForSingle.getDouble(1) == rowForMultiCols.getDouble(1) && - rowForSingle.getDouble(2) == rowForMultiCols.getDouble(2)) - } + testTransformerByGlobalCheckFunc[(Double, Double, Double)]( + df, + plForMultiCols, + "result1", + "result2", + "result3") { rows => + assert(rows === expected) + } } test("Multiple Columns: Comparing setting numBuckets with setting numBucketsArray " + @@ -359,18 +388,16 @@ class QuantileDiscretizerSuite .setOutputCols(Array("result1", "result2", "result3")) .setNumBucketsArray(Array(10, 10, 10)) - val result1 = discretizerSingleNumBuckets.fit(df).transform(df) - .select("result1", "result2", "result3") - .collect() - val result2 = discretizerNumBucketsArray.fit(df).transform(df) - .select("result1", "result2", "result3") - .collect() - - result1.zip(result2).foreach { - case (row1, row2) => - assert(row1.getDouble(0) == row2.getDouble(0) && - row1.getDouble(1) == row2.getDouble(1) && - row1.getDouble(2) == row2.getDouble(2)) + val model = discretizerSingleNumBuckets.fit(df) + val expected = model.transform(df).select("result1", "result2", "result3").collect() + + testTransformerByGlobalCheckFunc[(Double, Double, Double)]( + df, + discretizerNumBucketsArray.fit(df), + "result1", + "result2", + "result3") { rows => + assert(rows === expected) } } @@ -379,7 +406,12 @@ class QuantileDiscretizerSuite .setInputCols(Array("input1", "input2")) .setOutputCols(Array("result1", "result2")) .setNumBucketsArray(Array(5, 10)) - testDefaultReadWrite(discretizer) + + val readDiscretizer = testDefaultReadWrite(discretizer) + val data = Seq((1.0, 2.0), (2.0, 3.0), (3.0, 4.0)).toDF("input1", "input2") + readDiscretizer.fit(data) + assert(discretizer.hasDefault(discretizer.outputCol)) + assert(readDiscretizer.hasDefault(readDiscretizer.outputCol)) } test("Multiple Columns: Both inputCol and inputCols are set") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala index 5d09c90ec6dfa..27d570f0b68ad 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala @@ -17,18 +17,38 @@ package org.apache.spark.ml.feature -import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.ml.attribute._ -import org.apache.spark.ml.linalg.Vectors +import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} +import org.apache.spark.sql.{DataFrame, Encoder, Row} import org.apache.spark.sql.types.DoubleType -class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class RFormulaSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ + def testRFormulaTransform[A: Encoder]( + dataframe: DataFrame, + formulaModel: RFormulaModel, + expected: DataFrame, + expectedAttributes: AttributeGroup*): Unit = { + val resultSchema = formulaModel.transformSchema(dataframe.schema) + assert(resultSchema.json === expected.schema.json) + assert(resultSchema === expected.schema) + val (first +: rest) = expected.schema.fieldNames.toSeq + val expectedRows = expected.collect() + testTransformerByGlobalCheckFunc[A](dataframe, formulaModel, first, rest: _*) { rows => + assert(rows.head.schema.toString() == resultSchema.toString()) + for (expectedAttributeGroup <- expectedAttributes) { + val attributeGroup = + AttributeGroup.fromStructField(rows.head.schema(expectedAttributeGroup.name)) + assert(attributeGroup === expectedAttributeGroup) + } + assert(rows === expectedRows) + } + } + test("params") { ParamsSuite.checkParams(new RFormula()) } @@ -38,16 +58,11 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val original = Seq((0, 1.0, 3.0), (2, 2.0, 5.0)).toDF("id", "v1", "v2") val model = formula.fit(original) MLTestingUtils.checkCopyAndUids(formula, model) - val result = model.transform(original) - val resultSchema = model.transformSchema(original.schema) val expected = Seq( (0, 1.0, 3.0, Vectors.dense(1.0, 3.0), 0.0), (2, 2.0, 5.0, Vectors.dense(2.0, 5.0), 2.0) ).toDF("id", "v1", "v2", "features", "label") - // TODO(ekl) make schema comparisons ignore metadata, to avoid .toString - assert(result.schema.toString == resultSchema.toString) - assert(resultSchema == expected.schema) - assert(result.collect() === expected.collect()) + testRFormulaTransform[(Int, Double, Double)](original, model, expected) } test("features column already exists") { @@ -62,9 +77,13 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val formula = new RFormula().setFormula("y ~ x").setLabelCol("y") val original = Seq((0, 1.0), (2, 2.0)).toDF("x", "y") val model = formula.fit(original) + val expected = Seq( + (0, 1.0, Vectors.dense(0.0)), + (2, 2.0, Vectors.dense(2.0)) + ).toDF("x", "y", "features") val resultSchema = model.transformSchema(original.schema) assert(resultSchema.length == 3) - assert(resultSchema.toString == model.transform(original).schema.toString) + testRFormulaTransform[(Int, Double)](original, model, expected) } test("label column already exists but forceIndexLabel was set with true") { @@ -82,9 +101,11 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul intercept[IllegalArgumentException] { model.transformSchema(original.schema) } - intercept[IllegalArgumentException] { - model.transform(original) - } + testTransformerByInterceptingException[(Int, Boolean)]( + original, + model, + "Label column already exists and is not of type NumericType.", + "x") } test("allow missing label column for test datasets") { @@ -94,22 +115,23 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val resultSchema = model.transformSchema(original.schema) assert(resultSchema.length == 3) assert(!resultSchema.exists(_.name == "label")) - assert(resultSchema.toString == model.transform(original).schema.toString) + val expected = Seq( + (0, 1.0, Vectors.dense(0.0)), + (2, 2.0, Vectors.dense(2.0)) + ).toDF("x", "_not_y", "features") + testRFormulaTransform[(Int, Double)](original, model, expected) } test("allow empty label") { val original = Seq((1, 2.0, 3.0), (4, 5.0, 6.0), (7, 8.0, 9.0)).toDF("id", "a", "b") val formula = new RFormula().setFormula("~ a + b") val model = formula.fit(original) - val result = model.transform(original) - val resultSchema = model.transformSchema(original.schema) val expected = Seq( (1, 2.0, 3.0, Vectors.dense(2.0, 3.0)), (4, 5.0, 6.0, Vectors.dense(5.0, 6.0)), (7, 8.0, 9.0, Vectors.dense(8.0, 9.0)) ).toDF("id", "a", "b", "features") - assert(result.schema.toString == resultSchema.toString) - assert(result.collect() === expected.collect()) + testRFormulaTransform[(Int, Double, Double)](original, model, expected) } test("encodes string terms") { @@ -117,16 +139,13 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val original = Seq((1, "foo", 4), (2, "bar", 4), (3, "bar", 5), (4, "baz", 5)) .toDF("id", "a", "b") val model = formula.fit(original) - val result = model.transform(original) - val resultSchema = model.transformSchema(original.schema) val expected = Seq( (1, "foo", 4, Vectors.dense(0.0, 1.0, 4.0), 1.0), (2, "bar", 4, Vectors.dense(1.0, 0.0, 4.0), 2.0), (3, "bar", 5, Vectors.dense(1.0, 0.0, 5.0), 3.0), (4, "baz", 5, Vectors.dense(0.0, 0.0, 5.0), 4.0) ).toDF("id", "a", "b", "features", "label") - assert(result.schema.toString == resultSchema.toString) - assert(result.collect() === expected.collect()) + testRFormulaTransform[(Int, String, Int)](original, model, expected) } test("encodes string terms with string indexer order type") { @@ -164,10 +183,7 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul var idx = 0 for (orderType <- StringIndexer.supportedStringOrderType) { val model = formula.setStringIndexerOrderType(orderType).fit(original) - val result = model.transform(original) - val resultSchema = model.transformSchema(original.schema) - assert(result.schema.toString == resultSchema.toString) - assert(result.collect() === expected(idx).collect()) + testRFormulaTransform[(Int, String, Int)](original, model, expected(idx)) idx += 1 } } @@ -207,10 +223,7 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul ).toDF("id", "a", "b", "features", "label") val model = formula.fit(original) - val result = model.transform(original) - val resultSchema = model.transformSchema(original.schema) - assert(result.schema.toString == resultSchema.toString) - assert(result.collect() === expected.collect()) + testRFormulaTransform[(Int, String, Int)](original, model, expected) } test("formula w/o intercept, we should output reference category when encoding string terms") { @@ -243,19 +256,6 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val formula1 = new RFormula().setFormula("id ~ a + b + c - 1") .setStringIndexerOrderType(StringIndexer.alphabetDesc) val model1 = formula1.fit(original) - val result1 = model1.transform(original) - val resultSchema1 = model1.transformSchema(original.schema) - // Note the column order is different between R and Spark. - val expected1 = Seq( - (1, "foo", "zq", 4, Vectors.sparse(5, Array(0, 4), Array(1.0, 4.0)), 1.0), - (2, "bar", "zz", 4, Vectors.dense(0.0, 0.0, 1.0, 1.0, 4.0), 2.0), - (3, "bar", "zz", 5, Vectors.dense(0.0, 0.0, 1.0, 1.0, 5.0), 3.0), - (4, "baz", "zz", 5, Vectors.dense(0.0, 1.0, 0.0, 1.0, 5.0), 4.0) - ).toDF("id", "a", "b", "c", "features", "label") - assert(result1.schema.toString == resultSchema1.toString) - assert(result1.collect() === expected1.collect()) - - val attrs1 = AttributeGroup.fromStructField(result1.schema("features")) val expectedAttrs1 = new AttributeGroup( "features", Array[Attribute]( @@ -264,14 +264,20 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul new BinaryAttribute(Some("a_bar"), Some(3)), new BinaryAttribute(Some("b_zz"), Some(4)), new NumericAttribute(Some("c"), Some(5)))) - assert(attrs1 === expectedAttrs1) + // Note the column order is different between R and Spark. + val expected1 = Seq( + (1, "foo", "zq", 4, Vectors.sparse(5, Array(0, 4), Array(1.0, 4.0)), 1.0), + (2, "bar", "zz", 4, Vectors.dense(0.0, 0.0, 1.0, 1.0, 4.0), 2.0), + (3, "bar", "zz", 5, Vectors.dense(0.0, 0.0, 1.0, 1.0, 5.0), 3.0), + (4, "baz", "zz", 5, Vectors.dense(0.0, 1.0, 0.0, 1.0, 5.0), 4.0) + ).toDF("id", "a", "b", "c", "features", "label") + + testRFormulaTransform[(Int, String, String, Int)](original, model1, expected1, expectedAttrs1) // There is no impact for string terms interaction. val formula2 = new RFormula().setFormula("id ~ a:b + c - 1") .setStringIndexerOrderType(StringIndexer.alphabetDesc) val model2 = formula2.fit(original) - val result2 = model2.transform(original) - val resultSchema2 = model2.transformSchema(original.schema) // Note the column order is different between R and Spark. val expected2 = Seq( (1, "foo", "zq", 4, Vectors.sparse(7, Array(1, 6), Array(1.0, 4.0)), 1.0), @@ -279,10 +285,6 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul (3, "bar", "zz", 5, Vectors.sparse(7, Array(4, 6), Array(1.0, 5.0)), 3.0), (4, "baz", "zz", 5, Vectors.sparse(7, Array(2, 6), Array(1.0, 5.0)), 4.0) ).toDF("id", "a", "b", "c", "features", "label") - assert(result2.schema.toString == resultSchema2.toString) - assert(result2.collect() === expected2.collect()) - - val attrs2 = AttributeGroup.fromStructField(result2.schema("features")) val expectedAttrs2 = new AttributeGroup( "features", Array[Attribute]( @@ -293,7 +295,8 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul new NumericAttribute(Some("a_bar:b_zz"), Some(5)), new NumericAttribute(Some("a_bar:b_zq"), Some(6)), new NumericAttribute(Some("c"), Some(7)))) - assert(attrs2 === expectedAttrs2) + + testRFormulaTransform[(Int, String, String, Int)](original, model2, expected2, expectedAttrs2) } test("index string label") { @@ -302,15 +305,15 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul Seq(("male", "foo", 4), ("female", "bar", 4), ("female", "bar", 5), ("male", "baz", 5)) .toDF("id", "a", "b") val model = formula.fit(original) - val result = model.transform(original) + val attr = NominalAttribute.defaultAttr val expected = Seq( ("male", "foo", 4, Vectors.dense(0.0, 1.0, 4.0), 1.0), ("female", "bar", 4, Vectors.dense(1.0, 0.0, 4.0), 0.0), ("female", "bar", 5, Vectors.dense(1.0, 0.0, 5.0), 0.0), ("male", "baz", 5, Vectors.dense(0.0, 0.0, 5.0), 1.0) ).toDF("id", "a", "b", "features", "label") - // assert(result.schema.toString == resultSchema.toString) - assert(result.collect() === expected.collect()) + .select($"id", $"a", $"b", $"features", $"label".as("label", attr.toMetadata())) + testRFormulaTransform[(String, String, Int)](original, model, expected) } test("force to index label even it is numeric type") { @@ -319,15 +322,15 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul Seq((1.0, "foo", 4), (1.0, "bar", 4), (0.0, "bar", 5), (1.0, "baz", 5)) ).toDF("id", "a", "b") val model = formula.fit(original) - val result = model.transform(original) - val expected = spark.createDataFrame( - Seq( + val attr = NominalAttribute.defaultAttr + val expected = Seq( (1.0, "foo", 4, Vectors.dense(0.0, 1.0, 4.0), 0.0), (1.0, "bar", 4, Vectors.dense(1.0, 0.0, 4.0), 0.0), (0.0, "bar", 5, Vectors.dense(1.0, 0.0, 5.0), 1.0), (1.0, "baz", 5, Vectors.dense(0.0, 0.0, 5.0), 0.0)) - ).toDF("id", "a", "b", "features", "label") - assert(result.collect() === expected.collect()) + .toDF("id", "a", "b", "features", "label") + .select($"id", $"a", $"b", $"features", $"label".as("label", attr.toMetadata())) + testRFormulaTransform[(Double, String, Int)](original, model, expected) } test("attribute generation") { @@ -335,15 +338,20 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val original = Seq((1, "foo", 4), (2, "bar", 4), (3, "bar", 5), (4, "baz", 5)) .toDF("id", "a", "b") val model = formula.fit(original) - val result = model.transform(original) - val attrs = AttributeGroup.fromStructField(result.schema("features")) + val expected = Seq( + (1, "foo", 4, Vectors.dense(0.0, 1.0, 4.0), 1.0), + (2, "bar", 4, Vectors.dense(1.0, 0.0, 4.0), 2.0), + (3, "bar", 5, Vectors.dense(1.0, 0.0, 5.0), 3.0), + (4, "baz", 5, Vectors.dense(0.0, 0.0, 5.0), 4.0)) + .toDF("id", "a", "b", "features", "label") val expectedAttrs = new AttributeGroup( "features", Array( new BinaryAttribute(Some("a_bar"), Some(1)), new BinaryAttribute(Some("a_foo"), Some(2)), new NumericAttribute(Some("b"), Some(3)))) - assert(attrs === expectedAttrs) + testRFormulaTransform[(Int, String, Int)](original, model, expected, expectedAttrs) + } test("vector attribute generation") { @@ -351,14 +359,19 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val original = Seq((1, Vectors.dense(0.0, 1.0)), (2, Vectors.dense(1.0, 2.0))) .toDF("id", "vec") val model = formula.fit(original) - val result = model.transform(original) - val attrs = AttributeGroup.fromStructField(result.schema("features")) + val attrs = new AttributeGroup("vec", 2) + val expected = Seq( + (1, Vectors.dense(0.0, 1.0), Vectors.dense(0.0, 1.0), 1.0), + (2, Vectors.dense(1.0, 2.0), Vectors.dense(1.0, 2.0), 2.0)) + .toDF("id", "vec", "features", "label") + .select($"id", $"vec".as("vec", attrs.toMetadata()), $"features", $"label") val expectedAttrs = new AttributeGroup( "features", Array[Attribute]( new NumericAttribute(Some("vec_0"), Some(1)), new NumericAttribute(Some("vec_1"), Some(2)))) - assert(attrs === expectedAttrs) + + testRFormulaTransform[(Int, Vector)](original, model, expected, expectedAttrs) } test("vector attribute generation with unnamed input attrs") { @@ -372,31 +385,31 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul NumericAttribute.defaultAttr)).toMetadata() val original = base.select(base.col("id"), base.col("vec").as("vec2", metadata)) val model = formula.fit(original) - val result = model.transform(original) - val attrs = AttributeGroup.fromStructField(result.schema("features")) + val expected = Seq( + (1, Vectors.dense(0.0, 1.0), Vectors.dense(0.0, 1.0), 1.0), + (2, Vectors.dense(1.0, 2.0), Vectors.dense(1.0, 2.0), 2.0) + ).toDF("id", "vec2", "features", "label") + .select($"id", $"vec2".as("vec2", metadata), $"features", $"label") val expectedAttrs = new AttributeGroup( "features", Array[Attribute]( new NumericAttribute(Some("vec2_0"), Some(1)), new NumericAttribute(Some("vec2_1"), Some(2)))) - assert(attrs === expectedAttrs) + testRFormulaTransform[(Int, Vector)](original, model, expected, expectedAttrs) } test("numeric interaction") { val formula = new RFormula().setFormula("a ~ b:c:d") val original = Seq((1, 2, 4, 2), (2, 3, 4, 1)).toDF("a", "b", "c", "d") val model = formula.fit(original) - val result = model.transform(original) val expected = Seq( (1, 2, 4, 2, Vectors.dense(16.0), 1.0), (2, 3, 4, 1, Vectors.dense(12.0), 2.0) ).toDF("a", "b", "c", "d", "features", "label") - assert(result.collect() === expected.collect()) - val attrs = AttributeGroup.fromStructField(result.schema("features")) val expectedAttrs = new AttributeGroup( "features", Array[Attribute](new NumericAttribute(Some("b:c:d"), Some(1)))) - assert(attrs === expectedAttrs) + testRFormulaTransform[(Int, Int, Int, Int)](original, model, expected, expectedAttrs) } test("factor numeric interaction") { @@ -405,7 +418,6 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul Seq((1, "foo", 4), (2, "bar", 4), (3, "bar", 5), (4, "baz", 5), (4, "baz", 5), (4, "baz", 5)) .toDF("id", "a", "b") val model = formula.fit(original) - val result = model.transform(original) val expected = Seq( (1, "foo", 4, Vectors.dense(0.0, 0.0, 4.0), 1.0), (2, "bar", 4, Vectors.dense(0.0, 4.0, 0.0), 2.0), @@ -414,15 +426,13 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul (4, "baz", 5, Vectors.dense(5.0, 0.0, 0.0), 4.0), (4, "baz", 5, Vectors.dense(5.0, 0.0, 0.0), 4.0) ).toDF("id", "a", "b", "features", "label") - assert(result.collect() === expected.collect()) - val attrs = AttributeGroup.fromStructField(result.schema("features")) val expectedAttrs = new AttributeGroup( "features", Array[Attribute]( new NumericAttribute(Some("a_baz:b"), Some(1)), new NumericAttribute(Some("a_bar:b"), Some(2)), new NumericAttribute(Some("a_foo:b"), Some(3)))) - assert(attrs === expectedAttrs) + testRFormulaTransform[(Int, String, Int)](original, model, expected, expectedAttrs) } test("factor factor interaction") { @@ -430,14 +440,12 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val original = Seq((1, "foo", "zq"), (2, "bar", "zq"), (3, "bar", "zz")).toDF("id", "a", "b") val model = formula.fit(original) - val result = model.transform(original) val expected = Seq( (1, "foo", "zq", Vectors.dense(0.0, 0.0, 1.0, 0.0), 1.0), (2, "bar", "zq", Vectors.dense(1.0, 0.0, 0.0, 0.0), 2.0), (3, "bar", "zz", Vectors.dense(0.0, 1.0, 0.0, 0.0), 3.0) ).toDF("id", "a", "b", "features", "label") - assert(result.collect() === expected.collect()) - val attrs = AttributeGroup.fromStructField(result.schema("features")) + testRFormulaTransform[(Int, String, String)](original, model, expected) val expectedAttrs = new AttributeGroup( "features", Array[Attribute]( @@ -445,7 +453,7 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul new NumericAttribute(Some("a_bar:b_zz"), Some(2)), new NumericAttribute(Some("a_foo:b_zq"), Some(3)), new NumericAttribute(Some("a_foo:b_zz"), Some(4)))) - assert(attrs === expectedAttrs) + testRFormulaTransform[(Int, String, String)](original, model, expected, expectedAttrs) } test("read/write: RFormula") { @@ -508,11 +516,13 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul // Handle unseen features. val formula1 = new RFormula().setFormula("id ~ a + b") - intercept[SparkException] { - formula1.fit(df1).transform(df2).collect() - } - val result1 = formula1.setHandleInvalid("skip").fit(df1).transform(df2) - val result2 = formula1.setHandleInvalid("keep").fit(df1).transform(df2) + testTransformerByInterceptingException[(Int, String, String)]( + df2, + formula1.fit(df1), + "Unseen label:", + "features") + val model1 = formula1.setHandleInvalid("skip").fit(df1) + val model2 = formula1.setHandleInvalid("keep").fit(df1) val expected1 = Seq( (1, "foo", "zq", Vectors.dense(0.0, 1.0), 1.0), @@ -524,28 +534,62 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul (3, "bar", "zy", Vectors.dense(1.0, 0.0, 0.0, 0.0), 3.0) ).toDF("id", "a", "b", "features", "label") - assert(result1.collect() === expected1.collect()) - assert(result2.collect() === expected2.collect()) + testRFormulaTransform[(Int, String, String)](df2, model1, expected1) + testRFormulaTransform[(Int, String, String)](df2, model2, expected2) // Handle unseen labels. val formula2 = new RFormula().setFormula("b ~ a + id") - intercept[SparkException] { - formula2.fit(df1).transform(df2).collect() - } - val result3 = formula2.setHandleInvalid("skip").fit(df1).transform(df2) - val result4 = formula2.setHandleInvalid("keep").fit(df1).transform(df2) + testTransformerByInterceptingException[(Int, String, String)]( + df2, + formula2.fit(df1), + "Unseen label:", + "label") + val model3 = formula2.setHandleInvalid("skip").fit(df1) + val model4 = formula2.setHandleInvalid("keep").fit(df1) + + val attr = NominalAttribute.defaultAttr val expected3 = Seq( (1, "foo", "zq", Vectors.dense(0.0, 1.0), 0.0), (2, "bar", "zq", Vectors.dense(1.0, 2.0), 0.0) ).toDF("id", "a", "b", "features", "label") + .select($"id", $"a", $"b", $"features", $"label".as("label", attr.toMetadata())) + val expected4 = Seq( (1, "foo", "zq", Vectors.dense(0.0, 1.0, 1.0), 0.0), (2, "bar", "zq", Vectors.dense(1.0, 0.0, 2.0), 0.0), (3, "bar", "zy", Vectors.dense(1.0, 0.0, 3.0), 2.0) ).toDF("id", "a", "b", "features", "label") + .select($"id", $"a", $"b", $"features", $"label".as("label", attr.toMetadata())) + + testRFormulaTransform[(Int, String, String)](df2, model3, expected3) + testRFormulaTransform[(Int, String, String)](df2, model4, expected4) + } + + test("Use Vectors as inputs to formula.") { + val original = Seq( + (1, 4, Vectors.dense(0.0, 0.0, 4.0)), + (2, 4, Vectors.dense(1.0, 0.0, 4.0)), + (3, 5, Vectors.dense(1.0, 0.0, 5.0)), + (4, 5, Vectors.dense(0.0, 1.0, 5.0)) + ).toDF("id", "a", "b") + val formula = new RFormula().setFormula("id ~ a + b") + val (first +: rest) = Seq("id", "a", "b", "features", "label") + testTransformer[(Int, Int, Vector)](original, formula.fit(original), first, rest: _*) { + case Row(id: Int, a: Int, b: Vector, features: Vector, label: Double) => + assert(label === id) + assert(features.toArray === a +: b.toArray) + } - assert(result3.collect() === expected3.collect()) - assert(result4.collect() === expected4.collect()) + val group = new AttributeGroup("b", 3) + val vectorColWithMetadata = original("b").as("b", group.toMetadata()) + val dfWithMetadata = original.withColumn("b", vectorColWithMetadata) + val model = formula.fit(dfWithMetadata) + // model should work even when applied to dataframe without metadata. + testTransformer[(Int, Int, Vector)](original, model, first, rest: _*) { + case Row(id: Int, a: Int, b: Vector, features: Vector, label: Double) => + assert(label === id) + assert(features.toArray === a +: b.toArray) + } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala index 673a146e619f2..cf09418d8e0a2 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala @@ -17,15 +17,12 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.sql.types.{LongType, StructField, StructType} import org.apache.spark.storage.StorageLevel -class SQLTransformerSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class SQLTransformerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -37,14 +34,22 @@ class SQLTransformerSuite val original = Seq((0, 1.0, 3.0), (2, 2.0, 5.0)).toDF("id", "v1", "v2") val sqlTrans = new SQLTransformer().setStatement( "SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__") - val result = sqlTrans.transform(original) - val resultSchema = sqlTrans.transformSchema(original.schema) - val expected = Seq((0, 1.0, 3.0, 4.0, 3.0), (2, 2.0, 5.0, 7.0, 10.0)) + val expected = Seq((0, 1.0, 3.0, 4.0, 3.0), (2, 2.0, 5.0, 7.0, 10.0)) .toDF("id", "v1", "v2", "v3", "v4") - assert(result.schema.toString == resultSchema.toString) - assert(resultSchema == expected.schema) - assert(result.collect().toSeq == expected.collect().toSeq) - assert(original.sparkSession.catalog.listTables().count() == 0) + val resultSchema = sqlTrans.transformSchema(original.schema) + testTransformerByGlobalCheckFunc[(Int, Double, Double)]( + original, + sqlTrans, + "id", + "v1", + "v2", + "v3", + "v4") { rows => + assert(rows.head.schema.toString == resultSchema.toString) + assert(resultSchema == expected.schema) + assert(rows == expected.collect().toSeq) + assert(original.sparkSession.catalog.listTables().count() == 0) + } } test("read/write") { @@ -63,13 +68,13 @@ class SQLTransformerSuite } test("SPARK-22538: SQLTransformer should not unpersist given dataset") { - val df = spark.range(10) + val df = spark.range(10).toDF() df.cache() df.count() assert(df.storageLevel != StorageLevel.NONE) - new SQLTransformer() + val sqlTrans = new SQLTransformer() .setStatement("SELECT id + 1 AS id1 FROM __THIS__") - .transform(df) + testTransformerByGlobalCheckFunc[Long](df, sqlTrans, "id1") { _ => } assert(df.storageLevel != StorageLevel.NONE) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala index 350ba44baa1eb..c5c49d67194e4 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala @@ -17,16 +17,13 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{DataFrame, Row} -class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext - with DefaultReadWriteTest { +class StandardScalerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -60,12 +57,10 @@ class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext ) } - def assertResult(df: DataFrame): Unit = { - df.select("standardized_features", "expected").collect().foreach { - case Row(vector1: Vector, vector2: Vector) => - assert(vector1 ~== vector2 absTol 1E-5, - "The vector value is not correct after standardization.") - } + def assertResult: Row => Unit = { + case Row(vector1: Vector, vector2: Vector) => + assert(vector1 ~== vector2 absTol 1E-5, + "The vector value is not correct after standardization.") } test("params") { @@ -83,7 +78,8 @@ class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext val standardScaler0 = standardScalerEst0.fit(df0) MLTestingUtils.checkCopyAndUids(standardScalerEst0, standardScaler0) - assertResult(standardScaler0.transform(df0)) + testTransformer[(Vector, Vector)](df0, standardScaler0, "standardized_features", "expected")( + assertResult) } test("Standardization with setter") { @@ -112,9 +108,12 @@ class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext .setWithStd(false) .fit(df3) - assertResult(standardScaler1.transform(df1)) - assertResult(standardScaler2.transform(df2)) - assertResult(standardScaler3.transform(df3)) + testTransformer[(Vector, Vector)](df1, standardScaler1, "standardized_features", "expected")( + assertResult) + testTransformer[(Vector, Vector)](df2, standardScaler2, "standardized_features", "expected")( + assertResult) + testTransformer[(Vector, Vector)](df3, standardScaler3, "standardized_features", "expected")( + assertResult) } test("sparse data and withMean") { @@ -130,7 +129,8 @@ class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext .setWithMean(true) .setWithStd(false) .fit(df) - assertResult(standardScaler.transform(df)) + testTransformer[(Vector, Vector)](df, standardScaler, "standardized_features", "expected")( + assertResult) } test("StandardScaler read/write") { @@ -149,4 +149,5 @@ class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext assert(newInstance.std === instance.std) assert(newInstance.mean === instance.mean) } + } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala index 5262b146b184e..21259a50916d2 100755 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala @@ -17,28 +17,20 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{Dataset, Row} - -object StopWordsRemoverSuite extends SparkFunSuite { - def testStopWordsRemover(t: StopWordsRemover, dataset: Dataset[_]): Unit = { - t.transform(dataset) - .select("filtered", "expected") - .collect() - .foreach { case Row(tokens, wantedTokens) => - assert(tokens === wantedTokens) - } - } -} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.{DataFrame, Row} -class StopWordsRemoverSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class StopWordsRemoverSuite extends MLTest with DefaultReadWriteTest { - import StopWordsRemoverSuite._ import testImplicits._ + def testStopWordsRemover(t: StopWordsRemover, dataFrame: DataFrame): Unit = { + testTransformer[(Array[String], Array[String])](dataFrame, t, "filtered", "expected") { + case Row(tokens: Seq[_], wantedTokens: Seq[_]) => + assert(tokens === wantedTokens) + } + } + test("StopWordsRemover default") { val remover = new StopWordsRemover() .setInputCol("raw") @@ -151,9 +143,10 @@ class StopWordsRemoverSuite .setOutputCol(outputCol) val dataSet = Seq((Seq("The", "the", "swift"), Seq("swift"))).toDF("raw", outputCol) - val thrown = intercept[IllegalArgumentException] { - testStopWordsRemover(remover, dataSet) - } - assert(thrown.getMessage == s"requirement failed: Column $outputCol already exists.") + testTransformerByInterceptingException[(Array[String], Array[String])]( + dataSet, + remover, + s"requirement failed: Column $outputCol already exists.", + "expected") } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala index 775a04d3df050..df24367177011 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala @@ -17,17 +17,14 @@ package org.apache.spark.ml.feature -import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.ml.attribute.{Attribute, NominalAttribute} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.sql.Row import org.apache.spark.sql.functions.col import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType} -class StringIndexerSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class StringIndexerSuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -46,19 +43,23 @@ class StringIndexerSuite .setInputCol("label") .setOutputCol("labelIndex") val indexerModel = indexer.fit(df) - MLTestingUtils.checkCopyAndUids(indexer, indexerModel) - - val transformed = indexerModel.transform(df) - val attr = Attribute.fromStructField(transformed.schema("labelIndex")) - .asInstanceOf[NominalAttribute] - assert(attr.values.get === Array("a", "c", "b")) - val output = transformed.select("id", "labelIndex").rdd.map { r => - (r.getInt(0), r.getDouble(1)) - }.collect().toSet // a -> 0, b -> 2, c -> 1 - val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)) - assert(output === expected) + val expected = Seq( + (0, 0.0), + (1, 2.0), + (2, 1.0), + (3, 0.0), + (4, 0.0), + (5, 1.0) + ).toDF("id", "labelIndex") + + testTransformerByGlobalCheckFunc[(Int, String)](df, indexerModel, "id", "labelIndex") { rows => + val attr = Attribute.fromStructField(rows.head.schema("labelIndex")) + .asInstanceOf[NominalAttribute] + assert(attr.values.get === Array("a", "c", "b")) + assert(rows.seq === expected.collect().toSeq) + } } test("StringIndexerUnseen") { @@ -70,36 +71,38 @@ class StringIndexerSuite .setInputCol("label") .setOutputCol("labelIndex") .fit(df) + // Verify we throw by default with unseen values - intercept[SparkException] { - indexer.transform(df2).collect() - } + testTransformerByInterceptingException[(Int, String)]( + df2, + indexer, + "Unseen label:", + "labelIndex") - indexer.setHandleInvalid("skip") // Verify that we skip the c record - val transformedSkip = indexer.transform(df2) - val attrSkip = Attribute.fromStructField(transformedSkip.schema("labelIndex")) - .asInstanceOf[NominalAttribute] - assert(attrSkip.values.get === Array("b", "a")) - val outputSkip = transformedSkip.select("id", "labelIndex").rdd.map { r => - (r.getInt(0), r.getDouble(1)) - }.collect().toSet // a -> 1, b -> 0 - val expectedSkip = Set((0, 1.0), (1, 0.0)) - assert(outputSkip === expectedSkip) + indexer.setHandleInvalid("skip") + + val expectedSkip = Seq((0, 1.0), (1, 0.0)).toDF() + testTransformerByGlobalCheckFunc[(Int, String)](df2, indexer, "id", "labelIndex") { rows => + val attrSkip = Attribute.fromStructField(rows.head.schema("labelIndex")) + .asInstanceOf[NominalAttribute] + assert(attrSkip.values.get === Array("b", "a")) + assert(rows.seq === expectedSkip.collect().toSeq) + } indexer.setHandleInvalid("keep") - // Verify that we keep the unseen records - val transformedKeep = indexer.transform(df2) - val attrKeep = Attribute.fromStructField(transformedKeep.schema("labelIndex")) - .asInstanceOf[NominalAttribute] - assert(attrKeep.values.get === Array("b", "a", "__unknown")) - val outputKeep = transformedKeep.select("id", "labelIndex").rdd.map { r => - (r.getInt(0), r.getDouble(1)) - }.collect().toSet + // a -> 1, b -> 0, c -> 2, d -> 3 - val expectedKeep = Set((0, 1.0), (1, 0.0), (2, 2.0), (3, 2.0)) - assert(outputKeep === expectedKeep) + val expectedKeep = Seq((0, 1.0), (1, 0.0), (2, 2.0), (3, 2.0)).toDF() + + // Verify that we keep the unseen records + testTransformerByGlobalCheckFunc[(Int, String)](df2, indexer, "id", "labelIndex") { rows => + val attrKeep = Attribute.fromStructField(rows.head.schema("labelIndex")) + .asInstanceOf[NominalAttribute] + assert(attrKeep.values.get === Array("b", "a", "__unknown")) + assert(rows === expectedKeep.collect().toSeq) + } } test("StringIndexer with a numeric input column") { @@ -109,16 +112,14 @@ class StringIndexerSuite .setInputCol("label") .setOutputCol("labelIndex") .fit(df) - val transformed = indexer.transform(df) - val attr = Attribute.fromStructField(transformed.schema("labelIndex")) - .asInstanceOf[NominalAttribute] - assert(attr.values.get === Array("100", "300", "200")) - val output = transformed.select("id", "labelIndex").rdd.map { r => - (r.getInt(0), r.getDouble(1)) - }.collect().toSet // 100 -> 0, 200 -> 2, 300 -> 1 - val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)) - assert(output === expected) + val expected = Seq((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)).toDF() + testTransformerByGlobalCheckFunc[(Int, String)](df, indexer, "id", "labelIndex") { rows => + val attr = Attribute.fromStructField(rows.head.schema("labelIndex")) + .asInstanceOf[NominalAttribute] + assert(attr.values.get === Array("100", "300", "200")) + assert(rows === expected.collect().toSeq) + } } test("StringIndexer with NULLs") { @@ -133,37 +134,36 @@ class StringIndexerSuite withClue("StringIndexer should throw error when setHandleInvalid=error " + "when given NULL values") { - intercept[SparkException] { - indexer.setHandleInvalid("error") - indexer.fit(df).transform(df2).collect() - } + indexer.setHandleInvalid("error") + testTransformerByInterceptingException[(Int, String)]( + df2, + indexer.fit(df), + "StringIndexer encountered NULL value.", + "labelIndex") } indexer.setHandleInvalid("skip") - val transformedSkip = indexer.fit(df).transform(df2) - val attrSkip = Attribute - .fromStructField(transformedSkip.schema("labelIndex")) - .asInstanceOf[NominalAttribute] - assert(attrSkip.values.get === Array("b", "a")) - val outputSkip = transformedSkip.select("id", "labelIndex").rdd.map { r => - (r.getInt(0), r.getDouble(1)) - }.collect().toSet + val modelSkip = indexer.fit(df) // a -> 1, b -> 0 - val expectedSkip = Set((0, 1.0), (1, 0.0)) - assert(outputSkip === expectedSkip) + val expectedSkip = Seq((0, 1.0), (1, 0.0)).toDF() + testTransformerByGlobalCheckFunc[(Int, String)](df2, modelSkip, "id", "labelIndex") { rows => + val attrSkip = + Attribute.fromStructField(rows.head.schema("labelIndex")).asInstanceOf[NominalAttribute] + assert(attrSkip.values.get === Array("b", "a")) + assert(rows === expectedSkip.collect().toSeq) + } indexer.setHandleInvalid("keep") - val transformedKeep = indexer.fit(df).transform(df2) - val attrKeep = Attribute - .fromStructField(transformedKeep.schema("labelIndex")) - .asInstanceOf[NominalAttribute] - assert(attrKeep.values.get === Array("b", "a", "__unknown")) - val outputKeep = transformedKeep.select("id", "labelIndex").rdd.map { r => - (r.getInt(0), r.getDouble(1)) - }.collect().toSet // a -> 1, b -> 0, null -> 2 - val expectedKeep = Set((0, 1.0), (1, 0.0), (3, 2.0)) - assert(outputKeep === expectedKeep) + val expectedKeep = Seq((0, 1.0), (1, 0.0), (3, 2.0)).toDF() + val modelKeep = indexer.fit(df) + testTransformerByGlobalCheckFunc[(Int, String)](df2, modelKeep, "id", "labelIndex") { rows => + val attrKeep = Attribute + .fromStructField(rows.head.schema("labelIndex")) + .asInstanceOf[NominalAttribute] + assert(attrKeep.values.get === Array("b", "a", "__unknown")) + assert(rows === expectedKeep.collect().toSeq) + } } test("StringIndexerModel should keep silent if the input column does not exist.") { @@ -171,7 +171,9 @@ class StringIndexerSuite .setInputCol("label") .setOutputCol("labelIndex") val df = spark.range(0L, 10L).toDF() - assert(indexerModel.transform(df).collect().toSet === df.collect().toSet) + testTransformerByGlobalCheckFunc[Long](df, indexerModel, "id") { rows => + assert(rows.toSet === df.collect().toSet) + } } test("StringIndexerModel can't overwrite output column") { @@ -188,9 +190,12 @@ class StringIndexerSuite .setOutputCol("indexedInput") .fit(df) - intercept[IllegalArgumentException] { - indexer.setOutputCol("output").transform(df) - } + testTransformerByInterceptingException[(Int, String)]( + df, + indexer.setOutputCol("output"), + "Output column output already exists.", + "labelIndex") + } test("StringIndexer read/write") { @@ -223,7 +228,8 @@ class StringIndexerSuite .setInputCol("index") .setOutputCol("actual") .setLabels(labels) - idxToStr0.transform(df0).select("actual", "expected").collect().foreach { + + testTransformer[(Int, String)](df0, idxToStr0, "actual", "expected") { case Row(actual, expected) => assert(actual === expected) } @@ -234,7 +240,8 @@ class StringIndexerSuite val idxToStr1 = new IndexToString() .setInputCol("indexWithAttr") .setOutputCol("actual") - idxToStr1.transform(df1).select("actual", "expected").collect().foreach { + + testTransformer[(Int, String)](df1, idxToStr1, "actual", "expected") { case Row(actual, expected) => assert(actual === expected) } @@ -252,9 +259,10 @@ class StringIndexerSuite .setInputCol("labelIndex") .setOutputCol("sameLabel") .setLabels(indexer.labels) - idx2str.transform(transformed).select("label", "sameLabel").collect().foreach { - case Row(a: String, b: String) => - assert(a === b) + + testTransformer[(Int, String, Double)](transformed, idx2str, "sameLabel", "label") { + case Row(sameLabel, label) => + assert(sameLabel === label) } } @@ -286,10 +294,11 @@ class StringIndexerSuite .setInputCol("label") .setOutputCol("labelIndex") .fit(df) - val transformed = indexer.transform(df) - val attrs = - NominalAttribute.decodeStructField(transformed.schema("labelIndex"), preserveName = true) - assert(attrs.name.nonEmpty && attrs.name.get === "labelIndex") + testTransformerByGlobalCheckFunc[(Int, String)](df, indexer, "labelIndex") { rows => + val attrs = + NominalAttribute.decodeStructField(rows.head.schema("labelIndex"), preserveName = true) + assert(attrs.name.nonEmpty && attrs.name.get === "labelIndex") + } } test("StringIndexer order types") { @@ -299,18 +308,17 @@ class StringIndexerSuite .setInputCol("label") .setOutputCol("labelIndex") - val expected = Seq(Set((0, 0.0), (1, 0.0), (2, 2.0), (3, 1.0), (4, 1.0), (5, 0.0)), - Set((0, 2.0), (1, 2.0), (2, 0.0), (3, 1.0), (4, 1.0), (5, 2.0)), - Set((0, 1.0), (1, 1.0), (2, 0.0), (3, 2.0), (4, 2.0), (5, 1.0)), - Set((0, 1.0), (1, 1.0), (2, 2.0), (3, 0.0), (4, 0.0), (5, 1.0))) + val expected = Seq(Seq((0, 0.0), (1, 0.0), (2, 2.0), (3, 1.0), (4, 1.0), (5, 0.0)), + Seq((0, 2.0), (1, 2.0), (2, 0.0), (3, 1.0), (4, 1.0), (5, 2.0)), + Seq((0, 1.0), (1, 1.0), (2, 0.0), (3, 2.0), (4, 2.0), (5, 1.0)), + Seq((0, 1.0), (1, 1.0), (2, 2.0), (3, 0.0), (4, 0.0), (5, 1.0))) var idx = 0 for (orderType <- StringIndexer.supportedStringOrderType) { - val transformed = indexer.setStringOrderType(orderType).fit(df).transform(df) - val output = transformed.select("id", "labelIndex").rdd.map { r => - (r.getInt(0), r.getDouble(1)) - }.collect().toSet - assert(output === expected(idx)) + val model = indexer.setStringOrderType(orderType).fit(df) + testTransformerByGlobalCheckFunc[(Int, String)](df, model, "id", "labelIndex") { rows => + assert(rows === expected(idx).toDF().collect().toSeq) + } idx += 1 } } @@ -328,7 +336,11 @@ class StringIndexerSuite .setOutputCol("CITYIndexed") .fit(dfNoBristol) - val dfWithIndex = model.transform(dfNoBristol) - assert(dfWithIndex.filter($"CITYIndexed" === 1.0).count == 1) + testTransformerByGlobalCheckFunc[(String, String, String)]( + dfNoBristol, + model, + "CITYIndexed") { rows => + assert(rows.toList.count(_.getDouble(0) == 1.0) === 1) + } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala index c895659a2d8be..be59b0af2c78e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala @@ -19,16 +19,14 @@ package org.apache.spark.ml.feature import scala.beans.BeanInfo -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{Dataset, Row} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.{DataFrame, Row} @BeanInfo case class TokenizerTestData(rawText: String, wantedTokens: Array[String]) -class TokenizerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class TokenizerSuite extends MLTest with DefaultReadWriteTest { test("params") { ParamsSuite.checkParams(new Tokenizer) @@ -42,12 +40,17 @@ class TokenizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defau } } -class RegexTokenizerSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class RegexTokenizerSuite extends MLTest with DefaultReadWriteTest { - import org.apache.spark.ml.feature.RegexTokenizerSuite._ import testImplicits._ + def testRegexTokenizer(t: RegexTokenizer, dataframe: DataFrame): Unit = { + testTransformer[(String, Seq[String])](dataframe, t, "tokens", "wantedTokens") { + case Row(tokens, wantedTokens) => + assert(tokens === wantedTokens) + } + } + test("params") { ParamsSuite.checkParams(new RegexTokenizer) } @@ -105,14 +108,3 @@ class RegexTokenizerSuite } } -object RegexTokenizerSuite extends SparkFunSuite { - - def testRegexTokenizer(t: RegexTokenizer, dataset: Dataset[_]): Unit = { - t.transform(dataset) - .select("tokens", "wantedTokens") - .collect() - .foreach { case Row(tokens, wantedTokens) => - assert(tokens === wantedTokens) - } - } -} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala index 69a7b75e32eb7..e5675e31bbecf 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala @@ -19,18 +19,16 @@ package org.apache.spark.ml.feature import scala.beans.{BeanInfo, BeanProperty} -import org.apache.spark.{SparkException, SparkFunSuite} +import org.apache.spark.SparkException import org.apache.spark.internal.Logging import org.apache.spark.ml.attribute._ import org.apache.spark.ml.linalg.{SparseVector, Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.{DataFrame, Row} -class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext - with DefaultReadWriteTest with Logging { +class VectorIndexerSuite extends MLTest with DefaultReadWriteTest with Logging { import testImplicits._ import VectorIndexerSuite.FeatureData @@ -128,18 +126,27 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext MLTestingUtils.checkCopyAndUids(vectorIndexer, model) - model.transform(densePoints1) // should work - model.transform(sparsePoints1) // should work + testTransformer[FeatureData](densePoints1, model, "indexed") { _ => } + testTransformer[FeatureData](sparsePoints1, model, "indexed") { _ => } + // If the data is local Dataset, it throws AssertionError directly. - intercept[AssertionError] { - model.transform(densePoints2).collect() - logInfo("Did not throw error when fit, transform were called on vectors of different lengths") + withClue("Did not throw error when fit, transform were called on " + + "vectors of different lengths") { + testTransformerByInterceptingException[FeatureData]( + densePoints2, + model, + "VectorIndexerModel expected vector of length 3 but found length 4", + "indexed") } // If the data is distributed Dataset, it throws SparkException // which is the wrapper of AssertionError. - intercept[SparkException] { - model.transform(densePoints2.repartition(2)).collect() - logInfo("Did not throw error when fit, transform were called on vectors of different lengths") + withClue("Did not throw error when fit, transform were called " + + "on vectors of different lengths") { + testTransformerByInterceptingException[FeatureData]( + densePoints2.repartition(2), + model, + "VectorIndexerModel expected vector of length 3 but found length 4", + "indexed") } intercept[SparkException] { vectorIndexer.fit(badPoints) @@ -178,46 +185,48 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext val categoryMaps = model.categoryMaps // Chose correct categorical features assert(categoryMaps.keys.toSet === categoricalFeatures) - val transformed = model.transform(data).select("indexed") - val indexedRDD: RDD[Vector] = transformed.rdd.map(_.getAs[Vector](0)) - val featureAttrs = AttributeGroup.fromStructField(transformed.schema("indexed")) - assert(featureAttrs.name === "indexed") - assert(featureAttrs.attributes.get.length === model.numFeatures) - categoricalFeatures.foreach { feature: Int => - val origValueSet = collectedData.map(_(feature)).toSet - val targetValueIndexSet = Range(0, origValueSet.size).toSet - val catMap = categoryMaps(feature) - assert(catMap.keys.toSet === origValueSet) // Correct categories - assert(catMap.values.toSet === targetValueIndexSet) // Correct category indices - if (origValueSet.contains(0.0)) { - assert(catMap(0.0) === 0) // value 0 gets index 0 - } - // Check transformed data - assert(indexedRDD.map(_(feature)).collect().toSet === targetValueIndexSet) - // Check metadata - val featureAttr = featureAttrs(feature) - assert(featureAttr.index.get === feature) - featureAttr match { - case attr: BinaryAttribute => - assert(attr.values.get === origValueSet.toArray.sorted.map(_.toString)) - case attr: NominalAttribute => - assert(attr.values.get === origValueSet.toArray.sorted.map(_.toString)) - assert(attr.isOrdinal.get === false) - case _ => - throw new RuntimeException(errMsg + s". Categorical feature $feature failed" + - s" metadata check. Found feature attribute: $featureAttr.") + testTransformerByGlobalCheckFunc[FeatureData](data, model, "indexed") { rows => + val transformed = rows.map { r => Tuple1(r.getAs[Vector](0)) }.toDF("indexed") + val indexedRDD: RDD[Vector] = transformed.rdd.map(_.getAs[Vector](0)) + val featureAttrs = AttributeGroup.fromStructField(rows.head.schema("indexed")) + assert(featureAttrs.name === "indexed") + assert(featureAttrs.attributes.get.length === model.numFeatures) + categoricalFeatures.foreach { feature: Int => + val origValueSet = collectedData.map(_(feature)).toSet + val targetValueIndexSet = Range(0, origValueSet.size).toSet + val catMap = categoryMaps(feature) + assert(catMap.keys.toSet === origValueSet) // Correct categories + assert(catMap.values.toSet === targetValueIndexSet) // Correct category indices + if (origValueSet.contains(0.0)) { + assert(catMap(0.0) === 0) // value 0 gets index 0 + } + // Check transformed data + assert(indexedRDD.map(_(feature)).collect().toSet === targetValueIndexSet) + // Check metadata + val featureAttr = featureAttrs(feature) + assert(featureAttr.index.get === feature) + featureAttr match { + case attr: BinaryAttribute => + assert(attr.values.get === origValueSet.toArray.sorted.map(_.toString)) + case attr: NominalAttribute => + assert(attr.values.get === origValueSet.toArray.sorted.map(_.toString)) + assert(attr.isOrdinal.get === false) + case _ => + throw new RuntimeException(errMsg + s". Categorical feature $feature failed" + + s" metadata check. Found feature attribute: $featureAttr.") + } } - } - // Check numerical feature metadata. - Range(0, model.numFeatures).filter(feature => !categoricalFeatures.contains(feature)) - .foreach { feature: Int => - val featureAttr = featureAttrs(feature) - featureAttr match { - case attr: NumericAttribute => - assert(featureAttr.index.get === feature) - case _ => - throw new RuntimeException(errMsg + s". Numerical feature $feature failed" + - s" metadata check. Found feature attribute: $featureAttr.") + // Check numerical feature metadata. + Range(0, model.numFeatures).filter(feature => !categoricalFeatures.contains(feature)) + .foreach { feature: Int => + val featureAttr = featureAttrs(feature) + featureAttr match { + case attr: NumericAttribute => + assert(featureAttr.index.get === feature) + case _ => + throw new RuntimeException(errMsg + s". Numerical feature $feature failed" + + s" metadata check. Found feature attribute: $featureAttr.") + } } } } catch { @@ -236,25 +245,32 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext (sparsePoints1, sparsePoints1TestInvalid))) { val vectorIndexer = getIndexer.setMaxCategories(4).setHandleInvalid("error") val model = vectorIndexer.fit(points) - intercept[SparkException] { - model.transform(pointsTestInvalid).collect() - } + testTransformerByInterceptingException[FeatureData]( + pointsTestInvalid, + model, + "VectorIndexer encountered invalid value", + "indexed") val vectorIndexer1 = getIndexer.setMaxCategories(4).setHandleInvalid("skip") val model1 = vectorIndexer1.fit(points) - val invalidTransformed1 = model1.transform(pointsTestInvalid).select("indexed") - .collect().map(_(0)) - val transformed1 = model1.transform(points).select("indexed").collect().map(_(0)) - assert(transformed1 === invalidTransformed1) - + val expected = Seq( + Vectors.dense(1.0, 2.0, 0.0), + Vectors.dense(0.0, 1.0, 2.0), + Vectors.dense(0.0, 0.0, 1.0), + Vectors.dense(1.0, 3.0, 2.0)) + testTransformerByGlobalCheckFunc[FeatureData](pointsTestInvalid, model1, "indexed") { rows => + assert(rows.map(_(0)) == expected) + } + testTransformerByGlobalCheckFunc[FeatureData](points, model1, "indexed") { rows => + assert(rows.map(_(0)) == expected) + } val vectorIndexer2 = getIndexer.setMaxCategories(4).setHandleInvalid("keep") val model2 = vectorIndexer2.fit(points) - val invalidTransformed2 = model2.transform(pointsTestInvalid).select("indexed") - .collect().map(_(0)) - assert(invalidTransformed2 === transformed1 ++ Array( - Vectors.dense(2.0, 2.0, 0.0), - Vectors.dense(0.0, 4.0, 2.0), - Vectors.dense(1.0, 3.0, 3.0)) - ) + testTransformerByGlobalCheckFunc[FeatureData](pointsTestInvalid, model2, "indexed") { rows => + assert(rows.map(_(0)) == expected ++ Array( + Vectors.dense(2.0, 2.0, 0.0), + Vectors dense(0.0, 4.0, 2.0), + Vectors.dense(1.0, 3.0, 3.0))) + } } } @@ -263,12 +279,12 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext val points = data.collect().map(_.getAs[Vector](0)) val vectorIndexer = getIndexer.setMaxCategories(maxCategories) val model = vectorIndexer.fit(data) - val indexedPoints = - model.transform(data).select("indexed").rdd.map(_.getAs[Vector](0)).collect() - points.zip(indexedPoints).foreach { - case (orig: SparseVector, indexed: SparseVector) => - assert(orig.indices.length == indexed.indices.length) - case _ => throw new UnknownError("Unit test has a bug in it.") // should never happen + testTransformerByGlobalCheckFunc[FeatureData](data, model, "indexed") { rows => + points.zip(rows.map(_(0))).foreach { + case (orig: SparseVector, indexed: SparseVector) => + assert(orig.indices.length == indexed.indices.length) + case _ => throw new UnknownError("Unit test has a bug in it.") // should never happen + } } } checkSparsity(sparsePoints1, maxCategories = 2) @@ -286,17 +302,18 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext val vectorIndexer = getIndexer.setMaxCategories(2) val model = vectorIndexer.fit(densePoints1WithMeta) // Check that ML metadata are preserved. - val indexedPoints = model.transform(densePoints1WithMeta) - val transAttributes: Array[Attribute] = - AttributeGroup.fromStructField(indexedPoints.schema("indexed")).attributes.get - featureAttributes.zip(transAttributes).foreach { case (orig, trans) => - assert(orig.name === trans.name) - (orig, trans) match { - case (orig: NumericAttribute, trans: NumericAttribute) => - assert(orig.max.nonEmpty && orig.max === trans.max) - case _ => + testTransformerByGlobalCheckFunc[FeatureData](densePoints1WithMeta, model, "indexed") { rows => + val transAttributes: Array[Attribute] = + AttributeGroup.fromStructField(rows.head.schema("indexed")).attributes.get + featureAttributes.zip(transAttributes).foreach { case (orig, trans) => + assert(orig.name === trans.name) + (orig, trans) match { + case (orig: NumericAttribute, trans: NumericAttribute) => + assert(orig.max.nonEmpty && orig.max === trans.max) + case _ => // do nothing // TODO: Once input features marked as categorical are handled correctly, check that here. + } } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSizeHintSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSizeHintSuite.scala index f6c9a76599fae..d89d10b320d84 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSizeHintSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSizeHintSuite.scala @@ -17,17 +17,15 @@ package org.apache.spark.ml.feature -import org.apache.spark.{SparkException, SparkFunSuite} import org.apache.spark.ml.Pipeline import org.apache.spark.ml.attribute.AttributeGroup import org.apache.spark.ml.linalg.{Vector, Vectors} -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} import org.apache.spark.sql.execution.streaming.MemoryStream import org.apache.spark.sql.streaming.StreamTest class VectorSizeHintSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { + extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -40,16 +38,23 @@ class VectorSizeHintSuite val data = Seq((Vectors.dense(1, 2), 0)).toDF("vector", "intValue") val noSizeTransformer = new VectorSizeHint().setInputCol("vector") - intercept[NoSuchElementException] (noSizeTransformer.transform(data)) + testTransformerByInterceptingException[(Vector, Int)]( + data, + noSizeTransformer, + "Failed to find a default value for size", + "vector") intercept[NoSuchElementException] (noSizeTransformer.transformSchema(data.schema)) val noInputColTransformer = new VectorSizeHint().setSize(2) - intercept[NoSuchElementException] (noInputColTransformer.transform(data)) + testTransformerByInterceptingException[(Vector, Int)]( + data, + noInputColTransformer, + "Failed to find a default value for inputCol", + "vector") intercept[NoSuchElementException] (noInputColTransformer.transformSchema(data.schema)) } test("Adding size to column of vectors.") { - val size = 3 val vectorColName = "vector" val denseVector = Vectors.dense(1, 2, 3) @@ -66,12 +71,15 @@ class VectorSizeHintSuite .setInputCol(vectorColName) .setSize(size) .setHandleInvalid(handleInvalid) - val withSize = transformer.transform(dataFrame) - assert( - AttributeGroup.fromStructField(withSize.schema(vectorColName)).size == size, - "Transformer did not add expected size data.") - val numRows = withSize.collect().length - assert(numRows === data.length, s"Expecting ${data.length} rows, got $numRows.") + testTransformerByGlobalCheckFunc[Tuple1[Vector]](dataFrame, transformer, vectorColName) { + rows => { + assert( + AttributeGroup.fromStructField(rows.head.schema(vectorColName)).size == size, + "Transformer did not add expected size data.") + val numRows = rows.length + assert(numRows === data.length, s"Expecting ${data.length} rows, got $numRows.") + } + } } } @@ -93,14 +101,16 @@ class VectorSizeHintSuite .setInputCol(vectorColName) .setSize(size) .setHandleInvalid(handleInvalid) - val withSize = transformer.transform(dataFrameWithMetadata) - - val newGroup = AttributeGroup.fromStructField(withSize.schema(vectorColName)) - assert(newGroup.size === size, "Column has incorrect size metadata.") - assert( - newGroup.attributes.get === group.attributes.get, - "VectorSizeHint did not preserve attributes.") - withSize.collect + testTransformerByGlobalCheckFunc[(Int, Int, Int, Vector)]( + dataFrameWithMetadata, + transformer, + vectorColName) { rows => + val newGroup = AttributeGroup.fromStructField(rows.head.schema(vectorColName)) + assert(newGroup.size === size, "Column has incorrect size metadata.") + assert( + newGroup.attributes.get === group.attributes.get, + "VectorSizeHint did not preserve attributes.") + } } } @@ -120,7 +130,11 @@ class VectorSizeHintSuite .setInputCol(vectorColName) .setSize(size) .setHandleInvalid(handleInvalid) - intercept[IllegalArgumentException](transformer.transform(dataFrameWithMetadata)) + testTransformerByInterceptingException[(Int, Int, Int, Vector)]( + dataFrameWithMetadata, + transformer, + "Trying to set size of vectors in `vector` to 4 but size already set to 3.", + vectorColName) } } @@ -136,18 +150,36 @@ class VectorSizeHintSuite .setHandleInvalid("error") .setSize(3) - intercept[SparkException](sizeHint.transform(dataWithNull).collect()) - intercept[SparkException](sizeHint.transform(dataWithShort).collect()) + testTransformerByInterceptingException[Tuple1[Vector]]( + dataWithNull, + sizeHint, + "Got null vector in VectorSizeHint", + "vector") + + testTransformerByInterceptingException[Tuple1[Vector]]( + dataWithShort, + sizeHint, + "VectorSizeHint Expecting a vector of size 3 but got 1", + "vector") sizeHint.setHandleInvalid("skip") - assert(sizeHint.transform(dataWithNull).count() === 1) - assert(sizeHint.transform(dataWithShort).count() === 1) + testTransformerByGlobalCheckFunc[Tuple1[Vector]](dataWithNull, sizeHint, "vector") { rows => + assert(rows.length === 1) + } + testTransformerByGlobalCheckFunc[Tuple1[Vector]](dataWithShort, sizeHint, "vector") { rows => + assert(rows.length === 1) + } sizeHint.setHandleInvalid("optimistic") - assert(sizeHint.transform(dataWithNull).count() === 2) - assert(sizeHint.transform(dataWithShort).count() === 2) + testTransformerByGlobalCheckFunc[Tuple1[Vector]](dataWithNull, sizeHint, "vector") { rows => + assert(rows.length === 2) + } + testTransformerByGlobalCheckFunc[Tuple1[Vector]](dataWithShort, sizeHint, "vector") { rows => + assert(rows.length === 2) + } } + test("read/write") { val sizeHint = new VectorSizeHint() .setInputCol("myInputCol") diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala index 1746ce53107c4..3d90f9d9ac764 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala @@ -17,16 +17,16 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute} import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.DefaultReadWriteTest -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.Row import org.apache.spark.sql.types.{StructField, StructType} -class VectorSlicerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class VectorSlicerSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ test("params") { val slicer = new VectorSlicer().setInputCol("feature") @@ -84,12 +84,12 @@ class VectorSlicerSuite extends SparkFunSuite with MLlibTestSparkContext with De val vectorSlicer = new VectorSlicer().setInputCol("features").setOutputCol("result") - def validateResults(df: DataFrame): Unit = { - df.select("result", "expected").collect().foreach { case Row(vec1: Vector, vec2: Vector) => + def validateResults(rows: Seq[Row]): Unit = { + rows.foreach { case Row(vec1: Vector, vec2: Vector) => assert(vec1 === vec2) } - val resultMetadata = AttributeGroup.fromStructField(df.schema("result")) - val expectedMetadata = AttributeGroup.fromStructField(df.schema("expected")) + val resultMetadata = AttributeGroup.fromStructField(rows.head.schema("result")) + val expectedMetadata = AttributeGroup.fromStructField(rows.head.schema("expected")) assert(resultMetadata.numAttributes === expectedMetadata.numAttributes) resultMetadata.attributes.get.zip(expectedMetadata.attributes.get).foreach { case (a, b) => assert(a === b) @@ -97,13 +97,16 @@ class VectorSlicerSuite extends SparkFunSuite with MLlibTestSparkContext with De } vectorSlicer.setIndices(Array(1, 4)).setNames(Array.empty) - validateResults(vectorSlicer.transform(df)) + testTransformerByGlobalCheckFunc[(Vector, Vector)](df, vectorSlicer, "result", "expected")( + validateResults) vectorSlicer.setIndices(Array(1)).setNames(Array("f4")) - validateResults(vectorSlicer.transform(df)) + testTransformerByGlobalCheckFunc[(Vector, Vector)](df, vectorSlicer, "result", "expected")( + validateResults) vectorSlicer.setIndices(Array.empty).setNames(Array("f1", "f4")) - validateResults(vectorSlicer.transform(df)) + testTransformerByGlobalCheckFunc[(Vector, Vector)](df, vectorSlicer, "result", "expected")( + validateResults) } test("read/write") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala index 6183606a7b2ac..b59c4e7967338 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala @@ -17,17 +17,17 @@ package org.apache.spark.ml.feature -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamsSuite -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.mllib.feature.{Word2VecModel => OldWord2VecModel} -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.Row import org.apache.spark.util.Utils -class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class Word2VecSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ test("params") { ParamsSuite.checkParams(new Word2Vec) @@ -36,10 +36,6 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul } test("Word2Vec") { - - val spark = this.spark - import spark.implicits._ - val sentence = "a b " * 100 + "a c " * 10 val numOfWords = sentence.split(" ").size val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" ")) @@ -70,17 +66,13 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul // These expectations are just magic values, characterizing the current // behavior. The test needs to be updated to be more general, see SPARK-11502 val magicExp = Vectors.dense(0.30153007534417237, -0.6833061711354689, 0.5116530778733167) - model.transform(docDF).select("result", "expected").collect().foreach { + testTransformer[(Seq[String], Vector)](docDF, model, "result", "expected") { case Row(vector1: Vector, vector2: Vector) => assert(vector1 ~== magicExp absTol 1E-5, "Transformed vector is different with expected.") } } test("getVectors") { - - val spark = this.spark - import spark.implicits._ - val sentence = "a b " * 100 + "a c " * 10 val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" ")) @@ -119,9 +111,6 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul test("findSynonyms") { - val spark = this.spark - import spark.implicits._ - val sentence = "a b " * 100 + "a c " * 10 val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" ")) val docDF = doc.zip(doc).toDF("text", "alsotext") @@ -154,9 +143,6 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul test("window size") { - val spark = this.spark - import spark.implicits._ - val sentence = "a q s t q s t b b b s t m s t m q " * 100 + "a c " * 10 val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" ")) val docDF = doc.zip(doc).toDF("text", "alsotext") @@ -222,12 +208,11 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul val oldModel = new OldWord2VecModel(word2VecMap) val instance = new Word2VecModel("myWord2VecModel", oldModel) val newInstance = testDefaultReadWrite(instance) - assert(newInstance.getVectors.collect() === instance.getVectors.collect()) + assert(newInstance.getVectors.collect().sortBy(_.getString(0)) === + instance.getVectors.collect().sortBy(_.getString(0))) } test("Word2Vec works with input that is non-nullable (NGram)") { - val spark = this.spark - import spark.implicits._ val sentence = "a q s t q s t b b b s t m s t m q " val docDF = sc.parallelize(Seq(sentence, sentence)).map(_.split(" ")).toDF("text") @@ -242,7 +227,7 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul .fit(ngramDF) // Just test that this transformation succeeds - model.transform(ngramDF).collect() + testTransformerByGlobalCheckFunc[(Seq[String], Seq[String])](ngramDF, model, "result") { _ => } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/image/ImageSchemaSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/image/ImageSchemaSuite.scala index dba61cd1eb1cc..a8833c615865d 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/image/ImageSchemaSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/image/ImageSchemaSuite.scala @@ -53,11 +53,11 @@ class ImageSchemaSuite extends SparkFunSuite with MLlibTestSparkContext { assert(df.count === 1) df = readImages(imagePath, null, true, -1, false, 1.0, 0) - assert(df.count === 9) + assert(df.count === 10) df = readImages(imagePath, null, true, -1, true, 1.0, 0) val countTotal = df.count - assert(countTotal === 7) + assert(countTotal === 8) df = readImages(imagePath, null, true, -1, true, 0.5, 0) // Random number about half of the size of the original dataset @@ -103,6 +103,9 @@ class ImageSchemaSuite extends SparkFunSuite with MLlibTestSparkContext { -71, -58, -56, -73, -64))), "BGRA.png" -> (("CV_8UC4", Array[Byte](-128, -128, -8, -1, -128, -128, -8, -1, -128, - -128, -8, -1, 127, 127, -9, -1, 127, 127, -9, -1))) + -128, -8, -1, 127, 127, -9, -1, 127, 127, -9, -1))), + "BGRA_alpha_60.png" -> (("CV_8UC4", + Array[Byte](-128, -128, -8, 60, -128, -128, -8, 60, -128, + -128, -8, 60, 127, 127, -9, 60, 127, 127, -9, 60))) ) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala index 85198ad4c913a..36e06091d24de 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala @@ -20,8 +20,10 @@ package org.apache.spark.ml.param import java.io.{ByteArrayOutputStream, ObjectOutputStream} import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.{Estimator, Transformer} import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.util.MyParams +import org.apache.spark.sql.Dataset class ParamsSuite extends SparkFunSuite { @@ -430,4 +432,24 @@ object ParamsSuite extends SparkFunSuite { require(copyReturnType === obj.getClass, s"${clazz.getName}.copy should return ${clazz.getName} instead of ${copyReturnType.getName}.") } + + /** + * Checks that the class throws an exception in case multiple exclusive params are set. + * The params to be checked are passed as arguments with their value. + */ + def testExclusiveParams( + model: Params, + dataset: Dataset[_], + paramsAndValues: (String, Any)*): Unit = { + val m = model.copy(ParamMap.empty) + paramsAndValues.foreach { case (paramName, paramValue) => + m.set(m.getParam(paramName), paramValue) + } + intercept[IllegalArgumentException] { + m match { + case t: Transformer => t.transform(dataset) + case e: Estimator[_] => e.fit(dataset) + } + } + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/MLTest.scala b/mllib/src/test/scala/org/apache/spark/ml/util/MLTest.scala index 17678aa611a48..23e05acd40099 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/util/MLTest.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/util/MLTest.scala @@ -22,9 +22,10 @@ import java.io.File import org.scalatest.Suite import org.apache.spark.SparkContext -import org.apache.spark.ml.{PipelineModel, Transformer} +import org.apache.spark.ml.Transformer import org.apache.spark.sql.{DataFrame, Encoder, Row} import org.apache.spark.sql.execution.streaming.MemoryStream +import org.apache.spark.sql.functions.col import org.apache.spark.sql.streaming.StreamTest import org.apache.spark.sql.test.TestSparkSession import org.apache.spark.util.Utils @@ -62,8 +63,10 @@ trait MLTest extends StreamTest with TempDirectory { self: Suite => val columnNames = dataframe.schema.fieldNames val stream = MemoryStream[A] - val streamDF = stream.toDS().toDF(columnNames: _*) - + val columnsWithMetadata = dataframe.schema.map { structField => + col(structField.name).as(structField.name, structField.metadata) + } + val streamDF = stream.toDS().toDF(columnNames: _*).select(columnsWithMetadata: _*) val data = dataframe.as[A].collect() val streamOutput = transformer.transform(streamDF) @@ -108,5 +111,35 @@ trait MLTest extends StreamTest with TempDirectory { self: Suite => otherResultCols: _*)(globalCheckFunction) testTransformerOnDF(dataframe, transformer, firstResultCol, otherResultCols: _*)(globalCheckFunction) + } + + def testTransformerByInterceptingException[A : Encoder]( + dataframe: DataFrame, + transformer: Transformer, + expectedMessagePart : String, + firstResultCol: String) { + + def hasExpectedMessageDirectly(exception: Throwable): Boolean = + exception.getMessage.contains(expectedMessagePart) + + def hasExpectedMessage(exception: Throwable): Boolean = + hasExpectedMessageDirectly(exception) || ( + exception.getCause != null && ( + hasExpectedMessageDirectly(exception.getCause) || ( + exception.getCause.getCause != null && + hasExpectedMessageDirectly(exception.getCause.getCause)))) + + withClue(s"""Expected message part "${expectedMessagePart}" is not found in DF test.""") { + val exceptionOnDf = intercept[Throwable] { + testTransformerOnDF(dataframe, transformer, firstResultCol)(_ => Unit) + } + assert(hasExpectedMessage(exceptionOnDf)) + } + withClue(s"""Expected message part "${expectedMessagePart}" is not found in stream test.""") { + val exceptionOnStreamData = intercept[Throwable] { + testTransformerOnStreamData(dataframe, transformer, firstResultCol)(_ => Unit) + } + assert(hasExpectedMessage(exceptionOnStreamData)) + } } } diff --git a/pom.xml b/pom.xml index 1b37164376460..0e9354891f1a3 100644 --- a/pom.xml +++ b/pom.xml @@ -26,7 +26,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT pom Spark Project Parent POM http://spark.apache.org/ @@ -129,8 +129,8 @@ 1.2.1 10.12.1.1 - 1.8.2 - 1.4.1 + 1.8.3 + 1.4.4 nohive 1.6.0 9.3.20.v20170531 @@ -185,6 +185,10 @@ 2.8 1.8 1.0.0 + 0.8.0 ${java.home} @@ -1735,10 +1739,6 @@ org.apache.hive hive-storage-api - - io.airlift - slice - @@ -1752,6 +1752,10 @@ org.apache.hadoop hadoop-common + + org.apache.hadoop + hadoop-mapreduce-client-core + org.apache.orc orc-core diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala index 2ef0e7b40d940..adde213e361f0 100644 --- a/project/MimaBuild.scala +++ b/project/MimaBuild.scala @@ -88,7 +88,7 @@ object MimaBuild { def mimaSettings(sparkHome: File, projectRef: ProjectRef) = { val organization = "org.apache.spark" - val previousSparkVersion = "2.0.0" + val previousSparkVersion = "2.2.0" val project = projectRef.project val fullId = "spark-" + project + "_2.11" mimaDefaultSettings ++ diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 81584af6813ea..eec2e2b1757ae 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -36,6 +36,9 @@ object MimaExcludes { // Exclude rules for 2.3.x lazy val v23excludes = v22excludes ++ Seq( + // [SPARK-22897] Expose stageAttemptId in TaskContext + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.TaskContext.stageAttemptNumber"), + // SPARK-22789: Map-only continuous processing execution ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.streaming.StreamingQueryManager.startQuery$default$8"), ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.sql.streaming.StreamingQueryManager.startQuery$default$6"), @@ -95,7 +98,40 @@ object MimaExcludes { // [SPARK-21087] CrossValidator, TrainValidationSplit expose sub models after fitting: Scala ProblemFilters.exclude[FinalClassProblem]("org.apache.spark.ml.tuning.CrossValidatorModel$CrossValidatorModelWriter"), - ProblemFilters.exclude[FinalClassProblem]("org.apache.spark.ml.tuning.TrainValidationSplitModel$TrainValidationSplitModelWriter") + ProblemFilters.exclude[FinalClassProblem]("org.apache.spark.ml.tuning.TrainValidationSplitModel$TrainValidationSplitModelWriter"), + + // [SPARK-21728][CORE] Allow SparkSubmit to use Logging + ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.deploy.SparkSubmit.downloadFileList"), + ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.deploy.SparkSubmit.downloadFile"), + + // [SPARK-21714][CORE][YARN] Avoiding re-uploading remote resources in yarn client mode + ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.deploy.SparkSubmit.prepareSubmitEnvironment"), + + // [SPARK-22324][SQL][PYTHON] Upgrade Arrow to 0.8.0 + ProblemFilters.exclude[FinalMethodProblem]("org.apache.spark.network.util.AbstractFileRegion.transfered"), + + // [SPARK-20643][CORE] Add listener implementation to collect app state + ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.status.api.v1.TaskData.$default$5"), + + // [SPARK-20648][CORE] Port JobsTab and StageTab to the new UI backend + ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.status.api.v1.TaskData.$default$12"), + + // [SPARK-21462][SS] Added batchId to StreamingQueryProgress.json + // [SPARK-21409][SS] Expose state store memory usage in SQL metrics and progress updates + ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.sql.streaming.StateOperatorProgress.this"), + + // [SPARK-22278][SS] Expose current event time watermark and current processing time in GroupState + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.sql.streaming.GroupState.getCurrentWatermarkMs"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.sql.streaming.GroupState.getCurrentProcessingTimeMs"), + + // [SPARK-20542][ML][SQL] Add an API to Bucketizer that can bin multiple columns + ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.param.shared.HasOutputCols.org$apache$spark$ml$param$shared$HasOutputCols$_setter_$outputCols_="), + + // [SPARK-18619][ML] Make QuantileDiscretizer/Bucketizer/StringIndexer/RFormula inherit from HasHandleInvalid + ProblemFilters.exclude[FinalMethodProblem]("org.apache.spark.ml.feature.Bucketizer.getHandleInvalid"), + ProblemFilters.exclude[FinalMethodProblem]("org.apache.spark.ml.feature.StringIndexer.getHandleInvalid"), + ProblemFilters.exclude[FinalMethodProblem]("org.apache.spark.ml.feature.QuantileDiscretizer.getHandleInvalid"), + ProblemFilters.exclude[FinalMethodProblem]("org.apache.spark.ml.feature.StringIndexerModel.getHandleInvalid") ) // Exclude rules for 2.2.x diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 7469f11df0294..c2e5137645d76 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -728,7 +728,8 @@ object Unidoc { scalacOptions in (ScalaUnidoc, unidoc) ++= Seq( "-groups", // Group similar methods together based on the @group annotation. - "-skip-packages", "org.apache.hadoop" + "-skip-packages", "org.apache.hadoop", + "-sourcepath", (baseDirectory in ThisBuild).value.getAbsolutePath ) ++ ( // Add links to sources when generating Scaladoc for a non-snapshot release if (!isSnapshot.value) { diff --git a/python/README.md b/python/README.md index 3f17fdb98a081..61d2abf61d261 100644 --- a/python/README.md +++ b/python/README.md @@ -29,4 +29,4 @@ The Python packaging for Spark is not intended to replace all of the other use c ## Python Requirements -At its core PySpark depends on Py4J (currently version 0.10.6), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow). +At its core PySpark depends on Py4J (currently version 0.10.7), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow). diff --git a/python/docs/Makefile b/python/docs/Makefile index 09898f29950ed..b8e079483c90c 100644 --- a/python/docs/Makefile +++ b/python/docs/Makefile @@ -7,7 +7,7 @@ SPHINXBUILD ?= sphinx-build PAPER ?= BUILDDIR ?= _build -export PYTHONPATH=$(realpath ..):$(realpath ../lib/py4j-0.10.6-src.zip) +export PYTHONPATH=$(realpath ..):$(realpath ../lib/py4j-0.10.7-src.zip) # User-friendly check for sphinx-build ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) diff --git a/python/lib/py4j-0.10.6-src.zip b/python/lib/py4j-0.10.6-src.zip deleted file mode 100644 index 2f8edcc0c0b88..0000000000000 Binary files a/python/lib/py4j-0.10.6-src.zip and /dev/null differ diff --git a/python/lib/py4j-0.10.7-src.zip b/python/lib/py4j-0.10.7-src.zip new file mode 100644 index 0000000000000..128e321078793 Binary files /dev/null and b/python/lib/py4j-0.10.7-src.zip differ diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py index 4d142c91629cc..58218918693ca 100644 --- a/python/pyspark/__init__.py +++ b/python/pyspark/__init__.py @@ -54,6 +54,7 @@ from pyspark.taskcontext import TaskContext from pyspark.profiler import Profiler, BasicProfiler from pyspark.version import __version__ +from pyspark._globals import _NoValue def since(version): diff --git a/python/pyspark/_globals.py b/python/pyspark/_globals.py new file mode 100644 index 0000000000000..8e6099db09963 --- /dev/null +++ b/python/pyspark/_globals.py @@ -0,0 +1,70 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +Module defining global singleton classes. + +This module raises a RuntimeError if an attempt to reload it is made. In that +way the identities of the classes defined here are fixed and will remain so +even if pyspark itself is reloaded. In particular, a function like the following +will still work correctly after pyspark is reloaded: + + def foo(arg=pyspark._NoValue): + if arg is pyspark._NoValue: + ... + +See gh-7844 for a discussion of the reload problem that motivated this module. + +Note that this approach is taken after from NumPy. +""" + +__ALL__ = ['_NoValue'] + + +# Disallow reloading this module so as to preserve the identities of the +# classes defined here. +if '_is_loaded' in globals(): + raise RuntimeError('Reloading pyspark._globals is not allowed') +_is_loaded = True + + +class _NoValueType(object): + """Special keyword value. + + The instance of this class may be used as the default value assigned to a + deprecated keyword in order to check if it has been given a user defined + value. + + This class was copied from NumPy. + """ + __instance = None + + def __new__(cls): + # ensure that only one instance exists + if not cls.__instance: + cls.__instance = super(_NoValueType, cls).__new__(cls) + return cls.__instance + + # needed for python 2 to preserve identity through a pickle + def __reduce__(self): + return (self.__class__, ()) + + def __repr__(self): + return "" + + +_NoValue = _NoValueType() diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 24905f1c97b21..880559719d3a5 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -211,9 +211,21 @@ def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, for path in self._conf.get("spark.submit.pyFiles", "").split(","): if path != "": (dirname, filename) = os.path.split(path) - if filename[-4:].lower() in self.PACKAGE_EXTENSIONS: - self._python_includes.append(filename) - sys.path.insert(1, os.path.join(SparkFiles.getRootDirectory(), filename)) + try: + filepath = os.path.join(SparkFiles.getRootDirectory(), filename) + if not os.path.exists(filepath): + # In case of YARN with shell mode, 'spark.submit.pyFiles' files are + # not added via SparkContext.addFile. Here we check if the file exists, + # try to copy and then add it to the path. See SPARK-21945. + shutil.copyfile(path, filepath) + if filename[-4:].lower() in self.PACKAGE_EXTENSIONS: + self._python_includes.append(filename) + sys.path.insert(1, filepath) + except Exception: + warnings.warn( + "Failed to add file [%s] speficied in 'spark.submit.pyFiles' to " + "Python path:\n %s" % (path, "\n ".join(sys.path)), + RuntimeWarning) # Create a temporary directory inside spark.local.dir: local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf()) @@ -998,8 +1010,8 @@ def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False): # by runJob() in order to avoid having to pass a Python lambda into # SparkContext#runJob. mappedRDD = rdd.mapPartitions(partitionFunc) - port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions) - return list(_load_from_socket(port, mappedRDD._jrdd_deserializer)) + sock_info = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions) + return list(_load_from_socket(sock_info, mappedRDD._jrdd_deserializer)) def show_profiles(self): """ Print the profile stats to stdout """ diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py index 7f06d4288c872..e7d1e718c934a 100644 --- a/python/pyspark/daemon.py +++ b/python/pyspark/daemon.py @@ -29,7 +29,7 @@ from signal import SIGHUP, SIGTERM, SIGCHLD, SIG_DFL, SIG_IGN, SIGINT from pyspark.worker import main as worker_main -from pyspark.serializers import read_int, write_int +from pyspark.serializers import read_int, write_int, write_with_length, UTF8Deserializer def compute_real_exit_code(exit_code): @@ -40,7 +40,7 @@ def compute_real_exit_code(exit_code): return 1 -def worker(sock): +def worker(sock, authenticated): """ Called by a worker process after the fork(). """ @@ -56,6 +56,18 @@ def worker(sock): # otherwise writes also cause a seek that makes us miss data on the read side. infile = os.fdopen(os.dup(sock.fileno()), "rb", 65536) outfile = os.fdopen(os.dup(sock.fileno()), "wb", 65536) + + if not authenticated: + client_secret = UTF8Deserializer().loads(infile) + if os.environ["PYTHON_WORKER_FACTORY_SECRET"] == client_secret: + write_with_length("ok".encode("utf-8"), outfile) + outfile.flush() + else: + write_with_length("err".encode("utf-8"), outfile) + outfile.flush() + sock.close() + return 1 + exit_code = 0 try: worker_main(infile, outfile) @@ -153,8 +165,11 @@ def handle_sigterm(*args): write_int(os.getpid(), outfile) outfile.flush() outfile.close() + authenticated = False while True: - code = worker(sock) + code = worker(sock, authenticated) + if code == 0: + authenticated = True if not reuse or code: # wait for closing try: diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index 3e704fe9bf6ec..0afbe9dc6aa3e 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -21,16 +21,19 @@ import select import signal import shlex +import shutil import socket import platform +import tempfile +import time from subprocess import Popen, PIPE if sys.version >= '3': xrange = range -from py4j.java_gateway import java_import, JavaGateway, GatewayClient +from py4j.java_gateway import java_import, JavaGateway, GatewayParameters from pyspark.find_spark_home import _find_spark_home -from pyspark.serializers import read_int +from pyspark.serializers import read_int, write_with_length, UTF8Deserializer def launch_gateway(conf=None): @@ -41,6 +44,7 @@ def launch_gateway(conf=None): """ if "PYSPARK_GATEWAY_PORT" in os.environ: gateway_port = int(os.environ["PYSPARK_GATEWAY_PORT"]) + gateway_secret = os.environ["PYSPARK_GATEWAY_SECRET"] else: SPARK_HOME = _find_spark_home() # Launch the Py4j gateway using Spark's run command so that we pick up the @@ -59,40 +63,40 @@ def launch_gateway(conf=None): ]) command = command + shlex.split(submit_args) - # Start a socket that will be used by PythonGatewayServer to communicate its port to us - callback_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - callback_socket.bind(('127.0.0.1', 0)) - callback_socket.listen(1) - callback_host, callback_port = callback_socket.getsockname() - env = dict(os.environ) - env['_PYSPARK_DRIVER_CALLBACK_HOST'] = callback_host - env['_PYSPARK_DRIVER_CALLBACK_PORT'] = str(callback_port) - - # Launch the Java gateway. - # We open a pipe to stdin so that the Java gateway can die when the pipe is broken - if not on_windows: - # Don't send ctrl-c / SIGINT to the Java gateway: - def preexec_func(): - signal.signal(signal.SIGINT, signal.SIG_IGN) - proc = Popen(command, stdin=PIPE, preexec_fn=preexec_func, env=env) - else: - # preexec_fn not supported on Windows - proc = Popen(command, stdin=PIPE, env=env) - - gateway_port = None - # We use select() here in order to avoid blocking indefinitely if the subprocess dies - # before connecting - while gateway_port is None and proc.poll() is None: - timeout = 1 # (seconds) - readable, _, _ = select.select([callback_socket], [], [], timeout) - if callback_socket in readable: - gateway_connection = callback_socket.accept()[0] - # Determine which ephemeral port the server started on: - gateway_port = read_int(gateway_connection.makefile(mode="rb")) - gateway_connection.close() - callback_socket.close() - if gateway_port is None: - raise Exception("Java gateway process exited before sending the driver its port number") + # Create a temporary directory where the gateway server should write the connection + # information. + conn_info_dir = tempfile.mkdtemp() + try: + fd, conn_info_file = tempfile.mkstemp(dir=conn_info_dir) + os.close(fd) + os.unlink(conn_info_file) + + env = dict(os.environ) + env["_PYSPARK_DRIVER_CONN_INFO_PATH"] = conn_info_file + + # Launch the Java gateway. + # We open a pipe to stdin so that the Java gateway can die when the pipe is broken + if not on_windows: + # Don't send ctrl-c / SIGINT to the Java gateway: + def preexec_func(): + signal.signal(signal.SIGINT, signal.SIG_IGN) + proc = Popen(command, stdin=PIPE, preexec_fn=preexec_func, env=env) + else: + # preexec_fn not supported on Windows + proc = Popen(command, stdin=PIPE, env=env) + + # Wait for the file to appear, or for the process to exit, whichever happens first. + while not proc.poll() and not os.path.isfile(conn_info_file): + time.sleep(0.1) + + if not os.path.isfile(conn_info_file): + raise Exception("Java gateway process exited before sending its port number") + + with open(conn_info_file, "rb") as info: + gateway_port = read_int(info) + gateway_secret = UTF8Deserializer().loads(info) + finally: + shutil.rmtree(conn_info_dir) # In Windows, ensure the Java child processes do not linger after Python has exited. # In UNIX-based systems, the child process can kill itself on broken pipe (i.e. when @@ -111,7 +115,9 @@ def killChild(): atexit.register(killChild) # Connect to the gateway - gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=True) + gateway = JavaGateway( + gateway_parameters=GatewayParameters(port=gateway_port, auth_token=gateway_secret, + auto_convert=True)) # Import the classes used by PySpark java_import(gateway.jvm, "org.apache.spark.SparkConf") @@ -126,3 +132,16 @@ def killChild(): java_import(gateway.jvm, "scala.Tuple2") return gateway + + +def do_server_auth(conn, auth_secret): + """ + Performs the authentication protocol defined by the SocketAuthHelper class on the given + file-like object 'conn'. + """ + write_with_length(auth_secret.encode("utf-8"), conn) + conn.flush() + reply = UTF8Deserializer().loads(conn) + if reply != "ok": + conn.close() + raise Exception("Unexpected reply from iterator server.") diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 27ad1e80aa0d3..55d603070706b 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -1542,12 +1542,12 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, tol=1e-6, seed=None, layers=None, blockSize=128, stepSize=0.03, solver="l-bfgs", initialWeights=None, probabilityCol="probability", - rawPredicitionCol="rawPrediction"): + rawPredictionCol="rawPrediction"): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, tol=1e-6, seed=None, layers=None, blockSize=128, stepSize=0.03, \ solver="l-bfgs", initialWeights=None, probabilityCol="probability", \ - rawPredicitionCol="rawPrediction") + rawPredictionCol="rawPrediction") """ super(MultilayerPerceptronClassifier, self).__init__() self._java_obj = self._new_java_obj( @@ -1561,12 +1561,12 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, tol=1e-6, seed=None, layers=None, blockSize=128, stepSize=0.03, solver="l-bfgs", initialWeights=None, probabilityCol="probability", - rawPredicitionCol="rawPrediction"): + rawPredictionCol="rawPrediction"): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, tol=1e-6, seed=None, layers=None, blockSize=128, stepSize=0.03, \ solver="l-bfgs", initialWeights=None, probabilityCol="probability", \ - rawPredicitionCol="rawPrediction"): + rawPredictionCol="rawPrediction"): Sets params for MultilayerPerceptronClassifier. """ kwargs = self._input_kwargs diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py index aa8dbe708a115..0cbce9b40048f 100644 --- a/python/pyspark/ml/evaluation.py +++ b/python/pyspark/ml/evaluation.py @@ -334,7 +334,13 @@ class ClusteringEvaluator(JavaEvaluator, HasPredictionCol, HasFeaturesCol, .. note:: Experimental Evaluator for Clustering results, which expects two input - columns: prediction and features. + columns: prediction and features. The metric computes the Silhouette + measure using the squared Euclidean distance. + + The Silhouette is a measure for the validation of the consistency + within clusters. It ranges between 1 and -1, where a value close to + 1 means that the points in a cluster are close to the other points + in the same cluster and far from the points of the other clusters. >>> from pyspark.ml.linalg import Vectors >>> featureAndPredictions = map(lambda x: (Vectors.dense(x[0]), x[1]), diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index 13bf95cce40be..04b07e6a05481 100755 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -45,6 +45,7 @@ 'NGram', 'Normalizer', 'OneHotEncoder', + 'OneHotEncoderEstimator', 'OneHotEncoderModel', 'PCA', 'PCAModel', 'PolynomialExpansion', 'QuantileDiscretizer', @@ -740,9 +741,9 @@ class FeatureHasher(JavaTransformer, HasInputCols, HasOutputCol, HasNumFeatures, >>> df = spark.createDataFrame(data, cols) >>> hasher = FeatureHasher(inputCols=cols, outputCol="features") >>> hasher.transform(df).head().features - SparseVector(262144, {51871: 1.0, 63643: 1.0, 174475: 2.0, 253195: 1.0}) + SparseVector(262144, {174475: 2.0, 247670: 1.0, 257907: 1.0, 262126: 1.0}) >>> hasher.setCategoricalCols(["real"]).transform(df).head().features - SparseVector(262144, {51871: 1.0, 63643: 1.0, 171257: 1.0, 253195: 1.0}) + SparseVector(262144, {171257: 1.0, 247670: 1.0, 257907: 1.0, 262126: 1.0}) >>> hasherPath = temp_path + "/hasher" >>> hasher.save(hasherPath) >>> loadedHasher = FeatureHasher.load(hasherPath) @@ -1577,6 +1578,9 @@ class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, .. note:: This is different from scikit-learn's OneHotEncoder, which keeps all categories. The output vectors are sparse. + .. note:: Deprecated in 2.3.0. :py:class:`OneHotEncoderEstimator` will be renamed to + :py:class:`OneHotEncoder` and this :py:class:`OneHotEncoder` will be removed in 3.0.0. + .. seealso:: :py:class:`StringIndexer` for converting categorical values into @@ -1641,6 +1645,118 @@ def getDropLast(self): return self.getOrDefault(self.dropLast) +@inherit_doc +class OneHotEncoderEstimator(JavaEstimator, HasInputCols, HasOutputCols, HasHandleInvalid, + JavaMLReadable, JavaMLWritable): + """ + A one-hot encoder that maps a column of category indices to a column of binary vectors, with + at most a single one-value per row that indicates the input category index. + For example with 5 categories, an input value of 2.0 would map to an output vector of + `[0.0, 0.0, 1.0, 0.0]`. + The last category is not included by default (configurable via `dropLast`), + because it makes the vector entries sum up to one, and hence linearly dependent. + So an input value of 4.0 maps to `[0.0, 0.0, 0.0, 0.0]`. + + Note: This is different from scikit-learn's OneHotEncoder, which keeps all categories. + The output vectors are sparse. + + When `handleInvalid` is configured to 'keep', an extra "category" indicating invalid values is + added as last category. So when `dropLast` is true, invalid values are encoded as all-zeros + vector. + + Note: When encoding multi-column by using `inputCols` and `outputCols` params, input/output + cols come in pairs, specified by the order in the arrays, and each pair is treated + independently. + + See `StringIndexer` for converting categorical values into category indices + + >>> from pyspark.ml.linalg import Vectors + >>> df = spark.createDataFrame([(0.0,), (1.0,), (2.0,)], ["input"]) + >>> ohe = OneHotEncoderEstimator(inputCols=["input"], outputCols=["output"]) + >>> model = ohe.fit(df) + >>> model.transform(df).head().output + SparseVector(2, {0: 1.0}) + >>> ohePath = temp_path + "/oheEstimator" + >>> ohe.save(ohePath) + >>> loadedOHE = OneHotEncoderEstimator.load(ohePath) + >>> loadedOHE.getInputCols() == ohe.getInputCols() + True + >>> modelPath = temp_path + "/ohe-model" + >>> model.save(modelPath) + >>> loadedModel = OneHotEncoderModel.load(modelPath) + >>> loadedModel.categorySizes == model.categorySizes + True + + .. versionadded:: 2.3.0 + """ + + handleInvalid = Param(Params._dummy(), "handleInvalid", "How to handle invalid data during " + + "transform(). Options are 'keep' (invalid data presented as an extra " + + "categorical feature) or error (throw an error). Note that this Param " + + "is only used during transform; during fitting, invalid data will " + + "result in an error.", + typeConverter=TypeConverters.toString) + + dropLast = Param(Params._dummy(), "dropLast", "whether to drop the last category", + typeConverter=TypeConverters.toBoolean) + + @keyword_only + def __init__(self, inputCols=None, outputCols=None, handleInvalid="error", dropLast=True): + """ + __init__(self, inputCols=None, outputCols=None, handleInvalid="error", dropLast=True) + """ + super(OneHotEncoderEstimator, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.feature.OneHotEncoderEstimator", self.uid) + self._setDefault(handleInvalid="error", dropLast=True) + kwargs = self._input_kwargs + self.setParams(**kwargs) + + @keyword_only + @since("2.3.0") + def setParams(self, inputCols=None, outputCols=None, handleInvalid="error", dropLast=True): + """ + setParams(self, inputCols=None, outputCols=None, handleInvalid="error", dropLast=True) + Sets params for this OneHotEncoderEstimator. + """ + kwargs = self._input_kwargs + return self._set(**kwargs) + + @since("2.3.0") + def setDropLast(self, value): + """ + Sets the value of :py:attr:`dropLast`. + """ + return self._set(dropLast=value) + + @since("2.3.0") + def getDropLast(self): + """ + Gets the value of dropLast or its default value. + """ + return self.getOrDefault(self.dropLast) + + def _create_model(self, java_model): + return OneHotEncoderModel(java_model) + + +class OneHotEncoderModel(JavaModel, JavaMLReadable, JavaMLWritable): + """ + Model fitted by :py:class:`OneHotEncoderEstimator`. + + .. versionadded:: 2.3.0 + """ + + @property + @since("2.3.0") + def categorySizes(self): + """ + Original number of categories for each feature being encoded. + The array contains one value for each input column, in order. + """ + return self._call_java("categorySizes") + + @inherit_doc class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): @@ -3324,7 +3440,7 @@ class ChiSqSelector(JavaEstimator, HasFeaturesCol, HasOutputCol, HasLabelCol, Ja selectorType = Param(Params._dummy(), "selectorType", "The selector type of the ChisqSelector. " + - "Supported options: numTopFeatures (default), percentile and fpr.", + "Supported options: numTopFeatures (default), percentile, fpr, fdr, fwe.", typeConverter=TypeConverters.toString) numTopFeatures = \ diff --git a/python/pyspark/ml/fpm.py b/python/pyspark/ml/fpm.py index dd7dda5f03124..b8dafd49d354d 100644 --- a/python/pyspark/ml/fpm.py +++ b/python/pyspark/ml/fpm.py @@ -144,7 +144,7 @@ def freqItemsets(self): @since("2.2.0") def associationRules(self): """ - Data with three columns: + DataFrame with three columns: * `antecedent` - Array of the same type as the input column. * `consequent` - Array of the same type as the input column. * `confidence` - Confidence for the rule (`DoubleType`). diff --git a/python/pyspark/ml/image.py b/python/pyspark/ml/image.py index c9b840276f675..2d86c7f03860c 100644 --- a/python/pyspark/ml/image.py +++ b/python/pyspark/ml/image.py @@ -194,9 +194,9 @@ def readImages(self, path, recursive=False, numPartitions=-1, :return: a :class:`DataFrame` with a single column of "images", see ImageSchema for details. - >>> df = ImageSchema.readImages('python/test_support/image/kittens', recursive=True) + >>> df = ImageSchema.readImages('data/mllib/images/kittens', recursive=True) >>> df.count() - 4 + 5 .. versionadded:: 2.3.0 """ @@ -216,3 +216,25 @@ def readImages(self, path, recursive=False, numPartitions=-1, def _disallow_instance(_): raise RuntimeError("Creating instance of _ImageSchema class is disallowed.") _ImageSchema.__init__ = _disallow_instance + + +def _test(): + import doctest + import pyspark.ml.image + globs = pyspark.ml.image.__dict__.copy() + spark = SparkSession.builder\ + .master("local[2]")\ + .appName("ml.image tests")\ + .getOrCreate() + globs['spark'] = spark + + (failure_count, test_count) = doctest.testmod( + pyspark.ml.image, globs=globs, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) + spark.stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py index d55d209d09398..db951d81de1e7 100644 --- a/python/pyspark/ml/param/_shared_params_code_gen.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -119,10 +119,12 @@ def get$Name(self): ("inputCol", "input column name.", None, "TypeConverters.toString"), ("inputCols", "input column names.", None, "TypeConverters.toListString"), ("outputCol", "output column name.", "self.uid + '__output'", "TypeConverters.toString"), + ("outputCols", "output column names.", None, "TypeConverters.toListString"), ("numFeatures", "number of features.", None, "TypeConverters.toInt"), ("checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). " + - "E.g. 10 means that the cache will get checkpointed every 10 iterations.", None, - "TypeConverters.toInt"), + "E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: " + + "this setting will be ignored if the checkpoint directory is not set in the SparkContext.", + None, "TypeConverters.toInt"), ("seed", "random seed.", "hash(type(self).__name__)", "TypeConverters.toInt"), ("tol", "the convergence tolerance for iterative algorithms (>= 0).", None, "TypeConverters.toFloat"), diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index e5c5ddfba6c1f..474c38764e5a1 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -256,6 +256,29 @@ def getOutputCol(self): return self.getOrDefault(self.outputCol) +class HasOutputCols(Params): + """ + Mixin for param outputCols: output column names. + """ + + outputCols = Param(Params._dummy(), "outputCols", "output column names.", typeConverter=TypeConverters.toListString) + + def __init__(self): + super(HasOutputCols, self).__init__() + + def setOutputCols(self, value): + """ + Sets the value of :py:attr:`outputCols`. + """ + return self._set(outputCols=value) + + def getOutputCols(self): + """ + Gets the value of outputCols or its default value. + """ + return self.getOrDefault(self.outputCols) + + class HasNumFeatures(Params): """ Mixin for param numFeatures: number of features. @@ -281,10 +304,10 @@ def getNumFeatures(self): class HasCheckpointInterval(Params): """ - Mixin for param checkpointInterval: set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. + Mixin for param checkpointInterval: set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext. """ - checkpointInterval = Param(Params._dummy(), "checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations.", typeConverter=TypeConverters.toInt) + checkpointInterval = Param(Params._dummy(), "checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.", typeConverter=TypeConverters.toInt) def __init__(self): super(HasCheckpointInterval, self).__init__() diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 340bc3a6b7470..8dc30a42f74e0 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -39,9 +39,11 @@ else: from itertools import imap as map, ifilter as filter +from pyspark.java_gateway import do_server_auth from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \ BatchedSerializer, CloudPickleSerializer, PairDeserializer, \ - PickleSerializer, pack_long, AutoBatchedSerializer + PickleSerializer, pack_long, AutoBatchedSerializer, write_with_length, \ + UTF8Deserializer from pyspark.join import python_join, python_left_outer_join, \ python_right_outer_join, python_full_outer_join, python_cogroup from pyspark.statcounter import StatCounter @@ -51,6 +53,7 @@ from pyspark.shuffle import Aggregator, ExternalMerger, \ get_used_memory, ExternalSorter, ExternalGroupBy from pyspark.traceback_utils import SCCallSiteSync +from pyspark.util import fail_on_stopiteration __all__ = ["RDD"] @@ -68,8 +71,8 @@ class PythonEvalType(object): SQL_BATCHED_UDF = 100 - SQL_PANDAS_SCALAR_UDF = 200 - SQL_PANDAS_GROUP_MAP_UDF = 201 + SQL_SCALAR_PANDAS_UDF = 200 + SQL_GROUPED_MAP_PANDAS_UDF = 201 def portable_hash(x): @@ -135,7 +138,8 @@ def _parse_memory(s): return int(float(s[:-1]) * units[s[-1].lower()]) -def _load_from_socket(port, serializer): +def _load_from_socket(sock_info, serializer): + port, auth_secret = sock_info sock = None # Support for both IPv4 and IPv6. # On most of IPv6-ready systems, IPv6 will take precedence. @@ -155,8 +159,12 @@ def _load_from_socket(port, serializer): # The RDD materialization time is unpredicable, if we set a timeout for socket reading # operation, it will very possibly fail. See SPARK-18281. sock.settimeout(None) + + sockfile = sock.makefile("rwb", 65536) + do_server_auth(sockfile, auth_secret) + # The socket will be automatically closed when garbage-collected. - return serializer.load_stream(sock.makefile("rb", 65536)) + return serializer.load_stream(sockfile) def ignore_unicode_prefix(f): @@ -331,7 +339,7 @@ def map(self, f, preservesPartitioning=False): [('a', 1), ('b', 1), ('c', 1)] """ def func(_, iterator): - return map(f, iterator) + return map(fail_on_stopiteration(f), iterator) return self.mapPartitionsWithIndex(func, preservesPartitioning) def flatMap(self, f, preservesPartitioning=False): @@ -346,7 +354,7 @@ def flatMap(self, f, preservesPartitioning=False): [(2, 2), (2, 2), (3, 3), (3, 3), (4, 4), (4, 4)] """ def func(s, iterator): - return chain.from_iterable(map(f, iterator)) + return chain.from_iterable(map(fail_on_stopiteration(f), iterator)) return self.mapPartitionsWithIndex(func, preservesPartitioning) def mapPartitions(self, f, preservesPartitioning=False): @@ -409,7 +417,7 @@ def filter(self, f): [2, 4] """ def func(iterator): - return filter(f, iterator) + return filter(fail_on_stopiteration(f), iterator) return self.mapPartitions(func, True) def distinct(self, numPartitions=None): @@ -790,6 +798,8 @@ def foreach(self, f): >>> def f(x): print(x) >>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f) """ + f = fail_on_stopiteration(f) + def processPartition(iterator): for x in iterator: f(x) @@ -821,8 +831,8 @@ def collect(self): to be small, as all the data is loaded into the driver's memory. """ with SCCallSiteSync(self.context) as css: - port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) - return list(_load_from_socket(port, self._jrdd_deserializer)) + sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) + return list(_load_from_socket(sock_info, self._jrdd_deserializer)) def reduce(self, f): """ @@ -839,6 +849,8 @@ def reduce(self, f): ... ValueError: Can not reduce() empty RDD """ + f = fail_on_stopiteration(f) + def func(iterator): iterator = iter(iterator) try: @@ -910,6 +922,8 @@ def fold(self, zeroValue, op): >>> sc.parallelize([1, 2, 3, 4, 5]).fold(0, add) 15 """ + op = fail_on_stopiteration(op) + def func(iterator): acc = zeroValue for obj in iterator: @@ -942,6 +956,9 @@ def aggregate(self, zeroValue, seqOp, combOp): >>> sc.parallelize([]).aggregate((0, 0), seqOp, combOp) (0, 0) """ + seqOp = fail_on_stopiteration(seqOp) + combOp = fail_on_stopiteration(combOp) + def func(iterator): acc = zeroValue for obj in iterator: @@ -1635,6 +1652,8 @@ def reduceByKeyLocally(self, func): >>> sorted(rdd.reduceByKeyLocally(add).items()) [('a', 2), ('b', 1)] """ + func = fail_on_stopiteration(func) + def reducePartition(iterator): m = {} for k, v in iterator: @@ -2379,8 +2398,8 @@ def toLocalIterator(self): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] """ with SCCallSiteSync(self.context) as css: - port = self.ctx._jvm.PythonRDD.toLocalIteratorAndServe(self._jrdd.rdd()) - return _load_from_socket(port, self._jrdd_deserializer) + sock_info = self.ctx._jvm.PythonRDD.toLocalIteratorAndServe(self._jrdd.rdd()) + return _load_from_socket(sock_info, self._jrdd_deserializer) def _prepare_for_python_RDD(sc, command): diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 88d6a191babca..6d107f3069dc5 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -33,8 +33,9 @@ [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] >>> sc.stop() -PySpark serialize objects in batches; By default, the batch size is chosen based -on the size of objects, also configurable by SparkContext's C{batchSize} parameter: +PySpark serializes objects in batches; by default, the batch size is chosen based +on the size of objects and is also configurable by SparkContext's C{batchSize} +parameter: >>> sc = SparkContext('local', 'test', batchSize=2) >>> rdd = sc.parallelize(range(16), 4).map(lambda x: x) @@ -99,7 +100,7 @@ def load_stream(self, stream): def _load_stream_without_unbatching(self, stream): """ Return an iterator of deserialized batches (iterable) of objects from the input stream. - if the serializer does not operate on batches the default implementation returns an + If the serializer does not operate on batches the default implementation returns an iterator of single element lists. """ return map(lambda x: [x], self.load_stream(stream)) @@ -230,6 +231,10 @@ def create_array(s, t): s = _check_series_convert_timestamps_internal(s.fillna(0), timezone) # TODO: need cast after Arrow conversion, ns values cause error with pandas 0.19.2 return pa.Array.from_pandas(s, mask=mask).cast(t, safe=False) + elif t is not None and pa.types.is_string(t) and sys.version < '3': + # TODO: need decode before converting to Arrow in Python 2 + return pa.Array.from_pandas(s.apply( + lambda v: v.decode("utf-8") if isinstance(v, str) else v), mask=mask, type=t) return pa.Array.from_pandas(s, mask=mask, type=t) arrs = [create_array(s, t) for s, t in series] @@ -267,12 +272,15 @@ def load_stream(self, stream): """ Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series. """ - from pyspark.sql.types import _check_dataframe_localize_timestamps + from pyspark.sql.types import from_arrow_schema, _check_dataframe_convert_date, \ + _check_dataframe_localize_timestamps import pyarrow as pa reader = pa.open_stream(stream) + schema = from_arrow_schema(reader.schema) for batch in reader: - # NOTE: changed from pa.Columns.to_pandas, timezone issue in conversion fixed in 0.7.1 - pdf = _check_dataframe_localize_timestamps(batch.to_pandas(), self._timezone) + pdf = batch.to_pandas() + pdf = _check_dataframe_convert_date(pdf, schema) + pdf = _check_dataframe_localize_timestamps(pdf, self._timezone) yield [c for _, c in pdf.iteritems()] def __repr__(self): @@ -449,7 +457,7 @@ def dumps(self, obj): return obj -# Hook namedtuple, make it picklable +# Hack namedtuple, make it picklable __cls = {} @@ -513,15 +521,15 @@ def namedtuple(*args, **kwargs): cls = _old_namedtuple(*args, **kwargs) return _hack_namedtuple(cls) - # replace namedtuple with new one + # replace namedtuple with the new one collections.namedtuple.__globals__["_old_namedtuple_kwdefaults"] = _old_namedtuple_kwdefaults collections.namedtuple.__globals__["_old_namedtuple"] = _old_namedtuple collections.namedtuple.__globals__["_hack_namedtuple"] = _hack_namedtuple collections.namedtuple.__code__ = namedtuple.__code__ collections.namedtuple.__hijack = 1 - # hack the cls already generated by namedtuple - # those created in other module can be pickled as normal, + # hack the cls already generated by namedtuple. + # Those created in other modules can be pickled as normal, # so only hack those in __main__ module for n, o in sys.modules["__main__"].__dict__.items(): if (type(o) is type and o.__base__ is tuple @@ -604,7 +612,7 @@ def loads(self, obj): elif _type == b'P': return pickle.loads(obj[1:]) else: - raise ValueError("invalid sevialization type: %s" % _type) + raise ValueError("invalid serialization type: %s" % _type) class CompressedSerializer(FramedSerializer): diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py index e974cda9fc3e1..68f9df7e8b2a9 100644 --- a/python/pyspark/shuffle.py +++ b/python/pyspark/shuffle.py @@ -27,6 +27,7 @@ import pyspark.heapq3 as heapq from pyspark.serializers import BatchedSerializer, PickleSerializer, FlattenedValuesSerializer, \ CompressedSerializer, AutoBatchedSerializer +from pyspark.util import fail_on_stopiteration try: @@ -93,9 +94,9 @@ class Aggregator(object): """ def __init__(self, createCombiner, mergeValue, mergeCombiners): - self.createCombiner = createCombiner - self.mergeValue = mergeValue - self.mergeCombiners = mergeCombiners + self.createCombiner = fail_on_stopiteration(createCombiner) + self.mergeValue = fail_on_stopiteration(mergeValue) + self.mergeCombiners = fail_on_stopiteration(mergeCombiners) class SimpleAggregator(Aggregator): diff --git a/python/pyspark/sql/catalog.py b/python/pyspark/sql/catalog.py index 659bc65701a0c..6aef0f22340be 100644 --- a/python/pyspark/sql/catalog.py +++ b/python/pyspark/sql/catalog.py @@ -224,42 +224,17 @@ def dropGlobalTempView(self, viewName): """ self._jcatalog.dropGlobalTempView(viewName) - @ignore_unicode_prefix @since(2.0) - def registerFunction(self, name, f, returnType=StringType()): - """Registers a python function (including lambda function) as a UDF - so it can be used in SQL statements. - - In addition to a name and the function itself, the return type can be optionally specified. - When the return type is not given it default to a string and conversion will automatically - be done. For any other return type, the produced object must match the specified type. - - :param name: name of the UDF - :param f: python function - :param returnType: a :class:`pyspark.sql.types.DataType` object - :return: a wrapped :class:`UserDefinedFunction` - - >>> strlen = spark.catalog.registerFunction("stringLengthString", len) - >>> spark.sql("SELECT stringLengthString('test')").collect() - [Row(stringLengthString(test)=u'4')] - - >>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect() - [Row(stringLengthString(text)=u'3')] - - >>> from pyspark.sql.types import IntegerType - >>> _ = spark.catalog.registerFunction("stringLengthInt", len, IntegerType()) - >>> spark.sql("SELECT stringLengthInt('test')").collect() - [Row(stringLengthInt(test)=4)] - - >>> from pyspark.sql.types import IntegerType - >>> _ = spark.udf.register("stringLengthInt", len, IntegerType()) - >>> spark.sql("SELECT stringLengthInt('test')").collect() - [Row(stringLengthInt(test)=4)] + def registerFunction(self, name, f, returnType=None): + """An alias for :func:`spark.udf.register`. + See :meth:`pyspark.sql.UDFRegistration.register`. + + .. note:: Deprecated in 2.3.0. Use :func:`spark.udf.register` instead. """ - udf = UserDefinedFunction(f, returnType=returnType, name=name, - evalType=PythonEvalType.SQL_BATCHED_UDF) - self._jsparkSession.udf().registerPython(name, udf._judf) - return udf._wrapped() + warnings.warn( + "Deprecated in 2.3.0. Use spark.udf.register instead.", + DeprecationWarning) + return self._sparkSession.udf.register(name, f, returnType) @since(2.0) def isCached(self, tableName): diff --git a/python/pyspark/sql/conf.py b/python/pyspark/sql/conf.py index 792c420ca6386..d62ab91ef43f1 100644 --- a/python/pyspark/sql/conf.py +++ b/python/pyspark/sql/conf.py @@ -15,7 +15,9 @@ # limitations under the License. # -from pyspark import since +import sys + +from pyspark import since, _NoValue from pyspark.rdd import ignore_unicode_prefix @@ -37,15 +39,16 @@ def set(self, key, value): @ignore_unicode_prefix @since(2.0) - def get(self, key, default=None): + def get(self, key, default=_NoValue): """Returns the value of Spark runtime configuration property for the given key, assuming it is set. """ self._checkType(key, "key") - if default is None: + if default is _NoValue: return self._jconf.get(key) else: - self._checkType(default, "default") + if default is not None: + self._checkType(default, "default") return self._jconf.get(key, default) @ignore_unicode_prefix diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index b1e723cdecef3..673415f864343 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -22,16 +22,17 @@ if sys.version >= '3': basestring = unicode = str -from pyspark import since +from pyspark import since, _NoValue from pyspark.rdd import ignore_unicode_prefix from pyspark.sql.session import _monkey_patch_RDD, SparkSession from pyspark.sql.dataframe import DataFrame from pyspark.sql.readwriter import DataFrameReader from pyspark.sql.streaming import DataStreamReader from pyspark.sql.types import IntegerType, Row, StringType +from pyspark.sql.udf import UDFRegistration from pyspark.sql.utils import install_exception_handler -__all__ = ["SQLContext", "HiveContext", "UDFRegistration"] +__all__ = ["SQLContext", "HiveContext"] class SQLContext(object): @@ -123,11 +124,11 @@ def setConf(self, key, value): @ignore_unicode_prefix @since(1.3) - def getConf(self, key, defaultValue=None): + def getConf(self, key, defaultValue=_NoValue): """Returns the value of Spark SQL configuration property for the given key. - If the key is not set and defaultValue is not None, return - defaultValue. If the key is not set and defaultValue is None, return + If the key is not set and defaultValue is set, return + defaultValue. If the key is not set and defaultValue is not set, return the system default value. >>> sqlContext.getConf("spark.sql.shuffle.partitions") @@ -147,7 +148,7 @@ def udf(self): :return: :class:`UDFRegistration` """ - return UDFRegistration(self) + return self.sparkSession.udf @since(1.4) def range(self, start, end=None, step=1, numPartitions=None): @@ -172,82 +173,29 @@ def range(self, start, end=None, step=1, numPartitions=None): """ return self.sparkSession.range(start, end, step, numPartitions) - @ignore_unicode_prefix @since(1.2) - def registerFunction(self, name, f, returnType=StringType()): - """Registers a python function (including lambda function) as a UDF - so it can be used in SQL statements. - - In addition to a name and the function itself, the return type can be optionally specified. - When the return type is not given it default to a string and conversion will automatically - be done. For any other return type, the produced object must match the specified type. - - :param name: name of the UDF - :param f: python function - :param returnType: a :class:`pyspark.sql.types.DataType` object - :return: a wrapped :class:`UserDefinedFunction` - - >>> strlen = sqlContext.registerFunction("stringLengthString", lambda x: len(x)) - >>> sqlContext.sql("SELECT stringLengthString('test')").collect() - [Row(stringLengthString(test)=u'4')] - - >>> sqlContext.sql("SELECT 'foo' AS text").select(strlen("text")).collect() - [Row(stringLengthString(text)=u'3')] - - >>> from pyspark.sql.types import IntegerType - >>> _ = sqlContext.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) - >>> sqlContext.sql("SELECT stringLengthInt('test')").collect() - [Row(stringLengthInt(test)=4)] - - >>> from pyspark.sql.types import IntegerType - >>> _ = sqlContext.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) - >>> sqlContext.sql("SELECT stringLengthInt('test')").collect() - [Row(stringLengthInt(test)=4)] + def registerFunction(self, name, f, returnType=None): + """An alias for :func:`spark.udf.register`. + See :meth:`pyspark.sql.UDFRegistration.register`. + + .. note:: Deprecated in 2.3.0. Use :func:`spark.udf.register` instead. """ - return self.sparkSession.catalog.registerFunction(name, f, returnType) + warnings.warn( + "Deprecated in 2.3.0. Use spark.udf.register instead.", + DeprecationWarning) + return self.sparkSession.udf.register(name, f, returnType) - @ignore_unicode_prefix @since(2.1) def registerJavaFunction(self, name, javaClassName, returnType=None): - """Register a java UDF so it can be used in SQL statements. - - In addition to a name and the function itself, the return type can be optionally specified. - When the return type is not specified we would infer it via reflection. - :param name: name of the UDF - :param javaClassName: fully qualified name of java class - :param returnType: a :class:`pyspark.sql.types.DataType` object - - >>> sqlContext.registerJavaFunction("javaStringLength", - ... "test.org.apache.spark.sql.JavaStringLength", IntegerType()) - >>> sqlContext.sql("SELECT javaStringLength('test')").collect() - [Row(UDF:javaStringLength(test)=4)] - >>> sqlContext.registerJavaFunction("javaStringLength2", - ... "test.org.apache.spark.sql.JavaStringLength") - >>> sqlContext.sql("SELECT javaStringLength2('test')").collect() - [Row(UDF:javaStringLength2(test)=4)] + """An alias for :func:`spark.udf.registerJavaFunction`. + See :meth:`pyspark.sql.UDFRegistration.registerJavaFunction`. + .. note:: Deprecated in 2.3.0. Use :func:`spark.udf.registerJavaFunction` instead. """ - jdt = None - if returnType is not None: - jdt = self.sparkSession._jsparkSession.parseDataType(returnType.json()) - self.sparkSession._jsparkSession.udf().registerJava(name, javaClassName, jdt) - - @ignore_unicode_prefix - @since(2.3) - def registerJavaUDAF(self, name, javaClassName): - """Register a java UDAF so it can be used in SQL statements. - - :param name: name of the UDAF - :param javaClassName: fully qualified name of java class - - >>> sqlContext.registerJavaUDAF("javaUDAF", - ... "test.org.apache.spark.sql.MyDoubleAvg") - >>> df = sqlContext.createDataFrame([(1, "a"),(2, "b"), (3, "a")],["id", "name"]) - >>> df.registerTempTable("df") - >>> sqlContext.sql("SELECT name, javaUDAF(id) as avg from df group by name").collect() - [Row(name=u'b', avg=102.0), Row(name=u'a', avg=102.0)] - """ - self.sparkSession._jsparkSession.udf().registerJavaUDAF(name, javaClassName) + warnings.warn( + "Deprecated in 2.3.0. Use spark.udf.registerJavaFunction instead.", + DeprecationWarning) + return self.sparkSession.udf.registerJavaFunction(name, javaClassName, returnType) # TODO(andrew): delete this once we refactor things to take in SparkSession def _inferSchema(self, rdd, samplingRatio=None): @@ -559,24 +507,6 @@ def refreshTable(self, tableName): self._ssql_ctx.refreshTable(tableName) -class UDFRegistration(object): - """Wrapper for user-defined function registration.""" - - def __init__(self, sqlContext): - self.sqlContext = sqlContext - - def register(self, name, f, returnType=StringType()): - return self.sqlContext.registerFunction(name, f, returnType) - - def registerJavaFunction(self, name, javaClassName, returnType=None): - self.sqlContext.registerJavaFunction(name, javaClassName, returnType) - - def registerJavaUDAF(self, name, javaClassName): - self.sqlContext.registerJavaUDAF(name, javaClassName) - - register.__doc__ = SQLContext.registerFunction.__doc__ - - def _test(): import os import doctest diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 95eca76fa9888..d416b3be08b43 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -27,7 +27,7 @@ import warnings -from pyspark import copy_func, since +from pyspark import copy_func, since, _NoValue from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix from pyspark.serializers import ArrowSerializer, BatchedSerializer, PickleSerializer, \ UTF8Deserializer @@ -463,8 +463,8 @@ def collect(self): [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] """ with SCCallSiteSync(self._sc) as css: - port = self._jdf.collectToPython() - return list(_load_from_socket(port, BatchedSerializer(PickleSerializer()))) + sock_info = self._jdf.collectToPython() + return list(_load_from_socket(sock_info, BatchedSerializer(PickleSerializer()))) @ignore_unicode_prefix @since(2.0) @@ -477,8 +477,8 @@ def toLocalIterator(self): [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] """ with SCCallSiteSync(self._sc) as css: - port = self._jdf.toPythonIterator() - return _load_from_socket(port, BatchedSerializer(PickleSerializer())) + sock_info = self._jdf.toPythonIterator() + return _load_from_socket(sock_info, BatchedSerializer(PickleSerializer())) @ignore_unicode_prefix @since(1.3) @@ -819,6 +819,29 @@ def columns(self): """ return [f.name for f in self.schema.fields] + @since(2.3) + def colRegex(self, colName): + """ + Selects column based on the column name specified as a regex and returns it + as :class:`Column`. + + :param colName: string, column name specified as a regex. + + >>> df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["Col1", "Col2"]) + >>> df.select(df.colRegex("`(Col1)?+.+`")).show() + +----+ + |Col2| + +----+ + | 1| + | 2| + | 3| + +----+ + """ + if not isinstance(colName, basestring): + raise ValueError("colName should be provided as string") + jc = self._jdf.colRegex(colName) + return Column(jc) + @ignore_unicode_prefix @since(1.3) def alias(self, alias): @@ -1364,7 +1387,8 @@ def subtract(self, other): """ Return a new :class:`DataFrame` containing rows in this frame but not in another frame. - This is equivalent to `EXCEPT` in SQL. + This is equivalent to `EXCEPT DISTINCT` in SQL. + """ return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sql_ctx) @@ -1508,7 +1532,7 @@ def fillna(self, value, subset=None): return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx) @since(1.4) - def replace(self, to_replace, value=None, subset=None): + def replace(self, to_replace, value=_NoValue, subset=None): """Returns a new :class:`DataFrame` replacing a value with another value. :func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace` are aliases of each other. @@ -1521,8 +1545,8 @@ def replace(self, to_replace, value=None, subset=None): :param to_replace: bool, int, long, float, string, list or dict. Value to be replaced. - If the value is a dict, then `value` is ignored and `to_replace` must be a - mapping between a value and a replacement. + If the value is a dict, then `value` is ignored or can be omitted, and `to_replace` + must be a mapping between a value and a replacement. :param value: bool, int, long, float, string, list or None. The replacement value must be a bool, int, long, float, string or None. If `value` is a list, `value` should be of the same length and type as `to_replace`. @@ -1553,7 +1577,7 @@ def replace(self, to_replace, value=None, subset=None): |null| null|null| +----+------+----+ - >>> df4.na.replace('Alice').show() + >>> df4.na.replace({'Alice': None}).show() +----+------+----+ | age|height|name| +----+------+----+ @@ -1573,6 +1597,12 @@ def replace(self, to_replace, value=None, subset=None): |null| null|null| +----+------+----+ """ + if value is _NoValue: + if isinstance(to_replace, dict): + value = None + else: + raise TypeError("value argument is required when to_replace is not a dictionary.") + # Helper functions def all_of(types): """Given a type or tuple of types and a sequence of xs @@ -1805,11 +1835,15 @@ def withColumn(self, colName, col): Returns a new :class:`DataFrame` by adding a column or replacing the existing column that has the same name. + The column expression must be an expression over this DataFrame; attempting to add + a column from some other dataframe will raise an error. + :param colName: string, name of the new column. :param col: a :class:`Column` expression for the new column. >>> df.withColumn('age2', df.age + 2).collect() [Row(age=2, name=u'Alice', age2=4), Row(age=5, name=u'Bob', age2=7)] + """ assert isinstance(col, Column), "col should be Column" return DataFrame(self._jdf.withColumn(colName, col._jc), self.sql_ctx) @@ -1890,11 +1924,16 @@ def toPandas(self): .. note:: This method should only be used if the resulting Pandas's DataFrame is expected to be small, as all the data is loaded into the driver's memory. + .. note:: Usage with spark.sql.execution.arrow.enabled=True is experimental. + >>> df.toPandas() # doctest: +SKIP age name 0 2 Alice 1 5 Bob """ + from pyspark.sql.utils import require_minimum_pandas_version + require_minimum_pandas_version() + import pandas as pd if self.sql_ctx.getConf("spark.sql.execution.pandas.respectSessionTimeZone").lower() \ @@ -1905,21 +1944,26 @@ def toPandas(self): if self.sql_ctx.getConf("spark.sql.execution.arrow.enabled", "false").lower() == "true": try: - from pyspark.sql.types import _check_dataframe_localize_timestamps + from pyspark.sql.types import _check_dataframe_convert_date, \ + _check_dataframe_localize_timestamps, to_arrow_schema from pyspark.sql.utils import require_minimum_pyarrow_version - import pyarrow require_minimum_pyarrow_version() + import pyarrow + to_arrow_schema(self.schema) tables = self._collectAsArrow() if tables: table = pyarrow.concat_tables(tables) pdf = table.to_pandas() + pdf = _check_dataframe_convert_date(pdf, self.schema) return _check_dataframe_localize_timestamps(pdf, timezone) else: return pd.DataFrame.from_records([], columns=self.columns) - except ImportError as e: - msg = "note: pyarrow must be installed and available on calling Python process " \ - "if using spark.sql.execution.arrow.enabled=true" - raise ImportError("%s\n%s" % (_exception_message(e), msg)) + except Exception as e: + msg = ( + "Note: toPandas attempted Arrow optimization because " + "'spark.sql.execution.arrow.enabled' is set to true. Please set it to false " + "to disable this.") + raise RuntimeError("%s\n%s" % (_exception_message(e), msg)) else: pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns) @@ -1957,8 +2001,8 @@ def _collectAsArrow(self): .. note:: Experimental. """ with SCCallSiteSync(self._sc) as css: - port = self._jdf.collectAsArrowToPython() - return list(_load_from_socket(port, ArrowSerializer())) + sock_info = self._jdf.collectAsArrowToPython() + return list(_load_from_socket(sock_info, ArrowSerializer())) ########################################################################################## # Pandas compatibility @@ -1991,7 +2035,6 @@ def _to_corrected_pandas_type(dt): """ When converting Spark SQL records to Pandas DataFrame, the inferred data type may be wrong. This method gets the corrected data type for Pandas if that type may be inferred uncorrectly. - NOTE: DateType is inferred incorrectly as 'object', TimestampType is correct with datetime64[ns] """ import numpy as np if type(dt) == ByteType: @@ -2002,8 +2045,6 @@ def _to_corrected_pandas_type(dt): return np.int32 elif type(dt) == FloatType: return np.float32 - elif type(dt) == DateType: - return 'datetime64[ns]' else: return None @@ -2027,7 +2068,7 @@ def fill(self, value, subset=None): fill.__doc__ = DataFrame.fillna.__doc__ - def replace(self, to_replace, value=None, subset=None): + def replace(self, to_replace, value=_NoValue, subset=None): return self.df.replace(to_replace, value, subset) replace.__doc__ = DataFrame.replace.__doc__ diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index a4ed562ad48b4..9c02982e4ae22 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -106,18 +106,15 @@ def _(): _functions_1_4 = { # unary math functions - 'acos': 'Computes the cosine inverse of the given value; the returned angle is in the range' + - '0.0 through pi.', - 'asin': 'Computes the sine inverse of the given value; the returned angle is in the range' + - '-pi/2 through pi/2.', - 'atan': 'Computes the tangent inverse of the given value; the returned angle is in the range' + - '-pi/2 through pi/2', + 'acos': ':return: inverse cosine of `col`, as if computed by `java.lang.Math.acos()`', + 'asin': ':return: inverse sine of `col`, as if computed by `java.lang.Math.asin()`', + 'atan': ':return: inverse tangent of `col`, as if computed by `java.lang.Math.atan()`', 'cbrt': 'Computes the cube-root of the given value.', 'ceil': 'Computes the ceiling of the given value.', - 'cos': """Computes the cosine of the given value. - - :param col: :class:`DoubleType` column, units in radians.""", - 'cosh': 'Computes the hyperbolic cosine of the given value.', + 'cos': """:param col: angle in radians + :return: cosine of the angle, as if computed by `java.lang.Math.cos()`.""", + 'cosh': """:param col: hyperbolic angle + :return: hyperbolic cosine of the angle, as if computed by `java.lang.Math.cosh()`""", 'exp': 'Computes the exponential of the given value.', 'expm1': 'Computes the exponential of the given value minus one.', 'floor': 'Computes the floor of the given value.', @@ -127,14 +124,16 @@ def _(): 'rint': 'Returns the double value that is closest in value to the argument and' + ' is equal to a mathematical integer.', 'signum': 'Computes the signum of the given value.', - 'sin': """Computes the sine of the given value. - - :param col: :class:`DoubleType` column, units in radians.""", - 'sinh': 'Computes the hyperbolic sine of the given value.', - 'tan': """Computes the tangent of the given value. - - :param col: :class:`DoubleType` column, units in radians.""", - 'tanh': 'Computes the hyperbolic tangent of the given value.', + 'sin': """:param col: angle in radians + :return: sine of the angle, as if computed by `java.lang.Math.sin()`""", + 'sinh': """:param col: hyperbolic angle + :return: hyperbolic sine of the given value, + as if computed by `java.lang.Math.sinh()`""", + 'tan': """:param col: angle in radians + :return: tangent of the given value, as if computed by `java.lang.Math.tan()`""", + 'tanh': """:param col: hyperbolic angle + :return: hyperbolic tangent of the given value, + as if computed by `java.lang.Math.tanh()`""", 'toDegrees': '.. note:: Deprecated in 2.1, use :func:`degrees` instead.', 'toRadians': '.. note:: Deprecated in 2.1, use :func:`radians` instead.', 'bitwiseNOT': 'Computes bitwise not.', @@ -173,16 +172,31 @@ def _(): _functions_2_1 = { # unary math functions - 'degrees': 'Converts an angle measured in radians to an approximately equivalent angle ' + - 'measured in degrees.', - 'radians': 'Converts an angle measured in degrees to an approximately equivalent angle ' + - 'measured in radians.', + 'degrees': """ + Converts an angle measured in radians to an approximately equivalent angle + measured in degrees. + :param col: angle in radians + :return: angle in degrees, as if computed by `java.lang.Math.toDegrees()` + """, + 'radians': """ + Converts an angle measured in degrees to an approximately equivalent angle + measured in radians. + :param col: angle in degrees + :return: angle in radians, as if computed by `java.lang.Math.toRadians()` + """, } # math functions that take two arguments as input _binary_mathfunctions = { - 'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' + - 'polar coordinates (r, theta). Units in radians.', + 'atan2': """ + :param col1: coordinate on y-axis + :param col2: coordinate on x-axis + :return: the `theta` component of the point + (`r`, `theta`) + in polar coordinates that corresponds to the point + (`x`, `y`) in Cartesian coordinates, + as if computed by `java.lang.Math.atan2()` + """, 'hypot': 'Computes ``sqrt(a^2 + b^2)`` without intermediate overflow or underflow.', 'pow': 'Returns the value of the first argument raised to the power of the second argument.', } @@ -1705,10 +1719,12 @@ def unhex(col): @ignore_unicode_prefix @since(1.5) def length(col): - """Calculates the length of a string or binary expression. + """Computes the character length of string data or number of bytes of binary data. + The length of character data includes the trailing spaces. The length of binary data + includes binary zeros. - >>> spark.createDataFrame([('ABC',)], ['a']).select(length('a').alias('length')).collect() - [Row(length=3)] + >>> spark.createDataFrame([('ABC ',)], ['a']).select(length('a').alias('length')).collect() + [Row(length=4)] """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.length(_to_java_column(col))) @@ -1737,8 +1753,8 @@ def translate(srcCol, matching, replace): def create_map(*cols): """Creates a new map column. - :param cols: list of column names (string) or list of :class:`Column` expressions that grouped - as key-value pairs, e.g. (key1, value1, key2, value2, ...). + :param cols: list of column names (string) or list of :class:`Column` expressions that are + grouped as key-value pairs, e.g. (key1, value1, key2, value2, ...). >>> df.select(create_map('name', 'age').alias("map")).collect() [Row(map={u'Alice': 2}), Row(map={u'Bob': 5})] @@ -1849,14 +1865,14 @@ def explode_outer(col): +---+----------+----+-----+ >>> df.select("id", "a_map", explode_outer("an_array")).show() - +---+-------------+----+ - | id| a_map| col| - +---+-------------+----+ - | 1|Map(x -> 1.0)| foo| - | 1|Map(x -> 1.0)| bar| - | 2| Map()|null| - | 3| null|null| - +---+-------------+----+ + +---+----------+----+ + | id| a_map| col| + +---+----------+----+ + | 1|[x -> 1.0]| foo| + | 1|[x -> 1.0]| bar| + | 2| []|null| + | 3| null|null| + +---+----------+----+ """ sc = SparkContext._active_spark_context jc = sc._jvm.functions.explode_outer(_to_java_column(col)) @@ -1881,14 +1897,14 @@ def posexplode_outer(col): | 3| null|null|null| null| +---+----------+----+----+-----+ >>> df.select("id", "a_map", posexplode_outer("an_array")).show() - +---+-------------+----+----+ - | id| a_map| pos| col| - +---+-------------+----+----+ - | 1|Map(x -> 1.0)| 0| foo| - | 1|Map(x -> 1.0)| 1| bar| - | 2| Map()|null|null| - | 3| null|null|null| - +---+-------------+----+----+ + +---+----------+----+----+ + | id| a_map| pos| col| + +---+----------+----+----+ + | 1|[x -> 1.0]| 0| foo| + | 1|[x -> 1.0]| 1| bar| + | 2| []|null|null| + | 3| null|null|null| + +---+----------+----+----+ """ sc = SparkContext._active_spark_context jc = sc._jvm.functions.posexplode_outer(_to_java_column(col)) @@ -2085,9 +2101,9 @@ def map_values(col): class PandasUDFType(object): """Pandas UDF Types. See :meth:`pyspark.sql.functions.pandas_udf`. """ - SCALAR = PythonEvalType.SQL_PANDAS_SCALAR_UDF + SCALAR = PythonEvalType.SQL_SCALAR_PANDAS_UDF - GROUP_MAP = PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF + GROUPED_MAP = PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF @since(1.3) @@ -2103,12 +2119,15 @@ def udf(f=None, returnType=StringType()): >>> import random >>> random_udf = udf(lambda: int(random.random() * 100), IntegerType()).asNondeterministic() - .. note:: The user-defined functions do not support conditional expressions or short curcuiting + .. note:: The user-defined functions do not support conditional expressions or short circuiting in boolean expressions and it ends up with being executed all internally. If the functions can fail on special rows, the workaround is to incorporate the condition into the functions. + .. note:: The user-defined functions do not take keyword arguments on the calling side. + :param f: python function if used as a standalone function - :param returnType: a :class:`pyspark.sql.types.DataType` object + :param returnType: the return type of the user-defined function. The value can be either a + :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string. >>> from pyspark.sql.types import IntegerType >>> slen = udf(lambda s: len(s), IntegerType()) @@ -2148,10 +2167,13 @@ def pandas_udf(f=None, returnType=None, functionType=None): Creates a vectorized user defined function (UDF). :param f: user-defined function. A python function if used as a standalone function - :param returnType: a :class:`pyspark.sql.types.DataType` object + :param returnType: the return type of the user-defined function. The value can be either a + :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string. :param functionType: an enum value in :class:`pyspark.sql.functions.PandasUDFType`. Default: SCALAR. + .. note:: Experimental + The function type of the UDF can be one of the following: 1. SCALAR @@ -2184,20 +2206,26 @@ def pandas_udf(f=None, returnType=None, functionType=None): | 8| JOHN DOE| 22| +----------+--------------+------------+ - 2. GROUP_MAP + .. note:: The length of `pandas.Series` within a scalar UDF is not that of the whole input + column, but is the length of an internal batch used for each call to the function. + Therefore, this can be used, for example, to ensure the length of each returned + `pandas.Series`, and can not be used as the column length. + + 2. GROUPED_MAP - A group map UDF defines transformation: A `pandas.DataFrame` -> A `pandas.DataFrame` + A grouped map UDF defines transformation: A `pandas.DataFrame` -> A `pandas.DataFrame` The returnType should be a :class:`StructType` describing the schema of the returned `pandas.DataFrame`. - The length of the returned `pandas.DataFrame` can be arbitrary. + The length of the returned `pandas.DataFrame` can be arbitrary and the columns must be + indexed so that their position matches the corresponding field in the schema. - Group map UDFs are used with :meth:`pyspark.sql.GroupedData.apply`. + Grouped map UDFs are used with :meth:`pyspark.sql.GroupedData.apply`. >>> from pyspark.sql.functions import pandas_udf, PandasUDFType >>> df = spark.createDataFrame( ... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ... ("id", "v")) # doctest: +SKIP - >>> @pandas_udf("id long, v double", PandasUDFType.GROUP_MAP) # doctest: +SKIP + >>> @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP) # doctest: +SKIP ... def normalize(pdf): ... v = pdf.v ... return pdf.assign(v=(v - v.mean()) / v.std()) @@ -2212,13 +2240,31 @@ def pandas_udf(f=None, returnType=None, functionType=None): | 2| 1.1094003924504583| +---+-------------------+ + .. note:: If returning a new `pandas.DataFrame` constructed with a dictionary, it is + recommended to explicitly index the columns by name to ensure the positions are correct, + or alternatively use an `OrderedDict`. + For example, `pd.DataFrame({'id': ids, 'a': data}, columns=['id', 'a'])` or + `pd.DataFrame(OrderedDict([('id', ids), ('a', data)]))`. + .. seealso:: :meth:`pyspark.sql.GroupedData.apply` - .. note:: The user-defined function must be deterministic. + .. note:: The user-defined functions are considered deterministic by default. Due to + optimization, duplicate invocations may be eliminated or the function may even be invoked + more times than it is present in the query. If your function is not deterministic, call + `asNondeterministic` on the user defined function. E.g.: + + >>> @pandas_udf('double', PandasUDFType.SCALAR) # doctest: +SKIP + ... def random(v): + ... import numpy as np + ... import pandas as pd + ... return pd.Series(np.random.randn(len(v)) + >>> random = random.asNondeterministic() # doctest: +SKIP - .. note:: The user-defined functions do not support conditional expressions or short curcuiting + .. note:: The user-defined functions do not support conditional expressions or short circuiting in boolean expressions and it ends up with being executed all internally. If the functions can fail on special rows, the workaround is to incorporate the condition into the functions. + + .. note:: The user-defined functions do not take keyword arguments on the calling side. """ # decorator @pandas_udf(returnType, functionType) is_decorator = f is None or isinstance(f, (str, DataType)) @@ -2237,20 +2283,20 @@ def pandas_udf(f=None, returnType=None, functionType=None): eval_type = returnType else: # @pandas_udf(dataType) or @pandas_udf(returnType=dataType) - eval_type = PythonEvalType.SQL_PANDAS_SCALAR_UDF + eval_type = PythonEvalType.SQL_SCALAR_PANDAS_UDF else: return_type = returnType if functionType is not None: eval_type = functionType else: - eval_type = PythonEvalType.SQL_PANDAS_SCALAR_UDF + eval_type = PythonEvalType.SQL_SCALAR_PANDAS_UDF if return_type is None: raise ValueError("Invalid returnType: returnType can not be None") - if eval_type not in [PythonEvalType.SQL_PANDAS_SCALAR_UDF, - PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF]: + if eval_type not in [PythonEvalType.SQL_SCALAR_PANDAS_UDF, + PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF]: raise ValueError("Invalid functionType: " "functionType must be one the values from PandasUDFType") diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py index 09fae46adf014..bc6c094fa0ce2 100644 --- a/python/pyspark/sql/group.py +++ b/python/pyspark/sql/group.py @@ -212,13 +212,16 @@ def apply(self, udf): This function does not support partial aggregation, and requires shuffling all the data in the :class:`DataFrame`. - :param udf: A function object returned by :meth:`pyspark.sql.functions.pandas_udf` + .. note:: Experimental + + :param udf: a grouped map user-defined function returned by + :func:`pyspark.sql.functions.pandas_udf`. >>> from pyspark.sql.functions import pandas_udf, PandasUDFType >>> df = spark.createDataFrame( ... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ... ("id", "v")) - >>> @pandas_udf("id long, v double", PandasUDFType.GROUP_MAP) # doctest: +SKIP + >>> @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP) # doctest: +SKIP ... def normalize(pdf): ... v = pdf.v ... return pdf.assign(v=(v - v.mean()) / v.std()) @@ -238,9 +241,9 @@ def apply(self, udf): """ # Columns are special because hasattr always return True if isinstance(udf, Column) or not hasattr(udf, 'func') \ - or udf.evalType != PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF: + or udf.evalType != PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF: raise ValueError("Invalid udf: the udf argument must be a pandas_udf of type " - "GROUP_MAP.") + "GROUPED_MAP.") df = self._df udf_column = udf(*[df[col] for col in df.columns]) jdf = self._jgd.flatMapGroupsInPandas(udf_column._jc.expr()) diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py index 49af1bcee5ef8..28c10aa0cad5f 100644 --- a/python/pyspark/sql/readwriter.py +++ b/python/pyspark/sql/readwriter.py @@ -147,8 +147,8 @@ def load(self, path=None, format=None, schema=None, **options): or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``). :param options: all other string options - >>> df = spark.read.load('python/test_support/sql/parquet_partitioned', opt1=True, - ... opt2=1, opt3='str') + >>> df = spark.read.format("parquet").load('python/test_support/sql/parquet_partitioned', + ... opt1=True, opt2=1, opt3='str') >>> df.dtypes [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')] @@ -209,13 +209,13 @@ def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None, :param mode: allows a mode for dealing with corrupt records during parsing. If None is set, it uses the default value, ``PERMISSIVE``. - * ``PERMISSIVE`` : sets other fields to ``null`` when it meets a corrupted \ - record, and puts the malformed string into a field configured by \ - ``columnNameOfCorruptRecord``. To keep corrupt records, an user can set \ - a string type field named ``columnNameOfCorruptRecord`` in an user-defined \ - schema. If a schema does not have the field, it drops corrupt records during \ - parsing. When inferring a schema, it implicitly adds a \ - ``columnNameOfCorruptRecord`` field in an output schema. + * ``PERMISSIVE`` : when it meets a corrupted record, puts the malformed string \ + into a field configured by ``columnNameOfCorruptRecord``, and sets other \ + fields to ``null``. To keep corrupt records, an user can set a string type \ + field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \ + schema does not have the field, it drops corrupt records during parsing. \ + When inferring a schema, it implicitly adds a ``columnNameOfCorruptRecord`` \ + field in an output schema. * ``DROPMALFORMED`` : ignores the whole corrupted records. * ``FAILFAST`` : throws an exception when it meets corrupted records. @@ -393,13 +393,15 @@ def csv(self, path, schema=None, sep=None, encoding=None, quote=None, escape=Non :param mode: allows a mode for dealing with corrupt records during parsing. If None is set, it uses the default value, ``PERMISSIVE``. - * ``PERMISSIVE`` : sets other fields to ``null`` when it meets a corrupted \ - record, and puts the malformed string into a field configured by \ - ``columnNameOfCorruptRecord``. To keep corrupt records, an user can set \ - a string type field named ``columnNameOfCorruptRecord`` in an \ - user-defined schema. If a schema does not have the field, it drops corrupt \ - records during parsing. When a length of parsed CSV tokens is shorter than \ - an expected length of a schema, it sets `null` for extra fields. + * ``PERMISSIVE`` : when it meets a corrupted record, puts the malformed string \ + into a field configured by ``columnNameOfCorruptRecord``, and sets other \ + fields to ``null``. To keep corrupt records, an user can set a string type \ + field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \ + schema does not have the field, it drops corrupt records during parsing. \ + A record with less/more tokens than schema is not a corrupted record to CSV. \ + When it meets a record having fewer tokens than the length of the schema, \ + sets ``null`` to extra fields. When the record has more tokens than the \ + length of the schema, it drops extra tokens. * ``DROPMALFORMED`` : ignores the whole corrupted records. * ``FAILFAST`` : throws an exception when it meets corrupted records. @@ -954,7 +956,7 @@ def _test(): globs = pyspark.sql.readwriter.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') try: - spark = SparkSession.builder.enableHiveSupport().getOrCreate() + spark = SparkSession.builder.getOrCreate() except py4j.protocol.Py4JError: spark = SparkSession(sc) diff --git a/python/pyspark/sql/session.py b/python/pyspark/sql/session.py index 6e5eec48e8aca..a459cb5f25ee5 100644 --- a/python/pyspark/sql/session.py +++ b/python/pyspark/sql/session.py @@ -29,7 +29,6 @@ from pyspark import since from pyspark.rdd import RDD, ignore_unicode_prefix -from pyspark.sql.catalog import Catalog from pyspark.sql.conf import RuntimeConfig from pyspark.sql.dataframe import DataFrame from pyspark.sql.readwriter import DataFrameReader @@ -280,6 +279,7 @@ def catalog(self): :return: :class:`Catalog` """ + from pyspark.sql.catalog import Catalog if not hasattr(self, "_catalog"): self._catalog = Catalog(self) return self._catalog @@ -291,8 +291,8 @@ def udf(self): :return: :class:`UDFRegistration` """ - from pyspark.sql.context import UDFRegistration - return UDFRegistration(self._wrapped) + from pyspark.sql.udf import UDFRegistration + return UDFRegistration(self) @since(2.0) def range(self, start, end=None, step=1, numPartitions=None): @@ -325,11 +325,12 @@ def range(self, start, end=None, step=1, numPartitions=None): return DataFrame(jdf, self._wrapped) - def _inferSchemaFromList(self, data): + def _inferSchemaFromList(self, data, names=None): """ Infer schema from list of Row or tuple. :param data: list of Row or tuple + :param names: list of column names :return: :class:`pyspark.sql.types.StructType` """ if not data: @@ -338,12 +339,12 @@ def _inferSchemaFromList(self, data): if type(first) is dict: warnings.warn("inferring schema from dict is deprecated," "please use pyspark.sql.Row instead") - schema = reduce(_merge_type, map(_infer_schema, data)) + schema = reduce(_merge_type, (_infer_schema(row, names) for row in data)) if _has_nulltype(schema): raise ValueError("Some of types cannot be determined after inferring") return schema - def _inferSchema(self, rdd, samplingRatio=None): + def _inferSchema(self, rdd, samplingRatio=None, names=None): """ Infer schema from an RDD of Row or tuple. @@ -360,10 +361,10 @@ def _inferSchema(self, rdd, samplingRatio=None): "Use pyspark.sql.Row instead") if samplingRatio is None: - schema = _infer_schema(first) + schema = _infer_schema(first, names=names) if _has_nulltype(schema): for row in rdd.take(100)[1:]: - schema = _merge_type(schema, _infer_schema(row)) + schema = _merge_type(schema, _infer_schema(row, names=names)) if not _has_nulltype(schema): break else: @@ -372,7 +373,7 @@ def _inferSchema(self, rdd, samplingRatio=None): else: if samplingRatio < 0.99: rdd = rdd.sample(False, float(samplingRatio)) - schema = rdd.map(_infer_schema).reduce(_merge_type) + schema = rdd.map(lambda row: _infer_schema(row, names)).reduce(_merge_type) return schema def _createFromRDD(self, rdd, schema, samplingRatio): @@ -380,7 +381,7 @@ def _createFromRDD(self, rdd, schema, samplingRatio): Create an RDD for DataFrame from an existing RDD, returns the RDD and schema. """ if schema is None or isinstance(schema, (list, tuple)): - struct = self._inferSchema(rdd, samplingRatio) + struct = self._inferSchema(rdd, samplingRatio, names=schema) converter = _create_converter(struct) rdd = rdd.map(converter) if isinstance(schema, (list, tuple)): @@ -406,7 +407,7 @@ def _createFromLocal(self, data, schema): data = list(data) if schema is None or isinstance(schema, (list, tuple)): - struct = self._inferSchemaFromList(data) + struct = self._inferSchemaFromList(data, names=schema) converter = _create_converter(struct) data = map(converter, data) if isinstance(schema, (list, tuple)): @@ -458,21 +459,23 @@ def _convert_from_pandas(self, pdf, schema, timezone): # TODO: handle nested timestamps, such as ArrayType(TimestampType())? if isinstance(field.dataType, TimestampType): s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone) - if not copied and s is not pdf[field.name]: - # Copy once if the series is modified to prevent the original Pandas - # DataFrame from being updated - pdf = pdf.copy() - copied = True - pdf[field.name] = s + if s is not pdf[field.name]: + if not copied: + # Copy once if the series is modified to prevent the original + # Pandas DataFrame from being updated + pdf = pdf.copy() + copied = True + pdf[field.name] = s else: for column, series in pdf.iteritems(): - s = _check_series_convert_timestamps_tz_local(pdf[column], timezone) - if not copied and s is not pdf[column]: - # Copy once if the series is modified to prevent the original Pandas - # DataFrame from being updated - pdf = pdf.copy() - copied = True - pdf[column] = s + s = _check_series_convert_timestamps_tz_local(series, timezone) + if s is not series: + if not copied: + # Copy once if the series is modified to prevent the original + # Pandas DataFrame from being updated + pdf = pdf.copy() + copied = True + pdf[column] = s # Convert pandas.DataFrame to list of numpy records np_records = pdf.to_records(index=False) @@ -575,6 +578,8 @@ def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=Tr .. versionchanged:: 2.1 Added verifySchema. + .. note:: Usage with spark.sql.execution.arrow.enabled=True is experimental. + >>> l = [('Alice', 1)] >>> spark.createDataFrame(l).collect() [Row(_1=u'Alice', _2=1)] @@ -637,6 +642,9 @@ def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=Tr except Exception: has_pandas = False if has_pandas and isinstance(data, pandas.DataFrame): + from pyspark.sql.utils import require_minimum_pandas_version + require_minimum_pandas_version() + if self.conf.get("spark.sql.execution.pandas.respectSessionTimeZone").lower() \ == "true": timezone = self.conf.get("spark.sql.session.timeZone") @@ -645,7 +653,9 @@ def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=Tr # If no schema supplied by user then get the names of columns only if schema is None: - schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in data.columns] + schema = [str(x) if not isinstance(x, basestring) else + (x.encode('utf-8') if not isinstance(x, str) else x) + for x in data.columns] if self.conf.get("spark.sql.execution.arrow.enabled", "false").lower() == "true" \ and len(data) > 0: diff --git a/python/pyspark/sql/streaming.py b/python/pyspark/sql/streaming.py index fb228f99ba7ab..cc622decfd682 100644 --- a/python/pyspark/sql/streaming.py +++ b/python/pyspark/sql/streaming.py @@ -442,13 +442,13 @@ def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None, :param mode: allows a mode for dealing with corrupt records during parsing. If None is set, it uses the default value, ``PERMISSIVE``. - * ``PERMISSIVE`` : sets other fields to ``null`` when it meets a corrupted \ - record, and puts the malformed string into a field configured by \ - ``columnNameOfCorruptRecord``. To keep corrupt records, an user can set \ - a string type field named ``columnNameOfCorruptRecord`` in an user-defined \ - schema. If a schema does not have the field, it drops corrupt records during \ - parsing. When inferring a schema, it implicitly adds a \ - ``columnNameOfCorruptRecord`` field in an output schema. + * ``PERMISSIVE`` : when it meets a corrupted record, puts the malformed string \ + into a field configured by ``columnNameOfCorruptRecord``, and sets other \ + fields to ``null``. To keep corrupt records, an user can set a string type \ + field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \ + schema does not have the field, it drops corrupt records during parsing. \ + When inferring a schema, it implicitly adds a ``columnNameOfCorruptRecord`` \ + field in an output schema. * ``DROPMALFORMED`` : ignores the whole corrupted records. * ``FAILFAST`` : throws an exception when it meets corrupted records. @@ -621,13 +621,15 @@ def csv(self, path, schema=None, sep=None, encoding=None, quote=None, escape=Non :param mode: allows a mode for dealing with corrupt records during parsing. If None is set, it uses the default value, ``PERMISSIVE``. - * ``PERMISSIVE`` : sets other fields to ``null`` when it meets a corrupted \ - record, and puts the malformed string into a field configured by \ - ``columnNameOfCorruptRecord``. To keep corrupt records, an user can set \ - a string type field named ``columnNameOfCorruptRecord`` in an \ - user-defined schema. If a schema does not have the field, it drops corrupt \ - records during parsing. When a length of parsed CSV tokens is shorter than \ - an expected length of a schema, it sets `null` for extra fields. + * ``PERMISSIVE`` : when it meets a corrupted record, puts the malformed string \ + into a field configured by ``columnNameOfCorruptRecord``, and sets other \ + fields to ``null``. To keep corrupt records, an user can set a string type \ + field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \ + schema does not have the field, it drops corrupt records during parsing. \ + A record with less/more tokens than schema is not a corrupted record to CSV. \ + When it meets a record having fewer tokens than the length of the schema, \ + sets ``null`` to extra fields. When the record has more tokens than the \ + length of the schema, it drops extra tokens. * ``DROPMALFORMED`` : ignores the whole corrupted records. * ``FAILFAST`` : throws an exception when it meets corrupted records. @@ -786,35 +788,54 @@ def queryName(self, queryName): @keyword_only @since(2.0) - def trigger(self, processingTime=None, once=None): + def trigger(self, processingTime=None, once=None, continuous=None): """Set the trigger for the stream query. If this is not set it will run the query as fast as possible, which is equivalent to setting the trigger to ``processingTime='0 seconds'``. .. note:: Evolving. :param processingTime: a processing time interval as a string, e.g. '5 seconds', '1 minute'. + Set a trigger that runs a query periodically based on the processing + time. Only one trigger can be set. + :param once: if set to True, set a trigger that processes only one batch of data in a + streaming query then terminates the query. Only one trigger can be set. >>> # trigger the query for execution every 5 seconds >>> writer = sdf.writeStream.trigger(processingTime='5 seconds') >>> # trigger the query for just once batch of data >>> writer = sdf.writeStream.trigger(once=True) + >>> # trigger the query for execution every 5 seconds + >>> writer = sdf.writeStream.trigger(continuous='5 seconds') """ + params = [processingTime, once, continuous] + + if params.count(None) == 3: + raise ValueError('No trigger provided') + elif params.count(None) < 2: + raise ValueError('Multiple triggers not allowed.') + jTrigger = None if processingTime is not None: - if once is not None: - raise ValueError('Multiple triggers not allowed.') if type(processingTime) != str or len(processingTime.strip()) == 0: raise ValueError('Value for processingTime must be a non empty string. Got: %s' % processingTime) interval = processingTime.strip() jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.ProcessingTime( interval) + elif once is not None: if once is not True: raise ValueError('Value for once must be True. Got: %s' % once) jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.Once() + else: - raise ValueError('No trigger provided') + if type(continuous) != str or len(continuous.strip()) == 0: + raise ValueError('Value for continuous must be a non empty string. Got: %s' % + continuous) + interval = continuous.strip() + jTrigger = self._spark._sc._jvm.org.apache.spark.sql.streaming.Trigger.Continuous( + interval) + self._jwrite = self._jwrite.trigger(jTrigger) return self diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 67bdb3d72d93b..aa7d8eba1f692 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -33,6 +33,7 @@ import array import ctypes import py4j +from contextlib import contextmanager try: import xmlrunner @@ -48,19 +49,26 @@ else: import unittest -_have_pandas = False -_have_old_pandas = False +_pandas_requirement_message = None try: - import pandas - try: - from pyspark.sql.utils import require_minimum_pandas_version - require_minimum_pandas_version() - _have_pandas = True - except: - _have_old_pandas = True -except: - # No Pandas, but that's okay, we'll skip those tests - pass + from pyspark.sql.utils import require_minimum_pandas_version + require_minimum_pandas_version() +except ImportError as e: + from pyspark.util import _exception_message + # If Pandas version requirement is not satisfied, skip related tests. + _pandas_requirement_message = _exception_message(e) + +_pyarrow_requirement_message = None +try: + from pyspark.sql.utils import require_minimum_pyarrow_version + require_minimum_pyarrow_version() +except ImportError as e: + from pyspark.util import _exception_message + # If Arrow version requirement is not satisfied, skip related tests. + _pyarrow_requirement_message = _exception_message(e) + +_have_pandas = _pandas_requirement_message is None +_have_pyarrow = _pyarrow_requirement_message is None from pyspark import SparkContext from pyspark.sql import SparkSession, SQLContext, HiveContext, Column, Row @@ -68,21 +76,13 @@ from pyspark.sql.types import UserDefinedType, _infer_type, _make_type_verifier from pyspark.sql.types import _array_signed_int_typecode_ctype_mappings, _array_type_mappings from pyspark.sql.types import _array_unsigned_int_typecode_ctype_mappings +from pyspark.sql.types import _merge_type from pyspark.tests import QuietTest, ReusedPySparkTestCase, SparkSubmitTests from pyspark.sql.functions import UserDefinedFunction, sha2, lit from pyspark.sql.window import Window from pyspark.sql.utils import AnalysisException, ParseException, IllegalArgumentException -_have_arrow = False -try: - import pyarrow - _have_arrow = True -except: - # No Arrow, but that's okay, we'll skip those tests - pass - - class UTCOffsetTimezone(datetime.tzinfo): """ Specifies timezone in UTC offset @@ -185,7 +185,38 @@ def __init__(self, key, value): self.value = value -class ReusedSQLTestCase(ReusedPySparkTestCase): +class SQLTestUtils(object): + """ + This util assumes the instance of this to have 'spark' attribute, having a spark session. + It is usually used with 'ReusedSQLTestCase' class but can be used if you feel sure the + the implementation of this class has 'spark' attribute. + """ + + @contextmanager + def sql_conf(self, pairs): + """ + A convenient context manager to test some configuration specific logic. This sets + `value` to the configuration `key` and then restores it back when it exits. + """ + assert isinstance(pairs, dict), "pairs should be a dictionary." + assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session." + + keys = pairs.keys() + new_values = pairs.values() + old_values = [self.spark.conf.get(key, None) for key in keys] + for key, new_value in zip(keys, new_values): + self.spark.conf.set(key, new_value) + try: + yield + finally: + for key, old_value in zip(keys, old_values): + if old_value is None: + self.spark.conf.unset(key) + else: + self.spark.conf.set(key, old_value) + + +class ReusedSQLTestCase(ReusedPySparkTestCase, SQLTestUtils): @classmethod def setUpClass(cls): ReusedPySparkTestCase.setUpClass() @@ -196,6 +227,12 @@ def tearDownClass(cls): ReusedPySparkTestCase.tearDownClass() cls.spark.stop() + def assertPandasEqual(self, expected, result): + msg = ("DataFrames are not equal: " + + "\n\nExpected:\n%s\n%s" % (expected, expected.dtypes) + + "\n\nResult:\n%s\n%s" % (result, result.dtypes)) + self.assertTrue(expected.equals(result), msg=msg) + class DataTypeTests(unittest.TestCase): # regression test for SPARK-6055 @@ -371,6 +408,12 @@ def test_udf(self): [row] = self.spark.sql("SELECT twoArgs('test', 1)").collect() self.assertEqual(row[0], 5) + # This is to check if a deprecated 'SQLContext.registerFunction' can call its alias. + sqlContext = self.spark._wrapped + sqlContext.registerFunction("oneArg", lambda x: len(x), IntegerType()) + [row] = sqlContext.sql("SELECT oneArg('test')").collect() + self.assertEqual(row[0], 4) + def test_udf2(self): self.spark.catalog.registerFunction("strlen", lambda string: len(string), IntegerType()) self.spark.createDataFrame(self.sc.parallelize([Row(a="test")]))\ @@ -378,6 +421,81 @@ def test_udf2(self): [res] = self.spark.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect() self.assertEqual(4, res[0]) + def test_udf3(self): + two_args = self.spark.catalog.registerFunction( + "twoArgs", UserDefinedFunction(lambda x, y: len(x) + y)) + self.assertEqual(two_args.deterministic, True) + [row] = self.spark.sql("SELECT twoArgs('test', 1)").collect() + self.assertEqual(row[0], u'5') + + def test_udf_registration_return_type_none(self): + two_args = self.spark.catalog.registerFunction( + "twoArgs", UserDefinedFunction(lambda x, y: len(x) + y, "integer"), None) + self.assertEqual(two_args.deterministic, True) + [row] = self.spark.sql("SELECT twoArgs('test', 1)").collect() + self.assertEqual(row[0], 5) + + def test_udf_registration_return_type_not_none(self): + with QuietTest(self.sc): + with self.assertRaisesRegexp(TypeError, "Invalid returnType"): + self.spark.catalog.registerFunction( + "f", UserDefinedFunction(lambda x, y: len(x) + y, StringType()), StringType()) + + def test_nondeterministic_udf(self): + # Test that nondeterministic UDFs are evaluated only once in chained UDF evaluations + from pyspark.sql.functions import udf + import random + udf_random_col = udf(lambda: int(100 * random.random()), IntegerType()).asNondeterministic() + self.assertEqual(udf_random_col.deterministic, False) + df = self.spark.createDataFrame([Row(1)]).select(udf_random_col().alias('RAND')) + udf_add_ten = udf(lambda rand: rand + 10, IntegerType()) + [row] = df.withColumn('RAND_PLUS_TEN', udf_add_ten('RAND')).collect() + self.assertEqual(row[0] + 10, row[1]) + + def test_nondeterministic_udf2(self): + import random + from pyspark.sql.functions import udf + random_udf = udf(lambda: random.randint(6, 6), IntegerType()).asNondeterministic() + self.assertEqual(random_udf.deterministic, False) + random_udf1 = self.spark.catalog.registerFunction("randInt", random_udf) + self.assertEqual(random_udf1.deterministic, False) + [row] = self.spark.sql("SELECT randInt()").collect() + self.assertEqual(row[0], 6) + [row] = self.spark.range(1).select(random_udf1()).collect() + self.assertEqual(row[0], 6) + [row] = self.spark.range(1).select(random_udf()).collect() + self.assertEqual(row[0], 6) + # render_doc() reproduces the help() exception without printing output + pydoc.render_doc(udf(lambda: random.randint(6, 6), IntegerType())) + pydoc.render_doc(random_udf) + pydoc.render_doc(random_udf1) + pydoc.render_doc(udf(lambda x: x).asNondeterministic) + + def test_nondeterministic_udf3(self): + # regression test for SPARK-23233 + from pyspark.sql.functions import udf + f = udf(lambda x: x) + # Here we cache the JVM UDF instance. + self.spark.range(1).select(f("id")) + # This should reset the cache to set the deterministic status correctly. + f = f.asNondeterministic() + # Check the deterministic status of udf. + df = self.spark.range(1).select(f("id")) + deterministic = df._jdf.logicalPlan().projectList().head().deterministic() + self.assertFalse(deterministic) + + def test_nondeterministic_udf_in_aggregate(self): + from pyspark.sql.functions import udf, sum + import random + udf_random_col = udf(lambda: int(100 * random.random()), 'int').asNondeterministic() + df = self.spark.range(10) + + with QuietTest(self.sc): + with self.assertRaisesRegexp(AnalysisException, "nondeterministic"): + df.groupby('id').agg(sum(udf_random_col())).collect() + with self.assertRaisesRegexp(AnalysisException, "nondeterministic"): + df.agg(sum(udf_random_col())).collect() + def test_chained_udf(self): self.spark.catalog.registerFunction("double", lambda x: x + x, IntegerType()) [row] = self.spark.sql("SELECT double(1)").collect() @@ -435,15 +553,6 @@ def test_udf_with_array_type(self): self.assertEqual(list(range(3)), l1) self.assertEqual(1, l2) - def test_nondeterministic_udf(self): - from pyspark.sql.functions import udf - import random - udf_random_col = udf(lambda: int(100 * random.random()), IntegerType()).asNondeterministic() - df = self.spark.createDataFrame([Row(1)]).select(udf_random_col().alias('RAND')) - udf_add_ten = udf(lambda rand: rand + 10, IntegerType()) - [row] = df.withColumn('RAND_PLUS_TEN', udf_add_ten('RAND')).collect() - self.assertEqual(row[0] + 10, row[1]) - def test_broadcast_in_udf(self): bar = {"a": "aa", "b": "bb", "c": "abc"} foo = self.sc.broadcast(bar) @@ -523,11 +632,25 @@ def test_udf_registration_returns_udf(self): df.select(add_three("id").alias("plus_three")).collect() ) + # This is to check if a 'SQLContext.udf' can call its alias. + sqlContext = self.spark._wrapped + add_four = sqlContext.udf.register("add_four", lambda x: x + 4, IntegerType()) + + self.assertListEqual( + df.selectExpr("add_four(id) AS plus_four").collect(), + df.select(add_four("id").alias("plus_four")).collect() + ) + def test_non_existed_udf(self): spark = self.spark self.assertRaisesRegexp(AnalysisException, "Can not load class non_existed_udf", lambda: spark.udf.registerJavaFunction("udf1", "non_existed_udf")) + # This is to check if a deprecated 'SQLContext.registerJavaFunction' can call its alias. + sqlContext = spark._wrapped + self.assertRaisesRegexp(AnalysisException, "Can not load class non_existed_udf", + lambda: sqlContext.registerJavaFunction("udf1", "non_existed_udf")) + def test_non_existed_udaf(self): spark = self.spark self.assertRaisesRegexp(AnalysisException, "Can not load class non_existed_udaf", @@ -567,7 +690,6 @@ def test_read_multiple_orc_file(self): def test_udf_with_input_file_name(self): from pyspark.sql.functions import udf, input_file_name - from pyspark.sql.types import StringType sourceFile = udf(lambda path: path, StringType()) filePath = "python/test_support/sql/people1.json" row = self.spark.read.json(filePath).select(sourceFile(input_file_name())).first() @@ -575,7 +697,6 @@ def test_udf_with_input_file_name(self): def test_udf_with_input_file_name_for_hadooprdd(self): from pyspark.sql.functions import udf, input_file_name - from pyspark.sql.types import StringType def filename(path): return path @@ -635,7 +756,6 @@ def test_udf_with_string_return_type(self): def test_udf_shouldnt_accept_noncallable_object(self): from pyspark.sql.functions import UserDefinedFunction - from pyspark.sql.types import StringType non_callable = None self.assertRaises(TypeError, UserDefinedFunction, non_callable, StringType()) @@ -861,6 +981,15 @@ def test_infer_schema(self): result = self.spark.sql("SELECT l[0].a from test2 where d['key'].d = '2'") self.assertEqual(1, result.head()[0]) + def test_infer_schema_not_enough_names(self): + df = self.spark.createDataFrame([["a", "b"]], ["col1"]) + self.assertEqual(df.columns, ['col1', '_2']) + + def test_infer_schema_fails(self): + with self.assertRaisesRegexp(TypeError, 'field a'): + self.spark.createDataFrame(self.spark.sparkContext.parallelize([[1, 1], ["x", 1]]), + schema=["a", "b"], samplingRatio=0.99) + def test_infer_nested_schema(self): NestedRow = Row("f1", "f2") nestedRdd1 = self.sc.parallelize([NestedRow([1, 2], {"row1": 1.0}), @@ -881,6 +1010,10 @@ def test_infer_nested_schema(self): df = self.spark.createDataFrame(rdd) self.assertEqual(Row(field1=1, field2=u'row1'), df.first()) + def test_create_dataframe_from_dict_respects_schema(self): + df = self.spark.createDataFrame([{'a': 1}], ["b"]) + self.assertEqual(df.columns, ['b']) + def test_create_dataframe_from_objects(self): data = [MyObject(1, "1"), MyObject(2, "2")] df = self.spark.createDataFrame(data) @@ -1016,6 +1149,14 @@ def myudf(x): rows = [r[0] for r in df.selectExpr("udf(id)").take(2)] self.assertEqual(rows, [None, PythonOnlyPoint(1, 1)]) + def test_nonparam_udf_with_aggregate(self): + import pyspark.sql.functions as f + + df = self.spark.createDataFrame([(1, 2), (1, 2)]) + f_udf = f.udf(lambda: "const_str") + rows = df.distinct().withColumn("a", f_udf()).collect() + self.assertEqual(rows, [Row(_1=1, _2=2, a=u'const_str')]) + def test_infer_schema_with_udt(self): from pyspark.sql.tests import ExamplePoint, ExamplePointUDT row = Row(label=1.0, point=ExamplePoint(1.0, 2.0)) @@ -1105,6 +1246,17 @@ def test_union_with_udt(self): ] ) + def test_cast_to_string_with_udt(self): + from pyspark.sql.tests import ExamplePointUDT, ExamplePoint + from pyspark.sql.functions import col + row = (ExamplePoint(1.0, 2.0), PythonOnlyPoint(3.0, 4.0)) + schema = StructType([StructField("point", ExamplePointUDT(), False), + StructField("pypoint", PythonOnlyUDT(), False)]) + df = self.spark.createDataFrame([row], schema) + + result = df.select(col('point').cast('string'), col('pypoint').cast('string')).head() + self.assertEqual(result, Row(point=u'(1.0, 2.0)', pypoint=u'[3.0, 4.0]')) + def test_column_operators(self): ci = self.df.key cs = self.df.value @@ -1299,7 +1451,6 @@ def test_between_function(self): df.filter(df.a.between(df.b, df.c)).collect()) def test_struct_type(self): - from pyspark.sql.types import StructType, StringType, StructField struct1 = StructType().add("f1", StringType(), True).add("f2", StringType(), True, None) struct2 = StructType([StructField("f1", StringType(), True), StructField("f2", StringType(), True, None)]) @@ -1368,7 +1519,6 @@ def test_parse_datatype_string(self): _parse_datatype_string("a INT, c DOUBLE")) def test_metadata_null(self): - from pyspark.sql.types import StructType, StringType, StructField schema = StructType([StructField("f1", StringType(), True, None), StructField("f2", StringType(), True, {'a': None})]) rdd = self.sc.parallelize([["a", "b"], ["c", "d"]]) @@ -1456,6 +1606,12 @@ def test_stream_trigger(self): except ValueError: pass + # Should not take multiple args + try: + df.writeStream.trigger(processingTime='5 seconds', continuous='1 second') + except ValueError: + pass + # Should take only keyword args try: df.writeStream.trigger('5 seconds') @@ -1737,6 +1893,92 @@ def test_infer_long_type(self): self.assertEqual(_infer_type(2**61), LongType()) self.assertEqual(_infer_type(2**71), LongType()) + def test_merge_type(self): + self.assertEqual(_merge_type(LongType(), NullType()), LongType()) + self.assertEqual(_merge_type(NullType(), LongType()), LongType()) + + self.assertEqual(_merge_type(LongType(), LongType()), LongType()) + + self.assertEqual(_merge_type( + ArrayType(LongType()), + ArrayType(LongType()) + ), ArrayType(LongType())) + with self.assertRaisesRegexp(TypeError, 'element in array'): + _merge_type(ArrayType(LongType()), ArrayType(DoubleType())) + + self.assertEqual(_merge_type( + MapType(StringType(), LongType()), + MapType(StringType(), LongType()) + ), MapType(StringType(), LongType())) + with self.assertRaisesRegexp(TypeError, 'key of map'): + _merge_type( + MapType(StringType(), LongType()), + MapType(DoubleType(), LongType())) + with self.assertRaisesRegexp(TypeError, 'value of map'): + _merge_type( + MapType(StringType(), LongType()), + MapType(StringType(), DoubleType())) + + self.assertEqual(_merge_type( + StructType([StructField("f1", LongType()), StructField("f2", StringType())]), + StructType([StructField("f1", LongType()), StructField("f2", StringType())]) + ), StructType([StructField("f1", LongType()), StructField("f2", StringType())])) + with self.assertRaisesRegexp(TypeError, 'field f1'): + _merge_type( + StructType([StructField("f1", LongType()), StructField("f2", StringType())]), + StructType([StructField("f1", DoubleType()), StructField("f2", StringType())])) + + self.assertEqual(_merge_type( + StructType([StructField("f1", StructType([StructField("f2", LongType())]))]), + StructType([StructField("f1", StructType([StructField("f2", LongType())]))]) + ), StructType([StructField("f1", StructType([StructField("f2", LongType())]))])) + with self.assertRaisesRegexp(TypeError, 'field f2 in field f1'): + _merge_type( + StructType([StructField("f1", StructType([StructField("f2", LongType())]))]), + StructType([StructField("f1", StructType([StructField("f2", StringType())]))])) + + self.assertEqual(_merge_type( + StructType([StructField("f1", ArrayType(LongType())), StructField("f2", StringType())]), + StructType([StructField("f1", ArrayType(LongType())), StructField("f2", StringType())]) + ), StructType([StructField("f1", ArrayType(LongType())), StructField("f2", StringType())])) + with self.assertRaisesRegexp(TypeError, 'element in array field f1'): + _merge_type( + StructType([ + StructField("f1", ArrayType(LongType())), + StructField("f2", StringType())]), + StructType([ + StructField("f1", ArrayType(DoubleType())), + StructField("f2", StringType())])) + + self.assertEqual(_merge_type( + StructType([ + StructField("f1", MapType(StringType(), LongType())), + StructField("f2", StringType())]), + StructType([ + StructField("f1", MapType(StringType(), LongType())), + StructField("f2", StringType())]) + ), StructType([ + StructField("f1", MapType(StringType(), LongType())), + StructField("f2", StringType())])) + with self.assertRaisesRegexp(TypeError, 'value of map field f1'): + _merge_type( + StructType([ + StructField("f1", MapType(StringType(), LongType())), + StructField("f2", StringType())]), + StructType([ + StructField("f1", MapType(StringType(), DoubleType())), + StructField("f2", StringType())])) + + self.assertEqual(_merge_type( + StructType([StructField("f1", ArrayType(MapType(StringType(), LongType())))]), + StructType([StructField("f1", ArrayType(MapType(StringType(), LongType())))]) + ), StructType([StructField("f1", ArrayType(MapType(StringType(), LongType())))])) + with self.assertRaisesRegexp(TypeError, 'key of map element in array field f1'): + _merge_type( + StructType([StructField("f1", ArrayType(MapType(StringType(), LongType())))]), + StructType([StructField("f1", ArrayType(MapType(DoubleType(), LongType())))]) + ) + def test_filter_with_datetime(self): time = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000) date = time.date() @@ -2033,11 +2275,6 @@ def test_replace(self): .replace(False, True).first()) self.assertTupleEqual(row, (True, True)) - # replace list while value is not given (default to None) - row = self.spark.createDataFrame( - [(u'Alice', 10, 80.0)], schema).replace(["Alice", "Bob"]).first() - self.assertTupleEqual(row, (None, 10, 80.0)) - # replace string with None and then drop None rows row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace(u'Alice', None).dropna() @@ -2073,6 +2310,12 @@ def test_replace(self): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({u"Alice": u"Bob", 10: 20}).first() + with self.assertRaisesRegexp( + TypeError, + 'value argument is required when to_replace is not a dictionary.'): + self.spark.createDataFrame( + [(u'Alice', 10, 80.0)], schema).replace(["Alice", "Bob"]).first() + def test_capture_analysis_exception(self): self.assertRaises(AnalysisException, lambda: self.spark.sql("select abc")) self.assertRaises(AnalysisException, lambda: self.df.selectExpr("a + b")) @@ -2198,17 +2441,13 @@ def test_join_without_on(self): df1 = self.spark.range(1).toDF("a") df2 = self.spark.range(1).toDF("b") - try: - self.spark.conf.set("spark.sql.crossJoin.enabled", "false") + with self.sql_conf({"spark.sql.crossJoin.enabled": False}): self.assertRaises(AnalysisException, lambda: df1.join(df2, how="inner").collect()) - self.spark.conf.set("spark.sql.crossJoin.enabled", "true") + with self.sql_conf({"spark.sql.crossJoin.enabled": True}): actual = df1.join(df2, how="inner").collect() expected = [Row(a=0, b=0)] self.assertEqual(actual, expected) - finally: - # We should unset this. Otherwise, other tests are affected. - self.spark.conf.unset("spark.sql.crossJoin.enabled") # Regression test for invalid join methods when on is None, Spark-14761 def test_invalid_join_method(self): @@ -2241,6 +2480,17 @@ def test_conf(self): spark.conf.unset("bogo") self.assertEqual(spark.conf.get("bogo", "colombia"), "colombia") + self.assertEqual(spark.conf.get("hyukjin", None), None) + + # This returns 'STATIC' because it's the default value of + # 'spark.sql.sources.partitionOverwriteMode', and `defaultValue` in + # `spark.conf.get` is unset. + self.assertEqual(spark.conf.get("spark.sql.sources.partitionOverwriteMode"), "STATIC") + + # This returns None because 'spark.sql.sources.partitionOverwriteMode' is unset, but + # `defaultValue` in `spark.conf.get` is set to None. + self.assertEqual(spark.conf.get("spark.sql.sources.partitionOverwriteMode", None), None) + def test_current_database(self): spark = self.spark spark.catalog._reset() @@ -2582,7 +2832,6 @@ def count_bucketed_cols(names, table="pyspark_bucket"): def _to_pandas(self): from datetime import datetime, date - import numpy as np schema = StructType().add("a", IntegerType()).add("b", StringType())\ .add("c", BooleanType()).add("d", FloatType())\ .add("dt", DateType()).add("ts", TimestampType()) @@ -2595,7 +2844,7 @@ def _to_pandas(self): df = self.spark.createDataFrame(data, schema) return df.toPandas() - @unittest.skipIf(not _have_pandas, "Pandas not installed") + @unittest.skipIf(not _have_pandas, _pandas_requirement_message) def test_to_pandas(self): import numpy as np pdf = self._to_pandas() @@ -2604,16 +2853,16 @@ def test_to_pandas(self): self.assertEquals(types[1], np.object) self.assertEquals(types[2], np.bool) self.assertEquals(types[3], np.float32) - self.assertEquals(types[4], 'datetime64[ns]') + self.assertEquals(types[4], np.object) # datetime.date self.assertEquals(types[5], 'datetime64[ns]') - @unittest.skipIf(not _have_old_pandas, "Old Pandas not installed") - def test_to_pandas_old(self): + @unittest.skipIf(_have_pandas, "Required Pandas was found.") + def test_to_pandas_required_pandas_not_found(self): with QuietTest(self.sc): with self.assertRaisesRegexp(ImportError, 'Pandas >= .* must be installed'): self._to_pandas() - @unittest.skipIf(not _have_pandas, "Pandas not installed") + @unittest.skipIf(not _have_pandas, _pandas_requirement_message) def test_to_pandas_avoid_astype(self): import numpy as np schema = StructType().add("a", IntegerType()).add("b", StringType())\ @@ -2631,7 +2880,7 @@ def test_create_dataframe_from_array_of_long(self): df = self.spark.createDataFrame(data) self.assertEqual(df.first(), Row(longarray=[-9223372036854775808, 0, 9223372036854775807])) - @unittest.skipIf(not _have_pandas, "Pandas not installed") + @unittest.skipIf(not _have_pandas, _pandas_requirement_message) def test_create_dataframe_from_pandas_with_timestamp(self): import pandas as pd from datetime import datetime @@ -2646,19 +2895,66 @@ def test_create_dataframe_from_pandas_with_timestamp(self): self.assertTrue(isinstance(df.schema['ts'].dataType, TimestampType)) self.assertTrue(isinstance(df.schema['d'].dataType, DateType)) - @unittest.skipIf(not _have_old_pandas, "Old Pandas not installed") - def test_create_dataframe_from_old_pandas(self): - import pandas as pd - from datetime import datetime - pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)], - "d": [pd.Timestamp.now().date()]}) + @unittest.skipIf(_have_pandas, "Required Pandas was found.") + def test_create_dataframe_required_pandas_not_found(self): with QuietTest(self.sc): - with self.assertRaisesRegexp(ImportError, 'Pandas >= .* must be installed'): + with self.assertRaisesRegexp( + ImportError, + "(Pandas >= .* must be installed|No module named '?pandas'?)"): + import pandas as pd + from datetime import datetime + pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)], + "d": [pd.Timestamp.now().date()]}) self.spark.createDataFrame(pdf) + # Regression test for SPARK-23360 + @unittest.skipIf(not _have_pandas, _pandas_requirement_message) + def test_create_dateframe_from_pandas_with_dst(self): + import pandas as pd + from datetime import datetime + + pdf = pd.DataFrame({'time': [datetime(2015, 10, 31, 22, 30)]}) + + df = self.spark.createDataFrame(pdf) + self.assertPandasEqual(pdf, df.toPandas()) + + orig_env_tz = os.environ.get('TZ', None) + try: + tz = 'America/Los_Angeles' + os.environ['TZ'] = tz + time.tzset() + with self.sql_conf({'spark.sql.session.timeZone': tz}): + df = self.spark.createDataFrame(pdf) + self.assertPandasEqual(pdf, df.toPandas()) + finally: + del os.environ['TZ'] + if orig_env_tz is not None: + os.environ['TZ'] = orig_env_tz + time.tzset() + class HiveSparkSubmitTests(SparkSubmitTests): + @classmethod + def setUpClass(cls): + # get a SparkContext to check for availability of Hive + sc = SparkContext('local[4]', cls.__name__) + cls.hive_available = True + try: + sc._jvm.org.apache.hadoop.hive.conf.HiveConf() + except py4j.protocol.Py4JError: + cls.hive_available = False + except TypeError: + cls.hive_available = False + finally: + # we don't need this SparkContext for the test + sc.stop() + + def setUp(self): + super(HiveSparkSubmitTests, self).setUp() + if not self.hive_available: + self.skipTest("Hive is not available.") + def test_hivecontext(self): # This test checks that HiveContext is using Hive metastore (SPARK-16224). # It sets a metastore url and checks if there is a derby dir created by @@ -2713,6 +3009,64 @@ def test_sparksession_with_stopped_sparkcontext(self): sc.stop() +class QueryExecutionListenerTests(unittest.TestCase, SQLTestUtils): + # These tests are separate because it uses 'spark.sql.queryExecutionListeners' which is + # static and immutable. This can't be set or unset, for example, via `spark.conf`. + + @classmethod + def setUpClass(cls): + import glob + from pyspark.find_spark_home import _find_spark_home + + SPARK_HOME = _find_spark_home() + filename_pattern = ( + "sql/core/target/scala-*/test-classes/org/apache/spark/sql/" + "TestQueryExecutionListener.class") + if not glob.glob(os.path.join(SPARK_HOME, filename_pattern)): + raise unittest.SkipTest( + "'org.apache.spark.sql.TestQueryExecutionListener' is not " + "available. Will skip the related tests.") + + # Note that 'spark.sql.queryExecutionListeners' is a static immutable configuration. + cls.spark = SparkSession.builder \ + .master("local[4]") \ + .appName(cls.__name__) \ + .config( + "spark.sql.queryExecutionListeners", + "org.apache.spark.sql.TestQueryExecutionListener") \ + .getOrCreate() + + @classmethod + def tearDownClass(cls): + cls.spark.stop() + + def tearDown(self): + self.spark._jvm.OnSuccessCall.clear() + + def test_query_execution_listener_on_collect(self): + self.assertFalse( + self.spark._jvm.OnSuccessCall.isCalled(), + "The callback from the query execution listener should not be called before 'collect'") + self.spark.sql("SELECT * FROM range(1)").collect() + self.assertTrue( + self.spark._jvm.OnSuccessCall.isCalled(), + "The callback from the query execution listener should be called after 'collect'") + + @unittest.skipIf( + not _have_pandas or not _have_pyarrow, + _pandas_requirement_message or _pyarrow_requirement_message) + def test_query_execution_listener_on_collect_with_arrow(self): + with self.sql_conf({"spark.sql.execution.arrow.enabled": True}): + self.assertFalse( + self.spark._jvm.OnSuccessCall.isCalled(), + "The callback from the query execution listener should not be " + "called before 'toPandas'") + self.spark.sql("SELECT * FROM range(1)").toPandas() + self.assertTrue( + self.spark._jvm.OnSuccessCall.isCalled(), + "The callback from the query execution listener should be called after 'toPandas'") + + class UDFInitializationTests(unittest.TestCase): def tearDown(self): if SparkSession._instantiatedSession is not None: @@ -3145,12 +3499,14 @@ def __init__(self, **kwargs): _make_type_verifier(data_type, nullable=False)(obj) -@unittest.skipIf(not _have_pandas or not _have_arrow, "Pandas or Arrow not installed") +@unittest.skipIf( + not _have_pandas or not _have_pyarrow, + _pandas_requirement_message or _pyarrow_requirement_message) class ArrowTests(ReusedSQLTestCase): @classmethod def setUpClass(cls): - from datetime import datetime + from datetime import date, datetime from decimal import Decimal ReusedSQLTestCase.setUpClass() @@ -3172,11 +3528,11 @@ def setUpClass(cls): StructField("7_date_t", DateType(), True), StructField("8_timestamp_t", TimestampType(), True)]) cls.data = [(u"a", 1, 10, 0.2, 2.0, Decimal("2.0"), - datetime(1969, 1, 1), datetime(1969, 1, 1, 1, 1, 1)), + date(1969, 1, 1), datetime(1969, 1, 1, 1, 1, 1)), (u"b", 2, 20, 0.4, 4.0, Decimal("4.0"), - datetime(2012, 2, 2), datetime(2012, 2, 2, 2, 2, 2)), + date(2012, 2, 2), datetime(2012, 2, 2, 2, 2, 2)), (u"c", 3, 30, 0.8, 6.0, Decimal("6.0"), - datetime(2100, 3, 3), datetime(2100, 3, 3, 3, 3, 3))] + date(2100, 3, 3), datetime(2100, 3, 3, 3, 3, 3))] @classmethod def tearDownClass(cls): @@ -3186,12 +3542,6 @@ def tearDownClass(cls): time.tzset() ReusedSQLTestCase.tearDownClass() - def assertFramesEqual(self, df_with_arrow, df_without): - msg = ("DataFrame from Arrow is not equal" + - ("\n\nWith Arrow:\n%s\n%s" % (df_with_arrow, df_with_arrow.dtypes)) + - ("\n\nWithout:\n%s\n%s" % (df_without, df_without.dtypes))) - self.assertTrue(df_without.equals(df_with_arrow), msg=msg) - def create_pandas_data_frame(self): import pandas as pd import numpy as np @@ -3207,7 +3557,14 @@ def test_unsupported_datatype(self): schema = StructType([StructField("map", MapType(StringType(), IntegerType()), True)]) df = self.spark.createDataFrame([(None,)], schema=schema) with QuietTest(self.sc): - with self.assertRaisesRegexp(Exception, 'Unsupported data type'): + with self.assertRaisesRegexp(Exception, 'Unsupported type'): + df.toPandas() + + df = self.spark.createDataFrame([(None,)], schema="a binary") + with QuietTest(self.sc): + with self.assertRaisesRegexp( + Exception, + 'Unsupported type.*\nNote: toPandas attempted Arrow optimization because'): df.toPandas() def test_null_conversion(self): @@ -3218,33 +3575,35 @@ def test_null_conversion(self): self.assertTrue(all([c == 1 for c in null_counts])) def _toPandas_arrow_toggle(self, df): - self.spark.conf.set("spark.sql.execution.arrow.enabled", "false") - try: + with self.sql_conf({"spark.sql.execution.arrow.enabled": False}): pdf = df.toPandas() - finally: - self.spark.conf.set("spark.sql.execution.arrow.enabled", "true") + pdf_arrow = df.toPandas() + return pdf, pdf_arrow def test_toPandas_arrow_toggle(self): df = self.spark.createDataFrame(self.data, schema=self.schema) pdf, pdf_arrow = self._toPandas_arrow_toggle(df) - self.assertFramesEqual(pdf_arrow, pdf) + expected = self.create_pandas_data_frame() + self.assertPandasEqual(expected, pdf) + self.assertPandasEqual(expected, pdf_arrow) def test_toPandas_respect_session_timezone(self): df = self.spark.createDataFrame(self.data, schema=self.schema) - orig_tz = self.spark.conf.get("spark.sql.session.timeZone") - try: - timezone = "America/New_York" - self.spark.conf.set("spark.sql.session.timeZone", timezone) - self.spark.conf.set("spark.sql.execution.pandas.respectSessionTimeZone", "false") - try: - pdf_la, pdf_arrow_la = self._toPandas_arrow_toggle(df) - self.assertFramesEqual(pdf_arrow_la, pdf_la) - finally: - self.spark.conf.set("spark.sql.execution.pandas.respectSessionTimeZone", "true") + + timezone = "America/New_York" + with self.sql_conf({ + "spark.sql.execution.pandas.respectSessionTimeZone": False, + "spark.sql.session.timeZone": timezone}): + pdf_la, pdf_arrow_la = self._toPandas_arrow_toggle(df) + self.assertPandasEqual(pdf_arrow_la, pdf_la) + + with self.sql_conf({ + "spark.sql.execution.pandas.respectSessionTimeZone": True, + "spark.sql.session.timeZone": timezone}): pdf_ny, pdf_arrow_ny = self._toPandas_arrow_toggle(df) - self.assertFramesEqual(pdf_arrow_ny, pdf_ny) + self.assertPandasEqual(pdf_arrow_ny, pdf_ny) self.assertFalse(pdf_ny.equals(pdf_la)) @@ -3254,15 +3613,13 @@ def test_toPandas_respect_session_timezone(self): if isinstance(field.dataType, TimestampType): pdf_la_corrected[field.name] = _check_series_convert_timestamps_local_tz( pdf_la_corrected[field.name], timezone) - self.assertFramesEqual(pdf_ny, pdf_la_corrected) - finally: - self.spark.conf.set("spark.sql.session.timeZone", orig_tz) + self.assertPandasEqual(pdf_ny, pdf_la_corrected) def test_pandas_round_trip(self): pdf = self.create_pandas_data_frame() df = self.spark.createDataFrame(self.data, schema=self.schema) pdf_arrow = df.toPandas() - self.assertFramesEqual(pdf_arrow, pdf) + self.assertPandasEqual(pdf_arrow, pdf) def test_filtered_frame(self): df = self.spark.range(3).toDF("i") @@ -3272,12 +3629,11 @@ def test_filtered_frame(self): self.assertTrue(pdf.empty) def _createDataFrame_toggle(self, pdf, schema=None): - self.spark.conf.set("spark.sql.execution.arrow.enabled", "false") - try: + with self.sql_conf({"spark.sql.execution.arrow.enabled": False}): df_no_arrow = self.spark.createDataFrame(pdf, schema=schema) - finally: - self.spark.conf.set("spark.sql.execution.arrow.enabled", "true") + df_arrow = self.spark.createDataFrame(pdf, schema=schema) + return df_no_arrow, df_arrow def test_createDataFrame_toggle(self): @@ -3288,18 +3644,18 @@ def test_createDataFrame_toggle(self): def test_createDataFrame_respect_session_timezone(self): from datetime import timedelta pdf = self.create_pandas_data_frame() - orig_tz = self.spark.conf.get("spark.sql.session.timeZone") - try: - timezone = "America/New_York" - self.spark.conf.set("spark.sql.session.timeZone", timezone) - self.spark.conf.set("spark.sql.execution.pandas.respectSessionTimeZone", "false") - try: - df_no_arrow_la, df_arrow_la = self._createDataFrame_toggle(pdf, schema=self.schema) - result_la = df_no_arrow_la.collect() - result_arrow_la = df_arrow_la.collect() - self.assertEqual(result_la, result_arrow_la) - finally: - self.spark.conf.set("spark.sql.execution.pandas.respectSessionTimeZone", "true") + timezone = "America/New_York" + with self.sql_conf({ + "spark.sql.execution.pandas.respectSessionTimeZone": False, + "spark.sql.session.timeZone": timezone}): + df_no_arrow_la, df_arrow_la = self._createDataFrame_toggle(pdf, schema=self.schema) + result_la = df_no_arrow_la.collect() + result_arrow_la = df_arrow_la.collect() + self.assertEqual(result_la, result_arrow_la) + + with self.sql_conf({ + "spark.sql.execution.pandas.respectSessionTimeZone": True, + "spark.sql.session.timeZone": timezone}): df_no_arrow_ny, df_arrow_ny = self._createDataFrame_toggle(pdf, schema=self.schema) result_ny = df_no_arrow_ny.collect() result_arrow_ny = df_arrow_ny.collect() @@ -3312,15 +3668,13 @@ def test_createDataFrame_respect_session_timezone(self): for k, v in row.asDict().items()}) for row in result_la] self.assertEqual(result_ny, result_la_corrected) - finally: - self.spark.conf.set("spark.sql.session.timeZone", orig_tz) def test_createDataFrame_with_schema(self): pdf = self.create_pandas_data_frame() df = self.spark.createDataFrame(pdf, schema=self.schema) self.assertEquals(self.schema, df.schema) pdf_arrow = df.toPandas() - self.assertFramesEqual(pdf_arrow, pdf) + self.assertPandasEqual(pdf_arrow, pdf) def test_createDataFrame_with_incorrect_schema(self): pdf = self.create_pandas_data_frame() @@ -3397,8 +3751,34 @@ def test_toPandas_with_array_type(self): self.assertTrue(expected[r][e] == result_arrow[r][e] and result[r][e] == result_arrow[r][e]) + def test_createDataFrame_with_int_col_names(self): + import numpy as np + import pandas as pd + pdf = pd.DataFrame(np.random.rand(4, 2)) + df, df_arrow = self._createDataFrame_toggle(pdf) + pdf_col_names = [str(c) for c in pdf.columns] + self.assertEqual(pdf_col_names, df.columns) + self.assertEqual(pdf_col_names, df_arrow.columns) + + # Regression test for SPARK-23314 + def test_timestamp_dst(self): + import pandas as pd + # Daylight saving time for Los Angeles for 2015 is Sun, Nov 1 at 2:00 am + dt = [datetime.datetime(2015, 11, 1, 0, 30), + datetime.datetime(2015, 11, 1, 1, 30), + datetime.datetime(2015, 11, 1, 2, 30)] + pdf = pd.DataFrame({'time': dt}) + + df_from_python = self.spark.createDataFrame(dt, 'timestamp').toDF('time') + df_from_pandas = self.spark.createDataFrame(pdf) + + self.assertPandasEqual(pdf, df_from_python.toPandas()) + self.assertPandasEqual(pdf, df_from_pandas.toPandas()) -@unittest.skipIf(not _have_pandas or not _have_arrow, "Pandas or Arrow not installed") + +@unittest.skipIf( + not _have_pandas or not _have_pyarrow, + _pandas_requirement_message or _pyarrow_requirement_message) class PandasUDFTests(ReusedSQLTestCase): def test_pandas_udf_basic(self): from pyspark.rdd import PythonEvalType @@ -3406,34 +3786,34 @@ def test_pandas_udf_basic(self): udf = pandas_udf(lambda x: x, DoubleType()) self.assertEqual(udf.returnType, DoubleType()) - self.assertEqual(udf.evalType, PythonEvalType.SQL_PANDAS_SCALAR_UDF) + self.assertEqual(udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) udf = pandas_udf(lambda x: x, DoubleType(), PandasUDFType.SCALAR) self.assertEqual(udf.returnType, DoubleType()) - self.assertEqual(udf.evalType, PythonEvalType.SQL_PANDAS_SCALAR_UDF) + self.assertEqual(udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) udf = pandas_udf(lambda x: x, 'double', PandasUDFType.SCALAR) self.assertEqual(udf.returnType, DoubleType()) - self.assertEqual(udf.evalType, PythonEvalType.SQL_PANDAS_SCALAR_UDF) + self.assertEqual(udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) udf = pandas_udf(lambda x: x, StructType([StructField("v", DoubleType())]), - PandasUDFType.GROUP_MAP) + PandasUDFType.GROUPED_MAP) self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType())])) - self.assertEqual(udf.evalType, PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF) + self.assertEqual(udf.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF) - udf = pandas_udf(lambda x: x, 'v double', PandasUDFType.GROUP_MAP) + udf = pandas_udf(lambda x: x, 'v double', PandasUDFType.GROUPED_MAP) self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType())])) - self.assertEqual(udf.evalType, PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF) + self.assertEqual(udf.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF) udf = pandas_udf(lambda x: x, 'v double', - functionType=PandasUDFType.GROUP_MAP) + functionType=PandasUDFType.GROUPED_MAP) self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType())])) - self.assertEqual(udf.evalType, PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF) + self.assertEqual(udf.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF) udf = pandas_udf(lambda x: x, returnType='v double', - functionType=PandasUDFType.GROUP_MAP) + functionType=PandasUDFType.GROUPED_MAP) self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType())])) - self.assertEqual(udf.evalType, PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF) + self.assertEqual(udf.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF) def test_pandas_udf_decorator(self): from pyspark.rdd import PythonEvalType @@ -3444,45 +3824,45 @@ def test_pandas_udf_decorator(self): def foo(x): return x self.assertEqual(foo.returnType, DoubleType()) - self.assertEqual(foo.evalType, PythonEvalType.SQL_PANDAS_SCALAR_UDF) + self.assertEqual(foo.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) @pandas_udf(returnType=DoubleType()) def foo(x): return x self.assertEqual(foo.returnType, DoubleType()) - self.assertEqual(foo.evalType, PythonEvalType.SQL_PANDAS_SCALAR_UDF) + self.assertEqual(foo.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) schema = StructType([StructField("v", DoubleType())]) - @pandas_udf(schema, PandasUDFType.GROUP_MAP) + @pandas_udf(schema, PandasUDFType.GROUPED_MAP) def foo(x): return x self.assertEqual(foo.returnType, schema) - self.assertEqual(foo.evalType, PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF) + self.assertEqual(foo.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF) - @pandas_udf('v double', PandasUDFType.GROUP_MAP) + @pandas_udf('v double', PandasUDFType.GROUPED_MAP) def foo(x): return x self.assertEqual(foo.returnType, schema) - self.assertEqual(foo.evalType, PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF) + self.assertEqual(foo.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF) - @pandas_udf(schema, functionType=PandasUDFType.GROUP_MAP) + @pandas_udf(schema, functionType=PandasUDFType.GROUPED_MAP) def foo(x): return x self.assertEqual(foo.returnType, schema) - self.assertEqual(foo.evalType, PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF) + self.assertEqual(foo.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF) - @pandas_udf(returnType='v double', functionType=PandasUDFType.SCALAR) + @pandas_udf(returnType='double', functionType=PandasUDFType.SCALAR) def foo(x): return x - self.assertEqual(foo.returnType, schema) - self.assertEqual(foo.evalType, PythonEvalType.SQL_PANDAS_SCALAR_UDF) + self.assertEqual(foo.returnType, DoubleType()) + self.assertEqual(foo.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) - @pandas_udf(returnType=schema, functionType=PandasUDFType.GROUP_MAP) + @pandas_udf(returnType=schema, functionType=PandasUDFType.GROUPED_MAP) def foo(x): return x self.assertEqual(foo.returnType, schema) - self.assertEqual(foo.evalType, PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF) + self.assertEqual(foo.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF) def test_udf_wrong_arg(self): from pyspark.sql.functions import pandas_udf, PandasUDFType @@ -3509,21 +3889,61 @@ def zero_with_type(): return 1 with self.assertRaisesRegexp(TypeError, 'Invalid returnType'): - @pandas_udf(returnType=PandasUDFType.GROUP_MAP) + @pandas_udf(returnType=PandasUDFType.GROUPED_MAP) def foo(df): return df - with self.assertRaisesRegexp(ValueError, 'Invalid returnType'): - @pandas_udf(returnType='double', functionType=PandasUDFType.GROUP_MAP) + with self.assertRaisesRegexp(TypeError, 'Invalid returnType'): + @pandas_udf(returnType='double', functionType=PandasUDFType.GROUPED_MAP) def foo(df): return df with self.assertRaisesRegexp(ValueError, 'Invalid function'): - @pandas_udf(returnType='k int, v double', functionType=PandasUDFType.GROUP_MAP) + @pandas_udf(returnType='k int, v double', functionType=PandasUDFType.GROUPED_MAP) def foo(k, v): return k + def test_stopiteration_in_udf(self): + from pyspark.sql.functions import udf, pandas_udf, PandasUDFType + from py4j.protocol import Py4JJavaError -@unittest.skipIf(not _have_pandas or not _have_arrow, "Pandas or Arrow not installed") -class VectorizedUDFTests(ReusedSQLTestCase): + def foo(x): + raise StopIteration() + + def foofoo(x, y): + raise StopIteration() + + exc_message = "Caught StopIteration thrown from user's code; failing the task" + df = self.spark.range(0, 100) + + # plain udf (test for SPARK-23754) + self.assertRaisesRegexp( + Py4JJavaError, + exc_message, + df.withColumn('v', udf(foo)('id')).collect + ) + + # pandas scalar udf + self.assertRaisesRegexp( + Py4JJavaError, + exc_message, + df.withColumn( + 'v', pandas_udf(foo, 'double', PandasUDFType.SCALAR)('id') + ).collect + ) + + # pandas grouped map + self.assertRaisesRegexp( + Py4JJavaError, + exc_message, + df.groupBy('id').apply( + pandas_udf(foo, df.schema, PandasUDFType.GROUPED_MAP) + ).collect + ) + + +@unittest.skipIf( + not _have_pandas or not _have_pyarrow, + _pandas_requirement_message or _pyarrow_requirement_message) +class ScalarPandasUDFTests(ReusedSQLTestCase): @classmethod def setUpClass(cls): @@ -3546,8 +3966,20 @@ def tearDownClass(cls): time.tzset() ReusedSQLTestCase.tearDownClass() + @property + def nondeterministic_vectorized_udf(self): + from pyspark.sql.functions import pandas_udf + + @pandas_udf('double') + def random_udf(v): + import pandas as pd + import numpy as np + return pd.Series(np.random.random(len(v))) + random_udf = random_udf.asNondeterministic() + return random_udf + def test_vectorized_udf_basic(self): - from pyspark.sql.functions import pandas_udf, col + from pyspark.sql.functions import pandas_udf, col, array df = self.spark.range(10).select( col('id').cast('string').alias('str'), col('id').cast('int').alias('int'), @@ -3555,7 +3987,8 @@ def test_vectorized_udf_basic(self): col('id').cast('float').alias('float'), col('id').cast('double').alias('double'), col('id').cast('decimal').alias('decimal'), - col('id').cast('boolean').alias('bool')) + col('id').cast('boolean').alias('bool'), + array(col('id')).alias('array_long')) f = lambda x: x str_f = pandas_udf(f, StringType()) int_f = pandas_udf(f, IntegerType()) @@ -3564,12 +3997,28 @@ def test_vectorized_udf_basic(self): double_f = pandas_udf(f, DoubleType()) decimal_f = pandas_udf(f, DecimalType()) bool_f = pandas_udf(f, BooleanType()) + array_long_f = pandas_udf(f, ArrayType(LongType())) res = df.select(str_f(col('str')), int_f(col('int')), long_f(col('long')), float_f(col('float')), double_f(col('double')), decimal_f('decimal'), - bool_f(col('bool'))) + bool_f(col('bool')), array_long_f('array_long')) self.assertEquals(df.collect(), res.collect()) + def test_register_nondeterministic_vectorized_udf_basic(self): + from pyspark.sql.functions import pandas_udf + from pyspark.rdd import PythonEvalType + import random + random_pandas_udf = pandas_udf( + lambda x: random.randint(6, 6) + x, IntegerType()).asNondeterministic() + self.assertEqual(random_pandas_udf.deterministic, False) + self.assertEqual(random_pandas_udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) + nondeterministic_pandas_udf = self.spark.catalog.registerFunction( + "randomPandasUDF", random_pandas_udf) + self.assertEqual(nondeterministic_pandas_udf.deterministic, False) + self.assertEqual(nondeterministic_pandas_udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) + [row] = self.spark.sql("SELECT randomPandasUDF(1)").collect() + self.assertEqual(row[0], 7) + def test_vectorized_udf_null_boolean(self): from pyspark.sql.functions import pandas_udf, col data = [(True,), (True,), (None,), (False,)] @@ -3652,6 +4101,15 @@ def test_vectorized_udf_null_string(self): res = df.select(str_f(col('str'))) self.assertEquals(df.collect(), res.collect()) + def test_vectorized_udf_string_in_udf(self): + from pyspark.sql.functions import pandas_udf, col + import pandas as pd + df = self.spark.range(10) + str_f = pandas_udf(lambda x: pd.Series(map(str, x)), StringType()) + actual = df.select(str_f(col('id'))) + expected = df.select(col('id').cast('string')) + self.assertEquals(expected.collect(), actual.collect()) + def test_vectorized_udf_datatype_string(self): from pyspark.sql.functions import pandas_udf, col df = self.spark.range(10).select( @@ -3748,10 +4206,11 @@ def test_vectorized_udf_chained(self): def test_vectorized_udf_wrong_return_type(self): from pyspark.sql.functions import pandas_udf, col df = self.spark.range(10) - f = pandas_udf(lambda x: x * 1.0, MapType(LongType(), LongType())) with QuietTest(self.sc): - with self.assertRaisesRegexp(Exception, 'Unsupported.*type.*conversion'): - df.select(f(col('id'))).collect() + with self.assertRaisesRegexp( + NotImplementedError, + 'Invalid returnType.*scalar Pandas UDF.*MapType'): + pandas_udf(lambda x: x * 1.0, MapType(LongType(), LongType())) def test_vectorized_udf_return_scalar(self): from pyspark.sql.functions import pandas_udf, col @@ -3786,26 +4245,55 @@ def test_vectorized_udf_varargs(self): self.assertEquals(df.collect(), res.collect()) def test_vectorized_udf_unsupported_types(self): - from pyspark.sql.functions import pandas_udf, col - schema = StructType([StructField("map", MapType(StringType(), IntegerType()), True)]) - df = self.spark.createDataFrame([(None,)], schema=schema) - f = pandas_udf(lambda x: x, MapType(StringType(), IntegerType())) + from pyspark.sql.functions import pandas_udf + with QuietTest(self.sc): + with self.assertRaisesRegexp( + NotImplementedError, + 'Invalid returnType.*scalar Pandas UDF.*MapType'): + pandas_udf(lambda x: x, MapType(StringType(), IntegerType())) + with QuietTest(self.sc): - with self.assertRaisesRegexp(Exception, 'Unsupported data type'): - df.select(f(col('map'))).collect() + with self.assertRaisesRegexp( + NotImplementedError, + 'Invalid returnType.*scalar Pandas UDF.*BinaryType'): + pandas_udf(lambda x: x, BinaryType()) - def test_vectorized_udf_null_date(self): + def test_vectorized_udf_dates(self): from pyspark.sql.functions import pandas_udf, col from datetime import date - schema = StructType().add("date", DateType()) - data = [(date(1969, 1, 1),), - (date(2012, 2, 2),), - (None,), - (date(2100, 4, 4),)] + schema = StructType().add("idx", LongType()).add("date", DateType()) + data = [(0, date(1969, 1, 1),), + (1, date(2012, 2, 2),), + (2, None,), + (3, date(2100, 4, 4),)] df = self.spark.createDataFrame(data, schema=schema) - date_f = pandas_udf(lambda t: t, returnType=DateType()) - res = df.select(date_f(col("date"))) - self.assertEquals(df.collect(), res.collect()) + + date_copy = pandas_udf(lambda t: t, returnType=DateType()) + df = df.withColumn("date_copy", date_copy(col("date"))) + + @pandas_udf(returnType=StringType()) + def check_data(idx, date, date_copy): + import pandas as pd + msgs = [] + is_equal = date.isnull() + for i in range(len(idx)): + if (is_equal[i] and data[idx[i]][1] is None) or \ + date[i] == data[idx[i]][1]: + msgs.append(None) + else: + msgs.append( + "date values are not equal (date='%s': data[%d][1]='%s')" + % (date[i], idx[i], data[idx[i]][1])) + return pd.Series(msgs) + + result = df.withColumn("check_data", + check_data(col("idx"), col("date"), col("date_copy"))).collect() + + self.assertEquals(len(data), len(result)) + for i in range(len(result)): + self.assertEquals(data[i][1], result[i][1]) # "date" col + self.assertEquals(data[i][1], result[i][2]) # "date_copy" col + self.assertIsNone(result[i][3]) # "check_data" col def test_vectorized_udf_timestamps(self): from pyspark.sql.functions import pandas_udf, col @@ -3846,6 +4334,7 @@ def check_data(idx, timestamp, timestamp_copy): self.assertEquals(len(data), len(result)) for i in range(len(result)): self.assertEquals(data[i][1], result[i][1]) # "timestamp" col + self.assertEquals(data[i][1], result[i][2]) # "timestamp_copy" col self.assertIsNone(result[i][3]) # "check_data" col def test_vectorized_udf_return_timestamp_tz(self): @@ -3869,9 +4358,7 @@ def gen_timestamps(id): def test_vectorized_udf_check_config(self): from pyspark.sql.functions import pandas_udf, col import pandas as pd - orig_value = self.spark.conf.get("spark.sql.execution.arrow.maxRecordsPerBatch", None) - self.spark.conf.set("spark.sql.execution.arrow.maxRecordsPerBatch", 3) - try: + with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": 3}): df = self.spark.range(10, numPartitions=1) @pandas_udf(returnType=LongType()) @@ -3881,11 +4368,6 @@ def check_records_per_batch(x): result = df.select(check_records_per_batch(col("id"))).collect() for (r,) in result: self.assertTrue(r <= 3) - finally: - if orig_value is None: - self.spark.conf.unset("spark.sql.execution.arrow.maxRecordsPerBatch") - else: - self.spark.conf.set("spark.sql.execution.arrow.maxRecordsPerBatch", orig_value) def test_vectorized_udf_timestamps_respect_session_timezone(self): from pyspark.sql.functions import pandas_udf, col @@ -3904,40 +4386,107 @@ def test_vectorized_udf_timestamps_respect_session_timezone(self): internal_value = pandas_udf( lambda ts: ts.apply(lambda ts: ts.value if ts is not pd.NaT else None), LongType()) - orig_tz = self.spark.conf.get("spark.sql.session.timeZone") - try: - timezone = "America/New_York" - self.spark.conf.set("spark.sql.session.timeZone", timezone) - self.spark.conf.set("spark.sql.execution.pandas.respectSessionTimeZone", "false") - try: - df_la = df.withColumn("tscopy", f_timestamp_copy(col("timestamp"))) \ - .withColumn("internal_value", internal_value(col("timestamp"))) - result_la = df_la.select(col("idx"), col("internal_value")).collect() - # Correct result_la by adjusting 3 hours difference between Los Angeles and New York - diff = 3 * 60 * 60 * 1000 * 1000 * 1000 - result_la_corrected = \ - df_la.select(col("idx"), col("tscopy"), col("internal_value") + diff).collect() - finally: - self.spark.conf.set("spark.sql.execution.pandas.respectSessionTimeZone", "true") + timezone = "America/New_York" + with self.sql_conf({ + "spark.sql.execution.pandas.respectSessionTimeZone": False, + "spark.sql.session.timeZone": timezone}): + df_la = df.withColumn("tscopy", f_timestamp_copy(col("timestamp"))) \ + .withColumn("internal_value", internal_value(col("timestamp"))) + result_la = df_la.select(col("idx"), col("internal_value")).collect() + # Correct result_la by adjusting 3 hours difference between Los Angeles and New York + diff = 3 * 60 * 60 * 1000 * 1000 * 1000 + result_la_corrected = \ + df_la.select(col("idx"), col("tscopy"), col("internal_value") + diff).collect() + with self.sql_conf({ + "spark.sql.execution.pandas.respectSessionTimeZone": True, + "spark.sql.session.timeZone": timezone}): df_ny = df.withColumn("tscopy", f_timestamp_copy(col("timestamp"))) \ .withColumn("internal_value", internal_value(col("timestamp"))) result_ny = df_ny.select(col("idx"), col("tscopy"), col("internal_value")).collect() self.assertNotEqual(result_ny, result_la) self.assertEqual(result_ny, result_la_corrected) - finally: - self.spark.conf.set("spark.sql.session.timeZone", orig_tz) + def test_nondeterministic_vectorized_udf(self): + # Test that nondeterministic UDFs are evaluated only once in chained UDF evaluations + from pyspark.sql.functions import udf, pandas_udf, col -@unittest.skipIf(not _have_pandas or not _have_arrow, "Pandas or Arrow not installed") -class GroupbyApplyTests(ReusedSQLTestCase): + @pandas_udf('double') + def plus_ten(v): + return v + 10 + random_udf = self.nondeterministic_vectorized_udf - def assertFramesEqual(self, expected, result): - msg = ("DataFrames are not equal: " + - ("\n\nExpected:\n%s\n%s" % (expected, expected.dtypes)) + - ("\n\nResult:\n%s\n%s" % (result, result.dtypes))) - self.assertTrue(expected.equals(result), msg=msg) + df = self.spark.range(10).withColumn('rand', random_udf(col('id'))) + result1 = df.withColumn('plus_ten(rand)', plus_ten(df['rand'])).toPandas() + + self.assertEqual(random_udf.deterministic, False) + self.assertTrue(result1['plus_ten(rand)'].equals(result1['rand'] + 10)) + + def test_nondeterministic_vectorized_udf_in_aggregate(self): + from pyspark.sql.functions import pandas_udf, sum + + df = self.spark.range(10) + random_udf = self.nondeterministic_vectorized_udf + + with QuietTest(self.sc): + with self.assertRaisesRegexp(AnalysisException, 'nondeterministic'): + df.groupby(df.id).agg(sum(random_udf(df.id))).collect() + with self.assertRaisesRegexp(AnalysisException, 'nondeterministic'): + df.agg(sum(random_udf(df.id))).collect() + + def test_register_vectorized_udf_basic(self): + from pyspark.rdd import PythonEvalType + from pyspark.sql.functions import pandas_udf, col, expr + df = self.spark.range(10).select( + col('id').cast('int').alias('a'), + col('id').cast('int').alias('b')) + original_add = pandas_udf(lambda x, y: x + y, IntegerType()) + self.assertEqual(original_add.deterministic, True) + self.assertEqual(original_add.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF) + new_add = self.spark.catalog.registerFunction("add1", original_add) + res1 = df.select(new_add(col('a'), col('b'))) + res2 = self.spark.sql( + "SELECT add1(t.a, t.b) FROM (SELECT id as a, id as b FROM range(10)) t") + expected = df.select(expr('a + b')) + self.assertEquals(expected.collect(), res1.collect()) + self.assertEquals(expected.collect(), res2.collect()) + + # Regression test for SPARK-23314 + def test_timestamp_dst(self): + from pyspark.sql.functions import pandas_udf + # Daylight saving time for Los Angeles for 2015 is Sun, Nov 1 at 2:00 am + dt = [datetime.datetime(2015, 11, 1, 0, 30), + datetime.datetime(2015, 11, 1, 1, 30), + datetime.datetime(2015, 11, 1, 2, 30)] + df = self.spark.createDataFrame(dt, 'timestamp').toDF('time') + foo_udf = pandas_udf(lambda x: x, 'timestamp') + result = df.withColumn('time', foo_udf(df.time)) + self.assertEquals(df.collect(), result.collect()) + + @unittest.skipIf(sys.version_info[:2] < (3, 5), "Type hints are supported from Python 3.5.") + def test_type_annotation(self): + from pyspark.sql.functions import pandas_udf + # Regression test to check if type hints can be used. See SPARK-23569. + # Note that it throws an error during compilation in lower Python versions if 'exec' + # is not used. Also, note that we explicitly use another dictionary to avoid modifications + # in the current 'locals()'. + # + # Hyukjin: I think it's an ugly way to test issues about syntax specific in + # higher versions of Python, which we shouldn't encourage. This was the last resort + # I could come up with at that time. + _locals = {} + exec( + "import pandas as pd\ndef noop(col: pd.Series) -> pd.Series: return col", + _locals) + df = self.spark.range(1).select(pandas_udf(f=_locals['noop'], returnType='bigint')('id')) + self.assertEqual(df.first()[0], 0) + + +@unittest.skipIf( + not _have_pandas or not _have_pyarrow, + _pandas_requirement_message or _pyarrow_requirement_message) +class GroupedMapPandasUDFTests(ReusedSQLTestCase): @property def data(self): @@ -3946,23 +4495,53 @@ def data(self): .withColumn("vs", array([lit(i) for i in range(20, 30)])) \ .withColumn("v", explode(col('vs'))).drop('vs') - def test_simple(self): - from pyspark.sql.functions import pandas_udf, PandasUDFType - df = self.data + def test_supported_types(self): + from pyspark.sql.functions import pandas_udf, PandasUDFType, array, col + df = self.data.withColumn("arr", array(col("id"))) foo_udf = pandas_udf( lambda pdf: pdf.assign(v1=pdf.v * pdf.id * 1.0, v2=pdf.v + pdf.id), StructType( [StructField('id', LongType()), StructField('v', IntegerType()), + StructField('arr', ArrayType(LongType())), StructField('v1', DoubleType()), StructField('v2', LongType())]), - PandasUDFType.GROUP_MAP + PandasUDFType.GROUPED_MAP ) result = df.groupby('id').apply(foo_udf).sort('id').toPandas() expected = df.toPandas().groupby('id').apply(foo_udf.func).reset_index(drop=True) - self.assertFramesEqual(expected, result) + self.assertPandasEqual(expected, result) + + def test_array_type_correct(self): + from pyspark.sql.functions import pandas_udf, PandasUDFType, array, col + + df = self.data.withColumn("arr", array(col("id"))).repartition(1, "id") + + output_schema = StructType( + [StructField('id', LongType()), + StructField('v', IntegerType()), + StructField('arr', ArrayType(LongType()))]) + + udf = pandas_udf( + lambda pdf: pdf, + output_schema, + PandasUDFType.GROUPED_MAP + ) + + result = df.groupby('id').apply(udf).sort('id').toPandas() + expected = df.toPandas().groupby('id').apply(udf.func).reset_index(drop=True) + self.assertPandasEqual(expected, result) + + def test_register_grouped_map_udf(self): + from pyspark.sql.functions import pandas_udf, PandasUDFType + + foo_udf = pandas_udf(lambda x: x, "id long", PandasUDFType.GROUPED_MAP) + with QuietTest(self.sc): + with self.assertRaisesRegexp(ValueError, 'f must be either SQL_BATCHED_UDF or ' + 'SQL_SCALAR_PANDAS_UDF'): + self.spark.catalog.registerFunction("foo_udf", foo_udf) def test_decorator(self): from pyspark.sql.functions import pandas_udf, PandasUDFType @@ -3970,14 +4549,14 @@ def test_decorator(self): @pandas_udf( 'id long, v int, v1 double, v2 long', - PandasUDFType.GROUP_MAP + PandasUDFType.GROUPED_MAP ) def foo(pdf): return pdf.assign(v1=pdf.v * pdf.id * 1.0, v2=pdf.v + pdf.id) result = df.groupby('id').apply(foo).sort('id').toPandas() expected = df.toPandas().groupby('id').apply(foo.func).reset_index(drop=True) - self.assertFramesEqual(expected, result) + self.assertPandasEqual(expected, result) def test_coerce(self): from pyspark.sql.functions import pandas_udf, PandasUDFType @@ -3986,13 +4565,13 @@ def test_coerce(self): foo = pandas_udf( lambda pdf: pdf, 'id long, v double', - PandasUDFType.GROUP_MAP + PandasUDFType.GROUPED_MAP ) result = df.groupby('id').apply(foo).sort('id').toPandas() expected = df.toPandas().groupby('id').apply(foo.func).reset_index(drop=True) expected = expected.assign(v=expected.v.astype('float64')) - self.assertFramesEqual(expected, result) + self.assertPandasEqual(expected, result) def test_complex_groupby(self): from pyspark.sql.functions import pandas_udf, col, PandasUDFType @@ -4000,7 +4579,7 @@ def test_complex_groupby(self): @pandas_udf( 'id long, v int, norm double', - PandasUDFType.GROUP_MAP + PandasUDFType.GROUPED_MAP ) def normalize(pdf): v = pdf.v @@ -4011,7 +4590,7 @@ def normalize(pdf): expected = pdf.groupby(pdf['id'] % 2 == 0).apply(normalize.func) expected = expected.sort_values(['id', 'v']).reset_index(drop=True) expected = expected.assign(norm=expected.norm.astype('float64')) - self.assertFramesEqual(expected, result) + self.assertPandasEqual(expected, result) def test_empty_groupby(self): from pyspark.sql.functions import pandas_udf, col, PandasUDFType @@ -4019,7 +4598,7 @@ def test_empty_groupby(self): @pandas_udf( 'id long, v int, norm double', - PandasUDFType.GROUP_MAP + PandasUDFType.GROUPED_MAP ) def normalize(pdf): v = pdf.v @@ -4030,7 +4609,7 @@ def normalize(pdf): expected = normalize.func(pdf) expected = expected.sort_values(['id', 'v']).reset_index(drop=True) expected = expected.assign(norm=expected.norm.astype('float64')) - self.assertFramesEqual(expected, result) + self.assertPandasEqual(expected, result) def test_datatype_string(self): from pyspark.sql.functions import pandas_udf, PandasUDFType @@ -4039,26 +4618,24 @@ def test_datatype_string(self): foo_udf = pandas_udf( lambda pdf: pdf.assign(v1=pdf.v * pdf.id * 1.0, v2=pdf.v + pdf.id), 'id long, v int, v1 double, v2 long', - PandasUDFType.GROUP_MAP + PandasUDFType.GROUPED_MAP ) result = df.groupby('id').apply(foo_udf).sort('id').toPandas() expected = df.toPandas().groupby('id').apply(foo_udf.func).reset_index(drop=True) - self.assertFramesEqual(expected, result) + self.assertPandasEqual(expected, result) def test_wrong_return_type(self): from pyspark.sql.functions import pandas_udf, PandasUDFType - df = self.data - - foo = pandas_udf( - lambda pdf: pdf, - 'id long, v map', - PandasUDFType.GROUP_MAP - ) with QuietTest(self.sc): - with self.assertRaisesRegexp(Exception, 'Unsupported.*type.*conversion'): - df.groupby('id').apply(foo).sort('id').toPandas() + with self.assertRaisesRegexp( + NotImplementedError, + 'Invalid returnType.*grouped map Pandas UDF.*MapType'): + pandas_udf( + lambda pdf: pdf, + 'id long, v map', + PandasUDFType.GROUPED_MAP) def test_wrong_args(self): from pyspark.sql.functions import udf, pandas_udf, sum, PandasUDFType @@ -4077,23 +4654,42 @@ def test_wrong_args(self): df.groupby('id').apply( pandas_udf(lambda: 1, StructType([StructField("d", DoubleType())]))) with self.assertRaisesRegexp(ValueError, 'Invalid udf'): + df.groupby('id').apply(pandas_udf(lambda x, y: x, DoubleType())) + with self.assertRaisesRegexp(ValueError, 'Invalid udf.*GROUPED_MAP'): df.groupby('id').apply( - pandas_udf(lambda x, y: x, StructType([StructField("d", DoubleType())]))) - with self.assertRaisesRegexp(ValueError, 'Invalid udf.*GROUP_MAP'): - df.groupby('id').apply( - pandas_udf(lambda x, y: x, StructType([StructField("d", DoubleType())]), - PandasUDFType.SCALAR)) + pandas_udf(lambda x, y: x, DoubleType(), PandasUDFType.SCALAR)) def test_unsupported_types(self): - from pyspark.sql.functions import pandas_udf, col, PandasUDFType + from pyspark.sql.functions import pandas_udf, PandasUDFType schema = StructType( [StructField("id", LongType(), True), StructField("map", MapType(StringType(), IntegerType()), True)]) - df = self.spark.createDataFrame([(1, None,)], schema=schema) - f = pandas_udf(lambda x: x, df.schema, PandasUDFType.GROUP_MAP) with QuietTest(self.sc): - with self.assertRaisesRegexp(Exception, 'Unsupported data type'): - df.groupby('id').apply(f).collect() + with self.assertRaisesRegexp( + NotImplementedError, + 'Invalid returnType.*grouped map Pandas UDF.*MapType'): + pandas_udf(lambda x: x, schema, PandasUDFType.GROUPED_MAP) + + schema = StructType( + [StructField("id", LongType(), True), + StructField("arr_ts", ArrayType(TimestampType()), True)]) + with QuietTest(self.sc): + with self.assertRaisesRegexp( + NotImplementedError, + 'Invalid returnType.*grouped map Pandas UDF.*ArrayType.*TimestampType'): + pandas_udf(lambda x: x, schema, PandasUDFType.GROUPED_MAP) + + # Regression test for SPARK-23314 + def test_timestamp_dst(self): + from pyspark.sql.functions import pandas_udf, PandasUDFType + # Daylight saving time for Los Angeles for 2015 is Sun, Nov 1 at 2:00 am + dt = [datetime.datetime(2015, 11, 1, 0, 30), + datetime.datetime(2015, 11, 1, 1, 30), + datetime.datetime(2015, 11, 1, 2, 30)] + df = self.spark.createDataFrame(dt, 'timestamp').toDF('time') + foo_udf = pandas_udf(lambda pdf: pdf, 'time timestamp', PandasUDFType.GROUPED_MAP) + result = df.groupby('time').apply(foo_udf).sort('time') + self.assertPandasEqual(df.toPandas(), result.toPandas()) if __name__ == "__main__": diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py index 146e673ae9756..cd857402db8f7 100644 --- a/python/pyspark/sql/types.py +++ b/python/pyspark/sql/types.py @@ -455,9 +455,6 @@ class StructType(DataType): Iterating a :class:`StructType` will iterate its :class:`StructField`\\s. A contained :class:`StructField` can be accessed by name or position. - .. note:: `names` attribute is deprecated in 2.3. Use `fieldNames` method instead - to get a list of field names. - >>> struct1 = StructType([StructField("f1", StringType(), True)]) >>> struct1["f1"] StructField(f1,StringType,true) @@ -1073,7 +1070,7 @@ def _infer_type(obj): raise TypeError("not supported type: %s" % type(obj)) -def _infer_schema(row): +def _infer_schema(row, names=None): """Infer the schema from dict/namedtuple/object""" if isinstance(row, dict): items = sorted(row.items()) @@ -1084,7 +1081,10 @@ def _infer_schema(row): elif hasattr(row, "_fields"): # namedtuple items = zip(row._fields, tuple(row)) else: - names = ['_%d' % i for i in range(1, len(row) + 1)] + if names is None: + names = ['_%d' % i for i in range(1, len(row) + 1)] + elif len(names) < len(row): + names.extend('_%d' % i for i in range(len(names) + 1, len(row) + 1)) items = zip(names, row) elif hasattr(row, "__dict__"): # object @@ -1109,19 +1109,27 @@ def _has_nulltype(dt): return isinstance(dt, NullType) -def _merge_type(a, b): +def _merge_type(a, b, name=None): + if name is None: + new_msg = lambda msg: msg + new_name = lambda n: "field %s" % n + else: + new_msg = lambda msg: "%s: %s" % (name, msg) + new_name = lambda n: "field %s in %s" % (n, name) + if isinstance(a, NullType): return b elif isinstance(b, NullType): return a elif type(a) is not type(b): # TODO: type cast (such as int -> long) - raise TypeError("Can not merge type %s and %s" % (type(a), type(b))) + raise TypeError(new_msg("Can not merge type %s and %s" % (type(a), type(b)))) # same type if isinstance(a, StructType): nfs = dict((f.name, f.dataType) for f in b.fields) - fields = [StructField(f.name, _merge_type(f.dataType, nfs.get(f.name, NullType()))) + fields = [StructField(f.name, _merge_type(f.dataType, nfs.get(f.name, NullType()), + name=new_name(f.name))) for f in a.fields] names = set([f.name for f in fields]) for n in nfs: @@ -1130,11 +1138,12 @@ def _merge_type(a, b): return StructType(fields) elif isinstance(a, ArrayType): - return ArrayType(_merge_type(a.elementType, b.elementType), True) + return ArrayType(_merge_type(a.elementType, b.elementType, + name='element in array %s' % name), True) elif isinstance(a, MapType): - return MapType(_merge_type(a.keyType, b.keyType), - _merge_type(a.valueType, b.valueType), + return MapType(_merge_type(a.keyType, b.keyType, name='key of map %s' % name), + _merge_type(a.valueType, b.valueType, name='value of map %s' % name), True) else: return a @@ -1626,6 +1635,8 @@ def to_arrow_type(dt): # Timestamps should be in UTC, JVM Arrow timestamps require a timezone to be read arrow_type = pa.timestamp('us', tz='UTC') elif type(dt) == ArrayType: + if type(dt.elementType) == TimestampType: + raise TypeError("Unsupported type in conversion to Arrow: " + str(dt)) arrow_type = pa.list_(to_arrow_type(dt.elementType)) else: raise TypeError("Unsupported type in conversion to Arrow: " + str(dt)) @@ -1668,6 +1679,8 @@ def from_arrow_type(at): elif types.is_timestamp(at): spark_type = TimestampType() elif types.is_list(at): + if types.is_timestamp(at.value_type): + raise TypeError("Unsupported type in conversion from Arrow: " + str(at)) spark_type = ArrayType(from_arrow_type(at.value_type)) else: raise TypeError("Unsupported type in conversion from Arrow: " + str(at)) @@ -1682,6 +1695,36 @@ def from_arrow_schema(arrow_schema): for field in arrow_schema]) +def _check_dataframe_convert_date(pdf, schema): + """ Correct date type value to use datetime.date. + + Pandas DataFrame created from PyArrow uses datetime64[ns] for date type values, but we should + use datetime.date to match the behavior with when Arrow optimization is disabled. + + :param pdf: pandas.DataFrame + :param schema: a Spark schema of the pandas.DataFrame + """ + for field in schema: + if type(field.dataType) == DateType: + pdf[field.name] = pdf[field.name].dt.date + return pdf + + +def _get_local_timezone(): + """ Get local timezone using pytz with environment variable, or dateutil. + + If there is a 'TZ' environment variable, pass it to pandas to use pytz and use it as timezone + string, otherwise use the special word 'dateutil/:' which means that pandas uses dateutil and + it reads system configuration to know the system local timezone. + + See also: + - https://github.com/pandas-dev/pandas/blob/0.19.x/pandas/tslib.pyx#L1753 + - https://github.com/dateutil/dateutil/blob/2.6.1/dateutil/tz/tz.py#L1338 + """ + import os + return os.environ.get('TZ', 'dateutil/:') + + def _check_dataframe_localize_timestamps(pdf, timezone): """ Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone @@ -1694,7 +1737,7 @@ def _check_dataframe_localize_timestamps(pdf, timezone): require_minimum_pandas_version() from pandas.api.types import is_datetime64tz_dtype - tz = timezone or 'tzlocal()' + tz = timezone or _get_local_timezone() for column, series in pdf.iteritems(): # TODO: handle nested timestamps, such as ArrayType(TimestampType())? if is_datetime64tz_dtype(series.dtype): @@ -1717,8 +1760,38 @@ def _check_series_convert_timestamps_internal(s, timezone): from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype # TODO: handle nested timestamps, such as ArrayType(TimestampType())? if is_datetime64_dtype(s.dtype): - tz = timezone or 'tzlocal()' - return s.dt.tz_localize(tz).dt.tz_convert('UTC') + # When tz_localize a tz-naive timestamp, the result is ambiguous if the tz-naive + # timestamp is during the hour when the clock is adjusted backward during due to + # daylight saving time (dst). + # E.g., for America/New_York, the clock is adjusted backward on 2015-11-01 2:00 to + # 2015-11-01 1:00 from dst-time to standard time, and therefore, when tz_localize + # a tz-naive timestamp 2015-11-01 1:30 with America/New_York timezone, it can be either + # dst time (2015-01-01 1:30-0400) or standard time (2015-11-01 1:30-0500). + # + # Here we explicit choose to use standard time. This matches the default behavior of + # pytz. + # + # Here are some code to help understand this behavior: + # >>> import datetime + # >>> import pandas as pd + # >>> import pytz + # >>> + # >>> t = datetime.datetime(2015, 11, 1, 1, 30) + # >>> ts = pd.Series([t]) + # >>> tz = pytz.timezone('America/New_York') + # >>> + # >>> ts.dt.tz_localize(tz, ambiguous=True) + # 0 2015-11-01 01:30:00-04:00 + # dtype: datetime64[ns, America/New_York] + # >>> + # >>> ts.dt.tz_localize(tz, ambiguous=False) + # 0 2015-11-01 01:30:00-05:00 + # dtype: datetime64[ns, America/New_York] + # >>> + # >>> str(tz.localize(t)) + # '2015-11-01 01:30:00-05:00' + tz = timezone or _get_local_timezone() + return s.dt.tz_localize(tz, ambiguous=False).dt.tz_convert('UTC') elif is_datetime64tz_dtype(s.dtype): return s.dt.tz_convert('UTC') else: @@ -1739,15 +1812,16 @@ def _check_series_convert_timestamps_localize(s, from_timezone, to_timezone): import pandas as pd from pandas.api.types import is_datetime64tz_dtype, is_datetime64_dtype - from_tz = from_timezone or 'tzlocal()' - to_tz = to_timezone or 'tzlocal()' + from_tz = from_timezone or _get_local_timezone() + to_tz = to_timezone or _get_local_timezone() # TODO: handle nested timestamps, such as ArrayType(TimestampType())? if is_datetime64tz_dtype(s.dtype): return s.dt.tz_convert(to_tz).dt.tz_localize(None) elif is_datetime64_dtype(s.dtype) and from_tz != to_tz: # `s.dt.tz_localize('tzlocal()')` doesn't work properly when including NaT. - return s.apply(lambda ts: ts.tz_localize(from_tz).tz_convert(to_tz).tz_localize(None) - if ts is not pd.NaT else pd.NaT) + return s.apply( + lambda ts: ts.tz_localize(from_tz, ambiguous=False).tz_convert(to_tz).tz_localize(None) + if ts is not pd.NaT else pd.NaT) else: return s diff --git a/python/pyspark/sql/udf.py b/python/pyspark/sql/udf.py index 54b5a8656e1c8..671e5680b8e7b 100644 --- a/python/pyspark/sql/udf.py +++ b/python/pyspark/sql/udf.py @@ -19,10 +19,13 @@ """ import functools -from pyspark import SparkContext -from pyspark.rdd import _prepare_for_python_RDD, PythonEvalType +from pyspark import SparkContext, since +from pyspark.rdd import _prepare_for_python_RDD, PythonEvalType, ignore_unicode_prefix from pyspark.sql.column import Column, _to_java_column, _to_seq -from pyspark.sql.types import StringType, DataType, StructType, _parse_datatype_string +from pyspark.sql.types import StringType, DataType, StructType, _parse_datatype_string, \ + to_arrow_type, to_arrow_schema + +__all__ = ["UDFRegistration"] def _wrap_function(sc, func, returnType): @@ -34,29 +37,37 @@ def _wrap_function(sc, func, returnType): def _create_udf(f, returnType, evalType): - if evalType == PythonEvalType.SQL_PANDAS_SCALAR_UDF or \ - evalType == PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF: + if evalType in (PythonEvalType.SQL_SCALAR_PANDAS_UDF, + PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF): import inspect + import sys from pyspark.sql.utils import require_minimum_pyarrow_version require_minimum_pyarrow_version() - argspec = inspect.getargspec(f) - if evalType == PythonEvalType.SQL_PANDAS_SCALAR_UDF and len(argspec.args) == 0 and \ + if sys.version_info[0] < 3: + # `getargspec` is deprecated since python3.0 (incompatible with function annotations). + # See SPARK-23569. + argspec = inspect.getargspec(f) + else: + argspec = inspect.getfullargspec(f) + + if evalType == PythonEvalType.SQL_SCALAR_PANDAS_UDF and len(argspec.args) == 0 and \ argspec.varargs is None: raise ValueError( "Invalid function: 0-arg pandas_udfs are not supported. " "Instead, create a 1-arg pandas_udf and ignore the arg in your function." ) - if evalType == PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF and len(argspec.args) != 1: + if evalType == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF and len(argspec.args) != 1: raise ValueError( - "Invalid function: pandas_udfs with function type GROUP_MAP " + "Invalid function: pandas_udfs with function type GROUPED_MAP " "must take a single arg that is a pandas DataFrame." ) # Set the name of the UserDefinedFunction object to be the name of function f - udf_obj = UserDefinedFunction(f, returnType=returnType, name=None, evalType=evalType) + udf_obj = UserDefinedFunction( + f, returnType=returnType, name=None, evalType=evalType, deterministic=True) return udf_obj._wrapped() @@ -67,8 +78,10 @@ class UserDefinedFunction(object): .. versionadded:: 1.3 """ def __init__(self, func, - returnType=StringType(), name=None, - evalType=PythonEvalType.SQL_BATCHED_UDF): + returnType=StringType(), + name=None, + evalType=PythonEvalType.SQL_BATCHED_UDF, + deterministic=True): if not callable(func): raise TypeError( "Invalid function: not a function or callable (__call__ is not defined): " @@ -92,7 +105,7 @@ def __init__(self, func, func.__name__ if hasattr(func, '__name__') else func.__class__.__name__) self.evalType = evalType - self._deterministic = True + self.deterministic = deterministic @property def returnType(self): @@ -104,10 +117,24 @@ def returnType(self): else: self._returnType_placeholder = _parse_datatype_string(self._returnType) - if self.evalType == PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF \ - and not isinstance(self._returnType_placeholder, StructType): - raise ValueError("Invalid returnType: returnType must be a StructType for " - "pandas_udf with function type GROUP_MAP") + if self.evalType == PythonEvalType.SQL_SCALAR_PANDAS_UDF: + try: + to_arrow_type(self._returnType_placeholder) + except TypeError: + raise NotImplementedError( + "Invalid returnType with scalar Pandas UDFs: %s is " + "not supported" % str(self._returnType_placeholder)) + elif self.evalType == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF: + if isinstance(self._returnType_placeholder, StructType): + try: + to_arrow_schema(self._returnType_placeholder) + except TypeError: + raise NotImplementedError( + "Invalid returnType with grouped map Pandas UDFs: " + "%s is not supported" % str(self._returnType_placeholder)) + else: + raise TypeError("Invalid returnType for grouped map Pandas " + "UDFs: returnType must be a StructType.") return self._returnType_placeholder @@ -130,7 +157,7 @@ def _create_judf(self): wrapped_func = _wrap_function(sc, self.func, self.returnType) jdt = spark._jsparkSession.parseDataType(self.returnType.json()) judf = sc._jvm.org.apache.spark.sql.execution.python.UserDefinedPythonFunction( - self._name, wrapped_func, jdt, self.evalType, self._deterministic) + self._name, wrapped_func, jdt, self.evalType, self.deterministic) return judf def __call__(self, *cols): @@ -138,6 +165,9 @@ def __call__(self, *cols): sc = SparkContext._active_spark_context return Column(judf.apply(_to_seq(sc, cols, _to_java_column))) + # This function is for improving the online help system in the interactive interpreter. + # For example, the built-in help / pydoc.help. It wraps the UDF with the docstring and + # argument annotation. (See: SPARK-19161) def _wrapped(self): """ Wrap this udf with a function and attach docstring from func @@ -162,8 +192,9 @@ def wrapper(*args): wrapper.func = self.func wrapper.returnType = self.returnType wrapper.evalType = self.evalType - wrapper.asNondeterministic = self.asNondeterministic - + wrapper.deterministic = self.deterministic + wrapper.asNondeterministic = functools.wraps( + self.asNondeterministic)(lambda: self.asNondeterministic()._wrapped()) return wrapper def asNondeterministic(self): @@ -172,5 +203,198 @@ def asNondeterministic(self): .. versionadded:: 2.3 """ - self._deterministic = False + # Here, we explicitly clean the cache to create a JVM UDF instance + # with 'deterministic' updated. See SPARK-23233. + self._judf_placeholder = None + self.deterministic = False return self + + +class UDFRegistration(object): + """ + Wrapper for user-defined function registration. This instance can be accessed by + :attr:`spark.udf` or :attr:`sqlContext.udf`. + + .. versionadded:: 1.3.1 + """ + + def __init__(self, sparkSession): + self.sparkSession = sparkSession + + @ignore_unicode_prefix + @since("1.3.1") + def register(self, name, f, returnType=None): + """Register a Python function (including lambda function) or a user-defined function + as a SQL function. + + :param name: name of the user-defined function in SQL statements. + :param f: a Python function, or a user-defined function. The user-defined function can + be either row-at-a-time or vectorized. See :meth:`pyspark.sql.functions.udf` and + :meth:`pyspark.sql.functions.pandas_udf`. + :param returnType: the return type of the registered user-defined function. The value can + be either a :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string. + :return: a user-defined function. + + To register a nondeterministic Python function, users need to first build + a nondeterministic user-defined function for the Python function and then register it + as a SQL function. + + `returnType` can be optionally specified when `f` is a Python function but not + when `f` is a user-defined function. Please see below. + + 1. When `f` is a Python function: + + `returnType` defaults to string type and can be optionally specified. The produced + object must match the specified type. In this case, this API works as if + `register(name, f, returnType=StringType())`. + + >>> strlen = spark.udf.register("stringLengthString", lambda x: len(x)) + >>> spark.sql("SELECT stringLengthString('test')").collect() + [Row(stringLengthString(test)=u'4')] + + >>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect() + [Row(stringLengthString(text)=u'3')] + + >>> from pyspark.sql.types import IntegerType + >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) + >>> spark.sql("SELECT stringLengthInt('test')").collect() + [Row(stringLengthInt(test)=4)] + + >>> from pyspark.sql.types import IntegerType + >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) + >>> spark.sql("SELECT stringLengthInt('test')").collect() + [Row(stringLengthInt(test)=4)] + + 2. When `f` is a user-defined function: + + Spark uses the return type of the given user-defined function as the return type of + the registered user-defined function. `returnType` should not be specified. + In this case, this API works as if `register(name, f)`. + + >>> from pyspark.sql.types import IntegerType + >>> from pyspark.sql.functions import udf + >>> slen = udf(lambda s: len(s), IntegerType()) + >>> _ = spark.udf.register("slen", slen) + >>> spark.sql("SELECT slen('test')").collect() + [Row(slen(test)=4)] + + >>> import random + >>> from pyspark.sql.functions import udf + >>> from pyspark.sql.types import IntegerType + >>> random_udf = udf(lambda: random.randint(0, 100), IntegerType()).asNondeterministic() + >>> new_random_udf = spark.udf.register("random_udf", random_udf) + >>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP + [Row(random_udf()=82)] + + >>> from pyspark.sql.functions import pandas_udf, PandasUDFType + >>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP + ... def add_one(x): + ... return x + 1 + ... + >>> _ = spark.udf.register("add_one", add_one) # doctest: +SKIP + >>> spark.sql("SELECT add_one(id) FROM range(3)").collect() # doctest: +SKIP + [Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)] + + .. note:: Registration for a user-defined function (case 2.) was added from + Spark 2.3.0. + """ + + # This is to check whether the input function is from a user-defined function or + # Python function. + if hasattr(f, 'asNondeterministic'): + if returnType is not None: + raise TypeError( + "Invalid returnType: data type can not be specified when f is" + "a user-defined function, but got %s." % returnType) + if f.evalType not in [PythonEvalType.SQL_BATCHED_UDF, + PythonEvalType.SQL_SCALAR_PANDAS_UDF]: + raise ValueError( + "Invalid f: f must be either SQL_BATCHED_UDF or SQL_SCALAR_PANDAS_UDF") + register_udf = UserDefinedFunction(f.func, returnType=f.returnType, name=name, + evalType=f.evalType, + deterministic=f.deterministic) + return_udf = f + else: + if returnType is None: + returnType = StringType() + register_udf = UserDefinedFunction(f, returnType=returnType, name=name, + evalType=PythonEvalType.SQL_BATCHED_UDF) + return_udf = register_udf._wrapped() + self.sparkSession._jsparkSession.udf().registerPython(name, register_udf._judf) + return return_udf + + @ignore_unicode_prefix + @since(2.3) + def registerJavaFunction(self, name, javaClassName, returnType=None): + """Register a Java user-defined function as a SQL function. + + In addition to a name and the function itself, the return type can be optionally specified. + When the return type is not specified we would infer it via reflection. + + :param name: name of the user-defined function + :param javaClassName: fully qualified name of java class + :param returnType: the return type of the registered Java function. The value can be either + a :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string. + + >>> from pyspark.sql.types import IntegerType + >>> spark.udf.registerJavaFunction( + ... "javaStringLength", "test.org.apache.spark.sql.JavaStringLength", IntegerType()) + >>> spark.sql("SELECT javaStringLength('test')").collect() + [Row(UDF:javaStringLength(test)=4)] + + >>> spark.udf.registerJavaFunction( + ... "javaStringLength2", "test.org.apache.spark.sql.JavaStringLength") + >>> spark.sql("SELECT javaStringLength2('test')").collect() + [Row(UDF:javaStringLength2(test)=4)] + + >>> spark.udf.registerJavaFunction( + ... "javaStringLength3", "test.org.apache.spark.sql.JavaStringLength", "integer") + >>> spark.sql("SELECT javaStringLength3('test')").collect() + [Row(UDF:javaStringLength3(test)=4)] + """ + + jdt = None + if returnType is not None: + if not isinstance(returnType, DataType): + returnType = _parse_datatype_string(returnType) + jdt = self.sparkSession._jsparkSession.parseDataType(returnType.json()) + self.sparkSession._jsparkSession.udf().registerJava(name, javaClassName, jdt) + + @ignore_unicode_prefix + @since(2.3) + def registerJavaUDAF(self, name, javaClassName): + """Register a Java user-defined aggregate function as a SQL function. + + :param name: name of the user-defined aggregate function + :param javaClassName: fully qualified name of java class + + >>> spark.udf.registerJavaUDAF("javaUDAF", "test.org.apache.spark.sql.MyDoubleAvg") + >>> df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "a")],["id", "name"]) + >>> df.createOrReplaceTempView("df") + >>> spark.sql("SELECT name, javaUDAF(id) as avg from df group by name").collect() + [Row(name=u'b', avg=102.0), Row(name=u'a', avg=102.0)] + """ + + self.sparkSession._jsparkSession.udf().registerJavaUDAF(name, javaClassName) + + +def _test(): + import doctest + from pyspark.sql import SparkSession + import pyspark.sql.udf + globs = pyspark.sql.udf.__dict__.copy() + spark = SparkSession.builder\ + .master("local[4]")\ + .appName("sql.udf tests")\ + .getOrCreate() + globs['spark'] = spark + (failure_count, test_count) = doctest.testmod( + pyspark.sql.udf, globs=globs, + optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) + spark.stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/sql/utils.py b/python/pyspark/sql/utils.py index 08c34c6dccc5e..578298632dd4c 100644 --- a/python/pyspark/sql/utils.py +++ b/python/pyspark/sql/utils.py @@ -115,18 +115,32 @@ def toJArray(gateway, jtype, arr): def require_minimum_pandas_version(): """ Raise ImportError if minimum version of Pandas is not installed """ + # TODO(HyukjinKwon): Relocate and deduplicate the version specification. + minimum_pandas_version = "0.19.2" + from distutils.version import LooseVersion - import pandas - if LooseVersion(pandas.__version__) < LooseVersion('0.19.2'): - raise ImportError("Pandas >= 0.19.2 must be installed on calling Python process; " - "however, your version was %s." % pandas.__version__) + try: + import pandas + except ImportError: + raise ImportError("Pandas >= %s must be installed; however, " + "it was not found." % minimum_pandas_version) + if LooseVersion(pandas.__version__) < LooseVersion(minimum_pandas_version): + raise ImportError("Pandas >= %s must be installed; however, " + "your version was %s." % (minimum_pandas_version, pandas.__version__)) def require_minimum_pyarrow_version(): """ Raise ImportError if minimum version of pyarrow is not installed """ + # TODO(HyukjinKwon): Relocate and deduplicate the version specification. + minimum_pyarrow_version = "0.8.0" + from distutils.version import LooseVersion - import pyarrow - if LooseVersion(pyarrow.__version__) < LooseVersion('0.8.0'): - raise ImportError("pyarrow >= 0.8.0 must be installed on calling Python process; " - "however, your version was %s." % pyarrow.__version__) + try: + import pyarrow + except ImportError: + raise ImportError("PyArrow >= %s must be installed; however, " + "it was not found." % minimum_pyarrow_version) + if LooseVersion(pyarrow.__version__) < LooseVersion(minimum_pyarrow_version): + raise ImportError("PyArrow >= %s must be installed; however, " + "your version was %s." % (minimum_pyarrow_version, pyarrow.__version__)) diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py index fdb9308604489..ed2e0e7d10fa2 100644 --- a/python/pyspark/streaming/kafka.py +++ b/python/pyspark/streaming/kafka.py @@ -104,7 +104,8 @@ def createDirectStream(ssc, topics, kafkaParams, fromOffsets=None, :param topics: list of topic_name to consume. :param kafkaParams: Additional params for Kafka. :param fromOffsets: Per-topic/partition Kafka offsets defining the (inclusive) starting - point of the stream. + point of the stream (a dictionary mapping `TopicAndPartition` to + integers). :param keyDecoder: A function used to decode key (default is utf8_decoder). :param valueDecoder: A function used to decode value (default is utf8_decoder). :param messageHandler: A function used to convert KafkaMessageAndMetadata. You can assess diff --git a/python/pyspark/streaming/listener.py b/python/pyspark/streaming/listener.py index b830797f5c0a0..d4ecc215aea99 100644 --- a/python/pyspark/streaming/listener.py +++ b/python/pyspark/streaming/listener.py @@ -23,6 +23,12 @@ class StreamingListener(object): def __init__(self): pass + def onStreamingStarted(self, streamingStarted): + """ + Called when the streaming has been started. + """ + pass + def onReceiverStarted(self, receiverStarted): """ Called when a receiver has been started diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 5b86c1cb2c390..1ec418a7cea91 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -507,6 +507,10 @@ def __init__(self): self.batchInfosCompleted = [] self.batchInfosStarted = [] self.batchInfosSubmitted = [] + self.streamingStartedTime = [] + + def onStreamingStarted(self, streamingStarted): + self.streamingStartedTime.append(streamingStarted.time) def onBatchSubmitted(self, batchSubmitted): self.batchInfosSubmitted.append(batchSubmitted.batchInfo()) @@ -530,9 +534,12 @@ def func(dstream): batchInfosSubmitted = batch_collector.batchInfosSubmitted batchInfosStarted = batch_collector.batchInfosStarted batchInfosCompleted = batch_collector.batchInfosCompleted + streamingStartedTime = batch_collector.streamingStartedTime self.wait_for(batchInfosCompleted, 4) + self.assertEqual(len(streamingStartedTime), 1) + self.assertGreaterEqual(len(batchInfosSubmitted), 4) for info in batchInfosSubmitted: self.assertGreaterEqual(info.batchTime().milliseconds(), 0) @@ -1503,10 +1510,13 @@ def search_flume_assembly_jar(): return jars[0] -def search_kinesis_asl_assembly_jar(): +def _kinesis_asl_assembly_dir(): SPARK_HOME = os.environ["SPARK_HOME"] - kinesis_asl_assembly_dir = os.path.join(SPARK_HOME, "external/kinesis-asl-assembly") - jars = search_jar(kinesis_asl_assembly_dir, "spark-streaming-kinesis-asl-assembly") + return os.path.join(SPARK_HOME, "external/kinesis-asl-assembly") + + +def search_kinesis_asl_assembly_jar(): + jars = search_jar(_kinesis_asl_assembly_dir(), "spark-streaming-kinesis-asl-assembly") if not jars: return None elif len(jars) > 1: @@ -1569,7 +1579,7 @@ def search_kinesis_asl_assembly_jar(): else: raise Exception( ("Failed to find Spark Streaming Kinesis assembly jar in %s. " - % kinesis_asl_assembly_dir) + + % _kinesis_asl_assembly_dir()) + "You need to build Spark with 'build/sbt -Pkinesis-asl " "assembly/package streaming-kinesis-asl-assembly/assembly'" "or 'build/mvn -Pkinesis-asl package' before running this test.") diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index da99872da2f0e..81bff4b253586 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -161,6 +161,37 @@ def gen_gs(N, step=1): self.assertEqual(k, len(vs)) self.assertEqual(list(range(k)), list(vs)) + def test_stopiteration_is_raised(self): + + def stopit(*args, **kwargs): + raise StopIteration() + + def legit_create_combiner(x): + return [x] + + def legit_merge_value(x, y): + return x.append(y) or x + + def legit_merge_combiners(x, y): + return x.extend(y) or x + + data = [(x % 2, x) for x in range(100)] + + # wrong create combiner + m = ExternalMerger(Aggregator(stopit, legit_merge_value, legit_merge_combiners), 20) + with self.assertRaises((Py4JJavaError, RuntimeError)) as cm: + m.mergeValues(data) + + # wrong merge value + m = ExternalMerger(Aggregator(legit_create_combiner, stopit, legit_merge_combiners), 20) + with self.assertRaises((Py4JJavaError, RuntimeError)) as cm: + m.mergeValues(data) + + # wrong merge combiners + m = ExternalMerger(Aggregator(legit_create_combiner, legit_merge_value, stopit), 20) + with self.assertRaises((Py4JJavaError, RuntimeError)) as cm: + m.mergeCombiners(map(lambda x_y1: (x_y1[0], [x_y1[1]]), data)) + class SorterTests(unittest.TestCase): def test_in_memory_sort(self): @@ -1239,6 +1270,35 @@ def test_pipe_functions(self): self.assertRaises(Py4JJavaError, rdd.pipe('grep 4', checkCode=True).collect) self.assertEqual([], rdd.pipe('grep 4').collect()) + def test_stopiteration_in_user_code(self): + + def stopit(*x): + raise StopIteration() + + seq_rdd = self.sc.parallelize(range(10)) + keyed_rdd = self.sc.parallelize((x % 2, x) for x in range(10)) + msg = "Caught StopIteration thrown from user's code; failing the task" + + self.assertRaisesRegexp(Py4JJavaError, msg, seq_rdd.map(stopit).collect) + self.assertRaisesRegexp(Py4JJavaError, msg, seq_rdd.filter(stopit).collect) + self.assertRaisesRegexp(Py4JJavaError, msg, seq_rdd.foreach, stopit) + self.assertRaisesRegexp(Py4JJavaError, msg, seq_rdd.reduce, stopit) + self.assertRaisesRegexp(Py4JJavaError, msg, seq_rdd.fold, 0, stopit) + self.assertRaisesRegexp(Py4JJavaError, msg, seq_rdd.foreach, stopit) + self.assertRaisesRegexp(Py4JJavaError, msg, + seq_rdd.cartesian(seq_rdd).flatMap(stopit).collect) + + # these methods call the user function both in the driver and in the executor + # the exception raised is different according to where the StopIteration happens + # RuntimeError is raised if in the driver + # Py4JJavaError is raised if in the executor (wraps the RuntimeError raised in the worker) + self.assertRaisesRegexp((Py4JJavaError, RuntimeError), msg, + keyed_rdd.reduceByKeyLocally, stopit) + self.assertRaisesRegexp((Py4JJavaError, RuntimeError), msg, + seq_rdd.aggregate, 0, stopit, lambda *x: 1) + self.assertRaisesRegexp((Py4JJavaError, RuntimeError), msg, + seq_rdd.aggregate, 0, lambda *x: 1, stopit) + class ProfilerTests(PySparkTestCase): @@ -2286,6 +2346,17 @@ def set(self, x=None, other=None, other_x=None): self.assertEqual(b._x, 2) +class UtilTests(PySparkTestCase): + def test_py4j_exception_message(self): + from pyspark.util import _exception_message + + with self.assertRaises(Py4JJavaError) as context: + # This attempts java.lang.String(null) which throws an NPE. + self.sc._jvm.java.lang.String(None) + + self.assertTrue('NullPointerException' in _exception_message(context.exception)) + + @unittest.skipIf(not _have_scipy, "SciPy not installed") class SciPyTests(PySparkTestCase): diff --git a/python/pyspark/util.py b/python/pyspark/util.py index e5d332ce54429..94f51eec9d71a 100644 --- a/python/pyspark/util.py +++ b/python/pyspark/util.py @@ -15,6 +15,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # +from py4j.protocol import Py4JJavaError __all__ = [] @@ -33,11 +34,34 @@ def _exception_message(excp): >>> msg == _exception_message(excp) True """ + if isinstance(excp, Py4JJavaError): + # 'Py4JJavaError' doesn't contain the stack trace available on the Java side in 'message' + # attribute in Python 2. We should call 'str' function on this exception in general but + # 'Py4JJavaError' has an issue about addressing non-ascii strings. So, here we work + # around by the direct call, '__str__()'. Please see SPARK-23517. + return excp.__str__() if hasattr(excp, "message"): return excp.message return str(excp) +def fail_on_stopiteration(f): + """ + Wraps the input function to fail on 'StopIteration' by raising a 'RuntimeError' + prevents silent loss of data when 'f' is used in a for loop in Spark code + """ + def wrapper(*args, **kwargs): + try: + return f(*args, **kwargs) + except StopIteration as exc: + raise RuntimeError( + "Caught StopIteration thrown from user's code; failing the task", + exc + ) + + return wrapper + + if __name__ == "__main__": import doctest (failure_count, test_count) = doctest.testmod() diff --git a/python/pyspark/version.py b/python/pyspark/version.py index 12dd53b9d2902..ed1cbdd58f252 100644 --- a/python/pyspark/version.py +++ b/python/pyspark/version.py @@ -16,4 +16,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "2.3.0.dev0" +__version__ = "2.3.2.dev0" diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py index e6737ae1c1285..788b3237e1799 100644 --- a/python/pyspark/worker.py +++ b/python/pyspark/worker.py @@ -27,6 +27,7 @@ from pyspark.accumulators import _accumulatorRegistry from pyspark.broadcast import Broadcast, _broadcastRegistry +from pyspark.java_gateway import do_server_auth from pyspark.taskcontext import TaskContext from pyspark.files import SparkFiles from pyspark.rdd import PythonEvalType @@ -34,6 +35,7 @@ write_long, read_int, SpecialLengths, UTF8Deserializer, PickleSerializer, \ BatchedSerializer, ArrowStreamPandasSerializer from pyspark.sql.types import to_arrow_type +from pyspark.util import fail_on_stopiteration from pyspark import shuffle pickleSer = PickleSerializer() @@ -74,13 +76,13 @@ def wrap_udf(f, return_type): return lambda *a: f(*a) -def wrap_pandas_scalar_udf(f, return_type): +def wrap_scalar_pandas_udf(f, return_type): arrow_return_type = to_arrow_type(return_type) def verify_result_length(*a): result = f(*a) if not hasattr(result, "__len__"): - raise TypeError("Return type of the user-defined functon should be " + raise TypeError("Return type of the user-defined function should be " "Pandas.Series, but is {}".format(type(result))) if len(result) != len(a[0]): raise RuntimeError("Result vector from pandas_udf was not the required length: " @@ -90,7 +92,7 @@ def verify_result_length(*a): return lambda *a: (verify_result_length(*a), arrow_return_type) -def wrap_pandas_group_map_udf(f, return_type): +def wrap_grouped_map_pandas_udf(f, return_type): def wrapped(*series): import pandas as pd @@ -121,13 +123,17 @@ def read_single_udf(pickleSer, infile, eval_type): else: row_func = chain(row_func, f) + # make sure StopIteration's raised in the user code are not ignored + # when they are processed in a for loop, raise them as RuntimeError's instead + func = fail_on_stopiteration(row_func) + # the last returnType will be the return type of UDF - if eval_type == PythonEvalType.SQL_PANDAS_SCALAR_UDF: - return arg_offsets, wrap_pandas_scalar_udf(row_func, return_type) - elif eval_type == PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF: - return arg_offsets, wrap_pandas_group_map_udf(row_func, return_type) + if eval_type == PythonEvalType.SQL_SCALAR_PANDAS_UDF: + return arg_offsets, wrap_scalar_pandas_udf(func, return_type) + elif eval_type == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF: + return arg_offsets, wrap_grouped_map_pandas_udf(func, return_type) else: - return arg_offsets, wrap_udf(row_func, return_type) + return arg_offsets, wrap_udf(func, return_type) def read_udfs(pickleSer, infile, eval_type): @@ -148,8 +154,8 @@ def read_udfs(pickleSer, infile, eval_type): func = lambda _, it: map(mapper, it) - if eval_type == PythonEvalType.SQL_PANDAS_SCALAR_UDF \ - or eval_type == PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF: + if eval_type in (PythonEvalType.SQL_SCALAR_PANDAS_UDF, + PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF): timezone = utf8_deserializer.loads(infile) ser = ArrowStreamPandasSerializer(timezone) else: @@ -260,9 +266,11 @@ def process(): if __name__ == '__main__': - # Read a local port to connect to from stdin - java_port = int(sys.stdin.readline()) + # Read information about how to connect back to the JVM from the environment. + java_port = int(os.environ["PYTHON_WORKER_FACTORY_PORT"]) + auth_secret = os.environ["PYTHON_WORKER_FACTORY_SECRET"] sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect(("127.0.0.1", java_port)) sock_file = sock.makefile("rwb", 65536) + do_server_auth(sock_file, auth_secret) main(sock_file, sock_file) diff --git a/python/run-tests.py b/python/run-tests.py index 1341086f02db0..3539c76b911a4 100755 --- a/python/run-tests.py +++ b/python/run-tests.py @@ -31,6 +31,7 @@ import Queue else: import queue as Queue +from distutils.version import LooseVersion # Append `SPARK_HOME/dev` to the Python path so that we can import the sparktestsupport module @@ -39,7 +40,7 @@ from sparktestsupport import SPARK_HOME # noqa (suppress pep8 warnings) from sparktestsupport.shellutils import which, subprocess_check_output # noqa -from sparktestsupport.modules import all_modules # noqa +from sparktestsupport.modules import all_modules, pyspark_sql # noqa python_modules = dict((m.name, m) for m in all_modules if m.python_test_goals if m.name != 'root') @@ -151,6 +152,55 @@ def parse_opts(): return opts +def _check_dependencies(python_exec, modules_to_test): + # If we should test 'pyspark-sql', it checks if PyArrow and Pandas are installed and + # explicitly prints out. See SPARK-23300. + if pyspark_sql in modules_to_test: + # TODO(HyukjinKwon): Relocate and deduplicate these version specifications. + minimum_pyarrow_version = '0.8.0' + minimum_pandas_version = '0.19.2' + + try: + pyarrow_version = subprocess_check_output( + [python_exec, "-c", "import pyarrow; print(pyarrow.__version__)"], + universal_newlines=True, + stderr=open(os.devnull, 'w')).strip() + if LooseVersion(pyarrow_version) >= LooseVersion(minimum_pyarrow_version): + LOGGER.info("Will test PyArrow related features against Python executable " + "'%s' in '%s' module." % (python_exec, pyspark_sql.name)) + else: + LOGGER.warning( + "Will skip PyArrow related features against Python executable " + "'%s' in '%s' module. PyArrow >= %s is required; however, PyArrow " + "%s was found." % ( + python_exec, pyspark_sql.name, minimum_pyarrow_version, pyarrow_version)) + except: + LOGGER.warning( + "Will skip PyArrow related features against Python executable " + "'%s' in '%s' module. PyArrow >= %s is required; however, PyArrow " + "was not found." % (python_exec, pyspark_sql.name, minimum_pyarrow_version)) + + try: + pandas_version = subprocess_check_output( + [python_exec, "-c", "import pandas; print(pandas.__version__)"], + universal_newlines=True, + stderr=open(os.devnull, 'w')).strip() + if LooseVersion(pandas_version) >= LooseVersion(minimum_pandas_version): + LOGGER.info("Will test Pandas related features against Python executable " + "'%s' in '%s' module." % (python_exec, pyspark_sql.name)) + else: + LOGGER.warning( + "Will skip Pandas related features against Python executable " + "'%s' in '%s' module. Pandas >= %s is required; however, Pandas " + "%s was found." % ( + python_exec, pyspark_sql.name, minimum_pandas_version, pandas_version)) + except: + LOGGER.warning( + "Will skip Pandas related features against Python executable " + "'%s' in '%s' module. Pandas >= %s is required; however, Pandas " + "was not found." % (python_exec, pyspark_sql.name, minimum_pandas_version)) + + def main(): opts = parse_opts() if (opts.verbose): @@ -175,6 +225,10 @@ def main(): task_queue = Queue.PriorityQueue() for python_exec in python_execs: + # Check if the python executable has proper dependencies installed to run tests + # for given modules properly. + _check_dependencies(python_exec, modules_to_test) + python_implementation = subprocess_check_output( [python_exec, "-c", "import platform; print(platform.python_implementation())"], universal_newlines=True).strip() diff --git a/python/setup.py b/python/setup.py index 251d4526d4dd0..80cf0d7b329bd 100644 --- a/python/setup.py +++ b/python/setup.py @@ -100,6 +100,11 @@ def _supports_symlinks(): file=sys.stderr) exit(-1) +# If you are changing the versions here, please also change ./python/pyspark/sql/utils.py and +# ./python/run-tests.py. In case of Arrow, you should also check ./pom.xml. +_minimum_pandas_version = "0.19.2" +_minimum_pyarrow_version = "0.8.0" + try: # We copy the shell script to be under pyspark/python/pyspark so that the launcher scripts # find it where expected. The rest of the files aren't copied because they are accessed @@ -196,12 +201,15 @@ def _supports_symlinks(): 'pyspark.examples.src.main.python': ['*.py', '*/*.py']}, scripts=scripts, license='http://www.apache.org/licenses/LICENSE-2.0', - install_requires=['py4j==0.10.6'], + install_requires=['py4j==0.10.7'], setup_requires=['pypandoc'], extras_require={ 'ml': ['numpy>=1.7'], 'mllib': ['numpy>=1.7'], - 'sql': ['pandas>=0.19.2', 'pyarrow>=0.8.0'] + 'sql': [ + 'pandas>=%s' % _minimum_pandas_version, + 'pyarrow>=%s' % _minimum_pyarrow_version, + ] }, classifiers=[ 'Development Status :: 5 - Production/Stable', diff --git a/repl/pom.xml b/repl/pom.xml index 1cb0098d0eca3..bc10cce2d1aea 100644 --- a/repl/pom.xml +++ b/repl/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/resource-managers/kubernetes/core/pom.xml b/resource-managers/kubernetes/core/pom.xml index 7d35aea8a4142..3e0aa0872b99b 100644 --- a/resource-managers/kubernetes/core/pom.xml +++ b/resource-managers/kubernetes/core/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../../pom.xml diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Config.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Config.scala index e5d79d9a9d9da..471196ac0e3f6 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Config.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Config.scala @@ -29,17 +29,23 @@ private[spark] object Config extends Logging { .stringConf .createWithDefault("default") + val CONTAINER_IMAGE = + ConfigBuilder("spark.kubernetes.container.image") + .doc("Container image to use for Spark containers. Individual container types " + + "(e.g. driver or executor) can also be configured to use different images if desired, " + + "by setting the container type-specific image name.") + .stringConf + .createOptional + val DRIVER_CONTAINER_IMAGE = ConfigBuilder("spark.kubernetes.driver.container.image") .doc("Container image to use for the driver.") - .stringConf - .createOptional + .fallbackConf(CONTAINER_IMAGE) val EXECUTOR_CONTAINER_IMAGE = ConfigBuilder("spark.kubernetes.executor.container.image") .doc("Container image to use for the executors.") - .stringConf - .createOptional + .fallbackConf(CONTAINER_IMAGE) val CONTAINER_IMAGE_PULL_POLICY = ConfigBuilder("spark.kubernetes.container.image.pullPolicy") @@ -148,8 +154,7 @@ private[spark] object Config extends Logging { val INIT_CONTAINER_IMAGE = ConfigBuilder("spark.kubernetes.initContainer.image") .doc("Image for the driver and executor's init-container for downloading dependencies.") - .stringConf - .createOptional + .fallbackConf(CONTAINER_IMAGE) val INIT_CONTAINER_MOUNT_TIMEOUT = ConfigBuilder("spark.kubernetes.mountDependencies.timeout") diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Constants.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Constants.scala index 111cb2a3b75e5..9411956996843 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Constants.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Constants.scala @@ -60,10 +60,9 @@ private[spark] object Constants { val ENV_APPLICATION_ID = "SPARK_APPLICATION_ID" val ENV_EXECUTOR_ID = "SPARK_EXECUTOR_ID" val ENV_EXECUTOR_POD_IP = "SPARK_EXECUTOR_POD_IP" - val ENV_EXECUTOR_EXTRA_CLASSPATH = "SPARK_EXECUTOR_EXTRA_CLASSPATH" val ENV_MOUNTED_CLASSPATH = "SPARK_MOUNTED_CLASSPATH" val ENV_JAVA_OPT_PREFIX = "SPARK_JAVA_OPT_" - val ENV_SUBMIT_EXTRA_CLASSPATH = "SPARK_SUBMIT_EXTRA_CLASSPATH" + val ENV_CLASSPATH = "SPARK_CLASSPATH" val ENV_DRIVER_MAIN_CLASS = "SPARK_DRIVER_CLASS" val ENV_DRIVER_ARGS = "SPARK_DRIVER_ARGS" val ENV_DRIVER_JAVA_OPTS = "SPARK_DRIVER_JAVA_OPTS" diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/InitContainerBootstrap.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/InitContainerBootstrap.scala index dfeccf9e2bd1c..f6a57dfe00171 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/InitContainerBootstrap.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/InitContainerBootstrap.scala @@ -77,6 +77,7 @@ private[spark] class InitContainerBootstrap( .withMountPath(INIT_CONTAINER_PROPERTIES_FILE_DIR) .endVolumeMount() .addToVolumeMounts(sharedVolumeMounts: _*) + .addToArgs("init") .addToArgs(INIT_CONTAINER_PROPERTIES_FILE_PATH) .build() diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/MountSecretsBootstrap.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/MountSecretsBootstrap.scala index 8286546ce0641..c35e7db51d407 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/MountSecretsBootstrap.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/MountSecretsBootstrap.scala @@ -24,26 +24,36 @@ import io.fabric8.kubernetes.api.model.{Container, ContainerBuilder, Pod, PodBui private[spark] class MountSecretsBootstrap(secretNamesToMountPaths: Map[String, String]) { /** - * Mounts Kubernetes secrets as secret volumes into the given container in the given pod. + * Add new secret volumes for the secrets specified in secretNamesToMountPaths into the given pod. * * @param pod the pod into which the secret volumes are being added. - * @param container the container into which the secret volumes are being mounted. - * @return the updated pod and container with the secrets mounted. + * @return the updated pod with the secret volumes added. */ - def mountSecrets(pod: Pod, container: Container): (Pod, Container) = { + def addSecretVolumes(pod: Pod): Pod = { var podBuilder = new PodBuilder(pod) secretNamesToMountPaths.keys.foreach { name => podBuilder = podBuilder .editOrNewSpec() .addNewVolume() - .withName(secretVolumeName(name)) - .withNewSecret() - .withSecretName(name) - .endSecret() - .endVolume() + .withName(secretVolumeName(name)) + .withNewSecret() + .withSecretName(name) + .endSecret() + .endVolume() .endSpec() } + podBuilder.build() + } + + /** + * Mounts Kubernetes secret volumes of the secrets specified in secretNamesToMountPaths into the + * given container. + * + * @param container the container into which the secret volumes are being mounted. + * @return the updated container with the secrets mounted. + */ + def mountSecrets(container: Container): Container = { var containerBuilder = new ContainerBuilder(container) secretNamesToMountPaths.foreach { case (name, path) => containerBuilder = containerBuilder @@ -53,7 +63,7 @@ private[spark] class MountSecretsBootstrap(secretNamesToMountPaths: Map[String, .endVolumeMount() } - (podBuilder.build(), containerBuilder.build()) + containerBuilder.build() } private def secretVolumeName(secretName: String): String = { diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/rest/k8s/SparkPodInitContainer.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/SparkPodInitContainer.scala similarity index 99% rename from resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/rest/k8s/SparkPodInitContainer.scala rename to resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/SparkPodInitContainer.scala index 4a4b628aedbbf..c0f08786b76a1 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/rest/k8s/SparkPodInitContainer.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/SparkPodInitContainer.scala @@ -14,7 +14,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package org.apache.spark.deploy.rest.k8s +package org.apache.spark.deploy.k8s import java.io.File import java.util.concurrent.TimeUnit diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/DriverConfigOrchestrator.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/DriverConfigOrchestrator.scala index 00c9c4ee49177..ae70904621184 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/DriverConfigOrchestrator.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/DriverConfigOrchestrator.scala @@ -20,7 +20,7 @@ import java.util.UUID import com.google.common.primitives.Longs -import org.apache.spark.SparkConf +import org.apache.spark.{SparkConf, SparkException} import org.apache.spark.deploy.k8s.{KubernetesUtils, MountSecretsBootstrap} import org.apache.spark.deploy.k8s.Config._ import org.apache.spark.deploy.k8s.Constants._ @@ -117,6 +117,12 @@ private[spark] class DriverConfigOrchestrator( .map(_.split(",")) .getOrElse(Array.empty[String]) + // TODO(SPARK-23153): remove once submission client local dependencies are supported. + if (existSubmissionLocalFiles(sparkJars) || existSubmissionLocalFiles(sparkFiles)) { + throw new SparkException("The Kubernetes mode does not yet support referencing application " + + "dependencies in the local file system.") + } + val dependencyResolutionStep = if (sparkJars.nonEmpty || sparkFiles.nonEmpty) { Seq(new DependencyResolutionStep( sparkJars, @@ -127,6 +133,12 @@ private[spark] class DriverConfigOrchestrator( Nil } + val mountSecretsStep = if (secretNamesToMountPaths.nonEmpty) { + Seq(new DriverMountSecretsStep(new MountSecretsBootstrap(secretNamesToMountPaths))) + } else { + Nil + } + val initContainerBootstrapStep = if (existNonContainerLocalFiles(sparkJars ++ sparkFiles)) { val orchestrator = new InitContainerConfigOrchestrator( sparkJars, @@ -147,19 +159,19 @@ private[spark] class DriverConfigOrchestrator( Nil } - val mountSecretsStep = if (secretNamesToMountPaths.nonEmpty) { - Seq(new DriverMountSecretsStep(new MountSecretsBootstrap(secretNamesToMountPaths))) - } else { - Nil - } - Seq( initialSubmissionStep, serviceBootstrapStep, kubernetesCredentialsStep) ++ dependencyResolutionStep ++ - initContainerBootstrapStep ++ - mountSecretsStep + mountSecretsStep ++ + initContainerBootstrapStep + } + + private def existSubmissionLocalFiles(files: Seq[String]): Boolean = { + files.exists { uri => + Utils.resolveURI(uri).getScheme == "file" + } } private def existNonContainerLocalFiles(files: Seq[String]): Boolean = { diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/BasicDriverConfigurationStep.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/BasicDriverConfigurationStep.scala index b7a69a7dfd472..164e2e5594778 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/BasicDriverConfigurationStep.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/BasicDriverConfigurationStep.scala @@ -66,7 +66,7 @@ private[spark] class BasicDriverConfigurationStep( override def configureDriver(driverSpec: KubernetesDriverSpec): KubernetesDriverSpec = { val driverExtraClasspathEnv = driverExtraClasspath.map { classPath => new EnvVarBuilder() - .withName(ENV_SUBMIT_EXTRA_CLASSPATH) + .withName(ENV_CLASSPATH) .withValue(classPath) .build() } @@ -119,7 +119,7 @@ private[spark] class BasicDriverConfigurationStep( .endEnv() .addNewEnv() .withName(ENV_DRIVER_ARGS) - .withValue(appArgs.map(arg => "\"" + arg + "\"").mkString(" ")) + .withValue(appArgs.mkString(" ")) .endEnv() .addNewEnv() .withName(ENV_DRIVER_BIND_ADDRESS) @@ -133,6 +133,7 @@ private[spark] class BasicDriverConfigurationStep( .addToLimits("memory", driverMemoryLimitQuantity) .addToLimits(maybeCpuLimitQuantity.toMap.asJava) .endResources() + .addToArgs("driver") .build() val baseDriverPod = new PodBuilder(driverSpec.driverPod) diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/DriverMountSecretsStep.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/DriverMountSecretsStep.scala index f872e0f4b65d1..91e9a9f211335 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/DriverMountSecretsStep.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/DriverMountSecretsStep.scala @@ -28,8 +28,8 @@ private[spark] class DriverMountSecretsStep( bootstrap: MountSecretsBootstrap) extends DriverConfigurationStep { override def configureDriver(driverSpec: KubernetesDriverSpec): KubernetesDriverSpec = { - val (pod, container) = bootstrap.mountSecrets( - driverSpec.driverPod, driverSpec.driverContainer) + val pod = bootstrap.addSecretVolumes(driverSpec.driverPod) + val container = bootstrap.mountSecrets(driverSpec.driverContainer) driverSpec.copy( driverPod = pod, driverContainer = container diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/DriverServiceBootstrapStep.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/DriverServiceBootstrapStep.scala index eb594e4f16ec0..34af7cde6c1a9 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/DriverServiceBootstrapStep.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/DriverServiceBootstrapStep.scala @@ -83,7 +83,7 @@ private[spark] class DriverServiceBootstrapStep( .build() val namespace = sparkConf.get(KUBERNETES_NAMESPACE) - val driverHostname = s"${driverService.getMetadata.getName}.$namespace.svc.cluster.local" + val driverHostname = s"${driverService.getMetadata.getName}.$namespace.svc" val resolvedSparkConf = driverSpec.driverSparkConf.clone() .set(DRIVER_HOST_KEY, driverHostname) .set("spark.driver.port", driverPort.toString) diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerMountSecretsStep.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerMountSecretsStep.scala index c0e7bb20cce8c..0daa7b95e8aae 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerMountSecretsStep.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerMountSecretsStep.scala @@ -28,12 +28,9 @@ private[spark] class InitContainerMountSecretsStep( bootstrap: MountSecretsBootstrap) extends InitContainerConfigurationStep { override def configureInitContainer(spec: InitContainerSpec) : InitContainerSpec = { - val (driverPod, initContainer) = bootstrap.mountSecrets( - spec.driverPod, - spec.initContainer) - spec.copy( - driverPod = driverPod, - initContainer = initContainer - ) + // Mount the secret volumes given that the volumes have already been added to the driver pod + // when mounting the secrets into the main driver container. + val initContainer = bootstrap.mountSecrets(spec.initContainer) + spec.copy(initContainer = initContainer) } } diff --git a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactory.scala b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactory.scala index ba5d891f4c77e..141bd2827e7c5 100644 --- a/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactory.scala +++ b/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactory.scala @@ -94,6 +94,8 @@ private[spark] class ExecutorPodFactory( private val executorCores = sparkConf.getDouble("spark.executor.cores", 1) private val executorLimitCores = sparkConf.get(KUBERNETES_EXECUTOR_LIMIT_CORES) + private val executorJarsDownloadDir = sparkConf.get(JARS_DOWNLOAD_LOCATION) + /** * Configure and construct an executor pod with the given parameters. */ @@ -126,7 +128,7 @@ private[spark] class ExecutorPodFactory( .build() val executorExtraClasspathEnv = executorExtraClasspath.map { cp => new EnvVarBuilder() - .withName(ENV_EXECUTOR_EXTRA_CLASSPATH) + .withName(ENV_CLASSPATH) .withValue(cp) .build() } @@ -145,7 +147,8 @@ private[spark] class ExecutorPodFactory( (ENV_EXECUTOR_CORES, math.ceil(executorCores).toInt.toString), (ENV_EXECUTOR_MEMORY, executorMemoryString), (ENV_APPLICATION_ID, applicationId), - (ENV_EXECUTOR_ID, executorId)) ++ executorEnvs) + (ENV_EXECUTOR_ID, executorId), + (ENV_MOUNTED_CLASSPATH, s"$executorJarsDownloadDir/*")) ++ executorEnvs) .map(env => new EnvVarBuilder() .withName(env._1) .withValue(env._2) @@ -178,6 +181,7 @@ private[spark] class ExecutorPodFactory( .endResources() .addAllToEnv(executorEnv.asJava) .withPorts(requiredPorts.asJava) + .addToArgs("executor") .build() val executorPod = new PodBuilder() @@ -214,7 +218,7 @@ private[spark] class ExecutorPodFactory( val (maybeSecretsMountedPod, maybeSecretsMountedContainer) = mountSecretsBootstrap.map { bootstrap => - bootstrap.mountSecrets(executorPod, containerWithLimitCores) + (bootstrap.addSecretVolumes(executorPod), bootstrap.mountSecrets(containerWithLimitCores)) }.getOrElse((executorPod, containerWithLimitCores)) val (bootstrappedPod, bootstrappedContainer) = @@ -227,7 +231,9 @@ private[spark] class ExecutorPodFactory( val (pod, mayBeSecretsMountedInitContainer) = initContainerMountSecretsBootstrap.map { bootstrap => - bootstrap.mountSecrets(podWithInitContainer.pod, podWithInitContainer.initContainer) + // Mount the secret volumes given that the volumes have already been added to the + // executor pod when mounting the secrets into the main executor container. + (podWithInitContainer.pod, bootstrap.mountSecrets(podWithInitContainer.initContainer)) }.getOrElse((podWithInitContainer.pod, podWithInitContainer.initContainer)) val bootstrappedPod = KubernetesUtils.appendInitContainer( diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/SecretVolumeUtils.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/SecretVolumeUtils.scala similarity index 71% rename from resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/SecretVolumeUtils.scala rename to resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/SecretVolumeUtils.scala index 8388c16ded268..16780584a674a 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/SecretVolumeUtils.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/SecretVolumeUtils.scala @@ -14,7 +14,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package org.apache.spark.deploy.k8s.submit +package org.apache.spark.deploy.k8s import scala.collection.JavaConverters._ @@ -22,15 +22,15 @@ import io.fabric8.kubernetes.api.model.{Container, Pod} private[spark] object SecretVolumeUtils { - def podHasVolume(driverPod: Pod, volumeName: String): Boolean = { - driverPod.getSpec.getVolumes.asScala.exists(volume => volume.getName == volumeName) + def podHasVolume(pod: Pod, volumeName: String): Boolean = { + pod.getSpec.getVolumes.asScala.exists { volume => + volume.getName == volumeName + } } - def containerHasVolume( - driverContainer: Container, - volumeName: String, - mountPath: String): Boolean = { - driverContainer.getVolumeMounts.asScala.exists(volumeMount => - volumeMount.getName == volumeName && volumeMount.getMountPath == mountPath) + def containerHasVolume(container: Container, volumeName: String, mountPath: String): Boolean = { + container.getVolumeMounts.asScala.exists { volumeMount => + volumeMount.getName == volumeName && volumeMount.getMountPath == mountPath + } } } diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/rest/k8s/SparkPodInitContainerSuite.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/SparkPodInitContainerSuite.scala similarity index 98% rename from resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/rest/k8s/SparkPodInitContainerSuite.scala rename to resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/SparkPodInitContainerSuite.scala index 6c557ec4a7c9a..e0f29ecd0fb53 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/rest/k8s/SparkPodInitContainerSuite.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/SparkPodInitContainerSuite.scala @@ -14,7 +14,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package org.apache.spark.deploy.rest.k8s +package org.apache.spark.deploy.k8s import java.io.File import java.util.UUID diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/DriverConfigOrchestratorSuite.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/DriverConfigOrchestratorSuite.scala index f193b1f4d3664..033d303e946fd 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/DriverConfigOrchestratorSuite.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/DriverConfigOrchestratorSuite.scala @@ -16,7 +16,7 @@ */ package org.apache.spark.deploy.k8s.submit -import org.apache.spark.{SparkConf, SparkFunSuite} +import org.apache.spark.{SparkConf, SparkException, SparkFunSuite} import org.apache.spark.deploy.k8s.Config._ import org.apache.spark.deploy.k8s.submit.steps._ @@ -34,8 +34,7 @@ class DriverConfigOrchestratorSuite extends SparkFunSuite { private val SECRET_MOUNT_PATH = "/etc/secrets/driver" test("Base submission steps with a main app resource.") { - val sparkConf = new SparkConf(false) - .set(DRIVER_CONTAINER_IMAGE, DRIVER_IMAGE) + val sparkConf = new SparkConf(false).set(CONTAINER_IMAGE, DRIVER_IMAGE) val mainAppResource = JavaMainAppResource("local:///var/apps/jars/main.jar") val orchestrator = new DriverConfigOrchestrator( APP_ID, @@ -55,8 +54,7 @@ class DriverConfigOrchestratorSuite extends SparkFunSuite { } test("Base submission steps without a main app resource.") { - val sparkConf = new SparkConf(false) - .set(DRIVER_CONTAINER_IMAGE, DRIVER_IMAGE) + val sparkConf = new SparkConf(false).set(CONTAINER_IMAGE, DRIVER_IMAGE) val orchestrator = new DriverConfigOrchestrator( APP_ID, LAUNCH_TIME, @@ -75,8 +73,8 @@ class DriverConfigOrchestratorSuite extends SparkFunSuite { test("Submission steps with an init-container.") { val sparkConf = new SparkConf(false) - .set(DRIVER_CONTAINER_IMAGE, DRIVER_IMAGE) - .set(INIT_CONTAINER_IMAGE, IC_IMAGE) + .set(CONTAINER_IMAGE, DRIVER_IMAGE) + .set(INIT_CONTAINER_IMAGE.key, IC_IMAGE) .set("spark.jars", "hdfs://localhost:9000/var/apps/jars/jar1.jar") val mainAppResource = JavaMainAppResource("local:///var/apps/jars/main.jar") val orchestrator = new DriverConfigOrchestrator( @@ -98,7 +96,7 @@ class DriverConfigOrchestratorSuite extends SparkFunSuite { test("Submission steps with driver secrets to mount") { val sparkConf = new SparkConf(false) - .set(DRIVER_CONTAINER_IMAGE, DRIVER_IMAGE) + .set(CONTAINER_IMAGE, DRIVER_IMAGE) .set(s"$KUBERNETES_DRIVER_SECRETS_PREFIX$SECRET_FOO", SECRET_MOUNT_PATH) .set(s"$KUBERNETES_DRIVER_SECRETS_PREFIX$SECRET_BAR", SECRET_MOUNT_PATH) val mainAppResource = JavaMainAppResource("local:///var/apps/jars/main.jar") @@ -119,6 +117,35 @@ class DriverConfigOrchestratorSuite extends SparkFunSuite { classOf[DriverMountSecretsStep]) } + test("Submission using client local dependencies") { + val sparkConf = new SparkConf(false) + .set(CONTAINER_IMAGE, DRIVER_IMAGE) + var orchestrator = new DriverConfigOrchestrator( + APP_ID, + LAUNCH_TIME, + Some(JavaMainAppResource("file:///var/apps/jars/main.jar")), + APP_NAME, + MAIN_CLASS, + APP_ARGS, + sparkConf) + assertThrows[SparkException] { + orchestrator.getAllConfigurationSteps + } + + sparkConf.set("spark.files", "/path/to/file1,/path/to/file2") + orchestrator = new DriverConfigOrchestrator( + APP_ID, + LAUNCH_TIME, + Some(JavaMainAppResource("local:///var/apps/jars/main.jar")), + APP_NAME, + MAIN_CLASS, + APP_ARGS, + sparkConf) + assertThrows[SparkException] { + orchestrator.getAllConfigurationSteps + } + } + private def validateStepTypes( orchestrator: DriverConfigOrchestrator, types: Class[_ <: DriverConfigurationStep]*): Unit = { diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/BasicDriverConfigurationStepSuite.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/BasicDriverConfigurationStepSuite.scala index e864c6a16eeb1..b136f2c02ffba 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/BasicDriverConfigurationStepSuite.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/BasicDriverConfigurationStepSuite.scala @@ -33,7 +33,7 @@ class BasicDriverConfigurationStepSuite extends SparkFunSuite { private val CONTAINER_IMAGE_PULL_POLICY = "IfNotPresent" private val APP_NAME = "spark-test" private val MAIN_CLASS = "org.apache.spark.examples.SparkPi" - private val APP_ARGS = Array("arg1", "arg2", "arg 3") + private val APP_ARGS = Array("arg1", "arg2", "\"arg 3\"") private val CUSTOM_ANNOTATION_KEY = "customAnnotation" private val CUSTOM_ANNOTATION_VALUE = "customAnnotationValue" private val DRIVER_CUSTOM_ENV_KEY1 = "customDriverEnv1" @@ -47,7 +47,7 @@ class BasicDriverConfigurationStepSuite extends SparkFunSuite { .set(KUBERNETES_DRIVER_LIMIT_CORES, "4") .set(org.apache.spark.internal.config.DRIVER_MEMORY.key, "256M") .set(org.apache.spark.internal.config.DRIVER_MEMORY_OVERHEAD, 200L) - .set(DRIVER_CONTAINER_IMAGE, "spark-driver:latest") + .set(CONTAINER_IMAGE, "spark-driver:latest") .set(s"$KUBERNETES_DRIVER_ANNOTATION_PREFIX$CUSTOM_ANNOTATION_KEY", CUSTOM_ANNOTATION_VALUE) .set(s"$KUBERNETES_DRIVER_ENV_KEY$DRIVER_CUSTOM_ENV_KEY1", "customDriverEnv1") .set(s"$KUBERNETES_DRIVER_ENV_KEY$DRIVER_CUSTOM_ENV_KEY2", "customDriverEnv2") @@ -79,10 +79,10 @@ class BasicDriverConfigurationStepSuite extends SparkFunSuite { .asScala .map(env => (env.getName, env.getValue)) .toMap - assert(envs(ENV_SUBMIT_EXTRA_CLASSPATH) === "/opt/spark/spark-examples.jar") + assert(envs(ENV_CLASSPATH) === "/opt/spark/spark-examples.jar") assert(envs(ENV_DRIVER_MEMORY) === "256M") assert(envs(ENV_DRIVER_MAIN_CLASS) === MAIN_CLASS) - assert(envs(ENV_DRIVER_ARGS) === "\"arg1\" \"arg2\" \"arg 3\"") + assert(envs(ENV_DRIVER_ARGS) === "arg1 arg2 \"arg 3\"") assert(envs(DRIVER_CUSTOM_ENV_KEY1) === "customDriverEnv1") assert(envs(DRIVER_CUSTOM_ENV_KEY2) === "customDriverEnv2") diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/DriverMountSecretsStepSuite.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/DriverMountSecretsStepSuite.scala index 9ec0cb55de5aa..960d0bda1d011 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/DriverMountSecretsStepSuite.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/DriverMountSecretsStepSuite.scala @@ -17,8 +17,8 @@ package org.apache.spark.deploy.k8s.submit.steps import org.apache.spark.{SparkConf, SparkFunSuite} -import org.apache.spark.deploy.k8s.MountSecretsBootstrap -import org.apache.spark.deploy.k8s.submit.{KubernetesDriverSpec, SecretVolumeUtils} +import org.apache.spark.deploy.k8s.{MountSecretsBootstrap, SecretVolumeUtils} +import org.apache.spark.deploy.k8s.submit.KubernetesDriverSpec class DriverMountSecretsStepSuite extends SparkFunSuite { diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/DriverServiceBootstrapStepSuite.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/DriverServiceBootstrapStepSuite.scala index 006ce2668f8a0..78c8c3ba1afbd 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/DriverServiceBootstrapStepSuite.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/DriverServiceBootstrapStepSuite.scala @@ -85,7 +85,7 @@ class DriverServiceBootstrapStepSuite extends SparkFunSuite with BeforeAndAfter val resolvedDriverSpec = configurationStep.configureDriver(baseDriverSpec) val expectedServiceName = SHORT_RESOURCE_NAME_PREFIX + DriverServiceBootstrapStep.DRIVER_SVC_POSTFIX - val expectedHostName = s"$expectedServiceName.my-namespace.svc.cluster.local" + val expectedHostName = s"$expectedServiceName.my-namespace.svc" verifySparkConfHostNames(resolvedDriverSpec.driverSparkConf, expectedHostName) } @@ -120,7 +120,7 @@ class DriverServiceBootstrapStepSuite extends SparkFunSuite with BeforeAndAfter val driverService = resolvedDriverSpec.otherKubernetesResources.head.asInstanceOf[Service] val expectedServiceName = s"spark-10000${DriverServiceBootstrapStep.DRIVER_SVC_POSTFIX}" assert(driverService.getMetadata.getName === expectedServiceName) - val expectedHostName = s"$expectedServiceName.my-namespace.svc.cluster.local" + val expectedHostName = s"$expectedServiceName.my-namespace.svc" verifySparkConfHostNames(resolvedDriverSpec.driverSparkConf, expectedHostName) } diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerConfigOrchestratorSuite.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerConfigOrchestratorSuite.scala index 20f2e5bc15df3..09b42e4484d86 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerConfigOrchestratorSuite.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerConfigOrchestratorSuite.scala @@ -40,7 +40,7 @@ class InitContainerConfigOrchestratorSuite extends SparkFunSuite { test("including basic configuration step") { val sparkConf = new SparkConf(true) - .set(INIT_CONTAINER_IMAGE, DOCKER_IMAGE) + .set(CONTAINER_IMAGE, DOCKER_IMAGE) .set(s"$KUBERNETES_DRIVER_LABEL_PREFIX$CUSTOM_LABEL_KEY", CUSTOM_LABEL_VALUE) val orchestrator = new InitContainerConfigOrchestrator( @@ -59,7 +59,7 @@ class InitContainerConfigOrchestratorSuite extends SparkFunSuite { test("including step to mount user-specified secrets") { val sparkConf = new SparkConf(false) - .set(INIT_CONTAINER_IMAGE, DOCKER_IMAGE) + .set(CONTAINER_IMAGE, DOCKER_IMAGE) .set(s"$KUBERNETES_DRIVER_SECRETS_PREFIX$SECRET_FOO", SECRET_MOUNT_PATH) .set(s"$KUBERNETES_DRIVER_SECRETS_PREFIX$SECRET_BAR", SECRET_MOUNT_PATH) diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerMountSecretsStepSuite.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerMountSecretsStepSuite.scala index eab4e17659456..7ac0bde80dfe6 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerMountSecretsStepSuite.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/submit/steps/initcontainer/InitContainerMountSecretsStepSuite.scala @@ -19,8 +19,7 @@ package org.apache.spark.deploy.k8s.submit.steps.initcontainer import io.fabric8.kubernetes.api.model.{ContainerBuilder, PodBuilder} import org.apache.spark.SparkFunSuite -import org.apache.spark.deploy.k8s.MountSecretsBootstrap -import org.apache.spark.deploy.k8s.submit.SecretVolumeUtils +import org.apache.spark.deploy.k8s.{MountSecretsBootstrap, SecretVolumeUtils} class InitContainerMountSecretsStepSuite extends SparkFunSuite { @@ -44,12 +43,8 @@ class InitContainerMountSecretsStepSuite extends SparkFunSuite { val initContainerMountSecretsStep = new InitContainerMountSecretsStep(mountSecretsBootstrap) val configuredInitContainerSpec = initContainerMountSecretsStep.configureInitContainer( baseInitContainerSpec) - - val podWithSecretsMounted = configuredInitContainerSpec.driverPod val initContainerWithSecretsMounted = configuredInitContainerSpec.initContainer - Seq(s"$SECRET_FOO-volume", s"$SECRET_BAR-volume").foreach(volumeName => - assert(SecretVolumeUtils.podHasVolume(podWithSecretsMounted, volumeName))) Seq(s"$SECRET_FOO-volume", s"$SECRET_BAR-volume").foreach(volumeName => assert(SecretVolumeUtils.containerHasVolume( initContainerWithSecretsMounted, volumeName, SECRET_MOUNT_PATH))) diff --git a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactorySuite.scala b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactorySuite.scala index 7121a802c69c1..a3c615be031d2 100644 --- a/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactorySuite.scala +++ b/resource-managers/kubernetes/core/src/test/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactorySuite.scala @@ -25,7 +25,7 @@ import org.mockito.Mockito._ import org.scalatest.{BeforeAndAfter, BeforeAndAfterEach} import org.apache.spark.{SparkConf, SparkFunSuite} -import org.apache.spark.deploy.k8s.{InitContainerBootstrap, MountSecretsBootstrap, PodWithDetachedInitContainer} +import org.apache.spark.deploy.k8s.{InitContainerBootstrap, MountSecretsBootstrap, PodWithDetachedInitContainer, SecretVolumeUtils} import org.apache.spark.deploy.k8s.Config._ import org.apache.spark.deploy.k8s.Constants._ @@ -54,7 +54,7 @@ class ExecutorPodFactorySuite extends SparkFunSuite with BeforeAndAfter with Bef baseConf = new SparkConf() .set(KUBERNETES_DRIVER_POD_NAME, driverPodName) .set(KUBERNETES_EXECUTOR_POD_NAME_PREFIX, executorPrefix) - .set(EXECUTOR_CONTAINER_IMAGE, executorImage) + .set(CONTAINER_IMAGE, executorImage) } test("basic executor pod has reasonable defaults") { @@ -107,7 +107,7 @@ class ExecutorPodFactorySuite extends SparkFunSuite with BeforeAndAfter with Bef checkEnv(executor, Map("SPARK_JAVA_OPT_0" -> "foo=bar", - "SPARK_EXECUTOR_EXTRA_CLASSPATH" -> "bar=baz", + ENV_CLASSPATH -> "bar=baz", "qux" -> "quux")) checkOwnerReferences(executor, driverPodUid) } @@ -165,17 +165,19 @@ class ExecutorPodFactorySuite extends SparkFunSuite with BeforeAndAfter with Bef val factory = new ExecutorPodFactory( conf, - None, + Some(secretsBootstrap), Some(initContainerBootstrap), Some(secretsBootstrap)) val executor = factory.createExecutorPod( "1", "dummy", "dummy", Seq[(String, String)](), driverPod, Map[String, Int]()) + assert(executor.getSpec.getVolumes.size() === 1) + assert(SecretVolumeUtils.podHasVolume(executor, "secret1-volume")) + assert(SecretVolumeUtils.containerHasVolume( + executor.getSpec.getContainers.get(0), "secret1-volume", "/var/secret1")) assert(executor.getSpec.getInitContainers.size() === 1) - assert(executor.getSpec.getInitContainers.get(0).getVolumeMounts.get(0).getName - === "secret1-volume") - assert(executor.getSpec.getInitContainers.get(0).getVolumeMounts.get(0) - .getMountPath === "/var/secret1") + assert(SecretVolumeUtils.containerHasVolume( + executor.getSpec.getInitContainers.get(0), "secret1-volume", "/var/secret1")) checkOwnerReferences(executor, driverPodUid) } @@ -195,7 +197,8 @@ class ExecutorPodFactorySuite extends SparkFunSuite with BeforeAndAfter with Bef ENV_EXECUTOR_CORES -> "1", ENV_EXECUTOR_MEMORY -> "1g", ENV_APPLICATION_ID -> "dummy", - ENV_EXECUTOR_POD_IP -> null) ++ additionalEnvVars + ENV_EXECUTOR_POD_IP -> null, + ENV_MOUNTED_CLASSPATH -> "/var/spark-data/spark-jars/*") ++ additionalEnvVars assert(executor.getSpec.getContainers.size() === 1) assert(executor.getSpec.getContainers.get(0).getEnv.size() === defaultEnvs.size) diff --git a/resource-managers/kubernetes/docker/src/main/dockerfiles/driver/Dockerfile b/resource-managers/kubernetes/docker/src/main/dockerfiles/driver/Dockerfile deleted file mode 100644 index 45fbcd9cd0deb..0000000000000 --- a/resource-managers/kubernetes/docker/src/main/dockerfiles/driver/Dockerfile +++ /dev/null @@ -1,35 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -FROM spark-base - -# Before building the docker image, first build and make a Spark distribution following -# the instructions in http://spark.apache.org/docs/latest/building-spark.html. -# If this docker file is being used in the context of building your images from a Spark -# distribution, the docker build command should be invoked from the top level directory -# of the Spark distribution. E.g.: -# docker build -t spark-driver:latest -f kubernetes/dockerfiles/driver/Dockerfile . - -COPY examples /opt/spark/examples - -CMD SPARK_CLASSPATH="${SPARK_HOME}/jars/*" && \ - env | grep SPARK_JAVA_OPT_ | sed 's/[^=]*=\(.*\)/\1/g' > /tmp/java_opts.txt && \ - readarray -t SPARK_DRIVER_JAVA_OPTS < /tmp/java_opts.txt && \ - if ! [ -z ${SPARK_MOUNTED_CLASSPATH+x} ]; then SPARK_CLASSPATH="$SPARK_MOUNTED_CLASSPATH:$SPARK_CLASSPATH"; fi && \ - if ! [ -z ${SPARK_SUBMIT_EXTRA_CLASSPATH+x} ]; then SPARK_CLASSPATH="$SPARK_SUBMIT_EXTRA_CLASSPATH:$SPARK_CLASSPATH"; fi && \ - if ! [ -z ${SPARK_MOUNTED_FILES_DIR+x} ]; then cp -R "$SPARK_MOUNTED_FILES_DIR/." .; fi && \ - ${JAVA_HOME}/bin/java "${SPARK_DRIVER_JAVA_OPTS[@]}" -cp "$SPARK_CLASSPATH" -Xms$SPARK_DRIVER_MEMORY -Xmx$SPARK_DRIVER_MEMORY -Dspark.driver.bindAddress=$SPARK_DRIVER_BIND_ADDRESS $SPARK_DRIVER_CLASS $SPARK_DRIVER_ARGS diff --git a/resource-managers/kubernetes/docker/src/main/dockerfiles/executor/Dockerfile b/resource-managers/kubernetes/docker/src/main/dockerfiles/executor/Dockerfile deleted file mode 100644 index 0f806cf7e148e..0000000000000 --- a/resource-managers/kubernetes/docker/src/main/dockerfiles/executor/Dockerfile +++ /dev/null @@ -1,35 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -FROM spark-base - -# Before building the docker image, first build and make a Spark distribution following -# the instructions in http://spark.apache.org/docs/latest/building-spark.html. -# If this docker file is being used in the context of building your images from a Spark -# distribution, the docker build command should be invoked from the top level directory -# of the Spark distribution. E.g.: -# docker build -t spark-executor:latest -f kubernetes/dockerfiles/executor/Dockerfile . - -COPY examples /opt/spark/examples - -CMD SPARK_CLASSPATH="${SPARK_HOME}/jars/*" && \ - env | grep SPARK_JAVA_OPT_ | sed 's/[^=]*=\(.*\)/\1/g' > /tmp/java_opts.txt && \ - readarray -t SPARK_EXECUTOR_JAVA_OPTS < /tmp/java_opts.txt && \ - if ! [ -z ${SPARK_MOUNTED_CLASSPATH}+x} ]; then SPARK_CLASSPATH="$SPARK_MOUNTED_CLASSPATH:$SPARK_CLASSPATH"; fi && \ - if ! [ -z ${SPARK_EXECUTOR_EXTRA_CLASSPATH+x} ]; then SPARK_CLASSPATH="$SPARK_EXECUTOR_EXTRA_CLASSPATH:$SPARK_CLASSPATH"; fi && \ - if ! [ -z ${SPARK_MOUNTED_FILES_DIR+x} ]; then cp -R "$SPARK_MOUNTED_FILES_DIR/." .; fi && \ - ${JAVA_HOME}/bin/java "${SPARK_EXECUTOR_JAVA_OPTS[@]}" -Xms$SPARK_EXECUTOR_MEMORY -Xmx$SPARK_EXECUTOR_MEMORY -cp "$SPARK_CLASSPATH" org.apache.spark.executor.CoarseGrainedExecutorBackend --driver-url $SPARK_DRIVER_URL --executor-id $SPARK_EXECUTOR_ID --cores $SPARK_EXECUTOR_CORES --app-id $SPARK_APPLICATION_ID --hostname $SPARK_EXECUTOR_POD_IP diff --git a/resource-managers/kubernetes/docker/src/main/dockerfiles/init-container/Dockerfile b/resource-managers/kubernetes/docker/src/main/dockerfiles/init-container/Dockerfile deleted file mode 100644 index 055493188fcb7..0000000000000 --- a/resource-managers/kubernetes/docker/src/main/dockerfiles/init-container/Dockerfile +++ /dev/null @@ -1,24 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -FROM spark-base - -# If this docker file is being used in the context of building your images from a Spark distribution, the docker build -# command should be invoked from the top level directory of the Spark distribution. E.g.: -# docker build -t spark-init:latest -f kubernetes/dockerfiles/init-container/Dockerfile . - -ENTRYPOINT [ "/opt/entrypoint.sh", "/opt/spark/bin/spark-class", "org.apache.spark.deploy.rest.k8s.SparkPodInitContainer" ] diff --git a/resource-managers/kubernetes/docker/src/main/dockerfiles/spark-base/entrypoint.sh b/resource-managers/kubernetes/docker/src/main/dockerfiles/spark-base/entrypoint.sh deleted file mode 100755 index 82559889f4beb..0000000000000 --- a/resource-managers/kubernetes/docker/src/main/dockerfiles/spark-base/entrypoint.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# echo commands to the terminal output -set -ex - -# Check whether there is a passwd entry for the container UID -myuid=$(id -u) -mygid=$(id -g) -uidentry=$(getent passwd $myuid) - -# If there is no passwd entry for the container UID, attempt to create one -if [ -z "$uidentry" ] ; then - if [ -w /etc/passwd ] ; then - echo "$myuid:x:$myuid:$mygid:anonymous uid:$SPARK_HOME:/bin/false" >> /etc/passwd - else - echo "Container ENTRYPOINT failed to add passwd entry for anonymous UID" - fi -fi - -# Execute the container CMD under tini for better hygiene -/sbin/tini -s -- "$@" diff --git a/resource-managers/kubernetes/docker/src/main/dockerfiles/spark-base/Dockerfile b/resource-managers/kubernetes/docker/src/main/dockerfiles/spark/Dockerfile similarity index 85% rename from resource-managers/kubernetes/docker/src/main/dockerfiles/spark-base/Dockerfile rename to resource-managers/kubernetes/docker/src/main/dockerfiles/spark/Dockerfile index 222e777db3a82..491b7cf692478 100644 --- a/resource-managers/kubernetes/docker/src/main/dockerfiles/spark-base/Dockerfile +++ b/resource-managers/kubernetes/docker/src/main/dockerfiles/spark/Dockerfile @@ -17,12 +17,15 @@ FROM openjdk:8-alpine +ARG spark_jars=jars +ARG img_path=kubernetes/dockerfiles + # Before building the docker image, first build and make a Spark distribution following # the instructions in http://spark.apache.org/docs/latest/building-spark.html. # If this docker file is being used in the context of building your images from a Spark # distribution, the docker build command should be invoked from the top level directory # of the Spark distribution. E.g.: -# docker build -t spark-base:latest -f kubernetes/dockerfiles/spark-base/Dockerfile . +# docker build -t spark:latest -f kubernetes/dockerfiles/spark/Dockerfile . RUN set -ex && \ apk upgrade --no-cache && \ @@ -34,11 +37,13 @@ RUN set -ex && \ ln -sv /bin/bash /bin/sh && \ chgrp root /etc/passwd && chmod ug+rw /etc/passwd -COPY jars /opt/spark/jars +COPY ${spark_jars} /opt/spark/jars COPY bin /opt/spark/bin COPY sbin /opt/spark/sbin COPY conf /opt/spark/conf -COPY kubernetes/dockerfiles/spark-base/entrypoint.sh /opt/ +COPY ${img_path}/spark/entrypoint.sh /opt/ +COPY examples /opt/spark/examples +COPY data /opt/spark/data ENV SPARK_HOME /opt/spark diff --git a/resource-managers/kubernetes/docker/src/main/dockerfiles/spark/entrypoint.sh b/resource-managers/kubernetes/docker/src/main/dockerfiles/spark/entrypoint.sh new file mode 100755 index 0000000000000..3d67b0a702dd4 --- /dev/null +++ b/resource-managers/kubernetes/docker/src/main/dockerfiles/spark/entrypoint.sh @@ -0,0 +1,97 @@ +#!/bin/bash +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# echo commands to the terminal output +set -ex + +# Check whether there is a passwd entry for the container UID +myuid=$(id -u) +mygid=$(id -g) +uidentry=$(getent passwd $myuid) + +# If there is no passwd entry for the container UID, attempt to create one +if [ -z "$uidentry" ] ; then + if [ -w /etc/passwd ] ; then + echo "$myuid:x:$myuid:$mygid:anonymous uid:$SPARK_HOME:/bin/false" >> /etc/passwd + else + echo "Container ENTRYPOINT failed to add passwd entry for anonymous UID" + fi +fi + +SPARK_K8S_CMD="$1" +if [ -z "$SPARK_K8S_CMD" ]; then + echo "No command to execute has been provided." 1>&2 + exit 1 +fi +shift 1 + +SPARK_CLASSPATH="$SPARK_CLASSPATH:${SPARK_HOME}/jars/*" +env | grep SPARK_JAVA_OPT_ | sort -t_ -k4 -n | sed 's/[^=]*=\(.*\)/\1/g' > /tmp/java_opts.txt +readarray -t SPARK_JAVA_OPTS < /tmp/java_opts.txt +if [ -n "$SPARK_MOUNTED_CLASSPATH" ]; then + SPARK_CLASSPATH="$SPARK_CLASSPATH:$SPARK_MOUNTED_CLASSPATH" +fi +if [ -n "$SPARK_MOUNTED_FILES_DIR" ]; then + cp -R "$SPARK_MOUNTED_FILES_DIR/." . +fi + +case "$SPARK_K8S_CMD" in + driver) + CMD=( + ${JAVA_HOME}/bin/java + "${SPARK_JAVA_OPTS[@]}" + -cp "$SPARK_CLASSPATH" + -Xms$SPARK_DRIVER_MEMORY + -Xmx$SPARK_DRIVER_MEMORY + -Dspark.driver.bindAddress=$SPARK_DRIVER_BIND_ADDRESS + $SPARK_DRIVER_CLASS + $SPARK_DRIVER_ARGS + ) + ;; + + executor) + CMD=( + ${JAVA_HOME}/bin/java + "${SPARK_JAVA_OPTS[@]}" + -Xms$SPARK_EXECUTOR_MEMORY + -Xmx$SPARK_EXECUTOR_MEMORY + -cp "$SPARK_CLASSPATH" + org.apache.spark.executor.CoarseGrainedExecutorBackend + --driver-url $SPARK_DRIVER_URL + --executor-id $SPARK_EXECUTOR_ID + --cores $SPARK_EXECUTOR_CORES + --app-id $SPARK_APPLICATION_ID + --hostname $SPARK_EXECUTOR_POD_IP + ) + ;; + + init) + CMD=( + "$SPARK_HOME/bin/spark-class" + "org.apache.spark.deploy.k8s.SparkPodInitContainer" + "$@" + ) + ;; + + *) + echo "Unknown command: $SPARK_K8S_CMD" 1>&2 + exit 1 +esac + +# Execute the container CMD under tini for better hygiene +exec /sbin/tini -s -- "${CMD[@]}" diff --git a/resource-managers/mesos/pom.xml b/resource-managers/mesos/pom.xml index 70d0c1750b14e..cf153476ce9f9 100644 --- a/resource-managers/mesos/pom.xml +++ b/resource-managers/mesos/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala index d224a7325820a..b36f46456f9a5 100644 --- a/resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala +++ b/resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala @@ -530,9 +530,9 @@ private[spark] class MesosClusterScheduler( .filter { case (key, _) => !replicatedOptionsBlacklist.contains(key) } .toMap (defaultConf ++ driverConf).foreach { case (key, value) => - options ++= Seq("--conf", s""""$key=${shellEscape(value)}"""".stripMargin) } + options ++= Seq("--conf", s"${key}=${value}") } - options + options.map(shellEscape) } /** diff --git a/resource-managers/yarn/pom.xml b/resource-managers/yarn/pom.xml index 43a7ce95bd3de..71ddbb5d4db08 100644 --- a/resource-managers/yarn/pom.xml +++ b/resource-managers/yarn/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index b2576b0d72633..6e35d23def6f0 100644 --- a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -30,6 +30,7 @@ import scala.util.control.NonFatal import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.util.StringUtils import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.conf.YarnConfiguration @@ -417,7 +418,19 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments) extends } private def sparkContextInitialized(sc: SparkContext) = { - sparkContextPromise.success(sc) + sparkContextPromise.synchronized { + // Notify runDriver function that SparkContext is available + sparkContextPromise.success(sc) + // Pause the user class thread in order to make proper initialization in runDriver function. + sparkContextPromise.wait() + } + } + + private def resumeDriver(): Unit = { + // When initialization in runDriver happened the user class thread has to be resumed. + sparkContextPromise.synchronized { + sparkContextPromise.notify() + } } private def registerAM( @@ -427,11 +440,8 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments) extends uiAddress: Option[String]) = { val appId = client.getAttemptId().getApplicationId().toString() val attemptId = client.getAttemptId().getAttemptId().toString() - val historyAddress = - _sparkConf.get(HISTORY_SERVER_ADDRESS) - .map { text => SparkHadoopUtil.get.substituteHadoopVariables(text, yarnConf) } - .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}/${attemptId}" } - .getOrElse("") + val historyAddress = ApplicationMaster + .getHistoryServerAddress(_sparkConf, yarnConf, appId, attemptId) val driverUrl = RpcEndpointAddress( _sparkConf.get("spark.driver.host"), @@ -499,6 +509,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments) extends // if the user app did not create a SparkContext. throw new IllegalStateException("User did not initialize spark context!") } + resumeDriver() userClassThread.join() } catch { case e: SparkException if e.getCause().isInstanceOf[TimeoutException] => @@ -508,6 +519,8 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments) extends finish(FinalApplicationStatus.FAILED, ApplicationMaster.EXIT_SC_NOT_INITED, "Timed out waiting for SparkContext.") + } finally { + resumeDriver() } } @@ -721,7 +734,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments) extends logError("User class threw exception: " + cause, cause) finish(FinalApplicationStatus.FAILED, ApplicationMaster.EXIT_EXCEPTION_USER_CLASS, - "User class threw exception: " + cause) + "User class threw exception: " + StringUtils.stringifyException(cause)) } sparkContextPromise.tryFailure(e.getCause()) } finally { @@ -834,6 +847,16 @@ object ApplicationMaster extends Logging { master.getAttemptId } + private[spark] def getHistoryServerAddress( + sparkConf: SparkConf, + yarnConf: YarnConfiguration, + appId: String, + attemptId: String): String = { + sparkConf.get(HISTORY_SERVER_ADDRESS) + .map { text => SparkHadoopUtil.get.substituteHadoopVariables(text, yarnConf) } + .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}/${attemptId}" } + .getOrElse("") + } } /** diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 15328d08b3b5c..c3ba48efe7de8 100644 --- a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -696,7 +696,13 @@ private[spark] class Client( } } - Seq("HADOOP_CONF_DIR", "YARN_CONF_DIR").foreach { envKey => + // SPARK-23630: during testing, Spark scripts filter out hadoop conf dirs so that user's + // environments do not interfere with tests. This allows a special env variable during + // tests so that custom conf dirs can be used by unit tests. + val confDirs = Seq("HADOOP_CONF_DIR", "YARN_CONF_DIR") ++ + (if (Utils.isTesting) Seq("SPARK_TEST_HADOOP_CONF_DIR") else Nil) + + confDirs.foreach { envKey => sys.env.get(envKey).foreach { path => val dir = new File(path) if (dir.isDirectory()) { @@ -753,7 +759,7 @@ private[spark] class Client( // Save the YARN configuration into a separate file that will be overlayed on top of the // cluster's Hadoop conf. - confStream.putNextEntry(new ZipEntry(SPARK_HADOOP_CONF_FILE)) + confStream.putNextEntry(new ZipEntry(SparkHadoopUtil.SPARK_HADOOP_CONF_FILE)) hadoopConf.writeXml(confStream) confStream.closeEntry() @@ -1176,7 +1182,7 @@ private[spark] class Client( val pyArchivesFile = new File(pyLibPath, "pyspark.zip") require(pyArchivesFile.exists(), s"$pyArchivesFile not found; cannot run pyspark application in YARN mode.") - val py4jFile = new File(pyLibPath, "py4j-0.10.6-src.zip") + val py4jFile = new File(pyLibPath, "py4j-0.10.7-src.zip") require(py4jFile.exists(), s"$py4jFile not found; cannot run pyspark application in YARN mode.") Seq(pyArchivesFile.getAbsolutePath(), py4jFile.getAbsolutePath()) @@ -1220,10 +1226,6 @@ private object Client extends Logging { // Name of the file in the conf archive containing Spark configuration. val SPARK_CONF_FILE = "__spark_conf__.properties" - // Name of the file containing the gateway's Hadoop configuration, to be overlayed on top of the - // cluster's Hadoop config. - val SPARK_HADOOP_CONF_FILE = "__spark_hadoop_conf__.xml" - // Subdirectory where the user's python files (not archives) will be placed. val LOCALIZED_PYTHON_DIR = "__pyfiles__" @@ -1421,15 +1423,20 @@ private object Client extends Logging { } /** - * Return whether the two file systems are the same. + * Return whether two URI represent file system are the same */ - private def compareFs(srcFs: FileSystem, destFs: FileSystem): Boolean = { - val srcUri = srcFs.getUri() - val dstUri = destFs.getUri() + private[spark] def compareUri(srcUri: URI, dstUri: URI): Boolean = { + if (srcUri.getScheme() == null || srcUri.getScheme() != dstUri.getScheme()) { return false } + val srcAuthority = srcUri.getAuthority() + val dstAuthority = dstUri.getAuthority() + if (srcAuthority != null && !srcAuthority.equalsIgnoreCase(dstAuthority)) { + return false + } + var srcHost = srcUri.getHost() var dstHost = dstUri.getHost() @@ -1447,6 +1454,17 @@ private object Client extends Logging { } Objects.equal(srcHost, dstHost) && srcUri.getPort() == dstUri.getPort() + + } + + /** + * Return whether the two file systems are the same. + */ + protected def compareFs(srcFs: FileSystem, destFs: FileSystem): Boolean = { + val srcUri = srcFs.getUri() + val dstUri = destFs.getUri() + + compareUri(srcUri, dstUri) } /** diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala index 506adb363aa90..b2d960bb468ec 100644 --- a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala @@ -81,7 +81,8 @@ private[yarn] class YarnAllocator( private val releasedContainers = Collections.newSetFromMap[ContainerId]( new ConcurrentHashMap[ContainerId, java.lang.Boolean]) - private val numExecutorsRunning = new AtomicInteger(0) + private val runningExecutors = Collections.newSetFromMap[String]( + new ConcurrentHashMap[String, java.lang.Boolean]()) private val numExecutorsStarting = new AtomicInteger(0) @@ -166,7 +167,7 @@ private[yarn] class YarnAllocator( clock = newClock } - def getNumExecutorsRunning: Int = numExecutorsRunning.get() + def getNumExecutorsRunning: Int = runningExecutors.size() def getNumExecutorsFailed: Int = synchronized { val endTime = clock.getTimeMillis() @@ -242,12 +243,11 @@ private[yarn] class YarnAllocator( * Request that the ResourceManager release the container running the specified executor. */ def killExecutor(executorId: String): Unit = synchronized { - if (executorIdToContainer.contains(executorId)) { - val container = executorIdToContainer.get(executorId).get - internalReleaseContainer(container) - numExecutorsRunning.decrementAndGet() - } else { - logWarning(s"Attempted to kill unknown executor $executorId!") + executorIdToContainer.get(executorId) match { + case Some(container) if !releasedContainers.contains(container.getId) => + internalReleaseContainer(container) + runningExecutors.remove(executorId) + case _ => logWarning(s"Attempted to kill unknown executor $executorId!") } } @@ -274,7 +274,7 @@ private[yarn] class YarnAllocator( "Launching executor count: %d. Cluster resources: %s.") .format( allocatedContainers.size, - numExecutorsRunning.get, + runningExecutors.size, numExecutorsStarting.get, allocateResponse.getAvailableResources)) @@ -286,7 +286,7 @@ private[yarn] class YarnAllocator( logDebug("Completed %d containers".format(completedContainers.size)) processCompletedContainers(completedContainers.asScala) logDebug("Finished processing %d completed containers. Current running executor count: %d." - .format(completedContainers.size, numExecutorsRunning.get)) + .format(completedContainers.size, runningExecutors.size)) } } @@ -300,9 +300,9 @@ private[yarn] class YarnAllocator( val pendingAllocate = getPendingAllocate val numPendingAllocate = pendingAllocate.size val missing = targetNumExecutors - numPendingAllocate - - numExecutorsStarting.get - numExecutorsRunning.get + numExecutorsStarting.get - runningExecutors.size logDebug(s"Updating resource requests, target: $targetNumExecutors, " + - s"pending: $numPendingAllocate, running: ${numExecutorsRunning.get}, " + + s"pending: $numPendingAllocate, running: ${runningExecutors.size}, " + s"executorsStarting: ${numExecutorsStarting.get}") if (missing > 0) { @@ -502,7 +502,7 @@ private[yarn] class YarnAllocator( s"for executor with ID $executorId") def updateInternalState(): Unit = synchronized { - numExecutorsRunning.incrementAndGet() + runningExecutors.add(executorId) numExecutorsStarting.decrementAndGet() executorIdToContainer(executorId) = container containerIdToExecutorId(container.getId) = executorId @@ -513,7 +513,7 @@ private[yarn] class YarnAllocator( allocatedContainerToHostMap.put(containerId, executorHostname) } - if (numExecutorsRunning.get < targetNumExecutors) { + if (runningExecutors.size() < targetNumExecutors) { numExecutorsStarting.incrementAndGet() if (launchContainers) { launcherPool.execute(new Runnable { @@ -554,7 +554,7 @@ private[yarn] class YarnAllocator( } else { logInfo(("Skip launching executorRunnable as running executors count: %d " + "reached target executors count: %d.").format( - numExecutorsRunning.get, targetNumExecutors)) + runningExecutors.size, targetNumExecutors)) } } } @@ -569,7 +569,11 @@ private[yarn] class YarnAllocator( val exitReason = if (!alreadyReleased) { // Decrement the number of executors running. The next iteration of // the ApplicationMaster's reporting thread will take care of allocating. - numExecutorsRunning.decrementAndGet() + containerIdToExecutorId.get(containerId) match { + case Some(executorId) => runningExecutors.remove(executorId) + case None => logWarning(s"Cannot find executorId for container: ${containerId.toString}") + } + logInfo("Completed container %s%s (state: %s, exit status: %s)".format( containerId, onHostStr, diff --git a/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/ApplicationMasterSuite.scala b/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/ApplicationMasterSuite.scala new file mode 100644 index 0000000000000..695a82f3583e6 --- /dev/null +++ b/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/ApplicationMasterSuite.scala @@ -0,0 +1,43 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import org.apache.hadoop.yarn.conf.YarnConfiguration + +import org.apache.spark.{SparkConf, SparkFunSuite} + +class ApplicationMasterSuite extends SparkFunSuite { + + test("history url with hadoop and spark substitutions") { + val host = "rm.host.com" + val port = 18080 + val sparkConf = new SparkConf() + + sparkConf.set("spark.yarn.historyServer.address", + "http://${hadoopconf-yarn.resourcemanager.hostname}:${spark.history.ui.port}") + val yarnConf = new YarnConfiguration() + yarnConf.set("yarn.resourcemanager.hostname", host) + val appId = "application_123_1" + val attemptId = appId + "_1" + + val shsAddr = ApplicationMaster + .getHistoryServerAddress(sparkConf, yarnConf, appId, attemptId) + + assert(shsAddr === s"http://${host}:${port}/history/${appId}/${attemptId}") + } +} diff --git a/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index 9d5f5eb621118..7fa597167f3f0 100644 --- a/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -357,6 +357,39 @@ class ClientSuite extends SparkFunSuite with Matchers { sparkConf.get(SECONDARY_JARS) should be (Some(Seq(new File(jar2.toURI).getName))) } + private val matching = Seq( + ("files URI match test1", "file:///file1", "file:///file2"), + ("files URI match test2", "file:///c:file1", "file://c:file2"), + ("files URI match test3", "file://host/file1", "file://host/file2"), + ("wasb URI match test", "wasb://bucket1@user", "wasb://bucket1@user/"), + ("hdfs URI match test", "hdfs:/path1", "hdfs:/path1") + ) + + matching.foreach { t => + test(t._1) { + assert(Client.compareUri(new URI(t._2), new URI(t._3)), + s"No match between ${t._2} and ${t._3}") + } + } + + private val unmatching = Seq( + ("files URI unmatch test1", "file:///file1", "file://host/file2"), + ("files URI unmatch test2", "file://host/file1", "file:///file2"), + ("files URI unmatch test3", "file://host/file1", "file://host2/file2"), + ("wasb URI unmatch test1", "wasb://bucket1@user", "wasb://bucket2@user/"), + ("wasb URI unmatch test2", "wasb://bucket1@user", "wasb://bucket1@user2/"), + ("s3 URI unmatch test", "s3a://user@pass:bucket1/", "s3a://user2@pass2:bucket1/"), + ("hdfs URI unmatch test1", "hdfs://namenode1/path1", "hdfs://namenode1:8080/path2"), + ("hdfs URI unmatch test2", "hdfs://namenode1:8020/path1", "hdfs://namenode1:8080/path2") + ) + + unmatching.foreach { t => + test(t._1) { + assert(!Client.compareUri(new URI(t._2), new URI(t._3)), + s"match between ${t._2} and ${t._3}") + } + } + object Fixtures { val knownDefYarnAppCP: Seq[String] = diff --git a/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala b/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala index cb1e3c5268510..525abb6f2b350 100644 --- a/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala +++ b/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala @@ -251,11 +251,55 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter ContainerStatus.newInstance(c.getId(), ContainerState.COMPLETE, "Finished", 0) } handler.updateResourceRequests() - handler.processCompletedContainers(statuses.toSeq) + handler.processCompletedContainers(statuses) handler.getNumExecutorsRunning should be (0) handler.getPendingAllocate.size should be (1) } + test("kill same executor multiple times") { + val handler = createAllocator(2) + handler.updateResourceRequests() + handler.getNumExecutorsRunning should be (0) + handler.getPendingAllocate.size should be (2) + + val container1 = createContainer("host1") + val container2 = createContainer("host2") + handler.handleAllocatedContainers(Array(container1, container2)) + handler.getNumExecutorsRunning should be (2) + handler.getPendingAllocate.size should be (0) + + val executorToKill = handler.executorIdToContainer.keys.head + handler.killExecutor(executorToKill) + handler.getNumExecutorsRunning should be (1) + handler.killExecutor(executorToKill) + handler.killExecutor(executorToKill) + handler.killExecutor(executorToKill) + handler.getNumExecutorsRunning should be (1) + handler.requestTotalExecutorsWithPreferredLocalities(2, 0, Map.empty, Set.empty) + handler.updateResourceRequests() + handler.getPendingAllocate.size should be (1) + } + + test("process same completed container multiple times") { + val handler = createAllocator(2) + handler.updateResourceRequests() + handler.getNumExecutorsRunning should be (0) + handler.getPendingAllocate.size should be (2) + + val container1 = createContainer("host1") + val container2 = createContainer("host2") + handler.handleAllocatedContainers(Array(container1, container2)) + handler.getNumExecutorsRunning should be (2) + handler.getPendingAllocate.size should be (0) + + val statuses = Seq(container1, container1, container2).map { c => + ContainerStatus.newInstance(c.getId(), ContainerState.COMPLETE, "Finished", 0) + } + handler.processCompletedContainers(statuses) + handler.getNumExecutorsRunning should be (0) + + } + test("lost executor removed from backend") { val handler = createAllocator(4) handler.updateResourceRequests() @@ -272,7 +316,7 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter ContainerStatus.newInstance(c.getId(), ContainerState.COMPLETE, "Failed", -1) } handler.updateResourceRequests() - handler.processCompletedContainers(statuses.toSeq) + handler.processCompletedContainers(statuses) handler.updateResourceRequests() handler.getNumExecutorsRunning should be (0) handler.getPendingAllocate.size should be (2) diff --git a/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index 061f653b97b7a..4210737310c6b 100644 --- a/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -45,8 +45,7 @@ import org.apache.spark.util.Utils /** * Integration tests for YARN; these tests use a mini Yarn cluster to run Spark-on-YARN - * applications, and require the Spark assembly to be built before they can be successfully - * run. + * applications. */ @ExtendedYarnTest class YarnClusterSuite extends BaseYarnClusterSuite { @@ -115,12 +114,25 @@ class YarnClusterSuite extends BaseYarnClusterSuite { )) } - test("yarn-cluster should respect conf overrides in SparkHadoopUtil (SPARK-16414)") { + test("yarn-cluster should respect conf overrides in SparkHadoopUtil (SPARK-16414, SPARK-23630)") { + // Create a custom hadoop config file, to make sure it's contents are propagated to the driver. + val customConf = Utils.createTempDir() + val coreSite = """ + | + | + | spark.test.key + | testvalue + | + | + |""".stripMargin + Files.write(coreSite, new File(customConf, "core-site.xml"), StandardCharsets.UTF_8) + val result = File.createTempFile("result", null, tempDir) val finalState = runSpark(false, mainClassName(YarnClusterDriverUseSparkHadoopUtilConf.getClass), - appArgs = Seq("key=value", result.getAbsolutePath()), - extraConf = Map("spark.hadoop.key" -> "value")) + appArgs = Seq("key=value", "spark.test.key=testvalue", result.getAbsolutePath()), + extraConf = Map("spark.hadoop.key" -> "value"), + extraEnv = Map("SPARK_TEST_HADOOP_CONF_DIR" -> customConf.getAbsolutePath())) checkResult(finalState, result) } @@ -152,7 +164,7 @@ class YarnClusterSuite extends BaseYarnClusterSuite { } test("run Python application in yarn-cluster mode using " + - " spark.yarn.appMasterEnv to override local envvar") { + "spark.yarn.appMasterEnv to override local envvar") { testPySpark( clientMode = false, extraConf = Map( @@ -245,22 +257,17 @@ class YarnClusterSuite extends BaseYarnClusterSuite { // needed locations. val sparkHome = sys.props("spark.test.home") val pythonPath = Seq( - s"$sparkHome/python/lib/py4j-0.10.6-src.zip", + s"$sparkHome/python/lib/py4j-0.10.7-src.zip", s"$sparkHome/python") val extraEnvVars = Map( "PYSPARK_ARCHIVES_PATH" -> pythonPath.map("local:" + _).mkString(File.pathSeparator), "PYTHONPATH" -> pythonPath.mkString(File.pathSeparator)) ++ extraEnv - val moduleDir = - if (clientMode) { - // In client-mode, .py files added with --py-files are not visible in the driver. - // This is something that the launcher library would have to handle. - tempDir - } else { - val subdir = new File(tempDir, "pyModules") - subdir.mkdir() - subdir - } + val moduleDir = { + val subdir = new File(tempDir, "pyModules") + subdir.mkdir() + subdir + } val pyModule = new File(moduleDir, "mod1.py") Files.write(TEST_PYMODULE, pyModule, StandardCharsets.UTF_8) @@ -320,13 +327,13 @@ private object YarnClusterDriverWithFailure extends Logging with Matchers { private object YarnClusterDriverUseSparkHadoopUtilConf extends Logging with Matchers { def main(args: Array[String]): Unit = { - if (args.length != 2) { + if (args.length < 2) { // scalastyle:off println System.err.println( s""" |Invalid command line: ${args.mkString(" ")} | - |Usage: YarnClusterDriverUseSparkHadoopUtilConf [hadoopConfKey=value] [result file] + |Usage: YarnClusterDriverUseSparkHadoopUtilConf [hadoopConfKey=value]+ [result file] """.stripMargin) // scalastyle:on println System.exit(1) @@ -336,11 +343,16 @@ private object YarnClusterDriverUseSparkHadoopUtilConf extends Logging with Matc .set("spark.extraListeners", classOf[SaveExecutorInfo].getName) .setAppName("yarn test using SparkHadoopUtil's conf")) - val kv = args(0).split("=") - val status = new File(args(1)) + val kvs = args.take(args.length - 1).map { kv => + val parsed = kv.split("=") + (parsed(0), parsed(1)) + } + val status = new File(args.last) var result = "failure" try { - SparkHadoopUtil.get.conf.get(kv(0)) should be (kv(1)) + kvs.foreach { case (k, v) => + SparkHadoopUtil.get.conf.get(k) should be (v) + } result = "success" } finally { Files.write(result, status, StandardCharsets.UTF_8) @@ -381,7 +393,9 @@ private object YarnClusterDriver extends Logging with Matchers { // Verify that the config archive is correctly placed in the classpath of all containers. val confFile = "/" + Client.SPARK_CONF_FILE - assert(getClass().getResource(confFile) != null) + if (conf.getOption(SparkLauncher.DEPLOY_MODE) == Some("cluster")) { + assert(getClass().getResource(confFile) != null) + } val configFromExecutors = sc.parallelize(1 to 4, 4) .map { _ => Option(getClass().getResource(confFile)).map(_.toString).orNull } .collect() diff --git a/sbin/build-push-docker-images.sh b/sbin/build-push-docker-images.sh deleted file mode 100755 index b3137598692d8..0000000000000 --- a/sbin/build-push-docker-images.sh +++ /dev/null @@ -1,69 +0,0 @@ -#!/usr/bin/env bash - -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# This script builds and pushes docker images when run from a release of Spark -# with Kubernetes support. - -declare -A path=( [spark-driver]=kubernetes/dockerfiles/driver/Dockerfile \ - [spark-executor]=kubernetes/dockerfiles/executor/Dockerfile \ - [spark-init]=kubernetes/dockerfiles/init-container/Dockerfile ) - -function build { - docker build -t spark-base -f kubernetes/dockerfiles/spark-base/Dockerfile . - for image in "${!path[@]}"; do - docker build -t ${REPO}/$image:${TAG} -f ${path[$image]} . - done -} - - -function push { - for image in "${!path[@]}"; do - docker push ${REPO}/$image:${TAG} - done -} - -function usage { - echo "This script must be run from a runnable distribution of Apache Spark." - echo "Usage: ./sbin/build-push-docker-images.sh -r -t build" - echo " ./sbin/build-push-docker-images.sh -r -t push" - echo "for example: ./sbin/build-push-docker-images.sh -r docker.io/myrepo -t v2.3.0 push" -} - -if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then - usage - exit 0 -fi - -while getopts r:t: option -do - case "${option}" - in - r) REPO=${OPTARG};; - t) TAG=${OPTARG};; - esac -done - -if [ -z "$REPO" ] || [ -z "$TAG" ]; then - usage -else - case "${@: -1}" in - build) build;; - push) push;; - *) usage;; - esac -fi diff --git a/sbin/spark-config.sh b/sbin/spark-config.sh index bac154e10ae62..bf3da18c3706e 100755 --- a/sbin/spark-config.sh +++ b/sbin/spark-config.sh @@ -28,6 +28,6 @@ export SPARK_CONF_DIR="${SPARK_CONF_DIR:-"${SPARK_HOME}/conf"}" # Add the PySpark classes to the PYTHONPATH: if [ -z "${PYSPARK_PYTHONPATH_SET}" ]; then export PYTHONPATH="${SPARK_HOME}/python:${PYTHONPATH}" - export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.6-src.zip:${PYTHONPATH}" + export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.7-src.zip:${PYTHONPATH}" export PYSPARK_PYTHONPATH_SET=1 fi diff --git a/scalastyle-config.xml b/scalastyle-config.xml index 7bdd3fac773a3..e2fa5754afaee 100644 --- a/scalastyle-config.xml +++ b/scalastyle-config.xml @@ -93,7 +93,7 @@ This file is divided into 3 sections: - + diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml index 9e2ced30407d4..04a3380b02115 100644 --- a/sql/catalyst/pom.xml +++ b/sql/catalyst/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml @@ -134,7 +134,7 @@ org.scalatest scalatest-maven-plugin - -ea -Xmx4g -Xss4m -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=512m + -ea -Xmx4g -Xss4m -XX:ReservedCodeCacheSize=${CodeCacheSize} diff --git a/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4 b/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4 index 6fe995f650d55..5fa75fe348e68 100644 --- a/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4 +++ b/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4 @@ -73,18 +73,22 @@ statement | ALTER DATABASE identifier SET DBPROPERTIES tablePropertyList #setDatabaseProperties | DROP DATABASE (IF EXISTS)? identifier (RESTRICT | CASCADE)? #dropDatabase | createTableHeader ('(' colTypeList ')')? tableProvider - (OPTIONS options=tablePropertyList)? - (PARTITIONED BY partitionColumnNames=identifierList)? - bucketSpec? locationSpec? - (COMMENT comment=STRING)? - (TBLPROPERTIES tableProps=tablePropertyList)? + ((OPTIONS options=tablePropertyList) | + (PARTITIONED BY partitionColumnNames=identifierList) | + bucketSpec | + locationSpec | + (COMMENT comment=STRING) | + (TBLPROPERTIES tableProps=tablePropertyList))* (AS? query)? #createTable | createTableHeader ('(' columns=colTypeList ')')? - (COMMENT comment=STRING)? - (PARTITIONED BY '(' partitionColumns=colTypeList ')')? - bucketSpec? skewSpec? - rowFormat? createFileFormat? locationSpec? - (TBLPROPERTIES tablePropertyList)? + ((COMMENT comment=STRING) | + (PARTITIONED BY '(' partitionColumns=colTypeList ')') | + bucketSpec | + skewSpec | + rowFormat | + createFileFormat | + locationSpec | + (TBLPROPERTIES tableProps=tablePropertyList))* (AS? query)? #createHiveTable | CREATE TABLE (IF NOT EXISTS)? target=tableIdentifier LIKE source=tableIdentifier locationSpec? #createTableLike @@ -137,7 +141,7 @@ statement (LIKE? pattern=STRING)? #showTables | SHOW TABLE EXTENDED ((FROM | IN) db=identifier)? LIKE pattern=STRING partitionSpec? #showTable - | SHOW DATABASES (LIKE pattern=STRING)? #showDatabases + | SHOW DATABASES (LIKE? pattern=STRING)? #showDatabases | SHOW TBLPROPERTIES table=tableIdentifier ('(' key=tablePropertyKey ')')? #showTblProperties | SHOW COLUMNS (FROM | IN) tableIdentifier diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java index d18542b188f71..bf7b98a62998e 100644 --- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java @@ -72,7 +72,7 @@ public static int calculateHeaderPortionInBytes(int numFields) { private long elementOffset; private long getElementOffset(int ordinal, int elementSize) { - return elementOffset + ordinal * elementSize; + return elementOffset + ordinal * (long)elementSize; } public Object getBaseObject() { return baseObject; } @@ -402,7 +402,7 @@ public byte[] toByteArray() { public short[] toShortArray() { short[] values = new short[numElements]; Platform.copyMemory( - baseObject, elementOffset, values, Platform.SHORT_ARRAY_OFFSET, numElements * 2); + baseObject, elementOffset, values, Platform.SHORT_ARRAY_OFFSET, numElements * 2L); return values; } @@ -410,7 +410,7 @@ public short[] toShortArray() { public int[] toIntArray() { int[] values = new int[numElements]; Platform.copyMemory( - baseObject, elementOffset, values, Platform.INT_ARRAY_OFFSET, numElements * 4); + baseObject, elementOffset, values, Platform.INT_ARRAY_OFFSET, numElements * 4L); return values; } @@ -418,7 +418,7 @@ public int[] toIntArray() { public long[] toLongArray() { long[] values = new long[numElements]; Platform.copyMemory( - baseObject, elementOffset, values, Platform.LONG_ARRAY_OFFSET, numElements * 8); + baseObject, elementOffset, values, Platform.LONG_ARRAY_OFFSET, numElements * 8L); return values; } @@ -426,7 +426,7 @@ public long[] toLongArray() { public float[] toFloatArray() { float[] values = new float[numElements]; Platform.copyMemory( - baseObject, elementOffset, values, Platform.FLOAT_ARRAY_OFFSET, numElements * 4); + baseObject, elementOffset, values, Platform.FLOAT_ARRAY_OFFSET, numElements * 4L); return values; } @@ -434,14 +434,14 @@ public float[] toFloatArray() { public double[] toDoubleArray() { double[] values = new double[numElements]; Platform.copyMemory( - baseObject, elementOffset, values, Platform.DOUBLE_ARRAY_OFFSET, numElements * 8); + baseObject, elementOffset, values, Platform.DOUBLE_ARRAY_OFFSET, numElements * 8L); return values; } private static UnsafeArrayData fromPrimitiveArray( Object arr, int offset, int length, int elementSize) { final long headerInBytes = calculateHeaderPortionInBytes(length); - final long valueRegionInBytes = elementSize * length; + final long valueRegionInBytes = (long)elementSize * length; final long totalSizeInLongs = (headerInBytes + valueRegionInBytes + 7) / 8; if (totalSizeInLongs > Integer.MAX_VALUE / 8) { throw new UnsupportedOperationException("Cannot convert this array to unsafe format as " + diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/VariableLengthRowBasedKeyValueBatch.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/VariableLengthRowBasedKeyValueBatch.java index 905e6820ce6e2..c823de4810f2b 100644 --- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/VariableLengthRowBasedKeyValueBatch.java +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/VariableLengthRowBasedKeyValueBatch.java @@ -41,7 +41,7 @@ public final class VariableLengthRowBasedKeyValueBatch extends RowBasedKeyValueB @Override public UnsafeRow appendRow(Object kbase, long koff, int klen, Object vbase, long voff, int vlen) { - final long recordLength = 8 + klen + vlen + 8; + final long recordLength = 8L + klen + vlen + 8; // if run out of max supported rows or page size, return null if (numRows >= capacity || page == null || page.size() - pageCursor < recordLength) { return null; diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/codegen/UTF8StringBuilder.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/codegen/UTF8StringBuilder.java new file mode 100644 index 0000000000000..f0f66bae245fd --- /dev/null +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/codegen/UTF8StringBuilder.java @@ -0,0 +1,78 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions.codegen; + +import org.apache.spark.unsafe.Platform; +import org.apache.spark.unsafe.array.ByteArrayMethods; +import org.apache.spark.unsafe.types.UTF8String; + +/** + * A helper class to write {@link UTF8String}s to an internal buffer and build the concatenated + * {@link UTF8String} at the end. + */ +public class UTF8StringBuilder { + + private static final int ARRAY_MAX = ByteArrayMethods.MAX_ROUNDED_ARRAY_LENGTH; + + private byte[] buffer; + private int cursor = Platform.BYTE_ARRAY_OFFSET; + + public UTF8StringBuilder() { + // Since initial buffer size is 16 in `StringBuilder`, we set the same size here + this.buffer = new byte[16]; + } + + // Grows the buffer by at least `neededSize` + private void grow(int neededSize) { + if (neededSize > ARRAY_MAX - totalSize()) { + throw new UnsupportedOperationException( + "Cannot grow internal buffer by size " + neededSize + " because the size after growing " + + "exceeds size limitation " + ARRAY_MAX); + } + final int length = totalSize() + neededSize; + if (buffer.length < length) { + int newLength = length < ARRAY_MAX / 2 ? length * 2 : ARRAY_MAX; + final byte[] tmp = new byte[newLength]; + Platform.copyMemory( + buffer, + Platform.BYTE_ARRAY_OFFSET, + tmp, + Platform.BYTE_ARRAY_OFFSET, + totalSize()); + buffer = tmp; + } + } + + private int totalSize() { + return cursor - Platform.BYTE_ARRAY_OFFSET; + } + + public void append(UTF8String value) { + grow(value.numBytes()); + value.writeToMemory(buffer, cursor); + cursor += value.numBytes(); + } + + public void append(String value) { + append(UTF8String.fromString(value)); + } + + public UTF8String build() { + return UTF8String.fromBytes(buffer, 0, totalSize()); + } +} diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/xml/UDFXPathUtil.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/xml/UDFXPathUtil.java index d224332d8a6c9..023ec139652c5 100644 --- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/xml/UDFXPathUtil.java +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/xml/UDFXPathUtil.java @@ -21,6 +21,9 @@ import java.io.Reader; import javax.xml.namespace.QName; +import javax.xml.parsers.DocumentBuilder; +import javax.xml.parsers.DocumentBuilderFactory; +import javax.xml.parsers.ParserConfigurationException; import javax.xml.xpath.XPath; import javax.xml.xpath.XPathConstants; import javax.xml.xpath.XPathExpression; @@ -37,9 +40,15 @@ * This is based on Hive's UDFXPathUtil implementation. */ public class UDFXPathUtil { + public static final String SAX_FEATURE_PREFIX = "http://xml.org/sax/features/"; + public static final String EXTERNAL_GENERAL_ENTITIES_FEATURE = "external-general-entities"; + public static final String EXTERNAL_PARAMETER_ENTITIES_FEATURE = "external-parameter-entities"; + private DocumentBuilderFactory dbf = DocumentBuilderFactory.newInstance(); + private DocumentBuilder builder = null; private XPath xpath = XPathFactory.newInstance().newXPath(); private ReusableStringReader reader = new ReusableStringReader(); private InputSource inputSource = new InputSource(reader); + private XPathExpression expression = null; private String oldPath = null; @@ -65,14 +74,31 @@ public Object eval(String xml, String path, QName qname) throws XPathExpressionE return null; } + if (builder == null){ + try { + initializeDocumentBuilderFactory(); + builder = dbf.newDocumentBuilder(); + } catch (ParserConfigurationException e) { + throw new RuntimeException( + "Error instantiating DocumentBuilder, cannot build xml parser", e); + } + } + reader.set(xml); try { - return expression.evaluate(inputSource, qname); + return expression.evaluate(builder.parse(inputSource), qname); } catch (XPathExpressionException e) { throw new RuntimeException("Invalid XML document: " + e.getMessage() + "\n" + xml, e); + } catch (Exception e) { + throw new RuntimeException("Error loading expression '" + oldPath + "'", e); } } + private void initializeDocumentBuilderFactory() throws ParserConfigurationException { + dbf.setFeature(SAX_FEATURE_PREFIX + EXTERNAL_GENERAL_ENTITIES_FEATURE, false); + dbf.setFeature(SAX_FEATURE_PREFIX + EXTERNAL_PARAMETER_ENTITIES_FEATURE, false); + } + public Boolean evalBoolean(String xml, String path) throws XPathExpressionException { return (Boolean) eval(xml, path, XPathConstants.BOOLEAN); } diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/RecordBinaryComparator.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/RecordBinaryComparator.java new file mode 100644 index 0000000000000..bb77b5bf6de2a --- /dev/null +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/RecordBinaryComparator.java @@ -0,0 +1,70 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution; + +import org.apache.spark.unsafe.Platform; +import org.apache.spark.util.collection.unsafe.sort.RecordComparator; + +public final class RecordBinaryComparator extends RecordComparator { + + // TODO(jiangxb) Add test suite for this. + @Override + public int compare( + Object leftObj, long leftOff, int leftLen, Object rightObj, long rightOff, int rightLen) { + int i = 0; + int res = 0; + + // If the arrays have different length, the longer one is larger. + if (leftLen != rightLen) { + return leftLen - rightLen; + } + + // The following logic uses `leftLen` as the length for both `leftObj` and `rightObj`, since + // we have guaranteed `leftLen` == `rightLen`. + + // check if stars align and we can get both offsets to be aligned + if ((leftOff % 8) == (rightOff % 8)) { + while ((leftOff + i) % 8 != 0 && i < leftLen) { + res = (Platform.getByte(leftObj, leftOff + i) & 0xff) - + (Platform.getByte(rightObj, rightOff + i) & 0xff); + if (res != 0) return res; + i += 1; + } + } + // for architectures that support unaligned accesses, chew it up 8 bytes at a time + if (Platform.unaligned() || (((leftOff + i) % 8 == 0) && ((rightOff + i) % 8 == 0))) { + while (i <= leftLen - 8) { + res = (int) ((Platform.getLong(leftObj, leftOff + i) - + Platform.getLong(rightObj, rightOff + i)) % Integer.MAX_VALUE); + if (res != 0) return res; + i += 8; + } + } + // this will finish off the unaligned comparisons, or do the entire aligned comparison + // whichever is needed. + while (i < leftLen) { + res = (Platform.getByte(leftObj, leftOff + i) & 0xff) - + (Platform.getByte(rightObj, rightOff + i) & 0xff); + if (res != 0) return res; + i += 1; + } + + // The two arrays are equal. + return 0; + } +} diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java index 6b002f0d3f8e8..1b2f5eee5ccdd 100644 --- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution; import java.io.IOException; +import java.util.function.Supplier; import scala.collection.AbstractIterator; import scala.collection.Iterator; @@ -56,26 +57,50 @@ public abstract static class PrefixComputer { public static class Prefix { /** Key prefix value, or the null prefix value if isNull = true. **/ - long value; + public long value; /** Whether the key is null. */ - boolean isNull; + public boolean isNull; } /** * Computes prefix for the given row. For efficiency, the returned object may be reused in * further calls to a given PrefixComputer. */ - abstract Prefix computePrefix(InternalRow row); + public abstract Prefix computePrefix(InternalRow row); } - public UnsafeExternalRowSorter( + public static UnsafeExternalRowSorter createWithRecordComparator( + StructType schema, + Supplier recordComparatorSupplier, + PrefixComparator prefixComparator, + PrefixComputer prefixComputer, + long pageSizeBytes, + boolean canUseRadixSort) throws IOException { + return new UnsafeExternalRowSorter(schema, recordComparatorSupplier, prefixComparator, + prefixComputer, pageSizeBytes, canUseRadixSort); + } + + public static UnsafeExternalRowSorter create( StructType schema, Ordering ordering, PrefixComparator prefixComparator, PrefixComputer prefixComputer, long pageSizeBytes, boolean canUseRadixSort) throws IOException { + Supplier recordComparatorSupplier = + () -> new RowComparator(ordering, schema.length()); + return new UnsafeExternalRowSorter(schema, recordComparatorSupplier, prefixComparator, + prefixComputer, pageSizeBytes, canUseRadixSort); + } + + private UnsafeExternalRowSorter( + StructType schema, + Supplier recordComparatorSupplier, + PrefixComparator prefixComparator, + PrefixComputer prefixComputer, + long pageSizeBytes, + boolean canUseRadixSort) throws IOException { this.schema = schema; this.prefixComputer = prefixComputer; final SparkEnv sparkEnv = SparkEnv.get(); @@ -85,7 +110,7 @@ public UnsafeExternalRowSorter( sparkEnv.blockManager(), sparkEnv.serializerManager(), taskContext, - () -> new RowComparator(ordering, schema.length()), + recordComparatorSupplier, prefixComparator, sparkEnv.conf().getInt("spark.shuffle.sort.initialBufferSize", DEFAULT_INITIAL_SORT_BUFFER_SIZE), @@ -206,7 +231,13 @@ private static final class RowComparator extends RecordComparator { } @Override - public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff2) { + public int compare( + Object baseObj1, + long baseOff1, + int baseLen1, + Object baseObj2, + long baseOff2, + int baseLen2) { // Note that since ordering doesn't need the total length of the record, we just pass 0 // into the row. row1.pointTo(baseObj1, baseOff1, 0); diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/streaming/OutputMode.java b/sql/catalyst/src/main/java/org/apache/spark/sql/streaming/OutputMode.java index 2800b3068f87b..470c128ee6c3d 100644 --- a/sql/catalyst/src/main/java/org/apache/spark/sql/streaming/OutputMode.java +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/streaming/OutputMode.java @@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.streaming.InternalOutputModes; /** - * OutputMode is used to what data will be written to a streaming sink when there is + * OutputMode describes what data will be written to a streaming sink when there is * new data available in a streaming DataFrame/Dataset. * * @since 2.0.0 diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 65040f1af4b04..fabf8955330eb 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -63,6 +63,7 @@ object ScalaReflection extends ScalaReflection { private def dataTypeFor(tpe: `Type`): DataType = cleanUpReflectionObjects { tpe.dealias match { + case t if t <:< definitions.NullTpe => NullType case t if t <:< definitions.IntTpe => IntegerType case t if t <:< definitions.LongTpe => LongType case t if t <:< definitions.DoubleTpe => DoubleType @@ -381,22 +382,22 @@ object ScalaReflection extends ScalaReflection { val clsName = getClassNameFromType(fieldType) val newTypePath = s"""- field (class: "$clsName", name: "$fieldName")""" +: walkedTypePath // For tuples, we based grab the inner fields by ordinal instead of name. - if (cls.getName startsWith "scala.Tuple") { + val constructor = if (cls.getName startsWith "scala.Tuple") { deserializerFor( fieldType, Some(addToPathOrdinal(i, dataType, newTypePath)), newTypePath) } else { - val constructor = deserializerFor( + deserializerFor( fieldType, Some(addToPath(fieldName, dataType, newTypePath)), newTypePath) + } - if (!nullable) { - AssertNotNull(constructor, newTypePath) - } else { - constructor - } + if (!nullable) { + AssertNotNull(constructor, newTypePath) + } else { + constructor } } @@ -712,6 +713,9 @@ object ScalaReflection extends ScalaReflection { /** Returns a catalyst DataType and its nullability for the given Scala Type using reflection. */ def schemaFor(tpe: `Type`): Schema = cleanUpReflectionObjects { tpe.dealias match { + // this must be the first case, since all objects in scala are instances of Null, therefore + // Null type would wrongly match the first of them, which is Option as of now + case t if t <:< definitions.NullTpe => Schema(NullType, nullable = true) case t if t.typeSymbol.annotations.exists(_.tree.tpe =:= typeOf[SQLUserDefinedType]) => val udt = getClassFromType(t).getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance() Schema(udt, nullable = true) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AlreadyExistException.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AlreadyExistException.scala index 57f7a80bedc6c..6d587abd8fd4d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AlreadyExistException.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AlreadyExistException.scala @@ -31,7 +31,7 @@ class TableAlreadyExistsException(db: String, table: String) extends AnalysisException(s"Table or view '$table' already exists in database '$db'") class TempTableAlreadyExistsException(table: String) - extends AnalysisException(s"Temporary table '$table' already exists") + extends AnalysisException(s"Temporary view '$table' already exists") class PartitionAlreadyExistsException(db: String, table: String, spec: TablePartitionSpec) extends AnalysisException( diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 6d294d48c0ee7..8597d83d83000 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.catalyst.analysis import scala.collection.mutable.ArrayBuffer +import scala.util.Random import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst._ @@ -52,6 +53,7 @@ object SimpleAnalyzer extends Analyzer( /** * Provides a way to keep state during the analysis, this enables us to decouple the concerns * of analysis environment from the catalog. + * The state that is kept here is per-query. * * Note this is thread local. * @@ -70,6 +72,8 @@ object AnalysisContext { } def get: AnalysisContext = value.get() + def reset(): Unit = value.remove() + private def set(context: AnalysisContext): Unit = value.set(context) def withAnalysisContext[A](database: Option[String])(f: => A): A = { @@ -95,6 +99,30 @@ class Analyzer( this(catalog, conf, conf.optimizerMaxIterations) } + def executeAndCheck(plan: LogicalPlan): LogicalPlan = { + val analyzed = execute(plan) + try { + checkAnalysis(analyzed) + EliminateBarriers(analyzed) + } catch { + case e: AnalysisException => + val ae = new AnalysisException(e.message, e.line, e.startPosition, Option(analyzed)) + ae.setStackTrace(e.getStackTrace) + throw ae + } + } + + override def execute(plan: LogicalPlan): LogicalPlan = { + AnalysisContext.reset() + try { + executeSameContext(plan) + } finally { + AnalysisContext.reset() + } + } + + private def executeSameContext(plan: LogicalPlan): LogicalPlan = super.execute(plan) + def resolver: Resolver = conf.resolver protected val fixedPoint = FixedPoint(maxIterations) @@ -150,6 +178,7 @@ class Analyzer( TimeWindowing :: ResolveInlineTables(conf) :: ResolveTimeZone(conf) :: + ResolvedUuidExpressions :: TypeCoercion.typeCoercionRules(conf) ++ extendedResolutionRules : _*), Batch("Post-Hoc Resolution", Once, postHocResolutionRules: _*), @@ -164,8 +193,7 @@ class Analyzer( Batch("Subquery", Once, UpdateOuterReferences), Batch("Cleanup", fixedPoint, - CleanupAliases, - EliminateBarriers) + CleanupAliases) ) /** @@ -176,7 +204,7 @@ class Analyzer( case With(child, relations) => substituteCTE(child, relations.foldLeft(Seq.empty[(String, LogicalPlan)]) { case (resolved, (name, relation)) => - resolved :+ name -> execute(substituteCTE(relation, resolved)) + resolved :+ name -> executeSameContext(substituteCTE(relation, resolved)) }) case other => other } @@ -597,10 +625,10 @@ class Analyzer( if (AnalysisContext.get.nestedViewDepth > conf.maxNestedViewDepth) { view.failAnalysis(s"The depth of view ${view.desc.identifier} exceeds the maximum " + s"view resolution depth (${conf.maxNestedViewDepth}). Analysis is aborted to " + - "avoid errors. Increase the value of spark.sql.view.maxNestedViewDepth to work " + - "aroud this.") + s"avoid errors. Increase the value of ${SQLConf.MAX_NESTED_VIEW_DEPTH.key} to work " + + "around this.") } - execute(child) + executeSameContext(child) } view.copy(child = newChild) case p @ SubqueryAlias(_, view: View) => @@ -633,13 +661,13 @@ class Analyzer( try { catalog.lookupRelation(tableIdentWithDb) } catch { - case _: NoSuchTableException => - u.failAnalysis(s"Table or view not found: ${tableIdentWithDb.unquotedString}") + case e: NoSuchTableException => + u.failAnalysis(s"Table or view not found: ${tableIdentWithDb.unquotedString}", e) // If the database is defined and that database is not found, throw an AnalysisException. // Note that if the database is not defined, it is possible we are looking up a temp view. case e: NoSuchDatabaseException => u.failAnalysis(s"Table or view not found: ${tableIdentWithDb.unquotedString}, the " + - s"database ${e.db} doesn't exsits.") + s"database ${e.db} doesn't exist.", e) } } @@ -1269,7 +1297,7 @@ class Analyzer( do { // Try to resolve the subquery plan using the regular analyzer. previous = current - current = execute(current) + current = executeSameContext(current) // Use the outer references to resolve the subquery plan if it isn't resolved yet. val i = plans.iterator @@ -1392,7 +1420,7 @@ class Analyzer( grouping, Alias(cond, "havingCondition")() :: Nil, child) - val resolvedOperator = execute(aggregatedCondition) + val resolvedOperator = executeSameContext(aggregatedCondition) def resolvedAggregateFilter = resolvedOperator .asInstanceOf[Aggregate] @@ -1450,7 +1478,8 @@ class Analyzer( val aliasedOrdering = unresolvedSortOrders.map(o => Alias(o.child, "aggOrder")()) val aggregatedOrdering = aggregate.copy(aggregateExpressions = aliasedOrdering) - val resolvedAggregate: Aggregate = execute(aggregatedOrdering).asInstanceOf[Aggregate] + val resolvedAggregate: Aggregate = + executeSameContext(aggregatedOrdering).asInstanceOf[Aggregate] val resolvedAliasedOrdering: Seq[Alias] = resolvedAggregate.aggregateExpressions.asInstanceOf[Seq[Alias]] @@ -1466,7 +1495,7 @@ class Analyzer( // to push down this ordering expression and can reference the original aggregate // expression instead. val needsPushDown = ArrayBuffer.empty[NamedExpression] - val evaluatedOrderings = resolvedAliasedOrdering.zip(sortOrder).map { + val evaluatedOrderings = resolvedAliasedOrdering.zip(unresolvedSortOrders).map { case (evaluated, order) => val index = originalAggExprs.indexWhere { case Alias(child, _) => child semanticEquals evaluated.child @@ -1509,7 +1538,7 @@ class Analyzer( } /** - * Extracts [[Generator]] from the projectList of a [[Project]] operator and create [[Generate]] + * Extracts [[Generator]] from the projectList of a [[Project]] operator and creates [[Generate]] * operator under [[Project]]. * * This rule will throw [[AnalysisException]] for following cases: @@ -1967,6 +1996,20 @@ class Analyzer( } } + /** + * Set the seed for random number generation in Uuid expressions. + */ + object ResolvedUuidExpressions extends Rule[LogicalPlan] { + private lazy val random = new Random() + + override def apply(plan: LogicalPlan): LogicalPlan = plan.transformUp { + case p if p.resolved => p + case p => p transformExpressionsUp { + case Uuid(None) => Uuid(Some(random.nextLong())) + } + } + } + /** * Correctly handle null primitive inputs for UDF by adding extra [[If]] expression to do the * null check. When user defines a UDF with primitive parameters, there is no way to tell if the diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index bbcec5627bd49..0d189b4fd7743 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -348,6 +348,7 @@ trait CheckAnalysis extends PredicateHelper { } extendedCheckRules.foreach(_(plan)) plan.foreachUp { + case AnalysisBarrier(child) if !child.resolved => checkAnalysis(child) case o if !o.resolved => failAnalysis(s"unresolved operator ${o.simpleString}") case _ => } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecision.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecision.scala index a8100b9b24aac..ab63131b07573 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecision.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecision.scala @@ -21,6 +21,7 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.expressions.Literal._ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.types._ @@ -42,8 +43,10 @@ import org.apache.spark.sql.types._ * e1 / e2 p1 - s1 + s2 + max(6, s1 + p2 + 1) max(6, s1 + p2 + 1) * e1 % e2 min(p1-s1, p2-s2) + max(s1, s2) max(s1, s2) * e1 union e2 max(s1, s2) + max(p1-s1, p2-s2) max(s1, s2) - * sum(e1) p1 + 10 s1 - * avg(e1) p1 + 4 s1 + 4 + * + * When `spark.sql.decimalOperations.allowPrecisionLoss` is set to true, if the precision / scale + * needed are out of the range of available values, the scale is reduced up to 6, in order to + * prevent the truncation of the integer part of the decimals. * * To implement the rules for fixed-precision types, we introduce casts to turn them to unlimited * precision, do the math on unlimited-precision numbers, then introduce casts back to the @@ -56,6 +59,7 @@ import org.apache.spark.sql.types._ * - INT gets turned into DECIMAL(10, 0) * - LONG gets turned into DECIMAL(20, 0) * - FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE + * - Literals INT and LONG get turned into DECIMAL with the precision strictly needed by the value */ // scalastyle:on object DecimalPrecision extends TypeCoercionRule { @@ -93,41 +97,76 @@ object DecimalPrecision extends TypeCoercionRule { case e: BinaryArithmetic if e.left.isInstanceOf[PromotePrecision] => e case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) - CheckOverflow(Add(promotePrecision(e1, dt), promotePrecision(e2, dt)), dt) + val resultScale = max(s1, s2) + val resultType = if (SQLConf.get.decimalOperationsAllowPrecisionLoss) { + DecimalType.adjustPrecisionScale(max(p1 - s1, p2 - s2) + resultScale + 1, + resultScale) + } else { + DecimalType.bounded(max(p1 - s1, p2 - s2) + resultScale + 1, resultScale) + } + CheckOverflow(Add(promotePrecision(e1, resultType), promotePrecision(e2, resultType)), + resultType) case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) - CheckOverflow(Subtract(promotePrecision(e1, dt), promotePrecision(e2, dt)), dt) + val resultScale = max(s1, s2) + val resultType = if (SQLConf.get.decimalOperationsAllowPrecisionLoss) { + DecimalType.adjustPrecisionScale(max(p1 - s1, p2 - s2) + resultScale + 1, + resultScale) + } else { + DecimalType.bounded(max(p1 - s1, p2 - s2) + resultScale + 1, resultScale) + } + CheckOverflow(Subtract(promotePrecision(e1, resultType), promotePrecision(e2, resultType)), + resultType) case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - val resultType = DecimalType.bounded(p1 + p2 + 1, s1 + s2) + val resultType = if (SQLConf.get.decimalOperationsAllowPrecisionLoss) { + DecimalType.adjustPrecisionScale(p1 + p2 + 1, s1 + s2) + } else { + DecimalType.bounded(p1 + p2 + 1, s1 + s2) + } val widerType = widerDecimalType(p1, s1, p2, s2) CheckOverflow(Multiply(promotePrecision(e1, widerType), promotePrecision(e2, widerType)), resultType) case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - var intDig = min(DecimalType.MAX_SCALE, p1 - s1 + s2) - var decDig = min(DecimalType.MAX_SCALE, max(6, s1 + p2 + 1)) - val diff = (intDig + decDig) - DecimalType.MAX_SCALE - if (diff > 0) { - decDig -= diff / 2 + 1 - intDig = DecimalType.MAX_SCALE - decDig + val resultType = if (SQLConf.get.decimalOperationsAllowPrecisionLoss) { + // Precision: p1 - s1 + s2 + max(6, s1 + p2 + 1) + // Scale: max(6, s1 + p2 + 1) + val intDig = p1 - s1 + s2 + val scale = max(DecimalType.MINIMUM_ADJUSTED_SCALE, s1 + p2 + 1) + val prec = intDig + scale + DecimalType.adjustPrecisionScale(prec, scale) + } else { + var intDig = min(DecimalType.MAX_SCALE, p1 - s1 + s2) + var decDig = min(DecimalType.MAX_SCALE, max(6, s1 + p2 + 1)) + val diff = (intDig + decDig) - DecimalType.MAX_SCALE + if (diff > 0) { + decDig -= diff / 2 + 1 + intDig = DecimalType.MAX_SCALE - decDig + } + DecimalType.bounded(intDig + decDig, decDig) } - val resultType = DecimalType.bounded(intDig + decDig, decDig) val widerType = widerDecimalType(p1, s1, p2, s2) CheckOverflow(Divide(promotePrecision(e1, widerType), promotePrecision(e2, widerType)), resultType) case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) + val resultType = if (SQLConf.get.decimalOperationsAllowPrecisionLoss) { + DecimalType.adjustPrecisionScale(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) + } else { + DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) + } // resultType may have lower precision, so we cast them into wider type first. val widerType = widerDecimalType(p1, s1, p2, s2) CheckOverflow(Remainder(promotePrecision(e1, widerType), promotePrecision(e2, widerType)), resultType) case Pmod(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) + val resultType = if (SQLConf.get.decimalOperationsAllowPrecisionLoss) { + DecimalType.adjustPrecisionScale(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) + } else { + DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) + } // resultType may have lower precision, so we cast them into wider type first. val widerType = widerDecimalType(p1, s1, p2, s2) CheckOverflow(Pmod(promotePrecision(e1, widerType), promotePrecision(e2, widerType)), @@ -137,9 +176,6 @@ object DecimalPrecision extends TypeCoercionRule { e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => val resultType = widerDecimalType(p1, s1, p2, s2) b.makeCopy(Array(Cast(e1, resultType), Cast(e2, resultType))) - - // TODO: MaxOf, MinOf, etc might want other rules - // SUM and AVERAGE are handled by the implementations of those expressions } /** @@ -243,17 +279,35 @@ object DecimalPrecision extends TypeCoercionRule { // Promote integers inside a binary expression with fixed-precision decimals to decimals, // and fixed-precision decimals in an expression with floats / doubles to doubles case b @ BinaryOperator(left, right) if left.dataType != right.dataType => - (left.dataType, right.dataType) match { - case (t: IntegralType, DecimalType.Fixed(p, s)) => - b.makeCopy(Array(Cast(left, DecimalType.forType(t)), right)) - case (DecimalType.Fixed(p, s), t: IntegralType) => - b.makeCopy(Array(left, Cast(right, DecimalType.forType(t)))) - case (t, DecimalType.Fixed(p, s)) if isFloat(t) => - b.makeCopy(Array(left, Cast(right, DoubleType))) - case (DecimalType.Fixed(p, s), t) if isFloat(t) => - b.makeCopy(Array(Cast(left, DoubleType), right)) - case _ => - b + (left, right) match { + // Promote literal integers inside a binary expression with fixed-precision decimals to + // decimals. The precision and scale are the ones strictly needed by the integer value. + // Requiring more precision than necessary may lead to a useless loss of precision. + // Consider the following example: multiplying a column which is DECIMAL(38, 18) by 2. + // If we use the default precision and scale for the integer type, 2 is considered a + // DECIMAL(10, 0). According to the rules, the result would be DECIMAL(38 + 10 + 1, 18), + // which is out of range and therefore it will becomes DECIMAL(38, 7), leading to + // potentially loosing 11 digits of the fractional part. Using only the precision needed + // by the Literal, instead, the result would be DECIMAL(38 + 1 + 1, 18), which would + // become DECIMAL(38, 16), safely having a much lower precision loss. + case (l: Literal, r) if r.dataType.isInstanceOf[DecimalType] + && l.dataType.isInstanceOf[IntegralType] => + b.makeCopy(Array(Cast(l, DecimalType.fromLiteral(l)), r)) + case (l, r: Literal) if l.dataType.isInstanceOf[DecimalType] + && r.dataType.isInstanceOf[IntegralType] => + b.makeCopy(Array(l, Cast(r, DecimalType.fromLiteral(r)))) + // Promote integers inside a binary expression with fixed-precision decimals to decimals, + // and fixed-precision decimals in an expression with floats / doubles to doubles + case (l @ IntegralType(), r @ DecimalType.Expression(_, _)) => + b.makeCopy(Array(Cast(l, DecimalType.forType(l.dataType)), r)) + case (l @ DecimalType.Expression(_, _), r @ IntegralType()) => + b.makeCopy(Array(l, Cast(r, DecimalType.forType(r.dataType)))) + case (l, r @ DecimalType.Expression(_, _)) if isFloat(l.dataType) => + b.makeCopy(Array(l, Cast(r, DoubleType))) + case (l @ DecimalType.Expression(_, _), r) if isFloat(r.dataType) => + b.makeCopy(Array(Cast(l, DoubleType), r)) + case _ => b } } + } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala index 5ddb39822617d..f3dfd69d96529 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala @@ -526,7 +526,17 @@ object FunctionRegistry { // Otherwise, find a constructor method that matches the number of arguments, and use that. val params = Seq.fill(expressions.size)(classOf[Expression]) val f = constructors.find(_.getParameterTypes.toSeq == params).getOrElse { - throw new AnalysisException(s"Invalid number of arguments for function $name") + val validParametersCount = constructors + .filter(_.getParameterTypes.forall(_ == classOf[Expression])) + .map(_.getParameterCount).distinct.sorted + val expectedNumberOfParameters = if (validParametersCount.length == 1) { + validParametersCount.head.toString + } else { + validParametersCount.init.mkString("one of ", ", ", " and ") + + validParametersCount.last + } + throw new AnalysisException(s"Invalid number of arguments for function $name. " + + s"Expected: $expectedNumberOfParameters; Found: ${params.length}") } Try(f.newInstance(expressions : _*).asInstanceOf[Expression]) match { case Success(e) => e diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveInlineTables.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveInlineTables.scala index f2df3e132629f..71ed75454cd4d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveInlineTables.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveInlineTables.scala @@ -103,7 +103,7 @@ case class ResolveInlineTables(conf: SQLConf) extends Rule[LogicalPlan] with Cas castedExpr.eval() } catch { case NonFatal(ex) => - table.failAnalysis(s"failed to evaluate expression ${e.sql}: ${ex.getMessage}") + table.failAnalysis(s"failed to evaluate expression ${e.sql}: ${ex.getMessage}", ex) } }) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala index e9436367c7e2e..e8669c4637d06 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala @@ -54,6 +54,7 @@ object TypeCoercion { BooleanEquality :: FunctionArgumentConversion :: ConcatCoercion(conf) :: + EltCoercion(conf) :: CaseWhenCoercion :: IfCoercion :: StackCoercion :: @@ -684,6 +685,34 @@ object TypeCoercion { } } + /** + * Coerces the types of [[Elt]] children to expected ones. + * + * If `spark.sql.function.eltOutputAsString` is false and all children types are binary, + * the expected types are binary. Otherwise, the expected ones are strings. + */ + case class EltCoercion(conf: SQLConf) extends TypeCoercionRule { + + override protected def coerceTypes(plan: LogicalPlan): LogicalPlan = plan transform { case p => + p transformExpressionsUp { + // Skip nodes if unresolved or not enough children + case c @ Elt(children) if !c.childrenResolved || children.size < 2 => c + case c @ Elt(children) => + val index = children.head + val newIndex = ImplicitTypeCasts.implicitCast(index, IntegerType).getOrElse(index) + val newInputs = if (conf.eltOutputAsString || + !children.tail.map(_.dataType).forall(_ == BinaryType)) { + children.tail.map { e => + ImplicitTypeCasts.implicitCast(e, StringType).getOrElse(e) + } + } else { + children.tail + } + c.copy(children = newIndex +: newInputs) + } + } + } + /** * Turns Add/Subtract of DateType/TimestampType/StringType and CalendarIntervalType * to TimeAdd/TimeSub diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/UnsupportedOperationChecker.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/UnsupportedOperationChecker.scala index b55043c270644..ff9d6d7a7dded 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/UnsupportedOperationChecker.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/UnsupportedOperationChecker.scala @@ -345,7 +345,7 @@ object UnsupportedOperationChecker { plan.foreachUp { implicit subPlan => subPlan match { case (_: Project | _: Filter | _: MapElements | _: MapPartitions | - _: DeserializeToObject | _: SerializeFromObject) => + _: DeserializeToObject | _: SerializeFromObject | _: SubqueryAlias) => case node if node.nodeName == "StreamingRelationV2" => case node => throwError(s"Continuous processing does not support ${node.nodeName} operations.") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala index 7731336d247db..354a3fa0602a9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala @@ -41,6 +41,11 @@ package object analysis { def failAnalysis(msg: String): Nothing = { throw new AnalysisException(msg, t.origin.line, t.origin.startPosition) } + + /** Fails the analysis at the point where a specific tree node was parsed. */ + def failAnalysis(msg: String, cause: Throwable): Nothing = { + throw new AnalysisException(msg, t.origin.line, t.origin.startPosition, cause = Some(cause)) + } } /** Catches any AnalysisExceptions thrown by `f` and attaches `t`'s position if any. */ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala index d336f801d0770..a65f58fa61ff4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala @@ -294,7 +294,7 @@ case class UnresolvedStar(target: Option[Seq[String]]) extends Star with Unevalu } else { val from = input.inputSet.map(_.name).mkString(", ") val targetString = target.get.mkString(".") - throw new AnalysisException(s"cannot resolve '$targetString.*' give input columns '$from'") + throw new AnalysisException(s"cannot resolve '$targetString.*' given input columns '$from'") } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala index a129896230775..4b119c75260a7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala @@ -988,8 +988,11 @@ class SessionCatalog( // ------------------------------------------------------- /** - * Create a metastore function in the database specified in `funcDefinition`. + * Create a function in the database specified in `funcDefinition`. * If no such database is specified, create it in the current database. + * + * @param ignoreIfExists: When true, ignore if the function with the specified name exists + * in the specified database. */ def createFunction(funcDefinition: CatalogFunction, ignoreIfExists: Boolean): Unit = { val db = formatDatabaseName(funcDefinition.identifier.database.getOrElse(getCurrentDatabase)) @@ -1061,7 +1064,7 @@ class SessionCatalog( } /** - * Check if the specified function exists. + * Check if the function with the specified name exists */ def functionExists(name: FunctionIdentifier): Boolean = { val db = formatDatabaseName(name.database.getOrElse(getCurrentDatabase)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 59cb26d5e6c36..efb2eba655e15 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -21,6 +21,7 @@ import java.sql.{Date, Timestamp} import scala.language.implicitConversions +import org.apache.spark.api.java.function.FilterFunction import org.apache.spark.sql.Encoder import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ @@ -301,6 +302,8 @@ package object dsl { def filter[T : Encoder](func: T => Boolean): LogicalPlan = TypedFilter(func, logicalPlan) + def filter[T : Encoder](func: FilterFunction[T]): LogicalPlan = TypedFilter(func, logicalPlan) + def serialize[T : Encoder]: LogicalPlan = CatalystSerde.serialize[T](logicalPlan) def deserialize[T : Encoder]: LogicalPlan = CatalystSerde.deserialize[T](logicalPlan) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index 274d8813f16db..79b051670e9e4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -206,6 +206,85 @@ case class Cast(child: Expression, dataType: DataType, timeZoneId: Option[String case DateType => buildCast[Int](_, d => UTF8String.fromString(DateTimeUtils.dateToString(d))) case TimestampType => buildCast[Long](_, t => UTF8String.fromString(DateTimeUtils.timestampToString(t, timeZone))) + case ArrayType(et, _) => + buildCast[ArrayData](_, array => { + val builder = new UTF8StringBuilder + builder.append("[") + if (array.numElements > 0) { + val toUTF8String = castToString(et) + if (!array.isNullAt(0)) { + builder.append(toUTF8String(array.get(0, et)).asInstanceOf[UTF8String]) + } + var i = 1 + while (i < array.numElements) { + builder.append(",") + if (!array.isNullAt(i)) { + builder.append(" ") + builder.append(toUTF8String(array.get(i, et)).asInstanceOf[UTF8String]) + } + i += 1 + } + } + builder.append("]") + builder.build() + }) + case MapType(kt, vt, _) => + buildCast[MapData](_, map => { + val builder = new UTF8StringBuilder + builder.append("[") + if (map.numElements > 0) { + val keyArray = map.keyArray() + val valueArray = map.valueArray() + val keyToUTF8String = castToString(kt) + val valueToUTF8String = castToString(vt) + builder.append(keyToUTF8String(keyArray.get(0, kt)).asInstanceOf[UTF8String]) + builder.append(" ->") + if (!valueArray.isNullAt(0)) { + builder.append(" ") + builder.append(valueToUTF8String(valueArray.get(0, vt)).asInstanceOf[UTF8String]) + } + var i = 1 + while (i < map.numElements) { + builder.append(", ") + builder.append(keyToUTF8String(keyArray.get(i, kt)).asInstanceOf[UTF8String]) + builder.append(" ->") + if (!valueArray.isNullAt(i)) { + builder.append(" ") + builder.append(valueToUTF8String(valueArray.get(i, vt)) + .asInstanceOf[UTF8String]) + } + i += 1 + } + } + builder.append("]") + builder.build() + }) + case StructType(fields) => + buildCast[InternalRow](_, row => { + val builder = new UTF8StringBuilder + builder.append("[") + if (row.numFields > 0) { + val st = fields.map(_.dataType) + val toUTF8StringFuncs = st.map(castToString) + if (!row.isNullAt(0)) { + builder.append(toUTF8StringFuncs(0)(row.get(0, st(0))).asInstanceOf[UTF8String]) + } + var i = 1 + while (i < row.numFields) { + builder.append(",") + if (!row.isNullAt(i)) { + builder.append(" ") + builder.append(toUTF8StringFuncs(i)(row.get(i, st(i))).asInstanceOf[UTF8String]) + } + i += 1 + } + } + builder.append("]") + builder.build() + }) + case pudt: PythonUserDefinedType => castToString(pudt.sqlType) + case udt: UserDefinedType[_] => + buildCast[Any](_, o => UTF8String.fromString(udt.deserialize(o).toString)) case _ => buildCast[Any](_, o => UTF8String.fromString(o.toString)) } @@ -597,6 +676,123 @@ case class Cast(child: Expression, dataType: DataType, timeZoneId: Option[String """ } + private def writeArrayToStringBuilder( + et: DataType, + array: String, + buffer: String, + ctx: CodegenContext): String = { + val elementToStringCode = castToStringCode(et, ctx) + val funcName = ctx.freshName("elementToString") + val elementToStringFunc = ctx.addNewFunction(funcName, + s""" + |private UTF8String $funcName(${ctx.javaType(et)} element) { + | UTF8String elementStr = null; + | ${elementToStringCode("element", "elementStr", null /* resultIsNull won't be used */)} + | return elementStr; + |} + """.stripMargin) + + val loopIndex = ctx.freshName("loopIndex") + s""" + |$buffer.append("["); + |if ($array.numElements() > 0) { + | if (!$array.isNullAt(0)) { + | $buffer.append($elementToStringFunc(${ctx.getValue(array, et, "0")})); + | } + | for (int $loopIndex = 1; $loopIndex < $array.numElements(); $loopIndex++) { + | $buffer.append(","); + | if (!$array.isNullAt($loopIndex)) { + | $buffer.append(" "); + | $buffer.append($elementToStringFunc(${ctx.getValue(array, et, loopIndex)})); + | } + | } + |} + |$buffer.append("]"); + """.stripMargin + } + + private def writeMapToStringBuilder( + kt: DataType, + vt: DataType, + map: String, + buffer: String, + ctx: CodegenContext): String = { + + def dataToStringFunc(func: String, dataType: DataType) = { + val funcName = ctx.freshName(func) + val dataToStringCode = castToStringCode(dataType, ctx) + ctx.addNewFunction(funcName, + s""" + |private UTF8String $funcName(${ctx.javaType(dataType)} data) { + | UTF8String dataStr = null; + | ${dataToStringCode("data", "dataStr", null /* resultIsNull won't be used */)} + | return dataStr; + |} + """.stripMargin) + } + + val keyToStringFunc = dataToStringFunc("keyToString", kt) + val valueToStringFunc = dataToStringFunc("valueToString", vt) + val loopIndex = ctx.freshName("loopIndex") + s""" + |$buffer.append("["); + |if ($map.numElements() > 0) { + | $buffer.append($keyToStringFunc(${ctx.getValue(s"$map.keyArray()", kt, "0")})); + | $buffer.append(" ->"); + | if (!$map.valueArray().isNullAt(0)) { + | $buffer.append(" "); + | $buffer.append($valueToStringFunc(${ctx.getValue(s"$map.valueArray()", vt, "0")})); + | } + | for (int $loopIndex = 1; $loopIndex < $map.numElements(); $loopIndex++) { + | $buffer.append(", "); + | $buffer.append($keyToStringFunc(${ctx.getValue(s"$map.keyArray()", kt, loopIndex)})); + | $buffer.append(" ->"); + | if (!$map.valueArray().isNullAt($loopIndex)) { + | $buffer.append(" "); + | $buffer.append($valueToStringFunc( + | ${ctx.getValue(s"$map.valueArray()", vt, loopIndex)})); + | } + | } + |} + |$buffer.append("]"); + """.stripMargin + } + + private def writeStructToStringBuilder( + st: Seq[DataType], + row: String, + buffer: String, + ctx: CodegenContext): String = { + val structToStringCode = st.zipWithIndex.map { case (ft, i) => + val fieldToStringCode = castToStringCode(ft, ctx) + val field = ctx.freshName("field") + val fieldStr = ctx.freshName("fieldStr") + s""" + |${if (i != 0) s"""$buffer.append(",");""" else ""} + |if (!$row.isNullAt($i)) { + | ${if (i != 0) s"""$buffer.append(" ");""" else ""} + | + | // Append $i field into the string buffer + | ${ctx.javaType(ft)} $field = ${ctx.getValue(row, ft, s"$i")}; + | UTF8String $fieldStr = null; + | ${fieldToStringCode(field, fieldStr, null /* resultIsNull won't be used */)} + | $buffer.append($fieldStr); + |} + """.stripMargin + } + + val writeStructCode = ctx.splitExpressions( + expressions = structToStringCode, + funcName = "fieldToString", + arguments = ("InternalRow", row) :: (classOf[UTF8StringBuilder].getName, buffer) :: Nil) + + s""" + |$buffer.append("["); + |$writeStructCode + |$buffer.append("]"); + """.stripMargin + } + private[this] def castToStringCode(from: DataType, ctx: CodegenContext): CastFunction = { from match { case BinaryType => @@ -608,6 +804,47 @@ case class Cast(child: Expression, dataType: DataType, timeZoneId: Option[String val tz = ctx.addReferenceObj("timeZone", timeZone) (c, evPrim, evNull) => s"""$evPrim = UTF8String.fromString( org.apache.spark.sql.catalyst.util.DateTimeUtils.timestampToString($c, $tz));""" + case ArrayType(et, _) => + (c, evPrim, evNull) => { + val buffer = ctx.freshName("buffer") + val bufferClass = classOf[UTF8StringBuilder].getName + val writeArrayElemCode = writeArrayToStringBuilder(et, c, buffer, ctx) + s""" + |$bufferClass $buffer = new $bufferClass(); + |$writeArrayElemCode; + |$evPrim = $buffer.build(); + """.stripMargin + } + case MapType(kt, vt, _) => + (c, evPrim, evNull) => { + val buffer = ctx.freshName("buffer") + val bufferClass = classOf[UTF8StringBuilder].getName + val writeMapElemCode = writeMapToStringBuilder(kt, vt, c, buffer, ctx) + s""" + |$bufferClass $buffer = new $bufferClass(); + |$writeMapElemCode; + |$evPrim = $buffer.build(); + """.stripMargin + } + case StructType(fields) => + (c, evPrim, evNull) => { + val row = ctx.freshName("row") + val buffer = ctx.freshName("buffer") + val bufferClass = classOf[UTF8StringBuilder].getName + val writeStructCode = writeStructToStringBuilder(fields.map(_.dataType), row, buffer, ctx) + s""" + |InternalRow $row = $c; + |$bufferClass $buffer = new $bufferClass(); + |$writeStructCode + |$evPrim = $buffer.build(); + """.stripMargin + } + case pudt: PythonUserDefinedType => castToStringCode(pudt.sqlType, ctx) + case udt: UserDefinedType[_] => + val udtRef = ctx.addReferenceObj("udt", udt) + (c, evPrim, evNull) => { + s"$evPrim = UTF8String.fromString($udtRef.deserialize($c).toString());" + } case _ => (c, evPrim, evNull) => s"$evPrim = UTF8String.fromString(String.valueOf($c));" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala index 388ef42883ad3..989c02305620a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala @@ -49,6 +49,17 @@ case class ScalaUDF( udfDeterministic: Boolean = true) extends Expression with ImplicitCastInputTypes with NonSQLExpression with UserDefinedExpression { + // The constructor for SPARK 2.1 and 2.2 + def this( + function: AnyRef, + dataType: DataType, + children: Seq[Expression], + inputTypes: Seq[DataType], + udfName: Option[String]) = { + this( + function, dataType, children, inputTypes, udfName, nullable = true, udfDeterministic = true) + } + override lazy val deterministic: Boolean = udfDeterministic && children.forall(_.deterministic) override def toString: String = diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala index 149ac265e6ed5..a45854a3b5146 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala @@ -296,8 +296,8 @@ object ApproximatePercentile { Ints.BYTES + Doubles.BYTES + Longs.BYTES + // length of summary.sampled Ints.BYTES + - // summary.sampled, Array[Stat(value: Double, g: Int, delta: Int)] - summaries.sampled.length * (Doubles.BYTES + Ints.BYTES + Ints.BYTES) + // summary.sampled, Array[Stat(value: Double, g: Long, delta: Long)] + summaries.sampled.length * (Doubles.BYTES + Longs.BYTES + Longs.BYTES) } final def serialize(obj: PercentileDigest): Array[Byte] = { @@ -312,8 +312,8 @@ object ApproximatePercentile { while (i < summary.sampled.length) { val stat = summary.sampled(i) buffer.putDouble(stat.value) - buffer.putInt(stat.g) - buffer.putInt(stat.delta) + buffer.putLong(stat.g) + buffer.putLong(stat.delta) i += 1 } buffer.array() @@ -330,8 +330,8 @@ object ApproximatePercentile { var i = 0 while (i < sampledLength) { val value = buffer.getDouble() - val g = buffer.getInt() - val delta = buffer.getInt() + val g = buffer.getLong() + val delta = buffer.getLong() sampled(i) = Stats(value, g, delta) i += 1 } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala index 19abce01a26cf..e1d16a2cd38b0 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala @@ -190,17 +190,15 @@ abstract class AggregateFunction extends Expression { def defaultResult: Option[Literal] = None /** - * Wraps this [[AggregateFunction]] in an [[AggregateExpression]] because - * [[AggregateExpression]] is the container of an [[AggregateFunction]], aggregation mode, - * and the flag indicating if this aggregation is distinct aggregation or not. - * An [[AggregateFunction]] should not be used without being wrapped in - * an [[AggregateExpression]]. + * Creates [[AggregateExpression]] with `isDistinct` flag disabled. + * + * @see `toAggregateExpression(isDistinct: Boolean)` for detailed description */ def toAggregateExpression(): AggregateExpression = toAggregateExpression(isDistinct = false) /** - * Wraps this [[AggregateFunction]] in an [[AggregateExpression]] and set isDistinct - * field of the [[AggregateExpression]] to the given value because + * Wraps this [[AggregateFunction]] in an [[AggregateExpression]] and sets `isDistinct` + * flag of the [[AggregateExpression]] to the given value because * [[AggregateExpression]] is the container of an [[AggregateFunction]], aggregation mode, * and the flag indicating if this aggregation is distinct aggregation or not. * An [[AggregateFunction]] should not be used without being wrapped in diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index 2c714c228e6c9..9cf5839ff1c91 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -389,7 +389,7 @@ class CodegenContext { val equivalentExpressions: EquivalentExpressions = new EquivalentExpressions // Foreach expression that is participating in subexpression elimination, the state to use. - val subExprEliminationExprs = mutable.HashMap.empty[Expression, SubExprEliminationState] + var subExprEliminationExprs = Map.empty[Expression, SubExprEliminationState] // The collection of sub-expression result resetting methods that need to be called on each row. val subexprFunctions = mutable.ArrayBuffer.empty[String] @@ -564,14 +564,9 @@ class CodegenContext { } else { s"${freshNamePrefix}_$name" } - if (freshNameIds.contains(fullName)) { - val id = freshNameIds(fullName) - freshNameIds(fullName) = id + 1 - s"$fullName$id" - } else { - freshNameIds += fullName -> 1 - fullName - } + val id = freshNameIds.getOrElse(fullName, 0) + freshNameIds(fullName) = id + 1 + s"${fullName}_$id" } /** @@ -688,17 +683,13 @@ class CodegenContext { /** * Returns the specialized code to access a value from a column vector for a given `DataType`. */ - def getValue(vector: String, rowId: String, dataType: DataType): String = { - val jt = javaType(dataType) - dataType match { - case _ if isPrimitiveType(jt) => - s"$vector.get${primitiveTypeName(jt)}($rowId)" - case t: DecimalType => - s"$vector.getDecimal($rowId, ${t.precision}, ${t.scale})" - case StringType => - s"$vector.getUTF8String($rowId)" - case _ => - throw new IllegalArgumentException(s"cannot generate code for unsupported type: $dataType") + def getValueFromVector(vector: String, dataType: DataType, rowId: String): String = { + if (dataType.isInstanceOf[StructType]) { + // `ColumnVector.getStruct` is different from `InternalRow.getStruct`, it only takes an + // `ordinal` parameter. + s"$vector.getStruct($rowId)" + } else { + getValue(vector, dataType, rowId) } } @@ -773,8 +764,10 @@ class CodegenContext { */ def genEqual(dataType: DataType, c1: String, c2: String): String = dataType match { case BinaryType => s"java.util.Arrays.equals($c1, $c2)" - case FloatType => s"(java.lang.Float.isNaN($c1) && java.lang.Float.isNaN($c2)) || $c1 == $c2" - case DoubleType => s"(java.lang.Double.isNaN($c1) && java.lang.Double.isNaN($c2)) || $c1 == $c2" + case FloatType => + s"((java.lang.Float.isNaN($c1) && java.lang.Float.isNaN($c2)) || $c1 == $c2)" + case DoubleType => + s"((java.lang.Double.isNaN($c1) && java.lang.Double.isNaN($c2)) || $c1 == $c2)" case dt: DataType if isPrimitiveType(dt) => s"$c1 == $c2" case dt: DataType if dt.isInstanceOf[AtomicType] => s"$c1.equals($c2)" case array: ArrayType => genComp(array, c1, c2) + " == 0" @@ -1122,14 +1115,12 @@ class CodegenContext { newSubExprEliminationExprs: Map[Expression, SubExprEliminationState])( f: => Seq[ExprCode]): Seq[ExprCode] = { val oldsubExprEliminationExprs = subExprEliminationExprs - subExprEliminationExprs.clear - newSubExprEliminationExprs.foreach(subExprEliminationExprs += _) + subExprEliminationExprs = newSubExprEliminationExprs val genCodes = f // Restore previous subExprEliminationExprs - subExprEliminationExprs.clear - oldsubExprEliminationExprs.foreach(subExprEliminationExprs += _) + subExprEliminationExprs = oldsubExprEliminationExprs genCodes } @@ -1143,7 +1134,7 @@ class CodegenContext { def subexpressionEliminationForWholeStageCodegen(expressions: Seq[Expression]): SubExprCodes = { // Create a clear EquivalentExpressions and SubExprEliminationState mapping val equivalentExpressions: EquivalentExpressions = new EquivalentExpressions - val subExprEliminationExprs = mutable.HashMap.empty[Expression, SubExprEliminationState] + val localSubExprEliminationExprs = mutable.HashMap.empty[Expression, SubExprEliminationState] // Add each expression tree and compute the common subexpressions. expressions.foreach(equivalentExpressions.addExprTree) @@ -1156,10 +1147,10 @@ class CodegenContext { // Generate the code for this expression tree. val eval = expr.genCode(this) val state = SubExprEliminationState(eval.isNull, eval.value) - e.foreach(subExprEliminationExprs.put(_, state)) + e.foreach(localSubExprEliminationExprs.put(_, state)) eval.code.trim } - SubExprCodes(codes, subExprEliminationExprs.toMap) + SubExprCodes(codes, localSubExprEliminationExprs.toMap) } /** @@ -1207,7 +1198,7 @@ class CodegenContext { subexprFunctions += s"${addNewFunction(fnName, fn)}($INPUT_ROW);" val state = SubExprEliminationState(isNull, value) - e.foreach(subExprEliminationExprs.put(_, state)) + subExprEliminationExprs ++= e.map(_ -> state).toMap } } @@ -1249,6 +1240,32 @@ class CodegenContext { "" } } + + /** + * Returns the length of parameters for a Java method descriptor. `this` contributes one unit + * and a parameter of type long or double contributes two units. Besides, for nullable parameter, + * we also need to pass a boolean parameter for the null status. + */ + def calculateParamLength(params: Seq[Expression]): Int = { + def paramLengthForExpr(input: Expression): Int = { + val javaParamLength = javaType(input.dataType) match { + case JAVA_LONG | JAVA_DOUBLE => 2 + case _ => 1 + } + // For a nullable expression, we need to pass in an extra boolean parameter. + (if (input.nullable) 1 else 0) + javaParamLength + } + // Initial value is 1 for `this`. + 1 + params.map(paramLengthForExpr).sum + } + + /** + * In Java, a method descriptor is valid only if it represents method parameters with a total + * length less than a pre-defined constant. + */ + def isValidParamLength(paramLength: Int): Boolean = { + paramLength <= CodeGenerator.MAX_JVM_METHOD_PARAMS_LENGTH + } } /** @@ -1315,26 +1332,29 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin object CodeGenerator extends Logging { // This is the value of HugeMethodLimit in the OpenJDK JVM settings - val DEFAULT_JVM_HUGE_METHOD_LIMIT = 8000 + final val DEFAULT_JVM_HUGE_METHOD_LIMIT = 8000 + + // The max valid length of method parameters in JVM. + final val MAX_JVM_METHOD_PARAMS_LENGTH = 255 // This is the threshold over which the methods in an inner class are grouped in a single // method which is going to be called by the outer class instead of the many small ones - val MERGE_SPLIT_METHODS_THRESHOLD = 3 + final val MERGE_SPLIT_METHODS_THRESHOLD = 3 // The number of named constants that can exist in the class is limited by the Constant Pool // limit, 65,536. We cannot know how many constants will be inserted for a class, so we use a // threshold of 1000k bytes to determine when a function should be inlined to a private, inner // class. - val GENERATED_CLASS_SIZE_THRESHOLD = 1000000 + final val GENERATED_CLASS_SIZE_THRESHOLD = 1000000 // This is the threshold for the number of global variables, whose types are primitive type or // complex type (e.g. more than one-dimensional array), that will be placed at the outer class - val OUTER_CLASS_VARIABLES_THRESHOLD = 10000 + final val OUTER_CLASS_VARIABLES_THRESHOLD = 10000 // This is the maximum number of array elements to keep global variables in one Java array // 32767 is the maximum integer value that does not require a constant pool entry in a Java // bytecode instruction - val MUTABLESTATEARRAY_SIZE_LIMIT = 32768 + final val MUTABLESTATEARRAY_SIZE_LIMIT = 32768 /** * Compile the Java source code into a Java class, using Janino. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala index be5f5a73b5d47..febf7b0c96c2a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala @@ -70,7 +70,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U // --------------------- copy bitset from row 1 and row 2 --------------------------- // val copyBitset = Seq.tabulate(outputBitsetWords) { i => - val bits = if (bitset1Remainder > 0) { + val bits = if (bitset1Remainder > 0 && bitset2Words != 0) { if (i < bitset1Words - 1) { s"$getLong(obj1, offset1 + ${i * 8})" } else if (i == bitset1Words - 1) { @@ -152,7 +152,9 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U } else { // Number of bytes to increase for the offset. Note that since in UnsafeRow we store the // offset in the upper 32 bit of the words, we can just shift the offset to the left by - // 32 and increment that amount in place. + // 32 and increment that amount in place. However, we need to handle the important special + // case of a null field, in which case the offset should be zero and should not have a + // shift added to it. val shift = if (i < schema1.size) { s"${(outputBitsetWords - bitset1Words + schema2.size) * 8}L" @@ -160,14 +162,55 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U s"(${(outputBitsetWords - bitset2Words + schema1.size) * 8}L + numBytesVariableRow1)" } val cursor = offset + outputBitsetWords * 8 + i * 8 - s"$putLong(buf, $cursor, $getLong(buf, $cursor) + ($shift << 32));\n" + // UnsafeRow is a little underspecified, so in what follows we'll treat UnsafeRowWriter's + // output as a de-facto specification for the internal layout of data. + // + // Null-valued fields will always have a data offset of 0 because + // UnsafeRowWriter.setNullAt(ordinal) sets the null bit and stores 0 to in field's + // position in the fixed-length section of the row. As a result, we must NOT add + // `shift` to the offset for null fields. + // + // We could perform a null-check here by inspecting the null-tracking bitmap, but doing + // so could be expensive and will add significant bloat to the generated code. Instead, + // we'll rely on the invariant "stored offset == 0 for variable-length data type implies + // that the field's value is null." + // + // To establish that this invariant holds, we'll prove that a non-null field can never + // have a stored offset of 0. There are two cases to consider: + // + // 1. The non-null field's data is of non-zero length: reading this field's value + // must read data from the variable-length section of the row, so the stored offset + // will actually be used in address calculation and must be correct. The offsets + // count bytes from the start of the UnsafeRow so these offsets will always be + // non-zero because the storage of the offsets themselves takes up space at the + // start of the row. + // 2. The non-null field's data is of zero length (i.e. its data is empty). In this + // case, we have to worry about the possibility that an arbitrary offset value was + // stored because we never actually read any bytes using this offset and therefore + // would not crash if it was incorrect. The variable-sized data writing paths in + // UnsafeRowWriter unconditionally calls setOffsetAndSize(ordinal, numBytes) with + // no special handling for the case where `numBytes == 0`. Internally, + // setOffsetAndSize computes the offset without taking the size into account. Thus + // the stored offset is the same non-zero offset that would be used if the field's + // dataSize was non-zero (and in (1) above we've shown that case behaves as we + // expect). + // + // Thus it is safe to perform `existingOffset != 0` checks here in the place of + // more expensive null-bit checks. + s""" + |existingOffset = $getLong(buf, $cursor); + |if (existingOffset != 0) { + | $putLong(buf, $cursor, existingOffset + ($shift << 32)); + |} + """.stripMargin } } val updateOffsets = ctx.splitExpressions( expressions = updateOffset, funcName = "copyBitsetFunc", - arguments = ("long", "numBytesVariableRow1") :: Nil) + arguments = ("long", "numBytesVariableRow1") :: Nil, + makeSplitFunction = (s: String) => "long existingOffset;\n" + s) // ------------------------ Finally, put everything together --------------------------- // val codeBody = s""" @@ -200,6 +243,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U | $copyFixedLengthRow2 | $copyVariableLengthRow1 | $copyVariableLengthRow2 + | long existingOffset; | $updateOffsets | | out.pointTo(buf, sizeInBytes); diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala index 4270b987d6de0..d5e94d7348a39 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala @@ -21,7 +21,7 @@ import java.util.Comparator import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.analysis.TypeCheckResult import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, CodegenFallback, ExprCode} -import org.apache.spark.sql.catalyst.util.{ArrayData, GenericArrayData, MapData} +import org.apache.spark.sql.catalyst.util.{ArrayData, GenericArrayData, MapData, TypeUtils} import org.apache.spark.sql.types._ /** @@ -227,6 +227,9 @@ case class ArrayContains(left: Expression, right: Expression) override def dataType: DataType = BooleanType + @transient private lazy val ordering: Ordering[Any] = + TypeUtils.getInterpretedOrdering(right.dataType) + override def inputTypes: Seq[AbstractDataType] = right.dataType match { case NullType => Seq.empty case _ => left.dataType match { @@ -243,7 +246,7 @@ case class ArrayContains(left: Expression, right: Expression) TypeCheckResult.TypeCheckFailure( "Arguments must be an array followed by a value of same type as the array members") } else { - TypeCheckResult.TypeCheckSuccess + TypeUtils.checkForOrderingExpr(right.dataType, s"function $prettyName") } } @@ -256,7 +259,7 @@ case class ArrayContains(left: Expression, right: Expression) arr.asInstanceOf[ArrayData].foreach(right.dataType, (i, v) => if (v == null) { hasNull = true - } else if (v == value) { + } else if (ordering.equiv(v, value)) { return true } ) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala index 3dc2ee03a86e3..047b80ac5289c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala @@ -111,7 +111,7 @@ private [sql] object GenArrayData { val assignmentString = ctx.splitExpressionsWithCurrentInputs( expressions = assignments, funcName = "apply", - extraArguments = ("Object[]", arrayDataName) :: Nil) + extraArguments = ("Object[]", arrayName) :: Nil) (s"Object[] $arrayName = new Object[$numElements];", assignmentString, diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala index 7e53ca3908905..eed25b5d96931 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala @@ -21,7 +21,7 @@ import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode} -import org.apache.spark.sql.catalyst.util.{quoteIdentifier, ArrayData, GenericArrayData, MapData} +import org.apache.spark.sql.catalyst.util.{quoteIdentifier, ArrayData, GenericArrayData, MapData, TypeUtils} import org.apache.spark.sql.types._ //////////////////////////////////////////////////////////////////////////////////////////////////// @@ -301,7 +301,7 @@ case class GetMapValue(child: Expression, key: Expression) var i = 0 var found = false while (i < length && !found) { - if (keys.get(i, keyType) == ordinal) { + if (ordering.equiv(keys.get(i, keyType), ordinal)) { found = true } else { i += 1 @@ -352,4 +352,15 @@ case class GetMapValue(child: Expression, key: Expression) """ }) } + + @transient private lazy val ordering: Ordering[Any] = + TypeUtils.getInterpretedOrdering(keyType) + + override def checkInputDataTypes(): TypeCheckResult = { + super.checkInputDataTypes() match { + case f: TypeCheckResult.TypeCheckFailure => f + case TypeCheckResult.TypeCheckSuccess => + TypeUtils.checkForOrderingExpr(keyType, s"function $prettyName") + } + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala index 7a674ea7f4d76..7859cd83e7cf8 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala @@ -23,6 +23,8 @@ import java.util.{Calendar, TimeZone} import scala.util.control.NonFatal +import org.apache.commons.lang3.StringEscapeUtils + import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, CodegenFallback, ExprCode} import org.apache.spark.sql.catalyst.util.DateTimeUtils @@ -1008,7 +1010,7 @@ case class FromUTCTimestamp(left: Expression, right: Expression) override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { val dtu = DateTimeUtils.getClass.getName.stripSuffix("$") if (right.foldable) { - val tz = right.eval() + val tz = right.eval().asInstanceOf[UTF8String] if (tz == null) { ev.copy(code = s""" |boolean ${ev.isNull} = true; @@ -1017,8 +1019,9 @@ case class FromUTCTimestamp(left: Expression, right: Expression) } else { val tzClass = classOf[TimeZone].getName val dtu = DateTimeUtils.getClass.getName.stripSuffix("$") + val escapedTz = StringEscapeUtils.escapeJava(tz.toString) val tzTerm = ctx.addMutableState(tzClass, "tz", - v => s"""$v = $dtu.getTimeZone("$tz");""") + v => s"""$v = $dtu.getTimeZone("$escapedTz");""") val utcTerm = "tzUTC" ctx.addImmutableStateIfNotExists(tzClass, utcTerm, v => s"""$v = $dtu.getTimeZone("UTC");""") @@ -1185,7 +1188,7 @@ case class ToUTCTimestamp(left: Expression, right: Expression) override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { val dtu = DateTimeUtils.getClass.getName.stripSuffix("$") if (right.foldable) { - val tz = right.eval() + val tz = right.eval().asInstanceOf[UTF8String] if (tz == null) { ev.copy(code = s""" |boolean ${ev.isNull} = true; @@ -1194,8 +1197,9 @@ case class ToUTCTimestamp(left: Expression, right: Expression) } else { val tzClass = classOf[TimeZone].getName val dtu = DateTimeUtils.getClass.getName.stripSuffix("$") + val escapedTz = StringEscapeUtils.escapeJava(tz.toString) val tzTerm = ctx.addMutableState(tzClass, "tz", - v => s"""$v = $dtu.getTimeZone("$tz");""") + v => s"""$v = $dtu.getTimeZone("$escapedTz");""") val utcTerm = "tzUTC" ctx.addImmutableStateIfNotExists(tzClass, utcTerm, v => s"""$v = $dtu.getTimeZone("UTC");""") @@ -1430,14 +1434,14 @@ case class TruncDate(date: Expression, format: Expression) """, examples = """ Examples: - > SELECT _FUNC_('2015-03-05T09:32:05.359', 'YEAR'); - 2015-01-01T00:00:00 - > SELECT _FUNC_('2015-03-05T09:32:05.359', 'MM'); - 2015-03-01T00:00:00 - > SELECT _FUNC_('2015-03-05T09:32:05.359', 'DD'); - 2015-03-05T00:00:00 - > SELECT _FUNC_('2015-03-05T09:32:05.359', 'HOUR'); - 2015-03-05T09:00:00 + > SELECT _FUNC_('YEAR', '2015-03-05T09:32:05.359'); + 2015-01-01 00:00:00 + > SELECT _FUNC_('MM', '2015-03-05T09:32:05.359'); + 2015-03-01 00:00:00 + > SELECT _FUNC_('DD', '2015-03-05T09:32:05.359'); + 2015-03-05 00:00:00 + > SELECT _FUNC_('HOUR', '2015-03-05T09:32:05.359'); + 2015-03-05 09:00:00 """, since = "2.3.0") // scalastyle:on line.size.limit diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala index 18b4fed597447..34161f0f03f4a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala @@ -30,6 +30,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback import org.apache.spark.sql.catalyst.json._ import org.apache.spark.sql.catalyst.parser.CatalystSqlParser import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, ArrayData, BadRecordException, FailFastMode, GenericArrayData, MapData} +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.types._ import org.apache.spark.unsafe.types.UTF8String import org.apache.spark.util.Utils @@ -513,12 +514,16 @@ case class JsonToStructs( schema: DataType, options: Map[String, String], child: Expression, - timeZoneId: Option[String] = None) + timeZoneId: Option[String], + forceNullableSchema: Boolean) extends UnaryExpression with TimeZoneAwareExpression with CodegenFallback with ExpectsInputTypes { - override def nullable: Boolean = true - def this(schema: DataType, options: Map[String, String], child: Expression) = - this(schema, options, child, None) + // The JSON input data might be missing certain fields. We force the nullability + // of the user-provided schema to avoid data corruptions. In particular, the parquet-mr encoder + // can generate incorrect files if values are missing in columns declared as non-nullable. + val nullableSchema = if (forceNullableSchema) schema.asNullable else schema + + override def nullable: Boolean = true // Used in `FunctionRegistry` def this(child: Expression, schema: Expression) = @@ -526,31 +531,38 @@ case class JsonToStructs( schema = JsonExprUtils.validateSchemaLiteral(schema), options = Map.empty[String, String], child = child, - timeZoneId = None) + timeZoneId = None, + forceNullableSchema = SQLConf.get.getConf(SQLConf.FROM_JSON_FORCE_NULLABLE_SCHEMA)) def this(child: Expression, schema: Expression, options: Expression) = this( schema = JsonExprUtils.validateSchemaLiteral(schema), options = JsonExprUtils.convertToMapData(options), child = child, - timeZoneId = None) + timeZoneId = None, + forceNullableSchema = SQLConf.get.getConf(SQLConf.FROM_JSON_FORCE_NULLABLE_SCHEMA)) + + // Used in `org.apache.spark.sql.functions` + def this(schema: DataType, options: Map[String, String], child: Expression) = + this(schema, options, child, timeZoneId = None, + forceNullableSchema = SQLConf.get.getConf(SQLConf.FROM_JSON_FORCE_NULLABLE_SCHEMA)) - override def checkInputDataTypes(): TypeCheckResult = schema match { + override def checkInputDataTypes(): TypeCheckResult = nullableSchema match { case _: StructType | ArrayType(_: StructType, _) => super.checkInputDataTypes() case _ => TypeCheckResult.TypeCheckFailure( - s"Input schema ${schema.simpleString} must be a struct or an array of structs.") + s"Input schema ${nullableSchema.simpleString} must be a struct or an array of structs.") } @transient - lazy val rowSchema = schema match { + lazy val rowSchema = nullableSchema match { case st: StructType => st case ArrayType(st: StructType, _) => st } // This converts parsed rows to the desired output by the given schema. @transient - lazy val converter = schema match { + lazy val converter = nullableSchema match { case _: StructType => (rows: Seq[InternalRow]) => if (rows.length == 1) rows.head else null case ArrayType(_: StructType, _) => @@ -563,7 +575,7 @@ case class JsonToStructs( rowSchema, new JSONOptions(options + ("mode" -> FailFastMode.name), timeZoneId.get)) - override def dataType: DataType = schema + override def dataType: DataType = nullableSchema override def withTimeZone(timeZoneId: String): TimeZoneAwareExpression = copy(timeZoneId = Option(timeZoneId)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala index 383203a209833..cd176d941819f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala @@ -58,7 +58,7 @@ object Literal { case s: Short => Literal(s, ShortType) case s: String => Literal(UTF8String.fromString(s), StringType) case b: Boolean => Literal(b, BooleanType) - case d: BigDecimal => Literal(Decimal(d), DecimalType(Math.max(d.precision, d.scale), d.scale)) + case d: BigDecimal => Literal(Decimal(d), DecimalType.fromBigDecimal(d)) case d: JavaBigDecimal => Literal(Decimal(d), DecimalType(Math.max(d.precision, d.scale), d.scale())) case d: Decimal => Literal(d, DecimalType(Math.max(d.precision, d.scale), d.scale)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala index d8dc0862f1141..816ae9ae74729 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala @@ -168,9 +168,11 @@ case class Pi() extends LeafMathExpression(math.Pi, "PI") //////////////////////////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////////////////////////// -// scalastyle:off line.size.limit @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the inverse cosine (a.k.a. arccosine) of `expr` if -1<=`expr`<=1 or NaN otherwise.", + usage = """ + _FUNC_(expr) - Returns the inverse cosine (a.k.a. arc cosine) of `expr`, as if computed by + `java.lang.Math._FUNC_`. + """, examples = """ Examples: > SELECT _FUNC_(1); @@ -178,12 +180,13 @@ case class Pi() extends LeafMathExpression(math.Pi, "PI") > SELECT _FUNC_(2); NaN """) -// scalastyle:on line.size.limit case class Acos(child: Expression) extends UnaryMathExpression(math.acos, "ACOS") -// scalastyle:off line.size.limit @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the inverse sine (a.k.a. arcsine) the arc sin of `expr` if -1<=`expr`<=1 or NaN otherwise.", + usage = """ + _FUNC_(expr) - Returns the inverse sine (a.k.a. arc sine) the arc sin of `expr`, + as if computed by `java.lang.Math._FUNC_`. + """, examples = """ Examples: > SELECT _FUNC_(0); @@ -191,18 +194,18 @@ case class Acos(child: Expression) extends UnaryMathExpression(math.acos, "ACOS" > SELECT _FUNC_(2); NaN """) -// scalastyle:on line.size.limit case class Asin(child: Expression) extends UnaryMathExpression(math.asin, "ASIN") -// scalastyle:off line.size.limit @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the inverse tangent (a.k.a. arctangent).", + usage = """ + _FUNC_(expr) - Returns the inverse tangent (a.k.a. arc tangent) of `expr`, as if computed by + `java.lang.Math._FUNC_` + """, examples = """ Examples: > SELECT _FUNC_(0); 0.0 """) -// scalastyle:on line.size.limit case class Atan(child: Expression) extends UnaryMathExpression(math.atan, "ATAN") @ExpressionDescription( @@ -252,7 +255,14 @@ case class Ceil(child: Expression) extends UnaryMathExpression(math.ceil, "CEIL" } @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the cosine of `expr`.", + usage = """ + _FUNC_(expr) - Returns the cosine of `expr`, as if computed by + `java.lang.Math._FUNC_`. + """, + arguments = """ + Arguments: + * expr - angle in radians + """, examples = """ Examples: > SELECT _FUNC_(0); @@ -261,7 +271,14 @@ case class Ceil(child: Expression) extends UnaryMathExpression(math.ceil, "CEIL" case class Cos(child: Expression) extends UnaryMathExpression(math.cos, "COS") @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the hyperbolic cosine of `expr`.", + usage = """ + _FUNC_(expr) - Returns the hyperbolic cosine of `expr`, as if computed by + `java.lang.Math._FUNC_`. + """, + arguments = """ + Arguments: + * expr - hyperbolic angle + """, examples = """ Examples: > SELECT _FUNC_(0); @@ -512,7 +529,11 @@ case class Rint(child: Expression) extends UnaryMathExpression(math.rint, "ROUND case class Signum(child: Expression) extends UnaryMathExpression(math.signum, "SIGNUM") @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the sine of `expr`.", + usage = "_FUNC_(expr) - Returns the sine of `expr`, as if computed by `java.lang.Math._FUNC_`.", + arguments = """ + Arguments: + * expr - angle in radians + """, examples = """ Examples: > SELECT _FUNC_(0); @@ -521,7 +542,13 @@ case class Signum(child: Expression) extends UnaryMathExpression(math.signum, "S case class Sin(child: Expression) extends UnaryMathExpression(math.sin, "SIN") @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the hyperbolic sine of `expr`.", + usage = """ + _FUNC_(expr) - Returns hyperbolic sine of `expr`, as if computed by `java.lang.Math._FUNC_`. + """, + arguments = """ + Arguments: + * expr - hyperbolic angle + """, examples = """ Examples: > SELECT _FUNC_(0); @@ -539,7 +566,13 @@ case class Sinh(child: Expression) extends UnaryMathExpression(math.sinh, "SINH" case class Sqrt(child: Expression) extends UnaryMathExpression(math.sqrt, "SQRT") @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the tangent of `expr`.", + usage = """ + _FUNC_(expr) - Returns the tangent of `expr`, as if computed by `java.lang.Math._FUNC_`. + """, + arguments = """ + Arguments: + * expr - angle in radians + """, examples = """ Examples: > SELECT _FUNC_(0); @@ -548,7 +581,13 @@ case class Sqrt(child: Expression) extends UnaryMathExpression(math.sqrt, "SQRT" case class Tan(child: Expression) extends UnaryMathExpression(math.tan, "TAN") @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the cotangent of `expr`.", + usage = """ + _FUNC_(expr) - Returns the cotangent of `expr`, as if computed by `1/java.lang.Math._FUNC_`. + """, + arguments = """ + Arguments: + * expr - angle in radians + """, examples = """ Examples: > SELECT _FUNC_(1); @@ -562,7 +601,14 @@ case class Cot(child: Expression) } @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the hyperbolic tangent of `expr`.", + usage = """ + _FUNC_(expr) - Returns the hyperbolic tangent of `expr`, as if computed by + `java.lang.Math._FUNC_`. + """, + arguments = """ + Arguments: + * expr - hyperbolic angle + """, examples = """ Examples: > SELECT _FUNC_(0); @@ -572,6 +618,10 @@ case class Tanh(child: Expression) extends UnaryMathExpression(math.tanh, "TANH" @ExpressionDescription( usage = "_FUNC_(expr) - Converts radians to degrees.", + arguments = """ + Arguments: + * expr - angle in radians + """, examples = """ Examples: > SELECT _FUNC_(3.141592653589793); @@ -583,6 +633,10 @@ case class ToDegrees(child: Expression) extends UnaryMathExpression(math.toDegre @ExpressionDescription( usage = "_FUNC_(expr) - Converts degrees to radians.", + arguments = """ + Arguments: + * expr - angle in degrees + """, examples = """ Examples: > SELECT _FUNC_(180); @@ -768,15 +822,22 @@ case class Unhex(child: Expression) extends UnaryExpression with ImplicitCastInp //////////////////////////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////////////////////////// -// scalastyle:off line.size.limit @ExpressionDescription( - usage = "_FUNC_(expr1, expr2) - Returns the angle in radians between the positive x-axis of a plane and the point given by the coordinates (`expr1`, `expr2`).", + usage = """ + _FUNC_(exprY, exprX) - Returns the angle in radians between the positive x-axis of a plane + and the point given by the coordinates (`exprX`, `exprY`), as if computed by + `java.lang.Math._FUNC_`. + """, + arguments = """ + Arguments: + * exprY - coordinate on y-axis + * exprX - coordinate on x-axis + """, examples = """ Examples: > SELECT _FUNC_(0, 0); 0.0 """) -// scalastyle:on line.size.limit case class Atan2(left: Expression, right: Expression) extends BinaryMathExpression(math.atan2, "ATAN2") { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala index 4b9006ab5b423..cdbe611fdd064 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala @@ -21,6 +21,7 @@ import java.util.UUID import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.codegen._ +import org.apache.spark.sql.catalyst.util.RandomUUIDGenerator import org.apache.spark.sql.types._ import org.apache.spark.unsafe.types.UTF8String @@ -117,18 +118,34 @@ case class CurrentDatabase() extends LeafExpression with Unevaluable { 46707d92-02f4-4817-8116-a4c3b23e6266 """) // scalastyle:on line.size.limit -case class Uuid() extends LeafExpression { +case class Uuid(randomSeed: Option[Long] = None) extends LeafExpression with Nondeterministic { - override lazy val deterministic: Boolean = false + def this() = this(None) + + override lazy val resolved: Boolean = randomSeed.isDefined override def nullable: Boolean = false override def dataType: DataType = StringType - override def eval(input: InternalRow): Any = UTF8String.fromString(UUID.randomUUID().toString) + @transient private[this] var randomGenerator: RandomUUIDGenerator = _ + + + override protected def initializeInternal(partitionIndex: Int): Unit = + randomGenerator = RandomUUIDGenerator(randomSeed.get + partitionIndex) + + override protected def evalInternal(input: InternalRow): Any = + randomGenerator.getNextUUIDUTF8String() override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { - ev.copy(code = s"final UTF8String ${ev.value} = " + - s"UTF8String.fromString(java.util.UUID.randomUUID().toString());", isNull = "false") + val randomGen = ctx.freshName("randomGen") + ctx.addMutableState("org.apache.spark.sql.catalyst.util.RandomUUIDGenerator", randomGen, + forceInline = true, + useFreshName = false) + ctx.addPartitionInitializationStatement(s"$randomGen = " + + "new org.apache.spark.sql.catalyst.util.RandomUUIDGenerator(" + + s"${randomSeed.get}L + partitionIndex);") + ev.copy(code = s"final UTF8String ${ev.value} = $randomGen.getNextUUIDUTF8String();", + isNull = "false") } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala index b469f5cb7586a..a6d41ea7d00d4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala @@ -157,7 +157,8 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate { require(list != null, "list should not be null") override def checkInputDataTypes(): TypeCheckResult = { - val mismatchOpt = list.find(l => !DataType.equalsStructurally(l.dataType, value.dataType)) + val mismatchOpt = list.find(l => !DataType.equalsStructurally(l.dataType, value.dataType, + ignoreNullability = true)) if (mismatchOpt.isDefined) { list match { case ListQuery(_, _, _, childOutputs) :: Nil => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala index b0da55a4a961b..d7612e30b4c57 100755 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala @@ -58,7 +58,7 @@ case class Concat(children: Seq[Expression]) extends Expression { } else { val childTypes = children.map(_.dataType) if (childTypes.exists(tpe => !Seq(StringType, BinaryType).contains(tpe))) { - TypeCheckResult.TypeCheckFailure( + return TypeCheckResult.TypeCheckFailure( s"input to function $prettyName should have StringType or BinaryType, but it's " + childTypes.map(_.simpleString).mkString("[", ", ", "]")) } @@ -271,33 +271,45 @@ case class ConcatWs(children: Seq[Expression]) } } +/** + * An expression that returns the `n`-th input in given inputs. + * If all inputs are binary, `elt` returns an output as binary. Otherwise, it returns as string. + * If any input is null, `elt` returns null. + */ // scalastyle:off line.size.limit @ExpressionDescription( - usage = "_FUNC_(n, str1, str2, ...) - Returns the `n`-th string, e.g., returns `str2` when `n` is 2.", + usage = "_FUNC_(n, input1, input2, ...) - Returns the `n`-th input, e.g., returns `input2` when `n` is 2.", examples = """ Examples: > SELECT _FUNC_(1, 'scala', 'java'); scala """) // scalastyle:on line.size.limit -case class Elt(children: Seq[Expression]) - extends Expression with ImplicitCastInputTypes { +case class Elt(children: Seq[Expression]) extends Expression { private lazy val indexExpr = children.head - private lazy val stringExprs = children.tail.toArray + private lazy val inputExprs = children.tail.toArray /** This expression is always nullable because it returns null if index is out of range. */ override def nullable: Boolean = true - override def dataType: DataType = StringType - - override def inputTypes: Seq[DataType] = IntegerType +: Seq.fill(children.size - 1)(StringType) + override def dataType: DataType = inputExprs.map(_.dataType).headOption.getOrElse(StringType) override def checkInputDataTypes(): TypeCheckResult = { if (children.size < 2) { TypeCheckResult.TypeCheckFailure("elt function requires at least two arguments") } else { - super[ImplicitCastInputTypes].checkInputDataTypes() + val (indexType, inputTypes) = (indexExpr.dataType, inputExprs.map(_.dataType)) + if (indexType != IntegerType) { + return TypeCheckResult.TypeCheckFailure(s"first input to function $prettyName should " + + s"have IntegerType, but it's $indexType") + } + if (inputTypes.exists(tpe => !Seq(StringType, BinaryType).contains(tpe))) { + return TypeCheckResult.TypeCheckFailure( + s"input to function $prettyName should have StringType or BinaryType, but it's " + + inputTypes.map(_.simpleString).mkString("[", ", ", "]")) + } + TypeUtils.checkForSameTypeInputExpr(inputTypes, s"function $prettyName") } } @@ -307,27 +319,27 @@ case class Elt(children: Seq[Expression]) null } else { val index = indexObj.asInstanceOf[Int] - if (index <= 0 || index > stringExprs.length) { + if (index <= 0 || index > inputExprs.length) { null } else { - stringExprs(index - 1).eval(input) + inputExprs(index - 1).eval(input) } } } override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { val index = indexExpr.genCode(ctx) - val strings = stringExprs.map(_.genCode(ctx)) + val inputs = inputExprs.map(_.genCode(ctx)) val indexVal = ctx.freshName("index") val indexMatched = ctx.freshName("eltIndexMatched") - val stringVal = ctx.addMutableState(ctx.javaType(dataType), "stringVal") + val inputVal = ctx.addMutableState(ctx.javaType(dataType), "inputVal") - val assignStringValue = strings.zipWithIndex.map { case (eval, index) => + val assignInputValue = inputs.zipWithIndex.map { case (eval, index) => s""" |if ($indexVal == ${index + 1}) { | ${eval.code} - | $stringVal = ${eval.isNull} ? null : ${eval.value}; + | $inputVal = ${eval.isNull} ? null : ${eval.value}; | $indexMatched = true; | continue; |} @@ -335,7 +347,7 @@ case class Elt(children: Seq[Expression]) } val codes = ctx.splitExpressionsWithCurrentInputs( - expressions = assignStringValue, + expressions = assignInputValue, funcName = "eltFunc", extraArguments = ("int", indexVal) :: Nil, returnType = ctx.JAVA_BOOLEAN, @@ -361,11 +373,11 @@ case class Elt(children: Seq[Expression]) |${index.code} |final int $indexVal = ${index.value}; |${ctx.JAVA_BOOLEAN} $indexMatched = false; - |$stringVal = null; + |$inputVal = null; |do { | $codes |} while (false); - |final UTF8String ${ev.value} = $stringVal; + |final ${ctx.javaType(dataType)} ${ev.value} = $inputVal; |final boolean ${ev.isNull} = ${ev.value} == null; """.stripMargin) } @@ -1641,19 +1653,19 @@ case class Left(str: Expression, len: Expression, child: Expression) extends Run * A function that returns the char length of the given string expression or * number of bytes of the given binary expression. */ -// scalastyle:off line.size.limit @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the character length of `expr` or number of bytes in binary data.", + usage = "_FUNC_(expr) - Returns the character length of string data or number of bytes of " + + "binary data. The length of string data includes the trailing spaces. The length of binary " + + "data includes binary zeros.", examples = """ Examples: - > SELECT _FUNC_('Spark SQL'); - 9 - > SELECT CHAR_LENGTH('Spark SQL'); - 9 - > SELECT CHARACTER_LENGTH('Spark SQL'); - 9 + > SELECT _FUNC_('Spark SQL '); + 10 + > SELECT CHAR_LENGTH('Spark SQL '); + 10 + > SELECT CHARACTER_LENGTH('Spark SQL '); + 10 """) -// scalastyle:on line.size.limit case class Length(child: Expression) extends UnaryExpression with ImplicitCastInputTypes { override def dataType: DataType = IntegerType override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(StringType, BinaryType)) @@ -1675,7 +1687,7 @@ case class Length(child: Expression) extends UnaryExpression with ImplicitCastIn * A function that returns the bit length of the given string or binary expression. */ @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the bit length of `expr` or number of bits in binary data.", + usage = "_FUNC_(expr) - Returns the bit length of string data or number of bits of binary data.", examples = """ Examples: > SELECT _FUNC_('Spark SQL'); @@ -1696,13 +1708,16 @@ case class BitLength(child: Expression) extends UnaryExpression with ImplicitCas case BinaryType => defineCodeGen(ctx, ev, c => s"($c).length * 8") } } + + override def prettyName: String = "bit_length" } /** * A function that returns the byte length of the given string or binary expression. */ @ExpressionDescription( - usage = "_FUNC_(expr) - Returns the byte length of `expr` or number of bytes in binary data.", + usage = "_FUNC_(expr) - Returns the byte length of string data or number of bytes of binary " + + "data.", examples = """ Examples: > SELECT _FUNC_('Spark SQL'); @@ -1723,6 +1738,8 @@ case class OctetLength(child: Expression) extends UnaryExpression with ImplicitC case BinaryType => defineCodeGen(ctx, ev, c => s"($c).length") } } + + override def prettyName: String = "octet_length" } /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala index dd13d9a3bba51..f2e23473af66e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala @@ -363,7 +363,10 @@ abstract class OffsetWindowFunction override lazy val frame: WindowFrame = { val boundary = direction match { case Ascending => offset - case Descending => UnaryMinus(offset) + case Descending => UnaryMinus(offset) match { + case e: Expression if e.foldable => Literal.create(e.eval(EmptyRow), e.dataType) + case o => o + } } SpecifiedWindowFrame(RowFrame, boundary, boundary) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/xml/xpath.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/xml/xpath.scala index d0185562c9cfc..aacf1a44e2ad0 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/xml/xpath.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/xml/xpath.scala @@ -160,7 +160,7 @@ case class XPathFloat(xml: Expression, path: Expression) extends XPathExtract { """) // scalastyle:on line.size.limit case class XPathDouble(xml: Expression, path: Expression) extends XPathExtract { - override def prettyName: String = "xpath_float" + override def prettyName: String = "xpath_double" override def dataType: DataType = DoubleType override def nullSafeEval(xml: Any, path: Any): Any = { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala index 652412b34478a..190fcc605d043 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala @@ -31,7 +31,7 @@ import org.apache.spark.sql.catalyst.util._ * Most of these map directly to Jackson's internal options, specified in [[JsonParser.Feature]]. */ private[sql] class JSONOptions( - @transient private val parameters: CaseInsensitiveMap[String], + @transient val parameters: CaseInsensitiveMap[String], defaultTimeZoneId: String, defaultColumnNameOfCorruptRecord: String) extends Logging with Serializable { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonParser.scala index bd144c9575c72..7f6956994f31f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonParser.scala @@ -357,6 +357,9 @@ class JacksonParser( } } catch { case e @ (_: RuntimeException | _: JsonProcessingException) => + // JSON parser currently doesn't support partial results for corrupted records. + // For such records, all fields other than the field configured by + // `columnNameOfCorruptRecord` are set to `null`. throw BadRecordException(() => recordLiteral(record), () => None, e) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 0d4b02c6e7d8a..c77e0f82dc253 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -352,7 +352,6 @@ object LimitPushDown extends Rule[LogicalPlan] { // on both sides if it is applied multiple times. Therefore: // - If one side is already limited, stack another limit on top if the new limit is smaller. // The redundant limit will be collapsed by the CombineLimits rule. - // - If neither side is limited, limit the side that is estimated to be bigger. case LocalLimit(exp, join @ Join(left, right, joinType, _)) => val newJoin = joinType match { case RightOuter => join.copy(right = maybePushLocalLimit(exp, right)) @@ -795,7 +794,8 @@ object PushDownPredicate extends Rule[LogicalPlan] with PredicateHelper { project.copy(child = Filter(replaceAlias(condition, aliasMap), grandChild)) case filter @ Filter(condition, aggregate: Aggregate) - if aggregate.aggregateExpressions.forall(_.deterministic) => + if aggregate.aggregateExpressions.forall(_.deterministic) + && aggregate.groupingExpressions.nonEmpty => // Find all the aliased expressions in the aggregate list that don't include any actual // AggregateExpression, and create a map from the alias to the expression val aliasMap = AttributeMap(aggregate.aggregateExpressions.collect { @@ -1107,23 +1107,29 @@ object CheckCartesianProducts extends Rule[LogicalPlan] with PredicateHelper { */ def isCartesianProduct(join: Join): Boolean = { val conditions = join.condition.map(splitConjunctivePredicates).getOrElse(Nil) - !conditions.map(_.references).exists(refs => refs.exists(join.left.outputSet.contains) - && refs.exists(join.right.outputSet.contains)) + + conditions match { + case Seq(Literal.FalseLiteral) | Seq(Literal(null, BooleanType)) => false + case _ => !conditions.map(_.references).exists(refs => + refs.exists(join.left.outputSet.contains) && refs.exists(join.right.outputSet.contains)) + } } def apply(plan: LogicalPlan): LogicalPlan = if (SQLConf.get.crossJoinEnabled) { plan } else plan transform { - case j @ Join(left, right, Inner | LeftOuter | RightOuter | FullOuter, condition) + case j @ Join(left, right, Inner | LeftOuter | RightOuter | FullOuter, _) if isCartesianProduct(j) => throw new AnalysisException( - s"""Detected cartesian product for ${j.joinType.sql} join between logical plans + s"""Detected implicit cartesian product for ${j.joinType.sql} join between logical plans |${left.treeString(false).trim} |and |${right.treeString(false).trim} |Join condition is missing or trivial. - |Use the CROSS JOIN syntax to allow cartesian products between these relations.""" + |Either: use the CROSS JOIN syntax to allow cartesian products between these + |relations, or: enable implicit cartesian products by setting the configuration + |variable spark.sql.crossJoin.enabled=true""" .stripMargin) } } @@ -1221,7 +1227,13 @@ object ReplaceDeduplicateWithAggregate extends Rule[LogicalPlan] { Alias(new First(attr).toAggregateExpression(), attr.name)(attr.exprId) } } - Aggregate(keys, aggCols, child) + // SPARK-22951: Physical aggregate operators distinguishes global aggregation and grouping + // aggregations by checking the number of grouping keys. The key difference here is that a + // global aggregation always returns at least one row even if there are no input rows. Here + // we append a literal when the grouping key list is empty so that the result aggregate + // operator is properly treated as a grouping aggregation. + val nonemptyKeys = if (keys.isEmpty) Literal(1) :: Nil else keys + Aggregate(nonemptyKeys, aggCols, child) } } @@ -1292,8 +1304,12 @@ object RemoveLiteralFromGroupExpressions extends Rule[LogicalPlan] { */ object RemoveRepetitionFromGroupExpressions extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case a @ Aggregate(grouping, _, _) => + case a @ Aggregate(grouping, _, _) if grouping.size > 1 => val newGrouping = ExpressionSet(grouping).toSeq - a.copy(groupingExpressions = newGrouping) + if (newGrouping.size == grouping.size) { + a + } else { + a.copy(groupingExpressions = newGrouping) + } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelation.scala index a6e5aa6daca65..c3fdb924243df 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelation.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelation.scala @@ -17,10 +17,12 @@ package org.apache.spark.sql.catalyst.optimizer +import org.apache.spark.sql.catalyst.analysis.CastSupport import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules._ +import org.apache.spark.sql.internal.SQLConf /** * Collapse plans consisting empty local relations generated by [[PruneFilters]]. @@ -32,7 +34,7 @@ import org.apache.spark.sql.catalyst.rules._ * - Aggregate with all empty children and at least one grouping expression. * - Generate(Explode) with all empty children. Others like Hive UDTF may return results. */ -object PropagateEmptyRelation extends Rule[LogicalPlan] with PredicateHelper { +object PropagateEmptyRelation extends Rule[LogicalPlan] with PredicateHelper with CastSupport { private def isEmptyLocalRelation(plan: LogicalPlan): Boolean = plan match { case p: LocalRelation => p.data.isEmpty case _ => false @@ -43,7 +45,9 @@ object PropagateEmptyRelation extends Rule[LogicalPlan] with PredicateHelper { // Construct a project list from plan's output, while the value is always NULL. private def nullValueProjectList(plan: LogicalPlan): Seq[NamedExpression] = - plan.output.map{ a => Alias(Literal(null), a.name)(a.exprId) } + plan.output.map{ a => Alias(cast(Literal(null), a.dataType), a.name)(a.exprId) } + + override def conf: SQLConf = SQLConf.get def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { case p: Union if p.children.forall(isEmptyLocalRelation) => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceExceptWithFilter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceExceptWithFilter.scala index 89bfcee078fba..45edf266bbce4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceExceptWithFilter.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceExceptWithFilter.scala @@ -46,18 +46,27 @@ object ReplaceExceptWithFilter extends Rule[LogicalPlan] { } plan.transform { - case Except(left, right) if isEligible(left, right) => - Distinct(Filter(Not(transformCondition(left, skipProject(right))), left)) + case e @ Except(left, right) if isEligible(left, right) => + val newCondition = transformCondition(left, skipProject(right)) + newCondition.map { c => + Distinct(Filter(Not(c), left)) + }.getOrElse { + e + } } } - private def transformCondition(left: LogicalPlan, right: LogicalPlan): Expression = { + private def transformCondition(left: LogicalPlan, right: LogicalPlan): Option[Expression] = { val filterCondition = InferFiltersFromConstraints(combineFilters(right)).asInstanceOf[Filter].condition val attributeNameMap: Map[String, Attribute] = left.output.map(x => (x.name, x)).toMap - filterCondition.transform { case a : AttributeReference => attributeNameMap(a.name) } + if (filterCondition.references.forall(r => attributeNameMap.contains(r.name))) { + Some(filterCondition.transform { case a: AttributeReference => attributeNameMap(a.name) }) + } else { + None + } } // TODO: This can be further extended in the future. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/expressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/expressions.scala index 7d830bbb7dc32..1c0b7bd806801 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/expressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/expressions.scala @@ -506,18 +506,21 @@ object NullPropagation extends Rule[LogicalPlan] { /** - * Propagate foldable expressions: * Replace attributes with aliases of the original foldable expressions if possible. - * Other optimizations will take advantage of the propagated foldable expressions. - * + * Other optimizations will take advantage of the propagated foldable expressions. For example, + * this rule can optimize * {{{ * SELECT 1.0 x, 'abc' y, Now() z ORDER BY x, y, 3 - * ==> SELECT 1.0 x, 'abc' y, Now() z ORDER BY 1.0, 'abc', Now() * }}} + * to + * {{{ + * SELECT 1.0 x, 'abc' y, Now() z ORDER BY 1.0, 'abc', Now() + * }}} + * and other rules can further optimize it and remove the ORDER BY operator. */ object FoldablePropagation extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = { - val foldableMap = AttributeMap(plan.flatMap { + var foldableMap = AttributeMap(plan.flatMap { case Project(projectList, _) => projectList.collect { case a: Alias if a.child.foldable => (a.toAttribute, a) } @@ -530,38 +533,44 @@ object FoldablePropagation extends Rule[LogicalPlan] { if (foldableMap.isEmpty) { plan } else { - var stop = false CleanupAliases(plan.transformUp { - // A leaf node should not stop the folding process (note that we are traversing up the - // tree, starting at the leaf nodes); so we are allowing it. - case l: LeafNode => - l - // We can only propagate foldables for a subset of unary nodes. - case u: UnaryNode if !stop && canPropagateFoldables(u) => + case u: UnaryNode if foldableMap.nonEmpty && canPropagateFoldables(u) => u.transformExpressions(replaceFoldable) - // Allow inner joins. We do not allow outer join, although its output attributes are - // derived from its children, they are actually different attributes: the output of outer - // join is not always picked from its children, but can also be null. + // Join derives the output attributes from its child while they are actually not the + // same attributes. For example, the output of outer join is not always picked from its + // children, but can also be null. We should exclude these miss-derived attributes when + // propagating the foldable expressions. // TODO(cloud-fan): It seems more reasonable to use new attributes as the output attributes // of outer join. - case j @ Join(_, _, Inner, _) if !stop => - j.transformExpressions(replaceFoldable) - - // We can fold the projections an expand holds. However expand changes the output columns - // and often reuses the underlying attributes; so we cannot assume that a column is still - // foldable after the expand has been applied. - // TODO(hvanhovell): Expand should use new attributes as the output attributes. - case expand: Expand if !stop => - val newExpand = expand.copy(projections = expand.projections.map { projection => + case j @ Join(left, right, joinType, _) if foldableMap.nonEmpty => + val newJoin = j.transformExpressions(replaceFoldable) + val missDerivedAttrsSet: AttributeSet = AttributeSet(joinType match { + case _: InnerLike | LeftExistence(_) => Nil + case LeftOuter => right.output + case RightOuter => left.output + case FullOuter => left.output ++ right.output + }) + foldableMap = AttributeMap(foldableMap.baseMap.values.filterNot { + case (attr, _) => missDerivedAttrsSet.contains(attr) + }.toSeq) + newJoin + + // We can not replace the attributes in `Expand.output`. If there are other non-leaf + // operators that have the `output` field, we should put them here too. + case expand: Expand if foldableMap.nonEmpty => + expand.copy(projections = expand.projections.map { projection => projection.map(_.transform(replaceFoldable)) }) - stop = true - newExpand - case other => - stop = true + // For other plans, they are not safe to apply foldable propagation, and they should not + // propagate foldable expressions from children. + case other if foldableMap.nonEmpty => + val childrenOutputSet = AttributeSet(other.children.flatMap(_.output)) + foldableMap = AttributeMap(foldableMap.baseMap.values.filterNot { + case (attr, _) => childrenOutputSet.contains(attr) + }.toSeq) other }) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/subquery.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/subquery.scala index 2673bea648d09..709db6d8bec7d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/subquery.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/subquery.scala @@ -369,13 +369,14 @@ object RewriteCorrelatedScalarSubquery extends Rule[LogicalPlan] { case ne => (ne.exprId, evalAggOnZeroTups(ne)) }.toMap - case _ => sys.error(s"Unexpected operator in scalar subquery: $lp") + case _ => + sys.error(s"Unexpected operator in scalar subquery: $lp") } val resultMap = evalPlan(plan) // By convention, the scalar subquery result is the leftmost field. - resultMap(plan.output.head.exprId) + resultMap.getOrElse(plan.output.head.exprId, None) } /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/ParserUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/ParserUtils.scala index 9b127f91648e6..89347f4b1f7bf 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/ParserUtils.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/ParserUtils.scala @@ -16,6 +16,8 @@ */ package org.apache.spark.sql.catalyst.parser +import java.util + import scala.collection.mutable.StringBuilder import org.antlr.v4.runtime.{ParserRuleContext, Token} @@ -39,6 +41,13 @@ object ParserUtils { throw new ParseException(s"Operation not allowed: $message", ctx) } + def checkDuplicateClauses[T]( + nodes: util.List[T], clauseName: String, ctx: ParserRuleContext): Unit = { + if (nodes.size() > 1) { + throw new ParseException(s"Found duplicate clauses: $clauseName", ctx) + } + } + /** Check if duplicate keys exist in a set of key-value pairs. */ def checkDuplicateKeys[T](keyPairs: Seq[(String, T)], ctx: ParserRuleContext): Unit = { keyPairs.groupBy(_._1).filter(_._2.size > 1).foreach { case (key, _) => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala index ddf2cbf2ab911..64cb8c726772f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.catalyst.plans import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.trees.TreeNode +import org.apache.spark.sql.catalyst.trees.{CurrentOrigin, TreeNode} import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.types.{DataType, StructType} @@ -103,7 +103,9 @@ abstract class QueryPlan[PlanType <: QueryPlan[PlanType]] extends TreeNode[PlanT var changed = false @inline def transformExpression(e: Expression): Expression = { - val newE = f(e) + val newE = CurrentOrigin.withOrigin(e.origin) { + f(e) + } if (newE.fastEquals(e)) { e } else { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala index d73d7e73f28d5..b05508db786ad 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala @@ -43,10 +43,11 @@ object LocalRelation { } } -case class LocalRelation(output: Seq[Attribute], - data: Seq[InternalRow] = Nil, - // Indicates whether this relation has data from a streaming source. - override val isStreaming: Boolean = false) +case class LocalRelation( + output: Seq[Attribute], + data: Seq[InternalRow] = Nil, + // Indicates whether this relation has data from a streaming source. + override val isStreaming: Boolean = false) extends LeafNode with analysis.MultiInstanceRelation { // A local relation must have resolved output. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index a38458add7b5e..c8ccd9bd03994 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -247,12 +247,15 @@ abstract class UnaryNode extends LogicalPlan { protected def getAliasedConstraints(projectList: Seq[NamedExpression]): Set[Expression] = { var allConstraints = child.constraints.asInstanceOf[Set[Expression]] projectList.foreach { + case a @ Alias(l: Literal, _) => + allConstraints += EqualTo(a.toAttribute, l) case a @ Alias(e, _) => // For every alias in `projectList`, replace the reference in constraints by its attribute. allConstraints ++= allConstraints.map(_ transform { case expr: Expression if expr.semanticEquals(e) => a.toAttribute }) + allConstraints += EqualNullSafe(e, a.toAttribute) case _ => // Don't change. } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlanVisitor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlanVisitor.scala index e0748043c46e2..2c248d74869ce 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlanVisitor.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlanVisitor.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.catalyst.plans.logical /** - * A visitor pattern for traversing a [[LogicalPlan]] tree and compute some properties. + * A visitor pattern for traversing a [[LogicalPlan]] tree and computing some properties. */ trait LogicalPlanVisitor[T] { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/QueryPlanConstraints.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/QueryPlanConstraints.scala index b0f611fd38dea..5c7b8e5b97883 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/QueryPlanConstraints.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/QueryPlanConstraints.scala @@ -94,25 +94,16 @@ trait QueryPlanConstraints { self: LogicalPlan => case _ => Seq.empty[Attribute] } - // Collect aliases from expressions of the whole tree rooted by the current QueryPlan node, so - // we may avoid producing recursive constraints. - private lazy val aliasMap: AttributeMap[Expression] = AttributeMap( - expressions.collect { - case a: Alias => (a.toAttribute, a.child) - } ++ children.flatMap(_.asInstanceOf[QueryPlanConstraints].aliasMap)) - // Note: the explicit cast is necessary, since Scala compiler fails to infer the type. - /** * Infers an additional set of constraints from a given set of equality constraints. * For e.g., if an operator has constraints of the form (`a = 5`, `a = b`), this returns an * additional constraint of the form `b = 5`. */ private def inferAdditionalConstraints(constraints: Set[Expression]): Set[Expression] = { - val aliasedConstraints = eliminateAliasedExpressionInConstraints(constraints) var inferredConstraints = Set.empty[Expression] - aliasedConstraints.foreach { + constraints.foreach { case eq @ EqualTo(l: Attribute, r: Attribute) => - val candidateConstraints = aliasedConstraints - eq + val candidateConstraints = constraints - eq inferredConstraints ++= replaceConstraints(candidateConstraints, l, r) inferredConstraints ++= replaceConstraints(candidateConstraints, r, l) case _ => // No inference @@ -120,30 +111,6 @@ trait QueryPlanConstraints { self: LogicalPlan => inferredConstraints -- constraints } - /** - * Replace the aliased expression in [[Alias]] with the alias name if both exist in constraints. - * Thus non-converging inference can be prevented. - * E.g. `Alias(b, f(a)), a = b` infers `f(a) = f(f(a))` without eliminating aliased expressions. - * Also, the size of constraints is reduced without losing any information. - * When the inferred filters are pushed down the operators that generate the alias, - * the alias names used in filters are replaced by the aliased expressions. - */ - private def eliminateAliasedExpressionInConstraints(constraints: Set[Expression]) - : Set[Expression] = { - val attributesInEqualTo = constraints.flatMap { - case EqualTo(l: Attribute, r: Attribute) => l :: r :: Nil - case _ => Nil - } - var aliasedConstraints = constraints - attributesInEqualTo.foreach { a => - if (aliasMap.contains(a)) { - val child = aliasMap.get(a).get - aliasedConstraints = replaceConstraints(aliasedConstraints, child, a) - } - } - aliasedConstraints - } - private def replaceConstraints( constraints: Set[Expression], source: Expression, diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala index 95e099c340af1..a4fca790dd086 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala @@ -903,6 +903,7 @@ case class Deduplicate( * This analysis barrier will be removed at the end of analysis stage. */ case class AnalysisBarrier(child: LogicalPlan) extends LeafNode { + override protected def innerChildren: Seq[LogicalPlan] = Seq(child) override def output: Seq[Attribute] = child.output override def isStreaming: Boolean = child.isStreaming override def doCanonicalize(): LogicalPlan = child.canonicalized diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/BasicStatsPlanVisitor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/BasicStatsPlanVisitor.scala index ca0775a2e8408..b6c16079d1984 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/BasicStatsPlanVisitor.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/BasicStatsPlanVisitor.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.plans.logical.statsEstimation import org.apache.spark.sql.catalyst.plans.logical._ /** - * An [[LogicalPlanVisitor]] that computes a the statistics used in a cost-based optimizer. + * A [[LogicalPlanVisitor]] that computes the statistics for the cost-based optimizer. */ object BasicStatsPlanVisitor extends LogicalPlanVisitor[Statistics] { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/SizeInBytesOnlyStatsPlanVisitor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/SizeInBytesOnlyStatsPlanVisitor.scala index 5e1c4e0bd6069..85f67c7d66075 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/SizeInBytesOnlyStatsPlanVisitor.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/SizeInBytesOnlyStatsPlanVisitor.scala @@ -48,8 +48,8 @@ object SizeInBytesOnlyStatsPlanVisitor extends LogicalPlanVisitor[Statistics] { } /** - * For leaf nodes, use its computeStats. For other nodes, we assume the size in bytes is the - * sum of all of the children's. + * For leaf nodes, use its `computeStats`. For other nodes, we assume the size in bytes is the + * product of all of the children's `computeStats`. */ override def default(p: LogicalPlan): Statistics = p match { case p: LeafNode => p.computeStats() diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala index e57c842ce2a36..cc1a5e835d9cd 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala @@ -30,18 +30,43 @@ import org.apache.spark.sql.types.{DataType, IntegerType} * - Intra-partition ordering of data: In this case the distribution describes guarantees made * about how tuples are distributed within a single partition. */ -sealed trait Distribution +sealed trait Distribution { + /** + * The required number of partitions for this distribution. If it's None, then any number of + * partitions is allowed for this distribution. + */ + def requiredNumPartitions: Option[Int] + + /** + * Creates a default partitioning for this distribution, which can satisfy this distribution while + * matching the given number of partitions. + */ + def createPartitioning(numPartitions: Int): Partitioning +} /** * Represents a distribution where no promises are made about co-location of data. */ -case object UnspecifiedDistribution extends Distribution +case object UnspecifiedDistribution extends Distribution { + override def requiredNumPartitions: Option[Int] = None + + override def createPartitioning(numPartitions: Int): Partitioning = { + throw new IllegalStateException("UnspecifiedDistribution does not have default partitioning.") + } +} /** * Represents a distribution that only has a single partition and all tuples of the dataset * are co-located. */ -case object AllTuples extends Distribution +case object AllTuples extends Distribution { + override def requiredNumPartitions: Option[Int] = Some(1) + + override def createPartitioning(numPartitions: Int): Partitioning = { + assert(numPartitions == 1, "The default partitioning of AllTuples can only have 1 partition.") + SinglePartition + } +} /** * Represents data where tuples that share the same values for the `clustering` @@ -51,12 +76,44 @@ case object AllTuples extends Distribution */ case class ClusteredDistribution( clustering: Seq[Expression], - numPartitions: Option[Int] = None) extends Distribution { + requiredNumPartitions: Option[Int] = None) extends Distribution { require( clustering != Nil, "The clustering expressions of a ClusteredDistribution should not be Nil. " + "An AllTuples should be used to represent a distribution that only has " + "a single partition.") + + override def createPartitioning(numPartitions: Int): Partitioning = { + assert(requiredNumPartitions.isEmpty || requiredNumPartitions.get == numPartitions, + s"This ClusteredDistribution requires ${requiredNumPartitions.get} partitions, but " + + s"the actual number of partitions is $numPartitions.") + HashPartitioning(clustering, numPartitions) + } +} + +/** + * Represents data where tuples have been clustered according to the hash of the given + * `expressions`. The hash function is defined as `HashPartitioning.partitionIdExpression`, so only + * [[HashPartitioning]] can satisfy this distribution. + * + * This is a strictly stronger guarantee than [[ClusteredDistribution]]. Given a tuple and the + * number of partitions, this distribution strictly requires which partition the tuple should be in. + */ +case class HashClusteredDistribution( + expressions: Seq[Expression], + requiredNumPartitions: Option[Int] = None) extends Distribution { + require( + expressions != Nil, + "The expressions for hash of a HashClusteredDistribution should not be Nil. " + + "An AllTuples should be used to represent a distribution that only has " + + "a single partition.") + + override def createPartitioning(numPartitions: Int): Partitioning = { + assert(requiredNumPartitions.isEmpty || requiredNumPartitions.get == numPartitions, + s"This HashClusteredDistribution requires ${requiredNumPartitions.get} partitions, but " + + s"the actual number of partitions is $numPartitions.") + HashPartitioning(expressions, numPartitions) + } } /** @@ -73,48 +130,33 @@ case class OrderedDistribution(ordering: Seq[SortOrder]) extends Distribution { "An AllTuples should be used to represent a distribution that only has " + "a single partition.") - // TODO: This is not really valid... - def clustering: Set[Expression] = ordering.map(_.child).toSet + override def requiredNumPartitions: Option[Int] = None + + override def createPartitioning(numPartitions: Int): Partitioning = { + RangePartitioning(ordering, numPartitions) + } } /** * Represents data where tuples are broadcasted to every node. It is quite common that the * entire set of tuples is transformed into different data structure. */ -case class BroadcastDistribution(mode: BroadcastMode) extends Distribution +case class BroadcastDistribution(mode: BroadcastMode) extends Distribution { + override def requiredNumPartitions: Option[Int] = Some(1) + + override def createPartitioning(numPartitions: Int): Partitioning = { + assert(numPartitions == 1, + "The default partitioning of BroadcastDistribution can only have 1 partition.") + BroadcastPartitioning(mode) + } +} /** - * Describes how an operator's output is split across partitions. The `compatibleWith`, - * `guarantees`, and `satisfies` methods describe relationships between child partitionings, - * target partitionings, and [[Distribution]]s. These relations are described more precisely in - * their individual method docs, but at a high level: - * - * - `satisfies` is a relationship between partitionings and distributions. - * - `compatibleWith` is relationships between an operator's child output partitionings. - * - `guarantees` is a relationship between a child's existing output partitioning and a target - * output partitioning. - * - * Diagrammatically: - * - * +--------------+ - * | Distribution | - * +--------------+ - * ^ - * | - * satisfies - * | - * +--------------+ +--------------+ - * | Child | | Target | - * +----| Partitioning |----guarantees--->| Partitioning | - * | +--------------+ +--------------+ - * | ^ - * | | - * | compatibleWith - * | | - * +------------+ - * + * Describes how an operator's output is split across partitions. It has 2 major properties: + * 1. number of partitions. + * 2. if it can satisfy a given distribution. */ -sealed trait Partitioning { +trait Partitioning { /** Returns the number of partitions that the data is split across */ val numPartitions: Int @@ -123,113 +165,45 @@ sealed trait Partitioning { * to satisfy the partitioning scheme mandated by the `required` [[Distribution]], * i.e. the current dataset does not need to be re-partitioned for the `required` * Distribution (it is possible that tuples within a partition need to be reorganized). - */ - def satisfies(required: Distribution): Boolean - - /** - * Returns true iff we can say that the partitioning scheme of this [[Partitioning]] - * guarantees the same partitioning scheme described by `other`. * - * Compatibility of partitionings is only checked for operators that have multiple children - * and that require a specific child output [[Distribution]], such as joins. - * - * Intuitively, partitionings are compatible if they route the same partitioning key to the same - * partition. For instance, two hash partitionings are only compatible if they produce the same - * number of output partitionings and hash records according to the same hash function and - * same partitioning key schema. - * - * Put another way, two partitionings are compatible with each other if they satisfy all of the - * same distribution guarantees. + * A [[Partitioning]] can never satisfy a [[Distribution]] if its `numPartitions` does't match + * [[Distribution.requiredNumPartitions]]. */ - def compatibleWith(other: Partitioning): Boolean + final def satisfies(required: Distribution): Boolean = { + required.requiredNumPartitions.forall(_ == numPartitions) && satisfies0(required) + } /** - * Returns true iff we can say that the partitioning scheme of this [[Partitioning]] guarantees - * the same partitioning scheme described by `other`. If a `A.guarantees(B)`, then repartitioning - * the child's output according to `B` will be unnecessary. `guarantees` is used as a performance - * optimization to allow the exchange planner to avoid redundant repartitionings. By default, - * a partitioning only guarantees partitionings that are equal to itself (i.e. the same number - * of partitions, same strategy (range or hash), etc). - * - * In order to enable more aggressive optimization, this strict equality check can be relaxed. - * For example, say that the planner needs to repartition all of an operator's children so that - * they satisfy the [[AllTuples]] distribution. One way to do this is to repartition all children - * to have the [[SinglePartition]] partitioning. If one of the operator's children already happens - * to be hash-partitioned with a single partition then we do not need to re-shuffle this child; - * this repartitioning can be avoided if a single-partition [[HashPartitioning]] `guarantees` - * [[SinglePartition]]. - * - * The SinglePartition example given above is not particularly interesting; guarantees' real - * value occurs for more advanced partitioning strategies. SPARK-7871 will introduce a notion - * of null-safe partitionings, under which partitionings can specify whether rows whose - * partitioning keys contain null values will be grouped into the same partition or whether they - * will have an unknown / random distribution. If a partitioning does not require nulls to be - * clustered then a partitioning which _does_ cluster nulls will guarantee the null clustered - * partitioning. The converse is not true, however: a partitioning which clusters nulls cannot - * be guaranteed by one which does not cluster them. Thus, in general `guarantees` is not a - * symmetric relation. + * The actual method that defines whether this [[Partitioning]] can satisfy the given + * [[Distribution]], after the `numPartitions` check. * - * Another way to think about `guarantees`: if `A.guarantees(B)`, then any partitioning of rows - * produced by `A` could have also been produced by `B`. + * By default a [[Partitioning]] can satisfy [[UnspecifiedDistribution]], and [[AllTuples]] if + * the [[Partitioning]] only have one partition. Implementations can also overwrite this method + * with special logic. */ - def guarantees(other: Partitioning): Boolean = this == other -} - -object Partitioning { - def allCompatible(partitionings: Seq[Partitioning]): Boolean = { - // Note: this assumes transitivity - partitionings.sliding(2).map { - case Seq(a) => true - case Seq(a, b) => - if (a.numPartitions != b.numPartitions) { - assert(!a.compatibleWith(b) && !b.compatibleWith(a)) - false - } else { - a.compatibleWith(b) && b.compatibleWith(a) - } - }.forall(_ == true) - } -} - -case class UnknownPartitioning(numPartitions: Int) extends Partitioning { - override def satisfies(required: Distribution): Boolean = required match { + protected def satisfies0(required: Distribution): Boolean = required match { case UnspecifiedDistribution => true + case AllTuples => numPartitions == 1 case _ => false } - - override def compatibleWith(other: Partitioning): Boolean = false - - override def guarantees(other: Partitioning): Boolean = false } +case class UnknownPartitioning(numPartitions: Int) extends Partitioning + /** * Represents a partitioning where rows are distributed evenly across output partitions * by starting from a random target partition number and distributing rows in a round-robin * fashion. This partitioning is used when implementing the DataFrame.repartition() operator. */ -case class RoundRobinPartitioning(numPartitions: Int) extends Partitioning { - override def satisfies(required: Distribution): Boolean = required match { - case UnspecifiedDistribution => true - case _ => false - } - - override def compatibleWith(other: Partitioning): Boolean = false - - override def guarantees(other: Partitioning): Boolean = false -} +case class RoundRobinPartitioning(numPartitions: Int) extends Partitioning case object SinglePartition extends Partitioning { val numPartitions = 1 - override def satisfies(required: Distribution): Boolean = required match { + override def satisfies0(required: Distribution): Boolean = required match { case _: BroadcastDistribution => false - case ClusteredDistribution(_, desiredPartitions) => desiredPartitions.forall(_ == 1) case _ => true } - - override def compatibleWith(other: Partitioning): Boolean = other.numPartitions == 1 - - override def guarantees(other: Partitioning): Boolean = other.numPartitions == 1 } /** @@ -244,22 +218,18 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) override def nullable: Boolean = false override def dataType: DataType = IntegerType - override def satisfies(required: Distribution): Boolean = required match { - case UnspecifiedDistribution => true - case ClusteredDistribution(requiredClustering, desiredPartitions) => - expressions.forall(x => requiredClustering.exists(_.semanticEquals(x))) && - desiredPartitions.forall(_ == numPartitions) // if desiredPartitions = None, returns true - case _ => false - } - - override def compatibleWith(other: Partitioning): Boolean = other match { - case o: HashPartitioning => this.semanticEquals(o) - case _ => false - } - - override def guarantees(other: Partitioning): Boolean = other match { - case o: HashPartitioning => this.semanticEquals(o) - case _ => false + override def satisfies0(required: Distribution): Boolean = { + super.satisfies0(required) || { + required match { + case h: HashClusteredDistribution => + expressions.length == h.expressions.length && expressions.zip(h.expressions).forall { + case (l, r) => l.semanticEquals(r) + } + case ClusteredDistribution(requiredClustering, _) => + expressions.forall(x => requiredClustering.exists(_.semanticEquals(x))) + case _ => false + } + } } /** @@ -288,25 +258,17 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) override def nullable: Boolean = false override def dataType: DataType = IntegerType - override def satisfies(required: Distribution): Boolean = required match { - case UnspecifiedDistribution => true - case OrderedDistribution(requiredOrdering) => - val minSize = Seq(requiredOrdering.size, ordering.size).min - requiredOrdering.take(minSize) == ordering.take(minSize) - case ClusteredDistribution(requiredClustering, desiredPartitions) => - ordering.map(_.child).forall(x => requiredClustering.exists(_.semanticEquals(x))) && - desiredPartitions.forall(_ == numPartitions) // if desiredPartitions = None, returns true - case _ => false - } - - override def compatibleWith(other: Partitioning): Boolean = other match { - case o: RangePartitioning => this.semanticEquals(o) - case _ => false - } - - override def guarantees(other: Partitioning): Boolean = other match { - case o: RangePartitioning => this.semanticEquals(o) - case _ => false + override def satisfies0(required: Distribution): Boolean = { + super.satisfies0(required) || { + required match { + case OrderedDistribution(requiredOrdering) => + val minSize = Seq(requiredOrdering.size, ordering.size).min + requiredOrdering.take(minSize) == ordering.take(minSize) + case ClusteredDistribution(requiredClustering, _) => + ordering.map(_.child).forall(x => requiredClustering.exists(_.semanticEquals(x))) + case _ => false + } + } } } @@ -344,23 +306,9 @@ case class PartitioningCollection(partitionings: Seq[Partitioning]) * Returns true if any `partitioning` of this collection satisfies the given * [[Distribution]]. */ - override def satisfies(required: Distribution): Boolean = + override def satisfies0(required: Distribution): Boolean = partitionings.exists(_.satisfies(required)) - /** - * Returns true if any `partitioning` of this collection is compatible with - * the given [[Partitioning]]. - */ - override def compatibleWith(other: Partitioning): Boolean = - partitionings.exists(_.compatibleWith(other)) - - /** - * Returns true if any `partitioning` of this collection guarantees - * the given [[Partitioning]]. - */ - override def guarantees(other: Partitioning): Boolean = - partitionings.exists(_.guarantees(other)) - override def toString: String = { partitionings.map(_.toString).mkString("(", " or ", ")") } @@ -373,13 +321,8 @@ case class PartitioningCollection(partitionings: Seq[Partitioning]) case class BroadcastPartitioning(mode: BroadcastMode) extends Partitioning { override val numPartitions: Int = 1 - override def satisfies(required: Distribution): Boolean = required match { + override def satisfies0(required: Distribution): Boolean = required match { case BroadcastDistribution(m) if m == mode => true case _ => false } - - override def compatibleWith(other: Partitioning): Boolean = other match { - case BroadcastPartitioning(m) if m == mode => true - case _ => false - } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/QueryExecutionMetering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/QueryExecutionMetering.scala new file mode 100644 index 0000000000000..62f7541150a6e --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/QueryExecutionMetering.scala @@ -0,0 +1,91 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.rules + +import scala.collection.JavaConverters._ + +import com.google.common.util.concurrent.AtomicLongMap + +case class QueryExecutionMetering() { + private val timeMap = AtomicLongMap.create[String]() + private val numRunsMap = AtomicLongMap.create[String]() + private val numEffectiveRunsMap = AtomicLongMap.create[String]() + private val timeEffectiveRunsMap = AtomicLongMap.create[String]() + + /** Resets statistics about time spent running specific rules */ + def resetMetrics(): Unit = { + timeMap.clear() + numRunsMap.clear() + numEffectiveRunsMap.clear() + timeEffectiveRunsMap.clear() + } + + def totalTime: Long = { + timeMap.sum() + } + + def totalNumRuns: Long = { + numRunsMap.sum() + } + + def incExecutionTimeBy(ruleName: String, delta: Long): Unit = { + timeMap.addAndGet(ruleName, delta) + } + + def incTimeEffectiveExecutionBy(ruleName: String, delta: Long): Unit = { + timeEffectiveRunsMap.addAndGet(ruleName, delta) + } + + def incNumEffectiveExecution(ruleName: String): Unit = { + numEffectiveRunsMap.incrementAndGet(ruleName) + } + + def incNumExecution(ruleName: String): Unit = { + numRunsMap.incrementAndGet(ruleName) + } + + /** Dump statistics about time spent running specific rules. */ + def dumpTimeSpent(): String = { + val map = timeMap.asMap().asScala + val maxLengthRuleNames = map.keys.map(_.toString.length).max + + val colRuleName = "Rule".padTo(maxLengthRuleNames, " ").mkString + val colRunTime = "Effective Time / Total Time".padTo(len = 47, " ").mkString + val colNumRuns = "Effective Runs / Total Runs".padTo(len = 47, " ").mkString + + val ruleMetrics = map.toSeq.sortBy(_._2).reverseMap { case (name, time) => + val timeEffectiveRun = timeEffectiveRunsMap.get(name) + val numRuns = numRunsMap.get(name) + val numEffectiveRun = numEffectiveRunsMap.get(name) + + val ruleName = name.padTo(maxLengthRuleNames, " ").mkString + val runtimeValue = s"$timeEffectiveRun / $time".padTo(len = 47, " ").mkString + val numRunValue = s"$numEffectiveRun / $numRuns".padTo(len = 47, " ").mkString + s"$ruleName $runtimeValue $numRunValue" + }.mkString("\n", "\n", "") + + s""" + |=== Metrics of Analyzer/Optimizer Rules === + |Total number of runs: $totalNumRuns + |Total time: ${totalTime / 1000000000D} seconds + | + |$colRuleName $colRunTime $colNumRuns + |$ruleMetrics + """.stripMargin + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala index 7e4b784033bfc..dccb44ddebfa4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala @@ -17,10 +17,6 @@ package org.apache.spark.sql.catalyst.rules -import scala.collection.JavaConverters._ - -import com.google.common.util.concurrent.AtomicLongMap - import org.apache.spark.internal.Logging import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.trees.TreeNode @@ -28,18 +24,16 @@ import org.apache.spark.sql.catalyst.util.sideBySide import org.apache.spark.util.Utils object RuleExecutor { - protected val timeMap = AtomicLongMap.create[String]() - - /** Resets statistics about time spent running specific rules */ - def resetTime(): Unit = timeMap.clear() + protected val queryExecutionMeter = QueryExecutionMetering() /** Dump statistics about time spent running specific rules. */ def dumpTimeSpent(): String = { - val map = timeMap.asMap().asScala - val maxSize = map.keys.map(_.toString.length).max - map.toSeq.sortBy(_._2).reverseMap { case (k, v) => - s"${k.padTo(maxSize, " ").mkString} $v" - }.mkString("\n", "\n", "") + queryExecutionMeter.dumpTimeSpent() + } + + /** Resets statistics about time spent running specific rules */ + def resetMetrics(): Unit = { + queryExecutionMeter.resetMetrics() } } @@ -77,6 +71,7 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging { */ def execute(plan: TreeType): TreeType = { var curPlan = plan + val queryExecutionMetrics = RuleExecutor.queryExecutionMeter batches.foreach { batch => val batchStartPlan = curPlan @@ -91,15 +86,18 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging { val startTime = System.nanoTime() val result = rule(plan) val runTime = System.nanoTime() - startTime - RuleExecutor.timeMap.addAndGet(rule.ruleName, runTime) if (!result.fastEquals(plan)) { + queryExecutionMetrics.incNumEffectiveExecution(rule.ruleName) + queryExecutionMetrics.incTimeEffectiveExecutionBy(rule.ruleName, runTime) logTrace( s""" |=== Applying Rule ${rule.ruleName} === |${sideBySide(plan.treeString, result.treeString).mkString("\n")} """.stripMargin) } + queryExecutionMetrics.incExecutionTimeBy(rule.ruleName, runTime) + queryExecutionMetrics.incNumExecution(rule.ruleName) // Run the structural integrity checker against the plan after each rule. if (!isPlanIntegral(result)) { @@ -135,9 +133,9 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging { if (!batchStartPlan.fastEquals(curPlan)) { logDebug( s""" - |=== Result of Batch ${batch.name} === - |${sideBySide(batchStartPlan.treeString, curPlan.treeString).mkString("\n")} - """.stripMargin) + |=== Result of Batch ${batch.name} === + |${sideBySide(batchStartPlan.treeString, curPlan.treeString).mkString("\n")} + """.stripMargin) } else { logTrace(s"Batch ${batch.name} has no effect.") } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/QuantileSummaries.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/QuantileSummaries.scala index eb7941cf9e6af..b013add9c9778 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/QuantileSummaries.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/QuantileSummaries.scala @@ -105,7 +105,7 @@ class QuantileSummaries( if (newSamples.isEmpty || (sampleIdx == sampled.length && opsIdx == sorted.length - 1)) { 0 } else { - math.floor(2 * relativeError * currentCount).toInt + math.floor(2 * relativeError * currentCount).toLong } val tuple = Stats(currentSample, 1, delta) @@ -192,10 +192,10 @@ class QuantileSummaries( } // Target rank - val rank = math.ceil(quantile * count).toInt + val rank = math.ceil(quantile * count).toLong val targetError = relativeError * count // Minimum rank at current sample - var minRank = 0 + var minRank = 0L var i = 0 while (i < sampled.length - 1) { val curSample = sampled(i) @@ -235,7 +235,7 @@ object QuantileSummaries { * @param g the minimum rank jump from the previous value's minimum rank * @param delta the maximum span of the rank. */ - case class Stats(value: Double, g: Int, delta: Int) + case class Stats(value: Double, g: Long, delta: Long) private def compressImmut( currentSamples: IndexedSeq[Stats], diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/RandomUUIDGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/RandomUUIDGenerator.scala new file mode 100644 index 0000000000000..4fe07a071c1ca --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/RandomUUIDGenerator.scala @@ -0,0 +1,43 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.util + +import java.util.UUID + +import org.apache.commons.math3.random.MersenneTwister + +import org.apache.spark.unsafe.types.UTF8String + +/** + * This class is used to generate a UUID from Pseudo-Random Numbers. + * + * For the algorithm, see RFC 4122: A Universally Unique IDentifier (UUID) URN Namespace, + * section 4.4 "Algorithms for Creating a UUID from Truly Random or Pseudo-Random Numbers". + */ +case class RandomUUIDGenerator(randomSeed: Long) { + private val random = new MersenneTwister(randomSeed) + + def getNextUUID(): UUID = { + val mostSigBits = (random.nextLong() & 0xFFFFFFFFFFFF0FFFL) | 0x0000000000004000L + val leastSigBits = (random.nextLong() | 0x8000000000000000L) & 0xBFFFFFFFFFFFFFFFL + + new UUID(mostSigBits, leastSigBits) + } + + def getNextUUIDUTF8String(): UTF8String = UTF8String.fromString(getNextUUID().toString()) +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala index 4f77c54a7af57..74e0f609e2e52 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala @@ -27,11 +27,13 @@ import scala.util.matching.Regex import org.apache.hadoop.fs.Path +import org.apache.spark.{SparkContext, SparkEnv} import org.apache.spark.internal.Logging import org.apache.spark.internal.config._ import org.apache.spark.network.util.ByteUnit import org.apache.spark.sql.catalyst.analysis.Resolver import org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator +import org.apache.spark.util.Utils //////////////////////////////////////////////////////////////////////////////////////////////////// // This file defines the configuration options for Spark SQL. @@ -70,7 +72,7 @@ object SQLConf { * Default config. Only used when there is no active SparkSession for the thread. * See [[get]] for more information. */ - private val fallbackConf = new ThreadLocal[SQLConf] { + private lazy val fallbackConf = new ThreadLocal[SQLConf] { override def initialValue: SQLConf = new SQLConf } @@ -121,14 +123,12 @@ object SQLConf { .createWithDefault(10) val COMPRESS_CACHED = buildConf("spark.sql.inMemoryColumnarStorage.compressed") - .internal() .doc("When set to true Spark SQL will automatically select a compression codec for each " + "column based on statistics of the data.") .booleanConf .createWithDefault(true) val COLUMN_BATCH_SIZE = buildConf("spark.sql.inMemoryColumnarStorage.batchSize") - .internal() .doc("Controls the size of batches for columnar caching. Larger batch sizes can improve " + "memory utilization and compression, but risk OOMs when caching data.") .intConf @@ -141,6 +141,12 @@ object SQLConf { .booleanConf .createWithDefault(true) + val CACHE_VECTORIZED_READER_ENABLED = + buildConf("spark.sql.inMemoryColumnarStorage.enableVectorizedReader") + .doc("Enables vectorized reader for columnar caching.") + .booleanConf + .createWithDefault(true) + val COLUMN_VECTOR_OFFHEAP_ENABLED = buildConf("spark.sql.columnVector.offheap.enabled") .internal() @@ -247,7 +253,7 @@ object SQLConf { val CONSTRAINT_PROPAGATION_ENABLED = buildConf("spark.sql.constraintPropagation.enabled") .internal() .doc("When true, the query optimizer will infer and propagate data constraints in the query " + - "plan to optimize them. Constraint propagation can sometimes be computationally expensive" + + "plan to optimize them. Constraint propagation can sometimes be computationally expensive " + "for certain kinds of query plans (such as those with a large number of predicates and " + "aliases) which might negatively impact overall runtime.") .booleanConf @@ -261,6 +267,15 @@ object SQLConf { .booleanConf .createWithDefault(false) + val FILE_COMRESSION_FACTOR = buildConf("spark.sql.sources.fileCompressionFactor") + .internal() + .doc("When estimating the output data size of a table scan, multiply the file size with this " + + "factor as the estimated data size, in case the data is compressed in the file and lead to" + + " a heavily underestimated result.") + .doubleConf + .checkValue(_ > 0, "the value of fileDataSizeFactor must be larger than 0") + .createWithDefault(1.0) + val PARQUET_SCHEMA_MERGING_ENABLED = buildConf("spark.sql.parquet.mergeSchema") .doc("When true, the Parquet data source merges schemas collected from all data files, " + "otherwise the schema is picked from the summary file or a random data file " + @@ -323,11 +338,14 @@ object SQLConf { .createWithDefault(false) val PARQUET_COMPRESSION = buildConf("spark.sql.parquet.compression.codec") - .doc("Sets the compression codec use when writing Parquet files. Acceptable values include: " + - "uncompressed, snappy, gzip, lzo.") + .doc("Sets the compression codec used when writing Parquet files. If either `compression` or " + + "`parquet.compression` is specified in the table-specific options/properties, the " + + "precedence would be `compression`, `parquet.compression`, " + + "`spark.sql.parquet.compression.codec`. Acceptable values include: none, uncompressed, " + + "snappy, gzip, lzo.") .stringConf .transform(_.toLowerCase(Locale.ROOT)) - .checkValues(Set("uncompressed", "snappy", "gzip", "lzo")) + .checkValues(Set("none", "uncompressed", "snappy", "gzip", "lzo")) .createWithDefault("snappy") val PARQUET_FILTER_PUSHDOWN_ENABLED = buildConf("spark.sql.parquet.filterPushdown") @@ -364,8 +382,10 @@ object SQLConf { .createWithDefault(true) val ORC_COMPRESSION = buildConf("spark.sql.orc.compression.codec") - .doc("Sets the compression codec use when writing ORC files. Acceptable values include: " + - "none, uncompressed, snappy, zlib, lzo.") + .doc("Sets the compression codec used when writing ORC files. If either `compression` or " + + "`orc.compress` is specified in the table-specific options/properties, the precedence " + + "would be `compression`, `orc.compress`, `spark.sql.orc.compression.codec`." + + "Acceptable values include: none, uncompressed, snappy, zlib, lzo.") .stringConf .transform(_.toLowerCase(Locale.ROOT)) .checkValues(Set("none", "uncompressed", "snappy", "zlib", "lzo")) @@ -373,11 +393,23 @@ object SQLConf { val ORC_IMPLEMENTATION = buildConf("spark.sql.orc.impl") .doc("When native, use the native version of ORC support instead of the ORC library in Hive " + - "1.2.1. It is 'hive' by default prior to Spark 2.3.") + "1.2.1. It is 'hive' by default.") .internal() .stringConf .checkValues(Set("hive", "native")) - .createWithDefault("native") + .createWithDefault("hive") + + val ORC_VECTORIZED_READER_ENABLED = buildConf("spark.sql.orc.enableVectorizedReader") + .doc("Enables vectorized orc decoding.") + .booleanConf + .createWithDefault(true) + + val ORC_COPY_BATCH_TO_SPARK = buildConf("spark.sql.orc.copyBatchToSpark") + .doc("Whether or not to copy the ORC columnar batch to Spark columnar batch in the " + + "vectorized ORC reader.") + .internal() + .booleanConf + .createWithDefault(false) val ORC_FILTER_PUSHDOWN_ENABLED = buildConf("spark.sql.orc.filterPushdown") .doc("When true, enable filter pushdown for ORC files.") @@ -449,6 +481,14 @@ object SQLConf { .stringConf .createWithDefault("_corrupt_record") + val FROM_JSON_FORCE_NULLABLE_SCHEMA = buildConf("spark.sql.fromJsonForceNullableSchema") + .internal() + .doc("When true, force the output schema of the from_json() function to be nullable " + + "(including all the fields). Otherwise, the schema might not be compatible with" + + "actual data, which leads to curruptions.") + .booleanConf + .createWithDefault(true) + val BROADCAST_TIMEOUT = buildConf("spark.sql.broadcastTimeout") .doc("Timeout in seconds for the broadcast wait time in broadcast joins.") .timeConf(TimeUnit.SECONDS) @@ -601,6 +641,14 @@ object SQLConf { .booleanConf .createWithDefault(true) + val WHOLESTAGE_CODEGEN_USE_ID_IN_CLASS_NAME = + buildConf("spark.sql.codegen.useIdInClassName") + .internal() + .doc("When true, embed the (whole-stage) codegen stage ID into " + + "the class name of the generated class as a suffix") + .booleanConf + .createWithDefault(true) + val WHOLESTAGE_MAX_NUM_FIELDS = buildConf("spark.sql.codegen.maxFields") .internal() .doc("The maximum number of fields (including nested fields) that will be supported before" + @@ -626,12 +674,22 @@ object SQLConf { val WHOLESTAGE_HUGE_METHOD_LIMIT = buildConf("spark.sql.codegen.hugeMethodLimit") .internal() .doc("The maximum bytecode size of a single compiled Java function generated by whole-stage " + - "codegen. When the compiled function exceeds this threshold, " + - "the whole-stage codegen is deactivated for this subtree of the current query plan. " + - s"The default value is ${CodeGenerator.DEFAULT_JVM_HUGE_METHOD_LIMIT} and " + - "this is a limit in the OpenJDK JVM implementation.") + "codegen. When the compiled function exceeds this threshold, the whole-stage codegen is " + + "deactivated for this subtree of the current query plan. The default value is 65535, which " + + "is the largest bytecode size possible for a valid Java method. When running on HotSpot, " + + s"it may be preferable to set the value to ${CodeGenerator.DEFAULT_JVM_HUGE_METHOD_LIMIT} " + + "to match HotSpot's implementation.") .intConf - .createWithDefault(CodeGenerator.DEFAULT_JVM_HUGE_METHOD_LIMIT) + .createWithDefault(65535) + + val WHOLESTAGE_SPLIT_CONSUME_FUNC_BY_OPERATOR = + buildConf("spark.sql.codegen.splitConsumeFuncByOperator") + .internal() + .doc("When true, whole stage codegen would put the logic of consuming rows of each " + + "physical operator into individual methods, instead of a single big method. This can be " + + "used to avoid oversized function that can miss the opportunity of JIT optimization.") + .booleanConf + .createWithDefault(true) val FILES_MAX_PARTITION_BYTES = buildConf("spark.sql.files.maxPartitionBytes") .doc("The maximum number of bytes to pack into a single partition when reading files.") @@ -866,7 +924,7 @@ object SQLConf { .internal() .doc("The number of bins when generating histograms.") .intConf - .checkValue(num => num > 1, "The number of bins must be large than 1.") + .checkValue(num => num > 1, "The number of bins must be larger than 1.") .createWithDefault(254) val PERCENTILE_ACCURACY = @@ -998,17 +1056,16 @@ object SQLConf { val ARROW_EXECUTION_ENABLE = buildConf("spark.sql.execution.arrow.enabled") - .internal() - .doc("Make use of Apache Arrow for columnar data transfers. Currently available " + - "for use with pyspark.sql.DataFrame.toPandas with the following data types: " + - "StringType, BinaryType, BooleanType, DoubleType, FloatType, ByteType, IntegerType, " + - "LongType, ShortType") + .doc("When true, make use of Apache Arrow for columnar data transfers. Currently available " + + "for use with pyspark.sql.DataFrame.toPandas, and " + + "pyspark.sql.SparkSession.createDataFrame when its input is a Pandas DataFrame. " + + "The following data types are unsupported: " + + "BinaryType, MapType, ArrayType of TimestampType, and nested StructType.") .booleanConf .createWithDefault(false) val ARROW_EXECUTION_MAX_RECORDS_PER_BATCH = buildConf("spark.sql.execution.arrow.maxRecordsPerBatch") - .internal() .doc("When using Apache Arrow, limit the maximum number of records that can be written " + "to a single ArrowRecordBatch in memory. If set to zero or negative there is no limit.") .intConf @@ -1036,8 +1093,27 @@ object SQLConf { .booleanConf .createWithDefault(true) + val DECIMAL_OPERATIONS_ALLOW_PREC_LOSS = + buildConf("spark.sql.decimalOperations.allowPrecisionLoss") + .internal() + .doc("When true (default), establishing the result type of an arithmetic operation " + + "happens according to Hive behavior and SQL ANSI 2011 specification, ie. rounding the " + + "decimal part of the result if an exact representation is not possible. Otherwise, NULL " + + "is returned in those cases, as previously.") + .booleanConf + .createWithDefault(true) + + val SQL_OPTIONS_REDACTION_PATTERN = + buildConf("spark.sql.redaction.options.regex") + .doc("Regex to decide which keys in a Spark SQL command's options map contain sensitive " + + "information. The values of options whose names that match this regex will be redacted " + + "in the explain output. This redaction is applied on top of the global redaction " + + s"configuration defined by ${SECRET_REDACTION_PATTERN.key}.") + .regexConf + .createWithDefault("(?i)url".r) + val SQL_STRING_REDACTION_PATTERN = - ConfigBuilder("spark.sql.redaction.string.regex") + buildConf("spark.sql.redaction.string.regex") .doc("Regex to decide which parts of strings produced by Spark contain sensitive " + "information. When this regex matches a string part, that string part is replaced by a " + "dummy value. This is currently used to redact the output of SQL explain commands. " + @@ -1050,6 +1126,12 @@ object SQLConf { .booleanConf .createWithDefault(false) + val ELT_OUTPUT_AS_STRING = buildConf("spark.sql.function.eltOutputAsString") + .doc("When this option is set to false and all inputs are binary, `elt` returns " + + "an output as binary. Otherwise, it returns as a string. ") + .booleanConf + .createWithDefault(false) + val CONTINUOUS_STREAMING_EXECUTOR_QUEUE_SIZE = buildConf("spark.sql.streaming.continuous.executorQueueSize") .internal() @@ -1066,6 +1148,43 @@ object SQLConf { .timeConf(TimeUnit.MILLISECONDS) .createWithDefault(100) + val DISABLED_V2_STREAMING_WRITERS = buildConf("spark.sql.streaming.disabledV2Writers") + .internal() + .doc("A comma-separated list of fully qualified data source register class names for which" + + " StreamWriteSupport is disabled. Writes to these sources will fail back to the V1 Sink.") + .stringConf + .createWithDefault("") + + object PartitionOverwriteMode extends Enumeration { + val STATIC, DYNAMIC = Value + } + + val PARTITION_OVERWRITE_MODE = + buildConf("spark.sql.sources.partitionOverwriteMode") + .doc("When INSERT OVERWRITE a partitioned data source table, we currently support 2 modes: " + + "static and dynamic. In static mode, Spark deletes all the partitions that match the " + + "partition specification(e.g. PARTITION(a=1,b)) in the INSERT statement, before " + + "overwriting. In dynamic mode, Spark doesn't delete partitions ahead, and only overwrite " + + "those partitions that have data written into it at runtime. By default we use static " + + "mode to keep the same behavior of Spark prior to 2.3. Note that this config doesn't " + + "affect Hive serde tables, as they are always overwritten with dynamic mode.") + .stringConf + .transform(_.toUpperCase(Locale.ROOT)) + .checkValues(PartitionOverwriteMode.values.map(_.toString)) + .createWithDefault(PartitionOverwriteMode.STATIC.toString) + + val SORT_BEFORE_REPARTITION = + buildConf("spark.sql.execution.sortBeforeRepartition") + .internal() + .doc("When perform a repartition following a shuffle, the output row ordering would be " + + "nondeterministic. If some downstream stages fail and some tasks of the repartition " + + "stage retry, these tasks may generate different data, and that can lead to correctness " + + "issues. Turn on this config to insert a local sort before actually doing repartition " + + "to generate consistent repartition results. The performance of repartition() may go " + + "down since we insert extra local sort before it.") + .booleanConf + .createWithDefault(true) + object Deprecated { val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks" } @@ -1087,6 +1206,12 @@ object SQLConf { class SQLConf extends Serializable with Logging { import SQLConf._ + if (Utils.isTesting && SparkEnv.get != null) { + // assert that we're only accessing it on the driver. + assert(SparkEnv.get.executorId == SparkContext.DRIVER_IDENTIFIER, + "SQLConf should only be created and accessed on the driver.") + } + /** Only low degree of contention is expected for conf, thus NOT using ConcurrentHashMap. */ @transient protected[spark] val settings = java.util.Collections.synchronizedMap( new java.util.HashMap[String, String]()) @@ -1146,12 +1271,16 @@ class SQLConf extends Serializable with Logging { def orcCompressionCodec: String = getConf(ORC_COMPRESSION) + def orcVectorizedReaderEnabled: Boolean = getConf(ORC_VECTORIZED_READER_ENABLED) + def parquetCompressionCodec: String = getConf(PARQUET_COMPRESSION) def parquetVectorizedReaderEnabled: Boolean = getConf(PARQUET_VECTORIZED_READER_ENABLED) def columnBatchSize: Int = getConf(COLUMN_BATCH_SIZE) + def cacheVectorizedReaderEnabled: Boolean = getConf(CACHE_VECTORIZED_READER_ENABLED) + def numShufflePartitions: Int = getConf(SHUFFLE_PARTITIONS) def targetPostShuffleInputSize: Long = @@ -1185,6 +1314,8 @@ class SQLConf extends Serializable with Logging { def wholeStageEnabled: Boolean = getConf(WHOLESTAGE_CODEGEN_ENABLED) + def wholeStageUseIdInClassName: Boolean = getConf(WHOLESTAGE_CODEGEN_USE_ID_IN_CLASS_NAME) + def wholeStageMaxNumFields: Int = getConf(WHOLESTAGE_MAX_NUM_FIELDS) def codegenFallback: Boolean = getConf(CODEGEN_FALLBACK) @@ -1193,6 +1324,9 @@ class SQLConf extends Serializable with Logging { def hugeMethodLimit: Int = getConf(WHOLESTAGE_HUGE_METHOD_LIMIT) + def wholeStageSplitConsumeFuncByOperator: Boolean = + getConf(WHOLESTAGE_SPLIT_CONSUME_FUNC_BY_OPERATOR) + def tableRelationCacheSize: Int = getConf(StaticSQLConf.FILESOURCE_TABLE_RELATION_CACHE_SIZE) @@ -1204,7 +1338,11 @@ class SQLConf extends Serializable with Logging { def escapedStringLiterals: Boolean = getConf(ESCAPED_STRING_LITERALS) - def stringRedationPattern: Option[Regex] = SQL_STRING_REDACTION_PATTERN.readFrom(reader) + def fileCompressionFactor: Double = getConf(FILE_COMRESSION_FACTOR) + + def stringRedactionPattern: Option[Regex] = getConf(SQL_STRING_REDACTION_PATTERN) + + def sortBeforeRepartition: Boolean = getConf(SORT_BEFORE_REPARTITION) /** * Returns the [[Resolver]] for the current configuration, which can be used to determine if two @@ -1379,13 +1517,22 @@ class SQLConf extends Serializable with Logging { def replaceExceptWithFilter: Boolean = getConf(REPLACE_EXCEPT_WITH_FILTER) + def decimalOperationsAllowPrecisionLoss: Boolean = getConf(DECIMAL_OPERATIONS_ALLOW_PREC_LOSS) + def continuousStreamingExecutorQueueSize: Int = getConf(CONTINUOUS_STREAMING_EXECUTOR_QUEUE_SIZE) def continuousStreamingExecutorPollIntervalMs: Long = getConf(CONTINUOUS_STREAMING_EXECUTOR_POLL_INTERVAL_MS) + def disabledV2StreamingWriters: String = getConf(DISABLED_V2_STREAMING_WRITERS) + def concatBinaryAsString: Boolean = getConf(CONCAT_BINARY_AS_STRING) + def eltOutputAsString: Boolean = getConf(ELT_OUTPUT_AS_STRING) + + def partitionOverwriteMode: PartitionOverwriteMode.Value = + PartitionOverwriteMode.withName(getConf(PARTITION_OVERWRITE_MODE)) + /** ********************** SQLConf functionality methods ************ */ /** Set Spark SQL configuration properties. */ @@ -1492,6 +1639,17 @@ class SQLConf extends Serializable with Logging { }.toSeq } + /** + * Redacts the given option map according to the description of SQL_OPTIONS_REDACTION_PATTERN. + */ + def redactOptions(options: Map[String, String]): Map[String, String] = { + val regexes = Seq( + getConf(SQL_OPTIONS_REDACTION_PATTERN), + SECRET_REDACTION_PATTERN.readFrom(reader)) + + regexes.foldLeft(options.toSeq) { case (opts, r) => Utils.redact(Some(r), opts) }.toMap + } + /** * Return whether a given key is set in this [[SQLConf]]. */ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala index d6e0df12218ad..0bef11659fc9e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala @@ -295,25 +295,31 @@ object DataType { } /** - * Returns true if the two data types share the same "shape", i.e. the types (including - * nullability) are the same, but the field names don't need to be the same. + * Returns true if the two data types share the same "shape", i.e. the types + * are the same, but the field names don't need to be the same. + * + * @param ignoreNullability whether to ignore nullability when comparing the types */ - def equalsStructurally(from: DataType, to: DataType): Boolean = { + def equalsStructurally( + from: DataType, + to: DataType, + ignoreNullability: Boolean = false): Boolean = { (from, to) match { case (left: ArrayType, right: ArrayType) => equalsStructurally(left.elementType, right.elementType) && - left.containsNull == right.containsNull + (ignoreNullability || left.containsNull == right.containsNull) case (left: MapType, right: MapType) => equalsStructurally(left.keyType, right.keyType) && equalsStructurally(left.valueType, right.valueType) && - left.valueContainsNull == right.valueContainsNull + (ignoreNullability || left.valueContainsNull == right.valueContainsNull) case (StructType(fromFields), StructType(toFields)) => fromFields.length == toFields.length && fromFields.zip(toFields) .forall { case (l, r) => - equalsStructurally(l.dataType, r.dataType) && l.nullable == r.nullable + equalsStructurally(l.dataType, r.dataType) && + (ignoreNullability || l.nullable == r.nullable) } case (fromDataType, toDataType) => fromDataType == toDataType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala index 6e050c18b8acb..dbf51c398fa47 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala @@ -23,7 +23,7 @@ import scala.reflect.runtime.universe.typeTag import org.apache.spark.annotation.InterfaceStability import org.apache.spark.sql.AnalysisException -import org.apache.spark.sql.catalyst.expressions.Expression +import org.apache.spark.sql.catalyst.expressions.{Expression, Literal} /** @@ -117,6 +117,7 @@ object DecimalType extends AbstractDataType { val MAX_SCALE = 38 val SYSTEM_DEFAULT: DecimalType = DecimalType(MAX_PRECISION, 18) val USER_DEFAULT: DecimalType = DecimalType(10, 0) + val MINIMUM_ADJUSTED_SCALE = 6 // The decimal types compatible with other numeric types private[sql] val ByteDecimal = DecimalType(3, 0) @@ -136,10 +137,56 @@ object DecimalType extends AbstractDataType { case DoubleType => DoubleDecimal } + private[sql] def fromLiteral(literal: Literal): DecimalType = literal.value match { + case v: Short => fromBigDecimal(BigDecimal(v)) + case v: Int => fromBigDecimal(BigDecimal(v)) + case v: Long => fromBigDecimal(BigDecimal(v)) + case _ => forType(literal.dataType) + } + + private[sql] def fromBigDecimal(d: BigDecimal): DecimalType = { + DecimalType(Math.max(d.precision, d.scale), d.scale) + } + private[sql] def bounded(precision: Int, scale: Int): DecimalType = { DecimalType(min(precision, MAX_PRECISION), min(scale, MAX_SCALE)) } + /** + * Scale adjustment implementation is based on Hive's one, which is itself inspired to + * SQLServer's one. In particular, when a result precision is greater than + * {@link #MAX_PRECISION}, the corresponding scale is reduced to prevent the integral part of a + * result from being truncated. + * + * This method is used only when `spark.sql.decimalOperations.allowPrecisionLoss` is set to true. + */ + private[sql] def adjustPrecisionScale(precision: Int, scale: Int): DecimalType = { + // Assumption: + assert(precision >= scale) + + if (precision <= MAX_PRECISION) { + // Adjustment only needed when we exceed max precision + DecimalType(precision, scale) + } else if (scale < 0) { + // Decimal can have negative scale (SPARK-24468). In this case, we cannot allow a precision + // loss since we would cause a loss of digits in the integer part. + // In this case, we are likely to meet an overflow. + DecimalType(MAX_PRECISION, scale) + } else { + // Precision/scale exceed maximum precision. Result must be adjusted to MAX_PRECISION. + val intDigits = precision - scale + // If original scale is less than MINIMUM_ADJUSTED_SCALE, use original scale value; otherwise + // preserve at least MINIMUM_ADJUSTED_SCALE fractional digits + val minScaleValue = Math.min(scale, MINIMUM_ADJUSTED_SCALE) + // The resulting scale is the maximum between what is available without causing a loss of + // digits for the integer part of the decimal and the minimum guaranteed scale, which is + // computed above + val adjustedScale = Math.max(MAX_PRECISION - intDigits, minScaleValue) + + DecimalType(MAX_PRECISION, adjustedScale) + } + } + override private[sql] def defaultConcreteType: DataType = SYSTEM_DEFAULT override private[sql] def acceptsType(other: DataType): Boolean = { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala index e3b0969283a84..68d5f52f251a4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala @@ -264,7 +264,9 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru */ def apply(name: String): StructField = { nameToField.getOrElse(name, - throw new IllegalArgumentException(s"""Field "$name" does not exist.""")) + throw new IllegalArgumentException( + s"""Field "$name" does not exist. + |Available fields: ${fieldNames.mkString(", ")}""".stripMargin)) } /** @@ -277,7 +279,8 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru val nonExistFields = names -- fieldNamesSet if (nonExistFields.nonEmpty) { throw new IllegalArgumentException( - s"Field ${nonExistFields.mkString(",")} does not exist.") + s"""Nonexistent field(s): ${nonExistFields.mkString(", ")}. + |Available fields: ${fieldNames.mkString(", ")}""".stripMargin) } // Preserve the original order of fields. StructType(fields.filter(f => names.contains(f.name))) @@ -290,7 +293,9 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru */ def fieldIndex(name: String): Int = { nameToIndex.getOrElse(name, - throw new IllegalArgumentException(s"""Field "$name" does not exist.""")) + throw new IllegalArgumentException( + s"""Field "$name" does not exist. + |Available fields: ${fieldNames.mkString(", ")}""".stripMargin)) } private[sql] def getFieldIndex(name: String): Option[Int] = { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala index b47b8adfe5d55..39228102682b9 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala @@ -41,34 +41,127 @@ class DistributionSuite extends SparkFunSuite { } } - test("HashPartitioning (with nullSafe = true) is the output partitioning") { - // Cases which do not need an exchange between two data properties. + test("UnspecifiedDistribution and AllTuples") { + // except `BroadcastPartitioning`, all other partitioning can satisfy UnspecifiedDistribution checkSatisfied( - HashPartitioning(Seq('a, 'b, 'c), 10), + UnknownPartitioning(-1), UnspecifiedDistribution, true) checkSatisfied( - HashPartitioning(Seq('a, 'b, 'c), 10), - ClusteredDistribution(Seq('a, 'b, 'c)), + RoundRobinPartitioning(10), + UnspecifiedDistribution, true) checkSatisfied( - HashPartitioning(Seq('b, 'c), 10), - ClusteredDistribution(Seq('a, 'b, 'c)), + SinglePartition, + UnspecifiedDistribution, + true) + + checkSatisfied( + HashPartitioning(Seq('a), 10), + UnspecifiedDistribution, + true) + + checkSatisfied( + RangePartitioning(Seq('a.asc), 10), + UnspecifiedDistribution, + true) + + checkSatisfied( + BroadcastPartitioning(IdentityBroadcastMode), + UnspecifiedDistribution, + false) + + // except `BroadcastPartitioning`, all other partitioning can satisfy AllTuples if they have + // only one partition. + checkSatisfied( + UnknownPartitioning(1), + AllTuples, + true) + + checkSatisfied( + UnknownPartitioning(10), + AllTuples, + false) + + checkSatisfied( + RoundRobinPartitioning(1), + AllTuples, + true) + + checkSatisfied( + RoundRobinPartitioning(10), + AllTuples, + false) + + checkSatisfied( + SinglePartition, + AllTuples, + true) + + checkSatisfied( + HashPartitioning(Seq('a), 1), + AllTuples, true) + checkSatisfied( + HashPartitioning(Seq('a), 10), + AllTuples, + false) + + checkSatisfied( + RangePartitioning(Seq('a.asc), 1), + AllTuples, + true) + + checkSatisfied( + RangePartitioning(Seq('a.asc), 10), + AllTuples, + false) + + checkSatisfied( + BroadcastPartitioning(IdentityBroadcastMode), + AllTuples, + false) + } + + test("SinglePartition is the output partitioning") { + // SinglePartition can satisfy all the distributions except `BroadcastDistribution` checkSatisfied( SinglePartition, ClusteredDistribution(Seq('a, 'b, 'c)), true) + checkSatisfied( + SinglePartition, + HashClusteredDistribution(Seq('a, 'b, 'c)), + true) + checkSatisfied( SinglePartition, OrderedDistribution(Seq('a.asc, 'b.asc, 'c.asc)), true) - // Cases which need an exchange between two data properties. + checkSatisfied( + SinglePartition, + BroadcastDistribution(IdentityBroadcastMode), + false) + } + + test("HashPartitioning is the output partitioning") { + // HashPartitioning can satisfy ClusteredDistribution iff its hash expressions are a subset of + // the required clustering expressions. + checkSatisfied( + HashPartitioning(Seq('a, 'b, 'c), 10), + ClusteredDistribution(Seq('a, 'b, 'c)), + true) + + checkSatisfied( + HashPartitioning(Seq('b, 'c), 10), + ClusteredDistribution(Seq('a, 'b, 'c)), + true) + checkSatisfied( HashPartitioning(Seq('a, 'b, 'c), 10), ClusteredDistribution(Seq('b, 'c)), @@ -79,37 +172,43 @@ class DistributionSuite extends SparkFunSuite { ClusteredDistribution(Seq('d, 'e)), false) + // HashPartitioning can satisfy HashClusteredDistribution iff its hash expressions are exactly + // same with the required hash clustering expressions. checkSatisfied( HashPartitioning(Seq('a, 'b, 'c), 10), - AllTuples, + HashClusteredDistribution(Seq('a, 'b, 'c)), + true) + + checkSatisfied( + HashPartitioning(Seq('c, 'b, 'a), 10), + HashClusteredDistribution(Seq('a, 'b, 'c)), false) + checkSatisfied( + HashPartitioning(Seq('a, 'b), 10), + HashClusteredDistribution(Seq('a, 'b, 'c)), + false) + + // HashPartitioning cannot satisfy OrderedDistribution checkSatisfied( HashPartitioning(Seq('a, 'b, 'c), 10), OrderedDistribution(Seq('a.asc, 'b.asc, 'c.asc)), false) checkSatisfied( - HashPartitioning(Seq('b, 'c), 10), + HashPartitioning(Seq('a, 'b, 'c), 1), OrderedDistribution(Seq('a.asc, 'b.asc, 'c.asc)), - false) + false) // TODO: this can be relaxed. - // TODO: We should check functional dependencies - /* checkSatisfied( - ClusteredDistribution(Seq('b)), - ClusteredDistribution(Seq('b + 1)), - true) - */ + HashPartitioning(Seq('b, 'c), 10), + OrderedDistribution(Seq('a.asc, 'b.asc, 'c.asc)), + false) } test("RangePartitioning is the output partitioning") { - // Cases which do not need an exchange between two data properties. - checkSatisfied( - RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), - UnspecifiedDistribution, - true) - + // RangePartitioning can satisfy OrderedDistribution iff its ordering is a prefix + // of the required ordering, or the required ordering is a prefix of its ordering. checkSatisfied( RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), OrderedDistribution(Seq('a.asc, 'b.asc, 'c.asc)), @@ -125,6 +224,27 @@ class DistributionSuite extends SparkFunSuite { OrderedDistribution(Seq('a.asc, 'b.asc, 'c.asc, 'd.desc)), true) + // TODO: We can have an optimization to first sort the dataset + // by a.asc and then sort b, and c in a partition. This optimization + // should tradeoff the benefit of a less number of Exchange operators + // and the parallelism. + checkSatisfied( + RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), + OrderedDistribution(Seq('a.asc, 'b.desc, 'c.asc)), + false) + + checkSatisfied( + RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), + OrderedDistribution(Seq('b.asc, 'a.asc)), + false) + + checkSatisfied( + RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), + OrderedDistribution(Seq('a.asc, 'b.asc, 'd.desc)), + false) + + // RangePartitioning can satisfy ClusteredDistribution iff its ordering expressions are a subset + // of the required clustering expressions. checkSatisfied( RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), ClusteredDistribution(Seq('a, 'b, 'c)), @@ -140,34 +260,47 @@ class DistributionSuite extends SparkFunSuite { ClusteredDistribution(Seq('b, 'c, 'a, 'd)), true) - // Cases which need an exchange between two data properties. - // TODO: We can have an optimization to first sort the dataset - // by a.asc and then sort b, and c in a partition. This optimization - // should tradeoff the benefit of a less number of Exchange operators - // and the parallelism. checkSatisfied( RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), - OrderedDistribution(Seq('a.asc, 'b.desc, 'c.asc)), + ClusteredDistribution(Seq('a, 'b)), false) checkSatisfied( RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), - OrderedDistribution(Seq('b.asc, 'a.asc)), + ClusteredDistribution(Seq('c, 'd)), false) + // RangePartitioning cannot satisfy HashClusteredDistribution checkSatisfied( RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), - ClusteredDistribution(Seq('a, 'b)), + HashClusteredDistribution(Seq('a, 'b, 'c)), false) + } + test("Partitioning.numPartitions must match Distribution.requiredNumPartitions to satisfy it") { checkSatisfied( - RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), - ClusteredDistribution(Seq('c, 'd)), + SinglePartition, + ClusteredDistribution(Seq('a, 'b, 'c), Some(10)), + false) + + checkSatisfied( + SinglePartition, + HashClusteredDistribution(Seq('a, 'b, 'c), Some(10)), + false) + + checkSatisfied( + HashPartitioning(Seq('a, 'b, 'c), 10), + ClusteredDistribution(Seq('a, 'b, 'c), Some(5)), + false) + + checkSatisfied( + HashPartitioning(Seq('a, 'b, 'c), 10), + HashClusteredDistribution(Seq('a, 'b, 'c), Some(5)), false) checkSatisfied( RangePartitioning(Seq('a.asc, 'b.asc, 'c.asc), 10), - AllTuples, + ClusteredDistribution(Seq('a, 'b, 'c), Some(5)), false) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala deleted file mode 100644 index 5b802ccc637dd..0000000000000 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst - -import org.apache.spark.SparkFunSuite -import org.apache.spark.sql.catalyst.expressions.{InterpretedMutableProjection, Literal} -import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, HashPartitioning} - -class PartitioningSuite extends SparkFunSuite { - test("HashPartitioning compatibility should be sensitive to expression ordering (SPARK-9785)") { - val expressions = Seq(Literal(2), Literal(3)) - // Consider two HashPartitionings that have the same _set_ of hash expressions but which are - // created with different orderings of those expressions: - val partitioningA = HashPartitioning(expressions, 100) - val partitioningB = HashPartitioning(expressions.reverse, 100) - // These partitionings are not considered equal: - assert(partitioningA != partitioningB) - // However, they both satisfy the same clustered distribution: - val distribution = ClusteredDistribution(expressions) - assert(partitioningA.satisfies(distribution)) - assert(partitioningB.satisfies(distribution)) - // These partitionings compute different hashcodes for the same input row: - def computeHashCode(partitioning: HashPartitioning): Int = { - val hashExprProj = new InterpretedMutableProjection(partitioning.expressions, Seq.empty) - hashExprProj.apply(InternalRow.empty).hashCode() - } - assert(computeHashCode(partitioningA) != computeHashCode(partitioningB)) - // Thus, these partitionings are incompatible: - assert(!partitioningA.compatibleWith(partitioningB)) - assert(!partitioningB.compatibleWith(partitioningA)) - assert(!partitioningA.guarantees(partitioningB)) - assert(!partitioningB.guarantees(partitioningA)) - - // Just to be sure that we haven't cheated by having these methods always return false, - // check that identical partitionings are still compatible with and guarantee each other: - assert(partitioningA === partitioningA) - assert(partitioningA.guarantees(partitioningA)) - assert(partitioningA.compatibleWith(partitioningA)) - } -} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala index 23e866cdf4917..353b8344658f2 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala @@ -21,7 +21,7 @@ import java.sql.{Date, Timestamp} import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute -import org.apache.spark.sql.catalyst.expressions.{BoundReference, Literal, SpecificInternalRow, UpCast} +import org.apache.spark.sql.catalyst.expressions.{BoundReference, Expression, Literal, SpecificInternalRow, UpCast} import org.apache.spark.sql.catalyst.expressions.objects.{AssertNotNull, NewInstance} import org.apache.spark.sql.types._ import org.apache.spark.unsafe.types.UTF8String @@ -356,4 +356,23 @@ class ScalaReflectionSuite extends SparkFunSuite { assert(deserializerFor[Int].isInstanceOf[AssertNotNull]) assert(!deserializerFor[String].isInstanceOf[AssertNotNull]) } + + test("SPARK-23025: schemaFor should support Null type") { + val schema = schemaFor[(Int, Null)] + assert(schema === Schema( + StructType(Seq( + StructField("_1", IntegerType, nullable = false), + StructField("_2", NullType, nullable = true))), + nullable = true)) + } + + test("SPARK-23835: add null check to non-nullable types in Tuples") { + def numberOfCheckedArguments(deserializer: Expression): Int = { + assert(deserializer.isInstanceOf[NewInstance]) + deserializer.asInstanceOf[NewInstance].arguments.count(_.isInstanceOf[AssertNotNull]) + } + assert(numberOfCheckedArguments(deserializerFor[(Double, Double)]) == 2) + assert(numberOfCheckedArguments(deserializerFor[(java.lang.Double, Int)]) == 1) + assert(numberOfCheckedArguments(deserializerFor[(java.lang.Integer, java.lang.Integer)]) == 0) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index f4514205d3ae0..cd8579584eada 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -408,8 +408,8 @@ class AnalysisSuite extends AnalysisTest with Matchers { assertExpressionType(sum(Divide(1.0, 2.0)), DoubleType) assertExpressionType(sum(Divide(1, 2.0f)), DoubleType) assertExpressionType(sum(Divide(1.0f, 2)), DoubleType) - assertExpressionType(sum(Divide(1, Decimal(2))), DecimalType(31, 11)) - assertExpressionType(sum(Divide(Decimal(1), 2)), DecimalType(31, 11)) + assertExpressionType(sum(Divide(1, Decimal(2))), DecimalType(22, 11)) + assertExpressionType(sum(Divide(Decimal(1), 2)), DecimalType(26, 6)) assertExpressionType(sum(Divide(Decimal(1), 2.0)), DoubleType) assertExpressionType(sum(Divide(1.0, Decimal(2.0))), DoubleType) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala index 549a4355dfba3..3d7c91870133b 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala @@ -54,8 +54,7 @@ trait AnalysisTest extends PlanTest { expectedPlan: LogicalPlan, caseSensitive: Boolean = true): Unit = { val analyzer = getAnalyzer(caseSensitive) - val actualPlan = analyzer.execute(inputPlan) - analyzer.checkAnalysis(actualPlan) + val actualPlan = analyzer.executeAndCheck(inputPlan) comparePlans(actualPlan, expectedPlan) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala index 60e46a9910a8b..bd87ca6017e99 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala @@ -136,19 +136,19 @@ class DecimalPrecisionSuite extends AnalysisTest with BeforeAndAfter { test("maximum decimals") { for (expr <- Seq(d1, d2, i, u)) { - checkType(Add(expr, u), DecimalType.SYSTEM_DEFAULT) - checkType(Subtract(expr, u), DecimalType.SYSTEM_DEFAULT) + checkType(Add(expr, u), DecimalType(38, 17)) + checkType(Subtract(expr, u), DecimalType(38, 17)) } - checkType(Multiply(d1, u), DecimalType(38, 19)) - checkType(Multiply(d2, u), DecimalType(38, 20)) - checkType(Multiply(i, u), DecimalType(38, 18)) - checkType(Multiply(u, u), DecimalType(38, 36)) + checkType(Multiply(d1, u), DecimalType(38, 16)) + checkType(Multiply(d2, u), DecimalType(38, 14)) + checkType(Multiply(i, u), DecimalType(38, 7)) + checkType(Multiply(u, u), DecimalType(38, 6)) - checkType(Divide(u, d1), DecimalType(38, 18)) - checkType(Divide(u, d2), DecimalType(38, 19)) - checkType(Divide(u, i), DecimalType(38, 23)) - checkType(Divide(u, u), DecimalType(38, 18)) + checkType(Divide(u, d1), DecimalType(38, 17)) + checkType(Divide(u, d2), DecimalType(38, 16)) + checkType(Divide(u, i), DecimalType(38, 18)) + checkType(Divide(u, u), DecimalType(38, 6)) checkType(Remainder(d1, u), DecimalType(19, 18)) checkType(Remainder(d2, u), DecimalType(21, 18)) @@ -272,6 +272,15 @@ class DecimalPrecisionSuite extends AnalysisTest with BeforeAndAfter { } } + test("SPARK-24468: operations on decimals with negative scale") { + val a = AttributeReference("a", DecimalType(3, -10))() + val b = AttributeReference("b", DecimalType(1, -1))() + val c = AttributeReference("c", DecimalType(35, 1))() + checkType(Multiply(a, b), DecimalType(5, -11)) + checkType(Multiply(a, c), DecimalType(38, -9)) + checkType(Multiply(b, c), DecimalType(37, 0)) + } + /** strength reduction for integer/decimal comparisons */ def ruleTest(initial: Expression, transformed: Expression): Unit = { val testRelation = LocalRelation(AttributeReference("a", IntegerType)()) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ResolvedUuidExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ResolvedUuidExpressionsSuite.scala new file mode 100644 index 0000000000000..fe57c199b8744 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ResolvedUuidExpressionsSuite.scala @@ -0,0 +1,73 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.dsl.plans._ +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} + +/** + * Test suite for resolving Uuid expressions. + */ +class ResolvedUuidExpressionsSuite extends AnalysisTest { + + private lazy val a = 'a.int + private lazy val r = LocalRelation(a) + private lazy val uuid1 = Uuid().as('_uuid1) + private lazy val uuid2 = Uuid().as('_uuid2) + private lazy val uuid3 = Uuid().as('_uuid3) + private lazy val uuid1Ref = uuid1.toAttribute + + private val analyzer = getAnalyzer(caseSensitive = true) + + private def getUuidExpressions(plan: LogicalPlan): Seq[Uuid] = { + plan.flatMap { + case p => + p.expressions.flatMap(_.collect { + case u: Uuid => u + }) + } + } + + test("analyzed plan sets random seed for Uuid expression") { + val plan = r.select(a, uuid1) + val resolvedPlan = analyzer.executeAndCheck(plan) + getUuidExpressions(resolvedPlan).foreach { u => + assert(u.resolved) + assert(u.randomSeed.isDefined) + } + } + + test("Uuid expressions should have different random seeds") { + val plan = r.select(a, uuid1).groupBy(uuid1Ref)(uuid2, uuid3) + val resolvedPlan = analyzer.executeAndCheck(plan) + assert(getUuidExpressions(resolvedPlan).map(_.randomSeed.get).distinct.length == 3) + } + + test("Different analyzed plans should have different random seeds in Uuids") { + val plan = r.select(a, uuid1).groupBy(uuid1Ref)(uuid2, uuid3) + val resolvedPlan1 = analyzer.executeAndCheck(plan) + val resolvedPlan2 = analyzer.executeAndCheck(plan) + val uuids1 = getUuidExpressions(resolvedPlan1) + val uuids2 = getUuidExpressions(resolvedPlan2) + assert(uuids1.distinct.length == 3) + assert(uuids2.distinct.length == 3) + assert(uuids1.intersect(uuids2).length == 0) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercionSuite.scala index 3661530cd622b..52a7ebdafd7c7 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercionSuite.scala @@ -923,6 +923,60 @@ class TypeCoercionSuite extends AnalysisTest { } } + test("type coercion for Elt") { + val rule = TypeCoercion.EltCoercion(conf) + + ruleTest(rule, + Elt(Seq(Literal(1), Literal("ab"), Literal("cde"))), + Elt(Seq(Literal(1), Literal("ab"), Literal("cde")))) + ruleTest(rule, + Elt(Seq(Literal(1.toShort), Literal("ab"), Literal("cde"))), + Elt(Seq(Cast(Literal(1.toShort), IntegerType), Literal("ab"), Literal("cde")))) + ruleTest(rule, + Elt(Seq(Literal(2), Literal(null), Literal("abc"))), + Elt(Seq(Literal(2), Cast(Literal(null), StringType), Literal("abc")))) + ruleTest(rule, + Elt(Seq(Literal(2), Literal(1), Literal("234"))), + Elt(Seq(Literal(2), Cast(Literal(1), StringType), Literal("234")))) + ruleTest(rule, + Elt(Seq(Literal(3), Literal(1L), Literal(2.toByte), Literal(0.1))), + Elt(Seq(Literal(3), Cast(Literal(1L), StringType), Cast(Literal(2.toByte), StringType), + Cast(Literal(0.1), StringType)))) + ruleTest(rule, + Elt(Seq(Literal(2), Literal(true), Literal(0.1f), Literal(3.toShort))), + Elt(Seq(Literal(2), Cast(Literal(true), StringType), Cast(Literal(0.1f), StringType), + Cast(Literal(3.toShort), StringType)))) + ruleTest(rule, + Elt(Seq(Literal(1), Literal(1L), Literal(0.1))), + Elt(Seq(Literal(1), Cast(Literal(1L), StringType), Cast(Literal(0.1), StringType)))) + ruleTest(rule, + Elt(Seq(Literal(1), Literal(Decimal(10)))), + Elt(Seq(Literal(1), Cast(Literal(Decimal(10)), StringType)))) + ruleTest(rule, + Elt(Seq(Literal(1), Literal(BigDecimal.valueOf(10)))), + Elt(Seq(Literal(1), Cast(Literal(BigDecimal.valueOf(10)), StringType)))) + ruleTest(rule, + Elt(Seq(Literal(1), Literal(java.math.BigDecimal.valueOf(10)))), + Elt(Seq(Literal(1), Cast(Literal(java.math.BigDecimal.valueOf(10)), StringType)))) + ruleTest(rule, + Elt(Seq(Literal(2), Literal(new java.sql.Date(0)), Literal(new Timestamp(0)))), + Elt(Seq(Literal(2), Cast(Literal(new java.sql.Date(0)), StringType), + Cast(Literal(new Timestamp(0)), StringType)))) + + withSQLConf("spark.sql.function.eltOutputAsString" -> "true") { + ruleTest(rule, + Elt(Seq(Literal(1), Literal("123".getBytes), Literal("456".getBytes))), + Elt(Seq(Literal(1), Cast(Literal("123".getBytes), StringType), + Cast(Literal("456".getBytes), StringType)))) + } + + withSQLConf("spark.sql.function.eltOutputAsString" -> "false") { + ruleTest(rule, + Elt(Seq(Literal(1), Literal("123".getBytes), Literal("456".getBytes))), + Elt(Seq(Literal(1), Literal("123".getBytes), Literal("456".getBytes)))) + } + } + test("BooleanEquality type cast") { val be = TypeCoercion.BooleanEquality // Use something more than a literal to avoid triggering the simplification rules. diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalogSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalogSuite.scala index 95c87ffa20cb7..6abab0073cca3 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalogSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalogSuite.scala @@ -279,7 +279,7 @@ abstract class SessionCatalogSuite extends AnalysisTest { } } - test("create temp table") { + test("create temp view") { withBasicCatalog { catalog => val tempTable1 = Range(1, 10, 1, 10) val tempTable2 = Range(1, 20, 2, 10) @@ -288,11 +288,11 @@ abstract class SessionCatalogSuite extends AnalysisTest { assert(catalog.getTempView("tbl1") == Option(tempTable1)) assert(catalog.getTempView("tbl2") == Option(tempTable2)) assert(catalog.getTempView("tbl3").isEmpty) - // Temporary table already exists + // Temporary view already exists intercept[TempTableAlreadyExistsException] { catalog.createTempView("tbl1", tempTable1, overrideIfExists = false) } - // Temporary table already exists but we override it + // Temporary view already exists but we override it catalog.createTempView("tbl1", tempTable2, overrideIfExists = true) assert(catalog.getTempView("tbl1") == Option(tempTable2)) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala index 1dd040e4696a1..5b25bdf907c3a 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala @@ -853,4 +853,73 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper { cast("2", LongType).genCode(ctx) assert(ctx.inlinedMutableStates.length == 0) } + + test("SPARK-22825 Cast array to string") { + val ret1 = cast(Literal.create(Array(1, 2, 3, 4, 5)), StringType) + checkEvaluation(ret1, "[1, 2, 3, 4, 5]") + val ret2 = cast(Literal.create(Array("ab", "cde", "f")), StringType) + checkEvaluation(ret2, "[ab, cde, f]") + val ret3 = cast(Literal.create(Array("ab", null, "c")), StringType) + checkEvaluation(ret3, "[ab,, c]") + val ret4 = cast(Literal.create(Array("ab".getBytes, "cde".getBytes, "f".getBytes)), StringType) + checkEvaluation(ret4, "[ab, cde, f]") + val ret5 = cast( + Literal.create(Array("2014-12-03", "2014-12-04", "2014-12-06").map(Date.valueOf)), + StringType) + checkEvaluation(ret5, "[2014-12-03, 2014-12-04, 2014-12-06]") + val ret6 = cast( + Literal.create(Array("2014-12-03 13:01:00", "2014-12-04 15:05:00").map(Timestamp.valueOf)), + StringType) + checkEvaluation(ret6, "[2014-12-03 13:01:00, 2014-12-04 15:05:00]") + val ret7 = cast(Literal.create(Array(Array(1, 2, 3), Array(4, 5))), StringType) + checkEvaluation(ret7, "[[1, 2, 3], [4, 5]]") + val ret8 = cast( + Literal.create(Array(Array(Array("a"), Array("b", "c")), Array(Array("d")))), + StringType) + checkEvaluation(ret8, "[[[a], [b, c]], [[d]]]") + } + + test("SPARK-22973 Cast map to string") { + val ret1 = cast(Literal.create(Map(1 -> "a", 2 -> "b", 3 -> "c")), StringType) + checkEvaluation(ret1, "[1 -> a, 2 -> b, 3 -> c]") + val ret2 = cast( + Literal.create(Map("1" -> "a".getBytes, "2" -> null, "3" -> "c".getBytes)), + StringType) + checkEvaluation(ret2, "[1 -> a, 2 ->, 3 -> c]") + val ret3 = cast( + Literal.create(Map( + 1 -> Date.valueOf("2014-12-03"), + 2 -> Date.valueOf("2014-12-04"), + 3 -> Date.valueOf("2014-12-05"))), + StringType) + checkEvaluation(ret3, "[1 -> 2014-12-03, 2 -> 2014-12-04, 3 -> 2014-12-05]") + val ret4 = cast( + Literal.create(Map( + 1 -> Timestamp.valueOf("2014-12-03 13:01:00"), + 2 -> Timestamp.valueOf("2014-12-04 15:05:00"))), + StringType) + checkEvaluation(ret4, "[1 -> 2014-12-03 13:01:00, 2 -> 2014-12-04 15:05:00]") + val ret5 = cast( + Literal.create(Map( + 1 -> Array(1, 2, 3), + 2 -> Array(4, 5, 6))), + StringType) + checkEvaluation(ret5, "[1 -> [1, 2, 3], 2 -> [4, 5, 6]]") + } + + test("SPARK-22981 Cast struct to string") { + val ret1 = cast(Literal.create((1, "a", 0.1)), StringType) + checkEvaluation(ret1, "[1, a, 0.1]") + val ret2 = cast(Literal.create(Tuple3[Int, String, String](1, null, "a")), StringType) + checkEvaluation(ret2, "[1,, a]") + val ret3 = cast(Literal.create( + (Date.valueOf("2014-12-03"), Timestamp.valueOf("2014-12-03 15:05:00"))), StringType) + checkEvaluation(ret3, "[2014-12-03, 2014-12-03 15:05:00]") + val ret4 = cast(Literal.create(((1, "a"), 5, 0.1)), StringType) + checkEvaluation(ret4, "[[1, a], 5, 0.1]") + val ret5 = cast(Literal.create((Seq(1, 2, 3), "a", 0.1)), StringType) + checkEvaluation(ret5, "[[1, 2, 3], a, 0.1]") + val ret6 = cast(Literal.create((1, Map(1 -> "a", 2 -> "b", 3 -> "c"))), StringType) + checkEvaluation(ret6, "[1, [1 -> a, 2 -> b, 3 -> c]]") + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala index 676ba3956ddc8..d0d6318b8dd0f 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala @@ -436,4 +436,64 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper { ctx.addImmutableStateIfNotExists("String", mutableState2) assert(ctx.inlinedMutableStates.length == 2) } + + test("SPARK-23628: calculateParamLength should compute properly the param length") { + val ctx = new CodegenContext + assert(ctx.calculateParamLength(Seq.range(0, 100).map(Literal(_))) == 101) + assert(ctx.calculateParamLength(Seq.range(0, 100).map(x => Literal(x.toLong))) == 201) + } + + test("SPARK-23760: CodegenContext.withSubExprEliminationExprs should save/restore correctly") { + + val ref = BoundReference(0, IntegerType, true) + val add1 = Add(ref, ref) + val add2 = Add(add1, add1) + + // raw testing of basic functionality + { + val ctx = new CodegenContext + val e = ref.genCode(ctx) + // before + ctx.subExprEliminationExprs += ref -> SubExprEliminationState(e.isNull, e.value) + assert(ctx.subExprEliminationExprs.contains(ref)) + // call withSubExprEliminationExprs + ctx.withSubExprEliminationExprs(Map(add1 -> SubExprEliminationState("dummy", "dummy"))) { + assert(ctx.subExprEliminationExprs.contains(add1)) + assert(!ctx.subExprEliminationExprs.contains(ref)) + Seq.empty + } + // after + assert(ctx.subExprEliminationExprs.nonEmpty) + assert(ctx.subExprEliminationExprs.contains(ref)) + assert(!ctx.subExprEliminationExprs.contains(add1)) + } + + // emulate an actual codegen workload + { + val ctx = new CodegenContext + // before + ctx.generateExpressions(Seq(add2, add1), doSubexpressionElimination = true) // trigger CSE + assert(ctx.subExprEliminationExprs.contains(add1)) + // call withSubExprEliminationExprs + ctx.withSubExprEliminationExprs(Map(ref -> SubExprEliminationState("dummy", "dummy"))) { + assert(ctx.subExprEliminationExprs.contains(ref)) + assert(!ctx.subExprEliminationExprs.contains(add1)) + Seq.empty + } + // after + assert(ctx.subExprEliminationExprs.nonEmpty) + assert(ctx.subExprEliminationExprs.contains(add1)) + assert(!ctx.subExprEliminationExprs.contains(ref)) + } + } + + test("SPARK-23986: freshName can generate duplicated names") { + val ctx = new CodegenContext + val names1 = ctx.freshName("myName1") :: ctx.freshName("myName1") :: + ctx.freshName("myName11") :: Nil + assert(names1.distinct.length == 3) + val names2 = ctx.freshName("a") :: ctx.freshName("a") :: + ctx.freshName("a_1") :: ctx.freshName("a_0") :: Nil + assert(names2.distinct.length == 4) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionExpressionsSuite.scala index 020687e4b3a27..83e96ee331ed1 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionExpressionsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionExpressionsSuite.scala @@ -104,5 +104,32 @@ class CollectionExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper checkEvaluation(ArrayContains(a3, Literal("")), null) checkEvaluation(ArrayContains(a3, Literal.create(null, StringType)), null) + + // binary + val b0 = Literal.create(Seq[Array[Byte]](Array[Byte](5, 6), Array[Byte](1, 2)), + ArrayType(BinaryType)) + val b1 = Literal.create(Seq[Array[Byte]](Array[Byte](2, 1), Array[Byte](4, 3)), + ArrayType(BinaryType)) + val b2 = Literal.create(Seq[Array[Byte]](Array[Byte](2, 1), null), + ArrayType(BinaryType)) + val b3 = Literal.create(Seq[Array[Byte]](null, Array[Byte](1, 2)), + ArrayType(BinaryType)) + val be = Literal.create(Array[Byte](1, 2), BinaryType) + val nullBinary = Literal.create(null, BinaryType) + + checkEvaluation(ArrayContains(b0, be), true) + checkEvaluation(ArrayContains(b1, be), false) + checkEvaluation(ArrayContains(b0, nullBinary), null) + checkEvaluation(ArrayContains(b2, be), null) + checkEvaluation(ArrayContains(b3, be), true) + + // complex data types + val aa0 = Literal.create(Seq[Seq[Int]](Seq[Int](1, 2), Seq[Int](3, 4)), + ArrayType(ArrayType(IntegerType))) + val aa1 = Literal.create(Seq[Seq[Int]](Seq[Int](5, 6), Seq[Int](2, 1)), + ArrayType(ArrayType(IntegerType))) + val aae = Literal.create(Seq[Int](1, 2), ArrayType(IntegerType)) + checkEvaluation(ArrayContains(aa0, aae), true) + checkEvaluation(ArrayContains(aa1, aae), false) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala index 63f6ceeb21b96..786266a2c13c0 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala @@ -22,6 +22,7 @@ import java.text.SimpleDateFormat import java.util.{Calendar, Locale, TimeZone} import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection import org.apache.spark.sql.catalyst.util.DateTimeTestUtils._ import org.apache.spark.sql.catalyst.util.DateTimeUtils import org.apache.spark.sql.catalyst.util.DateTimeUtils.TimeZoneGMT @@ -791,6 +792,9 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { test(null, "UTC", null) test("2015-07-24 00:00:00", null, null) test(null, null, null) + // Test escaping of timezone + GenerateUnsafeProjection.generate( + ToUTCTimestamp(Literal(Timestamp.valueOf("2015-07-24 00:00:00")), Literal("\"quote")) :: Nil) } test("from_utc_timestamp") { @@ -811,5 +815,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { test(null, "UTC", null) test("2015-07-24 00:00:00", null, null) test(null, null, null) + // Test escaping of timezone + GenerateUnsafeProjection.generate(FromUTCTimestamp(Literal(0), Literal("\"quote")) :: Nil) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala index b4c8eab19c5cc..fad26f0055d6c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala @@ -162,6 +162,7 @@ trait ExpressionEvalHelper extends GeneratorDrivenPropertyChecks { Alias(expression, s"Optimized($expression)1")() :: Alias(expression, s"Optimized($expression)2")() :: Nil), expression) + plan.initialize(0) val unsafeRow = plan(inputRow) val input = if (inputRow == EmptyRow) "" else s", input: $inputRow" diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala index a0bbe02f92354..00e97637eee7e 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala @@ -22,11 +22,13 @@ import java.util.Calendar import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.plans.PlanTestBase import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, DateTimeTestUtils, DateTimeUtils, GenericArrayData, PermissiveMode} +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.types._ import org.apache.spark.unsafe.types.UTF8String -class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { +class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper with PlanTestBase { val json = """ |{"store":{"fruit":[{"weight":8,"type":"apple"},{"weight":9,"type":"pear"}], @@ -390,7 +392,7 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { val jsonData = """{"a": 1}""" val schema = StructType(StructField("a", IntegerType) :: Nil) checkEvaluation( - JsonToStructs(schema, Map.empty, Literal(jsonData), gmtId), + JsonToStructs(schema, Map.empty, Literal(jsonData), gmtId, true), InternalRow(1) ) } @@ -399,13 +401,13 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { val jsonData = """{"a" 1}""" val schema = StructType(StructField("a", IntegerType) :: Nil) checkEvaluation( - JsonToStructs(schema, Map.empty, Literal(jsonData), gmtId), + JsonToStructs(schema, Map.empty, Literal(jsonData), gmtId, true), null ) // Other modes should still return `null`. checkEvaluation( - JsonToStructs(schema, Map("mode" -> PermissiveMode.name), Literal(jsonData), gmtId), + JsonToStructs(schema, Map("mode" -> PermissiveMode.name), Literal(jsonData), gmtId, true), null ) } @@ -414,62 +416,62 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { val input = """[{"a": 1}, {"a": 2}]""" val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil)) val output = InternalRow(1) :: InternalRow(2) :: Nil - checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId), output) + checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId, true), output) } test("from_json - input=object, schema=array, output=array of single row") { val input = """{"a": 1}""" val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil)) val output = InternalRow(1) :: Nil - checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId), output) + checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId, true), output) } test("from_json - input=empty array, schema=array, output=empty array") { val input = "[ ]" val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil)) val output = Nil - checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId), output) + checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId, true), output) } test("from_json - input=empty object, schema=array, output=array of single row with null") { val input = "{ }" val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil)) val output = InternalRow(null) :: Nil - checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId), output) + checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId, true), output) } test("from_json - input=array of single object, schema=struct, output=single row") { val input = """[{"a": 1}]""" val schema = StructType(StructField("a", IntegerType) :: Nil) val output = InternalRow(1) - checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId), output) + checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId, true), output) } test("from_json - input=array, schema=struct, output=null") { val input = """[{"a": 1}, {"a": 2}]""" val schema = StructType(StructField("a", IntegerType) :: Nil) val output = null - checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId), output) + checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId, true), output) } test("from_json - input=empty array, schema=struct, output=null") { val input = """[]""" val schema = StructType(StructField("a", IntegerType) :: Nil) val output = null - checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId), output) + checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId, true), output) } test("from_json - input=empty object, schema=struct, output=single row with null") { val input = """{ }""" val schema = StructType(StructField("a", IntegerType) :: Nil) val output = InternalRow(null) - checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId), output) + checkEvaluation(JsonToStructs(schema, Map.empty, Literal(input), gmtId, true), output) } test("from_json null input column") { val schema = StructType(StructField("a", IntegerType) :: Nil) checkEvaluation( - JsonToStructs(schema, Map.empty, Literal.create(null, StringType), gmtId), + JsonToStructs(schema, Map.empty, Literal.create(null, StringType), gmtId, true), null ) } @@ -477,7 +479,7 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { test("SPARK-20549: from_json bad UTF-8") { val schema = StructType(StructField("a", IntegerType) :: Nil) checkEvaluation( - JsonToStructs(schema, Map.empty, Literal(badJson), gmtId), + JsonToStructs(schema, Map.empty, Literal(badJson), gmtId, true), null) } @@ -489,14 +491,14 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { c.set(2016, 0, 1, 0, 0, 0) c.set(Calendar.MILLISECOND, 123) checkEvaluation( - JsonToStructs(schema, Map.empty, Literal(jsonData1), gmtId), + JsonToStructs(schema, Map.empty, Literal(jsonData1), gmtId, true), InternalRow(c.getTimeInMillis * 1000L) ) // The result doesn't change because the json string includes timezone string ("Z" here), // which means the string represents the timestamp string in the timezone regardless of // the timeZoneId parameter. checkEvaluation( - JsonToStructs(schema, Map.empty, Literal(jsonData1), Option("PST")), + JsonToStructs(schema, Map.empty, Literal(jsonData1), Option("PST"), true), InternalRow(c.getTimeInMillis * 1000L) ) @@ -510,7 +512,8 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { schema, Map("timestampFormat" -> "yyyy-MM-dd'T'HH:mm:ss"), Literal(jsonData2), - Option(tz.getID)), + Option(tz.getID), + true), InternalRow(c.getTimeInMillis * 1000L) ) checkEvaluation( @@ -519,7 +522,8 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { Map("timestampFormat" -> "yyyy-MM-dd'T'HH:mm:ss", DateTimeUtils.TIMEZONE_OPTION -> tz.getID), Literal(jsonData2), - gmtId), + gmtId, + true), InternalRow(c.getTimeInMillis * 1000L) ) } @@ -528,7 +532,7 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { test("SPARK-19543: from_json empty input column") { val schema = StructType(StructField("a", IntegerType) :: Nil) checkEvaluation( - JsonToStructs(schema, Map.empty, Literal.create(" ", StringType), gmtId), + JsonToStructs(schema, Map.empty, Literal.create(" ", StringType), gmtId, true), null ) } @@ -680,4 +684,26 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { ) } } + + test("from_json missing fields") { + for (forceJsonNullableSchema <- Seq(false, true)) { + val input = + """{ + | "a": 1, + | "c": "foo" + |} + |""".stripMargin + val jsonSchema = new StructType() + .add("a", LongType, nullable = false) + .add("b", StringType, nullable = false) + .add("c", StringType, nullable = false) + val output = InternalRow(1L, null, UTF8String.fromString("foo")) + val expr = JsonToStructs( + jsonSchema, Map.empty, Literal.create(input, StringType), gmtId, forceJsonNullableSchema) + checkEvaluation(expr, output) + val schema = expr.dataType + val schemaToCompare = if (forceJsonNullableSchema) jsonSchema.asNullable else jsonSchema + assert(schemaToCompare == schema) + } + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscExpressionsSuite.scala index facc863081303..593bcd02078ce 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscExpressionsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscExpressionsSuite.scala @@ -17,8 +17,11 @@ package org.apache.spark.sql.catalyst.expressions +import scala.util.Random + import org.apache.spark.SparkFunSuite import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String class MiscExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { @@ -40,7 +43,23 @@ class MiscExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { } test("uuid") { - checkEvaluation(Length(Uuid()), 36) - assert(evaluate(Uuid()) !== evaluate(Uuid())) + def assertIncorrectEval(f: () => Unit): Unit = { + intercept[Exception] { + f() + }.getMessage().contains("Incorrect evaluation") + } + + checkEvaluation(Length(Uuid(Some(0))), 36) + val r = new Random() + val seed1 = Some(r.nextLong()) + val uuid1 = evaluate(Uuid(seed1)).asInstanceOf[UTF8String] + checkEvaluation(Uuid(seed1), uuid1.toString) + + val seed2 = Some(r.nextLong()) + val uuid2 = evaluate(Uuid(seed2)).asInstanceOf[UTF8String] + assertIncorrectEval(() => checkEvaluationWithoutCodegen(Uuid(seed1), uuid2)) + assertIncorrectEval(() => checkEvaluationWithGeneratedMutableProjection(Uuid(seed1), uuid2)) + assertIncorrectEval(() => checkEvalutionWithUnsafeProjection(Uuid(seed1), uuid2)) + assertIncorrectEval(() => checkEvaluationWithOptimization(Uuid(seed1), uuid2)) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala index 8a8f8e10225fa..1bfd180ae4393 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala @@ -442,4 +442,11 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper { InSet(Literal(1), Set(1, 2, 3, 4)).genCode(ctx) assert(ctx.inlinedMutableStates.isEmpty) } + + test("SPARK-24007: EqualNullSafe for FloatType and DoubleType might generate a wrong result") { + checkEvaluation(EqualNullSafe(Literal(null, FloatType), Literal(-1.0f)), false) + checkEvaluation(EqualNullSafe(Literal(-1.0f), Literal(null, FloatType)), false) + checkEvaluation(EqualNullSafe(Literal(null, DoubleType), Literal(-1.0d)), false) + checkEvaluation(EqualNullSafe(Literal(-1.0d), Literal(null, DoubleType)), false) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala index 54cde77176e27..97ddbeba2c5ca 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala @@ -51,6 +51,18 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper { checkEvaluation(Concat(strs.map(Literal.create(_, StringType))), strs.mkString, EmptyRow) } + test("SPARK-22771 Check Concat.checkInputDataTypes results") { + assert(Concat(Seq.empty[Expression]).checkInputDataTypes().isSuccess) + assert(Concat(Literal.create("a") :: Literal.create("b") :: Nil) + .checkInputDataTypes().isSuccess) + assert(Concat(Literal.create("a".getBytes) :: Literal.create("b".getBytes) :: Nil) + .checkInputDataTypes().isSuccess) + assert(Concat(Literal.create(1) :: Literal.create(2) :: Nil) + .checkInputDataTypes().isFailure) + assert(Concat(Literal.create("a") :: Literal.create("b".getBytes) :: Nil) + .checkInputDataTypes().isFailure) + } + test("concat_ws") { def testConcatWs(expected: String, sep: String, inputs: Any*): Unit = { val inputExprs = inputs.map { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerSuite.scala index f203f25ad10d4..75c6beeb32150 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerSuite.scala @@ -22,8 +22,10 @@ import scala.util.Random import org.apache.spark.SparkFunSuite import org.apache.spark.sql.RandomDataGenerator import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow} -import org.apache.spark.sql.catalyst.expressions.UnsafeProjection +import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder +import org.apache.spark.sql.catalyst.expressions.{JoinedRow, UnsafeProjection, UnsafeRow} import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String /** * Test suite for [[GenerateUnsafeRowJoiner]]. @@ -45,6 +47,32 @@ class GenerateUnsafeRowJoinerSuite extends SparkFunSuite { testConcat(64, 64, fixed) } + test("rows with all empty strings") { + val schema = StructType(Seq( + StructField("f1", StringType), StructField("f2", StringType))) + val row: UnsafeRow = UnsafeProjection.create(schema).apply( + InternalRow(UTF8String.EMPTY_UTF8, UTF8String.EMPTY_UTF8)) + testConcat(schema, row, schema, row) + } + + test("rows with all empty int arrays") { + val schema = StructType(Seq( + StructField("f1", ArrayType(IntegerType)), StructField("f2", ArrayType(IntegerType)))) + val emptyIntArray = + ExpressionEncoder[Array[Int]]().resolveAndBind().toRow(Array.emptyIntArray).getArray(0) + val row: UnsafeRow = UnsafeProjection.create(schema).apply( + InternalRow(emptyIntArray, emptyIntArray)) + testConcat(schema, row, schema, row) + } + + test("alternating empty and non-empty strings") { + val schema = StructType(Seq( + StructField("f1", StringType), StructField("f2", StringType))) + val row: UnsafeRow = UnsafeProjection.create(schema).apply( + InternalRow(UTF8String.EMPTY_UTF8, UTF8String.fromString("foo"))) + testConcat(schema, row, schema, row) + } + test("randomized fix width types") { for (i <- 0 until 20) { testConcatOnce(Random.nextInt(100), Random.nextInt(100), fixed) @@ -94,27 +122,84 @@ class GenerateUnsafeRowJoinerSuite extends SparkFunSuite { val extRow2 = RandomDataGenerator.forType(schema2, nullable = false).get.apply() val row1 = converter1.apply(internalConverter1.apply(extRow1).asInstanceOf[InternalRow]) val row2 = converter2.apply(internalConverter2.apply(extRow2).asInstanceOf[InternalRow]) + testConcat(schema1, row1, schema2, row2) + } + + private def testConcat( + schema1: StructType, + row1: UnsafeRow, + schema2: StructType, + row2: UnsafeRow) { // Run the joiner. val mergedSchema = StructType(schema1 ++ schema2) val concater = GenerateUnsafeRowJoiner.create(schema1, schema2) - val output = concater.join(row1, row2) + val output: UnsafeRow = concater.join(row1, row2) + + // We'll also compare to an UnsafeRow produced with JoinedRow + UnsafeProjection. This ensures + // that unused space in the row (e.g. leftover bits in the null-tracking bitmap) is written + // correctly. + val expectedOutput: UnsafeRow = { + val joinedRowProjection = UnsafeProjection.create(mergedSchema) + val joined = new JoinedRow() + joinedRowProjection.apply(joined.apply(row1, row2)) + } // Test everything equals ... for (i <- mergedSchema.indices) { + val dataType = mergedSchema(i).dataType if (i < schema1.size) { assert(output.isNullAt(i) === row1.isNullAt(i)) if (!output.isNullAt(i)) { - assert(output.get(i, mergedSchema(i).dataType) === row1.get(i, mergedSchema(i).dataType)) + assert(output.get(i, dataType) === row1.get(i, dataType)) + assert(output.get(i, dataType) === expectedOutput.get(i, dataType)) } } else { assert(output.isNullAt(i) === row2.isNullAt(i - schema1.size)) if (!output.isNullAt(i)) { - assert(output.get(i, mergedSchema(i).dataType) === - row2.get(i - schema1.size, mergedSchema(i).dataType)) + assert(output.get(i, dataType) === row2.get(i - schema1.size, dataType)) + assert(output.get(i, dataType) === expectedOutput.get(i, dataType)) } } } + + + assert( + expectedOutput.getSizeInBytes == output.getSizeInBytes, + "output isn't same size in bytes as slow path") + + // Compare the UnsafeRows byte-by-byte so that we can print more useful debug information in + // case this assertion fails: + val actualBytes = output.getBaseObject.asInstanceOf[Array[Byte]] + .take(output.getSizeInBytes) + val expectedBytes = expectedOutput.getBaseObject.asInstanceOf[Array[Byte]] + .take(expectedOutput.getSizeInBytes) + + val bitsetWidth = UnsafeRow.calculateBitSetWidthInBytes(expectedOutput.numFields()) + val actualBitset = actualBytes.take(bitsetWidth) + val expectedBitset = expectedBytes.take(bitsetWidth) + assert(actualBitset === expectedBitset, "bitsets were not equal") + + val fixedLengthSize = expectedOutput.numFields() * 8 + val actualFixedLength = actualBytes.slice(bitsetWidth, bitsetWidth + fixedLengthSize) + val expectedFixedLength = expectedBytes.slice(bitsetWidth, bitsetWidth + fixedLengthSize) + if (actualFixedLength !== expectedFixedLength) { + actualFixedLength.grouped(8) + .zip(expectedFixedLength.grouped(8)) + .zip(mergedSchema.fields.toIterator) + .foreach { + case ((actual, expected), field) => + assert(actual === expected, s"Fixed length sections are not equal for field $field") + } + fail("Fixed length sections were not equal") + } + + val variableLengthStart = bitsetWidth + fixedLengthSize + val actualVariableLength = actualBytes.drop(variableLengthStart) + val expectedVariableLength = expectedBytes.drop(variableLengthStart) + assert(actualVariableLength === expectedVariableLength, "fixed length sections were not equal") + + assert(output.hashCode() == expectedOutput.hashCode(), "hash codes were not equal") } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/xml/UDFXPathUtilSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/xml/UDFXPathUtilSuite.scala index c4cde7091154b..0fec15bc42c17 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/xml/UDFXPathUtilSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/xml/UDFXPathUtilSuite.scala @@ -77,6 +77,27 @@ class UDFXPathUtilSuite extends SparkFunSuite { assert(ret == "foo") } + test("embedFailure") { + import org.apache.commons.io.FileUtils + import java.io.File + val secretValue = String.valueOf(Math.random) + val tempFile = File.createTempFile("verifyembed", ".tmp") + tempFile.deleteOnExit() + val fname = tempFile.getAbsolutePath + + FileUtils.writeStringToFile(tempFile, secretValue) + + val xml = + s""" + | + |]> + |&embed; + """.stripMargin + val evaled = new UDFXPathUtil().evalString(xml, "/foo") + assert(evaled.isEmpty) + } + test("number eval") { var ret = util.evalNumber("truefalseb3c1-77", "a/c[2]") diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/xml/XPathExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/xml/XPathExpressionSuite.scala index bfa18a0919e45..c6f6d3abb860c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/xml/XPathExpressionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/xml/XPathExpressionSuite.scala @@ -40,8 +40,9 @@ class XPathExpressionSuite extends SparkFunSuite with ExpressionEvalHelper { // Test error message for invalid XML document val e1 = intercept[RuntimeException] { testExpr("/a>", "a", null.asInstanceOf[T]) } - assert(e1.getCause.getMessage.contains("Invalid XML document") && - e1.getCause.getMessage.contains("/a>")) + assert(e1.getCause.getCause.getMessage.contains( + "XML document structures must start and end within the same entity.")) + assert(e1.getMessage.contains("/a>")) // Test error message for invalid xpath val e2 = intercept[RuntimeException] { testExpr("", "!#$", null.asInstanceOf[T]) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/AggregateOptimizeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/AggregateOptimizeSuite.scala index a3184a4266c7c..f8ddc93597070 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/AggregateOptimizeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/AggregateOptimizeSuite.scala @@ -67,10 +67,9 @@ class AggregateOptimizeSuite extends PlanTest { } test("remove repetition in grouping expression") { - val input = LocalRelation('a.int, 'b.int, 'c.int) - val query = input.groupBy('a + 1, 'b + 2, Literal(1) + 'A, Literal(2) + 'B)(sum('c)) + val query = testRelation.groupBy('a + 1, 'b + 2, Literal(1) + 'A, Literal(2) + 'B)(sum('c)) val optimized = Optimize.execute(analyzer.execute(query)) - val correctAnswer = input.groupBy('a + 1, 'b + 2)(sum('c)).analyze + val correctAnswer = testRelation.groupBy('a + 1, 'b + 2)(sum('c)).analyze comparePlans(optimized, correctAnswer) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CheckCartesianProductsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CheckCartesianProductsSuite.scala index 21220b38968e8..788fedb3c8e8e 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CheckCartesianProductsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CheckCartesianProductsSuite.scala @@ -56,7 +56,7 @@ class CheckCartesianProductsSuite extends PlanTest { val thrownException = the [AnalysisException] thrownBy { performCartesianProductCheck(joinType) } - assert(thrownException.message.contains("Detected cartesian product")) + assert(thrownException.message.contains("Detected implicit cartesian product")) } } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index 85a5e979f6021..82a10254d846d 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -809,6 +809,19 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("aggregate: don't push filters if the aggregate has no grouping expressions") { + val originalQuery = LocalRelation.apply(testRelation.output, Seq.empty) + .select('a, 'b) + .groupBy()(count(1)) + .where(false) + + val optimized = Optimize.execute(originalQuery.analyze) + + val correctAnswer = originalQuery.analyze + + comparePlans(optimized, correctAnswer) + } + test("broadcast hint") { val originalQuery = ResolvedHint(testRelation) .where('a === 2L && 'b + Rand(10).as("rnd") === 3) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FoldablePropagationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FoldablePropagationSuite.scala index dccb32f0379a8..c28844642aed0 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FoldablePropagationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FoldablePropagationSuite.scala @@ -147,8 +147,8 @@ class FoldablePropagationSuite extends PlanTest { test("Propagate in expand") { val c1 = Literal(1).as('a) val c2 = Literal(2).as('b) - val a1 = c1.toAttribute.withNullability(true) - val a2 = c2.toAttribute.withNullability(true) + val a1 = c1.toAttribute.newInstance().withNullability(true) + val a2 = c2.toAttribute.newInstance().withNullability(true) val expand = Expand( Seq(Seq(Literal(null), 'b), Seq('a, Literal(null))), Seq(a1, a2), @@ -161,4 +161,23 @@ class FoldablePropagationSuite extends PlanTest { val correctAnswer = correctExpand.where(a1.isNotNull).select(a1, a2).analyze comparePlans(optimized, correctAnswer) } + + test("Propagate above outer join") { + val left = LocalRelation('a.int).select('a, Literal(1).as('b)) + val right = LocalRelation('c.int).select('c, Literal(1).as('d)) + + val join = left.join( + right, + joinType = LeftOuter, + condition = Some('a === 'c && 'b === 'd)) + val query = join.select(('b + 3).as('res)).analyze + val optimized = Optimize.execute(query) + + val correctAnswer = left.join( + right, + joinType = LeftOuter, + condition = Some('a === 'c && Literal(1) === Literal(1))) + .select((Literal(1) + 3).as('res)).analyze + comparePlans(optimized, correctAnswer) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/InferFiltersFromConstraintsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/InferFiltersFromConstraintsSuite.scala index 5580f8604ec72..178c4b8c270a0 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/InferFiltersFromConstraintsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/InferFiltersFromConstraintsSuite.scala @@ -34,6 +34,7 @@ class InferFiltersFromConstraintsSuite extends PlanTest { PushDownPredicate, InferFiltersFromConstraints, CombineFilters, + SimplifyBinaryComparison, BooleanSimplification) :: Nil } @@ -160,64 +161,6 @@ class InferFiltersFromConstraintsSuite extends PlanTest { comparePlans(optimized, correctAnswer) } - test("inner join with alias: don't generate constraints for recursive functions") { - val t1 = testRelation.subquery('t1) - val t2 = testRelation.subquery('t2) - - // We should prevent `Coalese(a, b)` from recursively creating complicated constraints through - // the constraint inference procedure. - val originalQuery = t1.select('a, 'b.as('d), Coalesce(Seq('a, 'b)).as('int_col)) - // We hide an `Alias` inside the child's child's expressions, to cover the situation reported - // in [SPARK-20700]. - .select('int_col, 'd, 'a).as("t") - .join(t2, Inner, - Some("t.a".attr === "t2.a".attr - && "t.d".attr === "t2.a".attr - && "t.int_col".attr === "t2.a".attr)) - .analyze - val correctAnswer = t1 - .where(IsNotNull('a) && IsNotNull(Coalesce(Seq('a, 'a))) && IsNotNull(Coalesce(Seq('b, 'a))) - && IsNotNull('b) && IsNotNull(Coalesce(Seq('b, 'b))) && IsNotNull(Coalesce(Seq('a, 'b))) - && 'a === 'b && 'a === Coalesce(Seq('a, 'a)) && 'a === Coalesce(Seq('a, 'b)) - && 'a === Coalesce(Seq('b, 'a)) && 'b === Coalesce(Seq('a, 'b)) - && 'b === Coalesce(Seq('b, 'a)) && 'b === Coalesce(Seq('b, 'b))) - .select('a, 'b.as('d), Coalesce(Seq('a, 'b)).as('int_col)) - .select('int_col, 'd, 'a).as("t") - .join( - t2.where(IsNotNull('a) && IsNotNull(Coalesce(Seq('a, 'a))) && - 'a === Coalesce(Seq('a, 'a))), - Inner, - Some("t.a".attr === "t2.a".attr && "t.d".attr === "t2.a".attr - && "t.int_col".attr === "t2.a".attr)) - .analyze - val optimized = Optimize.execute(originalQuery) - comparePlans(optimized, correctAnswer) - } - - test("inner join with EqualTo expressions containing part of each other: don't generate " + - "constraints for recursive functions") { - val t1 = testRelation.subquery('t1) - val t2 = testRelation.subquery('t2) - - // We should prevent `c = Coalese(a, b)` and `a = Coalese(b, c)` from recursively creating - // complicated constraints through the constraint inference procedure. - val originalQuery = t1 - .select('a, 'b, 'c, Coalesce(Seq('b, 'c)).as('d), Coalesce(Seq('a, 'b)).as('e)) - .where('a === 'd && 'c === 'e) - .join(t2, Inner, Some("t1.a".attr === "t2.a".attr && "t1.c".attr === "t2.c".attr)) - .analyze - val correctAnswer = t1 - .where(IsNotNull('a) && IsNotNull('c) && 'a === Coalesce(Seq('b, 'c)) && - 'c === Coalesce(Seq('a, 'b))) - .select('a, 'b, 'c, Coalesce(Seq('b, 'c)).as('d), Coalesce(Seq('a, 'b)).as('e)) - .join(t2.where(IsNotNull('a) && IsNotNull('c)), - Inner, - Some("t1.a".attr === "t2.a".attr && "t1.c".attr === "t2.c".attr)) - .analyze - val optimized = Optimize.execute(originalQuery) - comparePlans(optimized, correctAnswer) - } - test("generate correct filters for alias that don't produce recursive constraints") { val t1 = testRelation.subquery('t1) @@ -236,4 +179,17 @@ class InferFiltersFromConstraintsSuite extends PlanTest { comparePlans(optimized, originalQuery) } } + + test("constraints should be inferred from aliased literals") { + val originalLeft = testRelation.subquery('left).as("left") + val optimizedLeft = testRelation.subquery('left).where(IsNotNull('a) && 'a === 2).as("left") + + val right = Project(Seq(Literal(2).as("two")), testRelation.subquery('right)).as("right") + val condition = Some("left.a".attr === "right.two".attr) + + val original = originalLeft.join(right, Inner, condition) + val correct = optimizedLeft.join(right, Inner, condition) + + comparePlans(Optimize.execute(original.analyze), correct.analyze) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LimitPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LimitPushdownSuite.scala index cc98d2350c777..17fb9fc5d11e3 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LimitPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LimitPushdownSuite.scala @@ -93,7 +93,21 @@ class LimitPushdownSuite extends PlanTest { test("left outer join") { val originalQuery = x.join(y, LeftOuter).limit(1) val optimized = Optimize.execute(originalQuery.analyze) - val correctAnswer = Limit(1, LocalLimit(1, y).join(y, LeftOuter)).analyze + val correctAnswer = Limit(1, LocalLimit(1, x).join(y, LeftOuter)).analyze + comparePlans(optimized, correctAnswer) + } + + test("left outer join and left sides are limited") { + val originalQuery = x.limit(2).join(y, LeftOuter).limit(1) + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = Limit(1, LocalLimit(1, x).join(y, LeftOuter)).analyze + comparePlans(optimized, correctAnswer) + } + + test("left outer join and right sides are limited") { + val originalQuery = x.join(y.limit(2), LeftOuter).limit(1) + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = Limit(1, LocalLimit(1, x).join(Limit(2, y), LeftOuter)).analyze comparePlans(optimized, correctAnswer) } @@ -104,6 +118,20 @@ class LimitPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("right outer join and right sides are limited") { + val originalQuery = x.join(y.limit(2), RightOuter).limit(1) + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = Limit(1, x.join(LocalLimit(1, y), RightOuter)).analyze + comparePlans(optimized, correctAnswer) + } + + test("right outer join and left sides are limited") { + val originalQuery = x.limit(2).join(y, RightOuter).limit(1) + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = Limit(1, Limit(2, x).join(LocalLimit(1, y), RightOuter)).analyze + comparePlans(optimized, correctAnswer) + } + test("larger limits are not pushed on top of smaller ones in right outer join") { val originalQuery = x.join(y.limit(5), RightOuter).limit(10) val optimized = Optimize.execute(originalQuery.analyze) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelationSuite.scala index 3964508e3a55e..f1ce7543ffdc1 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelationSuite.scala @@ -25,7 +25,7 @@ import org.apache.spark.sql.catalyst.expressions.Literal import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan, Project} import org.apache.spark.sql.catalyst.rules.RuleExecutor -import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.types.{IntegerType, StructType} class PropagateEmptyRelationSuite extends PlanTest { object Optimize extends RuleExecutor[LogicalPlan] { @@ -37,7 +37,8 @@ class PropagateEmptyRelationSuite extends PlanTest { ReplaceIntersectWithSemiJoin, PushDownPredicate, PruneFilters, - PropagateEmptyRelation) :: Nil + PropagateEmptyRelation, + CollapseProject) :: Nil } object OptimizeWithoutPropagateEmptyRelation extends RuleExecutor[LogicalPlan] { @@ -48,7 +49,8 @@ class PropagateEmptyRelationSuite extends PlanTest { ReplaceExceptWithAntiJoin, ReplaceIntersectWithSemiJoin, PushDownPredicate, - PruneFilters) :: Nil + PruneFilters, + CollapseProject) :: Nil } val testRelation1 = LocalRelation.fromExternalRows(Seq('a.int), data = Seq(Row(1))) @@ -79,9 +81,11 @@ class PropagateEmptyRelationSuite extends PlanTest { (true, false, Inner, Some(LocalRelation('a.int, 'b.int))), (true, false, Cross, Some(LocalRelation('a.int, 'b.int))), - (true, false, LeftOuter, Some(Project(Seq('a, Literal(null).as('b)), testRelation1).analyze)), + (true, false, LeftOuter, + Some(Project(Seq('a, Literal(null).cast(IntegerType).as('b)), testRelation1).analyze)), (true, false, RightOuter, Some(LocalRelation('a.int, 'b.int))), - (true, false, FullOuter, Some(Project(Seq('a, Literal(null).as('b)), testRelation1).analyze)), + (true, false, FullOuter, + Some(Project(Seq('a, Literal(null).cast(IntegerType).as('b)), testRelation1).analyze)), (true, false, LeftAnti, Some(testRelation1)), (true, false, LeftSemi, Some(LocalRelation('a.int))), @@ -89,8 +93,9 @@ class PropagateEmptyRelationSuite extends PlanTest { (false, true, Cross, Some(LocalRelation('a.int, 'b.int))), (false, true, LeftOuter, Some(LocalRelation('a.int, 'b.int))), (false, true, RightOuter, - Some(Project(Seq(Literal(null).as('a), 'b), testRelation2).analyze)), - (false, true, FullOuter, Some(Project(Seq(Literal(null).as('a), 'b), testRelation2).analyze)), + Some(Project(Seq(Literal(null).cast(IntegerType).as('a), 'b), testRelation2).analyze)), + (false, true, FullOuter, + Some(Project(Seq(Literal(null).cast(IntegerType).as('a), 'b), testRelation2).analyze)), (false, true, LeftAnti, Some(LocalRelation('a.int))), (false, true, LeftSemi, Some(LocalRelation('a.int))), @@ -209,4 +214,11 @@ class PropagateEmptyRelationSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + + test("propagate empty relation keeps the plan resolved") { + val query = testRelation1.join( + LocalRelation('a.int, 'b.int), UsingJoin(FullOuter, "a" :: Nil), None) + val optimized = Optimize.execute(query.analyze) + assert(optimized.resolved) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala index 0fa1aaeb9e164..52dc2e9fb076c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.optimizer import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.dsl.plans._ -import org.apache.spark.sql.catalyst.expressions.{Alias, Not} +import org.apache.spark.sql.catalyst.expressions.{Alias, Literal, Not} import org.apache.spark.sql.catalyst.expressions.aggregate.First import org.apache.spark.sql.catalyst.plans.{LeftAnti, LeftSemi, PlanTest} import org.apache.spark.sql.catalyst.plans.logical._ @@ -168,6 +168,21 @@ class ReplaceOperatorSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("replace Except with Filter when only right filter can be applied to the left") { + val table = LocalRelation(Seq('a.int, 'b.int)) + val left = table.where('b < 1).select('a).as("left") + val right = table.where('b < 3).select('a).as("right") + + val query = Except(left, right) + val optimized = Optimize.execute(query.analyze) + + val correctAnswer = + Aggregate(left.output, right.output, + Join(left, right, LeftAnti, Option($"left.a" <=> $"right.a"))).analyze + + comparePlans(optimized, correctAnswer) + } + test("replace Distinct with Aggregate") { val input = LocalRelation('a.int, 'b.int) @@ -198,6 +213,14 @@ class ReplaceOperatorSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("add one grouping key if necessary when replace Deduplicate with Aggregate") { + val input = LocalRelation() + val query = Deduplicate(Seq.empty, input) // dropDuplicates() + val optimized = Optimize.execute(query.analyze) + val correctAnswer = Aggregate(Seq(Literal(1)), input.output, input) + comparePlans(optimized, correctAnswer) + } + test("don't replace streaming Deduplicate") { val input = LocalRelation(Seq('a.int, 'b.int), isStreaming = true) val attrA = input.output(0) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/TypedFilterOptimizationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/TypedFilterOptimizationSuite.scala index 56f096f3ecf8c..5fc99a3a57c0f 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/TypedFilterOptimizationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/TypedFilterOptimizationSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.optimizer import scala.reflect.runtime.universe.TypeTag +import org.apache.spark.api.java.function.FilterFunction import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.dsl.plans._ import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder @@ -38,18 +39,19 @@ class TypedFilterOptimizationSuite extends PlanTest { implicit private def productEncoder[T <: Product : TypeTag] = ExpressionEncoder[T]() + val testRelation = LocalRelation('_1.int, '_2.int) + test("filter after serialize with the same object type") { - val input = LocalRelation('_1.int, '_2.int) val f = (i: (Int, Int)) => i._1 > 0 - val query = input + val query = testRelation .deserialize[(Int, Int)] .serialize[(Int, Int)] .filter(f).analyze val optimized = Optimize.execute(query) - val expected = input + val expected = testRelation .deserialize[(Int, Int)] .where(callFunction(f, BooleanType, 'obj)) .serialize[(Int, Int)].analyze @@ -58,10 +60,9 @@ class TypedFilterOptimizationSuite extends PlanTest { } test("filter after serialize with different object types") { - val input = LocalRelation('_1.int, '_2.int) val f = (i: OtherTuple) => i._1 > 0 - val query = input + val query = testRelation .deserialize[(Int, Int)] .serialize[(Int, Int)] .filter(f).analyze @@ -70,17 +71,16 @@ class TypedFilterOptimizationSuite extends PlanTest { } test("filter before deserialize with the same object type") { - val input = LocalRelation('_1.int, '_2.int) val f = (i: (Int, Int)) => i._1 > 0 - val query = input + val query = testRelation .filter(f) .deserialize[(Int, Int)] .serialize[(Int, Int)].analyze val optimized = Optimize.execute(query) - val expected = input + val expected = testRelation .deserialize[(Int, Int)] .where(callFunction(f, BooleanType, 'obj)) .serialize[(Int, Int)].analyze @@ -89,10 +89,9 @@ class TypedFilterOptimizationSuite extends PlanTest { } test("filter before deserialize with different object types") { - val input = LocalRelation('_1.int, '_2.int) val f = (i: OtherTuple) => i._1 > 0 - val query = input + val query = testRelation .filter(f) .deserialize[(Int, Int)] .serialize[(Int, Int)].analyze @@ -101,21 +100,89 @@ class TypedFilterOptimizationSuite extends PlanTest { } test("back to back filter with the same object type") { - val input = LocalRelation('_1.int, '_2.int) val f1 = (i: (Int, Int)) => i._1 > 0 val f2 = (i: (Int, Int)) => i._2 > 0 - val query = input.filter(f1).filter(f2).analyze + val query = testRelation.filter(f1).filter(f2).analyze val optimized = Optimize.execute(query) assert(optimized.collect { case t: TypedFilter => t }.length == 1) } test("back to back filter with different object types") { - val input = LocalRelation('_1.int, '_2.int) val f1 = (i: (Int, Int)) => i._1 > 0 val f2 = (i: OtherTuple) => i._2 > 0 - val query = input.filter(f1).filter(f2).analyze + val query = testRelation.filter(f1).filter(f2).analyze + val optimized = Optimize.execute(query) + assert(optimized.collect { case t: TypedFilter => t }.length == 2) + } + + test("back to back FilterFunction with the same object type") { + val f1 = new FilterFunction[(Int, Int)] { + override def call(value: (Int, Int)): Boolean = value._1 > 0 + } + val f2 = new FilterFunction[(Int, Int)] { + override def call(value: (Int, Int)): Boolean = value._2 > 0 + } + + val query = testRelation.filter(f1).filter(f2).analyze + val optimized = Optimize.execute(query) + assert(optimized.collect { case t: TypedFilter => t }.length == 1) + } + + test("back to back FilterFunction with different object types") { + val f1 = new FilterFunction[(Int, Int)] { + override def call(value: (Int, Int)): Boolean = value._1 > 0 + } + val f2 = new FilterFunction[OtherTuple] { + override def call(value: OtherTuple): Boolean = value._2 > 0 + } + + val query = testRelation.filter(f1).filter(f2).analyze + val optimized = Optimize.execute(query) + assert(optimized.collect { case t: TypedFilter => t }.length == 2) + } + + test("FilterFunction and filter with the same object type") { + val f1 = new FilterFunction[(Int, Int)] { + override def call(value: (Int, Int)): Boolean = value._1 > 0 + } + val f2 = (i: (Int, Int)) => i._2 > 0 + + val query = testRelation.filter(f1).filter(f2).analyze + val optimized = Optimize.execute(query) + assert(optimized.collect { case t: TypedFilter => t }.length == 1) + } + + test("FilterFunction and filter with different object types") { + val f1 = new FilterFunction[(Int, Int)] { + override def call(value: (Int, Int)): Boolean = value._1 > 0 + } + val f2 = (i: OtherTuple) => i._2 > 0 + + val query = testRelation.filter(f1).filter(f2).analyze + val optimized = Optimize.execute(query) + assert(optimized.collect { case t: TypedFilter => t }.length == 2) + } + + test("filter and FilterFunction with the same object type") { + val f2 = (i: (Int, Int)) => i._1 > 0 + val f1 = new FilterFunction[(Int, Int)] { + override def call(value: (Int, Int)): Boolean = value._2 > 0 + } + + val query = testRelation.filter(f1).filter(f2).analyze + val optimized = Optimize.execute(query) + assert(optimized.collect { case t: TypedFilter => t }.length == 1) + } + + test("filter and FilterFunction with different object types") { + val f2 = (i: (Int, Int)) => i._1 > 0 + val f1 = new FilterFunction[OtherTuple] { + override def call(value: OtherTuple): Boolean = value._2 > 0 + } + + val query = testRelation.filter(f1).filter(f2).analyze val optimized = Optimize.execute(query) assert(optimized.collect { case t: TypedFilter => t }.length == 2) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/complexTypesSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/complexTypesSuite.scala index 0d11958876ce9..c4c21369746a3 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/complexTypesSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/complexTypesSuite.scala @@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext import org.apache.spark.sql.catalyst.plans.PlanTest import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan, Range} import org.apache.spark.sql.catalyst.rules.RuleExecutor +import org.apache.spark.sql.catalyst.util.GenericArrayData import org.apache.spark.sql.types._ /** @@ -31,7 +32,7 @@ import org.apache.spark.sql.types._ * i.e. {{{create_named_struct(square, `x` * `x`).square}}} can be simplified to {{{`x` * `x`}}}. * sam applies to create_array and create_map */ -class ComplexTypesSuite extends PlanTest{ +class ComplexTypesSuite extends PlanTest with ExpressionEvalHelper { object Optimizer extends RuleExecutor[LogicalPlan] { val batches = @@ -171,6 +172,11 @@ class ComplexTypesSuite extends PlanTest{ assert(ctx.inlinedMutableStates.length == 0) } + test("SPARK-23208: Test code splitting for create array related methods") { + val inputs = (1 to 2500).map(x => Literal(s"l_$x")) + checkEvaluation(CreateArray(inputs), new GenericArrayData(inputs.map(_.eval()))) + } + test("simplify map ops") { val rel = relation .select( @@ -325,4 +331,17 @@ class ComplexTypesSuite extends PlanTest{ .analyze comparePlans(Optimizer execute rel, expected) } + + test("SPARK-24313: support binary type as map keys in GetMapValue") { + val mb0 = Literal.create( + Map(Array[Byte](1, 2) -> "1", Array[Byte](3, 4) -> null, Array[Byte](2, 1) -> "2"), + MapType(BinaryType, StringType)) + val mb1 = Literal.create(Map[Array[Byte], String](), MapType(BinaryType, StringType)) + + checkEvaluation(GetMapValue(mb0, Literal(Array[Byte](1, 2, 3))), null) + + checkEvaluation(GetMapValue(mb1, Literal(Array[Byte](1, 2))), null) + checkEvaluation(GetMapValue(mb0, Literal(Array[Byte](2, 1), BinaryType)), "2") + checkEvaluation(GetMapValue(mb0, Literal(Array[Byte](3, 4))), null) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/parser/ExpressionParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/parser/ExpressionParserSuite.scala index 2b9783a3295c6..cb8a1fecb80a7 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/parser/ExpressionParserSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/parser/ExpressionParserSuite.scala @@ -249,8 +249,8 @@ class ExpressionParserSuite extends PlanTest { assertEqual("foo(*) over (partition by a, b)", windowed(Seq('a, 'b))) assertEqual("foo(*) over (distribute by a, b)", windowed(Seq('a, 'b))) assertEqual("foo(*) over (cluster by a, b)", windowed(Seq('a, 'b))) - assertEqual("foo(*) over (order by a desc, b asc)", windowed(Seq.empty, Seq('a.desc, 'b.asc ))) - assertEqual("foo(*) over (sort by a desc, b asc)", windowed(Seq.empty, Seq('a.desc, 'b.asc ))) + assertEqual("foo(*) over (order by a desc, b asc)", windowed(Seq.empty, Seq('a.desc, 'b.asc))) + assertEqual("foo(*) over (sort by a desc, b asc)", windowed(Seq.empty, Seq('a.desc, 'b.asc))) assertEqual("foo(*) over (partition by a, b order by c)", windowed(Seq('a, 'b), Seq('c.asc))) assertEqual("foo(*) over (distribute by a, b sort by c)", windowed(Seq('a, 'b), Seq('c.asc))) @@ -263,21 +263,62 @@ class ExpressionParserSuite extends PlanTest { "sum(product + 1) over (partition by ((product / 2) + 1) order by 2)", WindowExpression('sum.function('product + 1), WindowSpecDefinition(Seq('product / 2 + 1), Seq(Literal(2).asc), UnspecifiedFrame))) + } + + test("range/rows window function expressions") { + val func = 'foo.function(star()) + def windowed( + partitioning: Seq[Expression] = Seq.empty, + ordering: Seq[SortOrder] = Seq.empty, + frame: WindowFrame = UnspecifiedFrame): Expression = { + WindowExpression(func, WindowSpecDefinition(partitioning, ordering, frame)) + } - // Range/Row val frameTypes = Seq(("rows", RowFrame), ("range", RangeFrame)) val boundaries = Seq( - ("10 preceding", -Literal(10), CurrentRow), + // No between combinations + ("unbounded preceding", UnboundedPreceding, CurrentRow), ("2147483648 preceding", -Literal(2147483648L), CurrentRow), + ("10 preceding", -Literal(10), CurrentRow), + ("3 + 1 preceding", -Add(Literal(3), Literal(1)), CurrentRow), + ("0 preceding", -Literal(0), CurrentRow), + ("current row", CurrentRow, CurrentRow), + ("0 following", Literal(0), CurrentRow), ("3 + 1 following", Add(Literal(3), Literal(1)), CurrentRow), - ("unbounded preceding", UnboundedPreceding, CurrentRow), + ("10 following", Literal(10), CurrentRow), + ("2147483649 following", Literal(2147483649L), CurrentRow), ("unbounded following", UnboundedFollowing, CurrentRow), // Will fail during analysis + + // Between combinations + ("between unbounded preceding and 5 following", + UnboundedPreceding, Literal(5)), + ("between unbounded preceding and 3 + 1 following", + UnboundedPreceding, Add(Literal(3), Literal(1))), + ("between unbounded preceding and 2147483649 following", + UnboundedPreceding, Literal(2147483649L)), ("between unbounded preceding and current row", UnboundedPreceding, CurrentRow), - ("between unbounded preceding and unbounded following", - UnboundedPreceding, UnboundedFollowing), + ("between 2147483648 preceding and current row", -Literal(2147483648L), CurrentRow), ("between 10 preceding and current row", -Literal(10), CurrentRow), + ("between 3 + 1 preceding and current row", -Add(Literal(3), Literal(1)), CurrentRow), + ("between 0 preceding and current row", -Literal(0), CurrentRow), + ("between current row and current row", CurrentRow, CurrentRow), + ("between current row and 0 following", CurrentRow, Literal(0)), ("between current row and 5 following", CurrentRow, Literal(5)), - ("between 10 preceding and 5 following", -Literal(10), Literal(5)) + ("between current row and 3 + 1 following", CurrentRow, Add(Literal(3), Literal(1))), + ("between current row and 2147483649 following", CurrentRow, Literal(2147483649L)), + ("between current row and unbounded following", CurrentRow, UnboundedFollowing), + ("between 2147483648 preceding and unbounded following", + -Literal(2147483648L), UnboundedFollowing), + ("between 10 preceding and unbounded following", + -Literal(10), UnboundedFollowing), + ("between 3 + 1 preceding and unbounded following", + -Add(Literal(3), Literal(1)), UnboundedFollowing), + ("between 0 preceding and unbounded following", -Literal(0), UnboundedFollowing), + + // Between partial and full range + ("between 10 preceding and 5 following", -Literal(10), Literal(5)), + ("between unbounded preceding and unbounded following", + UnboundedPreceding, UnboundedFollowing) ) frameTypes.foreach { case (frameTypeSql, frameType) => diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/ConstraintPropagationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/ConstraintPropagationSuite.scala index 866ff0d33cbb2..a37e06d922642 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/ConstraintPropagationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/ConstraintPropagationSuite.scala @@ -134,6 +134,8 @@ class ConstraintPropagationSuite extends SparkFunSuite with PlanTest { verifyConstraints(aliasedRelation.analyze.constraints, ExpressionSet(Seq(resolveColumn(aliasedRelation.analyze, "x") > 10, IsNotNull(resolveColumn(aliasedRelation.analyze, "x")), + resolveColumn(aliasedRelation.analyze, "b") <=> resolveColumn(aliasedRelation.analyze, "y"), + resolveColumn(aliasedRelation.analyze, "z") <=> resolveColumn(aliasedRelation.analyze, "x"), resolveColumn(aliasedRelation.analyze, "z") > 10, IsNotNull(resolveColumn(aliasedRelation.analyze, "z"))))) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala index 82c5307d54360..6241d5cbb1d25 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala @@ -154,7 +154,7 @@ trait PlanTestBase extends PredicateHelper { self: Suite => } /** - * Sets all SQL configurations specified in `pairs`, calls `f`, and then restore all SQL + * Sets all SQL configurations specified in `pairs`, calls `f`, and then restores all SQL * configurations. */ protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/QueryPlanSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/QueryPlanSuite.scala new file mode 100644 index 0000000000000..27914ef5565c0 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/QueryPlanSuite.scala @@ -0,0 +1,42 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.plans + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.plans +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, Literal, NamedExpression} +import org.apache.spark.sql.catalyst.trees.{CurrentOrigin, Origin} +import org.apache.spark.sql.types.IntegerType + +class QueryPlanSuite extends SparkFunSuite { + + test("origin remains the same after mapExpressions (SPARK-23823)") { + CurrentOrigin.setPosition(0, 0) + val column = AttributeReference("column", IntegerType)(NamedExpression.newExprId) + val query = plans.DslLogicalPlan(plans.table("table")).select(column) + CurrentOrigin.reset() + + val mappedQuery = query mapExpressions { + case _: Expression => Literal(1) + } + + val mappedOrigin = mappedQuery.expressions.apply(0).origin + assert(mappedOrigin == Origin.apply(Some(0), Some(0))) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/RandomUUIDGeneratorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/RandomUUIDGeneratorSuite.scala new file mode 100644 index 0000000000000..b75739e5a3a65 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/RandomUUIDGeneratorSuite.scala @@ -0,0 +1,57 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.util + +import scala.util.Random + +import org.apache.spark.SparkFunSuite + +class RandomUUIDGeneratorSuite extends SparkFunSuite { + test("RandomUUIDGenerator should generate version 4, variant 2 UUIDs") { + val generator = RandomUUIDGenerator(new Random().nextLong()) + for (_ <- 0 to 100) { + val uuid = generator.getNextUUID() + assert(uuid.version() == 4) + assert(uuid.variant() == 2) + } + } + + test("UUID from RandomUUIDGenerator should be deterministic") { + val r1 = new Random(100) + val generator1 = RandomUUIDGenerator(r1.nextLong()) + val r2 = new Random(100) + val generator2 = RandomUUIDGenerator(r2.nextLong()) + val r3 = new Random(101) + val generator3 = RandomUUIDGenerator(r3.nextLong()) + + for (_ <- 0 to 100) { + val uuid1 = generator1.getNextUUID() + val uuid2 = generator2.getNextUUID() + val uuid3 = generator3.getNextUUID() + assert(uuid1 == uuid2) + assert(uuid1 != uuid3) + } + } + + test("Get UTF8String UUID") { + val generator = RandomUUIDGenerator(new Random().nextLong()) + val utf8StringUUID = generator.getNextUUIDUTF8String() + val uuid = java.util.UUID.fromString(utf8StringUUID.toString) + assert(uuid.version() == 4 && uuid.variant() == 2 && utf8StringUUID.toString == uuid.toString) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/StructTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/StructTypeSuite.scala new file mode 100644 index 0000000000000..c6ca8bb005429 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/StructTypeSuite.scala @@ -0,0 +1,40 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import org.apache.spark.SparkFunSuite + +class StructTypeSuite extends SparkFunSuite { + + val s = StructType.fromDDL("a INT, b STRING") + + test("lookup a single missing field should output existing fields") { + val e = intercept[IllegalArgumentException](s("c")).getMessage + assert(e.contains("Available fields: a, b")) + } + + test("lookup a set of missing fields should output existing fields") { + val e = intercept[IllegalArgumentException](s(Set("a", "c"))).getMessage + assert(e.contains("Available fields: a, b")) + } + + test("lookup fieldIndex for missing field should output existing fields") { + val e = intercept[IllegalArgumentException](s.fieldIndex("c")).getMessage + assert(e.contains("Available fields: a, b")) + } +} diff --git a/sql/core/pom.xml b/sql/core/pom.xml index 93010c606cf45..c9a05cc9e43f6 100644 --- a/sql/core/pom.xml +++ b/sql/core/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml @@ -195,7 +195,7 @@ org.scalatest scalatest-maven-plugin - -ea -Xmx4g -Xss4m -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=512m + -ea -Xmx4g -Xss4m -XX:ReservedCodeCacheSize=${CodeCacheSize} diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/BufferedRowIterator.java b/sql/core/src/main/java/org/apache/spark/sql/execution/BufferedRowIterator.java index 730a4ae8d5605..74c9c05992719 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/BufferedRowIterator.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/BufferedRowIterator.java @@ -62,10 +62,14 @@ public long durationMs() { */ public abstract void init(int index, Iterator[] iters); + /* + * Attributes of the following four methods are public. Thus, they can be also accessed from + * methods in inner classes. See SPARK-23598 + */ /** * Append a row to currentRows. */ - protected void append(InternalRow row) { + public void append(InternalRow row) { currentRows.add(row); } @@ -75,7 +79,7 @@ protected void append(InternalRow row) { * If it returns true, the caller should exit the loop that [[InputAdapter]] generates. * This interface is mainly used to limit the number of input rows. */ - protected boolean stopEarly() { + public boolean stopEarly() { return false; } @@ -84,14 +88,14 @@ protected boolean stopEarly() { * * If it returns true, the caller should exit the loop (return from processNext()). */ - protected boolean shouldStop() { + public boolean shouldStop() { return !currentRows.isEmpty(); } /** * Increase the peak execution memory for current task. */ - protected void incPeakExecutionMemory(long size) { + public void incPeakExecutionMemory(long size) { TaskContext.get().taskMetrics().incPeakExecutionMemory(size); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java index eb2fe82007af3..9eb03430a7db2 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java @@ -34,6 +34,7 @@ import org.apache.spark.storage.BlockManager; import org.apache.spark.unsafe.KVIterator; import org.apache.spark.unsafe.Platform; +import org.apache.spark.unsafe.array.LongArray; import org.apache.spark.unsafe.map.BytesToBytesMap; import org.apache.spark.unsafe.memory.MemoryBlock; import org.apache.spark.util.collection.unsafe.sort.*; @@ -98,19 +99,33 @@ public UnsafeKVExternalSorter( numElementsForSpillThreshold, canUseRadixSort); } else { - // The array will be used to do in-place sort, which require half of the space to be empty. - // Note: each record in the map takes two entries in the array, one is record pointer, - // another is the key prefix. - assert(map.numKeys() * 2 <= map.getArray().size() / 2); - // During spilling, the array in map will not be used, so we can borrow that and use it - // as the underlying array for in-memory sorter (it's always large enough). - // Since we will not grow the array, it's fine to pass `null` as consumer. + // During spilling, the pointer array in `BytesToBytesMap` will not be used, so we can borrow + // that and use it as the pointer array for `UnsafeInMemorySorter`. + LongArray pointerArray = map.getArray(); + // `BytesToBytesMap`'s pointer array is only guaranteed to hold all the distinct keys, but + // `UnsafeInMemorySorter`'s pointer array need to hold all the entries. Since + // `BytesToBytesMap` can have duplicated keys, here we need a check to make sure the pointer + // array can hold all the entries in `BytesToBytesMap`. + // The pointer array will be used to do in-place sort, which requires half of the space to be + // empty. Note: each record in the map takes two entries in the pointer array, one is record + // pointer, another is key prefix. So the required size of pointer array is `numRecords * 4`. + // TODO: It's possible to change UnsafeInMemorySorter to have multiple entries with same key, + // so that we can always reuse the pointer array. + if (map.numValues() > pointerArray.size() / 4) { + // Here we ask the map to allocate memory, so that the memory manager won't ask the map + // to spill, if the memory is not enough. + pointerArray = map.allocateArray(map.numValues() * 4L); + } + + // Since the pointer array(either reuse the one in the map, or create a new one) is guaranteed + // to be large enough, it's fine to pass `null` as consumer because we won't allocate more + // memory. final UnsafeInMemorySorter inMemSorter = new UnsafeInMemorySorter( null, taskMemoryManager, comparatorSupplier.get(), prefixComparator, - map.getArray(), + pointerArray, canUseRadixSort); // We cannot use the destructive iterator here because we are reusing the existing memory @@ -241,7 +256,13 @@ private static final class KVComparator extends RecordComparator { } @Override - public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff2) { + public int compare( + Object baseObj1, + long baseOff1, + int baseLen1, + Object baseObj2, + long baseOff2, + int baseLen2) { // Note that since ordering doesn't need the total length of the record, we just pass 0 // into the row. row1.pointTo(baseObj1, baseOff1 + 4, 0); diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/SchemaColumnConvertNotSupportedException.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/SchemaColumnConvertNotSupportedException.java new file mode 100644 index 0000000000000..82a1169cbe7ae --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/SchemaColumnConvertNotSupportedException.java @@ -0,0 +1,62 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.datasources; + +import org.apache.spark.annotation.InterfaceStability; + +/** + * Exception thrown when the parquet reader find column type mismatches. + */ +@InterfaceStability.Unstable +public class SchemaColumnConvertNotSupportedException extends RuntimeException { + + /** + * Name of the column which cannot be converted. + */ + private String column; + /** + * Physical column type in the actual parquet file. + */ + private String physicalType; + /** + * Logical column type in the parquet schema the parquet reader use to parse all files. + */ + private String logicalType; + + public String getColumn() { + return column; + } + + public String getPhysicalType() { + return physicalType; + } + + public String getLogicalType() { + return logicalType; + } + + public SchemaColumnConvertNotSupportedException( + String column, + String physicalType, + String logicalType) { + super(); + this.column = column; + this.physicalType = physicalType; + this.logicalType = logicalType; + } +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/orc/OrcColumnVector.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/orc/OrcColumnVector.java new file mode 100644 index 0000000000000..9bfad1e83ee7b --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/orc/OrcColumnVector.java @@ -0,0 +1,193 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.datasources.orc; + +import java.math.BigDecimal; + +import org.apache.orc.storage.ql.exec.vector.*; + +import org.apache.spark.sql.types.DataType; +import org.apache.spark.sql.types.Decimal; +import org.apache.spark.sql.types.TimestampType; +import org.apache.spark.sql.vectorized.ColumnarArray; +import org.apache.spark.sql.vectorized.ColumnarMap; +import org.apache.spark.unsafe.types.UTF8String; + +/** + * A column vector class wrapping Hive's ColumnVector. Because Spark ColumnarBatch only accepts + * Spark's vectorized.ColumnVector, this column vector is used to adapt Hive ColumnVector with + * Spark ColumnarVector. + */ +public class OrcColumnVector extends org.apache.spark.sql.vectorized.ColumnVector { + private ColumnVector baseData; + private LongColumnVector longData; + private DoubleColumnVector doubleData; + private BytesColumnVector bytesData; + private DecimalColumnVector decimalData; + private TimestampColumnVector timestampData; + private final boolean isTimestamp; + + private int batchSize; + + OrcColumnVector(DataType type, ColumnVector vector) { + super(type); + + if (type instanceof TimestampType) { + isTimestamp = true; + } else { + isTimestamp = false; + } + + baseData = vector; + if (vector instanceof LongColumnVector) { + longData = (LongColumnVector) vector; + } else if (vector instanceof DoubleColumnVector) { + doubleData = (DoubleColumnVector) vector; + } else if (vector instanceof BytesColumnVector) { + bytesData = (BytesColumnVector) vector; + } else if (vector instanceof DecimalColumnVector) { + decimalData = (DecimalColumnVector) vector; + } else if (vector instanceof TimestampColumnVector) { + timestampData = (TimestampColumnVector) vector; + } else { + throw new UnsupportedOperationException(); + } + } + + public void setBatchSize(int batchSize) { + this.batchSize = batchSize; + } + + @Override + public void close() { + + } + + @Override + public boolean hasNull() { + return !baseData.noNulls; + } + + @Override + public int numNulls() { + if (baseData.isRepeating) { + if (baseData.isNull[0]) { + return batchSize; + } else { + return 0; + } + } else if (baseData.noNulls) { + return 0; + } else { + int count = 0; + for (int i = 0; i < batchSize; i++) { + if (baseData.isNull[i]) count++; + } + return count; + } + } + + /* A helper method to get the row index in a column. */ + private int getRowIndex(int rowId) { + return baseData.isRepeating ? 0 : rowId; + } + + @Override + public boolean isNullAt(int rowId) { + return baseData.isNull[getRowIndex(rowId)]; + } + + @Override + public boolean getBoolean(int rowId) { + return longData.vector[getRowIndex(rowId)] == 1; + } + + @Override + public byte getByte(int rowId) { + return (byte) longData.vector[getRowIndex(rowId)]; + } + + @Override + public short getShort(int rowId) { + return (short) longData.vector[getRowIndex(rowId)]; + } + + @Override + public int getInt(int rowId) { + return (int) longData.vector[getRowIndex(rowId)]; + } + + @Override + public long getLong(int rowId) { + int index = getRowIndex(rowId); + if (isTimestamp) { + return timestampData.time[index] * 1000 + timestampData.nanos[index] / 1000 % 1000; + } else { + return longData.vector[index]; + } + } + + @Override + public float getFloat(int rowId) { + return (float) doubleData.vector[getRowIndex(rowId)]; + } + + @Override + public double getDouble(int rowId) { + return doubleData.vector[getRowIndex(rowId)]; + } + + @Override + public Decimal getDecimal(int rowId, int precision, int scale) { + if (isNullAt(rowId)) return null; + BigDecimal data = decimalData.vector[getRowIndex(rowId)].getHiveDecimal().bigDecimalValue(); + return Decimal.apply(data, precision, scale); + } + + @Override + public UTF8String getUTF8String(int rowId) { + if (isNullAt(rowId)) return null; + int index = getRowIndex(rowId); + BytesColumnVector col = bytesData; + return UTF8String.fromBytes(col.vector[index], col.start[index], col.length[index]); + } + + @Override + public byte[] getBinary(int rowId) { + if (isNullAt(rowId)) return null; + int index = getRowIndex(rowId); + byte[] binary = new byte[bytesData.length[index]]; + System.arraycopy(bytesData.vector[index], bytesData.start[index], binary, 0, binary.length); + return binary; + } + + @Override + public ColumnarArray getArray(int rowId) { + throw new UnsupportedOperationException(); + } + + @Override + public ColumnarMap getMap(int rowId) { + throw new UnsupportedOperationException(); + } + + @Override + public org.apache.spark.sql.vectorized.ColumnVector getChild(int ordinal) { + throw new UnsupportedOperationException(); + } +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/orc/OrcColumnarBatchReader.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/orc/OrcColumnarBatchReader.java new file mode 100644 index 0000000000000..94de59ec4875c --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/orc/OrcColumnarBatchReader.java @@ -0,0 +1,553 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.datasources.orc; + +import java.io.IOException; +import java.util.stream.IntStream; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.mapreduce.InputSplit; +import org.apache.hadoop.mapreduce.RecordReader; +import org.apache.hadoop.mapreduce.TaskAttemptContext; +import org.apache.hadoop.mapreduce.lib.input.FileSplit; +import org.apache.orc.OrcConf; +import org.apache.orc.OrcFile; +import org.apache.orc.Reader; +import org.apache.orc.TypeDescription; +import org.apache.orc.mapred.OrcInputFormat; +import org.apache.orc.storage.common.type.HiveDecimal; +import org.apache.orc.storage.ql.exec.vector.*; +import org.apache.orc.storage.serde2.io.HiveDecimalWritable; + +import org.apache.spark.memory.MemoryMode; +import org.apache.spark.sql.catalyst.InternalRow; +import org.apache.spark.sql.execution.vectorized.ColumnVectorUtils; +import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector; +import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector; +import org.apache.spark.sql.execution.vectorized.WritableColumnVector; +import org.apache.spark.sql.types.*; +import org.apache.spark.sql.vectorized.ColumnarBatch; + + +/** + * To support vectorization in WholeStageCodeGen, this reader returns ColumnarBatch. + * After creating, `initialize` and `initBatch` should be called sequentially. + */ +public class OrcColumnarBatchReader extends RecordReader { + // TODO: make this configurable. + private static final int CAPACITY = 4 * 1024; + + // Vectorized ORC Row Batch + private VectorizedRowBatch batch; + + /** + * The column IDs of the physical ORC file schema which are required by this reader. + * -1 means this required column doesn't exist in the ORC file. + */ + private int[] requestedColIds; + + // Record reader from ORC row batch. + private org.apache.orc.RecordReader recordReader; + + private StructField[] requiredFields; + + // The result columnar batch for vectorized execution by whole-stage codegen. + private ColumnarBatch columnarBatch; + + // Writable column vectors of the result columnar batch. + private WritableColumnVector[] columnVectors; + + // The wrapped ORC column vectors. It should be null if `copyToSpark` is true. + private org.apache.spark.sql.vectorized.ColumnVector[] orcVectorWrappers; + + // The memory mode of the columnarBatch + private final MemoryMode MEMORY_MODE; + + // Whether or not to copy the ORC columnar batch to Spark columnar batch. + private final boolean copyToSpark; + + public OrcColumnarBatchReader(boolean useOffHeap, boolean copyToSpark) { + MEMORY_MODE = useOffHeap ? MemoryMode.OFF_HEAP : MemoryMode.ON_HEAP; + this.copyToSpark = copyToSpark; + } + + + @Override + public Void getCurrentKey() { + return null; + } + + @Override + public ColumnarBatch getCurrentValue() { + return columnarBatch; + } + + @Override + public float getProgress() throws IOException { + return recordReader.getProgress(); + } + + @Override + public boolean nextKeyValue() throws IOException { + return nextBatch(); + } + + @Override + public void close() throws IOException { + if (columnarBatch != null) { + columnarBatch.close(); + columnarBatch = null; + } + if (recordReader != null) { + recordReader.close(); + recordReader = null; + } + } + + /** + * Initialize ORC file reader and batch record reader. + * Please note that `initBatch` is needed to be called after this. + */ + @Override + public void initialize( + InputSplit inputSplit, TaskAttemptContext taskAttemptContext) throws IOException { + FileSplit fileSplit = (FileSplit)inputSplit; + Configuration conf = taskAttemptContext.getConfiguration(); + Reader reader = OrcFile.createReader( + fileSplit.getPath(), + OrcFile.readerOptions(conf) + .maxLength(OrcConf.MAX_FILE_LENGTH.getLong(conf)) + .filesystem(fileSplit.getPath().getFileSystem(conf))); + Reader.Options options = + OrcInputFormat.buildOptions(conf, reader, fileSplit.getStart(), fileSplit.getLength()); + recordReader = reader.rows(options); + } + + /** + * Initialize columnar batch by setting required schema and partition information. + * With this information, this creates ColumnarBatch with the full schema. + */ + public void initBatch( + TypeDescription orcSchema, + int[] requestedColIds, + StructField[] requiredFields, + StructType partitionSchema, + InternalRow partitionValues) { + batch = orcSchema.createRowBatch(CAPACITY); + assert(!batch.selectedInUse); // `selectedInUse` should be initialized with `false`. + + this.requiredFields = requiredFields; + this.requestedColIds = requestedColIds; + assert(requiredFields.length == requestedColIds.length); + + StructType resultSchema = new StructType(requiredFields); + for (StructField f : partitionSchema.fields()) { + resultSchema = resultSchema.add(f); + } + + if (copyToSpark) { + if (MEMORY_MODE == MemoryMode.OFF_HEAP) { + columnVectors = OffHeapColumnVector.allocateColumns(CAPACITY, resultSchema); + } else { + columnVectors = OnHeapColumnVector.allocateColumns(CAPACITY, resultSchema); + } + + // Initialize the missing columns once. + for (int i = 0; i < requiredFields.length; i++) { + if (requestedColIds[i] == -1) { + columnVectors[i].putNulls(0, CAPACITY); + columnVectors[i].setIsConstant(); + } + } + + if (partitionValues.numFields() > 0) { + int partitionIdx = requiredFields.length; + for (int i = 0; i < partitionValues.numFields(); i++) { + ColumnVectorUtils.populate(columnVectors[i + partitionIdx], partitionValues, i); + columnVectors[i + partitionIdx].setIsConstant(); + } + } + + columnarBatch = new ColumnarBatch(columnVectors); + } else { + // Just wrap the ORC column vector instead of copying it to Spark column vector. + orcVectorWrappers = new org.apache.spark.sql.vectorized.ColumnVector[resultSchema.length()]; + + for (int i = 0; i < requiredFields.length; i++) { + DataType dt = requiredFields[i].dataType(); + int colId = requestedColIds[i]; + // Initialize the missing columns once. + if (colId == -1) { + OnHeapColumnVector missingCol = new OnHeapColumnVector(CAPACITY, dt); + missingCol.putNulls(0, CAPACITY); + missingCol.setIsConstant(); + orcVectorWrappers[i] = missingCol; + } else { + orcVectorWrappers[i] = new OrcColumnVector(dt, batch.cols[colId]); + } + } + + if (partitionValues.numFields() > 0) { + int partitionIdx = requiredFields.length; + for (int i = 0; i < partitionValues.numFields(); i++) { + DataType dt = partitionSchema.fields()[i].dataType(); + OnHeapColumnVector partitionCol = new OnHeapColumnVector(CAPACITY, dt); + ColumnVectorUtils.populate(partitionCol, partitionValues, i); + partitionCol.setIsConstant(); + orcVectorWrappers[partitionIdx + i] = partitionCol; + } + } + + columnarBatch = new ColumnarBatch(orcVectorWrappers); + } + } + + /** + * Return true if there exists more data in the next batch. If exists, prepare the next batch + * by copying from ORC VectorizedRowBatch columns to Spark ColumnarBatch columns. + */ + private boolean nextBatch() throws IOException { + recordReader.nextBatch(batch); + int batchSize = batch.size; + if (batchSize == 0) { + return false; + } + columnarBatch.setNumRows(batchSize); + + if (!copyToSpark) { + for (int i = 0; i < requiredFields.length; i++) { + if (requestedColIds[i] != -1) { + ((OrcColumnVector) orcVectorWrappers[i]).setBatchSize(batchSize); + } + } + return true; + } + + for (WritableColumnVector vector : columnVectors) { + vector.reset(); + } + + for (int i = 0; i < requiredFields.length; i++) { + StructField field = requiredFields[i]; + WritableColumnVector toColumn = columnVectors[i]; + + if (requestedColIds[i] >= 0) { + ColumnVector fromColumn = batch.cols[requestedColIds[i]]; + + if (fromColumn.isRepeating) { + putRepeatingValues(batchSize, field, fromColumn, toColumn); + } else if (fromColumn.noNulls) { + putNonNullValues(batchSize, field, fromColumn, toColumn); + } else { + putValues(batchSize, field, fromColumn, toColumn); + } + } + } + return true; + } + + private void putRepeatingValues( + int batchSize, + StructField field, + ColumnVector fromColumn, + WritableColumnVector toColumn) { + if (fromColumn.isNull[0]) { + toColumn.putNulls(0, batchSize); + } else { + DataType type = field.dataType(); + if (type instanceof BooleanType) { + toColumn.putBooleans(0, batchSize, ((LongColumnVector)fromColumn).vector[0] == 1); + } else if (type instanceof ByteType) { + toColumn.putBytes(0, batchSize, (byte)((LongColumnVector)fromColumn).vector[0]); + } else if (type instanceof ShortType) { + toColumn.putShorts(0, batchSize, (short)((LongColumnVector)fromColumn).vector[0]); + } else if (type instanceof IntegerType || type instanceof DateType) { + toColumn.putInts(0, batchSize, (int)((LongColumnVector)fromColumn).vector[0]); + } else if (type instanceof LongType) { + toColumn.putLongs(0, batchSize, ((LongColumnVector)fromColumn).vector[0]); + } else if (type instanceof TimestampType) { + toColumn.putLongs(0, batchSize, + fromTimestampColumnVector((TimestampColumnVector)fromColumn, 0)); + } else if (type instanceof FloatType) { + toColumn.putFloats(0, batchSize, (float)((DoubleColumnVector)fromColumn).vector[0]); + } else if (type instanceof DoubleType) { + toColumn.putDoubles(0, batchSize, ((DoubleColumnVector)fromColumn).vector[0]); + } else if (type instanceof StringType || type instanceof BinaryType) { + BytesColumnVector data = (BytesColumnVector)fromColumn; + int size = data.vector[0].length; + toColumn.arrayData().reserve(size); + toColumn.arrayData().putBytes(0, size, data.vector[0], 0); + for (int index = 0; index < batchSize; index++) { + toColumn.putArray(index, 0, size); + } + } else if (type instanceof DecimalType) { + DecimalType decimalType = (DecimalType)type; + putDecimalWritables( + toColumn, + batchSize, + decimalType.precision(), + decimalType.scale(), + ((DecimalColumnVector)fromColumn).vector[0]); + } else { + throw new UnsupportedOperationException("Unsupported Data Type: " + type); + } + } + } + + private void putNonNullValues( + int batchSize, + StructField field, + ColumnVector fromColumn, + WritableColumnVector toColumn) { + DataType type = field.dataType(); + if (type instanceof BooleanType) { + long[] data = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + toColumn.putBoolean(index, data[index] == 1); + } + } else if (type instanceof ByteType) { + long[] data = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + toColumn.putByte(index, (byte)data[index]); + } + } else if (type instanceof ShortType) { + long[] data = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + toColumn.putShort(index, (short)data[index]); + } + } else if (type instanceof IntegerType || type instanceof DateType) { + long[] data = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + toColumn.putInt(index, (int)data[index]); + } + } else if (type instanceof LongType) { + toColumn.putLongs(0, batchSize, ((LongColumnVector)fromColumn).vector, 0); + } else if (type instanceof TimestampType) { + TimestampColumnVector data = ((TimestampColumnVector)fromColumn); + for (int index = 0; index < batchSize; index++) { + toColumn.putLong(index, fromTimestampColumnVector(data, index)); + } + } else if (type instanceof FloatType) { + double[] data = ((DoubleColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + toColumn.putFloat(index, (float)data[index]); + } + } else if (type instanceof DoubleType) { + toColumn.putDoubles(0, batchSize, ((DoubleColumnVector)fromColumn).vector, 0); + } else if (type instanceof StringType || type instanceof BinaryType) { + BytesColumnVector data = ((BytesColumnVector)fromColumn); + WritableColumnVector arrayData = toColumn.arrayData(); + int totalNumBytes = IntStream.of(data.length).sum(); + arrayData.reserve(totalNumBytes); + for (int index = 0, pos = 0; index < batchSize; pos += data.length[index], index++) { + arrayData.putBytes(pos, data.length[index], data.vector[index], data.start[index]); + toColumn.putArray(index, pos, data.length[index]); + } + } else if (type instanceof DecimalType) { + DecimalType decimalType = (DecimalType)type; + DecimalColumnVector data = ((DecimalColumnVector)fromColumn); + if (decimalType.precision() > Decimal.MAX_LONG_DIGITS()) { + toColumn.arrayData().reserve(batchSize * 16); + } + for (int index = 0; index < batchSize; index++) { + putDecimalWritable( + toColumn, + index, + decimalType.precision(), + decimalType.scale(), + data.vector[index]); + } + } else { + throw new UnsupportedOperationException("Unsupported Data Type: " + type); + } + } + + private void putValues( + int batchSize, + StructField field, + ColumnVector fromColumn, + WritableColumnVector toColumn) { + DataType type = field.dataType(); + if (type instanceof BooleanType) { + long[] vector = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + toColumn.putBoolean(index, vector[index] == 1); + } + } + } else if (type instanceof ByteType) { + long[] vector = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + toColumn.putByte(index, (byte)vector[index]); + } + } + } else if (type instanceof ShortType) { + long[] vector = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + toColumn.putShort(index, (short)vector[index]); + } + } + } else if (type instanceof IntegerType || type instanceof DateType) { + long[] vector = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + toColumn.putInt(index, (int)vector[index]); + } + } + } else if (type instanceof LongType) { + long[] vector = ((LongColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + toColumn.putLong(index, vector[index]); + } + } + } else if (type instanceof TimestampType) { + TimestampColumnVector vector = ((TimestampColumnVector)fromColumn); + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + toColumn.putLong(index, fromTimestampColumnVector(vector, index)); + } + } + } else if (type instanceof FloatType) { + double[] vector = ((DoubleColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + toColumn.putFloat(index, (float)vector[index]); + } + } + } else if (type instanceof DoubleType) { + double[] vector = ((DoubleColumnVector)fromColumn).vector; + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + toColumn.putDouble(index, vector[index]); + } + } + } else if (type instanceof StringType || type instanceof BinaryType) { + BytesColumnVector vector = (BytesColumnVector)fromColumn; + WritableColumnVector arrayData = toColumn.arrayData(); + int totalNumBytes = IntStream.of(vector.length).sum(); + arrayData.reserve(totalNumBytes); + for (int index = 0, pos = 0; index < batchSize; pos += vector.length[index], index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + arrayData.putBytes(pos, vector.length[index], vector.vector[index], vector.start[index]); + toColumn.putArray(index, pos, vector.length[index]); + } + } + } else if (type instanceof DecimalType) { + DecimalType decimalType = (DecimalType)type; + HiveDecimalWritable[] vector = ((DecimalColumnVector)fromColumn).vector; + if (decimalType.precision() > Decimal.MAX_LONG_DIGITS()) { + toColumn.arrayData().reserve(batchSize * 16); + } + for (int index = 0; index < batchSize; index++) { + if (fromColumn.isNull[index]) { + toColumn.putNull(index); + } else { + putDecimalWritable( + toColumn, + index, + decimalType.precision(), + decimalType.scale(), + vector[index]); + } + } + } else { + throw new UnsupportedOperationException("Unsupported Data Type: " + type); + } + } + + /** + * Returns the number of micros since epoch from an element of TimestampColumnVector. + */ + private static long fromTimestampColumnVector(TimestampColumnVector vector, int index) { + return vector.time[index] * 1000 + (vector.nanos[index] / 1000 % 1000); + } + + /** + * Put a `HiveDecimalWritable` to a `WritableColumnVector`. + */ + private static void putDecimalWritable( + WritableColumnVector toColumn, + int index, + int precision, + int scale, + HiveDecimalWritable decimalWritable) { + HiveDecimal decimal = decimalWritable.getHiveDecimal(); + Decimal value = + Decimal.apply(decimal.bigDecimalValue(), decimal.precision(), decimal.scale()); + value.changePrecision(precision, scale); + + if (precision <= Decimal.MAX_INT_DIGITS()) { + toColumn.putInt(index, (int) value.toUnscaledLong()); + } else if (precision <= Decimal.MAX_LONG_DIGITS()) { + toColumn.putLong(index, value.toUnscaledLong()); + } else { + byte[] bytes = value.toJavaBigDecimal().unscaledValue().toByteArray(); + toColumn.arrayData().putBytes(index * 16, bytes.length, bytes, 0); + toColumn.putArray(index, index * 16, bytes.length); + } + } + + /** + * Put `HiveDecimalWritable`s to a `WritableColumnVector`. + */ + private static void putDecimalWritables( + WritableColumnVector toColumn, + int size, + int precision, + int scale, + HiveDecimalWritable decimalWritable) { + HiveDecimal decimal = decimalWritable.getHiveDecimal(); + Decimal value = + Decimal.apply(decimal.bigDecimalValue(), decimal.precision(), decimal.scale()); + value.changePrecision(precision, scale); + + if (precision <= Decimal.MAX_INT_DIGITS()) { + toColumn.putInts(0, size, (int) value.toUnscaledLong()); + } else if (precision <= Decimal.MAX_LONG_DIGITS()) { + toColumn.putLongs(0, size, value.toUnscaledLong()); + } else { + byte[] bytes = value.toJavaBigDecimal().unscaledValue().toByteArray(); + toColumn.arrayData().reserve(bytes.length); + toColumn.arrayData().putBytes(0, bytes.length, bytes, 0); + for (int index = 0; index < size; index++) { + toColumn.putArray(index, 0, bytes.length); + } + } + } +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java index 80c2f491b48ce..95fe130a54326 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java @@ -147,7 +147,8 @@ public void initialize(InputSplit inputSplit, TaskAttemptContext taskAttemptCont this.sparkSchema = StructType$.MODULE$.fromString(sparkRequestedSchemaString); this.reader = new ParquetFileReader( configuration, footer.getFileMetaData(), file, blocks, requestedSchema.getColumns()); - for (BlockMetaData block : blocks) { + // use the blocks from the reader in case some do not match filters and will not be read + for (BlockMetaData block : reader.getRowGroups()) { this.totalRowCount += block.getRowCount(); } @@ -170,7 +171,7 @@ public void initialize(InputSplit inputSplit, TaskAttemptContext taskAttemptCont * Returns the list of files at 'path' recursively. This skips files that are ignored normally * by MapReduce. */ - public static List listDirectory(File path) throws IOException { + public static List listDirectory(File path) { List result = new ArrayList<>(); if (path.isDirectory()) { for (File f: path.listFiles()) { @@ -225,13 +226,14 @@ protected void initialize(String path, List columns) throws IOException this.sparkSchema = new ParquetToSparkSchemaConverter(config).convert(requestedSchema); this.reader = new ParquetFileReader( config, footer.getFileMetaData(), file, blocks, requestedSchema.getColumns()); - for (BlockMetaData block : blocks) { + // use the blocks from the reader in case some do not match filters and will not be read + for (BlockMetaData block : reader.getRowGroups()) { this.totalRowCount += block.getRowCount(); } } @Override - public Void getCurrentKey() throws IOException, InterruptedException { + public Void getCurrentKey() { return null; } @@ -259,7 +261,7 @@ public ValuesReaderIntIterator(ValuesReader delegate) { } @Override - int nextInt() throws IOException { + int nextInt() { return delegate.readInteger(); } } @@ -279,15 +281,15 @@ int nextInt() throws IOException { protected static final class NullIntIterator extends IntIterator { @Override - int nextInt() throws IOException { return 0; } + int nextInt() { return 0; } } /** * Creates a reader for definition and repetition levels, returning an optimized one if * the levels are not needed. */ - protected static IntIterator createRLEIterator(int maxLevel, BytesInput bytes, - ColumnDescriptor descriptor) throws IOException { + protected static IntIterator createRLEIterator( + int maxLevel, BytesInput bytes, ColumnDescriptor descriptor) throws IOException { try { if (maxLevel == 0) return new NullIntIterator(); return new RLEIntIterator( diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java index c120863152a96..72f1d024b08ce 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution.datasources.parquet; import java.io.IOException; +import java.util.Arrays; import java.util.TimeZone; import org.apache.parquet.bytes.BytesUtils; @@ -31,6 +32,7 @@ import org.apache.parquet.schema.PrimitiveType; import org.apache.spark.sql.catalyst.util.DateTimeUtils; +import org.apache.spark.sql.execution.datasources.SchemaColumnConvertNotSupportedException; import org.apache.spark.sql.execution.vectorized.WritableColumnVector; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.DecimalType; @@ -231,6 +233,18 @@ private boolean shouldConvertTimestamps() { return convertTz != null && !convertTz.equals(UTC); } + /** + * Helper function to construct exception for parquet schema mismatch. + */ + private SchemaColumnConvertNotSupportedException constructConvertNotSupportedException( + ColumnDescriptor descriptor, + WritableColumnVector column) { + return new SchemaColumnConvertNotSupportedException( + Arrays.toString(descriptor.getPath()), + descriptor.getType().toString(), + column.dataType().toString()); + } + /** * Reads `num` values into column, decoding the values from `dictionaryIds` and `dictionary`. */ @@ -261,7 +275,7 @@ private void decodeDictionaryIds( } } } else { - throw new UnsupportedOperationException("Unimplemented type: " + column.dataType()); + throw constructConvertNotSupportedException(descriptor, column); } break; @@ -282,7 +296,7 @@ private void decodeDictionaryIds( } } } else { - throw new UnsupportedOperationException("Unimplemented type: " + column.dataType()); + throw constructConvertNotSupportedException(descriptor, column); } break; @@ -321,7 +335,7 @@ private void decodeDictionaryIds( } } } else { - throw new UnsupportedOperationException(); + throw constructConvertNotSupportedException(descriptor, column); } break; case BINARY: @@ -360,7 +374,7 @@ private void decodeDictionaryIds( } } } else { - throw new UnsupportedOperationException(); + throw constructConvertNotSupportedException(descriptor, column); } break; @@ -375,7 +389,9 @@ private void decodeDictionaryIds( */ private void readBooleanBatch(int rowId, int num, WritableColumnVector column) { - assert(column.dataType() == DataTypes.BooleanType); + if (column.dataType() != DataTypes.BooleanType) { + throw constructConvertNotSupportedException(descriptor, column); + } defColumn.readBooleans( num, column, rowId, maxDefLevel, (VectorizedValuesReader) dataColumn); } @@ -394,7 +410,7 @@ private void readIntBatch(int rowId, int num, WritableColumnVector column) { defColumn.readShorts( num, column, rowId, maxDefLevel, (VectorizedValuesReader) dataColumn); } else { - throw new UnsupportedOperationException("Unimplemented type: " + column.dataType()); + throw constructConvertNotSupportedException(descriptor, column); } } @@ -414,7 +430,7 @@ private void readLongBatch(int rowId, int num, WritableColumnVector column) { } } } else { - throw new UnsupportedOperationException("Unsupported conversion to: " + column.dataType()); + throw constructConvertNotSupportedException(descriptor, column); } } @@ -425,7 +441,7 @@ private void readFloatBatch(int rowId, int num, WritableColumnVector column) { defColumn.readFloats( num, column, rowId, maxDefLevel, (VectorizedValuesReader) dataColumn); } else { - throw new UnsupportedOperationException("Unsupported conversion to: " + column.dataType()); + throw constructConvertNotSupportedException(descriptor, column); } } @@ -436,7 +452,7 @@ private void readDoubleBatch(int rowId, int num, WritableColumnVector column) { defColumn.readDoubles( num, column, rowId, maxDefLevel, (VectorizedValuesReader) dataColumn); } else { - throw new UnsupportedOperationException("Unimplemented type: " + column.dataType()); + throw constructConvertNotSupportedException(descriptor, column); } } @@ -444,7 +460,8 @@ private void readBinaryBatch(int rowId, int num, WritableColumnVector column) { // This is where we implement support for the valid type conversions. // TODO: implement remaining type conversions VectorizedValuesReader data = (VectorizedValuesReader) dataColumn; - if (column.dataType() == DataTypes.StringType || column.dataType() == DataTypes.BinaryType) { + if (column.dataType() == DataTypes.StringType || column.dataType() == DataTypes.BinaryType + || DecimalType.isByteArrayDecimalType(column.dataType())) { defColumn.readBinarys(num, column, rowId, maxDefLevel, data); } else if (column.dataType() == DataTypes.TimestampType) { if (!shouldConvertTimestamps()) { @@ -470,7 +487,7 @@ private void readBinaryBatch(int rowId, int num, WritableColumnVector column) { } } } else { - throw new UnsupportedOperationException("Unimplemented type: " + column.dataType()); + throw constructConvertNotSupportedException(descriptor, column); } } @@ -509,7 +526,7 @@ private void readFixedLenByteArrayBatch( } } } else { - throw new UnsupportedOperationException("Unimplemented type: " + column.dataType()); + throw constructConvertNotSupportedException(descriptor, column); } } diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java index 6c157e85d411f..bb1b23611a7d7 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java @@ -31,10 +31,10 @@ import org.apache.spark.memory.MemoryMode; import org.apache.spark.sql.catalyst.InternalRow; import org.apache.spark.sql.execution.vectorized.ColumnVectorUtils; -import org.apache.spark.sql.execution.vectorized.ColumnarBatch; import org.apache.spark.sql.execution.vectorized.WritableColumnVector; import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector; import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector; +import org.apache.spark.sql.vectorized.ColumnarBatch; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType; @@ -50,6 +50,9 @@ * TODO: make this always return ColumnarBatches. */ public class VectorizedParquetRecordReader extends SpecificParquetRecordReaderBase { + // TODO: make this configurable. + private static final int CAPACITY = 4 * 1024; + /** * Batch of rows that we assemble and the current index we've returned. Every time this * batch is used up (batchIdx == numBatched), we populated the batch. @@ -152,7 +155,7 @@ public void close() throws IOException { } @Override - public boolean nextKeyValue() throws IOException, InterruptedException { + public boolean nextKeyValue() throws IOException { resultBatch(); if (returnColumnarBatch) return nextBatch(); @@ -165,13 +168,13 @@ public boolean nextKeyValue() throws IOException, InterruptedException { } @Override - public Object getCurrentValue() throws IOException, InterruptedException { + public Object getCurrentValue() { if (returnColumnarBatch) return columnarBatch; return columnarBatch.getRow(batchIdx - 1); } @Override - public float getProgress() throws IOException, InterruptedException { + public float getProgress() { return (float) rowsReturned / totalRowCount; } @@ -181,7 +184,7 @@ public float getProgress() throws IOException, InterruptedException { // Columns 0,1: data columns // Column 2: partitionValues[0] // Column 3: partitionValues[1] - public void initBatch( + private void initBatch( MemoryMode memMode, StructType partitionColumns, InternalRow partitionValues) { @@ -195,13 +198,12 @@ public void initBatch( } } - int capacity = ColumnarBatch.DEFAULT_BATCH_SIZE; if (memMode == MemoryMode.OFF_HEAP) { - columnVectors = OffHeapColumnVector.allocateColumns(capacity, batchSchema); + columnVectors = OffHeapColumnVector.allocateColumns(CAPACITY, batchSchema); } else { - columnVectors = OnHeapColumnVector.allocateColumns(capacity, batchSchema); + columnVectors = OnHeapColumnVector.allocateColumns(CAPACITY, batchSchema); } - columnarBatch = new ColumnarBatch(batchSchema, columnVectors, capacity); + columnarBatch = new ColumnarBatch(columnVectors); if (partitionColumns != null) { int partitionIdx = sparkSchema.fields().length; for (int i = 0; i < partitionColumns.fields().length; i++) { @@ -213,13 +215,13 @@ public void initBatch( // Initialize missing columns with nulls. for (int i = 0; i < missingColumns.length; i++) { if (missingColumns[i]) { - columnVectors[i].putNulls(0, columnarBatch.capacity()); + columnVectors[i].putNulls(0, CAPACITY); columnVectors[i].setIsConstant(); } } } - public void initBatch() { + private void initBatch() { initBatch(MEMORY_MODE, null, null); } @@ -248,11 +250,14 @@ public void enableReturningBatches() { * Advances to the next batch of rows. Returns false if there are no more. */ public boolean nextBatch() throws IOException { - columnarBatch.reset(); + for (WritableColumnVector vector : columnVectors) { + vector.reset(); + } + columnarBatch.setNumRows(0); if (rowsReturned >= totalRowCount) return false; checkEndOfRowGroup(); - int num = (int) Math.min((long) columnarBatch.capacity(), totalCountLoadedSoFar - rowsReturned); + int num = (int) Math.min((long) CAPACITY, totalCountLoadedSoFar - rowsReturned); for (int i = 0; i < columnReaders.length; ++i) { if (columnReaders[i] == null) continue; columnReaders[i].readBatch(num, columnVectors[i]); diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVector.java b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVector.java deleted file mode 100644 index dc7c1269bedd9..0000000000000 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVector.java +++ /dev/null @@ -1,208 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.sql.execution.vectorized; - -import org.apache.spark.sql.catalyst.util.MapData; -import org.apache.spark.sql.types.DataType; -import org.apache.spark.sql.types.Decimal; -import org.apache.spark.unsafe.types.UTF8String; - -/** - * This class represents in-memory values of a column and provides the main APIs to access the data. - * It supports all the types and contains get APIs as well as their batched versions. The batched - * versions are considered to be faster and preferable whenever possible. - * - * To handle nested schemas, ColumnVector has two types: Arrays and Structs. In both cases these - * columns have child columns. All of the data are stored in the child columns and the parent column - * only contains nullability. In the case of Arrays, the lengths and offsets are saved in the child - * column and are encoded identically to INTs. - * - * Maps are just a special case of a two field struct. - * - * Most of the APIs take the rowId as a parameter. This is the batch local 0-based row id for values - * in the current batch. - */ -public abstract class ColumnVector implements AutoCloseable { - - /** - * Returns the data type of this column. - */ - public final DataType dataType() { return type; } - - /** - * Cleans up memory for this column. The column is not usable after this. - */ - public abstract void close(); - - /** - * Returns the number of nulls in this column. - */ - public abstract int numNulls(); - - /** - * Returns whether the value at rowId is NULL. - */ - public abstract boolean isNullAt(int rowId); - - /** - * Returns the value for rowId. - */ - public abstract boolean getBoolean(int rowId); - - /** - * Gets values from [rowId, rowId + count) - */ - public abstract boolean[] getBooleans(int rowId, int count); - - /** - * Returns the value for rowId. - */ - public abstract byte getByte(int rowId); - - /** - * Gets values from [rowId, rowId + count) - */ - public abstract byte[] getBytes(int rowId, int count); - - /** - * Returns the value for rowId. - */ - public abstract short getShort(int rowId); - - /** - * Gets values from [rowId, rowId + count) - */ - public abstract short[] getShorts(int rowId, int count); - - /** - * Returns the value for rowId. - */ - public abstract int getInt(int rowId); - - /** - * Gets values from [rowId, rowId + count) - */ - public abstract int[] getInts(int rowId, int count); - - /** - * Returns the value for rowId. - */ - public abstract long getLong(int rowId); - - /** - * Gets values from [rowId, rowId + count) - */ - public abstract long[] getLongs(int rowId, int count); - - /** - * Returns the value for rowId. - */ - public abstract float getFloat(int rowId); - - /** - * Gets values from [rowId, rowId + count) - */ - public abstract float[] getFloats(int rowId, int count); - - /** - * Returns the value for rowId. - */ - public abstract double getDouble(int rowId); - - /** - * Gets values from [rowId, rowId + count) - */ - public abstract double[] getDoubles(int rowId, int count); - - /** - * Returns the length of the array for rowId. - */ - public abstract int getArrayLength(int rowId); - - /** - * Returns the offset of the array for rowId. - */ - public abstract int getArrayOffset(int rowId); - - /** - * Returns the struct for rowId. - */ - public final ColumnarRow getStruct(int rowId) { - return new ColumnarRow(this, rowId); - } - - /** - * A special version of {@link #getStruct(int)}, which is only used as an adapter for Spark - * codegen framework, the second parameter is totally ignored. - */ - public final ColumnarRow getStruct(int rowId, int size) { - return getStruct(rowId); - } - - /** - * Returns the array for rowId. - */ - public final ColumnarArray getArray(int rowId) { - return new ColumnarArray(arrayData(), getArrayOffset(rowId), getArrayLength(rowId)); - } - - /** - * Returns the map for rowId. - */ - public MapData getMap(int ordinal) { - throw new UnsupportedOperationException(); - } - - /** - * Returns the decimal for rowId. - */ - public abstract Decimal getDecimal(int rowId, int precision, int scale); - - /** - * Returns the UTF8String for rowId. Note that the returned UTF8String may point to the data of - * this column vector, please copy it if you want to keep it after this column vector is freed. - */ - public abstract UTF8String getUTF8String(int rowId); - - /** - * Returns the byte array for rowId. - */ - public abstract byte[] getBinary(int rowId); - - /** - * Returns the data for the underlying array. - */ - public abstract ColumnVector arrayData(); - - /** - * Returns the ordinal's child data column. - */ - public abstract ColumnVector getChildColumn(int ordinal); - - /** - * Data type for this column. - */ - protected DataType type; - - /** - * Sets up the common state and also handles creating the child columns if this is a nested - * type. - */ - protected ColumnVector(DataType type) { - this.type = type; - } -} diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVectorUtils.java b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVectorUtils.java index bc62bc43484e5..829f3ce750fe6 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVectorUtils.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVectorUtils.java @@ -20,14 +20,19 @@ import java.math.BigInteger; import java.nio.charset.StandardCharsets; import java.sql.Date; +import java.util.HashMap; import java.util.Iterator; import java.util.List; +import java.util.Map; import org.apache.spark.memory.MemoryMode; import org.apache.spark.sql.Row; import org.apache.spark.sql.catalyst.InternalRow; import org.apache.spark.sql.catalyst.util.DateTimeUtils; import org.apache.spark.sql.types.*; +import org.apache.spark.sql.vectorized.ColumnarArray; +import org.apache.spark.sql.vectorized.ColumnarBatch; +import org.apache.spark.sql.vectorized.ColumnarMap; import org.apache.spark.unsafe.types.CalendarInterval; import org.apache.spark.unsafe.types.UTF8String; @@ -83,8 +88,8 @@ public static void populate(WritableColumnVector col, InternalRow row, int field } } else if (t instanceof CalendarIntervalType) { CalendarInterval c = (CalendarInterval)row.get(fieldIdx, t); - col.getChildColumn(0).putInts(0, capacity, c.months); - col.getChildColumn(1).putLongs(0, capacity, c.microseconds); + col.getChild(0).putInts(0, capacity, c.months); + col.getChild(1).putLongs(0, capacity, c.microseconds); } else if (t instanceof DateType) { col.putInts(0, capacity, row.getInt(fieldIdx)); } else if (t instanceof TimestampType) { @@ -107,6 +112,18 @@ public static int[] toJavaIntArray(ColumnarArray array) { return array.toIntArray(); } + public static Map toJavaIntMap(ColumnarMap map) { + int[] keys = toJavaIntArray(map.keyArray()); + int[] values = toJavaIntArray(map.valueArray()); + assert keys.length == values.length; + + Map result = new HashMap<>(); + for (int i = 0; i < keys.length; i++) { + result.put(keys[i], values[i]); + } + return result; + } + private static void appendValue(WritableColumnVector dst, DataType t, Object o) { if (o == null) { if (t instanceof CalendarIntervalType) { @@ -116,19 +133,19 @@ private static void appendValue(WritableColumnVector dst, DataType t, Object o) } } else { if (t == DataTypes.BooleanType) { - dst.appendBoolean(((Boolean)o).booleanValue()); + dst.appendBoolean((Boolean) o); } else if (t == DataTypes.ByteType) { - dst.appendByte(((Byte) o).byteValue()); + dst.appendByte((Byte) o); } else if (t == DataTypes.ShortType) { - dst.appendShort(((Short)o).shortValue()); + dst.appendShort((Short) o); } else if (t == DataTypes.IntegerType) { - dst.appendInt(((Integer)o).intValue()); + dst.appendInt((Integer) o); } else if (t == DataTypes.LongType) { - dst.appendLong(((Long)o).longValue()); + dst.appendLong((Long) o); } else if (t == DataTypes.FloatType) { - dst.appendFloat(((Float)o).floatValue()); + dst.appendFloat((Float) o); } else if (t == DataTypes.DoubleType) { - dst.appendDouble(((Double)o).doubleValue()); + dst.appendDouble((Double) o); } else if (t == DataTypes.StringType) { byte[] b =((String)o).getBytes(StandardCharsets.UTF_8); dst.appendByteArray(b, 0, b.length); @@ -147,8 +164,8 @@ private static void appendValue(WritableColumnVector dst, DataType t, Object o) } else if (t instanceof CalendarIntervalType) { CalendarInterval c = (CalendarInterval)o; dst.appendStruct(false); - dst.getChildColumn(0).appendInt(c.months); - dst.getChildColumn(1).appendLong(c.microseconds); + dst.getChild(0).appendInt(c.months); + dst.getChild(1).appendLong(c.microseconds); } else if (t instanceof DateType) { dst.appendInt(DateTimeUtils.fromJavaDate((Date)o)); } else { @@ -177,7 +194,7 @@ private static void appendValue(WritableColumnVector dst, DataType t, Row src, i dst.appendStruct(false); Row c = src.getStruct(fieldIdx); for (int i = 0; i < st.fields().length; i++) { - appendValue(dst.getChildColumn(i), st.fields()[i].dataType(), c, i); + appendValue(dst.getChild(i), st.fields()[i].dataType(), c, i); } } } else { @@ -190,7 +207,7 @@ private static void appendValue(WritableColumnVector dst, DataType t, Row src, i */ public static ColumnarBatch toBatch( StructType schema, MemoryMode memMode, Iterator row) { - int capacity = ColumnarBatch.DEFAULT_BATCH_SIZE; + int capacity = 4 * 1024; WritableColumnVector[] columnVectors; if (memMode == MemoryMode.OFF_HEAP) { columnVectors = OffHeapColumnVector.allocateColumns(capacity, schema); @@ -206,7 +223,7 @@ public static ColumnarBatch toBatch( } n++; } - ColumnarBatch batch = new ColumnarBatch(schema, columnVectors, capacity); + ColumnarBatch batch = new ColumnarBatch(columnVectors); batch.setNumRows(n); return batch; } diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/MutableColumnarRow.java b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/MutableColumnarRow.java index 06602c147dfe9..4e4242fe8d9b9 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/MutableColumnarRow.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/MutableColumnarRow.java @@ -21,8 +21,12 @@ import org.apache.spark.sql.catalyst.InternalRow; import org.apache.spark.sql.catalyst.expressions.GenericInternalRow; -import org.apache.spark.sql.catalyst.util.MapData; import org.apache.spark.sql.types.*; +import org.apache.spark.sql.vectorized.ColumnarArray; +import org.apache.spark.sql.vectorized.ColumnarBatch; +import org.apache.spark.sql.vectorized.ColumnarMap; +import org.apache.spark.sql.vectorized.ColumnarRow; +import org.apache.spark.sql.vectorized.ColumnVector; import org.apache.spark.unsafe.types.CalendarInterval; import org.apache.spark.unsafe.types.UTF8String; @@ -123,45 +127,37 @@ public boolean anyNull() { @Override public Decimal getDecimal(int ordinal, int precision, int scale) { - if (columns[ordinal].isNullAt(rowId)) return null; return columns[ordinal].getDecimal(rowId, precision, scale); } @Override public UTF8String getUTF8String(int ordinal) { - if (columns[ordinal].isNullAt(rowId)) return null; return columns[ordinal].getUTF8String(rowId); } @Override public byte[] getBinary(int ordinal) { - if (columns[ordinal].isNullAt(rowId)) return null; return columns[ordinal].getBinary(rowId); } @Override public CalendarInterval getInterval(int ordinal) { - if (columns[ordinal].isNullAt(rowId)) return null; - final int months = columns[ordinal].getChildColumn(0).getInt(rowId); - final long microseconds = columns[ordinal].getChildColumn(1).getLong(rowId); - return new CalendarInterval(months, microseconds); + return columns[ordinal].getInterval(rowId); } @Override public ColumnarRow getStruct(int ordinal, int numFields) { - if (columns[ordinal].isNullAt(rowId)) return null; return columns[ordinal].getStruct(rowId); } @Override public ColumnarArray getArray(int ordinal) { - if (columns[ordinal].isNullAt(rowId)) return null; return columns[ordinal].getArray(rowId); } @Override - public MapData getMap(int ordinal) { - throw new UnsupportedOperationException(); + public ColumnarMap getMap(int ordinal) { + return columns[ordinal].getMap(rowId); } @Override diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OffHeapColumnVector.java b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OffHeapColumnVector.java index 1c45b846790b6..5e0cf7d370dd1 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OffHeapColumnVector.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OffHeapColumnVector.java @@ -60,7 +60,7 @@ public static OffHeapColumnVector[] allocateColumns(int capacity, StructField[] private long nulls; private long data; - // Set iff the type is array. + // Only set if type is Array or Map. private long lengthData; private long offsetData; @@ -123,7 +123,7 @@ public void putNulls(int rowId, int count) { @Override public void putNotNulls(int rowId, int count) { - if (numNulls == 0) return; + if (!hasNull()) return; long offset = nulls + rowId; for (int i = 0; i < count; ++i, ++offset) { Platform.putByte(null, offset, (byte) 0); @@ -215,12 +215,12 @@ protected UTF8String getBytesAsUTF8String(int rowId, int count) { @Override public void putShort(int rowId, short value) { - Platform.putShort(null, data + 2 * rowId, value); + Platform.putShort(null, data + 2L * rowId, value); } @Override public void putShorts(int rowId, int count, short value) { - long offset = data + 2 * rowId; + long offset = data + 2L * rowId; for (int i = 0; i < count; ++i, offset += 2) { Platform.putShort(null, offset, value); } @@ -228,20 +228,20 @@ public void putShorts(int rowId, int count, short value) { @Override public void putShorts(int rowId, int count, short[] src, int srcIndex) { - Platform.copyMemory(src, Platform.SHORT_ARRAY_OFFSET + srcIndex * 2, - null, data + 2 * rowId, count * 2); + Platform.copyMemory(src, Platform.SHORT_ARRAY_OFFSET + srcIndex * 2L, + null, data + 2L * rowId, count * 2L); } @Override public void putShorts(int rowId, int count, byte[] src, int srcIndex) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, - null, data + rowId * 2, count * 2); + null, data + rowId * 2L, count * 2L); } @Override public short getShort(int rowId) { if (dictionary == null) { - return Platform.getShort(null, data + 2 * rowId); + return Platform.getShort(null, data + 2L * rowId); } else { return (short) dictionary.decodeToInt(dictionaryIds.getDictId(rowId)); } @@ -251,7 +251,7 @@ public short getShort(int rowId) { public short[] getShorts(int rowId, int count) { assert(dictionary == null); short[] array = new short[count]; - Platform.copyMemory(null, data + rowId * 2, array, Platform.SHORT_ARRAY_OFFSET, count * 2); + Platform.copyMemory(null, data + rowId * 2L, array, Platform.SHORT_ARRAY_OFFSET, count * 2L); return array; } @@ -261,12 +261,12 @@ public short[] getShorts(int rowId, int count) { @Override public void putInt(int rowId, int value) { - Platform.putInt(null, data + 4 * rowId, value); + Platform.putInt(null, data + 4L * rowId, value); } @Override public void putInts(int rowId, int count, int value) { - long offset = data + 4 * rowId; + long offset = data + 4L * rowId; for (int i = 0; i < count; ++i, offset += 4) { Platform.putInt(null, offset, value); } @@ -274,24 +274,24 @@ public void putInts(int rowId, int count, int value) { @Override public void putInts(int rowId, int count, int[] src, int srcIndex) { - Platform.copyMemory(src, Platform.INT_ARRAY_OFFSET + srcIndex * 4, - null, data + 4 * rowId, count * 4); + Platform.copyMemory(src, Platform.INT_ARRAY_OFFSET + srcIndex * 4L, + null, data + 4L * rowId, count * 4L); } @Override public void putInts(int rowId, int count, byte[] src, int srcIndex) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, - null, data + rowId * 4, count * 4); + null, data + rowId * 4L, count * 4L); } @Override public void putIntsLittleEndian(int rowId, int count, byte[] src, int srcIndex) { if (!bigEndianPlatform) { Platform.copyMemory(src, srcIndex + Platform.BYTE_ARRAY_OFFSET, - null, data + 4 * rowId, count * 4); + null, data + 4L * rowId, count * 4L); } else { int srcOffset = srcIndex + Platform.BYTE_ARRAY_OFFSET; - long offset = data + 4 * rowId; + long offset = data + 4L * rowId; for (int i = 0; i < count; ++i, offset += 4, srcOffset += 4) { Platform.putInt(null, offset, java.lang.Integer.reverseBytes(Platform.getInt(src, srcOffset))); @@ -302,7 +302,7 @@ public void putIntsLittleEndian(int rowId, int count, byte[] src, int srcIndex) @Override public int getInt(int rowId) { if (dictionary == null) { - return Platform.getInt(null, data + 4 * rowId); + return Platform.getInt(null, data + 4L * rowId); } else { return dictionary.decodeToInt(dictionaryIds.getDictId(rowId)); } @@ -312,7 +312,7 @@ public int getInt(int rowId) { public int[] getInts(int rowId, int count) { assert(dictionary == null); int[] array = new int[count]; - Platform.copyMemory(null, data + rowId * 4, array, Platform.INT_ARRAY_OFFSET, count * 4); + Platform.copyMemory(null, data + rowId * 4L, array, Platform.INT_ARRAY_OFFSET, count * 4L); return array; } @@ -324,7 +324,7 @@ public int[] getInts(int rowId, int count) { public int getDictId(int rowId) { assert(dictionary == null) : "A ColumnVector dictionary should not have a dictionary for itself."; - return Platform.getInt(null, data + 4 * rowId); + return Platform.getInt(null, data + 4L * rowId); } // @@ -333,12 +333,12 @@ public int getDictId(int rowId) { @Override public void putLong(int rowId, long value) { - Platform.putLong(null, data + 8 * rowId, value); + Platform.putLong(null, data + 8L * rowId, value); } @Override public void putLongs(int rowId, int count, long value) { - long offset = data + 8 * rowId; + long offset = data + 8L * rowId; for (int i = 0; i < count; ++i, offset += 8) { Platform.putLong(null, offset, value); } @@ -346,24 +346,24 @@ public void putLongs(int rowId, int count, long value) { @Override public void putLongs(int rowId, int count, long[] src, int srcIndex) { - Platform.copyMemory(src, Platform.LONG_ARRAY_OFFSET + srcIndex * 8, - null, data + 8 * rowId, count * 8); + Platform.copyMemory(src, Platform.LONG_ARRAY_OFFSET + srcIndex * 8L, + null, data + 8L * rowId, count * 8L); } @Override public void putLongs(int rowId, int count, byte[] src, int srcIndex) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, - null, data + rowId * 8, count * 8); + null, data + rowId * 8L, count * 8L); } @Override public void putLongsLittleEndian(int rowId, int count, byte[] src, int srcIndex) { if (!bigEndianPlatform) { Platform.copyMemory(src, srcIndex + Platform.BYTE_ARRAY_OFFSET, - null, data + 8 * rowId, count * 8); + null, data + 8L * rowId, count * 8L); } else { int srcOffset = srcIndex + Platform.BYTE_ARRAY_OFFSET; - long offset = data + 8 * rowId; + long offset = data + 8L * rowId; for (int i = 0; i < count; ++i, offset += 8, srcOffset += 8) { Platform.putLong(null, offset, java.lang.Long.reverseBytes(Platform.getLong(src, srcOffset))); @@ -374,7 +374,7 @@ public void putLongsLittleEndian(int rowId, int count, byte[] src, int srcIndex) @Override public long getLong(int rowId) { if (dictionary == null) { - return Platform.getLong(null, data + 8 * rowId); + return Platform.getLong(null, data + 8L * rowId); } else { return dictionary.decodeToLong(dictionaryIds.getDictId(rowId)); } @@ -384,7 +384,7 @@ public long getLong(int rowId) { public long[] getLongs(int rowId, int count) { assert(dictionary == null); long[] array = new long[count]; - Platform.copyMemory(null, data + rowId * 8, array, Platform.LONG_ARRAY_OFFSET, count * 8); + Platform.copyMemory(null, data + rowId * 8L, array, Platform.LONG_ARRAY_OFFSET, count * 8L); return array; } @@ -394,12 +394,12 @@ public long[] getLongs(int rowId, int count) { @Override public void putFloat(int rowId, float value) { - Platform.putFloat(null, data + rowId * 4, value); + Platform.putFloat(null, data + rowId * 4L, value); } @Override public void putFloats(int rowId, int count, float value) { - long offset = data + 4 * rowId; + long offset = data + 4L * rowId; for (int i = 0; i < count; ++i, offset += 4) { Platform.putFloat(null, offset, value); } @@ -407,18 +407,18 @@ public void putFloats(int rowId, int count, float value) { @Override public void putFloats(int rowId, int count, float[] src, int srcIndex) { - Platform.copyMemory(src, Platform.FLOAT_ARRAY_OFFSET + srcIndex * 4, - null, data + 4 * rowId, count * 4); + Platform.copyMemory(src, Platform.FLOAT_ARRAY_OFFSET + srcIndex * 4L, + null, data + 4L * rowId, count * 4L); } @Override public void putFloats(int rowId, int count, byte[] src, int srcIndex) { if (!bigEndianPlatform) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, - null, data + rowId * 4, count * 4); + null, data + rowId * 4L, count * 4L); } else { ByteBuffer bb = ByteBuffer.wrap(src).order(ByteOrder.LITTLE_ENDIAN); - long offset = data + 4 * rowId; + long offset = data + 4L * rowId; for (int i = 0; i < count; ++i, offset += 4) { Platform.putFloat(null, offset, bb.getFloat(srcIndex + (4 * i))); } @@ -428,7 +428,7 @@ public void putFloats(int rowId, int count, byte[] src, int srcIndex) { @Override public float getFloat(int rowId) { if (dictionary == null) { - return Platform.getFloat(null, data + rowId * 4); + return Platform.getFloat(null, data + rowId * 4L); } else { return dictionary.decodeToFloat(dictionaryIds.getDictId(rowId)); } @@ -438,7 +438,7 @@ public float getFloat(int rowId) { public float[] getFloats(int rowId, int count) { assert(dictionary == null); float[] array = new float[count]; - Platform.copyMemory(null, data + rowId * 4, array, Platform.FLOAT_ARRAY_OFFSET, count * 4); + Platform.copyMemory(null, data + rowId * 4L, array, Platform.FLOAT_ARRAY_OFFSET, count * 4L); return array; } @@ -449,12 +449,12 @@ public float[] getFloats(int rowId, int count) { @Override public void putDouble(int rowId, double value) { - Platform.putDouble(null, data + rowId * 8, value); + Platform.putDouble(null, data + rowId * 8L, value); } @Override public void putDoubles(int rowId, int count, double value) { - long offset = data + 8 * rowId; + long offset = data + 8L * rowId; for (int i = 0; i < count; ++i, offset += 8) { Platform.putDouble(null, offset, value); } @@ -462,18 +462,18 @@ public void putDoubles(int rowId, int count, double value) { @Override public void putDoubles(int rowId, int count, double[] src, int srcIndex) { - Platform.copyMemory(src, Platform.DOUBLE_ARRAY_OFFSET + srcIndex * 8, - null, data + 8 * rowId, count * 8); + Platform.copyMemory(src, Platform.DOUBLE_ARRAY_OFFSET + srcIndex * 8L, + null, data + 8L * rowId, count * 8L); } @Override public void putDoubles(int rowId, int count, byte[] src, int srcIndex) { if (!bigEndianPlatform) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, - null, data + rowId * 8, count * 8); + null, data + rowId * 8L, count * 8L); } else { ByteBuffer bb = ByteBuffer.wrap(src).order(ByteOrder.LITTLE_ENDIAN); - long offset = data + 8 * rowId; + long offset = data + 8L * rowId; for (int i = 0; i < count; ++i, offset += 8) { Platform.putDouble(null, offset, bb.getDouble(srcIndex + (8 * i))); } @@ -483,7 +483,7 @@ public void putDoubles(int rowId, int count, byte[] src, int srcIndex) { @Override public double getDouble(int rowId) { if (dictionary == null) { - return Platform.getDouble(null, data + rowId * 8); + return Platform.getDouble(null, data + rowId * 8L); } else { return dictionary.decodeToDouble(dictionaryIds.getDictId(rowId)); } @@ -493,7 +493,7 @@ public double getDouble(int rowId) { public double[] getDoubles(int rowId, int count) { assert(dictionary == null); double[] array = new double[count]; - Platform.copyMemory(null, data + rowId * 8, array, Platform.DOUBLE_ARRAY_OFFSET, count * 8); + Platform.copyMemory(null, data + rowId * 8L, array, Platform.DOUBLE_ARRAY_OFFSET, count * 8L); return array; } @@ -503,26 +503,26 @@ public double[] getDoubles(int rowId, int count) { @Override public void putArray(int rowId, int offset, int length) { assert(offset >= 0 && offset + length <= childColumns[0].capacity); - Platform.putInt(null, lengthData + 4 * rowId, length); - Platform.putInt(null, offsetData + 4 * rowId, offset); + Platform.putInt(null, lengthData + 4L * rowId, length); + Platform.putInt(null, offsetData + 4L * rowId, offset); } @Override public int getArrayLength(int rowId) { - return Platform.getInt(null, lengthData + 4 * rowId); + return Platform.getInt(null, lengthData + 4L * rowId); } @Override public int getArrayOffset(int rowId) { - return Platform.getInt(null, offsetData + 4 * rowId); + return Platform.getInt(null, offsetData + 4L * rowId); } // APIs dealing with ByteArrays @Override public int putByteArray(int rowId, byte[] value, int offset, int length) { int result = arrayData().appendBytes(length, value, offset); - Platform.putInt(null, lengthData + 4 * rowId, length); - Platform.putInt(null, offsetData + 4 * rowId, result); + Platform.putInt(null, lengthData + 4L * rowId, length); + Platform.putInt(null, offsetData + 4L * rowId, result); return result; } @@ -530,21 +530,21 @@ public int putByteArray(int rowId, byte[] value, int offset, int length) { @Override protected void reserveInternal(int newCapacity) { int oldCapacity = (nulls == 0L) ? 0 : capacity; - if (isArray()) { + if (isArray() || type instanceof MapType) { this.lengthData = - Platform.reallocateMemory(lengthData, oldCapacity * 4, newCapacity * 4); + Platform.reallocateMemory(lengthData, oldCapacity * 4L, newCapacity * 4L); this.offsetData = - Platform.reallocateMemory(offsetData, oldCapacity * 4, newCapacity * 4); + Platform.reallocateMemory(offsetData, oldCapacity * 4L, newCapacity * 4L); } else if (type instanceof ByteType || type instanceof BooleanType) { this.data = Platform.reallocateMemory(data, oldCapacity, newCapacity); } else if (type instanceof ShortType) { - this.data = Platform.reallocateMemory(data, oldCapacity * 2, newCapacity * 2); + this.data = Platform.reallocateMemory(data, oldCapacity * 2L, newCapacity * 2L); } else if (type instanceof IntegerType || type instanceof FloatType || type instanceof DateType || DecimalType.is32BitDecimalType(type)) { - this.data = Platform.reallocateMemory(data, oldCapacity * 4, newCapacity * 4); + this.data = Platform.reallocateMemory(data, oldCapacity * 4L, newCapacity * 4L); } else if (type instanceof LongType || type instanceof DoubleType || DecimalType.is64BitDecimalType(type) || type instanceof TimestampType) { - this.data = Platform.reallocateMemory(data, oldCapacity * 8, newCapacity * 8); + this.data = Platform.reallocateMemory(data, oldCapacity * 8L, newCapacity * 8L); } else if (childColumns != null) { // Nothing to store. } else { diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OnHeapColumnVector.java b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OnHeapColumnVector.java index 1d538fe4181b7..577eab6ed14c8 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OnHeapColumnVector.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OnHeapColumnVector.java @@ -69,7 +69,7 @@ public static OnHeapColumnVector[] allocateColumns(int capacity, StructField[] f private float[] floatData; private double[] doubleData; - // Only set if type is Array. + // Only set if type is Array or Map. private int[] arrayLengths; private int[] arrayOffsets; @@ -119,7 +119,7 @@ public void putNulls(int rowId, int count) { @Override public void putNotNulls(int rowId, int count) { - if (numNulls == 0) return; + if (!hasNull()) return; for (int i = 0; i < count; ++i) { nulls[rowId + i] = (byte)0; } @@ -231,7 +231,7 @@ public void putShorts(int rowId, int count, short[] src, int srcIndex) { @Override public void putShorts(int rowId, int count, byte[] src, int srcIndex) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, shortData, - Platform.SHORT_ARRAY_OFFSET + rowId * 2, count * 2); + Platform.SHORT_ARRAY_OFFSET + rowId * 2L, count * 2L); } @Override @@ -276,7 +276,7 @@ public void putInts(int rowId, int count, int[] src, int srcIndex) { @Override public void putInts(int rowId, int count, byte[] src, int srcIndex) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, intData, - Platform.INT_ARRAY_OFFSET + rowId * 4, count * 4); + Platform.INT_ARRAY_OFFSET + rowId * 4L, count * 4L); } @Override @@ -342,7 +342,7 @@ public void putLongs(int rowId, int count, long[] src, int srcIndex) { @Override public void putLongs(int rowId, int count, byte[] src, int srcIndex) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, longData, - Platform.LONG_ARRAY_OFFSET + rowId * 8, count * 8); + Platform.LONG_ARRAY_OFFSET + rowId * 8L, count * 8L); } @Override @@ -394,7 +394,7 @@ public void putFloats(int rowId, int count, float[] src, int srcIndex) { public void putFloats(int rowId, int count, byte[] src, int srcIndex) { if (!bigEndianPlatform) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, floatData, - Platform.DOUBLE_ARRAY_OFFSET + rowId * 4, count * 4); + Platform.DOUBLE_ARRAY_OFFSET + rowId * 4L, count * 4L); } else { ByteBuffer bb = ByteBuffer.wrap(src).order(ByteOrder.LITTLE_ENDIAN); for (int i = 0; i < count; ++i) { @@ -443,7 +443,7 @@ public void putDoubles(int rowId, int count, double[] src, int srcIndex) { public void putDoubles(int rowId, int count, byte[] src, int srcIndex) { if (!bigEndianPlatform) { Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET + srcIndex, doubleData, - Platform.DOUBLE_ARRAY_OFFSET + rowId * 8, count * 8); + Platform.DOUBLE_ARRAY_OFFSET + rowId * 8L, count * 8L); } else { ByteBuffer bb = ByteBuffer.wrap(src).order(ByteOrder.LITTLE_ENDIAN); for (int i = 0; i < count; ++i) { @@ -503,7 +503,7 @@ public int putByteArray(int rowId, byte[] value, int offset, int length) { // Spilt this function out since it is the slow path. @Override protected void reserveInternal(int newCapacity) { - if (isArray()) { + if (isArray() || type instanceof MapType) { int[] newLengths = new int[newCapacity]; int[] newOffsets = new int[newCapacity]; if (this.arrayLengths != null) { diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/WritableColumnVector.java b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/WritableColumnVector.java index 5f6f125976e12..36a92b66e988a 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/WritableColumnVector.java +++ b/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/WritableColumnVector.java @@ -23,6 +23,9 @@ import org.apache.spark.sql.internal.SQLConf; import org.apache.spark.sql.types.*; +import org.apache.spark.sql.vectorized.ColumnVector; +import org.apache.spark.sql.vectorized.ColumnarArray; +import org.apache.spark.sql.vectorized.ColumnarMap; import org.apache.spark.unsafe.array.ByteArrayMethods; import org.apache.spark.unsafe.types.UTF8String; @@ -57,8 +60,8 @@ public void reset() { elementsAppended = 0; if (numNulls > 0) { putNotNulls(0, capacity); + numNulls = 0; } - numNulls = 0; } @Override @@ -78,7 +81,9 @@ public void close() { } public void reserve(int requiredCapacity) { - if (requiredCapacity > capacity) { + if (requiredCapacity < 0) { + throwUnsupportedException(requiredCapacity, null); + } else if (requiredCapacity > capacity) { int newCapacity = (int) Math.min(MAX_CAPACITY, requiredCapacity * 2L); if (requiredCapacity <= newCapacity) { try { @@ -93,13 +98,19 @@ public void reserve(int requiredCapacity) { } private void throwUnsupportedException(int requiredCapacity, Throwable cause) { - String message = "Cannot reserve additional contiguous bytes in the vectorized reader " + - "(requested = " + requiredCapacity + " bytes). As a workaround, you can disable the " + - "vectorized reader by setting " + SQLConf.PARQUET_VECTORIZED_READER_ENABLED().key() + - " to false."; + String message = "Cannot reserve additional contiguous bytes in the vectorized reader (" + + (requiredCapacity >= 0 ? "requested " + requiredCapacity + " bytes" : "integer overflow") + + "). As a workaround, you can disable the vectorized reader. For parquet file format, " + + "refer to " + SQLConf.PARQUET_VECTORIZED_READER_ENABLED().key() + "; for orc file format," + + " refer to " + SQLConf.ORC_VECTORIZED_READER_ENABLED().key() + "."; throw new RuntimeException(message, cause); } + @Override + public boolean hasNull() { + return numNulls > 0; + } + @Override public int numNulls() { return numNulls; } @@ -330,6 +341,7 @@ public final int putByteArray(int rowId, byte[] value) { @Override public Decimal getDecimal(int rowId, int precision, int scale) { + if (isNullAt(rowId)) return null; if (precision <= Decimal.MAX_INT_DIGITS()) { return Decimal.createUnsafe(getInt(rowId), precision, scale); } else if (precision <= Decimal.MAX_LONG_DIGITS()) { @@ -356,6 +368,7 @@ public void putDecimal(int rowId, Decimal value, int precision) { @Override public UTF8String getUTF8String(int rowId) { + if (isNullAt(rowId)) return null; if (dictionary == null) { return arrayData().getBytesAsUTF8String(getArrayOffset(rowId), getArrayLength(rowId)); } else { @@ -373,6 +386,7 @@ public UTF8String getUTF8String(int rowId) { @Override public byte[] getBinary(int rowId) { + if (isNullAt(rowId)) return null; if (dictionary == null) { return arrayData().getBytes(getArrayOffset(rowId), getArrayLength(rowId)); } else { @@ -585,11 +599,11 @@ public final int appendArray(int length) { public final int appendStruct(boolean isNull) { if (isNull) { appendNull(); - for (ColumnVector c: childColumns) { + for (WritableColumnVector c: childColumns) { if (c.type instanceof StructType) { - ((WritableColumnVector) c).appendStruct(true); + c.appendStruct(true); } else { - ((WritableColumnVector) c).appendNull(); + c.appendNull(); } } } else { @@ -598,17 +612,32 @@ public final int appendStruct(boolean isNull) { return elementsAppended; } - /** - * Returns the data for the underlying array. - */ + // `WritableColumnVector` puts the data of array in the first child column vector, and puts the + // array offsets and lengths in the current column vector. @Override - public WritableColumnVector arrayData() { return childColumns[0]; } + public final ColumnarArray getArray(int rowId) { + if (isNullAt(rowId)) return null; + return new ColumnarArray(arrayData(), getArrayOffset(rowId), getArrayLength(rowId)); + } - /** - * Returns the ordinal's child data column. - */ + // `WritableColumnVector` puts the key array in the first child column vector, value array in the + // second child column vector, and puts the offsets and lengths in the current column vector. @Override - public WritableColumnVector getChildColumn(int ordinal) { return childColumns[ordinal]; } + public final ColumnarMap getMap(int rowId) { + if (isNullAt(rowId)) return null; + return new ColumnarMap(getChild(0), getChild(1), getArrayOffset(rowId), getArrayLength(rowId)); + } + + public WritableColumnVector arrayData() { + return childColumns[0]; + } + + public abstract int getArrayLength(int rowId); + + public abstract int getArrayOffset(int rowId); + + @Override + public WritableColumnVector getChild(int ordinal) { return childColumns[ordinal]; } /** * Returns the elements appended. @@ -692,6 +721,11 @@ protected WritableColumnVector(int capacity, DataType type) { for (int i = 0; i < childColumns.length; ++i) { this.childColumns[i] = reserveNewColumn(capacity, st.fields()[i].dataType()); } + } else if (type instanceof MapType) { + MapType mapType = (MapType) type; + this.childColumns = new WritableColumnVector[2]; + this.childColumns[0] = reserveNewColumn(capacity, mapType.keyType()); + this.childColumns[1] = reserveNewColumn(capacity, mapType.valueType()); } else if (type instanceof CalendarIntervalType) { // Two columns. Months as int. Microseconds as Long. this.childColumns = new WritableColumnVector[2]; diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/ContinuousReadSupport.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ContinuousReadSupport.java similarity index 87% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/ContinuousReadSupport.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/ContinuousReadSupport.java index 3136cee1f655f..7df5a451ae5f3 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/ContinuousReadSupport.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ContinuousReadSupport.java @@ -15,19 +15,19 @@ * limitations under the License. */ -package org.apache.spark.sql.sources.v2.streaming; +package org.apache.spark.sql.sources.v2; import java.util.Optional; -import org.apache.spark.sql.sources.v2.DataSourceV2; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; -import org.apache.spark.sql.sources.v2.streaming.reader.ContinuousReader; +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.sources.v2.reader.streaming.ContinuousReader; import org.apache.spark.sql.types.StructType; /** * A mix-in interface for {@link DataSourceV2}. Data sources can implement this interface to * provide data reading ability for continuous stream processing. */ +@InterfaceStability.Evolving public interface ContinuousReadSupport extends DataSourceV2 { /** * Creates a {@link ContinuousReader} to scan the data from this data source. @@ -42,5 +42,5 @@ public interface ContinuousReadSupport extends DataSourceV2 { ContinuousReader createContinuousReader( Optional schema, String checkpointLocation, - DataSourceV2Options options); + DataSourceOptions options); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/DataSourceV2Options.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/DataSourceOptions.java similarity index 94% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/DataSourceV2Options.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/DataSourceOptions.java index ddc2acca693ac..c32053580f016 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/DataSourceV2Options.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/DataSourceOptions.java @@ -29,18 +29,18 @@ * data source options. */ @InterfaceStability.Evolving -public class DataSourceV2Options { +public class DataSourceOptions { private final Map keyLowerCasedMap; private String toLowerCase(String key) { return key.toLowerCase(Locale.ROOT); } - public static DataSourceV2Options empty() { - return new DataSourceV2Options(new HashMap<>()); + public static DataSourceOptions empty() { + return new DataSourceOptions(new HashMap<>()); } - public DataSourceV2Options(Map originalMap) { + public DataSourceOptions(Map originalMap) { keyLowerCasedMap = new HashMap<>(originalMap.size()); for (Map.Entry entry : originalMap.entrySet()) { keyLowerCasedMap.put(toLowerCase(entry.getKey()), entry.getValue()); diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/MicroBatchReadSupport.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/MicroBatchReadSupport.java similarity index 82% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/MicroBatchReadSupport.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/MicroBatchReadSupport.java index 3c87a3db68243..209ffa7a0b9fa 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/MicroBatchReadSupport.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/MicroBatchReadSupport.java @@ -15,14 +15,12 @@ * limitations under the License. */ -package org.apache.spark.sql.sources.v2.streaming; +package org.apache.spark.sql.sources.v2; import java.util.Optional; import org.apache.spark.annotation.InterfaceStability; -import org.apache.spark.sql.sources.v2.DataSourceV2; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; -import org.apache.spark.sql.sources.v2.streaming.reader.MicroBatchReader; +import org.apache.spark.sql.sources.v2.reader.streaming.MicroBatchReader; import org.apache.spark.sql.types.StructType; /** @@ -36,8 +34,8 @@ public interface MicroBatchReadSupport extends DataSourceV2 { * streaming query. * * The execution engine will create a micro-batch reader at the start of a streaming query, - * alternate calls to setOffsetRange and createReadTasks for each batch to process, and then - * call stop() when the execution is complete. Note that a single query may have multiple + * alternate calls to setOffsetRange and createDataReaderFactories for each batch to process, and + * then call stop() when the execution is complete. Note that a single query may have multiple * executions due to restart or failure recovery. * * @param schema the user provided schema, or empty() if none was provided @@ -50,5 +48,5 @@ public interface MicroBatchReadSupport extends DataSourceV2 { MicroBatchReader createMicroBatchReader( Optional schema, String checkpointLocation, - DataSourceV2Options options); + DataSourceOptions options); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupport.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupport.java index 948e20bacf4a2..0ea4dc6b5def3 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupport.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupport.java @@ -18,17 +18,17 @@ package org.apache.spark.sql.sources.v2; import org.apache.spark.annotation.InterfaceStability; -import org.apache.spark.sql.sources.v2.reader.DataSourceV2Reader; +import org.apache.spark.sql.sources.v2.reader.DataSourceReader; /** * A mix-in interface for {@link DataSourceV2}. Data sources can implement this interface to * provide data reading ability and scan the data from the data source. */ @InterfaceStability.Evolving -public interface ReadSupport { +public interface ReadSupport extends DataSourceV2 { /** - * Creates a {@link DataSourceV2Reader} to scan the data from this data source. + * Creates a {@link DataSourceReader} to scan the data from this data source. * * If this method fails (by throwing an exception), the action would fail and no Spark job was * submitted. @@ -36,5 +36,5 @@ public interface ReadSupport { * @param options the options for the returned data source reader, which is an immutable * case-insensitive string-to-string map. */ - DataSourceV2Reader createReader(DataSourceV2Options options); + DataSourceReader createReader(DataSourceOptions options); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupportWithSchema.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupportWithSchema.java index b69c6bed8d1b5..3801402268af1 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupportWithSchema.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupportWithSchema.java @@ -18,7 +18,7 @@ package org.apache.spark.sql.sources.v2; import org.apache.spark.annotation.InterfaceStability; -import org.apache.spark.sql.sources.v2.reader.DataSourceV2Reader; +import org.apache.spark.sql.sources.v2.reader.DataSourceReader; import org.apache.spark.sql.types.StructType; /** @@ -30,10 +30,10 @@ * supports both schema inference and user-specified schema. */ @InterfaceStability.Evolving -public interface ReadSupportWithSchema { +public interface ReadSupportWithSchema extends DataSourceV2 { /** - * Create a {@link DataSourceV2Reader} to scan the data from this data source. + * Create a {@link DataSourceReader} to scan the data from this data source. * * If this method fails (by throwing an exception), the action would fail and no Spark job was * submitted. @@ -45,5 +45,5 @@ public interface ReadSupportWithSchema { * @param options the options for the returned data source reader, which is an immutable * case-insensitive string-to-string map. */ - DataSourceV2Reader createReader(StructType schema, DataSourceV2Options options); + DataSourceReader createReader(StructType schema, DataSourceOptions options); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/SessionConfigSupport.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/SessionConfigSupport.java index 3cb020d2e0836..9d66805d79b9e 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/SessionConfigSupport.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/SessionConfigSupport.java @@ -25,7 +25,7 @@ * session. */ @InterfaceStability.Evolving -public interface SessionConfigSupport { +public interface SessionConfigSupport extends DataSourceV2 { /** * Key prefix of the session configs to propagate. Spark will extract all session configs that diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/ContinuousWriteSupport.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/StreamWriteSupport.java similarity index 71% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/ContinuousWriteSupport.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/StreamWriteSupport.java index dee493cadb71e..a77b01497269e 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/ContinuousWriteSupport.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/StreamWriteSupport.java @@ -15,42 +15,38 @@ * limitations under the License. */ -package org.apache.spark.sql.sources.v2.streaming; - -import java.util.Optional; +package org.apache.spark.sql.sources.v2; import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.execution.streaming.BaseStreamingSink; -import org.apache.spark.sql.sources.v2.DataSourceV2; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; -import org.apache.spark.sql.sources.v2.streaming.writer.ContinuousWriter; -import org.apache.spark.sql.sources.v2.writer.DataSourceV2Writer; +import org.apache.spark.sql.sources.v2.writer.DataSourceWriter; +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter; import org.apache.spark.sql.streaming.OutputMode; import org.apache.spark.sql.types.StructType; /** * A mix-in interface for {@link DataSourceV2}. Data sources can implement this interface to - * provide data writing ability for continuous stream processing. + * provide data writing ability for structured streaming. */ @InterfaceStability.Evolving -public interface ContinuousWriteSupport extends BaseStreamingSink { +public interface StreamWriteSupport extends DataSourceV2, BaseStreamingSink { /** - * Creates an optional {@link ContinuousWriter} to save the data to this data source. Data + * Creates an optional {@link StreamWriter} to save the data to this data source. Data * sources can return None if there is no writing needed to be done. * * @param queryId A unique string for the writing query. It's possible that there are many * writing queries running at the same time, and the returned - * {@link DataSourceV2Writer} can use this id to distinguish itself from others. + * {@link DataSourceWriter} can use this id to distinguish itself from others. * @param schema the schema of the data to be written. * @param mode the output mode which determines what successive epoch output means to this * sink, please refer to {@link OutputMode} for more details. * @param options the options for the returned data source writer, which is an immutable * case-insensitive string-to-string map. */ - Optional createContinuousWriter( + StreamWriter createStreamWriter( String queryId, StructType schema, OutputMode mode, - DataSourceV2Options options); + DataSourceOptions options); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/WriteSupport.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/WriteSupport.java index 1e3b644d8c4ae..cab56453816cc 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/WriteSupport.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/WriteSupport.java @@ -21,7 +21,7 @@ import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.SaveMode; -import org.apache.spark.sql.sources.v2.writer.DataSourceV2Writer; +import org.apache.spark.sql.sources.v2.writer.DataSourceWriter; import org.apache.spark.sql.types.StructType; /** @@ -29,17 +29,17 @@ * provide data writing ability and save the data to the data source. */ @InterfaceStability.Evolving -public interface WriteSupport { +public interface WriteSupport extends DataSourceV2 { /** - * Creates an optional {@link DataSourceV2Writer} to save the data to this data source. Data + * Creates an optional {@link DataSourceWriter} to save the data to this data source. Data * sources can return None if there is no writing needed to be done according to the save mode. * * If this method fails (by throwing an exception), the action would fail and no Spark job was * submitted. * * @param jobId A unique string for the writing job. It's possible that there are many writing - * jobs running at the same time, and the returned {@link DataSourceV2Writer} can + * jobs running at the same time, and the returned {@link DataSourceWriter} can * use this job id to distinguish itself from other jobs. * @param schema the schema of the data to be written. * @param mode the save mode which determines what to do when the data are already in this data @@ -47,6 +47,6 @@ public interface WriteSupport { * @param options the options for the returned data source writer, which is an immutable * case-insensitive string-to-string map. */ - Optional createWriter( - String jobId, StructType schema, SaveMode mode, DataSourceV2Options options); + Optional createWriter( + String jobId, StructType schema, SaveMode mode, DataSourceOptions options); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataReader.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataReader.java index 8f58c865b6201..bb9790a1c819e 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataReader.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataReader.java @@ -23,7 +23,7 @@ import org.apache.spark.annotation.InterfaceStability; /** - * A data reader returned by {@link ReadTask#createDataReader()} and is responsible for + * A data reader returned by {@link DataReaderFactory#createDataReader()} and is responsible for * outputting data for a RDD partition. * * Note that, Currently the type `T` can only be {@link org.apache.spark.sql.Row} for normal data diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/ReadTask.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataReaderFactory.java similarity index 65% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/ReadTask.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataReaderFactory.java index fa161cdb8b347..32e98e8f5d8bd 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/ReadTask.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataReaderFactory.java @@ -22,21 +22,23 @@ import org.apache.spark.annotation.InterfaceStability; /** - * A read task returned by {@link DataSourceV2Reader#createReadTasks()} and is responsible for - * creating the actual data reader. The relationship between {@link ReadTask} and {@link DataReader} + * A reader factory returned by {@link DataSourceReader#createDataReaderFactories()} and is + * responsible for creating the actual data reader. The relationship between + * {@link DataReaderFactory} and {@link DataReader} * is similar to the relationship between {@link Iterable} and {@link java.util.Iterator}. * - * Note that, the read task will be serialized and sent to executors, then the data reader will be - * created on executors and do the actual reading. So {@link ReadTask} must be serializable and - * {@link DataReader} doesn't need to be. + * Note that, the reader factory will be serialized and sent to executors, then the data reader + * will be created on executors and do the actual reading. So {@link DataReaderFactory} must be + * serializable and {@link DataReader} doesn't need to be. */ @InterfaceStability.Evolving -public interface ReadTask extends Serializable { +public interface DataReaderFactory extends Serializable { /** - * The preferred locations where this read task can run faster, but Spark does not guarantee that - * this task will always run on these locations. The implementations should make sure that it can - * be run on any location. The location is a string representing the host name. + * The preferred locations where the data reader returned by this reader factory can run faster, + * but Spark does not guarantee to run the data reader on these locations. + * The implementations should make sure that it can be run on any location. + * The location is a string representing the host name. * * Note that if a host name cannot be recognized by Spark, it will be ignored as it was not in * the returned locations. By default this method returns empty string array, which means this @@ -50,7 +52,7 @@ default String[] preferredLocations() { } /** - * Returns a data reader to do the actual reading work for this read task. + * Returns a data reader to do the actual reading work. * * If this method fails (by throwing an exception), the corresponding Spark task would fail and * get retried until hitting the maximum retry times. diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataSourceV2Reader.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataSourceReader.java similarity index 70% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataSourceV2Reader.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataSourceReader.java index 95ee4a8278322..a470bccc5aad2 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataSourceV2Reader.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataSourceReader.java @@ -21,16 +21,18 @@ import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.Row; +import org.apache.spark.sql.sources.v2.DataSourceOptions; +import org.apache.spark.sql.sources.v2.ReadSupport; +import org.apache.spark.sql.sources.v2.ReadSupportWithSchema; import org.apache.spark.sql.types.StructType; /** * A data source reader that is returned by - * {@link org.apache.spark.sql.sources.v2.ReadSupport#createReader( - * org.apache.spark.sql.sources.v2.DataSourceV2Options)} or - * {@link org.apache.spark.sql.sources.v2.ReadSupportWithSchema#createReader( - * StructType, org.apache.spark.sql.sources.v2.DataSourceV2Options)}. + * {@link ReadSupport#createReader(DataSourceOptions)} or + * {@link ReadSupportWithSchema#createReader(StructType, DataSourceOptions)}. * It can mix in various query optimization interfaces to speed up the data scan. The actual scan - * logic is delegated to {@link ReadTask}s that are returned by {@link #createReadTasks()}. + * logic is delegated to {@link DataReaderFactory}s that are returned by + * {@link #createDataReaderFactories()}. * * There are mainly 3 kinds of query optimizations: * 1. Operators push-down. E.g., filter push-down, required columns push-down(aka column @@ -38,7 +40,10 @@ * 2. Information Reporting. E.g., statistics reporting, ordering reporting, etc. * Names of these interfaces start with `SupportsReporting`. * 3. Special scans. E.g, columnar scan, unsafe row scan, etc. - * Names of these interfaces start with `SupportsScan`. + * Names of these interfaces start with `SupportsScan`. Note that a reader should only + * implement at most one of the special scans, if more than one special scans are implemented, + * only one of them would be respected, according to the priority list from high to low: + * {@link SupportsScanColumnarBatch}, {@link SupportsScanUnsafeRow}. * * If an exception was throw when applying any of these query optimizations, the action would fail * and no Spark job was submitted. @@ -48,7 +53,7 @@ * issues the scan request and does the actual data reading. */ @InterfaceStability.Evolving -public interface DataSourceV2Reader { +public interface DataSourceReader { /** * Returns the actual schema of this data source reader, which may be different from the physical @@ -60,9 +65,9 @@ public interface DataSourceV2Reader { StructType readSchema(); /** - * Returns a list of read tasks. Each task is responsible for outputting data for one RDD - * partition. That means the number of tasks returned here is same as the number of RDD - * partitions this scan outputs. + * Returns a list of reader factories. Each factory is responsible for creating a data reader to + * output data for one RDD partition. That means the number of factories returned here is same as + * the number of RDD partitions this scan outputs. * * Note that, this may not be a full scan if the data source reader mixes in other optimization * interfaces like column pruning, filter push-down, etc. These optimizations are applied before @@ -71,5 +76,5 @@ public interface DataSourceV2Reader { * If this method fails (by throwing an exception), the action would fail and no Spark job was * submitted. */ - List> createReadTasks(); + List> createDataReaderFactories(); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownCatalystFilters.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownCatalystFilters.java index f76c687f450c8..290d614805ac7 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownCatalystFilters.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownCatalystFilters.java @@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.expressions.Expression; /** - * A mix-in interface for {@link DataSourceV2Reader}. Data source readers can implement this + * A mix-in interface for {@link DataSourceReader}. Data source readers can implement this * interface to push down arbitrary expressions as predicates to the data source. * This is an experimental and unstable interface as {@link Expression} is not public and may get * changed in the future Spark versions. @@ -31,10 +31,10 @@ * process this interface. */ @InterfaceStability.Unstable -public interface SupportsPushDownCatalystFilters { +public interface SupportsPushDownCatalystFilters extends DataSourceReader { /** - * Pushes down filters, and returns unsupported filters. + * Pushes down filters, and returns filters that need to be evaluated after scanning. */ Expression[] pushCatalystFilters(Expression[] filters); diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownFilters.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownFilters.java index 6b0c9d417eeae..1cff024232a44 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownFilters.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownFilters.java @@ -21,7 +21,7 @@ import org.apache.spark.sql.sources.Filter; /** - * A mix-in interface for {@link DataSourceV2Reader}. Data source readers can implement this + * A mix-in interface for {@link DataSourceReader}. Data source readers can implement this * interface to push down filters to the data source and reduce the size of the data to be read. * * Note that, if data source readers implement both this interface and @@ -29,10 +29,10 @@ * {@link SupportsPushDownCatalystFilters}. */ @InterfaceStability.Evolving -public interface SupportsPushDownFilters { +public interface SupportsPushDownFilters extends DataSourceReader { /** - * Pushes down filters, and returns unsupported filters. + * Pushes down filters, and returns filters that need to be evaluated after scanning. */ Filter[] pushFilters(Filter[] filters); diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownRequiredColumns.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownRequiredColumns.java index fe0ac8ee0ee32..427b4d00a1128 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownRequiredColumns.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsPushDownRequiredColumns.java @@ -21,12 +21,12 @@ import org.apache.spark.sql.types.StructType; /** - * A mix-in interface for {@link DataSourceV2Reader}. Data source readers can implement this + * A mix-in interface for {@link DataSourceReader}. Data source readers can implement this * interface to push down required columns to the data source and only read these columns during * scan to reduce the size of the data to be read. */ @InterfaceStability.Evolving -public interface SupportsPushDownRequiredColumns { +public interface SupportsPushDownRequiredColumns extends DataSourceReader { /** * Applies column pruning w.r.t. the given requiredSchema. @@ -35,7 +35,7 @@ public interface SupportsPushDownRequiredColumns { * also OK to do the pruning partially, e.g., a data source may not be able to prune nested * fields, and only prune top-level columns. * - * Note that, data source readers should update {@link DataSourceV2Reader#readSchema()} after + * Note that, data source readers should update {@link DataSourceReader#readSchema()} after * applying column pruning. */ void pruneColumns(StructType requiredSchema); diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsReportPartitioning.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsReportPartitioning.java new file mode 100644 index 0000000000000..5405a916951b8 --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsReportPartitioning.java @@ -0,0 +1,34 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources.v2.reader; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.sources.v2.reader.partitioning.Partitioning; + +/** + * A mix in interface for {@link DataSourceReader}. Data source readers can implement this + * interface to report data partitioning and try to avoid shuffle at Spark side. + */ +@InterfaceStability.Evolving +public interface SupportsReportPartitioning extends DataSourceReader { + + /** + * Returns the output data partitioning that this reader guarantees. + */ + Partitioning outputPartitioning(); +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsReportStatistics.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsReportStatistics.java index c019d2f819ab7..11bb13fd3b211 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsReportStatistics.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsReportStatistics.java @@ -20,11 +20,11 @@ import org.apache.spark.annotation.InterfaceStability; /** - * A mix in interface for {@link DataSourceV2Reader}. Data source readers can implement this + * A mix in interface for {@link DataSourceReader}. Data source readers can implement this * interface to report statistics to Spark. */ @InterfaceStability.Evolving -public interface SupportsReportStatistics { +public interface SupportsReportStatistics extends DataSourceReader { /** * Returns the basic statistics of this data source. diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsScanColumnarBatch.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsScanColumnarBatch.java new file mode 100644 index 0000000000000..2e5cfa78511f0 --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsScanColumnarBatch.java @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources.v2.reader; + +import java.util.List; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.vectorized.ColumnarBatch; + +/** + * A mix-in interface for {@link DataSourceReader}. Data source readers can implement this + * interface to output {@link ColumnarBatch} and make the scan faster. + */ +@InterfaceStability.Evolving +public interface SupportsScanColumnarBatch extends DataSourceReader { + @Override + default List> createDataReaderFactories() { + throw new IllegalStateException( + "createDataReaderFactories not supported by default within SupportsScanColumnarBatch."); + } + + /** + * Similar to {@link DataSourceReader#createDataReaderFactories()}, but returns columnar data + * in batches. + */ + List> createBatchDataReaderFactories(); + + /** + * Returns true if the concrete data source reader can read data in batch according to the scan + * properties like required columns, pushes filters, etc. It's possible that the implementation + * can only support some certain columns with certain types. Users can overwrite this method and + * {@link #createDataReaderFactories()} to fallback to normal read path under some conditions. + */ + default boolean enableBatchRead() { + return true; + } +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsScanUnsafeRow.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsScanUnsafeRow.java index b90ec880dc85e..9cd749e8e4ce9 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsScanUnsafeRow.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/SupportsScanUnsafeRow.java @@ -24,22 +24,23 @@ import org.apache.spark.sql.catalyst.expressions.UnsafeRow; /** - * A mix-in interface for {@link DataSourceV2Reader}. Data source readers can implement this + * A mix-in interface for {@link DataSourceReader}. Data source readers can implement this * interface to output {@link UnsafeRow} directly and avoid the row copy at Spark side. * This is an experimental and unstable interface, as {@link UnsafeRow} is not public and may get * changed in the future Spark versions. */ @InterfaceStability.Unstable -public interface SupportsScanUnsafeRow extends DataSourceV2Reader { +public interface SupportsScanUnsafeRow extends DataSourceReader { @Override - default List> createReadTasks() { + default List> createDataReaderFactories() { throw new IllegalStateException( - "createReadTasks should not be called with SupportsScanUnsafeRow."); + "createDataReaderFactories not supported by default within SupportsScanUnsafeRow"); } /** - * Similar to {@link DataSourceV2Reader#createReadTasks()}, but returns data in unsafe row format. + * Similar to {@link DataSourceReader#createDataReaderFactories()}, + * but returns data in unsafe row format. */ - List> createUnsafeRowReadTasks(); + List> createUnsafeRowReaderFactories(); } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/ClusteredDistribution.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/ClusteredDistribution.java new file mode 100644 index 0000000000000..2d0ee50212b56 --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/ClusteredDistribution.java @@ -0,0 +1,39 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources.v2.reader.partitioning; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.sources.v2.reader.DataReader; + +/** + * A concrete implementation of {@link Distribution}. Represents a distribution where records that + * share the same values for the {@link #clusteredColumns} will be produced by the same + * {@link DataReader}. + */ +@InterfaceStability.Evolving +public class ClusteredDistribution implements Distribution { + + /** + * The names of the clustered columns. Note that they are order insensitive. + */ + public final String[] clusteredColumns; + + public ClusteredDistribution(String[] clusteredColumns) { + this.clusteredColumns = clusteredColumns; + } +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/Distribution.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/Distribution.java new file mode 100644 index 0000000000000..f6b111fdf220d --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/Distribution.java @@ -0,0 +1,40 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources.v2.reader.partitioning; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.sources.v2.reader.DataReader; + +/** + * An interface to represent data distribution requirement, which specifies how the records should + * be distributed among the data partitions(one {@link DataReader} outputs data for one partition). + * Note that this interface has nothing to do with the data ordering inside one + * partition(the output records of a single {@link DataReader}). + * + * The instance of this interface is created and provided by Spark, then consumed by + * {@link Partitioning#satisfy(Distribution)}. This means data source developers don't need to + * implement this interface, but need to catch as more concrete implementations of this interface + * as possible in {@link Partitioning#satisfy(Distribution)}. + * + * Concrete implementations until now: + *
    + *
  • {@link ClusteredDistribution}
    • + *
+ */ +@InterfaceStability.Evolving +public interface Distribution {} diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/Partitioning.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/Partitioning.java new file mode 100644 index 0000000000000..309d9e5de0a0f --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/partitioning/Partitioning.java @@ -0,0 +1,48 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources.v2.reader.partitioning; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.sources.v2.reader.DataReaderFactory; +import org.apache.spark.sql.sources.v2.reader.SupportsReportPartitioning; + +/** + * An interface to represent the output data partitioning for a data source, which is returned by + * {@link SupportsReportPartitioning#outputPartitioning()}. Note that this should work like a + * snapshot. Once created, it should be deterministic and always report the same number of + * partitions and the same "satisfy" result for a certain distribution. + */ +@InterfaceStability.Evolving +public interface Partitioning { + + /** + * Returns the number of partitions(i.e., {@link DataReaderFactory}s) the data source outputs. + */ + int numPartitions(); + + /** + * Returns true if this partitioning can satisfy the given distribution, which means Spark does + * not need to shuffle the output data of this data source for some certain operations. + * + * Note that, Spark may add new concrete implementations of {@link Distribution} in new releases. + * This method should be aware of it and always return false for unrecognized distributions. It's + * recommended to check every Spark new release and support new distributions if possible, to + * avoid shuffle at Spark side for more cases. + */ + boolean satisfy(Distribution distribution); +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/ContinuousDataReader.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/ContinuousDataReader.java similarity index 90% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/ContinuousDataReader.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/ContinuousDataReader.java index ca9a290e97a02..47d26440841fd 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/ContinuousDataReader.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/ContinuousDataReader.java @@ -15,13 +15,15 @@ * limitations under the License. */ -package org.apache.spark.sql.sources.v2.streaming.reader; +package org.apache.spark.sql.sources.v2.reader.streaming; +import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.sources.v2.reader.DataReader; /** * A variation on {@link DataReader} for use with streaming in continuous processing mode. */ +@InterfaceStability.Evolving public interface ContinuousDataReader extends DataReader { /** * Get the offset of the current record, or the start offset if no records have been read. diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/ContinuousReader.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/ContinuousReader.java similarity index 60% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/ContinuousReader.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/ContinuousReader.java index f0b205869ed6c..7fe7f00ac2fa8 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/ContinuousReader.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/ContinuousReader.java @@ -15,23 +15,28 @@ * limitations under the License. */ -package org.apache.spark.sql.sources.v2.streaming.reader; +package org.apache.spark.sql.sources.v2.reader.streaming; +import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.execution.streaming.BaseStreamingSource; -import org.apache.spark.sql.sources.v2.reader.DataSourceV2Reader; +import org.apache.spark.sql.sources.v2.reader.DataSourceReader; import java.util.Optional; /** - * A mix-in interface for {@link DataSourceV2Reader}. Data source readers can implement this + * A mix-in interface for {@link DataSourceReader}. Data source readers can implement this * interface to allow reading in a continuous processing mode stream. * - * Implementations must ensure each read task output is a {@link ContinuousDataReader}. + * Implementations must ensure each reader factory output is a {@link ContinuousDataReader}. + * + * Note: This class currently extends {@link BaseStreamingSource} to maintain compatibility with + * DataSource V1 APIs. This extension will be removed once we get rid of V1 completely. */ -public interface ContinuousReader extends BaseStreamingSource, DataSourceV2Reader { +@InterfaceStability.Evolving +public interface ContinuousReader extends BaseStreamingSource, DataSourceReader { /** - * Merge offsets coming from {@link ContinuousDataReader} instances in each partition to - * a single global offset. + * Merge partitioned offsets coming from {@link ContinuousDataReader} instances for each + * partition to a single global offset. */ Offset mergeOffsets(PartitionOffset[] offsets); @@ -42,23 +47,23 @@ public interface ContinuousReader extends BaseStreamingSource, DataSourceV2Reade Offset deserializeOffset(String json); /** - * Set the desired start offset for read tasks created from this reader. The scan will start - * from the first record after the provided offset, or from an implementation-defined inferred - * starting point if no offset is provided. + * Set the desired start offset for reader factories created from this reader. The scan will + * start from the first record after the provided offset, or from an implementation-defined + * inferred starting point if no offset is provided. */ - void setOffset(Optional start); + void setStartOffset(Optional start); /** * Return the specified or inferred start offset for this reader. * - * @throws IllegalStateException if setOffset has not been called + * @throws IllegalStateException if setStartOffset has not been called */ Offset getStartOffset(); /** - * The execution engine will call this method in every epoch to determine if new read tasks need - * to be generated, which may be required if for example the underlying source system has had - * partitions added or removed. + * The execution engine will call this method in every epoch to determine if new reader + * factories need to be generated, which may be required if for example the underlying + * source system has had partitions added or removed. * * If true, the query will be shut down and restarted with a new reader. */ diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/MicroBatchReader.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/MicroBatchReader.java similarity index 75% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/MicroBatchReader.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/MicroBatchReader.java index 70ff756806032..67ebde30d61a9 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/MicroBatchReader.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/MicroBatchReader.java @@ -15,22 +15,27 @@ * limitations under the License. */ -package org.apache.spark.sql.sources.v2.streaming.reader; +package org.apache.spark.sql.sources.v2.reader.streaming; -import org.apache.spark.sql.sources.v2.reader.DataSourceV2Reader; +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.sources.v2.reader.DataSourceReader; import org.apache.spark.sql.execution.streaming.BaseStreamingSource; import java.util.Optional; /** - * A mix-in interface for {@link DataSourceV2Reader}. Data source readers can implement this + * A mix-in interface for {@link DataSourceReader}. Data source readers can implement this * interface to indicate they allow micro-batch streaming reads. + * + * Note: This class currently extends {@link BaseStreamingSource} to maintain compatibility with + * DataSource V1 APIs. This extension will be removed once we get rid of V1 completely. */ -public interface MicroBatchReader extends DataSourceV2Reader, BaseStreamingSource { +@InterfaceStability.Evolving +public interface MicroBatchReader extends DataSourceReader, BaseStreamingSource { /** - * Set the desired offset range for read tasks created from this reader. Read tasks will - * generate only data within (`start`, `end`]; that is, from the first record after `start` to - * the record with offset `end`. + * Set the desired offset range for reader factories created from this reader. Reader factories + * will generate only data within (`start`, `end`]; that is, from the first record after `start` + * to the record with offset `end`. * * @param start The initial offset to scan from. If not specified, scan from an * implementation-specified start point, such as the earliest available record. diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/Offset.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/Offset.java similarity index 70% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/Offset.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/Offset.java index 60b87f2ac0756..e41c0351edc82 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/Offset.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/Offset.java @@ -15,14 +15,22 @@ * limitations under the License. */ -package org.apache.spark.sql.sources.v2.streaming.reader; +package org.apache.spark.sql.sources.v2.reader.streaming; + +import org.apache.spark.annotation.InterfaceStability; /** - * An abstract representation of progress through a [[MicroBatchReader]] or [[ContinuousReader]]. - * During execution, Offsets provided by the data source implementation will be logged and used as - * restart checkpoints. Sources should provide an Offset implementation which they can use to - * reconstruct the stream position where the offset was taken. + * An abstract representation of progress through a {@link MicroBatchReader} or + * {@link ContinuousReader}. + * During execution, offsets provided by the data source implementation will be logged and used as + * restart checkpoints. Each source should provide an offset implementation which the source can use + * to reconstruct a position in the stream up to which data has been seen/processed. + * + * Note: This class currently extends {@link org.apache.spark.sql.execution.streaming.Offset} to + * maintain compatibility with DataSource V1 APIs. This extension will be removed once we + * get rid of V1 completely. */ +@InterfaceStability.Evolving public abstract class Offset extends org.apache.spark.sql.execution.streaming.Offset { /** * A JSON-serialized representation of an Offset that is @@ -37,7 +45,7 @@ public abstract class Offset extends org.apache.spark.sql.execution.streaming.Of /** * Equality based on JSON string representation. We leverage the * JSON representation for normalization between the Offset's - * in memory and on disk representations. + * in deserialized and serialized representations. */ @Override public boolean equals(Object obj) { diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/PartitionOffset.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/PartitionOffset.java similarity index 88% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/PartitionOffset.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/PartitionOffset.java index eca0085c8a8ce..383e73db6762b 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/reader/PartitionOffset.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/streaming/PartitionOffset.java @@ -15,15 +15,18 @@ * limitations under the License. */ -package org.apache.spark.sql.sources.v2.streaming.reader; +package org.apache.spark.sql.sources.v2.reader.streaming; import java.io.Serializable; +import org.apache.spark.annotation.InterfaceStability; + /** * Used for per-partition offsets in continuous processing. ContinuousReader implementations will * provide a method to merge these into a global Offset. * * These offsets must be serializable. */ +@InterfaceStability.Evolving public interface PartitionOffset extends Serializable { } diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/MicroBatchWriteSupport.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/MicroBatchWriteSupport.java deleted file mode 100644 index 53ffa95ae0f4c..0000000000000 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/MicroBatchWriteSupport.java +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.sources.v2.streaming; - -import java.util.Optional; - -import org.apache.spark.annotation.InterfaceStability; -import org.apache.spark.sql.execution.streaming.BaseStreamingSink; -import org.apache.spark.sql.sources.v2.DataSourceV2; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; -import org.apache.spark.sql.sources.v2.writer.DataSourceV2Writer; -import org.apache.spark.sql.streaming.OutputMode; -import org.apache.spark.sql.types.StructType; - -/** - * A mix-in interface for {@link DataSourceV2}. Data sources can implement this interface to - * provide data writing ability and save the data from a microbatch to the data source. - */ -@InterfaceStability.Evolving -public interface MicroBatchWriteSupport extends BaseStreamingSink { - - /** - * Creates an optional {@link DataSourceV2Writer} to save the data to this data source. Data - * sources can return None if there is no writing needed to be done. - * - * @param queryId A unique string for the writing query. It's possible that there are many writing - * queries running at the same time, and the returned {@link DataSourceV2Writer} - * can use this id to distinguish itself from others. - * @param epochId The unique numeric ID of the batch within this writing query. This is an - * incrementing counter representing a consistent set of data; the same batch may - * be started multiple times in failure recovery scenarios, but it will always - * contain the same records. - * @param schema the schema of the data to be written. - * @param mode the output mode which determines what successive batch output means to this - * sink, please refer to {@link OutputMode} for more details. - * @param options the options for the returned data source writer, which is an immutable - * case-insensitive string-to-string map. - */ - Optional createMicroBatchWriter( - String queryId, - long epochId, - StructType schema, - OutputMode mode, - DataSourceV2Options options); -} diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/writer/ContinuousWriter.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/writer/ContinuousWriter.java deleted file mode 100644 index 723395bd1e963..0000000000000 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/streaming/writer/ContinuousWriter.java +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.sources.v2.streaming.writer; - -import org.apache.spark.annotation.InterfaceStability; -import org.apache.spark.sql.sources.v2.writer.DataSourceV2Writer; -import org.apache.spark.sql.sources.v2.writer.DataWriter; -import org.apache.spark.sql.sources.v2.writer.WriterCommitMessage; - -/** - * A {@link DataSourceV2Writer} for use with continuous stream processing. - */ -@InterfaceStability.Evolving -public interface ContinuousWriter extends DataSourceV2Writer { - /** - * Commits this writing job for the specified epoch with a list of commit messages. The commit - * messages are collected from successful data writers and are produced by - * {@link DataWriter#commit()}. - * - * If this method fails (by throwing an exception), this writing job is considered to have been - * failed, and the execution engine will attempt to call {@link #abort(WriterCommitMessage[])}. - */ - void commit(long epochId, WriterCommitMessage[] messages); - - default void commit(WriterCommitMessage[] messages) { - throw new UnsupportedOperationException( - "Commit without epoch should not be called with ContinuousWriter"); - } -} diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceV2Writer.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceWriter.java similarity index 93% rename from sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceV2Writer.java rename to sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceWriter.java index fc37b9a516f82..3da1be2e0ef76 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceV2Writer.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceWriter.java @@ -20,13 +20,17 @@ import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.Row; import org.apache.spark.sql.SaveMode; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; +import org.apache.spark.sql.sources.v2.DataSourceOptions; +import org.apache.spark.sql.sources.v2.StreamWriteSupport; import org.apache.spark.sql.sources.v2.WriteSupport; +import org.apache.spark.sql.streaming.OutputMode; import org.apache.spark.sql.types.StructType; /** * A data source writer that is returned by - * {@link WriteSupport#createWriter(String, StructType, SaveMode, DataSourceV2Options)}. + * {@link WriteSupport#createWriter(String, StructType, SaveMode, DataSourceOptions)}/ + * {@link StreamWriteSupport#createStreamWriter( + * String, StructType, OutputMode, DataSourceOptions)}. * It can mix in various writing optimization interfaces to speed up the data saving. The actual * writing logic is delegated to {@link DataWriter}. * @@ -49,7 +53,7 @@ * Please refer to the documentation of commit/abort methods for detailed specifications. */ @InterfaceStability.Evolving -public interface DataSourceV2Writer { +public interface DataSourceWriter { /** * Creates a writer factory which will be serialized and sent to executors. diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriter.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriter.java index 04b03e63de500..53941a89ba94e 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriter.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriter.java @@ -33,11 +33,11 @@ * * If this data writer succeeds(all records are successfully written and {@link #commit()} * succeeds), a {@link WriterCommitMessage} will be sent to the driver side and pass to - * {@link DataSourceV2Writer#commit(WriterCommitMessage[])} with commit messages from other data + * {@link DataSourceWriter#commit(WriterCommitMessage[])} with commit messages from other data * writers. If this data writer fails(one record fails to write or {@link #commit()} fails), an * exception will be sent to the driver side, and Spark will retry this writing task for some times, * each time {@link DataWriterFactory#createDataWriter(int, int)} gets a different `attemptNumber`, - * and finally call {@link DataSourceV2Writer#abort(WriterCommitMessage[])} if all retry fail. + * and finally call {@link DataSourceWriter#abort(WriterCommitMessage[])} if all retry fail. * * Besides the retry mechanism, Spark may launch speculative tasks if the existing writing task * takes too long to finish. Different from retried tasks, which are launched one by one after the @@ -69,11 +69,11 @@ public interface DataWriter { /** * Commits this writer after all records are written successfully, returns a commit message which * will be sent back to driver side and passed to - * {@link DataSourceV2Writer#commit(WriterCommitMessage[])}. + * {@link DataSourceWriter#commit(WriterCommitMessage[])}. * * The written data should only be visible to data source readers after - * {@link DataSourceV2Writer#commit(WriterCommitMessage[])} succeeds, which means this method - * should still "hide" the written data and ask the {@link DataSourceV2Writer} at driver side to + * {@link DataSourceWriter#commit(WriterCommitMessage[])} succeeds, which means this method + * should still "hide" the written data and ask the {@link DataSourceWriter} at driver side to * do the final commit via {@link WriterCommitMessage}. * * If this method fails (by throwing an exception), {@link #abort()} will be called and this @@ -91,7 +91,7 @@ public interface DataWriter { * failed. * * If this method fails(by throwing an exception), the underlying data source may have garbage - * that need to be cleaned by {@link DataSourceV2Writer#abort(WriterCommitMessage[])} or manually, + * that need to be cleaned by {@link DataSourceWriter#abort(WriterCommitMessage[])} or manually, * but these garbage should not be visible to data source readers. * * @throws IOException if failure happens during disk/network IO like writing files. diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriterFactory.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriterFactory.java index 18ec792f5a2c9..ea95442511ce5 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriterFactory.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriterFactory.java @@ -22,7 +22,7 @@ import org.apache.spark.annotation.InterfaceStability; /** - * A factory of {@link DataWriter} returned by {@link DataSourceV2Writer#createWriterFactory()}, + * A factory of {@link DataWriter} returned by {@link DataSourceWriter#createWriterFactory()}, * which is responsible for creating and initializing the actual data writer at executor side. * * Note that, the writer factory will be serialized and sent to executors, then the data writer diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/SupportsWriteInternalRow.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/SupportsWriteInternalRow.java index 3e0518814f458..d2cf7e01c08c8 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/SupportsWriteInternalRow.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/SupportsWriteInternalRow.java @@ -22,14 +22,14 @@ import org.apache.spark.sql.catalyst.InternalRow; /** - * A mix-in interface for {@link DataSourceV2Writer}. Data source writers can implement this + * A mix-in interface for {@link DataSourceWriter}. Data source writers can implement this * interface to write {@link InternalRow} directly and avoid the row conversion at Spark side. * This is an experimental and unstable interface, as {@link InternalRow} is not public and may get * changed in the future Spark versions. */ @InterfaceStability.Unstable -public interface SupportsWriteInternalRow extends DataSourceV2Writer { +public interface SupportsWriteInternalRow extends DataSourceWriter { @Override default DataWriterFactory createWriterFactory() { diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/WriterCommitMessage.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/WriterCommitMessage.java index 082d6b5dc409f..9e38836c0edf9 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/WriterCommitMessage.java +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/WriterCommitMessage.java @@ -23,10 +23,10 @@ /** * A commit message returned by {@link DataWriter#commit()} and will be sent back to the driver side - * as the input parameter of {@link DataSourceV2Writer#commit(WriterCommitMessage[])}. + * as the input parameter of {@link DataSourceWriter#commit(WriterCommitMessage[])}. * * This is an empty interface, data sources should define their own message class and use it in - * their {@link DataWriter#commit()} and {@link DataSourceV2Writer#commit(WriterCommitMessage[])} + * their {@link DataWriter#commit()} and {@link DataSourceWriter#commit(WriterCommitMessage[])} * implementations. */ @InterfaceStability.Evolving diff --git a/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/streaming/StreamWriter.java b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/streaming/StreamWriter.java new file mode 100644 index 0000000000000..4913341bd505d --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/streaming/StreamWriter.java @@ -0,0 +1,72 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.sources.v2.writer.streaming; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.sources.v2.writer.DataSourceWriter; +import org.apache.spark.sql.sources.v2.writer.DataWriter; +import org.apache.spark.sql.sources.v2.writer.WriterCommitMessage; + +/** + * A {@link DataSourceWriter} for use with structured streaming. This writer handles commits and + * aborts relative to an epoch ID determined by the execution engine. + * + * {@link DataWriter} implementations generated by a StreamWriter may be reused for multiple epochs, + * and so must reset any internal state after a successful commit. + */ +@InterfaceStability.Evolving +public interface StreamWriter extends DataSourceWriter { + /** + * Commits this writing job for the specified epoch with a list of commit messages. The commit + * messages are collected from successful data writers and are produced by + * {@link DataWriter#commit()}. + * + * If this method fails (by throwing an exception), this writing job is considered to have been + * failed, and the execution engine will attempt to call {@link #abort(WriterCommitMessage[])}. + * + * To support exactly-once processing, writer implementations should ensure that this method is + * idempotent. The execution engine may call commit() multiple times for the same epoch + * in some circumstances. + */ + void commit(long epochId, WriterCommitMessage[] messages); + + /** + * Aborts this writing job because some data writers are failed and keep failing when retry, or + * the Spark job fails with some unknown reasons, or {@link #commit(WriterCommitMessage[])} fails. + * + * If this method fails (by throwing an exception), the underlying data source may require manual + * cleanup. + * + * Unless the abort is triggered by the failure of commit, the given messages should have some + * null slots as there maybe only a few data writers that are committed before the abort + * happens, or some data writers were committed but their commit messages haven't reached the + * driver when the abort is triggered. So this is just a "best effort" for data sources to + * clean up the data left by data writers. + */ + void abort(long epochId, WriterCommitMessage[] messages); + + default void commit(WriterCommitMessage[] messages) { + throw new UnsupportedOperationException( + "Commit without epoch should not be called with StreamWriter"); + } + + default void abort(WriterCommitMessage[] messages) { + throw new UnsupportedOperationException( + "Abort without epoch should not be called with StreamWriter"); + } +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/streaming/Trigger.java b/sql/core/src/main/java/org/apache/spark/sql/streaming/Trigger.java index 33ae9a9e87668..5371a23230c98 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/streaming/Trigger.java +++ b/sql/core/src/main/java/org/apache/spark/sql/streaming/Trigger.java @@ -50,7 +50,7 @@ public static Trigger ProcessingTime(long intervalMs) { * * {{{ * import java.util.concurrent.TimeUnit - * df.writeStream.trigger(ProcessingTime.create(10, TimeUnit.SECONDS)) + * df.writeStream().trigger(Trigger.ProcessingTime(10, TimeUnit.SECONDS)) * }}} * * @since 2.2.0 @@ -66,7 +66,7 @@ public static Trigger ProcessingTime(long interval, TimeUnit timeUnit) { * * {{{ * import scala.concurrent.duration._ - * df.writeStream.trigger(ProcessingTime(10.seconds)) + * df.writeStream.trigger(Trigger.ProcessingTime(10.seconds)) * }}} * @since 2.2.0 */ diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ArrowColumnVector.java b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java similarity index 70% rename from sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ArrowColumnVector.java rename to sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java index af5673e26a501..f8e37e995a17f 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ArrowColumnVector.java +++ b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java @@ -15,38 +15,31 @@ * limitations under the License. */ -package org.apache.spark.sql.execution.vectorized; +package org.apache.spark.sql.vectorized; +import io.netty.buffer.ArrowBuf; import org.apache.arrow.vector.*; import org.apache.arrow.vector.complex.*; import org.apache.arrow.vector.holders.NullableVarCharHolder; +import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.execution.arrow.ArrowUtils; import org.apache.spark.sql.types.*; import org.apache.spark.unsafe.types.UTF8String; /** - * A column vector backed by Apache Arrow. + * A column vector backed by Apache Arrow. Currently calendar interval type and map type are not + * supported. */ +@InterfaceStability.Evolving public final class ArrowColumnVector extends ColumnVector { private final ArrowVectorAccessor accessor; private ArrowColumnVector[] childColumns; - private void ensureAccessible(int index) { - int valueCount = accessor.getValueCount(); - if (index < 0 || index >= valueCount) { - throw new IndexOutOfBoundsException( - String.format("index: %d, valueCount: %d", index, valueCount)); - } - } - - private void ensureAccessible(int index, int count) { - int valueCount = accessor.getValueCount(); - if (index < 0 || index + count > valueCount) { - throw new IndexOutOfBoundsException( - String.format("index range: [%d, %d), valueCount: %d", index, index + count, valueCount)); - } + @Override + public boolean hasNull() { + return accessor.getNullCount() > 0; } @Override @@ -59,218 +52,84 @@ public void close() { if (childColumns != null) { for (int i = 0; i < childColumns.length; i++) { childColumns[i].close(); + childColumns[i] = null; } + childColumns = null; } accessor.close(); } - // - // APIs dealing with nulls - // - @Override public boolean isNullAt(int rowId) { - ensureAccessible(rowId); return accessor.isNullAt(rowId); } - // - // APIs dealing with Booleans - // - @Override public boolean getBoolean(int rowId) { - ensureAccessible(rowId); return accessor.getBoolean(rowId); } - @Override - public boolean[] getBooleans(int rowId, int count) { - ensureAccessible(rowId, count); - boolean[] array = new boolean[count]; - for (int i = 0; i < count; ++i) { - array[i] = accessor.getBoolean(rowId + i); - } - return array; - } - - // - // APIs dealing with Bytes - // - @Override public byte getByte(int rowId) { - ensureAccessible(rowId); return accessor.getByte(rowId); } - @Override - public byte[] getBytes(int rowId, int count) { - ensureAccessible(rowId, count); - byte[] array = new byte[count]; - for (int i = 0; i < count; ++i) { - array[i] = accessor.getByte(rowId + i); - } - return array; - } - - // - // APIs dealing with Shorts - // - @Override public short getShort(int rowId) { - ensureAccessible(rowId); return accessor.getShort(rowId); } - @Override - public short[] getShorts(int rowId, int count) { - ensureAccessible(rowId, count); - short[] array = new short[count]; - for (int i = 0; i < count; ++i) { - array[i] = accessor.getShort(rowId + i); - } - return array; - } - - // - // APIs dealing with Ints - // - @Override public int getInt(int rowId) { - ensureAccessible(rowId); return accessor.getInt(rowId); } - @Override - public int[] getInts(int rowId, int count) { - ensureAccessible(rowId, count); - int[] array = new int[count]; - for (int i = 0; i < count; ++i) { - array[i] = accessor.getInt(rowId + i); - } - return array; - } - - // - // APIs dealing with Longs - // - @Override public long getLong(int rowId) { - ensureAccessible(rowId); return accessor.getLong(rowId); } - @Override - public long[] getLongs(int rowId, int count) { - ensureAccessible(rowId, count); - long[] array = new long[count]; - for (int i = 0; i < count; ++i) { - array[i] = accessor.getLong(rowId + i); - } - return array; - } - - // - // APIs dealing with floats - // - @Override public float getFloat(int rowId) { - ensureAccessible(rowId); return accessor.getFloat(rowId); } - @Override - public float[] getFloats(int rowId, int count) { - ensureAccessible(rowId, count); - float[] array = new float[count]; - for (int i = 0; i < count; ++i) { - array[i] = accessor.getFloat(rowId + i); - } - return array; - } - - // - // APIs dealing with doubles - // - @Override public double getDouble(int rowId) { - ensureAccessible(rowId); return accessor.getDouble(rowId); } - @Override - public double[] getDoubles(int rowId, int count) { - ensureAccessible(rowId, count); - double[] array = new double[count]; - for (int i = 0; i < count; ++i) { - array[i] = accessor.getDouble(rowId + i); - } - return array; - } - - // - // APIs dealing with Arrays - // - - @Override - public int getArrayLength(int rowId) { - ensureAccessible(rowId); - return accessor.getArrayLength(rowId); - } - - @Override - public int getArrayOffset(int rowId) { - ensureAccessible(rowId); - return accessor.getArrayOffset(rowId); - } - - // - // APIs dealing with Decimals - // - @Override public Decimal getDecimal(int rowId, int precision, int scale) { - ensureAccessible(rowId); + if (isNullAt(rowId)) return null; return accessor.getDecimal(rowId, precision, scale); } - // - // APIs dealing with UTF8Strings - // - @Override public UTF8String getUTF8String(int rowId) { - ensureAccessible(rowId); + if (isNullAt(rowId)) return null; return accessor.getUTF8String(rowId); } - // - // APIs dealing with Binaries - // - @Override public byte[] getBinary(int rowId) { - ensureAccessible(rowId); + if (isNullAt(rowId)) return null; return accessor.getBinary(rowId); } - /** - * Returns the data for the underlying array. - */ @Override - public ArrowColumnVector arrayData() { return childColumns[0]; } + public ColumnarArray getArray(int rowId) { + if (isNullAt(rowId)) return null; + return accessor.getArray(rowId); + } - /** - * Returns the ordinal's child data column. - */ @Override - public ArrowColumnVector getChildColumn(int ordinal) { return childColumns[ordinal]; } + public ColumnarMap getMap(int rowId) { + throw new UnsupportedOperationException(); + } + + @Override + public ArrowColumnVector getChild(int ordinal) { return childColumns[ordinal]; } public ArrowColumnVector(ValueVector vector) { super(ArrowUtils.fromArrowField(vector.getField())); @@ -302,11 +161,8 @@ public ArrowColumnVector(ValueVector vector) { } else if (vector instanceof ListVector) { ListVector listVector = (ListVector) vector; accessor = new ArrayAccessor(listVector); - - childColumns = new ArrowColumnVector[1]; - childColumns[0] = new ArrowColumnVector(listVector.getDataVector()); - } else if (vector instanceof MapVector) { - MapVector mapVector = (MapVector) vector; + } else if (vector instanceof NullableMapVector) { + NullableMapVector mapVector = (NullableMapVector) vector; accessor = new StructAccessor(mapVector); childColumns = new ArrowColumnVector[mapVector.size()]; @@ -331,10 +187,6 @@ boolean isNullAt(int rowId) { return vector.isNull(rowId); } - final int getValueCount() { - return vector.getValueCount(); - } - final int getNullCount() { return vector.getNullCount(); } @@ -383,11 +235,7 @@ byte[] getBinary(int rowId) { throw new UnsupportedOperationException(); } - int getArrayLength(int rowId) { - throw new UnsupportedOperationException(); - } - - int getArrayOffset(int rowId) { + ColumnarArray getArray(int rowId) { throw new UnsupportedOperationException(); } } @@ -584,10 +432,12 @@ final long getLong(int rowId) { private static class ArrayAccessor extends ArrowVectorAccessor { private final ListVector accessor; + private final ArrowColumnVector arrayData; ArrayAccessor(ListVector vector) { super(vector); this.accessor = vector; + this.arrayData = new ArrowColumnVector(vector.getDataVector()); } @Override @@ -601,19 +451,26 @@ final boolean isNullAt(int rowId) { } @Override - final int getArrayLength(int rowId) { - return accessor.getInnerValueCountAt(rowId); - } - - @Override - final int getArrayOffset(int rowId) { - return accessor.getOffsetBuffer().getInt(rowId * accessor.OFFSET_WIDTH); + final ColumnarArray getArray(int rowId) { + ArrowBuf offsets = accessor.getOffsetBuffer(); + int index = rowId * accessor.OFFSET_WIDTH; + int start = offsets.getInt(index); + int end = offsets.getInt(index + accessor.OFFSET_WIDTH); + return new ColumnarArray(arrayData, start, end - start); } } + /** + * Any call to "get" method will throw UnsupportedOperationException. + * + * Access struct values in a ArrowColumnVector doesn't use this accessor. Instead, it uses + * getStruct() method defined in the parent class. Any call to "get" method in this class is a + * bug in the code. + * + */ private static class StructAccessor extends ArrowVectorAccessor { - StructAccessor(MapVector vector) { + StructAccessor(NullableMapVector vector) { super(vector); } } diff --git a/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnVector.java b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnVector.java new file mode 100644 index 0000000000000..ad99b450a4809 --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnVector.java @@ -0,0 +1,303 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.sql.vectorized; + +import org.apache.spark.annotation.InterfaceStability; +import org.apache.spark.sql.types.DataType; +import org.apache.spark.sql.types.Decimal; +import org.apache.spark.unsafe.types.CalendarInterval; +import org.apache.spark.unsafe.types.UTF8String; + +/** + * An interface representing in-memory columnar data in Spark. This interface defines the main APIs + * to access the data, as well as their batched versions. The batched versions are considered to be + * faster and preferable whenever possible. + * + * Most of the APIs take the rowId as a parameter. This is the batch local 0-based row id for values + * in this ColumnVector. + * + * Spark only calls specific `get` method according to the data type of this {@link ColumnVector}, + * e.g. if it's int type, Spark is guaranteed to only call {@link #getInt(int)} or + * {@link #getInts(int, int)}. + * + * ColumnVector supports all the data types including nested types. To handle nested types, + * ColumnVector can have children and is a tree structure. Please refer to {@link #getStruct(int)}, + * {@link #getArray(int)} and {@link #getMap(int)} for the details about how to implement nested + * types. + * + * ColumnVector is expected to be reused during the entire data loading process, to avoid allocating + * memory again and again. + * + * ColumnVector is meant to maximize CPU efficiency but not to minimize storage footprint. + * Implementations should prefer computing efficiency over storage efficiency when design the + * format. Since it is expected to reuse the ColumnVector instance while loading data, the storage + * footprint is negligible. + */ +@InterfaceStability.Evolving +public abstract class ColumnVector implements AutoCloseable { + + /** + * Returns the data type of this column vector. + */ + public final DataType dataType() { return type; } + + /** + * Cleans up memory for this column vector. The column vector is not usable after this. + * + * This overwrites `AutoCloseable.close` to remove the `throws` clause, as column vector is + * in-memory and we don't expect any exception to happen during closing. + */ + @Override + public abstract void close(); + + /** + * Returns true if this column vector contains any null values. + */ + public abstract boolean hasNull(); + + /** + * Returns the number of nulls in this column vector. + */ + public abstract int numNulls(); + + /** + * Returns whether the value at rowId is NULL. + */ + public abstract boolean isNullAt(int rowId); + + /** + * Returns the boolean type value for rowId. The return value is undefined and can be anything, + * if the slot for rowId is null. + */ + public abstract boolean getBoolean(int rowId); + + /** + * Gets boolean type values from [rowId, rowId + count). The return values for the null slots + * are undefined and can be anything. + */ + public boolean[] getBooleans(int rowId, int count) { + boolean[] res = new boolean[count]; + for (int i = 0; i < count; i++) { + res[i] = getBoolean(rowId + i); + } + return res; + } + + /** + * Returns the byte type value for rowId. The return value is undefined and can be anything, + * if the slot for rowId is null. + */ + public abstract byte getByte(int rowId); + + /** + * Gets byte type values from [rowId, rowId + count). The return values for the null slots + * are undefined and can be anything. + */ + public byte[] getBytes(int rowId, int count) { + byte[] res = new byte[count]; + for (int i = 0; i < count; i++) { + res[i] = getByte(rowId + i); + } + return res; + } + + /** + * Returns the short type value for rowId. The return value is undefined and can be anything, + * if the slot for rowId is null. + */ + public abstract short getShort(int rowId); + + /** + * Gets short type values from [rowId, rowId + count). The return values for the null slots + * are undefined and can be anything. + */ + public short[] getShorts(int rowId, int count) { + short[] res = new short[count]; + for (int i = 0; i < count; i++) { + res[i] = getShort(rowId + i); + } + return res; + } + + /** + * Returns the int type value for rowId. The return value is undefined and can be anything, + * if the slot for rowId is null. + */ + public abstract int getInt(int rowId); + + /** + * Gets int type values from [rowId, rowId + count). The return values for the null slots + * are undefined and can be anything. + */ + public int[] getInts(int rowId, int count) { + int[] res = new int[count]; + for (int i = 0; i < count; i++) { + res[i] = getInt(rowId + i); + } + return res; + } + + /** + * Returns the long type value for rowId. The return value is undefined and can be anything, + * if the slot for rowId is null. + */ + public abstract long getLong(int rowId); + + /** + * Gets long type values from [rowId, rowId + count). The return values for the null slots + * are undefined and can be anything. + */ + public long[] getLongs(int rowId, int count) { + long[] res = new long[count]; + for (int i = 0; i < count; i++) { + res[i] = getLong(rowId + i); + } + return res; + } + + /** + * Returns the float type value for rowId. The return value is undefined and can be anything, + * if the slot for rowId is null. + */ + public abstract float getFloat(int rowId); + + /** + * Gets float type values from [rowId, rowId + count). The return values for the null slots + * are undefined and can be anything. + */ + public float[] getFloats(int rowId, int count) { + float[] res = new float[count]; + for (int i = 0; i < count; i++) { + res[i] = getFloat(rowId + i); + } + return res; + } + + /** + * Returns the double type value for rowId. The return value is undefined and can be anything, + * if the slot for rowId is null. + */ + public abstract double getDouble(int rowId); + + /** + * Gets double type values from [rowId, rowId + count). The return values for the null slots + * are undefined and can be anything. + */ + public double[] getDoubles(int rowId, int count) { + double[] res = new double[count]; + for (int i = 0; i < count; i++) { + res[i] = getDouble(rowId + i); + } + return res; + } + + /** + * Returns the struct type value for rowId. If the slot for rowId is null, it should return null. + * + * To support struct type, implementations must implement {@link #getChild(int)} and make this + * vector a tree structure. The number of child vectors must be same as the number of fields of + * the struct type, and each child vector is responsible to store the data for its corresponding + * struct field. + */ + public final ColumnarRow getStruct(int rowId) { + if (isNullAt(rowId)) return null; + return new ColumnarRow(this, rowId); + } + + /** + * Returns the array type value for rowId. If the slot for rowId is null, it should return null. + * + * To support array type, implementations must construct an {@link ColumnarArray} and return it in + * this method. {@link ColumnarArray} requires a {@link ColumnVector} that stores the data of all + * the elements of all the arrays in this vector, and an offset and length which points to a range + * in that {@link ColumnVector}, and the range represents the array for rowId. Implementations + * are free to decide where to put the data vector and offsets and lengths. For example, we can + * use the first child vector as the data vector, and store offsets and lengths in 2 int arrays in + * this vector. + */ + public abstract ColumnarArray getArray(int rowId); + + /** + * Returns the map type value for rowId. If the slot for rowId is null, it should return null. + * + * In Spark, map type value is basically a key data array and a value data array. A key from the + * key array with a index and a value from the value array with the same index contribute to + * an entry of this map type value. + * + * To support map type, implementations must construct a {@link ColumnarMap} and return it in + * this method. {@link ColumnarMap} requires a {@link ColumnVector} that stores the data of all + * the keys of all the maps in this vector, and another {@link ColumnVector} that stores the data + * of all the values of all the maps in this vector, and a pair of offset and length which + * specify the range of the key/value array that belongs to the map type value at rowId. + */ + public abstract ColumnarMap getMap(int ordinal); + + /** + * Returns the decimal type value for rowId. If the slot for rowId is null, it should return null. + */ + public abstract Decimal getDecimal(int rowId, int precision, int scale); + + /** + * Returns the string type value for rowId. If the slot for rowId is null, it should return null. + * Note that the returned UTF8String may point to the data of this column vector, please copy it + * if you want to keep it after this column vector is freed. + */ + public abstract UTF8String getUTF8String(int rowId); + + /** + * Returns the binary type value for rowId. If the slot for rowId is null, it should return null. + */ + public abstract byte[] getBinary(int rowId); + + /** + * Returns the calendar interval type value for rowId. If the slot for rowId is null, it should + * return null. + * + * In Spark, calendar interval type value is basically an integer value representing the number of + * months in this interval, and a long value representing the number of microseconds in this + * interval. An interval type vector is the same as a struct type vector with 2 fields: `months` + * and `microseconds`. + * + * To support interval type, implementations must implement {@link #getChild(int)} and define 2 + * child vectors: the first child vector is an int type vector, containing all the month values of + * all the interval values in this vector. The second child vector is a long type vector, + * containing all the microsecond values of all the interval values in this vector. + */ + public final CalendarInterval getInterval(int rowId) { + if (isNullAt(rowId)) return null; + final int months = getChild(0).getInt(rowId); + final long microseconds = getChild(1).getLong(rowId); + return new CalendarInterval(months, microseconds); + } + + /** + * @return child [[ColumnVector]] at the given ordinal. + */ + protected abstract ColumnVector getChild(int ordinal); + + /** + * Data type for this column. + */ + protected DataType type; + + /** + * Sets up the data type of this column vector. + */ + protected ColumnVector(DataType type) { + this.type = type; + } +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarArray.java b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarArray.java similarity index 90% rename from sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarArray.java rename to sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarArray.java index cbc39d1d0aec2..72a192d089b9f 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarArray.java +++ b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarArray.java @@ -14,18 +14,18 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package org.apache.spark.sql.execution.vectorized; +package org.apache.spark.sql.vectorized; +import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.catalyst.util.ArrayData; -import org.apache.spark.sql.catalyst.util.MapData; import org.apache.spark.sql.types.*; import org.apache.spark.unsafe.types.CalendarInterval; import org.apache.spark.unsafe.types.UTF8String; /** - * Array abstraction in {@link ColumnVector}. The instance of this class is intended - * to be reused, callers should copy the data out if it needs to be stored. + * Array abstraction in {@link ColumnVector}. */ +@InterfaceStability.Evolving public final class ColumnarArray extends ArrayData { // The data for this array. This array contains elements from // data[offset] to data[offset + length). @@ -33,7 +33,7 @@ public final class ColumnarArray extends ArrayData { private final int offset; private final int length; - ColumnarArray(ColumnVector data, int offset, int length) { + public ColumnarArray(ColumnVector data, int offset, int length) { this.data = data; this.offset = offset; this.length = length; @@ -134,9 +134,7 @@ public byte[] getBinary(int ordinal) { @Override public CalendarInterval getInterval(int ordinal) { - int month = data.getChildColumn(0).getInt(offset + ordinal); - long microseconds = data.getChildColumn(1).getLong(offset + ordinal); - return new CalendarInterval(month, microseconds); + return data.getInterval(offset + ordinal); } @Override @@ -150,8 +148,8 @@ public ColumnarArray getArray(int ordinal) { } @Override - public MapData getMap(int ordinal) { - throw new UnsupportedOperationException(); + public ColumnarMap getMap(int ordinal) { + return data.getMap(offset + ordinal); } @Override diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarBatch.java b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarBatch.java similarity index 60% rename from sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarBatch.java rename to sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarBatch.java index a9d09aa679726..d206c1df42abb 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarBatch.java +++ b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarBatch.java @@ -14,32 +14,21 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package org.apache.spark.sql.execution.vectorized; +package org.apache.spark.sql.vectorized; import java.util.*; +import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.catalyst.InternalRow; -import org.apache.spark.sql.types.StructType; +import org.apache.spark.sql.execution.vectorized.MutableColumnarRow; /** - * This class is the in memory representation of rows as they are streamed through operators. It - * is designed to maximize CPU efficiency and not storage footprint. Since it is expected that - * each operator allocates one of these objects, the storage footprint on the task is negligible. - * - * The layout is a columnar with values encoded in their native format. Each RowBatch contains - * a horizontal partitioning of the data, split into columns. - * - * The ColumnarBatch supports either on heap or offheap modes with (mostly) the identical API. - * - * TODO: - * - There are many TODOs for the existing APIs. They should throw a not implemented exception. - * - Compaction: The batch and columns should be able to compact based on a selection vector. + * This class wraps multiple ColumnVectors as a row-wise table. It provides a row view of this + * batch so that Spark can access the data row by row. Instance of it is meant to be reused during + * the entire data loading process. */ +@InterfaceStability.Evolving public final class ColumnarBatch { - public static final int DEFAULT_BATCH_SIZE = 4 * 1024; - - private final StructType schema; - private final int capacity; private int numRows; private final ColumnVector[] columns; @@ -57,7 +46,7 @@ public void close() { } /** - * Returns an iterator over the rows in this batch. This skips rows that are filtered out. + * Returns an iterator over the rows in this batch. */ public Iterator rowIterator() { final int maxRows = numRows; @@ -87,22 +76,9 @@ public void remove() { } /** - * Resets the batch for writing. - */ - public void reset() { - for (int i = 0; i < numCols(); ++i) { - if (columns[i] instanceof WritableColumnVector) { - ((WritableColumnVector) columns[i]).reset(); - } - } - this.numRows = 0; - } - - /** - * Sets the number of rows that are valid. + * Sets the number of rows in this batch. */ public void setNumRows(int numRows) { - assert(numRows <= this.capacity); this.numRows = numRows; } @@ -116,16 +92,6 @@ public void setNumRows(int numRows) { */ public int numRows() { return numRows; } - /** - * Returns the schema that makes up this batch. - */ - public StructType schema() { return schema; } - - /** - * Returns the max capacity (in number of rows) for this batch. - */ - public int capacity() { return capacity; } - /** * Returns the column at `ordinal`. */ @@ -140,10 +106,8 @@ public InternalRow getRow(int rowId) { return row; } - public ColumnarBatch(StructType schema, ColumnVector[] columns, int capacity) { - this.schema = schema; + public ColumnarBatch(ColumnVector[] columns) { this.columns = columns; - this.capacity = capacity; this.row = new MutableColumnarRow(columns); } } diff --git a/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarMap.java b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarMap.java new file mode 100644 index 0000000000000..35648e386c4f1 --- /dev/null +++ b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarMap.java @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.vectorized; + +import org.apache.spark.sql.catalyst.util.MapData; + +/** + * Map abstraction in {@link ColumnVector}. + */ +public final class ColumnarMap extends MapData { + private final ColumnarArray keys; + private final ColumnarArray values; + private final int length; + + public ColumnarMap(ColumnVector keys, ColumnVector values, int offset, int length) { + this.length = length; + this.keys = new ColumnarArray(keys, offset, length); + this.values = new ColumnarArray(values, offset, length); + } + + @Override + public int numElements() { return length; } + + @Override + public ColumnarArray keyArray() { + return keys; + } + + @Override + public ColumnarArray valueArray() { + return values; + } + + @Override + public ColumnarMap copy() { + throw new UnsupportedOperationException(); + } +} diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarRow.java b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarRow.java similarity index 70% rename from sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarRow.java rename to sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarRow.java index 8bb33ed5b78c0..f2f2279590023 100644 --- a/sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarRow.java +++ b/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarRow.java @@ -14,27 +14,27 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package org.apache.spark.sql.execution.vectorized; +package org.apache.spark.sql.vectorized; +import org.apache.spark.annotation.InterfaceStability; import org.apache.spark.sql.catalyst.InternalRow; import org.apache.spark.sql.catalyst.expressions.GenericInternalRow; -import org.apache.spark.sql.catalyst.util.MapData; import org.apache.spark.sql.types.*; import org.apache.spark.unsafe.types.CalendarInterval; import org.apache.spark.unsafe.types.UTF8String; /** - * Row abstraction in {@link ColumnVector}. The instance of this class is intended - * to be reused, callers should copy the data out if it needs to be stored. + * Row abstraction in {@link ColumnVector}. */ +@InterfaceStability.Evolving public final class ColumnarRow extends InternalRow { // The data for this row. - // E.g. the value of 3rd int field is `data.getChildColumn(3).getInt(rowId)`. + // E.g. the value of 3rd int field is `data.getChild(3).getInt(rowId)`. private final ColumnVector data; private final int rowId; private final int numFields; - ColumnarRow(ColumnVector data, int rowId) { + public ColumnarRow(ColumnVector data, int rowId) { assert (data.dataType() instanceof StructType); this.data = data; this.rowId = rowId; @@ -54,7 +54,7 @@ public InternalRow copy() { if (isNullAt(i)) { row.setNullAt(i); } else { - DataType dt = data.getChildColumn(i).dataType(); + DataType dt = data.getChild(i).dataType(); if (dt instanceof BooleanType) { row.setBoolean(i, getBoolean(i)); } else if (dt instanceof ByteType) { @@ -94,70 +94,62 @@ public boolean anyNull() { } @Override - public boolean isNullAt(int ordinal) { return data.getChildColumn(ordinal).isNullAt(rowId); } + public boolean isNullAt(int ordinal) { return data.getChild(ordinal).isNullAt(rowId); } @Override - public boolean getBoolean(int ordinal) { return data.getChildColumn(ordinal).getBoolean(rowId); } + public boolean getBoolean(int ordinal) { return data.getChild(ordinal).getBoolean(rowId); } @Override - public byte getByte(int ordinal) { return data.getChildColumn(ordinal).getByte(rowId); } + public byte getByte(int ordinal) { return data.getChild(ordinal).getByte(rowId); } @Override - public short getShort(int ordinal) { return data.getChildColumn(ordinal).getShort(rowId); } + public short getShort(int ordinal) { return data.getChild(ordinal).getShort(rowId); } @Override - public int getInt(int ordinal) { return data.getChildColumn(ordinal).getInt(rowId); } + public int getInt(int ordinal) { return data.getChild(ordinal).getInt(rowId); } @Override - public long getLong(int ordinal) { return data.getChildColumn(ordinal).getLong(rowId); } + public long getLong(int ordinal) { return data.getChild(ordinal).getLong(rowId); } @Override - public float getFloat(int ordinal) { return data.getChildColumn(ordinal).getFloat(rowId); } + public float getFloat(int ordinal) { return data.getChild(ordinal).getFloat(rowId); } @Override - public double getDouble(int ordinal) { return data.getChildColumn(ordinal).getDouble(rowId); } + public double getDouble(int ordinal) { return data.getChild(ordinal).getDouble(rowId); } @Override public Decimal getDecimal(int ordinal, int precision, int scale) { - if (data.getChildColumn(ordinal).isNullAt(rowId)) return null; - return data.getChildColumn(ordinal).getDecimal(rowId, precision, scale); + return data.getChild(ordinal).getDecimal(rowId, precision, scale); } @Override public UTF8String getUTF8String(int ordinal) { - if (data.getChildColumn(ordinal).isNullAt(rowId)) return null; - return data.getChildColumn(ordinal).getUTF8String(rowId); + return data.getChild(ordinal).getUTF8String(rowId); } @Override public byte[] getBinary(int ordinal) { - if (data.getChildColumn(ordinal).isNullAt(rowId)) return null; - return data.getChildColumn(ordinal).getBinary(rowId); + return data.getChild(ordinal).getBinary(rowId); } @Override public CalendarInterval getInterval(int ordinal) { - if (data.getChildColumn(ordinal).isNullAt(rowId)) return null; - final int months = data.getChildColumn(ordinal).getChildColumn(0).getInt(rowId); - final long microseconds = data.getChildColumn(ordinal).getChildColumn(1).getLong(rowId); - return new CalendarInterval(months, microseconds); + return data.getChild(ordinal).getInterval(rowId); } @Override public ColumnarRow getStruct(int ordinal, int numFields) { - if (data.getChildColumn(ordinal).isNullAt(rowId)) return null; - return data.getChildColumn(ordinal).getStruct(rowId); + return data.getChild(ordinal).getStruct(rowId); } @Override public ColumnarArray getArray(int ordinal) { - if (data.getChildColumn(ordinal).isNullAt(rowId)) return null; - return data.getChildColumn(ordinal).getArray(rowId); + return data.getChild(ordinal).getArray(rowId); } @Override - public MapData getMap(int ordinal) { - throw new UnsupportedOperationException(); + public ColumnarMap getMap(int ordinal) { + return data.getChild(ordinal).getMap(rowId); } @Override diff --git a/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister index 6cdfe2fae5642..0259c774bbf4a 100644 --- a/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister +++ b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister @@ -7,3 +7,4 @@ org.apache.spark.sql.execution.datasources.text.TextFileFormat org.apache.spark.sql.execution.streaming.ConsoleSinkProvider org.apache.spark.sql.execution.streaming.TextSocketSourceProvider org.apache.spark.sql.execution.streaming.RateSourceProvider +org.apache.spark.sql.execution.streaming.sources.RateSourceProviderV2 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala index e8d683a578f35..395e1c999f025 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala @@ -74,6 +74,10 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging { * infer the input schema automatically from data. By specifying the schema here, the underlying * data source can skip the schema inference step, and thus speed up data loading. * + * {{{ + * spark.read.schema("a INT, b STRING, c DOUBLE").csv("test.csv") + * }}} + * * @since 2.3.0 */ def schema(schemaString: String): DataFrameReader = { @@ -186,11 +190,14 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging { val cls = DataSource.lookupDataSource(source, sparkSession.sessionState.conf) if (classOf[DataSourceV2].isAssignableFrom(cls)) { val ds = cls.newInstance() - val options = new DataSourceV2Options((extraOptions ++ + val options = new DataSourceOptions((extraOptions ++ DataSourceV2Utils.extractSessionConfigs( ds = ds.asInstanceOf[DataSourceV2], conf = sparkSession.sessionState.conf)).asJava) + // Streaming also uses the data source V2 API. So it may be that the data source implements + // v2, but has no v2 implementation for batch reads. In that case, we fall back to loading + // the dataframe as a v1 source. val reader = (ds, userSpecifiedSchema) match { case (ds: ReadSupportWithSchema, Some(schema)) => ds.createReader(schema, options) @@ -208,23 +215,30 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging { } reader - case _ => - throw new AnalysisException(s"$cls does not support data reading.") + case _ => null // fall back to v1 } - Dataset.ofRows(sparkSession, DataSourceV2Relation(reader)) + if (reader == null) { + loadV1Source(paths: _*) + } else { + Dataset.ofRows(sparkSession, DataSourceV2Relation(reader)) + } } else { - // Code path for data source v1. - sparkSession.baseRelationToDataFrame( - DataSource.apply( - sparkSession, - paths = paths, - userSpecifiedSchema = userSpecifiedSchema, - className = source, - options = extraOptions.toMap).resolveRelation()) + loadV1Source(paths: _*) } } + private def loadV1Source(paths: String*) = { + // Code path for data source v1. + sparkSession.baseRelationToDataFrame( + DataSource.apply( + sparkSession, + paths = paths, + userSpecifiedSchema = userSpecifiedSchema, + className = source, + options = extraOptions.toMap).resolveRelation()) + } + /** * Construct a `DataFrame` representing the database table accessible via JDBC URL * url named table and connection properties. @@ -354,12 +368,12 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging { *
  • `mode` (default `PERMISSIVE`): allows a mode for dealing with corrupt records * during parsing. *
      - *
    • `PERMISSIVE` : sets other fields to `null` when it meets a corrupted record, and puts - * the malformed string into a field configured by `columnNameOfCorruptRecord`. To keep - * corrupt records, an user can set a string type field named `columnNameOfCorruptRecord` - * in an user-defined schema. If a schema does not have the field, it drops corrupt records - * during parsing. When inferring a schema, it implicitly adds a `columnNameOfCorruptRecord` - * field in an output schema.
      • + *
    • `PERMISSIVE` : when it meets a corrupted record, puts the malformed string into a + * field configured by `columnNameOfCorruptRecord`, and sets other fields to `null`. To + * keep corrupt records, an user can set a string type field named + * `columnNameOfCorruptRecord` in an user-defined schema. If a schema does not have the + * field, it drops corrupt records during parsing. When inferring a schema, it implicitly + * adds a `columnNameOfCorruptRecord` field in an output schema.
      • *
    • `DROPMALFORMED` : ignores the whole corrupted records.
      • *
    • `FAILFAST` : throws an exception when it meets corrupted records.
      • *
    @@ -559,12 +573,14 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging { *
  • `mode` (default `PERMISSIVE`): allows a mode for dealing with corrupt records * during parsing. It supports the following case-insensitive modes. *
      - *
    • `PERMISSIVE` : sets other fields to `null` when it meets a corrupted record, and puts - * the malformed string into a field configured by `columnNameOfCorruptRecord`. To keep + *
    • `PERMISSIVE` : when it meets a corrupted record, puts the malformed string into a + * field configured by `columnNameOfCorruptRecord`, and sets other fields to `null`. To keep * corrupt records, an user can set a string type field named `columnNameOfCorruptRecord` * in an user-defined schema. If a schema does not have the field, it drops corrupt records - * during parsing. When a length of parsed CSV tokens is shorter than an expected length - * of a schema, it sets `null` for extra fields.
      • + * during parsing. A record with less/more tokens than schema is not a corrupted record to + * CSV. When it meets a record having fewer tokens than the length of the schema, sets + * `null` to extra fields. When the record has more tokens than the length of the schema, + * it drops extra tokens. *
    • `DROPMALFORMED` : ignores the whole corrupted records.
      • *
    • `FAILFAST` : throws an exception when it meets corrupted records.
      • *
    diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala index 3304f368e1050..ed7a9100cc7f1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala @@ -174,7 +174,8 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) { * predicates on the partitioned columns. In order for partitioning to work well, the number * of distinct values in each column should typically be less than tens of thousands. * - * This is applicable for all file-based data sources (e.g. Parquet, JSON) staring Spark 2.1.0. + * This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark + * 2.1.0. * * @since 1.4.0 */ @@ -188,7 +189,8 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) { * Buckets the output by the given columns. If specified, the output is laid out on the file * system similar to Hive's bucketing scheme. * - * This is applicable for all file-based data sources (e.g. Parquet, JSON) staring Spark 2.1.0. + * This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark + * 2.1.0. * * @since 2.0 */ @@ -202,7 +204,8 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) { /** * Sorts the output in each bucket by the given columns. * - * This is applicable for all file-based data sources (e.g. Parquet, JSON) staring Spark 2.1.0. + * This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark + * 2.1.0. * * @since 2.0 */ @@ -240,7 +243,7 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) { val ds = cls.newInstance() ds match { case ws: WriteSupport => - val options = new DataSourceV2Options((extraOptions ++ + val options = new DataSourceOptions((extraOptions ++ DataSourceV2Utils.extractSessionConfigs( ds = ds.asInstanceOf[DataSourceV2], conf = df.sparkSession.sessionState.conf)).asJava) @@ -255,17 +258,24 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) { } } - case _ => throw new AnalysisException(s"$cls does not support data writing.") + // Streaming also uses the data source V2 API. So it may be that the data source implements + // v2, but has no v2 implementation for batch writes. In that case, we fall back to saving + // as though it's a V1 source. + case _ => saveToV1Source() } } else { - // Code path for data source v1. - runCommand(df.sparkSession, "save") { - DataSource( - sparkSession = df.sparkSession, - className = source, - partitionColumns = partitioningColumns.getOrElse(Nil), - options = extraOptions.toMap).planForWriting(mode, AnalysisBarrier(df.logicalPlan)) - } + saveToV1Source() + } + } + + private def saveToV1Source(): Unit = { + // Code path for data source v1. + runCommand(df.sparkSession, "save") { + DataSource( + sparkSession = df.sparkSession, + className = source, + partitionColumns = partitioningColumns.getOrElse(Nil), + options = extraOptions.toMap).planForWriting(mode, AnalysisBarrier(df.logicalPlan)) } } @@ -304,7 +314,7 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) { if (partitioningColumns.isDefined) { throw new AnalysisException( "insertInto() can't be used together with partitionBy(). " + - "Partition columns have already be defined for the table. " + + "Partition columns have already been defined for the table. " + "It is not necessary to use partitionBy()." ) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala index 77e571272920a..21c92506692e1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala @@ -62,7 +62,11 @@ import org.apache.spark.util.Utils private[sql] object Dataset { def apply[T: Encoder](sparkSession: SparkSession, logicalPlan: LogicalPlan): Dataset[T] = { - new Dataset(sparkSession, logicalPlan, implicitly[Encoder[T]]) + val dataset = new Dataset(sparkSession, logicalPlan, implicitly[Encoder[T]]) + // Eagerly bind the encoder so we verify that the encoder matches the underlying + // schema. The user will get an error if this is not the case. + dataset.deserializer + dataset } def ofRows(sparkSession: SparkSession, logicalPlan: LogicalPlan): DataFrame = { @@ -192,7 +196,7 @@ class Dataset[T] private[sql]( } // Wraps analyzed logical plans with an analysis barrier so we won't traverse/resolve it again. - @transient private val planWithBarrier = AnalysisBarrier(logicalPlan) + @transient private[sql] val planWithBarrier = AnalysisBarrier(logicalPlan) /** * Currently [[ExpressionEncoder]] is the only implementation of [[Encoder]], here we turn the @@ -204,7 +208,7 @@ class Dataset[T] private[sql]( // The deserializer expression which can be used to build a projection and turn rows to objects // of type T, after collecting rows to the driver side. - private val deserializer = + private lazy val deserializer = exprEnc.resolveAndBind(logicalPlan.output, sparkSession.sessionState.analyzer).deserializer private implicit def classTag = exprEnc.clsTag @@ -237,13 +241,20 @@ class Dataset[T] private[sql]( private[sql] def showString( _numRows: Int, truncate: Int = 20, vertical: Boolean = false): String = { val numRows = _numRows.max(0).min(Int.MaxValue - 1) - val takeResult = toDF().take(numRows + 1) + val newDf = toDF() + val castCols = newDf.logicalPlan.output.map { col => + // Since binary types in top-level schema fields have a specific format to print, + // so we do not cast them to strings here. + if (col.dataType == BinaryType) { + Column(col) + } else { + Column(col).cast(StringType) + } + } + val takeResult = newDf.select(castCols: _*).take(numRows + 1) val hasMoreData = takeResult.length > numRows val data = takeResult.take(numRows) - lazy val timeZone = - DateTimeUtils.getTimeZone(sparkSession.sessionState.conf.sessionLocalTimeZone) - // For array values, replace Seq and Array with square brackets // For cells that are beyond `truncate` characters, replace it with the // first `truncate-3` and "..." @@ -252,12 +263,6 @@ class Dataset[T] private[sql]( val str = cell match { case null => "null" case binary: Array[Byte] => binary.map("%02X".format(_)).mkString("[", " ", "]") - case array: Array[_] => array.mkString("[", ", ", "]") - case seq: Seq[_] => seq.mkString("[", ", ", "]") - case d: Date => - DateTimeUtils.dateToString(DateTimeUtils.fromJavaDate(d)) - case ts: Timestamp => - DateTimeUtils.timestampToString(DateTimeUtils.fromJavaTimestamp(ts), timeZone) case _ => cell.toString } if (truncate > 0 && str.length > truncate) { @@ -1193,7 +1198,7 @@ class Dataset[T] private[sql]( def orderBy(sortExprs: Column*): Dataset[T] = sort(sortExprs : _*) /** - * Selects column based on the column name and return it as a [[Column]]. + * Selects column based on the column name and returns it as a [[Column]]. * * @note The column name can also reference to a nested column like `a.b`. * @@ -1219,7 +1224,7 @@ class Dataset[T] private[sql]( } /** - * Selects column based on the column name and return it as a [[Column]]. + * Selects column based on the column name and returns it as a [[Column]]. * * @note The column name can also reference to a nested column like `a.b`. * @@ -1239,7 +1244,7 @@ class Dataset[T] private[sql]( } /** - * Selects column based on the column name specified as a regex and return it as [[Column]]. + * Selects column based on the column name specified as a regex and returns it as [[Column]]. * @group untypedrel * @since 2.3.0 */ @@ -1902,7 +1907,7 @@ class Dataset[T] private[sql]( /** * Returns a new Dataset containing rows in this Dataset but not in another Dataset. - * This is equivalent to `EXCEPT` in SQL. + * This is equivalent to `EXCEPT DISTINCT` in SQL. * * @note Equality checking is performed directly on the encoded representation of the data * and thus is not affected by a custom `equals` function defined on `T`. @@ -2145,6 +2150,9 @@ class Dataset[T] private[sql]( * Returns a new Dataset by adding a column or replacing the existing column that has * the same name. * + * `column`'s expression must only refer to attributes supplied by this Dataset. It is an + * error to add a column that refers to some other Dataset. + * * @group untypedrel * @since 2.0.0 */ @@ -2728,7 +2736,7 @@ class Dataset[T] private[sql]( } /** - * Return an iterator that contains all rows in this Dataset. + * Returns an iterator that contains all rows in this Dataset. * * The iterator will consume as much memory as the largest partition in this Dataset. * @@ -2817,6 +2825,7 @@ class Dataset[T] private[sql]( * * At least one partition-by expression must be specified. * When no explicit sort order is specified, "ascending nulls first" is assumed. + * Note, the rows are not sorted in each partition of the resulting Dataset. * * @group typedrel * @since 2.3.0 @@ -2840,6 +2849,7 @@ class Dataset[T] private[sql]( * * At least one partition-by expression must be specified. * When no explicit sort order is specified, "ascending nulls first" is assumed. + * Note, the rows are not sorted in each partition of the resulting Dataset. * * @group typedrel * @since 2.3.0 @@ -3177,12 +3187,12 @@ class Dataset[T] private[sql]( EvaluatePython.javaToPython(rdd) } - private[sql] def collectToPython(): Int = { + private[sql] def collectToPython(): Array[Any] = { EvaluatePython.registerPicklers() - withNewExecutionId { + withAction("collectToPython", queryExecution) { plan => val toJava: (Any) => Any = EvaluatePython.toJava(_, schema) - val iter = new SerDeUtil.AutoBatchedPickler( - queryExecution.executedPlan.executeCollect().iterator.map(toJava)) + val iter: Iterator[Array[Byte]] = new SerDeUtil.AutoBatchedPickler( + plan.executeCollect().iterator.map(toJava)) PythonRDD.serveIterator(iter, "serve-DataFrame") } } @@ -3190,14 +3200,15 @@ class Dataset[T] private[sql]( /** * Collect a Dataset as ArrowPayload byte arrays and serve to PySpark. */ - private[sql] def collectAsArrowToPython(): Int = { - withNewExecutionId { - val iter = toArrowPayload.collect().iterator.map(_.asPythonSerializable) + private[sql] def collectAsArrowToPython(): Array[Any] = { + withAction("collectAsArrowToPython", queryExecution) { plan => + val iter: Iterator[Array[Byte]] = + toArrowPayload(plan).collect().iterator.map(_.asPythonSerializable) PythonRDD.serveIterator(iter, "serve-Arrow") } } - private[sql] def toPythonIterator(): Int = { + private[sql] def toPythonIterator(): Array[Any] = { withNewExecutionId { PythonRDD.toLocalIteratorAndServe(javaToPython.rdd) } @@ -3301,14 +3312,19 @@ class Dataset[T] private[sql]( } /** Convert to an RDD of ArrowPayload byte arrays */ - private[sql] def toArrowPayload: RDD[ArrowPayload] = { + private[sql] def toArrowPayload(plan: SparkPlan): RDD[ArrowPayload] = { val schemaCaptured = this.schema val maxRecordsPerBatch = sparkSession.sessionState.conf.arrowMaxRecordsPerBatch val timeZoneId = sparkSession.sessionState.conf.sessionLocalTimeZone - queryExecution.toRdd.mapPartitionsInternal { iter => + plan.execute().mapPartitionsInternal { iter => val context = TaskContext.get() ArrowConverters.toPayloadIterator( iter, schemaCaptured, maxRecordsPerBatch, timeZoneId, context) } } + + // This is only used in tests, for now. + private[sql] def toArrowPayload: RDD[ArrowPayload] = { + toArrowPayload(queryExecution.executedPlan) + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala index 6bab21dca0cbd..36f6038aa9485 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala @@ -49,7 +49,7 @@ class KeyValueGroupedDataset[K, V] private[sql]( private implicit val kExprEnc = encoderFor(kEncoder) private implicit val vExprEnc = encoderFor(vEncoder) - private def logicalPlan = queryExecution.analyzed + private def logicalPlan = AnalysisBarrier(queryExecution.analyzed) private def sparkSession = queryExecution.sparkSession /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala index a009c00b0abc5..f79c0da85a73e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala @@ -64,17 +64,17 @@ class RelationalGroupedDataset protected[sql]( groupType match { case RelationalGroupedDataset.GroupByType => Dataset.ofRows( - df.sparkSession, Aggregate(groupingExprs, aliasedAgg, df.logicalPlan)) + df.sparkSession, Aggregate(groupingExprs, aliasedAgg, df.planWithBarrier)) case RelationalGroupedDataset.RollupType => Dataset.ofRows( - df.sparkSession, Aggregate(Seq(Rollup(groupingExprs)), aliasedAgg, df.logicalPlan)) + df.sparkSession, Aggregate(Seq(Rollup(groupingExprs)), aliasedAgg, df.planWithBarrier)) case RelationalGroupedDataset.CubeType => Dataset.ofRows( - df.sparkSession, Aggregate(Seq(Cube(groupingExprs)), aliasedAgg, df.logicalPlan)) + df.sparkSession, Aggregate(Seq(Cube(groupingExprs)), aliasedAgg, df.planWithBarrier)) case RelationalGroupedDataset.PivotType(pivotCol, values) => val aliasedGrps = groupingExprs.map(alias) Dataset.ofRows( - df.sparkSession, Pivot(aliasedGrps, pivotCol, values, aggExprs, df.logicalPlan)) + df.sparkSession, Pivot(aliasedGrps, pivotCol, values, aggExprs, df.planWithBarrier)) } } @@ -434,7 +434,7 @@ class RelationalGroupedDataset protected[sql]( df.exprEnc.schema, groupingAttributes, df.logicalPlan.output, - df.logicalPlan)) + df.planWithBarrier)) } /** @@ -450,8 +450,8 @@ class RelationalGroupedDataset protected[sql]( * workers. */ private[sql] def flatMapGroupsInPandas(expr: PythonUDF): DataFrame = { - require(expr.evalType == PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF, - "Must pass a group map udf") + require(expr.evalType == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF, + "Must pass a grouped map udf") require(expr.dataType.isInstanceOf[StructType], "The returnType of the udf must be a StructType") @@ -460,7 +460,7 @@ class RelationalGroupedDataset protected[sql]( case other => Alias(other, other.toString)() } val groupingAttributes = groupingNamedExpressions.map(_.toAttribute) - val child = df.logicalPlan + val child = df.planWithBarrier val project = Project(groupingNamedExpressions ++ child.output, child) val output = expr.dataType.asInstanceOf[StructType].toAttributes val plan = FlatMapGroupsInPandas(groupingAttributes, expr, output, project) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala b/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala index 272eb844226d4..b699ccd08ff40 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala @@ -44,7 +44,7 @@ import org.apache.spark.sql.sources.BaseRelation import org.apache.spark.sql.streaming._ import org.apache.spark.sql.types.{DataType, StructType} import org.apache.spark.sql.util.ExecutionListenerManager -import org.apache.spark.util.Utils +import org.apache.spark.util.{CallSite, Utils} /** @@ -81,6 +81,9 @@ class SparkSession private( @transient private[sql] val extensions: SparkSessionExtensions) extends Serializable with Closeable with Logging { self => + // The call site where this SparkSession was constructed. + private val creationSite: CallSite = Utils.getCallSite() + private[sql] def this(sc: SparkContext) { this(sc, None, None, new SparkSessionExtensions) } @@ -742,7 +745,10 @@ class SparkSession private( private[sql] def applySchemaToPythonRDD( rdd: RDD[Array[Any]], schema: StructType): DataFrame = { - val rowRdd = rdd.map(r => python.EvaluatePython.fromJava(r, schema).asInstanceOf[InternalRow]) + val rowRdd = rdd.mapPartitions { iter => + val fromJava = python.EvaluatePython.makeFromJava(schema) + iter.map(r => fromJava(r).asInstanceOf[InternalRow]) + } internalCreateDataFrame(rowRdd, schema) } @@ -760,7 +766,7 @@ class SparkSession private( @InterfaceStability.Stable -object SparkSession { +object SparkSession extends Logging { /** * Builder for [[SparkSession]]. @@ -948,7 +954,8 @@ object SparkSession { session = new SparkSession(sparkContext, None, None, extensions) options.foreach { case (k, v) => session.initialSessionOptions.put(k, v) } - defaultSession.set(session) + setDefaultSession(session) + setActiveSession(session) // Register a successfully instantiated context to the singleton. This should be at the // end of the class definition so that the singleton is updated only if there is no @@ -1075,4 +1082,20 @@ object SparkSession { } } + private[spark] def cleanupAnyExistingSession(): Unit = { + val session = getActiveSession.orElse(getDefaultSession) + if (session.isDefined) { + logWarning( + s"""An existing Spark session exists as the active or default session. + |This probably means another suite leaked it. Attempting to stop it before continuing. + |This existing Spark session was created at: + | + |${session.get.creationSite.longForm} + | + """.stripMargin) + session.get.stop() + SparkSession.clearActiveSession() + SparkSession.clearDefaultSession() + } + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala index dc2468a721e41..f94baef39dfad 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql -import java.lang.reflect.{ParameterizedType, Type} +import java.lang.reflect.ParameterizedType import scala.reflect.runtime.universe.TypeTag import scala.util.Try @@ -110,29 +110,29 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends /* register 0-22 were generated by this script - (0 to 22).map { x => + (0 to 22).foreach { x => val types = (1 to x).foldRight("RT")((i, s) => {s"A$i, $s"}) - val typeTags = (1 to x).map(i => s"A${i}: TypeTag").foldLeft("RT: TypeTag")(_ + ", " + _) + val typeTags = (1 to x).map(i => s"A$i: TypeTag").foldLeft("RT: TypeTag")(_ + ", " + _) val inputTypes = (1 to x).foldRight("Nil")((i, s) => {s"ScalaReflection.schemaFor[A$i].dataType :: $s"}) println(s""" - /** - * Registers a deterministic Scala closure of ${x} arguments as user-defined function (UDF). - * @tparam RT return type of UDF. - * @since 1.3.0 - */ - def register[$typeTags](name: String, func: Function$x[$types]): UserDefinedFunction = { - val ScalaReflection.Schema(dataType, nullable) = ScalaReflection.schemaFor[RT] - val inputTypes = Try($inputTypes).toOption - def builder(e: Seq[Expression]) = if (e.length == $x) { - ScalaUDF(func, dataType, e, inputTypes.getOrElse(Nil), Some(name), nullable, udfDeterministic = true) - } else { - throw new AnalysisException("Invalid number of arguments for function " + name + - ". Expected: $x; Found: " + e.length) - } - functionRegistry.createOrReplaceTempFunction(name, builder) - val udf = UserDefinedFunction(func, dataType, inputTypes).withName(name) - if (nullable) udf else udf.asNonNullable() - }""") + |/** + | * Registers a deterministic Scala closure of $x arguments as user-defined function (UDF). + | * @tparam RT return type of UDF. + | * @since 1.3.0 + | */ + |def register[$typeTags](name: String, func: Function$x[$types]): UserDefinedFunction = { + | val ScalaReflection.Schema(dataType, nullable) = ScalaReflection.schemaFor[RT] + | val inputTypes = Try($inputTypes).toOption + | def builder(e: Seq[Expression]) = if (e.length == $x) { + | ScalaUDF(func, dataType, e, inputTypes.getOrElse(Nil), Some(name), nullable, udfDeterministic = true) + | } else { + | throw new AnalysisException("Invalid number of arguments for function " + name + + | ". Expected: $x; Found: " + e.length) + | } + | functionRegistry.createOrReplaceTempFunction(name, builder) + | val udf = UserDefinedFunction(func, dataType, inputTypes).withName(name) + | if (nullable) udf else udf.asNonNullable() + |}""".stripMargin) } (0 to 22).foreach { i => @@ -144,7 +144,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends val funcCall = if (i == 0) "() => func" else "func" println(s""" |/** - | * Register a user-defined function with ${i} arguments. + | * Register a deterministic Java UDF$i instance as user-defined function (UDF). | * @since $version | */ |def register(name: String, f: UDF$i[$extTypeArgs], returnType: DataType): Unit = { @@ -689,7 +689,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 0 arguments. + * Register a deterministic Java UDF0 instance as user-defined function (UDF). * @since 2.3.0 */ def register(name: String, f: UDF0[_], returnType: DataType): Unit = { @@ -704,7 +704,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 1 arguments. + * Register a deterministic Java UDF1 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF1[_, _], returnType: DataType): Unit = { @@ -719,7 +719,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 2 arguments. + * Register a deterministic Java UDF2 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF2[_, _, _], returnType: DataType): Unit = { @@ -734,7 +734,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 3 arguments. + * Register a deterministic Java UDF3 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF3[_, _, _, _], returnType: DataType): Unit = { @@ -749,7 +749,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 4 arguments. + * Register a deterministic Java UDF4 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF4[_, _, _, _, _], returnType: DataType): Unit = { @@ -764,7 +764,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 5 arguments. + * Register a deterministic Java UDF5 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF5[_, _, _, _, _, _], returnType: DataType): Unit = { @@ -779,7 +779,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 6 arguments. + * Register a deterministic Java UDF6 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF6[_, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -794,7 +794,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 7 arguments. + * Register a deterministic Java UDF7 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF7[_, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -809,7 +809,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 8 arguments. + * Register a deterministic Java UDF8 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF8[_, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -824,7 +824,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 9 arguments. + * Register a deterministic Java UDF9 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF9[_, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -839,7 +839,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 10 arguments. + * Register a deterministic Java UDF10 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -854,7 +854,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 11 arguments. + * Register a deterministic Java UDF11 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF11[_, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -869,7 +869,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 12 arguments. + * Register a deterministic Java UDF12 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF12[_, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -884,7 +884,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 13 arguments. + * Register a deterministic Java UDF13 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF13[_, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -899,7 +899,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 14 arguments. + * Register a deterministic Java UDF14 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF14[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -914,7 +914,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 15 arguments. + * Register a deterministic Java UDF15 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF15[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -929,7 +929,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 16 arguments. + * Register a deterministic Java UDF16 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF16[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -944,7 +944,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 17 arguments. + * Register a deterministic Java UDF17 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF17[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -959,7 +959,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 18 arguments. + * Register a deterministic Java UDF18 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF18[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -974,7 +974,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 19 arguments. + * Register a deterministic Java UDF19 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF19[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -989,7 +989,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 20 arguments. + * Register a deterministic Java UDF20 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF20[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -1004,7 +1004,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 21 arguments. + * Register a deterministic Java UDF21 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF21[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { @@ -1019,7 +1019,7 @@ class UDFRegistration private[sql] (functionRegistry: FunctionRegistry) extends } /** - * Register a user-defined function with 22 arguments. + * Register a deterministic Java UDF22 instance as user-defined function (UDF). * @since 1.3.0 */ def register(name: String, f: UDF22[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType): Unit = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala index b05fe49a6ac3b..d68aeb275afda 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala @@ -26,7 +26,7 @@ import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.spark.internal.Logging import org.apache.spark.sql.{Dataset, SparkSession} import org.apache.spark.sql.catalyst.expressions.SubqueryExpression -import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics} +import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, ResolvedHint} import org.apache.spark.sql.execution.columnar.InMemoryRelation import org.apache.spark.sql.execution.datasources.{HadoopFsRelation, LogicalRelation} import org.apache.spark.storage.StorageLevel @@ -169,6 +169,13 @@ class CacheManager extends Logging { /** Replaces segments of the given logical plan with cached versions where possible. */ def useCachedData(plan: LogicalPlan): LogicalPlan = { val newPlan = plan transformDown { + // Do not lookup the cache by hint node. Hint node is special, we should ignore it when + // canonicalizing plans, so that plans which are same except hint can hit the same cache. + // However, we also want to keep the hint info after cache lookup. Here we skip the hint + // node, so that the returned caching plan won't replace the hint node and drop the hint info + // from the original plan. + case hint: ResolvedHint => hint + case currentFragment => lookupCachedData(currentFragment) .map(_.cachedRepresentation.withOutput(currentFragment.output)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala index 782cec5e292ba..04f2619ed7541 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala @@ -17,21 +17,24 @@ package org.apache.spark.sql.execution -import org.apache.spark.sql.catalyst.expressions.UnsafeRow +import org.apache.spark.sql.catalyst.expressions.{BoundReference, UnsafeRow} import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode} import org.apache.spark.sql.execution.metric.SQLMetrics -import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, ColumnVector} import org.apache.spark.sql.types.DataType +import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector} /** - * Helper trait for abstracting scan functionality using - * [[org.apache.spark.sql.execution.vectorized.ColumnarBatch]]es. + * Helper trait for abstracting scan functionality using [[ColumnarBatch]]es. */ private[sql] trait ColumnarBatchScan extends CodegenSupport { def vectorTypes: Option[Seq[String]] = None + protected def supportsBatch: Boolean = true + + protected def needsUnsafeRowConversion: Boolean = true + override lazy val metrics = Map( "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"), "scanTime" -> SQLMetrics.createTimingMetric(sparkContext, "scan time")) @@ -47,7 +50,7 @@ private[sql] trait ColumnarBatchScan extends CodegenSupport { dataType: DataType, nullable: Boolean): ExprCode = { val javaType = ctx.javaType(dataType) - val value = ctx.getValue(columnVar, dataType, ordinal) + val value = ctx.getValueFromVector(columnVar, dataType, ordinal) val isNullVar = if (nullable) { ctx.freshName("isNull") } else { "false" } val valueVar = ctx.freshName("value") val str = s"columnVector[$columnVar, $ordinal, ${dataType.simpleString}]" @@ -71,7 +74,14 @@ private[sql] trait ColumnarBatchScan extends CodegenSupport { // PhysicalRDD always just has one input val input = ctx.addMutableState("scala.collection.Iterator", "input", v => s"$v = inputs[0];") + if (supportsBatch) { + produceBatches(ctx, input) + } else { + produceRows(ctx, input) + } + } + private def produceBatches(ctx: CodegenContext, input: String): String = { // metrics val numOutputRows = metricTerm(ctx, "numOutputRows") val scanTimeMetric = metricTerm(ctx, "scanTime") @@ -137,4 +147,25 @@ private[sql] trait ColumnarBatchScan extends CodegenSupport { """.stripMargin } + private def produceRows(ctx: CodegenContext, input: String): String = { + val numOutputRows = metricTerm(ctx, "numOutputRows") + val row = ctx.freshName("row") + + ctx.INPUT_ROW = row + ctx.currentVars = null + // Always provide `outputVars`, so that the framework can help us build unsafe row if the input + // row is not unsafe row, i.e. `needsUnsafeRowConversion` is true. + val outputVars = output.zipWithIndex.map { case (a, i) => + BoundReference(i, a.dataType, a.nullable).genCode(ctx) + } + val inputRow = if (needsUnsafeRowConversion) null else row + s""" + |while ($input.hasNext()) { + | InternalRow $row = (InternalRow) $input.next(); + | $numOutputRows.add(1); + | ${consume(ctx, outputVars, inputRow).trim} + | if (shouldStop()) return; + |} + """.stripMargin + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala index d1ff82c7c06bc..1a98fe0f3fa33 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala @@ -69,7 +69,7 @@ trait DataSourceScanExec extends LeafExecNode with CodegenSupport { * Shorthand for calling redactString() without specifying redacting rules */ private def redact(text: String): String = { - Utils.redact(sqlContext.sessionState.conf.stringRedationPattern, text) + Utils.redact(sqlContext.sessionState.conf.stringRedactionPattern, text) } } @@ -164,13 +164,15 @@ case class FileSourceScanExec( override val tableIdentifier: Option[TableIdentifier]) extends DataSourceScanExec with ColumnarBatchScan { - val supportsBatch: Boolean = relation.fileFormat.supportBatch( + override val supportsBatch: Boolean = relation.fileFormat.supportBatch( relation.sparkSession, StructType.fromAttributes(output)) - val needsUnsafeRowConversion: Boolean = if (relation.fileFormat.isInstanceOf[ParquetSource]) { - SparkSession.getActiveSession.get.sessionState.conf.parquetVectorizedReaderEnabled - } else { - false + override val needsUnsafeRowConversion: Boolean = { + if (relation.fileFormat.isInstanceOf[ParquetSource]) { + SparkSession.getActiveSession.get.sessionState.conf.parquetVectorizedReaderEnabled + } else { + false + } } override def vectorTypes: Option[Seq[String]] = @@ -322,7 +324,7 @@ case class FileSourceScanExec( // in the case of fallback, this batched scan should never fail because of: // 1) only primitive types are supported // 2) the number of columns should be smaller than spark.sql.codegen.maxFields - WholeStageCodegenExec(this).execute() + WholeStageCodegenExec(this)(codegenStageId = 0).execute() } else { val unsafeRows = { val scan = inputRDD @@ -346,33 +348,6 @@ case class FileSourceScanExec( override val nodeNamePrefix: String = "File" - override protected def doProduce(ctx: CodegenContext): String = { - if (supportsBatch) { - return super.doProduce(ctx) - } - val numOutputRows = metricTerm(ctx, "numOutputRows") - // PhysicalRDD always just has one input - val input = ctx.addMutableState("scala.collection.Iterator", "input", v => s"$v = inputs[0];") - val row = ctx.freshName("row") - - ctx.INPUT_ROW = row - ctx.currentVars = null - // Always provide `outputVars`, so that the framework can help us build unsafe row if the input - // row is not unsafe row, i.e. `needsUnsafeRowConversion` is true. - val outputVars = output.zipWithIndex.map{ case (a, i) => - BoundReference(i, a.dataType, a.nullable).genCode(ctx) - } - val inputRow = if (needsUnsafeRowConversion) null else row - s""" - |while ($input.hasNext()) { - | InternalRow $row = (InternalRow) $input.next(); - | $numOutputRows.add(1); - | ${consume(ctx, outputVars, inputRow).trim} - | if (shouldStop()) return; - |} - """.stripMargin - } - /** * Create an RDD for bucketed reads. * The non-bucketed variant of this function is [[createNonBucketedReadRDD]]. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuery.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuery.scala index 18f6f697bc857..dc4aff9f12580 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuery.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuery.scala @@ -17,6 +17,9 @@ package org.apache.spark.sql.execution +import java.util.Locale + +import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.catalog.{HiveTableRelation, SessionCatalog} import org.apache.spark.sql.catalyst.expressions._ @@ -80,8 +83,13 @@ case class OptimizeMetadataOnlyQuery(catalog: SessionCatalog) extends Rule[Logic private def getPartitionAttrs( partitionColumnNames: Seq[String], relation: LogicalPlan): Seq[Attribute] = { - val partColumns = partitionColumnNames.map(_.toLowerCase).toSet - relation.output.filter(a => partColumns.contains(a.name.toLowerCase)) + val attrMap = relation.output.map(a => a.name.toLowerCase(Locale.ROOT) -> a).toMap + partitionColumnNames.map { colName => + attrMap.getOrElse(colName.toLowerCase(Locale.ROOT), + throw new AnalysisException(s"Unable to find the column `$colName` " + + s"given [${relation.output.map(_.name).mkString(", ")}]") + ) + } } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala index 8bfe3eff0c3b3..3112b306c365e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala @@ -44,19 +44,7 @@ class QueryExecution(val sparkSession: SparkSession, val logical: LogicalPlan) { // TODO: Move the planner an optimizer into here from SessionState. protected def planner = sparkSession.sessionState.planner - def assertAnalyzed(): Unit = { - // Analyzer is invoked outside the try block to avoid calling it again from within the - // catch block below. - analyzed - try { - sparkSession.sessionState.analyzer.checkAnalysis(analyzed) - } catch { - case e: AnalysisException => - val ae = new AnalysisException(e.message, e.line, e.startPosition, Option(analyzed)) - ae.setStackTrace(e.getStackTrace) - throw ae - } - } + def assertAnalyzed(): Unit = analyzed def assertSupported(): Unit = { if (sparkSession.sessionState.conf.isUnsupportedOperationCheckEnabled) { @@ -66,7 +54,7 @@ class QueryExecution(val sparkSession: SparkSession, val logical: LogicalPlan) { lazy val analyzed: LogicalPlan = { SparkSession.setActiveSession(sparkSession) - sparkSession.sessionState.analyzer.execute(logical) + sparkSession.sessionState.analyzer.executeAndCheck(logical) } lazy val withCachedData: LogicalPlan = { @@ -167,6 +155,7 @@ class QueryExecution(val sparkSession: SparkSession, val logical: LogicalPlan) { case (null, _) => "null" case (s: String, StringType) => "\"" + s + "\"" case (decimal, DecimalType()) => decimal.toString + case (interval, CalendarIntervalType) => interval.toString case (other, tpe) if primitiveTypes contains tpe => other.toString } @@ -190,6 +179,7 @@ class QueryExecution(val sparkSession: SparkSession, val logical: LogicalPlan) { DateTimeUtils.getTimeZone(sparkSession.sessionState.conf.sessionLocalTimeZone)) case (bin: Array[Byte], BinaryType) => new String(bin, StandardCharsets.UTF_8) case (decimal: java.math.BigDecimal, DecimalType()) => formatDecimal(decimal) + case (interval, CalendarIntervalType) => interval.toString case (other, tpe) if primitiveTypes.contains(tpe) => other.toString } } @@ -235,7 +225,7 @@ class QueryExecution(val sparkSession: SparkSession, val logical: LogicalPlan) { * Redact the sensitive information in the given string. */ private def withRedaction(message: String): String = { - Utils.redact(sparkSession.sessionState.conf.stringRedationPattern, message) + Utils.redact(sparkSession.sessionState.conf.stringRedactionPattern, message) } /** A special namespace for commands that can be used to debug query execution. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecutionException.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecutionException.scala index 16806c620635f..cffd97baea6a2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecutionException.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecutionException.scala @@ -17,4 +17,5 @@ package org.apache.spark.sql.execution -class QueryExecutionException(message: String) extends Exception(message) +class QueryExecutionException(message: String, cause: Throwable = null) + extends Exception(message, cause) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala index e991da7df0bde..2c5102b1e5ee7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala @@ -88,7 +88,7 @@ object SQLExecution { /** * Wrap an action with a known executionId. When running a different action in a different * thread from the original one, this method can be used to connect the Spark jobs in this action - * with the known executionId, e.g., `BroadcastHashJoin.broadcastFuture`. + * with the known executionId, e.g., `BroadcastExchangeExec.relationFuture`. */ def withExecutionId[T](sc: SparkContext, executionId: String)(body: => T): T = { val oldExecutionId = sc.getLocalProperty(SQLExecution.EXECUTION_ID_KEY) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortExec.scala index ef1bb1c2a4468..ac1c34d41c4f1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortExec.scala @@ -84,7 +84,7 @@ case class SortExec( } val pageSize = SparkEnv.get.memoryManager.pageSizeBytes - val sorter = new UnsafeExternalRowSorter( + val sorter = UnsafeExternalRowSorter.create( schema, ordering, prefixComparator, prefixComputer, pageSize, canUseRadixSort) if (testSpillFrequency > 0) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala index 787c1cfbfb3d8..398758a3331b4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala @@ -94,7 +94,21 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ /** Specifies how data is partitioned across different nodes in the cluster. */ def outputPartitioning: Partitioning = UnknownPartitioning(0) // TODO: WRONG WIDTH! - /** Specifies any partition requirements on the input data for this operator. */ + /** + * Specifies the data distribution requirements of all the children for this operator. By default + * it's [[UnspecifiedDistribution]] for each child, which means each child can have any + * distribution. + * + * If an operator overwrites this method, and specifies distribution requirements(excluding + * [[UnspecifiedDistribution]] and [[BroadcastDistribution]]) for more than one child, Spark + * guarantees that the outputs of these children will have same number of partitions, so that the + * operator can safely zip partitions of these children's result RDDs. Some operators can leverage + * this guarantee to satisfy some interesting requirement, e.g., non-broadcast joins can specify + * HashClusteredDistribution(a,b) for its left child, and specify HashClusteredDistribution(c,d) + * for its right child, then it's guaranteed that left and right child are co-partitioned by + * a,b/c,d, which means tuples of same value are in the partitions of same index, e.g., + * (a=1,b=2) and (c=1,d=2) are both in the second partition of left and right child. + */ def requiredChildDistribution: Seq[Distribution] = Seq.fill(children.size)(UnspecifiedDistribution) @@ -337,8 +351,9 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ if (buf.isEmpty) { numPartsToTry = partsScanned * limitScaleUpFactor } else { - // the left side of max is >=1 whenever partsScanned >= 2 - numPartsToTry = Math.max((1.5 * n * partsScanned / buf.size).toInt - partsScanned, 1) + val left = n - buf.size + // As left > 0, numPartsToTry is always >= 1 + numPartsToTry = Math.ceil(1.5 * left * partsScanned / buf.size).toInt numPartsToTry = Math.min(numPartsToTry, partsScanned * limitScaleUpFactor) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlParser.scala index 29b584b55972c..4828fa60a7b58 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlParser.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlParser.scala @@ -327,7 +327,7 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { } /** - * Create a [[DescribeTableCommand]] logical plan. + * Create a [[DescribeColumnCommand]] or [[DescribeTableCommand]] logical commands. */ override def visitDescribeTable(ctx: DescribeTableContext): LogicalPlan = withOrigin(ctx) { val isExtended = ctx.EXTENDED != null || ctx.FORMATTED != null @@ -383,16 +383,19 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { * {{{ * CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db_name.]table_name * USING table_provider - * [OPTIONS table_property_list] - * [PARTITIONED BY (col_name, col_name, ...)] - * [CLUSTERED BY (col_name, col_name, ...) - * [SORTED BY (col_name [ASC|DESC], ...)] - * INTO num_buckets BUCKETS - * ] - * [LOCATION path] - * [COMMENT table_comment] - * [TBLPROPERTIES (property_name=property_value, ...)] + * create_table_clauses * [[AS] select_statement]; + * + * create_table_clauses (order insensitive): + * [OPTIONS table_property_list] + * [PARTITIONED BY (col_name, col_name, ...)] + * [CLUSTERED BY (col_name, col_name, ...) + * [SORTED BY (col_name [ASC|DESC], ...)] + * INTO num_buckets BUCKETS + * ] + * [LOCATION path] + * [COMMENT table_comment] + * [TBLPROPERTIES (property_name=property_value, ...)] * }}} */ override def visitCreateTable(ctx: CreateTableContext): LogicalPlan = withOrigin(ctx) { @@ -400,6 +403,14 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { if (external) { operationNotAllowed("CREATE EXTERNAL TABLE ... USING", ctx) } + + checkDuplicateClauses(ctx.TBLPROPERTIES, "TBLPROPERTIES", ctx) + checkDuplicateClauses(ctx.OPTIONS, "OPTIONS", ctx) + checkDuplicateClauses(ctx.PARTITIONED, "PARTITIONED BY", ctx) + checkDuplicateClauses(ctx.COMMENT, "COMMENT", ctx) + checkDuplicateClauses(ctx.bucketSpec(), "CLUSTERED BY", ctx) + checkDuplicateClauses(ctx.locationSpec, "LOCATION", ctx) + val options = Option(ctx.options).map(visitPropertyKeyValues).getOrElse(Map.empty) val provider = ctx.tableProvider.qualifiedName.getText val schema = Option(ctx.colTypeList()).map(createSchema) @@ -408,9 +419,9 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { .map(visitIdentifierList(_).toArray) .getOrElse(Array.empty[String]) val properties = Option(ctx.tableProps).map(visitPropertyKeyValues).getOrElse(Map.empty) - val bucketSpec = Option(ctx.bucketSpec()).map(visitBucketSpec) + val bucketSpec = ctx.bucketSpec().asScala.headOption.map(visitBucketSpec) - val location = Option(ctx.locationSpec).map(visitLocationSpec) + val location = ctx.locationSpec.asScala.headOption.map(visitLocationSpec) val storage = DataSource.buildStorageFormatFromOptions(options) if (location.isDefined && storage.locationUri.isDefined) { @@ -1087,13 +1098,16 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { * {{{ * CREATE [EXTERNAL] TABLE [IF NOT EXISTS] [db_name.]table_name * [(col1[:] data_type [COMMENT col_comment], ...)] - * [COMMENT table_comment] - * [PARTITIONED BY (col2[:] data_type [COMMENT col_comment], ...)] - * [ROW FORMAT row_format] - * [STORED AS file_format] - * [LOCATION path] - * [TBLPROPERTIES (property_name=property_value, ...)] + * create_table_clauses * [AS select_statement]; + * + * create_table_clauses (order insensitive): + * [COMMENT table_comment] + * [PARTITIONED BY (col2[:] data_type [COMMENT col_comment], ...)] + * [ROW FORMAT row_format] + * [STORED AS file_format] + * [LOCATION path] + * [TBLPROPERTIES (property_name=property_value, ...)] * }}} */ override def visitCreateHiveTable(ctx: CreateHiveTableContext): LogicalPlan = withOrigin(ctx) { @@ -1104,15 +1118,23 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { "CREATE TEMPORARY TABLE is not supported yet. " + "Please use CREATE TEMPORARY VIEW as an alternative.", ctx) } - if (ctx.skewSpec != null) { + if (ctx.skewSpec.size > 0) { operationNotAllowed("CREATE TABLE ... SKEWED BY", ctx) } + checkDuplicateClauses(ctx.TBLPROPERTIES, "TBLPROPERTIES", ctx) + checkDuplicateClauses(ctx.PARTITIONED, "PARTITIONED BY", ctx) + checkDuplicateClauses(ctx.COMMENT, "COMMENT", ctx) + checkDuplicateClauses(ctx.bucketSpec(), "CLUSTERED BY", ctx) + checkDuplicateClauses(ctx.createFileFormat, "STORED AS/BY", ctx) + checkDuplicateClauses(ctx.rowFormat, "ROW FORMAT", ctx) + checkDuplicateClauses(ctx.locationSpec, "LOCATION", ctx) + val dataCols = Option(ctx.columns).map(visitColTypeList).getOrElse(Nil) val partitionCols = Option(ctx.partitionColumns).map(visitColTypeList).getOrElse(Nil) - val properties = Option(ctx.tablePropertyList).map(visitPropertyKeyValues).getOrElse(Map.empty) + val properties = Option(ctx.tableProps).map(visitPropertyKeyValues).getOrElse(Map.empty) val selectQuery = Option(ctx.query).map(plan) - val bucketSpec = Option(ctx.bucketSpec()).map(visitBucketSpec) + val bucketSpec = ctx.bucketSpec().asScala.headOption.map(visitBucketSpec) // Note: Hive requires partition columns to be distinct from the schema, so we need // to include the partition columns here explicitly @@ -1120,12 +1142,12 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { // Storage format val defaultStorage = HiveSerDe.getDefaultStorage(conf) - validateRowFormatFileFormat(ctx.rowFormat, ctx.createFileFormat, ctx) - val fileStorage = Option(ctx.createFileFormat).map(visitCreateFileFormat) + validateRowFormatFileFormat(ctx.rowFormat.asScala, ctx.createFileFormat.asScala, ctx) + val fileStorage = ctx.createFileFormat.asScala.headOption.map(visitCreateFileFormat) .getOrElse(CatalogStorageFormat.empty) - val rowStorage = Option(ctx.rowFormat).map(visitRowFormat) + val rowStorage = ctx.rowFormat.asScala.headOption.map(visitRowFormat) .getOrElse(CatalogStorageFormat.empty) - val location = Option(ctx.locationSpec).map(visitLocationSpec) + val location = ctx.locationSpec.asScala.headOption.map(visitLocationSpec) // If we are creating an EXTERNAL table, then the LOCATION field is required if (external && location.isEmpty) { operationNotAllowed("CREATE EXTERNAL TABLE must be accompanied by LOCATION", ctx) @@ -1180,7 +1202,7 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { ctx) } - val hasStorageProperties = (ctx.createFileFormat != null) || (ctx.rowFormat != null) + val hasStorageProperties = (ctx.createFileFormat.size != 0) || (ctx.rowFormat.size != 0) if (conf.convertCTAS && !hasStorageProperties) { // At here, both rowStorage.serdeProperties and fileStorage.serdeProperties // are empty Maps. @@ -1366,6 +1388,15 @@ class SparkSqlAstBuilder(conf: SQLConf) extends AstBuilder(conf) { } } + private def validateRowFormatFileFormat( + rowFormatCtx: Seq[RowFormatContext], + createFileFormatCtx: Seq[CreateFileFormatContext], + parentCtx: ParserRuleContext): Unit = { + if (rowFormatCtx.size == 1 && createFileFormatCtx.size == 1) { + validateRowFormatFileFormat(rowFormatCtx.head, createFileFormatCtx.head, parentCtx) + } + } + /** * Create or replace a view. This creates a [[CreateViewCommand]] command. * diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 910294853c318..a0a641bc9667e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -70,12 +70,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case Limit(IntegerLiteral(limit), Project(projectList, Sort(order, true, child))) => TakeOrderedAndProjectExec(limit, order, projectList, planLater(child)) :: Nil case Limit(IntegerLiteral(limit), child) => - // With whole stage codegen, Spark releases resources only when all the output data of the - // query plan are consumed. It's possible that `CollectLimitExec` only consumes a little - // data from child plan and finishes the query without releasing resources. Here we wrap - // the child plan with `LocalLimitExec`, to stop the processing of whole stage codegen and - // trigger the resource releasing work, after we consume `limit` rows. - CollectLimitExec(limit, LocalLimitExec(limit, planLater(child))) :: Nil + CollectLimitExec(limit, planLater(child)) :: Nil case other => planLater(other) :: Nil } case Limit(IntegerLiteral(limit), Sort(order, true, child)) => @@ -90,23 +85,58 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { * Select the proper physical plan for join based on joining keys and size of logical plan. * * At first, uses the [[ExtractEquiJoinKeys]] pattern to find joins where at least some of the - * predicates can be evaluated by matching join keys. If found, Join implementations are chosen + * predicates can be evaluated by matching join keys. If found, join implementations are chosen * with the following precedence: * - * - Broadcast: We prefer to broadcast the join side with an explicit broadcast hint(e.g. the - * user applied the [[org.apache.spark.sql.functions.broadcast()]] function to a DataFrame). - * If both sides have the broadcast hint, we prefer to broadcast the side with a smaller - * estimated physical size. If neither one of the sides has the broadcast hint, - * we only broadcast the join side if its estimated physical size that is smaller than - * the user-configurable [[SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]] threshold. + * - Broadcast hash join (BHJ): + * BHJ is not supported for full outer join. For right outer join, we only can broadcast the + * left side. For left outer, left semi, left anti and the internal join type ExistenceJoin, + * we only can broadcast the right side. For inner like join, we can broadcast both sides. + * Normally, BHJ can perform faster than the other join algorithms when the broadcast side is + * small. However, broadcasting tables is a network-intensive operation. It could cause OOM + * or perform worse than the other join algorithms, especially when the build/broadcast side + * is big. + * + * For the supported cases, users can specify the broadcast hint (e.g. the user applied the + * [[org.apache.spark.sql.functions.broadcast()]] function to a DataFrame) and session-based + * [[SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]] threshold to adjust whether BHJ is used and + * which join side is broadcast. + * + * 1) Broadcast the join side with the broadcast hint, even if the size is larger than + * [[SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]]. If both sides have the hint (only when the type + * is inner like join), the side with a smaller estimated physical size will be broadcast. + * 2) Respect the [[SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]] threshold and broadcast the side + * whose estimated physical size is smaller than the threshold. If both sides are below the + * threshold, broadcast the smaller side. If neither is smaller, BHJ is not used. + * * - Shuffle hash join: if the average size of a single partition is small enough to build a hash * table. + * * - Sort merge: if the matching join keys are sortable. * * If there is no joining keys, Join implementations are chosen with the following precedence: - * - BroadcastNestedLoopJoin: if one side of the join could be broadcasted - * - CartesianProduct: for Inner join - * - BroadcastNestedLoopJoin + * - BroadcastNestedLoopJoin (BNLJ): + * BNLJ supports all the join types but the impl is OPTIMIZED for the following scenarios: + * For right outer join, the left side is broadcast. For left outer, left semi, left anti + * and the internal join type ExistenceJoin, the right side is broadcast. For inner like + * joins, either side is broadcast. + * + * Like BHJ, users still can specify the broadcast hint and session-based + * [[SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]] threshold to impact which side is broadcast. + * + * 1) Broadcast the join side with the broadcast hint, even if the size is larger than + * [[SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]]. If both sides have the hint (i.e., just for + * inner-like join), the side with a smaller estimated physical size will be broadcast. + * 2) Respect the [[SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]] threshold and broadcast the side + * whose estimated physical size is smaller than the threshold. If both sides are below the + * threshold, broadcast the smaller side. If neither is smaller, BNLJ is not used. + * + * - CartesianProduct: for inner like join, CartesianProduct is the fallback option. + * + * - BroadcastNestedLoopJoin (BNLJ): + * For the other join types, BNLJ is the fallback option. Here, we just pick the broadcast + * side with the broadcast hint. If neither side has a hint, we broadcast the side with + * the smaller estimated physical size. */ object JoinSelection extends Strategy with PredicateHelper { @@ -139,8 +169,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { } private def canBuildRight(joinType: JoinType): Boolean = joinType match { - case _: InnerLike | LeftOuter | LeftSemi | LeftAnti => true - case j: ExistenceJoin => true + case _: InnerLike | LeftOuter | LeftSemi | LeftAnti | _: ExistenceJoin => true case _ => false } @@ -243,7 +272,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { // --- Without joining keys ------------------------------------------------------------ - // Pick BroadcastNestedLoopJoin if one side could be broadcasted + // Pick BroadcastNestedLoopJoin if one side could be broadcast case j @ logical.Join(left, right, joinType, condition) if canBroadcastByHints(joinType, left, right) => val buildSide = broadcastSideByHints(joinType, left, right) @@ -338,9 +367,9 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { val (functionsWithDistinct, functionsWithoutDistinct) = aggregateExpressions.partition(_.isDistinct) - if (functionsWithDistinct.map(_.aggregateFunction.children).distinct.length > 1) { + if (functionsWithDistinct.map(_.aggregateFunction.children.toSet).distinct.length > 1) { // This is a sanity check. We should not reach here when we have multiple distinct - // column sets. Our MultipleDistinctRewriter should take care this case. + // column sets. Our `RewriteDistinctAggregates` should take care this case. sys.error("You hit a query analyzer bug. Please report your query to " + "Spark user mailing list.") } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala index 065954559e487..0e525b1e22eb9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala @@ -18,6 +18,9 @@ package org.apache.spark.sql.execution import java.util.Locale +import java.util.function.Supplier + +import scala.collection.mutable import org.apache.spark.broadcast import org.apache.spark.rdd.RDD @@ -58,7 +61,7 @@ trait CodegenSupport extends SparkPlan { } /** - * Whether this SparkPlan support whole stage codegen or not. + * Whether this SparkPlan supports whole stage codegen or not. */ def supportCodegen: Boolean = true @@ -106,6 +109,31 @@ trait CodegenSupport extends SparkPlan { */ protected def doProduce(ctx: CodegenContext): String + private def prepareRowVar(ctx: CodegenContext, row: String, colVars: Seq[ExprCode]): ExprCode = { + if (row != null) { + ExprCode("", "false", row) + } else { + if (colVars.nonEmpty) { + val colExprs = output.zipWithIndex.map { case (attr, i) => + BoundReference(i, attr.dataType, attr.nullable) + } + val evaluateInputs = evaluateVariables(colVars) + // generate the code to create a UnsafeRow + ctx.INPUT_ROW = row + ctx.currentVars = colVars + val ev = GenerateUnsafeProjection.createCode(ctx, colExprs, false) + val code = s""" + |$evaluateInputs + |${ev.code.trim} + """.stripMargin.trim + ExprCode(code, "false", ev.value) + } else { + // There is no columns + ExprCode("", "false", "unsafeRow") + } + } + } + /** * Consume the generated columns or row from current SparkPlan, call its parent's `doConsume()`. * @@ -126,28 +154,7 @@ trait CodegenSupport extends SparkPlan { } } - val rowVar = if (row != null) { - ExprCode("", "false", row) - } else { - if (outputVars.nonEmpty) { - val colExprs = output.zipWithIndex.map { case (attr, i) => - BoundReference(i, attr.dataType, attr.nullable) - } - val evaluateInputs = evaluateVariables(outputVars) - // generate the code to create a UnsafeRow - ctx.INPUT_ROW = row - ctx.currentVars = outputVars - val ev = GenerateUnsafeProjection.createCode(ctx, colExprs, false) - val code = s""" - |$evaluateInputs - |${ev.code.trim} - """.stripMargin.trim - ExprCode(code, "false", ev.value) - } else { - // There is no columns - ExprCode("", "false", "unsafeRow") - } - } + val rowVar = prepareRowVar(ctx, row, outputVars) // Set up the `currentVars` in the codegen context, as we generate the code of `inputVars` // before calling `parent.doConsume`. We can't set up `INPUT_ROW`, because parent needs to @@ -156,13 +163,96 @@ trait CodegenSupport extends SparkPlan { ctx.INPUT_ROW = null ctx.freshNamePrefix = parent.variablePrefix val evaluated = evaluateRequiredVariables(output, inputVars, parent.usedInputs) + + // Under certain conditions, we can put the logic to consume the rows of this operator into + // another function. So we can prevent a generated function too long to be optimized by JIT. + // The conditions: + // 1. The config "spark.sql.codegen.splitConsumeFuncByOperator" is enabled. + // 2. `inputVars` are all materialized. That is guaranteed to be true if the parent plan uses + // all variables in output (see `requireAllOutput`). + // 3. The number of output variables must less than maximum number of parameters in Java method + // declaration. + val confEnabled = SQLConf.get.wholeStageSplitConsumeFuncByOperator + val requireAllOutput = output.forall(parent.usedInputs.contains(_)) + val paramLength = ctx.calculateParamLength(output) + (if (row != null) 1 else 0) + val consumeFunc = if (confEnabled && requireAllOutput && ctx.isValidParamLength(paramLength)) { + constructDoConsumeFunction(ctx, inputVars, row) + } else { + parent.doConsume(ctx, inputVars, rowVar) + } s""" |${ctx.registerComment(s"CONSUME: ${parent.simpleString}")} |$evaluated - |${parent.doConsume(ctx, inputVars, rowVar)} + |$consumeFunc + """.stripMargin + } + + /** + * To prevent concatenated function growing too long to be optimized by JIT. We can separate the + * parent's `doConsume` codes of a `CodegenSupport` operator into a function to call. + */ + private def constructDoConsumeFunction( + ctx: CodegenContext, + inputVars: Seq[ExprCode], + row: String): String = { + val (args, params, inputVarsInFunc) = constructConsumeParameters(ctx, output, inputVars, row) + val rowVar = prepareRowVar(ctx, row, inputVarsInFunc) + + val doConsume = ctx.freshName("doConsume") + ctx.currentVars = inputVarsInFunc + ctx.INPUT_ROW = null + + val doConsumeFuncName = ctx.addNewFunction(doConsume, + s""" + | private void $doConsume(${params.mkString(", ")}) throws java.io.IOException { + | ${parent.doConsume(ctx, inputVarsInFunc, rowVar)} + | } + """.stripMargin) + + s""" + | $doConsumeFuncName(${args.mkString(", ")}); """.stripMargin } + /** + * Returns arguments for calling method and method definition parameters of the consume function. + * And also returns the list of `ExprCode` for the parameters. + */ + private def constructConsumeParameters( + ctx: CodegenContext, + attributes: Seq[Attribute], + variables: Seq[ExprCode], + row: String): (Seq[String], Seq[String], Seq[ExprCode]) = { + val arguments = mutable.ArrayBuffer[String]() + val parameters = mutable.ArrayBuffer[String]() + val paramVars = mutable.ArrayBuffer[ExprCode]() + + if (row != null) { + arguments += row + parameters += s"InternalRow $row" + } + + variables.zipWithIndex.foreach { case (ev, i) => + val paramName = ctx.freshName(s"expr_$i") + val paramType = ctx.javaType(attributes(i).dataType) + + arguments += ev.value + parameters += s"$paramType $paramName" + val paramIsNull = if (!attributes(i).nullable) { + // Use constant `false` without passing `isNull` for non-nullable variable. + "false" + } else { + val isNull = ctx.freshName(s"exprIsNull_$i") + arguments += ev.isNull + parameters += s"boolean $isNull" + isNull + } + + paramVars += ExprCode("", paramIsNull, paramName) + } + (arguments, parameters, paramVars) + } + /** * Returns source code to evaluate all the variables, and clear the code of them, to prevent * them to be evaluated twice. @@ -325,6 +415,58 @@ object WholeStageCodegenExec { } } +object WholeStageCodegenId { + // codegenStageId: ID for codegen stages within a query plan. + // It does not affect equality, nor does it participate in destructuring pattern matching + // of WholeStageCodegenExec. + // + // This ID is used to help differentiate between codegen stages. It is included as a part + // of the explain output for physical plans, e.g. + // + // == Physical Plan == + // *(5) SortMergeJoin [x#3L], [y#9L], Inner + // :- *(2) Sort [x#3L ASC NULLS FIRST], false, 0 + // : +- Exchange hashpartitioning(x#3L, 200) + // : +- *(1) Project [(id#0L % 2) AS x#3L] + // : +- *(1) Filter isnotnull((id#0L % 2)) + // : +- *(1) Range (0, 5, step=1, splits=8) + // +- *(4) Sort [y#9L ASC NULLS FIRST], false, 0 + // +- Exchange hashpartitioning(y#9L, 200) + // +- *(3) Project [(id#6L % 2) AS y#9L] + // +- *(3) Filter isnotnull((id#6L % 2)) + // +- *(3) Range (0, 5, step=1, splits=8) + // + // where the ID makes it obvious that not all adjacent codegen'd plan operators are of the + // same codegen stage. + // + // The codegen stage ID is also optionally included in the name of the generated classes as + // a suffix, so that it's easier to associate a generated class back to the physical operator. + // This is controlled by SQLConf: spark.sql.codegen.useIdInClassName + // + // The ID is also included in various log messages. + // + // Within a query, a codegen stage in a plan starts counting from 1, in "insertion order". + // WholeStageCodegenExec operators are inserted into a plan in depth-first post-order. + // See CollapseCodegenStages.insertWholeStageCodegen for the definition of insertion order. + // + // 0 is reserved as a special ID value to indicate a temporary WholeStageCodegenExec object + // is created, e.g. for special fallback handling when an existing WholeStageCodegenExec + // failed to generate/compile code. + + private val codegenStageCounter = ThreadLocal.withInitial(new Supplier[Integer] { + override def get() = 1 // TODO: change to Scala lambda syntax when upgraded to Scala 2.12+ + }) + + def resetPerQuery(): Unit = codegenStageCounter.set(1) + + def getNextStageId(): Int = { + val counter = codegenStageCounter + val id = counter.get() + counter.set(id + 1) + id + } +} + /** * WholeStageCodegen compiles a subtree of plans that support codegen together into single Java * function. @@ -353,7 +495,8 @@ object WholeStageCodegenExec { * `doCodeGen()` will create a `CodeGenContext`, which will hold a list of variables for input, * used to generated code for [[BoundReference]]. */ -case class WholeStageCodegenExec(child: SparkPlan) extends UnaryExecNode with CodegenSupport { +case class WholeStageCodegenExec(child: SparkPlan)(val codegenStageId: Int) + extends UnaryExecNode with CodegenSupport { override def output: Seq[Attribute] = child.output @@ -365,6 +508,12 @@ case class WholeStageCodegenExec(child: SparkPlan) extends UnaryExecNode with Co "pipelineTime" -> SQLMetrics.createTimingMetric(sparkContext, WholeStageCodegenExec.PIPELINE_DURATION_METRIC)) + def generatedClassName(): String = if (conf.wholeStageUseIdInClassName) { + s"GeneratedIteratorForCodegenStage$codegenStageId" + } else { + "GeneratedIterator" + } + /** * Generates code for this subtree. * @@ -382,19 +531,23 @@ case class WholeStageCodegenExec(child: SparkPlan) extends UnaryExecNode with Co } """, inlineToOuterClass = true) + val className = generatedClassName() + val source = s""" public Object generate(Object[] references) { - return new GeneratedIterator(references); + return new $className(references); } - ${ctx.registerComment(s"""Codegend pipeline for\n${child.treeString.trim}""")} - final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator { + ${ctx.registerComment( + s"""Codegend pipeline for stage (id=$codegenStageId) + |${this.treeString.trim}""".stripMargin)} + final class $className extends ${classOf[BufferedRowIterator].getName} { private Object[] references; private scala.collection.Iterator[] inputs; ${ctx.declareMutableStates()} - public GeneratedIterator(Object[] references) { + public $className(Object[] references) { this.references = references; } @@ -427,7 +580,7 @@ case class WholeStageCodegenExec(child: SparkPlan) extends UnaryExecNode with Co } catch { case _: Exception if !Utils.isTesting && sqlContext.conf.codegenFallback => // We should already saw the error message - logWarning(s"Whole-stage codegen disabled for this plan:\n $treeString") + logWarning(s"Whole-stage codegen disabled for plan (id=$codegenStageId):\n $treeString") return child.execute() } @@ -436,7 +589,7 @@ case class WholeStageCodegenExec(child: SparkPlan) extends UnaryExecNode with Co logInfo(s"Found too long generated codes and JIT optimization might not work: " + s"the bytecode size ($maxCodeSize) is above the limit " + s"${sqlContext.conf.hugeMethodLimit}, and the whole-stage codegen was disabled " + - s"for this plan. To avoid this, you can raise the limit " + + s"for this plan (id=$codegenStageId). To avoid this, you can raise the limit " + s"`${SQLConf.WHOLESTAGE_HUGE_METHOD_LIMIT.key}`:\n$treeString") child match { // The fallback solution of batch file source scan still uses WholeStageCodegenExec @@ -514,10 +667,12 @@ case class WholeStageCodegenExec(child: SparkPlan) extends UnaryExecNode with Co verbose: Boolean, prefix: String = "", addSuffix: Boolean = false): StringBuilder = { - child.generateTreeString(depth, lastChildren, builder, verbose, "*") + child.generateTreeString(depth, lastChildren, builder, verbose, s"*($codegenStageId) ") } override def needStopCheck: Boolean = true + + override protected def otherCopyArgs: Seq[AnyRef] = Seq(codegenStageId.asInstanceOf[Integer]) } @@ -568,13 +723,14 @@ case class CollapseCodegenStages(conf: SQLConf) extends Rule[SparkPlan] { case plan if plan.output.length == 1 && plan.output.head.dataType.isInstanceOf[ObjectType] => plan.withNewChildren(plan.children.map(insertWholeStageCodegen)) case plan: CodegenSupport if supportCodegen(plan) => - WholeStageCodegenExec(insertInputAdapter(plan)) + WholeStageCodegenExec(insertInputAdapter(plan))(WholeStageCodegenId.getNextStageId()) case other => other.withNewChildren(other.children.map(insertWholeStageCodegen)) } def apply(plan: SparkPlan): SparkPlan = { if (conf.wholeStageEnabled) { + WholeStageCodegenId.resetPerQuery() insertWholeStageCodegen(plan) } else { plan diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala index 9a6f1c6dfa6a9..ce3c68810f3b6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala @@ -28,7 +28,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen._ import org.apache.spark.sql.catalyst.plans.physical._ import org.apache.spark.sql.execution._ import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics} -import org.apache.spark.sql.execution.vectorized.{ColumnarRow, MutableColumnarRow} +import org.apache.spark.sql.execution.vectorized.MutableColumnarRow import org.apache.spark.sql.types.{DecimalType, StringType, StructType} import org.apache.spark.unsafe.KVIterator import org.apache.spark.util.Utils diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TypedAggregateExpression.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TypedAggregateExpression.scala index aab8cc50b9526..6d44890704f49 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TypedAggregateExpression.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TypedAggregateExpression.scala @@ -29,6 +29,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.GenerateSafeProjection import org.apache.spark.sql.catalyst.expressions.objects.Invoke import org.apache.spark.sql.expressions.Aggregator import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils object TypedAggregateExpression { def apply[BUF : Encoder, OUT : Encoder]( @@ -109,7 +110,9 @@ trait TypedAggregateExpression extends AggregateFunction { s"$nodeName($input)" } - override def nodeName: String = aggregator.getClass.getSimpleName.stripSuffix("$") + // aggregator.getClass.getSimpleName can cause Malformed class name error, + // call safer `Utils.getSimpleName` instead + override def nodeName: String = Utils.getSimpleName(aggregator.getClass).stripSuffix("$"); } // TODO: merge these 2 implementations once we refactor the `AggregateFunction` interface. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/VectorizedHashMapGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/VectorizedHashMapGenerator.scala index 0380ee8b09d63..633eeac180974 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/VectorizedHashMapGenerator.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/VectorizedHashMapGenerator.scala @@ -20,8 +20,9 @@ package org.apache.spark.sql.execution.aggregate import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext -import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, MutableColumnarRow, OnHeapColumnVector} +import org.apache.spark.sql.execution.vectorized.{MutableColumnarRow, OnHeapColumnVector} import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.ColumnarBatch /** * This is a helper class to generate an append-only vectorized hash map that can act as a 'cache' @@ -93,7 +94,7 @@ class VectorizedHashMapGenerator( | | public $generatedClassName() { | vectors = ${classOf[OnHeapColumnVector].getName}.allocateColumns(capacity, schema); - | batch = new ${classOf[ColumnarBatch].getName}(schema, vectors, capacity); + | batch = new ${classOf[ColumnarBatch].getName}(vectors); | | // Generates a projection to return the aggregate buffer only. | ${classOf[OnHeapColumnVector].getName}[] aggBufferVectors = @@ -126,8 +127,8 @@ class VectorizedHashMapGenerator( def genEqualsForKeys(groupingKeys: Seq[Buffer]): String = { groupingKeys.zipWithIndex.map { case (key: Buffer, ordinal: Int) => - s"""(${ctx.genEqual(key.dataType, ctx.getValue(s"vectors[$ordinal]", "buckets[idx]", - key.dataType), key.name)})""" + val value = ctx.getValueFromVector(s"vectors[$ordinal]", key.dataType, "buckets[idx]") + s"(${ctx.genEqual(key.dataType, value, key.name)})" }.mkString(" && ") } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala index bcfc412430263..7487564ed64da 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala @@ -32,8 +32,8 @@ import org.apache.spark.TaskContext import org.apache.spark.api.java.JavaRDD import org.apache.spark.sql.{DataFrame, SQLContext} import org.apache.spark.sql.catalyst.InternalRow -import org.apache.spark.sql.execution.vectorized.{ArrowColumnVector, ColumnarBatch, ColumnVector} import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.{ArrowColumnVector, ColumnarBatch, ColumnVector} import org.apache.spark.util.Utils @@ -175,7 +175,7 @@ private[sql] object ArrowConverters { new ArrowColumnVector(vector).asInstanceOf[ColumnVector] }.toArray - val batch = new ColumnarBatch(schemaRead, columns, root.getRowCount) + val batch = new ColumnarBatch(columns) batch.setNumRows(root.getRowCount) batch.rowIterator().asScala } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala index 22b63513548fe..66888fce7f9f5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala @@ -133,6 +133,14 @@ private[arrow] abstract class ArrowFieldWriter { valueVector match { case fixedWidthVector: BaseFixedWidthVector => fixedWidthVector.reset() case variableWidthVector: BaseVariableWidthVector => variableWidthVector.reset() + case listVector: ListVector => + // Manual "reset" the underlying buffer. + // TODO: When we upgrade to Arrow 0.10.0, we can simply remove this and call + // `listVector.reset()`. + val buffers = listVector.getBuffers(false) + buffers.foreach(buf => buf.setZero(0, buf.capacity())) + listVector.setValueCount(0) + listVector.setLastSet(0) case _ => } count = 0 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryRelation.scala index 51928d914841e..2579046e30708 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryRelation.scala @@ -24,8 +24,9 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.plans.logical -import org.apache.spark.sql.catalyst.plans.logical.Statistics +import org.apache.spark.sql.catalyst.plans.logical.{HintInfo, Statistics} import org.apache.spark.sql.execution.SparkPlan import org.apache.spark.storage.StorageLevel import org.apache.spark.util.LongAccumulator @@ -62,22 +63,36 @@ case class InMemoryRelation( @transient child: SparkPlan, tableName: Option[String])( @transient var _cachedColumnBuffers: RDD[CachedBatch] = null, - val batchStats: LongAccumulator = child.sqlContext.sparkContext.longAccumulator, - statsOfPlanToCache: Statistics = null) + val sizeInBytesStats: LongAccumulator = child.sqlContext.sparkContext.longAccumulator, + statsOfPlanToCache: Statistics) extends logical.LeafNode with MultiInstanceRelation { override protected def innerChildren: Seq[SparkPlan] = Seq(child) + override def doCanonicalize(): logical.LogicalPlan = + copy(output = output.map(QueryPlan.normalizeExprId(_, child.output)), + storageLevel = StorageLevel.NONE, + child = child.canonicalized, + tableName = None)( + _cachedColumnBuffers, + sizeInBytesStats, + statsOfPlanToCache) + override def producedAttributes: AttributeSet = outputSet @transient val partitionStatistics = new PartitionStatistics(output) override def computeStats(): Statistics = { - if (batchStats.value == 0L) { - // Underlying columnar RDD hasn't been materialized, use the stats from the plan to cache - statsOfPlanToCache + if (sizeInBytesStats.value == 0L) { + // Underlying columnar RDD hasn't been materialized, use the stats from the plan to cache. + // Note that we should drop the hint info here. We may cache a plan whose root node is a hint + // node. When we lookup the cache with a semantically same plan without hint info, the plan + // returned by cache lookup should not have hint info. If we lookup the cache with a + // semantically same plan with a different hint info, `CacheManager.useCachedData` will take + // care of it and retain the hint info in the lookup input plan. + statsOfPlanToCache.copy(hints = HintInfo()) } else { - Statistics(sizeInBytes = batchStats.value.longValue) + Statistics(sizeInBytes = sizeInBytesStats.value.longValue) } } @@ -122,7 +137,7 @@ case class InMemoryRelation( rowCount += 1 } - batchStats.add(totalSize) + sizeInBytesStats.add(totalSize) val stats = InternalRow.fromSeq( columnBuilders.flatMap(_.columnStats.collectedStatistics)) @@ -144,7 +159,7 @@ case class InMemoryRelation( def withOutput(newOutput: Seq[Attribute]): InMemoryRelation = { InMemoryRelation( newOutput, useCompression, batchSize, storageLevel, child, tableName)( - _cachedColumnBuffers, batchStats, statsOfPlanToCache) + _cachedColumnBuffers, sizeInBytesStats, statsOfPlanToCache) } override def newInstance(): this.type = { @@ -156,12 +171,12 @@ case class InMemoryRelation( child, tableName)( _cachedColumnBuffers, - batchStats, + sizeInBytesStats, statsOfPlanToCache).asInstanceOf[this.type] } def cachedColumnBuffers: RDD[CachedBatch] = _cachedColumnBuffers override protected def otherCopyArgs: Seq[AnyRef] = - Seq(_cachedColumnBuffers, batchStats, statsOfPlanToCache) + Seq(_cachedColumnBuffers, sizeInBytesStats, statsOfPlanToCache) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala index 3e73393b12850..08b2751ba5789 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala @@ -24,9 +24,10 @@ import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, Partitioning} -import org.apache.spark.sql.execution.{ColumnarBatchScan, LeafExecNode, WholeStageCodegenExec} +import org.apache.spark.sql.execution.{ColumnarBatchScan, LeafExecNode, SparkPlan, WholeStageCodegenExec} import org.apache.spark.sql.execution.vectorized._ import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector} case class InMemoryTableScanExec( @@ -37,6 +38,11 @@ case class InMemoryTableScanExec( override protected def innerChildren: Seq[QueryPlan[_]] = Seq(relation) ++ super.innerChildren + override def doCanonicalize(): SparkPlan = + copy(attributes = attributes.map(QueryPlan.normalizeExprId(_, relation.output)), + predicates = predicates.map(QueryPlan.normalizeExprId(_, relation.output)), + relation = relation.canonicalized.asInstanceOf[InMemoryRelation]) + override def vectorTypes: Option[Seq[String]] = Option(Seq.fill(attributes.length)( if (!conf.offHeapColumnVectorEnabled) { @@ -48,18 +54,23 @@ case class InMemoryTableScanExec( /** * If true, get data from ColumnVector in ColumnarBatch, which are generally faster. - * If false, get data from UnsafeRow build from ColumnVector + * If false, get data from UnsafeRow build from CachedBatch */ - override val supportCodegen: Boolean = { + override val supportsBatch: Boolean = { // In the initial implementation, for ease of review // support only primitive data types and # of fields is less than wholeStageMaxNumFields - relation.schema.fields.forall(f => f.dataType match { + conf.cacheVectorizedReaderEnabled && relation.schema.fields.forall(f => f.dataType match { case BooleanType | ByteType | ShortType | IntegerType | LongType | FloatType | DoubleType => true case _ => false }) && !WholeStageCodegenExec.isTooManyFields(conf, relation.schema) } + // TODO: revisit this. Shall we always turn off whole stage codegen if the output data are rows? + override def supportCodegen: Boolean = supportsBatch + + override protected def needsUnsafeRowConversion: Boolean = false + private val columnIndices = attributes.map(a => relation.output.map(o => o.exprId).indexOf(a.exprId)).toArray @@ -67,19 +78,20 @@ case class InMemoryTableScanExec( private lazy val columnarBatchSchema = new StructType(columnIndices.map(i => relationSchema(i))) - private def createAndDecompressColumn(cachedColumnarBatch: CachedBatch): ColumnarBatch = { + private def createAndDecompressColumn( + cachedColumnarBatch: CachedBatch, + offHeapColumnVectorEnabled: Boolean): ColumnarBatch = { val rowCount = cachedColumnarBatch.numRows val taskContext = Option(TaskContext.get()) - val columnVectors = if (!conf.offHeapColumnVectorEnabled || taskContext.isEmpty) { + val columnVectors = if (!offHeapColumnVectorEnabled || taskContext.isEmpty) { OnHeapColumnVector.allocateColumns(rowCount, columnarBatchSchema) } else { OffHeapColumnVector.allocateColumns(rowCount, columnarBatchSchema) } - val columnarBatch = new ColumnarBatch( - columnarBatchSchema, columnVectors.asInstanceOf[Array[ColumnVector]], rowCount) + val columnarBatch = new ColumnarBatch(columnVectors.asInstanceOf[Array[ColumnVector]]) columnarBatch.setNumRows(rowCount) - for (i <- 0 until attributes.length) { + for (i <- attributes.indices) { ColumnAccessor.decompress( cachedColumnarBatch.buffers(columnIndices(i)), columnarBatch.column(i).asInstanceOf[WritableColumnVector], @@ -89,14 +101,59 @@ case class InMemoryTableScanExec( columnarBatch } - override def inputRDDs(): Seq[RDD[InternalRow]] = { - assert(supportCodegen) + private lazy val inputRDD: RDD[InternalRow] = { val buffers = filteredCachedBatches() - // HACK ALERT: This is actually an RDD[ColumnarBatch]. - // We're taking advantage of Scala's type erasure here to pass these batches along. - Seq(buffers.map(createAndDecompressColumn(_)).asInstanceOf[RDD[InternalRow]]) + val offHeapColumnVectorEnabled = conf.offHeapColumnVectorEnabled + if (supportsBatch) { + // HACK ALERT: This is actually an RDD[ColumnarBatch]. + // We're taking advantage of Scala's type erasure here to pass these batches along. + buffers + .map(createAndDecompressColumn(_, offHeapColumnVectorEnabled)) + .asInstanceOf[RDD[InternalRow]] + } else { + val numOutputRows = longMetric("numOutputRows") + + if (enableAccumulatorsForTest) { + readPartitions.setValue(0) + readBatches.setValue(0) + } + + // Using these variables here to avoid serialization of entire objects (if referenced + // directly) within the map Partitions closure. + val relOutput: AttributeSeq = relation.output + + filteredCachedBatches().mapPartitionsInternal { cachedBatchIterator => + // Find the ordinals and data types of the requested columns. + val (requestedColumnIndices, requestedColumnDataTypes) = + attributes.map { a => + relOutput.indexOf(a.exprId) -> a.dataType + }.unzip + + // update SQL metrics + val withMetrics = cachedBatchIterator.map { batch => + if (enableAccumulatorsForTest) { + readBatches.add(1) + } + numOutputRows += batch.numRows + batch + } + + val columnTypes = requestedColumnDataTypes.map { + case udt: UserDefinedType[_] => udt.sqlType + case other => other + }.toArray + val columnarIterator = GenerateColumnAccessor.generate(columnTypes) + columnarIterator.initialize(withMetrics, columnTypes, requestedColumnIndices.toArray) + if (enableAccumulatorsForTest && columnarIterator.hasNext) { + readPartitions.add(1) + } + columnarIterator + } + } } + override def inputRDDs(): Seq[RDD[InternalRow]] = Seq(inputRDD) + override def output: Seq[Attribute] = attributes private def updateAttribute(expr: Expression): Expression = { @@ -122,11 +179,13 @@ case class InMemoryTableScanExec( override def outputOrdering: Seq[SortOrder] = relation.child.outputOrdering.map(updateAttribute(_).asInstanceOf[SortOrder]) - private def statsFor(a: Attribute) = relation.partitionStatistics.forAttribute(a) + // Keeps relation's partition statistics because we don't serialize relation. + private val stats = relation.partitionStatistics + private def statsFor(a: Attribute) = stats.forAttribute(a) // Returned filter predicate should return false iff it is impossible for the input expression // to evaluate to `true' based on statistics collected about this partition batch. - @transient val buildFilter: PartialFunction[Expression, Expression] = { + @transient lazy val buildFilter: PartialFunction[Expression, Expression] = { case And(lhs: Expression, rhs: Expression) if buildFilter.isDefinedAt(lhs) || buildFilter.isDefinedAt(rhs) => (buildFilter.lift(lhs) ++ buildFilter.lift(rhs)).reduce(_ && _) @@ -166,14 +225,14 @@ case class InMemoryTableScanExec( l.asInstanceOf[Literal] <= statsFor(a).upperBound).reduce(_ || _) } - val partitionFilters: Seq[Expression] = { + lazy val partitionFilters: Seq[Expression] = { predicates.flatMap { p => val filter = buildFilter.lift(p) val boundFilter = filter.map( BindReferences.bindReference( _, - relation.partitionStatistics.schema, + stats.schema, allowFailures = true)) boundFilter.foreach(_ => @@ -184,7 +243,7 @@ case class InMemoryTableScanExec( } } - lazy val enableAccumulators: Boolean = + lazy val enableAccumulatorsForTest: Boolean = sqlContext.getConf("spark.sql.inMemoryTableScanStatistics.enable", "false").toBoolean // Accumulators used for testing purposes @@ -196,7 +255,7 @@ case class InMemoryTableScanExec( private def filteredCachedBatches(): RDD[CachedBatch] = { // Using these variables here to avoid serialization of entire objects (if referenced directly) // within the map Partitions closure. - val schema = relation.partitionStatistics.schema + val schema = stats.schema val schemaIndex = schema.zipWithIndex val buffers = relation.cachedColumnBuffers @@ -229,43 +288,10 @@ case class InMemoryTableScanExec( } protected override def doExecute(): RDD[InternalRow] = { - val numOutputRows = longMetric("numOutputRows") - - if (enableAccumulators) { - readPartitions.setValue(0) - readBatches.setValue(0) - } - - // Using these variables here to avoid serialization of entire objects (if referenced directly) - // within the map Partitions closure. - val relOutput: AttributeSeq = relation.output - - filteredCachedBatches().mapPartitionsInternal { cachedBatchIterator => - // Find the ordinals and data types of the requested columns. - val (requestedColumnIndices, requestedColumnDataTypes) = - attributes.map { a => - relOutput.indexOf(a.exprId) -> a.dataType - }.unzip - - // update SQL metrics - val withMetrics = cachedBatchIterator.map { batch => - if (enableAccumulators) { - readBatches.add(1) - } - numOutputRows += batch.numRows - batch - } - - val columnTypes = requestedColumnDataTypes.map { - case udt: UserDefinedType[_] => udt.sqlType - case other => other - }.toArray - val columnarIterator = GenerateColumnAccessor.generate(columnTypes) - columnarIterator.initialize(withMetrics, columnTypes, requestedColumnIndices.toArray) - if (enableAccumulators && columnarIterator.hasNext) { - readPartitions.add(1) - } - columnarIterator + if (supportsBatch) { + WholeStageCodegenExec(this)(codegenStageId = 0).execute() + } else { + inputRDD } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/compression/compressionSchemes.scala index 79dcf3a6105ce..00a1d54b41709 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/compression/compressionSchemes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/compression/compressionSchemes.scala @@ -116,7 +116,7 @@ private[columnar] case object PassThrough extends CompressionScheme { while (pos < capacity) { if (pos != nextNullIndex) { val len = nextNullIndex - pos - assert(len * unitSize < Int.MaxValue) + assert(len * unitSize.toLong < Int.MaxValue) putFunction(columnVector, pos, bufferPos, len) bufferPos += len * unitSize pos += len diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/CommandUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/CommandUtils.scala index 1a0d67fc71fbc..c27048626c8eb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/CommandUtils.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/CommandUtils.scala @@ -116,8 +116,8 @@ object CommandUtils extends Logging { oldStats: Option[CatalogStatistics], newTotalSize: BigInt, newRowCount: Option[BigInt]): Option[CatalogStatistics] = { - val oldTotalSize = oldStats.map(_.sizeInBytes.toLong).getOrElse(-1L) - val oldRowCount = oldStats.flatMap(_.rowCount.map(_.toLong)).getOrElse(-1L) + val oldTotalSize = oldStats.map(_.sizeInBytes).getOrElse(BigInt(-1)) + val oldRowCount = oldStats.flatMap(_.rowCount).getOrElse(BigInt(-1)) var newStats: Option[CatalogStatistics] = None if (newTotalSize >= 0 && newTotalSize != oldTotalSize) { newStats = Some(CatalogStatistics(sizeInBytes = newTotalSize)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/DataWritingCommand.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/DataWritingCommand.scala index e56f8105fc9a7..e11dbd201004d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/DataWritingCommand.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/DataWritingCommand.scala @@ -19,7 +19,6 @@ package org.apache.spark.sql.execution.command import org.apache.hadoop.conf.Configuration -import org.apache.spark.SparkContext import org.apache.spark.sql.{Row, SparkSession} import org.apache.spark.sql.catalyst.expressions.Attribute import org.apache.spark.sql.catalyst.plans.logical.{Command, LogicalPlan} @@ -45,15 +44,7 @@ trait DataWritingCommand extends Command { // Output columns of the analyzed input query plan def outputColumns: Seq[Attribute] - lazy val metrics: Map[String, SQLMetric] = { - val sparkContext = SparkContext.getActive.get - Map( - "numFiles" -> SQLMetrics.createMetric(sparkContext, "number of written files"), - "numOutputBytes" -> SQLMetrics.createMetric(sparkContext, "bytes of written output"), - "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"), - "numParts" -> SQLMetrics.createMetric(sparkContext, "number of dynamic part") - ) - } + lazy val metrics: Map[String, SQLMetric] = BasicWriteJobStatsTracker.metrics def basicWriteJobStatsTracker(hadoopConf: Configuration): BasicWriteJobStatsTracker = { val serializableHadoopConf = new SerializableConfiguration(hadoopConf) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/SetCommand.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/SetCommand.scala index 7477d025dfe89..3c900be839aa9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/SetCommand.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/SetCommand.scala @@ -91,8 +91,8 @@ case class SetCommand(kv: Option[(String, Option[String])]) extends RunnableComm if (sparkSession.conf.get(CATALOG_IMPLEMENTATION.key).equals("hive") && key.startsWith("hive.")) { logWarning(s"'SET $key=$value' might not work, since Spark doesn't support changing " + - "the Hive config dynamically. Please passing the Hive-specific config by adding the " + - s"prefix spark.hadoop (e.g., spark.hadoop.$key) when starting a Spark application. " + + "the Hive config dynamically. Please pass the Hive-specific config by adding the " + + s"prefix spark.hadoop (e.g. spark.hadoop.$key) when starting a Spark application. " + "For details, see the link: https://spark.apache.org/docs/latest/configuration.html#" + "dynamically-loading-spark-properties.") } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/createDataSourceTables.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/createDataSourceTables.scala index 306f43dc4214a..e9747769dfcfc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/createDataSourceTables.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/createDataSourceTables.scala @@ -21,7 +21,9 @@ import java.net.URI import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.catalog._ +import org.apache.spark.sql.catalyst.expressions.Attribute import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.execution.SparkPlan import org.apache.spark.sql.execution.datasources._ import org.apache.spark.sql.sources.BaseRelation import org.apache.spark.sql.types.StructType @@ -136,12 +138,11 @@ case class CreateDataSourceTableCommand(table: CatalogTable, ignoreIfExists: Boo case class CreateDataSourceTableAsSelectCommand( table: CatalogTable, mode: SaveMode, - query: LogicalPlan) - extends RunnableCommand { - - override protected def innerChildren: Seq[LogicalPlan] = Seq(query) + query: LogicalPlan, + outputColumns: Seq[Attribute]) + extends DataWritingCommand { - override def run(sparkSession: SparkSession): Seq[Row] = { + override def run(sparkSession: SparkSession, child: SparkPlan): Seq[Row] = { assert(table.tableType != CatalogTableType.VIEW) assert(table.provider.isDefined) @@ -163,7 +164,7 @@ case class CreateDataSourceTableAsSelectCommand( } saveDataIntoTable( - sparkSession, table, table.storage.locationUri, query, SaveMode.Append, tableExists = true) + sparkSession, table, table.storage.locationUri, child, SaveMode.Append, tableExists = true) } else { assert(table.schema.isEmpty) @@ -173,7 +174,7 @@ case class CreateDataSourceTableAsSelectCommand( table.storage.locationUri } val result = saveDataIntoTable( - sparkSession, table, tableLocation, query, SaveMode.Overwrite, tableExists = false) + sparkSession, table, tableLocation, child, SaveMode.Overwrite, tableExists = false) val newTable = table.copy( storage = table.storage.copy(locationUri = tableLocation), // We will use the schema of resolved.relation as the schema of the table (instead of @@ -198,10 +199,10 @@ case class CreateDataSourceTableAsSelectCommand( session: SparkSession, table: CatalogTable, tableLocation: Option[URI], - data: LogicalPlan, + physicalPlan: SparkPlan, mode: SaveMode, tableExists: Boolean): BaseRelation = { - // Create the relation based on the input logical plan: `data`. + // Create the relation based on the input logical plan: `query`. val pathOption = tableLocation.map("path" -> CatalogUtils.URIToString(_)) val dataSource = DataSource( session, @@ -212,7 +213,7 @@ case class CreateDataSourceTableAsSelectCommand( catalogTable = if (tableExists) Some(table) else None) try { - dataSource.writeAndRead(mode, query) + dataSource.writeAndRead(mode, query, outputColumns, physicalPlan) } catch { case ex: AnalysisException => logError(s"Failed to write to table ${table.identifier.unquotedString}", ex) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala index 0142f17ce62e2..0f4831b348ce6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala @@ -314,8 +314,8 @@ case class AlterTableChangeColumnCommand( val resolver = sparkSession.sessionState.conf.resolver DDLUtils.verifyAlterTableType(catalog, table, isView = false) - // Find the origin column from schema by column name. - val originColumn = findColumnByName(table.schema, columnName, resolver) + // Find the origin column from dataSchema by column name. + val originColumn = findColumnByName(table.dataSchema, columnName, resolver) // Throw an AnalysisException if the column name/dataType is changed. if (!columnEqual(originColumn, newColumn, resolver)) { throw new AnalysisException( @@ -324,7 +324,7 @@ case class AlterTableChangeColumnCommand( s"'${newColumn.name}' with type '${newColumn.dataType}'") } - val newSchema = table.schema.fields.map { field => + val newDataSchema = table.dataSchema.fields.map { field => if (field.name == originColumn.name) { // Create a new column from the origin column with the new comment. addComment(field, newColumn.getComment) @@ -332,8 +332,7 @@ case class AlterTableChangeColumnCommand( field } } - val newTable = table.copy(schema = StructType(newSchema)) - catalog.alterTable(newTable) + catalog.alterTableDataSchema(tableName, StructType(newDataSchema)) Seq.empty[Row] } @@ -345,7 +344,8 @@ case class AlterTableChangeColumnCommand( schema.fields.collectFirst { case field if resolver(field.name, name) => field }.getOrElse(throw new AnalysisException( - s"Invalid column reference '$name', table schema is '${schema}'")) + s"Can't find column `$name` given table data columns " + + s"${schema.fieldNames.mkString("[`", "`, `", "`]")}")) } // Add the comment to a column, if comment is empty, return the original column. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/functions.scala index 4f92ffee687aa..1f7808c2f8e80 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/functions.scala @@ -40,6 +40,10 @@ import org.apache.spark.sql.types.{StringType, StructField, StructType} * CREATE [OR REPLACE] FUNCTION [IF NOT EXISTS] [databaseName.]functionName * AS className [USING JAR\FILE 'uri' [, JAR|FILE 'uri']] * }}} + * + * @param ignoreIfExists: When true, ignore if the function with the specified name exists + * in the specified database. + * @param replace: When true, alter the function with the specified name */ case class CreateFunctionCommand( databaseName: Option[String], @@ -47,17 +51,17 @@ case class CreateFunctionCommand( className: String, resources: Seq[FunctionResource], isTemp: Boolean, - ifNotExists: Boolean, + ignoreIfExists: Boolean, replace: Boolean) extends RunnableCommand { - if (ifNotExists && replace) { + if (ignoreIfExists && replace) { throw new AnalysisException("CREATE FUNCTION with both IF NOT EXISTS and REPLACE" + " is not allowed.") } // Disallow to define a temporary function with `IF NOT EXISTS` - if (ifNotExists && isTemp) { + if (ignoreIfExists && isTemp) { throw new AnalysisException( "It is not allowed to define a TEMPORARY function with IF NOT EXISTS.") } @@ -79,12 +83,12 @@ case class CreateFunctionCommand( // Handles `CREATE OR REPLACE FUNCTION AS ... USING ...` if (replace && catalog.functionExists(func.identifier)) { // alter the function in the metastore - catalog.alterFunction(CatalogFunction(func.identifier, className, resources)) + catalog.alterFunction(func) } else { // For a permanent, we will store the metadata into underlying external catalog. // This function will be loaded into the FunctionRegistry when a query uses it. // We do not load it into FunctionRegistry right now. - catalog.createFunction(CatalogFunction(func.identifier, className, resources), ifNotExists) + catalog.createFunction(func, ignoreIfExists) } } Seq.empty[Row] diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/BasicWriteStatsTracker.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/BasicWriteStatsTracker.scala index 9dbbe9946ee99..69c03d862391e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/BasicWriteStatsTracker.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/BasicWriteStatsTracker.scala @@ -153,12 +153,29 @@ class BasicWriteJobStatsTracker( totalNumOutput += summary.numRows } - metrics("numFiles").add(numFiles) - metrics("numOutputBytes").add(totalNumBytes) - metrics("numOutputRows").add(totalNumOutput) - metrics("numParts").add(numPartitions) + metrics(BasicWriteJobStatsTracker.NUM_FILES_KEY).add(numFiles) + metrics(BasicWriteJobStatsTracker.NUM_OUTPUT_BYTES_KEY).add(totalNumBytes) + metrics(BasicWriteJobStatsTracker.NUM_OUTPUT_ROWS_KEY).add(totalNumOutput) + metrics(BasicWriteJobStatsTracker.NUM_PARTS_KEY).add(numPartitions) val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY) SQLMetrics.postDriverMetricUpdates(sparkContext, executionId, metrics.values.toList) } } + +object BasicWriteJobStatsTracker { + private val NUM_FILES_KEY = "numFiles" + private val NUM_OUTPUT_BYTES_KEY = "numOutputBytes" + private val NUM_OUTPUT_ROWS_KEY = "numOutputRows" + private val NUM_PARTS_KEY = "numParts" + + def metrics: Map[String, SQLMetric] = { + val sparkContext = SparkContext.getActive.get + Map( + NUM_FILES_KEY -> SQLMetrics.createMetric(sparkContext, "number of written files"), + NUM_OUTPUT_BYTES_KEY -> SQLMetrics.createMetric(sparkContext, "bytes of written output"), + NUM_OUTPUT_ROWS_KEY -> SQLMetrics.createMetric(sparkContext, "number of output rows"), + NUM_PARTS_KEY -> SQLMetrics.createMetric(sparkContext, "number of dynamic part") + ) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/CodecStreams.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/CodecStreams.scala index 54549f698aca5..c0df6c779d7bd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/CodecStreams.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/CodecStreams.scala @@ -45,11 +45,11 @@ object CodecStreams { } /** - * Creates an input stream from the string path and add a closure for the input stream to be + * Creates an input stream from the given path and add a closure for the input stream to be * closed on task completion. */ - def createInputStreamWithCloseResource(config: Configuration, path: String): InputStream = { - val inputStream = createInputStream(config, new Path(path)) + def createInputStreamWithCloseResource(config: Configuration, path: Path): InputStream = { + val inputStream = createInputStream(config, path) Option(TaskContext.get()).foreach(_.addTaskCompletionListener(_ => inputStream.close())) inputStream } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala index 25e1210504273..6e1b5727e3fd5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala @@ -31,8 +31,10 @@ import org.apache.spark.internal.Logging import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute import org.apache.spark.sql.catalyst.catalog.{BucketSpec, CatalogStorageFormat, CatalogTable, CatalogUtils} +import org.apache.spark.sql.catalyst.expressions.Attribute import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap +import org.apache.spark.sql.execution.SparkPlan import org.apache.spark.sql.execution.datasources.csv.CSVFileFormat import org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider import org.apache.spark.sql.execution.datasources.json.JsonFileFormat @@ -435,10 +437,11 @@ case class DataSource( } /** - * Writes the given [[LogicalPlan]] out in this [[FileFormat]]. + * Creates a command node to write the given [[LogicalPlan]] out to the given [[FileFormat]]. + * The returned command is unresolved and need to be analyzed. */ private def planForWritingFileFormat( - format: FileFormat, mode: SaveMode, data: LogicalPlan): LogicalPlan = { + format: FileFormat, mode: SaveMode, data: LogicalPlan): InsertIntoHadoopFsRelationCommand = { // Don't glob path for the write path. The contracts here are: // 1. Only one output path can be specified on the write path; // 2. Output path must be a legal HDFS style file system path; @@ -482,9 +485,24 @@ case class DataSource( /** * Writes the given [[LogicalPlan]] out to this [[DataSource]] and returns a [[BaseRelation]] for * the following reading. + * + * @param mode The save mode for this writing. + * @param data The input query plan that produces the data to be written. Note that this plan + * is analyzed and optimized. + * @param outputColumns The original output columns of the input query plan. The optimizer may not + * preserve the output column's names' case, so we need this parameter + * instead of `data.output`. + * @param physicalPlan The physical plan of the input query plan. We should run the writing + * command with this physical plan instead of creating a new physical plan, + * so that the metrics can be correctly linked to the given physical plan and + * shown in the web UI. */ - def writeAndRead(mode: SaveMode, data: LogicalPlan): BaseRelation = { - if (data.schema.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) { + def writeAndRead( + mode: SaveMode, + data: LogicalPlan, + outputColumns: Seq[Attribute], + physicalPlan: SparkPlan): BaseRelation = { + if (outputColumns.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) { throw new AnalysisException("Cannot save interval data type into external storage.") } @@ -493,9 +511,23 @@ case class DataSource( dataSource.createRelation( sparkSession.sqlContext, mode, caseInsensitiveOptions, Dataset.ofRows(sparkSession, data)) case format: FileFormat => - sparkSession.sessionState.executePlan(planForWritingFileFormat(format, mode, data)).toRdd + val cmd = planForWritingFileFormat(format, mode, data) + val resolvedPartCols = cmd.partitionColumns.map { col => + // The partition columns created in `planForWritingFileFormat` should always be + // `UnresolvedAttribute` with a single name part. + assert(col.isInstanceOf[UnresolvedAttribute]) + val unresolved = col.asInstanceOf[UnresolvedAttribute] + assert(unresolved.nameParts.length == 1) + val name = unresolved.nameParts.head + outputColumns.find(a => equality(a.name, name)).getOrElse { + throw new AnalysisException( + s"Unable to resolve $name given [${data.output.map(_.name).mkString(", ")}]") + } + } + val resolved = cmd.copy(partitionColumns = resolvedPartCols, outputColumns = outputColumns) + resolved.run(sparkSession, physicalPlan) // Replace the schema with that of the DataFrame we just wrote out to avoid re-inferring - copy(userSpecifiedSchema = Some(data.schema.asNullable)).resolveRelation() + copy(userSpecifiedSchema = Some(outputColumns.toStructType.asNullable)).resolveRelation() case _ => sys.error(s"${providingClass.getCanonicalName} does not allow create table as select.") } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala index d94c5bbccdd84..3f41612c08065 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala @@ -139,7 +139,7 @@ case class DataSourceAnalysis(conf: SQLConf) extends Rule[LogicalPlan] with Cast case CreateTable(tableDesc, mode, Some(query)) if query.resolved && DDLUtils.isDatasourceTable(tableDesc) => DDLUtils.checkDataColNames(tableDesc.copy(schema = query.schema)) - CreateDataSourceTableAsSelectCommand(tableDesc, mode, query) + CreateDataSourceTableAsSelectCommand(tableDesc, mode, query, query.output) case InsertIntoTable(l @ LogicalRelation(_: InsertableRelation, _, _, _), parts, query, overwrite, false) if parts.isEmpty => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileScanRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileScanRDD.scala index 8731ee88f87f2..28c36b6020d33 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileScanRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileScanRDD.scala @@ -21,12 +21,15 @@ import java.io.{FileNotFoundException, IOException} import scala.collection.mutable +import org.apache.parquet.io.ParquetDecodingException + import org.apache.spark.{Partition => RDDPartition, TaskContext, TaskKilledException} import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.rdd.{InputFileBlockHolder, RDD} import org.apache.spark.sql.SparkSession import org.apache.spark.sql.catalyst.InternalRow -import org.apache.spark.sql.execution.vectorized.ColumnarBatch +import org.apache.spark.sql.execution.QueryExecutionException +import org.apache.spark.sql.vectorized.ColumnarBatch import org.apache.spark.util.NextIterator /** @@ -179,7 +182,23 @@ class FileScanRDD( currentIterator = readCurrentFile() } - hasNext + try { + hasNext + } catch { + case e: SchemaColumnConvertNotSupportedException => + val message = "Parquet column cannot be converted in " + + s"file ${currentFile.filePath}. Column: ${e.getColumn}, " + + s"Expected: ${e.getLogicalType}, Found: ${e.getPhysicalType}" + throw new QueryExecutionException(message, e) + case e: ParquetDecodingException => + if (e.getMessage.contains("Can not read value at")) { + val message = "Encounter error while reading parquet files. " + + "One possible cause: Parquet column cannot be converted in the " + + "corresponding files. Details: " + throw new QueryExecutionException(message, e) + } + throw e + } } else { currentFile = null InputFileBlockHolder.unset() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileSourceStrategy.scala index 16b22717b8d92..0a568d6b8adce 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileSourceStrategy.scala @@ -76,7 +76,10 @@ object FileSourceStrategy extends Strategy with Logging { fsRelation.partitionSchema, fsRelation.sparkSession.sessionState.analyzer.resolver) val partitionSet = AttributeSet(partitionColumns) val partitionKeyFilters = - ExpressionSet(normalizedFilters.filter(_.references.subsetOf(partitionSet))) + ExpressionSet(normalizedFilters + .filterNot(SubqueryExpression.hasSubquery(_)) + .filter(_.references.subsetOf(partitionSet))) + logInfo(s"Pruning directories with: ${partitionKeyFilters.mkString(",")}") val dataColumns = diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelation.scala index 89d8a85a9cbd2..b2f73b7f8d1fc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelation.scala @@ -67,6 +67,9 @@ case class HadoopFsRelation( } } + // When data and partition schemas have overlapping columns, the output + // schema respects the order of the data schema for the overlapping columns, and it + // respects the data types of the partition schema. val schema: StructType = { StructType(dataSchema.map(f => overlappedPartCols.getOrElse(getColName(f), f)) ++ partitionSchema.filterNot(f => overlappedPartCols.contains(getColName(f)))) @@ -82,7 +85,11 @@ case class HadoopFsRelation( } } - override def sizeInBytes: Long = location.sizeInBytes + override def sizeInBytes: Long = { + val compressionFactor = sqlContext.conf.fileCompressionFactor + (location.sizeInBytes * compressionFactor).toLong + } + override def inputFiles: Array[String] = location.inputFiles } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InMemoryFileIndex.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InMemoryFileIndex.scala index 318ada0ceefc5..4925831743465 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InMemoryFileIndex.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InMemoryFileIndex.scala @@ -294,9 +294,12 @@ object InMemoryFileIndex extends Logging { if (filter != null) allFiles.filter(f => filter.accept(f.getPath)) else allFiles } - allLeafStatuses.filterNot(status => shouldFilterOut(status.getPath.getName)).map { + val missingFiles = mutable.ArrayBuffer.empty[String] + val filteredLeafStatuses = allLeafStatuses.filterNot( + status => shouldFilterOut(status.getPath.getName)) + val resolvedLeafStatuses = filteredLeafStatuses.flatMap { case f: LocatedFileStatus => - f + Some(f) // NOTE: // @@ -311,14 +314,27 @@ object InMemoryFileIndex extends Logging { // The other constructor of LocatedFileStatus will call FileStatus.getPermission(), // which is very slow on some file system (RawLocalFileSystem, which is launch a // subprocess and parse the stdout). - val locations = fs.getFileBlockLocations(f, 0, f.getLen) - val lfs = new LocatedFileStatus(f.getLen, f.isDirectory, f.getReplication, f.getBlockSize, - f.getModificationTime, 0, null, null, null, null, f.getPath, locations) - if (f.isSymlink) { - lfs.setSymlink(f.getSymlink) + try { + val locations = fs.getFileBlockLocations(f, 0, f.getLen) + val lfs = new LocatedFileStatus(f.getLen, f.isDirectory, f.getReplication, f.getBlockSize, + f.getModificationTime, 0, null, null, null, null, f.getPath, locations) + if (f.isSymlink) { + lfs.setSymlink(f.getSymlink) + } + Some(lfs) + } catch { + case _: FileNotFoundException => + missingFiles += f.getPath.toString + None } - lfs } + + if (missingFiles.nonEmpty) { + logWarning( + s"the following files were missing during file scan:\n ${missingFiles.mkString("\n ")}") + } + + resolvedLeafStatuses } /** Checks if we should filter out this path name. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSourceCommand.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSourceCommand.scala index a813829d50cb1..80d7608a22891 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSourceCommand.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSourceCommand.scala @@ -38,9 +38,8 @@ case class InsertIntoDataSourceCommand( override def run(sparkSession: SparkSession): Seq[Row] = { val relation = logicalRelation.relation.asInstanceOf[InsertableRelation] val data = Dataset.ofRows(sparkSession, query) - // Apply the schema of the existing table to the new data. - val df = sparkSession.internalCreateDataFrame(data.queryExecution.toRdd, logicalRelation.schema) - relation.insert(df, overwrite) + // Data has been casted to the target relation's schema by the PreprocessTableInsertion rule. + relation.insert(data, overwrite) // Re-cache all cached plans(including this relation itself, if it's cached) that refer to this // data source relation. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelationCommand.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelationCommand.scala index ad24e280d942a..dd7ef0d15c140 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelationCommand.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelationCommand.scala @@ -29,6 +29,7 @@ import org.apache.spark.sql.catalyst.expressions.Attribute import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.SparkPlan import org.apache.spark.sql.execution.command._ +import org.apache.spark.sql.internal.SQLConf.PartitionOverwriteMode import org.apache.spark.sql.util.SchemaUtils /** @@ -89,13 +90,19 @@ case class InsertIntoHadoopFsRelationCommand( } val pathExists = fs.exists(qualifiedOutputPath) - // If we are appending data to an existing dir. - val isAppend = pathExists && (mode == SaveMode.Append) + + val enableDynamicOverwrite = + sparkSession.sessionState.conf.partitionOverwriteMode == PartitionOverwriteMode.DYNAMIC + // This config only makes sense when we are overwriting a partitioned dataset with dynamic + // partition columns. + val dynamicPartitionOverwrite = enableDynamicOverwrite && mode == SaveMode.Overwrite && + staticPartitions.size < partitionColumns.length val committer = FileCommitProtocol.instantiate( sparkSession.sessionState.conf.fileCommitProtocolClass, jobId = java.util.UUID.randomUUID().toString, - outputPath = outputPath.toString) + outputPath = outputPath.toString, + dynamicPartitionOverwrite = dynamicPartitionOverwrite) val doInsertion = (mode, pathExists) match { case (SaveMode.ErrorIfExists, true) => @@ -103,6 +110,9 @@ case class InsertIntoHadoopFsRelationCommand( case (SaveMode.Overwrite, true) => if (ifPartitionNotExists && matchingPartitions.nonEmpty) { false + } else if (dynamicPartitionOverwrite) { + // For dynamic partition overwrite, do not delete partition directories ahead. + true } else { deleteMatchingPartitions(fs, qualifiedOutputPath, customPartitionLocations, committer) true @@ -126,7 +136,9 @@ case class InsertIntoHadoopFsRelationCommand( catalogTable.get.identifier, newPartitions.toSeq.map(p => (p, None)), ifNotExists = true).run(sparkSession) } - if (mode == SaveMode.Overwrite) { + // For dynamic partition overwrite, we never remove partitions but only update existing + // ones. + if (mode == SaveMode.Overwrite && !dynamicPartitionOverwrite) { val deletedPartitions = initialMatchingPartitions.toSet -- updatedPartitions if (deletedPartitions.nonEmpty) { AlterTableDropPartitionCommand( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala index 472bf82d3604d..379acb67f7c71 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala @@ -407,6 +407,34 @@ object PartitioningUtils { Literal(bigDecimal) } + val dateTry = Try { + // try and parse the date, if no exception occurs this is a candidate to be resolved as + // DateType + DateTimeUtils.getThreadLocalDateFormat.parse(raw) + // SPARK-23436: Casting the string to date may still return null if a bad Date is provided. + // This can happen since DateFormat.parse may not use the entire text of the given string: + // so if there are extra-characters after the date, it returns correctly. + // We need to check that we can cast the raw string since we later can use Cast to get + // the partition values with the right DataType (see + // org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.inferPartitioning) + val dateValue = Cast(Literal(raw), DateType).eval() + // Disallow DateType if the cast returned null + require(dateValue != null) + Literal.create(dateValue, DateType) + } + + val timestampTry = Try { + val unescapedRaw = unescapePathName(raw) + // try and parse the date, if no exception occurs this is a candidate to be resolved as + // TimestampType + DateTimeUtils.getThreadLocalTimestampFormat(timeZone).parse(unescapedRaw) + // SPARK-23436: see comment for date + val timestampValue = Cast(Literal(unescapedRaw), TimestampType, Some(timeZone.getID)).eval() + // Disallow TimestampType if the cast returned null + require(timestampValue != null) + Literal.create(timestampValue, TimestampType) + } + if (typeInference) { // First tries integral types Try(Literal.create(Integer.parseInt(raw), IntegerType)) @@ -415,16 +443,8 @@ object PartitioningUtils { // Then falls back to fractional types .orElse(Try(Literal.create(JDouble.parseDouble(raw), DoubleType))) // Then falls back to date/timestamp types - .orElse(Try( - Literal.create( - DateTimeUtils.getThreadLocalTimestampFormat(timeZone) - .parse(unescapePathName(raw)).getTime * 1000L, - TimestampType))) - .orElse(Try( - Literal.create( - DateTimeUtils.millisToDays( - DateTimeUtils.getThreadLocalDateFormat.parse(raw).getTime), - DateType))) + .orElse(timestampTry) + .orElse(dateTry) // Then falls back to string .getOrElse { if (raw == DEFAULT_PARTITION_NAME) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PruneFileSourcePartitions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PruneFileSourcePartitions.scala index 3b830accb83f0..16b2367bfdd5c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PruneFileSourcePartitions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PruneFileSourcePartitions.scala @@ -55,7 +55,9 @@ private[sql] object PruneFileSourcePartitions extends Rule[LogicalPlan] { partitionSchema, sparkSession.sessionState.analyzer.resolver) val partitionSet = AttributeSet(partitionColumns) val partitionKeyFilters = - ExpressionSet(normalizedFilters.filter(_.references.subsetOf(partitionSet))) + ExpressionSet(normalizedFilters + .filterNot(SubqueryExpression.hasSubquery(_)) + .filter(_.references.subsetOf(partitionSet))) if (partitionKeyFilters.nonEmpty) { val prunedFileIndex = catalogFileIndex.filterPartitions(partitionKeyFilters.toSeq) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SQLHadoopMapReduceCommitProtocol.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SQLHadoopMapReduceCommitProtocol.scala index 40825a1f724b1..39c594a9bc618 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SQLHadoopMapReduceCommitProtocol.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SQLHadoopMapReduceCommitProtocol.scala @@ -29,11 +29,15 @@ import org.apache.spark.sql.internal.SQLConf * A variant of [[HadoopMapReduceCommitProtocol]] that allows specifying the actual * Hadoop output committer using an option specified in SQLConf. */ -class SQLHadoopMapReduceCommitProtocol(jobId: String, path: String) - extends HadoopMapReduceCommitProtocol(jobId, path) with Serializable with Logging { +class SQLHadoopMapReduceCommitProtocol( + jobId: String, + path: String, + dynamicPartitionOverwrite: Boolean = false) + extends HadoopMapReduceCommitProtocol(jobId, path, dynamicPartitionOverwrite) + with Serializable with Logging { override protected def setupCommitter(context: TaskAttemptContext): OutputCommitter = { - var committer = context.getOutputFormatClass.newInstance().getOutputCommitter(context) + var committer = super.setupCommitter(context) val configuration = context.getConfiguration val clazz = diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SaveIntoDataSourceCommand.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SaveIntoDataSourceCommand.scala index 568e953a5db66..00b1b5dedb593 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SaveIntoDataSourceCommand.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SaveIntoDataSourceCommand.scala @@ -17,13 +17,12 @@ package org.apache.spark.sql.execution.datasources -import org.apache.spark.SparkEnv import org.apache.spark.sql.{Dataset, Row, SaveMode, SparkSession} import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.command.RunnableCommand +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.sources.CreatableRelationProvider -import org.apache.spark.util.Utils /** * Saves the results of `query` in to a data source. @@ -50,7 +49,7 @@ case class SaveIntoDataSourceCommand( } override def simpleString: String = { - val redacted = Utils.redact(SparkEnv.get.conf, options.toSeq).toMap + val redacted = SQLConf.get.redactOptions(options) s"SaveIntoDataSourceCommand ${dataSource}, ${redacted}, ${mode}" } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala index 2031381dd2e10..fffad1753513a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala @@ -17,11 +17,12 @@ package org.apache.spark.sql.execution.datasources.csv +import java.net.URI import java.nio.charset.{Charset, StandardCharsets} import com.univocity.parsers.csv.CsvParser import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.FileStatus +import org.apache.hadoop.fs.{FileStatus, Path} import org.apache.hadoop.io.{LongWritable, Text} import org.apache.hadoop.mapred.TextInputFormat import org.apache.hadoop.mapreduce.Job @@ -32,7 +33,6 @@ import org.apache.spark.input.{PortableDataStream, StreamInputFormat} import org.apache.spark.rdd.{BinaryFileRDD, RDD} import org.apache.spark.sql.{Dataset, Encoders, SparkSession} import org.apache.spark.sql.catalyst.InternalRow -import org.apache.spark.sql.execution.SQLExecution import org.apache.spark.sql.execution.datasources._ import org.apache.spark.sql.execution.datasources.text.TextFileFormat import org.apache.spark.sql.types.StructType @@ -184,7 +184,8 @@ object TextInputCSVDataSource extends CSVDataSource { DataSource.apply( sparkSession, paths = paths, - className = classOf[TextFileFormat].getName + className = classOf[TextFileFormat].getName, + options = options.parameters ).resolveRelation(checkFilesExist = false)) .select("value").as[String](Encoders.STRING) } else { @@ -206,7 +207,7 @@ object MultiLineCSVDataSource extends CSVDataSource { parser: UnivocityParser, schema: StructType): Iterator[InternalRow] = { UnivocityParser.parseStream( - CodecStreams.createInputStreamWithCloseResource(conf, file.filePath), + CodecStreams.createInputStreamWithCloseResource(conf, new Path(new URI(file.filePath))), parser.options.headerFlag, parser, schema) @@ -218,8 +219,9 @@ object MultiLineCSVDataSource extends CSVDataSource { parsedOptions: CSVOptions): StructType = { val csv = createBaseRdd(sparkSession, inputPaths, parsedOptions) csv.flatMap { lines => + val path = new Path(lines.getPath()) UnivocityParser.tokenizeStream( - CodecStreams.createInputStreamWithCloseResource(lines.getConfiguration, lines.getPath()), + CodecStreams.createInputStreamWithCloseResource(lines.getConfiguration, path), shouldDropHeader = false, new CsvParser(parsedOptions.asParserSettings)) }.take(1).headOption match { @@ -230,7 +232,7 @@ object MultiLineCSVDataSource extends CSVDataSource { UnivocityParser.tokenizeStream( CodecStreams.createInputStreamWithCloseResource( lines.getConfiguration, - lines.getPath()), + new Path(lines.getPath())), parsedOptions.headerFlag, new CsvParser(parsedOptions.asParserSettings)) } @@ -247,7 +249,8 @@ object MultiLineCSVDataSource extends CSVDataSource { options: CSVOptions): RDD[PortableDataStream] = { val paths = inputPaths.map(_.getPath) val name = paths.mkString(",") - val job = Job.getInstance(sparkSession.sessionState.newHadoopConf()) + val job = Job.getInstance(sparkSession.sessionState.newHadoopConfWithOptions( + options.parameters)) FileInputFormat.setInputPaths(job, paths: _*) val conf = job.getConfiguration diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala index c16790630ce17..6347af619d46a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala @@ -27,7 +27,7 @@ import org.apache.spark.internal.Logging import org.apache.spark.sql.catalyst.util._ class CSVOptions( - @transient private val parameters: CaseInsensitiveMap[String], + @transient val parameters: CaseInsensitiveMap[String], defaultTimeZoneId: String, defaultColumnNameOfCorruptRecord: String) extends Logging with Serializable { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala index 7d6d7e7eef926..99557a1ceb0c8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala @@ -19,8 +19,6 @@ package org.apache.spark.sql.execution.datasources.csv import java.io.InputStream import java.math.BigDecimal -import java.text.NumberFormat -import java.util.Locale import scala.util.Try import scala.util.control.NonFatal @@ -203,6 +201,8 @@ class UnivocityParser( case _: BadRecordException => None } } + // For records with less or more tokens than the schema, tries to return partial results + // if possible. throw BadRecordException( () => getCurrentInput, () => getPartialResult(), @@ -218,6 +218,9 @@ class UnivocityParser( row } catch { case NonFatal(e) => + // For corrupted records with the number of tokens same as the schema, + // CSV reader doesn't support partial results. All fields other than the field + // configured by `columnNameOfCorruptRecord` are set to `null`. throw BadRecordException(() => getCurrentInput, () => None, e) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala index 7a6c0f9fed2f9..1723596de1db2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala @@ -32,6 +32,13 @@ import org.apache.spark.util.Utils */ object DriverRegistry extends Logging { + /** + * Load DriverManager first to avoid any race condition between + * DriverManager static initialization block and specific driver class's + * static initialization block. e.g. PhoenixDriver + */ + DriverManager.getDrivers + private val wrapperMap: mutable.Map[String, DriverWrapper] = mutable.Map.empty def register(className: String): Unit = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala index 8b7c2709afde1..8a0fe5374b912 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala @@ -18,11 +18,12 @@ package org.apache.spark.sql.execution.datasources.json import java.io.InputStream +import java.net.URI import com.fasterxml.jackson.core.{JsonFactory, JsonParser} import com.google.common.io.ByteStreams import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.FileStatus +import org.apache.hadoop.fs.{FileStatus, Path} import org.apache.hadoop.io.Text import org.apache.hadoop.mapreduce.Job import org.apache.hadoop.mapreduce.lib.input.FileInputFormat @@ -91,7 +92,7 @@ object TextInputJsonDataSource extends JsonDataSource { sparkSession: SparkSession, inputPaths: Seq[FileStatus], parsedOptions: JSONOptions): StructType = { - val json: Dataset[String] = createBaseDataset(sparkSession, inputPaths) + val json: Dataset[String] = createBaseDataset(sparkSession, inputPaths, parsedOptions) inferFromDataset(json, parsedOptions) } @@ -103,13 +104,15 @@ object TextInputJsonDataSource extends JsonDataSource { private def createBaseDataset( sparkSession: SparkSession, - inputPaths: Seq[FileStatus]): Dataset[String] = { + inputPaths: Seq[FileStatus], + parsedOptions: JSONOptions): Dataset[String] = { val paths = inputPaths.map(_.getPath.toString) sparkSession.baseRelationToDataFrame( DataSource.apply( sparkSession, paths = paths, - className = classOf[TextFileFormat].getName + className = classOf[TextFileFormat].getName, + options = parsedOptions.parameters ).resolveRelation(checkFilesExist = false)) .select("value").as(Encoders.STRING) } @@ -143,16 +146,18 @@ object MultiLineJsonDataSource extends JsonDataSource { sparkSession: SparkSession, inputPaths: Seq[FileStatus], parsedOptions: JSONOptions): StructType = { - val json: RDD[PortableDataStream] = createBaseRdd(sparkSession, inputPaths) + val json: RDD[PortableDataStream] = createBaseRdd(sparkSession, inputPaths, parsedOptions) val sampled: RDD[PortableDataStream] = JsonUtils.sample(json, parsedOptions) JsonInferSchema.infer(sampled, parsedOptions, createParser) } private def createBaseRdd( sparkSession: SparkSession, - inputPaths: Seq[FileStatus]): RDD[PortableDataStream] = { + inputPaths: Seq[FileStatus], + parsedOptions: JSONOptions): RDD[PortableDataStream] = { val paths = inputPaths.map(_.getPath) - val job = Job.getInstance(sparkSession.sessionState.newHadoopConf()) + val job = Job.getInstance(sparkSession.sessionState.newHadoopConfWithOptions( + parsedOptions.parameters)) val conf = job.getConfiguration val name = paths.mkString(",") FileInputFormat.setInputPaths(job, paths: _*) @@ -168,9 +173,10 @@ object MultiLineJsonDataSource extends JsonDataSource { } private def createParser(jsonFactory: JsonFactory, record: PortableDataStream): JsonParser = { + val path = new Path(record.getPath()) CreateJacksonParser.inputStream( jsonFactory, - CodecStreams.createInputStreamWithCloseResource(record.getConfiguration, record.getPath())) + CodecStreams.createInputStreamWithCloseResource(record.getConfiguration, path)) } override def readFile( @@ -180,7 +186,7 @@ object MultiLineJsonDataSource extends JsonDataSource { schema: StructType): Iterator[InternalRow] = { def partitionedFileString(ignored: Any): UTF8String = { Utils.tryWithResource { - CodecStreams.createInputStreamWithCloseResource(conf, file.filePath) + CodecStreams.createInputStreamWithCloseResource(conf, new Path(new URI(file.filePath))) } { inputStream => UTF8String.fromBytes(ByteStreams.toByteArray(inputStream)) } @@ -193,6 +199,6 @@ object MultiLineJsonDataSource extends JsonDataSource { parser.options.columnNameOfCorruptRecord) safeParser.parse( - CodecStreams.createInputStreamWithCloseResource(conf, file.filePath)) + CodecStreams.createInputStreamWithCloseResource(conf, new Path(new URI(file.filePath)))) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala index f7471cd7debce..94403c3be8c7b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFileFormat.scala @@ -38,6 +38,7 @@ import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection import org.apache.spark.sql.execution.datasources._ +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.sources._ import org.apache.spark.sql.types._ import org.apache.spark.util.SerializableConfiguration @@ -118,6 +119,13 @@ class OrcFileFormat } } + override def supportBatch(sparkSession: SparkSession, schema: StructType): Boolean = { + val conf = sparkSession.sessionState.conf + conf.orcVectorizedReaderEnabled && conf.wholeStageEnabled && + schema.length <= conf.wholeStageMaxNumFields && + schema.forall(_.dataType.isInstanceOf[AtomicType]) + } + override def isSplitable( sparkSession: SparkSession, options: Map[String, String], @@ -139,6 +147,12 @@ class OrcFileFormat } } + val resultSchema = StructType(requiredSchema.fields ++ partitionSchema.fields) + val sqlConf = sparkSession.sessionState.conf + val enableOffHeapColumnVector = sqlConf.offHeapColumnVectorEnabled + val enableVectorizedReader = supportBatch(sparkSession, resultSchema) + val copyToSpark = sparkSession.sessionState.conf.getConf(SQLConf.ORC_COPY_BATCH_TO_SPARK) + val broadcastedConf = sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf)) val isCaseSensitive = sparkSession.sessionState.conf.caseSensitiveAnalysis @@ -146,8 +160,14 @@ class OrcFileFormat (file: PartitionedFile) => { val conf = broadcastedConf.value.value + val filePath = new Path(new URI(file.filePath)) + + val fs = filePath.getFileSystem(conf) + val readerOptions = OrcFile.readerOptions(conf).filesystem(fs) + val reader = OrcFile.createReader(filePath, readerOptions) + val requestedColIdsOrEmptyFile = OrcUtils.requestedColumnIds( - isCaseSensitive, dataSchema, requiredSchema, new Path(new URI(file.filePath)), conf) + isCaseSensitive, dataSchema, requiredSchema, reader, conf) if (requestedColIdsOrEmptyFile.isEmpty) { Iterator.empty @@ -155,29 +175,50 @@ class OrcFileFormat val requestedColIds = requestedColIdsOrEmptyFile.get assert(requestedColIds.length == requiredSchema.length, "[BUG] requested column IDs do not match required schema") - conf.set(OrcConf.INCLUDE_COLUMNS.getAttribute, + val taskConf = new Configuration(conf) + taskConf.set(OrcConf.INCLUDE_COLUMNS.getAttribute, requestedColIds.filter(_ != -1).sorted.mkString(",")) - val fileSplit = - new FileSplit(new Path(new URI(file.filePath)), file.start, file.length, Array.empty) + val fileSplit = new FileSplit(filePath, file.start, file.length, Array.empty) val attemptId = new TaskAttemptID(new TaskID(new JobID(), TaskType.MAP, 0), 0) - val taskAttemptContext = new TaskAttemptContextImpl(conf, attemptId) - - val orcRecordReader = new OrcInputFormat[OrcStruct] - .createRecordReader(fileSplit, taskAttemptContext) - val iter = new RecordReaderIterator[OrcStruct](orcRecordReader) - Option(TaskContext.get()).foreach(_.addTaskCompletionListener(_ => iter.close())) - - val fullSchema = requiredSchema.toAttributes ++ partitionSchema.toAttributes - val unsafeProjection = GenerateUnsafeProjection.generate(fullSchema, fullSchema) - val deserializer = new OrcDeserializer(dataSchema, requiredSchema, requestedColIds) - - if (partitionSchema.length == 0) { - iter.map(value => unsafeProjection(deserializer.deserialize(value))) + val taskAttemptContext = new TaskAttemptContextImpl(taskConf, attemptId) + + val taskContext = Option(TaskContext.get()) + if (enableVectorizedReader) { + val batchReader = new OrcColumnarBatchReader( + enableOffHeapColumnVector && taskContext.isDefined, copyToSpark) + // SPARK-23399 Register a task completion listener first to call `close()` in all cases. + // There is a possibility that `initialize` and `initBatch` hit some errors (like OOM) + // after opening a file. + val iter = new RecordReaderIterator(batchReader) + Option(TaskContext.get()).foreach(_.addTaskCompletionListener(_ => iter.close())) + + batchReader.initialize(fileSplit, taskAttemptContext) + batchReader.initBatch( + reader.getSchema, + requestedColIds, + requiredSchema.fields, + partitionSchema, + file.partitionValues) + + iter.asInstanceOf[Iterator[InternalRow]] } else { - val joinedRow = new JoinedRow() - iter.map(value => - unsafeProjection(joinedRow(deserializer.deserialize(value), file.partitionValues))) + val orcRecordReader = new OrcInputFormat[OrcStruct] + .createRecordReader(fileSplit, taskAttemptContext) + val iter = new RecordReaderIterator[OrcStruct](orcRecordReader) + Option(TaskContext.get()).foreach(_.addTaskCompletionListener(_ => iter.close())) + + val fullSchema = requiredSchema.toAttributes ++ partitionSchema.toAttributes + val unsafeProjection = GenerateUnsafeProjection.generate(fullSchema, fullSchema) + val deserializer = new OrcDeserializer(dataSchema, requiredSchema, requestedColIds) + + if (partitionSchema.length == 0) { + iter.map(value => unsafeProjection(deserializer.deserialize(value))) + } else { + val joinedRow = new JoinedRow() + iter.map(value => + unsafeProjection(joinedRow(deserializer.deserialize(value), file.partitionValues))) + } } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcOptions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcOptions.scala index c866dd834a525..0ad3862f6cf01 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcOptions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcOptions.scala @@ -67,4 +67,6 @@ object OrcOptions { "snappy" -> "SNAPPY", "zlib" -> "ZLIB", "lzo" -> "LZO") + + def getORCCompressionCodecName(name: String): String = shortOrcCompressionCodecNames(name) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala index b03ee06d04a16..460194ba61c8b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala @@ -21,8 +21,9 @@ import scala.collection.JavaConverters._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileStatus, Path} -import org.apache.orc.{OrcFile, TypeDescription} +import org.apache.orc.{OrcFile, Reader, TypeDescription} +import org.apache.spark.SparkException import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.internal.Logging import org.apache.spark.sql.SparkSession @@ -50,23 +51,35 @@ object OrcUtils extends Logging { paths } - def readSchema(file: Path, conf: Configuration): Option[TypeDescription] = { + def readSchema(file: Path, conf: Configuration, ignoreCorruptFiles: Boolean) + : Option[TypeDescription] = { val fs = file.getFileSystem(conf) val readerOptions = OrcFile.readerOptions(conf).filesystem(fs) - val reader = OrcFile.createReader(file, readerOptions) - val schema = reader.getSchema - if (schema.getFieldNames.size == 0) { - None - } else { - Some(schema) + try { + val reader = OrcFile.createReader(file, readerOptions) + val schema = reader.getSchema + if (schema.getFieldNames.size == 0) { + None + } else { + Some(schema) + } + } catch { + case e: org.apache.orc.FileFormatException => + if (ignoreCorruptFiles) { + logWarning(s"Skipped the footer in the corrupted file: $file", e) + None + } else { + throw new SparkException(s"Could not read footer for file: $file", e) + } } } def readSchema(sparkSession: SparkSession, files: Seq[FileStatus]) : Option[StructType] = { + val ignoreCorruptFiles = sparkSession.sessionState.conf.ignoreCorruptFiles val conf = sparkSession.sessionState.newHadoopConf() // TODO: We need to support merge schema. Please see SPARK-11412. - files.map(_.getPath).flatMap(readSchema(_, conf)).headOption.map { schema => + files.map(_.getPath).flatMap(readSchema(_, conf, ignoreCorruptFiles)).headOption.map { schema => logDebug(s"Reading schema from file $files, got Hive schema string: $schema") CatalystSqlParser.parseDataType(schema.toString).asInstanceOf[StructType] } @@ -80,11 +93,8 @@ object OrcUtils extends Logging { isCaseSensitive: Boolean, dataSchema: StructType, requiredSchema: StructType, - file: Path, + reader: Reader, conf: Configuration): Option[Array[Int]] = { - val fs = file.getFileSystem(conf) - val readerOptions = OrcFile.readerOptions(conf).filesystem(fs) - val reader = OrcFile.createReader(file, readerOptions) val orcFieldNames = reader.getSchema.getFieldNames.asScala if (orcFieldNames.isEmpty) { // SPARK-8501: Some old empty ORC files always have an empty schema stored in their footer. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala index 45bedf70f975c..b0ba21e47df45 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala @@ -108,8 +108,7 @@ class ParquetFileFormat ParquetOutputFormat.setWriteSupportClass(job, classOf[ParquetWriteSupport]) - // We want to clear this temporary metadata from saving into Parquet file. - // This metadata is only useful for detecting optional columns when pushdowning filters. + // This metadata is useful for keeping UDTs like Vector/Matrix. ParquetWriteSupport.setSchema(dataSchema, conf) // Sets flags for `ParquetWriteSupport`, which converts Catalyst schema to Parquet @@ -322,19 +321,6 @@ class ParquetFileFormat SQLConf.PARQUET_INT96_AS_TIMESTAMP.key, sparkSession.sessionState.conf.isParquetINT96AsTimestamp) - // Try to push down filters when filter push-down is enabled. - val pushed = - if (sparkSession.sessionState.conf.parquetFilterPushDown) { - filters - // Collects all converted Parquet filter predicates. Notice that not all predicates can be - // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap` - // is used here. - .flatMap(ParquetFilters.createFilter(requiredSchema, _)) - .reduceOption(FilterApi.and) - } else { - None - } - val broadcastedHadoopConf = sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf)) @@ -351,12 +337,26 @@ class ParquetFileFormat sparkSession.sessionState.conf.parquetRecordFilterEnabled val timestampConversion: Boolean = sparkSession.sessionState.conf.isParquetINT96TimestampConversion + val enableParquetFilterPushDown: Boolean = + sparkSession.sessionState.conf.parquetFilterPushDown // Whole stage codegen (PhysicalRDD) is able to deal with batches directly val returningBatch = supportBatch(sparkSession, resultSchema) (file: PartitionedFile) => { assert(file.partitionValues.numFields == partitionSchema.size) + // Try to push down filters when filter push-down is enabled. + val pushed = if (enableParquetFilterPushDown) { + filters + // Collects all converted Parquet filter predicates. Notice that not all predicates can be + // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap` + // is used here. + .flatMap(ParquetFilters.createFilter(requiredSchema, _)) + .reduceOption(FilterApi.and) + } else { + None + } + val fileSplit = new FileSplit(new Path(new URI(file.filePath)), file.start, file.length, Array.empty) @@ -395,16 +395,21 @@ class ParquetFileFormat ParquetInputFormat.setFilterPredicate(hadoopAttemptContext.getConfiguration, pushed.get) } val taskContext = Option(TaskContext.get()) - val parquetReader = if (enableVectorizedReader) { + if (enableVectorizedReader) { val vectorizedReader = new VectorizedParquetRecordReader( convertTz.orNull, enableOffHeapColumnVector && taskContext.isDefined) + val iter = new RecordReaderIterator(vectorizedReader) + // SPARK-23457 Register a task completion lister before `initialization`. + taskContext.foreach(_.addTaskCompletionListener(_ => iter.close())) vectorizedReader.initialize(split, hadoopAttemptContext) logDebug(s"Appending $partitionSchema ${file.partitionValues}") vectorizedReader.initBatch(partitionSchema, file.partitionValues) if (returningBatch) { vectorizedReader.enableReturningBatches() } - vectorizedReader + + // UnsafeRowParquetRecordReader appends the columns internally to avoid another copy. + iter.asInstanceOf[Iterator[InternalRow]] } else { logDebug(s"Falling back to parquet-mr") // ParquetRecordReader returns UnsafeRow @@ -414,18 +419,11 @@ class ParquetFileFormat } else { new ParquetRecordReader[UnsafeRow](new ParquetReadSupport(convertTz)) } + val iter = new RecordReaderIterator(reader) + // SPARK-23457 Register a task completion lister before `initialization`. + taskContext.foreach(_.addTaskCompletionListener(_ => iter.close())) reader.initialize(split, hadoopAttemptContext) - reader - } - val iter = new RecordReaderIterator(parquetReader) - taskContext.foreach(_.addTaskCompletionListener(_ => iter.close())) - - // UnsafeRowParquetRecordReader appends the columns internally to avoid another copy. - if (parquetReader.isInstanceOf[VectorizedParquetRecordReader] && - enableVectorizedReader) { - iter.asInstanceOf[Iterator[InternalRow]] - } else { val fullSchema = requiredSchema.toAttributes ++ partitionSchema.toAttributes val joinedRow = new JoinedRow() val appendPartitionColumns = GenerateUnsafeProjection.generate(fullSchema, fullSchema) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetOptions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetOptions.scala index 772d4565de548..f36a89a4c3c5f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetOptions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetOptions.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.execution.datasources.parquet import java.util.Locale +import org.apache.parquet.hadoop.ParquetOutputFormat import org.apache.parquet.hadoop.metadata.CompressionCodecName import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap @@ -27,7 +28,7 @@ import org.apache.spark.sql.internal.SQLConf /** * Options for the Parquet data source. */ -private[parquet] class ParquetOptions( +class ParquetOptions( @transient private val parameters: CaseInsensitiveMap[String], @transient private val sqlConf: SQLConf) extends Serializable { @@ -42,8 +43,15 @@ private[parquet] class ParquetOptions( * Acceptable values are defined in [[shortParquetCompressionCodecNames]]. */ val compressionCodecClassName: String = { - val codecName = parameters.getOrElse("compression", - sqlConf.parquetCompressionCodec).toLowerCase(Locale.ROOT) + // `compression`, `parquet.compression`(i.e., ParquetOutputFormat.COMPRESSION), and + // `spark.sql.parquet.compression.codec` + // are in order of precedence from highest to lowest. + val parquetCompressionConf = parameters.get(ParquetOutputFormat.COMPRESSION) + val codecName = parameters + .get("compression") + .orElse(parquetCompressionConf) + .getOrElse(sqlConf.parquetCompressionCodec) + .toLowerCase(Locale.ROOT) if (!shortParquetCompressionCodecNames.contains(codecName)) { val availableCodecs = shortParquetCompressionCodecNames.keys.map(_.toLowerCase(Locale.ROOT)) @@ -74,4 +82,8 @@ object ParquetOptions { "snappy" -> CompressionCodecName.SNAPPY, "gzip" -> CompressionCodecName.GZIP, "lzo" -> CompressionCodecName.LZO) + + def getParquetCompressionCodecName(name: String): String = { + shortParquetCompressionCodecNames(name).name() + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala index f64e079539c4f..cab00251622b8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala @@ -22,7 +22,7 @@ import java.util.Locale import org.apache.spark.sql.{AnalysisException, SaveMode, SparkSession} import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.catalog._ -import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, Expression, InputFileBlockLength, InputFileBlockStart, InputFileName, RowOrdering} +import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, Cast, Expression, InputFileBlockLength, InputFileBlockStart, InputFileName, RowOrdering} import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules.Rule import org.apache.spark.sql.execution.command.DDLUtils @@ -32,7 +32,7 @@ import org.apache.spark.sql.types.{AtomicType, StructType} import org.apache.spark.sql.util.SchemaUtils /** - * Try to replaces [[UnresolvedRelation]]s if the plan is for direct query on files. + * Replaces [[UnresolvedRelation]]s if the plan is for direct query on files. */ class ResolveSQLOnFile(sparkSession: SparkSession) extends Rule[LogicalPlan] { private def maybeSQLFile(u: UnresolvedRelation): Boolean = { @@ -61,7 +61,7 @@ class ResolveSQLOnFile(sparkSession: SparkSession) extends Rule[LogicalPlan] { case _: ClassNotFoundException => u case e: Exception => // the provider is valid, but failed to create a logical plan - u.failAnalysis(e.getMessage) + u.failAnalysis(e.getMessage, e) } } } @@ -118,6 +118,14 @@ case class PreprocessTableCreation(sparkSession: SparkSession) extends Rule[Logi s"`${existingProvider.getSimpleName}`. It doesn't match the specified format " + s"`${specifiedProvider.getSimpleName}`.") } + tableDesc.storage.locationUri match { + case Some(location) if location.getPath != existingTable.location.getPath => + throw new AnalysisException( + s"The location of the existing table ${tableIdentWithDB.quotedString} is " + + s"`${existingTable.location}`. It doesn't match the specified location " + + s"`${tableDesc.location}`.") + case _ => + } if (query.schema.length != existingTable.schema.length) { throw new AnalysisException( @@ -178,7 +186,8 @@ case class PreprocessTableCreation(sparkSession: SparkSession) extends Rule[Logi c.copy( tableDesc = existingTable, - query = Some(newQuery)) + query = Some(DDLPreprocessingUtils.castAndRenameQueryOutput( + newQuery, existingTable.schema.toAttributes, conf))) // Here we normalize partition, bucket and sort column names, w.r.t. the case sensitivity // config, and do various checks: @@ -316,7 +325,7 @@ case class PreprocessTableCreation(sparkSession: SparkSession) extends Rule[Logi * table. It also does data type casting and field renaming, to make sure that the columns to be * inserted have the correct data type and fields have the correct names. */ -case class PreprocessTableInsertion(conf: SQLConf) extends Rule[LogicalPlan] with CastSupport { +case class PreprocessTableInsertion(conf: SQLConf) extends Rule[LogicalPlan] { private def preprocess( insert: InsertIntoTable, tblName: String, @@ -336,6 +345,8 @@ case class PreprocessTableInsertion(conf: SQLConf) extends Rule[LogicalPlan] wit s"including ${staticPartCols.size} partition column(s) having constant value(s).") } + val newQuery = DDLPreprocessingUtils.castAndRenameQueryOutput( + insert.query, expectedColumns, conf) if (normalizedPartSpec.nonEmpty) { if (normalizedPartSpec.size != partColNames.length) { throw new AnalysisException( @@ -346,37 +357,11 @@ case class PreprocessTableInsertion(conf: SQLConf) extends Rule[LogicalPlan] wit """.stripMargin) } - castAndRenameChildOutput(insert.copy(partition = normalizedPartSpec), expectedColumns) + insert.copy(query = newQuery, partition = normalizedPartSpec) } else { // All partition columns are dynamic because the InsertIntoTable command does // not explicitly specify partitioning columns. - castAndRenameChildOutput(insert, expectedColumns) - .copy(partition = partColNames.map(_ -> None).toMap) - } - } - - private def castAndRenameChildOutput( - insert: InsertIntoTable, - expectedOutput: Seq[Attribute]): InsertIntoTable = { - val newChildOutput = expectedOutput.zip(insert.query.output).map { - case (expected, actual) => - if (expected.dataType.sameType(actual.dataType) && - expected.name == actual.name && - expected.metadata == actual.metadata) { - actual - } else { - // Renaming is needed for handling the following cases like - // 1) Column names/types do not match, e.g., INSERT INTO TABLE tab1 SELECT 1, 2 - // 2) Target tables have column metadata - Alias(cast(actual, expected.dataType), expected.name)( - explicitMetadata = Option(expected.metadata)) - } - } - - if (newChildOutput == insert.query.output) { - insert - } else { - insert.copy(query = Project(newChildOutput, insert.query)) + insert.copy(query = newQuery, partition = partColNames.map(_ -> None).toMap) } } @@ -491,3 +476,36 @@ object PreWriteCheck extends (LogicalPlan => Unit) { } } } + +object DDLPreprocessingUtils { + + /** + * Adjusts the name and data type of the input query output columns, to match the expectation. + */ + def castAndRenameQueryOutput( + query: LogicalPlan, + expectedOutput: Seq[Attribute], + conf: SQLConf): LogicalPlan = { + val newChildOutput = expectedOutput.zip(query.output).map { + case (expected, actual) => + if (expected.dataType.sameType(actual.dataType) && + expected.name == actual.name && + expected.metadata == actual.metadata) { + actual + } else { + // Renaming is needed for handling the following cases like + // 1) Column names/types do not match, e.g., INSERT INTO TABLE tab1 SELECT 1, 2 + // 2) Target tables have column metadata + Alias( + Cast(actual, expected.dataType, Option(conf.sessionLocalTimeZone)), + expected.name)(explicitMetadata = Option(expected.metadata)) + } + } + + if (newChildOutput == query.output) { + query + } else { + Project(newChildOutput, query) + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourcePartitioning.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourcePartitioning.scala new file mode 100644 index 0000000000000..33079d5912506 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourcePartitioning.scala @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.datasources.v2 + +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, Expression} +import org.apache.spark.sql.catalyst.plans.physical +import org.apache.spark.sql.sources.v2.reader.partitioning.{ClusteredDistribution, Partitioning} + +/** + * An adapter from public data source partitioning to catalyst internal `Partitioning`. + */ +class DataSourcePartitioning( + partitioning: Partitioning, + colNames: AttributeMap[String]) extends physical.Partitioning { + + override val numPartitions: Int = partitioning.numPartitions() + + override def satisfies0(required: physical.Distribution): Boolean = { + super.satisfies0(required) || { + required match { + case d: physical.ClusteredDistribution if isCandidate(d.clustering) => + val attrs = d.clustering.map(_.asInstanceOf[Attribute]) + partitioning.satisfy( + new ClusteredDistribution(attrs.map { a => + val name = colNames.get(a) + assert(name.isDefined, s"Attribute ${a.name} is not found in the data source output") + name.get + }.toArray)) + + case _ => false + } + } + } + + private def isCandidate(clustering: Seq[Expression]): Boolean = { + clustering.forall { + case a: Attribute => colNames.contains(a) + case _ => false + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceRDD.scala index 5f30be5ed4af1..5ed0ba71e94c7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceRDD.scala @@ -18,30 +18,30 @@ package org.apache.spark.sql.execution.datasources.v2 import scala.collection.JavaConverters._ +import scala.reflect.ClassTag import org.apache.spark.{InterruptibleIterator, Partition, SparkContext, TaskContext} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.UnsafeRow -import org.apache.spark.sql.sources.v2.reader.ReadTask +import org.apache.spark.sql.sources.v2.reader.DataReaderFactory -class DataSourceRDDPartition(val index: Int, val readTask: ReadTask[UnsafeRow]) +class DataSourceRDDPartition[T : ClassTag](val index: Int, val readerFactory: DataReaderFactory[T]) extends Partition with Serializable -class DataSourceRDD( +class DataSourceRDD[T: ClassTag]( sc: SparkContext, - @transient private val readTasks: java.util.List[ReadTask[UnsafeRow]]) - extends RDD[UnsafeRow](sc, Nil) { + @transient private val readerFactories: java.util.List[DataReaderFactory[T]]) + extends RDD[T](sc, Nil) { override protected def getPartitions: Array[Partition] = { - readTasks.asScala.zipWithIndex.map { - case (readTask, index) => new DataSourceRDDPartition(index, readTask) + readerFactories.asScala.zipWithIndex.map { + case (readerFactory, index) => new DataSourceRDDPartition(index, readerFactory) }.toArray } - override def compute(split: Partition, context: TaskContext): Iterator[UnsafeRow] = { - val reader = split.asInstanceOf[DataSourceRDDPartition].readTask.createDataReader() + override def compute(split: Partition, context: TaskContext): Iterator[T] = { + val reader = split.asInstanceOf[DataSourceRDDPartition[T]].readerFactory.createDataReader() context.addTaskCompletionListener(_ => reader.close()) - val iter = new Iterator[UnsafeRow] { + val iter = new Iterator[T] { private[this] var valuePrepared = false override def hasNext: Boolean = { @@ -51,7 +51,7 @@ class DataSourceRDD( valuePrepared } - override def next(): UnsafeRow = { + override def next(): T = { if (!hasNext) { throw new java.util.NoSuchElementException("End of stream") } @@ -63,6 +63,6 @@ class DataSourceRDD( } override def getPreferredLocations(split: Partition): Seq[String] = { - split.asInstanceOf[DataSourceRDDPartition].readTask.preferredLocations() + split.asInstanceOf[DataSourceRDDPartition[T]].readerFactory.preferredLocations() } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceReaderHolder.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceReaderHolder.scala index 6093df26630cd..81219e9771bd8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceReaderHolder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceReaderHolder.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.datasources.v2 import java.util.Objects -import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference} +import org.apache.spark.sql.catalyst.expressions.Attribute import org.apache.spark.sql.sources.v2.reader._ /** @@ -28,14 +28,14 @@ import org.apache.spark.sql.sources.v2.reader._ trait DataSourceReaderHolder { /** - * The full output of the data source reader, without column pruning. + * The output of the data source reader, w.r.t. column pruning. */ - def fullOutput: Seq[AttributeReference] + def output: Seq[Attribute] /** * The held data source reader. */ - def reader: DataSourceV2Reader + def reader: DataSourceReader /** * The metadata of this data source reader that can be used for equality test. @@ -46,7 +46,7 @@ trait DataSourceReaderHolder { case s: SupportsPushDownFilters => s.pushedFilters().toSet case _ => Nil } - Seq(fullOutput, reader.getClass, reader.readSchema(), filters) + Seq(output, reader.getClass, filters) } def canEqual(other: Any): Boolean @@ -61,8 +61,4 @@ trait DataSourceReaderHolder { override def hashCode(): Int = { metadata.map(Objects.hashCode).foldLeft(0)((a, b) => 31 * a + b) } - - lazy val output: Seq[Attribute] = reader.readSchema().map(_.name).map { name => - fullOutput.find(_.name == name).get - } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Relation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Relation.scala index 7eb99a645001a..38f6b15224788 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Relation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Relation.scala @@ -17,13 +17,15 @@ package org.apache.spark.sql.execution.datasources.v2 +import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation import org.apache.spark.sql.catalyst.expressions.AttributeReference import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, Statistics} import org.apache.spark.sql.sources.v2.reader._ case class DataSourceV2Relation( - fullOutput: Seq[AttributeReference], - reader: DataSourceV2Reader) extends LeafNode with DataSourceReaderHolder { + output: Seq[AttributeReference], + reader: DataSourceReader) + extends LeafNode with MultiInstanceRelation with DataSourceReaderHolder { override def canEqual(other: Any): Boolean = other.isInstanceOf[DataSourceV2Relation] @@ -33,10 +35,24 @@ case class DataSourceV2Relation( case _ => Statistics(sizeInBytes = conf.defaultSizeInBytes) } + + override def newInstance(): DataSourceV2Relation = { + copy(output = output.map(_.newInstance())) + } +} + +/** + * A specialization of DataSourceV2Relation with the streaming bit set to true. Otherwise identical + * to the non-streaming relation. + */ +class StreamingDataSourceV2Relation( + output: Seq[AttributeReference], + reader: DataSourceReader) extends DataSourceV2Relation(output, reader) { + override def isStreaming: Boolean = true } object DataSourceV2Relation { - def apply(reader: DataSourceV2Reader): DataSourceV2Relation = { + def apply(reader: DataSourceReader): DataSourceV2Relation = { new DataSourceV2Relation(reader.readSchema().toAttributes, reader) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExec.scala index 49c506bc560cf..7d9581be4db89 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExec.scala @@ -24,65 +24,88 @@ import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.encoders.{ExpressionEncoder, RowEncoder} import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.execution.LeafExecNode -import org.apache.spark.sql.execution.metric.SQLMetrics -import org.apache.spark.sql.execution.streaming.StreamExecution -import org.apache.spark.sql.execution.streaming.continuous.{ContinuousDataSourceRDD, ContinuousExecution, EpochCoordinatorRef, SetReaderPartitions} +import org.apache.spark.sql.catalyst.plans.physical +import org.apache.spark.sql.execution.{ColumnarBatchScan, LeafExecNode, WholeStageCodegenExec} +import org.apache.spark.sql.execution.streaming.continuous._ import org.apache.spark.sql.sources.v2.reader._ -import org.apache.spark.sql.sources.v2.streaming.reader.ContinuousReader +import org.apache.spark.sql.sources.v2.reader.streaming.ContinuousReader import org.apache.spark.sql.types.StructType /** * Physical plan node for scanning data from a data source. */ case class DataSourceV2ScanExec( - fullOutput: Seq[AttributeReference], - @transient reader: DataSourceV2Reader) extends LeafExecNode with DataSourceReaderHolder { + output: Seq[AttributeReference], + @transient reader: DataSourceReader) + extends LeafExecNode with DataSourceReaderHolder with ColumnarBatchScan { override def canEqual(other: Any): Boolean = other.isInstanceOf[DataSourceV2ScanExec] - override def references: AttributeSet = AttributeSet.empty + override def outputPartitioning: physical.Partitioning = reader match { + case s: SupportsReportPartitioning => + new DataSourcePartitioning( + s.outputPartitioning(), AttributeMap(output.map(a => a -> a.name))) - override lazy val metrics = Map( - "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows")) + case _ => super.outputPartitioning + } - override protected def doExecute(): RDD[InternalRow] = { - val readTasks: java.util.List[ReadTask[UnsafeRow]] = reader match { - case r: SupportsScanUnsafeRow => r.createUnsafeRowReadTasks() - case _ => - reader.createReadTasks().asScala.map { - new RowToUnsafeRowReadTask(_, reader.readSchema()): ReadTask[UnsafeRow] - }.asJava - } + private lazy val readerFactories: java.util.List[DataReaderFactory[UnsafeRow]] = reader match { + case r: SupportsScanUnsafeRow => r.createUnsafeRowReaderFactories() + case _ => + reader.createDataReaderFactories().asScala.map { + new RowToUnsafeRowDataReaderFactory(_, reader.readSchema()): DataReaderFactory[UnsafeRow] + }.asJava + } - val inputRDD = reader match { - case _: ContinuousReader => - EpochCoordinatorRef.get( - sparkContext.getLocalProperty(ContinuousExecution.RUN_ID_KEY), sparkContext.env) - .askSync[Unit](SetReaderPartitions(readTasks.size())) + private lazy val inputRDD: RDD[InternalRow] = reader match { + case r: SupportsScanColumnarBatch if r.enableBatchRead() => + assert(!reader.isInstanceOf[ContinuousReader], + "continuous stream reader does not support columnar read yet.") + new DataSourceRDD(sparkContext, r.createBatchDataReaderFactories()) + .asInstanceOf[RDD[InternalRow]] + + case _: ContinuousReader => + EpochCoordinatorRef.get( + sparkContext.getLocalProperty(ContinuousExecution.EPOCH_COORDINATOR_ID_KEY), + sparkContext.env) + .askSync[Unit](SetReaderPartitions(readerFactories.size())) + new ContinuousDataSourceRDD(sparkContext, sqlContext, readerFactories) + .asInstanceOf[RDD[InternalRow]] + + case _ => + new DataSourceRDD(sparkContext, readerFactories).asInstanceOf[RDD[InternalRow]] + } - new ContinuousDataSourceRDD(sparkContext, sqlContext, readTasks) + override def inputRDDs(): Seq[RDD[InternalRow]] = Seq(inputRDD) - case _ => - new DataSourceRDD(sparkContext, readTasks) - } + override val supportsBatch: Boolean = reader match { + case r: SupportsScanColumnarBatch if r.enableBatchRead() => true + case _ => false + } - val numOutputRows = longMetric("numOutputRows") - inputRDD.asInstanceOf[RDD[InternalRow]].map { r => - numOutputRows += 1 - r + override protected def needsUnsafeRowConversion: Boolean = false + + override protected def doExecute(): RDD[InternalRow] = { + if (supportsBatch) { + WholeStageCodegenExec(this)(codegenStageId = 0).execute() + } else { + val numOutputRows = longMetric("numOutputRows") + inputRDD.map { r => + numOutputRows += 1 + r + } } } } -class RowToUnsafeRowReadTask(rowReadTask: ReadTask[Row], schema: StructType) - extends ReadTask[UnsafeRow] { +class RowToUnsafeRowDataReaderFactory(rowReaderFactory: DataReaderFactory[Row], schema: StructType) + extends DataReaderFactory[UnsafeRow] { - override def preferredLocations: Array[String] = rowReadTask.preferredLocations + override def preferredLocations: Array[String] = rowReaderFactory.preferredLocations override def createDataReader: DataReader[UnsafeRow] = { new RowToUnsafeDataReader( - rowReadTask.createDataReader, RowEncoder.apply(schema).resolveAndBind()) + rowReaderFactory.createDataReader, RowEncoder.apply(schema).resolveAndBind()) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownOperatorsToDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownOperatorsToDataSource.scala index df034adf1e7d6..1ca6cbf061b4e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownOperatorsToDataSource.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownOperatorsToDataSource.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.execution.datasources.v2 -import org.apache.spark.sql.catalyst.expressions.{And, Attribute, AttributeMap, Expression, NamedExpression, PredicateHelper} +import org.apache.spark.sql.catalyst.expressions.{And, Attribute, AttributeMap, AttributeSet, Expression, NamedExpression, PredicateHelper} import org.apache.spark.sql.catalyst.optimizer.RemoveRedundantProject import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan, Project} import org.apache.spark.sql.catalyst.rules.Rule @@ -81,35 +81,45 @@ object PushDownOperatorsToDataSource extends Rule[LogicalPlan] with PredicateHel // TODO: add more push down rules. - // TODO: nested fields pruning - def pushDownRequiredColumns(plan: LogicalPlan, requiredByParent: Seq[Attribute]): Unit = { - plan match { - case Project(projectList, child) => - val required = projectList.filter(requiredByParent.contains).flatMap(_.references) - pushDownRequiredColumns(child, required) - - case Filter(condition, child) => - val required = requiredByParent ++ condition.references - pushDownRequiredColumns(child, required) - - case DataSourceV2Relation(fullOutput, reader) => reader match { - case r: SupportsPushDownRequiredColumns => - // Match original case of attributes. - val attrMap = AttributeMap(fullOutput.zip(fullOutput)) - val requiredColumns = requiredByParent.map(attrMap) - r.pruneColumns(requiredColumns.toStructType) - case _ => - } + val columnPruned = pushDownRequiredColumns(filterPushed, filterPushed.outputSet) + // After column pruning, we may have redundant PROJECT nodes in the query plan, remove them. + RemoveRedundantProject(columnPruned) + } - // TODO: there may be more operators can be used to calculate required columns, we can add - // more and more in the future. - case _ => plan.children.foreach(child => pushDownRequiredColumns(child, child.output)) + // TODO: nested fields pruning + private def pushDownRequiredColumns( + plan: LogicalPlan, requiredByParent: AttributeSet): LogicalPlan = { + plan match { + case p @ Project(projectList, child) => + val required = projectList.flatMap(_.references) + p.copy(child = pushDownRequiredColumns(child, AttributeSet(required))) + + case f @ Filter(condition, child) => + val required = requiredByParent ++ condition.references + f.copy(child = pushDownRequiredColumns(child, required)) + + case relation: DataSourceV2Relation => relation.reader match { + case reader: SupportsPushDownRequiredColumns => + // TODO: Enable the below assert after we make `DataSourceV2Relation` immutable. Fow now + // it's possible that the mutable reader being updated by someone else, and we need to + // always call `reader.pruneColumns` here to correct it. + // assert(relation.output.toStructType == reader.readSchema(), + // "Schema of data source reader does not match the relation plan.") + + val requiredColumns = relation.output.filter(requiredByParent.contains) + reader.pruneColumns(requiredColumns.toStructType) + + val nameToAttr = relation.output.map(_.name).zip(relation.output).toMap + val newOutput = reader.readSchema().map(_.name).map(nameToAttr) + relation.copy(output = newOutput) + + case _ => relation } - } - pushDownRequiredColumns(filterPushed, filterPushed.output) - // After column pruning, we may have redundant PROJECT nodes in the query plan, remove them. - RemoveRedundantProject(filterPushed) + // TODO: there may be more operators that can be used to calculate the required columns. We + // can add more and more in the future. + case _ => plan.mapChildren(c => pushDownRequiredColumns(c, c.outputSet)) + } } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/WriteToDataSourceV2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/WriteToDataSourceV2.scala index f0bdf84bb7a84..d02faacd9c19a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/WriteToDataSourceV2.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/WriteToDataSourceV2.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql.execution.datasources.v2 +import scala.util.control.NonFatal + import org.apache.spark.{SparkEnv, SparkException, TaskContext} import org.apache.spark.internal.Logging import org.apache.spark.rdd.RDD @@ -28,15 +30,15 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.SparkPlan import org.apache.spark.sql.execution.streaming.StreamExecution import org.apache.spark.sql.execution.streaming.continuous.{CommitPartitionEpoch, ContinuousExecution, EpochCoordinatorRef, SetWriterPartitions} -import org.apache.spark.sql.sources.v2.streaming.writer.ContinuousWriter import org.apache.spark.sql.sources.v2.writer._ +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter import org.apache.spark.sql.types.StructType import org.apache.spark.util.Utils /** * The logical plan for writing data into data source v2. */ -case class WriteToDataSourceV2(writer: DataSourceV2Writer, query: LogicalPlan) extends LogicalPlan { +case class WriteToDataSourceV2(writer: DataSourceWriter, query: LogicalPlan) extends LogicalPlan { override def children: Seq[LogicalPlan] = Seq(query) override def output: Seq[Attribute] = Nil } @@ -44,7 +46,7 @@ case class WriteToDataSourceV2(writer: DataSourceV2Writer, query: LogicalPlan) e /** * The physical plan for writing data into data source v2. */ -case class WriteToDataSourceV2Exec(writer: DataSourceV2Writer, query: SparkPlan) extends SparkPlan { +case class WriteToDataSourceV2Exec(writer: DataSourceWriter, query: SparkPlan) extends SparkPlan { override def children: Seq[SparkPlan] = Seq(query) override def output: Seq[Attribute] = Nil @@ -62,9 +64,12 @@ case class WriteToDataSourceV2Exec(writer: DataSourceV2Writer, query: SparkPlan) try { val runTask = writer match { - case w: ContinuousWriter => + // This case means that we're doing continuous processing. In microbatch streaming, the + // StreamWriter is wrapped in a MicroBatchWriter, which is executed as a normal batch. + case w: StreamWriter => EpochCoordinatorRef.get( - sparkContext.getLocalProperty(ContinuousExecution.RUN_ID_KEY), sparkContext.env) + sparkContext.getLocalProperty(ContinuousExecution.EPOCH_COORDINATOR_ID_KEY), + sparkContext.env) .askSync[Unit](SetWriterPartitions(rdd.getNumPartitions)) (context: TaskContext, iter: Iterator[InternalRow]) => @@ -81,11 +86,13 @@ case class WriteToDataSourceV2Exec(writer: DataSourceV2Writer, query: SparkPlan) (index, message: WriterCommitMessage) => messages(index) = message ) - logInfo(s"Data source writer $writer is committing.") - writer.commit(messages) - logInfo(s"Data source writer $writer committed.") + if (!writer.isInstanceOf[StreamWriter]) { + logInfo(s"Data source writer $writer is committing.") + writer.commit(messages) + logInfo(s"Data source writer $writer committed.") + } } catch { - case _: InterruptedException if writer.isInstanceOf[ContinuousWriter] => + case _: InterruptedException if writer.isInstanceOf[StreamWriter] => // Interruption is how continuous queries are ended, so accept and ignore the exception. case cause: Throwable => logError(s"Data source writer $writer is aborting.") @@ -98,7 +105,13 @@ case class WriteToDataSourceV2Exec(writer: DataSourceV2Writer, query: SparkPlan) throw new SparkException("Writing job failed.", cause) } logError(s"Data source writer $writer aborted.") - throw new SparkException("Writing job aborted.", cause) + cause match { + // Do not wrap interruption exceptions that will be handled by streaming specially. + case _ if StreamExecution.isInterruptionException(cause) => throw cause + // Only wrap non fatal exceptions. + case NonFatal(e) => throw new SparkException("Writing job aborted.", e) + case _ => throw cause + } } sparkContext.emptyRDD @@ -133,7 +146,7 @@ object DataWritingSparkTask extends Logging { iter: Iterator[InternalRow]): WriterCommitMessage = { val dataWriter = writeTask.createDataWriter(context.partitionId(), context.attemptNumber()) val epochCoordinator = EpochCoordinatorRef.get( - context.getLocalProperty(ContinuousExecution.RUN_ID_KEY), + context.getLocalProperty(ContinuousExecution.EPOCH_COORDINATOR_ID_KEY), SparkEnv.get) val currentMsg: WriterCommitMessage = null var currentEpoch = context.getLocalProperty(ContinuousExecution.START_EPOCH_KEY).toLong diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala index c8e236be28b42..ad95879d86f42 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.execution.exchange +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import org.apache.spark.sql.catalyst.expressions._ @@ -46,23 +47,6 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] { if (minNumPostShufflePartitions > 0) Some(minNumPostShufflePartitions) else None } - /** - * Given a required distribution, returns a partitioning that satisfies that distribution. - * @param requiredDistribution The distribution that is required by the operator - * @param numPartitions Used when the distribution doesn't require a specific number of partitions - */ - private def createPartitioning( - requiredDistribution: Distribution, - numPartitions: Int): Partitioning = { - requiredDistribution match { - case AllTuples => SinglePartition - case ClusteredDistribution(clustering, desiredPartitions) => - HashPartitioning(clustering, desiredPartitions.getOrElse(numPartitions)) - case OrderedDistribution(ordering) => RangePartitioning(ordering, numPartitions) - case dist => sys.error(s"Do not know how to satisfy distribution $dist") - } - } - /** * Adds [[ExchangeCoordinator]] to [[ShuffleExchangeExec]]s if adaptive query execution is enabled * and partitioning schemes of these [[ShuffleExchangeExec]]s support [[ExchangeCoordinator]]. @@ -88,8 +72,9 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] { // shuffle data when we have more than one children because data generated by // these children may not be partitioned in the same way. // Please see the comment in withCoordinator for more details. - val supportsDistribution = - requiredChildDistributions.forall(_.isInstanceOf[ClusteredDistribution]) + val supportsDistribution = requiredChildDistributions.forall { dist => + dist.isInstanceOf[ClusteredDistribution] || dist.isInstanceOf[HashClusteredDistribution] + } children.length > 1 && supportsDistribution } @@ -142,8 +127,7 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] { // // It will be great to introduce a new Partitioning to represent the post-shuffle // partitions when one post-shuffle partition includes multiple pre-shuffle partitions. - val targetPartitioning = - createPartitioning(distribution, defaultNumPreShufflePartitions) + val targetPartitioning = distribution.createPartitioning(defaultNumPreShufflePartitions) assert(targetPartitioning.isInstanceOf[HashPartitioning]) ShuffleExchangeExec(targetPartitioning, child, Some(coordinator)) } @@ -162,71 +146,56 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] { assert(requiredChildDistributions.length == children.length) assert(requiredChildOrderings.length == children.length) - // Ensure that the operator's children satisfy their output distribution requirements: + // Ensure that the operator's children satisfy their output distribution requirements. children = children.zip(requiredChildDistributions).map { case (child, distribution) if child.outputPartitioning.satisfies(distribution) => child case (child, BroadcastDistribution(mode)) => BroadcastExchangeExec(mode, child) case (child, distribution) => - ShuffleExchangeExec(createPartitioning(distribution, defaultNumPreShufflePartitions), child) + val numPartitions = distribution.requiredNumPartitions + .getOrElse(defaultNumPreShufflePartitions) + ShuffleExchangeExec(distribution.createPartitioning(numPartitions), child) } - // If the operator has multiple children and specifies child output distributions (e.g. join), - // then the children's output partitionings must be compatible: - def requireCompatiblePartitioning(distribution: Distribution): Boolean = distribution match { - case UnspecifiedDistribution => false - case BroadcastDistribution(_) => false + // Get the indexes of children which have specified distribution requirements and need to have + // same number of partitions. + val childrenIndexes = requiredChildDistributions.zipWithIndex.filter { + case (UnspecifiedDistribution, _) => false + case (_: BroadcastDistribution, _) => false case _ => true - } - if (children.length > 1 - && requiredChildDistributions.exists(requireCompatiblePartitioning) - && !Partitioning.allCompatible(children.map(_.outputPartitioning))) { + }.map(_._2) - // First check if the existing partitions of the children all match. This means they are - // partitioned by the same partitioning into the same number of partitions. In that case, - // don't try to make them match `defaultPartitions`, just use the existing partitioning. - val maxChildrenNumPartitions = children.map(_.outputPartitioning.numPartitions).max - val useExistingPartitioning = children.zip(requiredChildDistributions).forall { - case (child, distribution) => - child.outputPartitioning.guarantees( - createPartitioning(distribution, maxChildrenNumPartitions)) + val childrenNumPartitions = + childrenIndexes.map(children(_).outputPartitioning.numPartitions).toSet + + if (childrenNumPartitions.size > 1) { + // Get the number of partitions which is explicitly required by the distributions. + val requiredNumPartitions = { + val numPartitionsSet = childrenIndexes.flatMap { + index => requiredChildDistributions(index).requiredNumPartitions + }.toSet + assert(numPartitionsSet.size <= 1, + s"$operator have incompatible requirements of the number of partitions for its children") + numPartitionsSet.headOption } - children = if (useExistingPartitioning) { - // We do not need to shuffle any child's output. - children - } else { - // We need to shuffle at least one child's output. - // Now, we will determine the number of partitions that will be used by created - // partitioning schemes. - val numPartitions = { - // Let's see if we need to shuffle all child's outputs when we use - // maxChildrenNumPartitions. - val shufflesAllChildren = children.zip(requiredChildDistributions).forall { - case (child, distribution) => - !child.outputPartitioning.guarantees( - createPartitioning(distribution, maxChildrenNumPartitions)) - } - // If we need to shuffle all children, we use defaultNumPreShufflePartitions as the - // number of partitions. Otherwise, we use maxChildrenNumPartitions. - if (shufflesAllChildren) defaultNumPreShufflePartitions else maxChildrenNumPartitions - } + val targetNumPartitions = requiredNumPartitions.getOrElse(childrenNumPartitions.max) - children.zip(requiredChildDistributions).map { - case (child, distribution) => - val targetPartitioning = createPartitioning(distribution, numPartitions) - if (child.outputPartitioning.guarantees(targetPartitioning)) { - child - } else { - child match { - // If child is an exchange, we replace it with - // a new one having targetPartitioning. - case ShuffleExchangeExec(_, c, _) => ShuffleExchangeExec(targetPartitioning, c) - case _ => ShuffleExchangeExec(targetPartitioning, child) - } + children = children.zip(requiredChildDistributions).zipWithIndex.map { + case ((child, distribution), index) if childrenIndexes.contains(index) => + if (child.outputPartitioning.numPartitions == targetNumPartitions) { + child + } else { + val defaultPartitioning = distribution.createPartitioning(targetNumPartitions) + child match { + // If child is an exchange, we replace it with a new one having defaultPartitioning. + case ShuffleExchangeExec(_, c, _) => ShuffleExchangeExec(defaultPartitioning, c) + case _ => ShuffleExchangeExec(defaultPartitioning, child) + } } - } + + case ((child, _), _) => child } } @@ -259,9 +228,16 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] { currentOrderOfKeys: Seq[Expression]): (Seq[Expression], Seq[Expression]) = { val leftKeysBuffer = ArrayBuffer[Expression]() val rightKeysBuffer = ArrayBuffer[Expression]() + val pickedIndexes = mutable.Set[Int]() + val keysAndIndexes = currentOrderOfKeys.zipWithIndex expectedOrderOfKeys.foreach(expression => { - val index = currentOrderOfKeys.indexWhere(e => e.semanticEquals(expression)) + val index = keysAndIndexes.find { case (e, idx) => + // As we may have the same key used many times, we need to filter out its occurrence we + // have already used. + e.semanticEquals(expression) && !pickedIndexes.contains(idx) + }.map(_._2).get + pickedIndexes += index leftKeysBuffer.append(leftKeys(index)) rightKeysBuffer.append(rightKeys(index)) }) @@ -302,7 +278,7 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] { * partitioning of the join nodes' children. */ private def reorderJoinPredicates(plan: SparkPlan): SparkPlan = { - plan.transformUp { + plan match { case BroadcastHashJoinExec(leftKeys, rightKeys, joinType, buildSide, condition, left, right) => val (reorderedLeftKeys, reorderedRightKeys) = @@ -320,14 +296,16 @@ case class EnsureRequirements(conf: SQLConf) extends Rule[SparkPlan] { val (reorderedLeftKeys, reorderedRightKeys) = reorderJoinKeys(leftKeys, rightKeys, left.outputPartitioning, right.outputPartitioning) SortMergeJoinExec(reorderedLeftKeys, reorderedRightKeys, joinType, condition, left, right) + + case other => other } } def apply(plan: SparkPlan): SparkPlan = plan.transformUp { - case operator @ ShuffleExchangeExec(partitioning, child, _) => - child.children match { - case ShuffleExchangeExec(childPartitioning, baseChild, _)::Nil => - if (childPartitioning.guarantees(partitioning)) child else operator + // TODO: remove this after we create a physical operator for `RepartitionByExpression`. + case operator @ ShuffleExchangeExec(upper: HashPartitioning, child, _) => + child.outputPartitioning match { + case lower: HashPartitioning if upper.semanticEquals(lower) => child case _ => operator } case operator: SparkPlan => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala index 5a1e217082bc2..b89203719541b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution.exchange import java.util.Random +import java.util.function.Supplier import org.apache.spark._ import org.apache.spark.rdd.RDD @@ -25,13 +26,15 @@ import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.sort.SortShuffleManager import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.errors._ -import org.apache.spark.sql.catalyst.expressions.{Attribute, UnsafeProjection} +import org.apache.spark.sql.catalyst.expressions.{Attribute, UnsafeProjection, UnsafeRow} import org.apache.spark.sql.catalyst.expressions.codegen.LazilyGeneratedOrdering import org.apache.spark.sql.catalyst.plans.physical._ import org.apache.spark.sql.execution._ import org.apache.spark.sql.execution.metric.SQLMetrics import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.StructType import org.apache.spark.util.MutablePair +import org.apache.spark.util.collection.unsafe.sort.{PrefixComparators, RecordComparator} /** * Performs a shuffle that will result in the desired `newPartitioning`. @@ -150,12 +153,9 @@ object ShuffleExchangeExec { * See SPARK-2967, SPARK-4479, and SPARK-7375 for more discussion of this issue. * * @param partitioner the partitioner for the shuffle - * @param serializer the serializer that will be used to write rows * @return true if rows should be copied before being shuffled, false otherwise */ - private def needToCopyObjectsBeforeShuffle( - partitioner: Partitioner, - serializer: Serializer): Boolean = { + private def needToCopyObjectsBeforeShuffle(partitioner: Partitioner): Boolean = { // Note: even though we only use the partitioner's `numPartitions` field, we require it to be // passed instead of directly passing the number of partitions in order to guard against // corner-cases where a partitioner constructed with `numPartitions` partitions may output @@ -164,22 +164,24 @@ object ShuffleExchangeExec { val shuffleManager = SparkEnv.get.shuffleManager val sortBasedShuffleOn = shuffleManager.isInstanceOf[SortShuffleManager] val bypassMergeThreshold = conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) + val numParts = partitioner.numPartitions if (sortBasedShuffleOn) { - val bypassIsSupported = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager] - if (bypassIsSupported && partitioner.numPartitions <= bypassMergeThreshold) { + if (numParts <= bypassMergeThreshold) { // If we're using the original SortShuffleManager and the number of output partitions is // sufficiently small, then Spark will fall back to the hash-based shuffle write path, which // doesn't buffer deserialized records. // Note that we'll have to remove this case if we fix SPARK-6026 and remove this bypass. false - } else if (serializer.supportsRelocationOfSerializedObjects) { + } else if (numParts <= SortShuffleManager.MAX_SHUFFLE_OUTPUT_PARTITIONS_FOR_SERIALIZED_MODE) { // SPARK-4550 and SPARK-7081 extended sort-based shuffle to serialize individual records // prior to sorting them. This optimization is only applied in cases where shuffle // dependency does not specify an aggregator or ordering and the record serializer has - // certain properties. If this optimization is enabled, we can safely avoid the copy. + // certain properties and the number of partitions doesn't exceed the limitation. If this + // optimization is enabled, we can safely avoid the copy. // - // Exchange never configures its ShuffledRDDs with aggregators or key orderings, so we only - // need to check whether the optimization is enabled and supported by our serializer. + // Exchange never configures its ShuffledRDDs with aggregators or key orderings, and the + // serializer in Spark SQL always satisfy the properties, so we only need to check whether + // the number of partitions exceeds the limitation. false } else { // Spark's SortShuffleManager uses `ExternalSorter` to buffer records in memory, so we must @@ -247,14 +249,61 @@ object ShuffleExchangeExec { case RangePartitioning(_, _) | SinglePartition => identity case _ => sys.error(s"Exchange not implemented for $newPartitioning") } + val rddWithPartitionIds: RDD[Product2[Int, InternalRow]] = { - if (needToCopyObjectsBeforeShuffle(part, serializer)) { + // [SPARK-23207] Have to make sure the generated RoundRobinPartitioning is deterministic, + // otherwise a retry task may output different rows and thus lead to data loss. + // + // Currently we following the most straight-forward way that perform a local sort before + // partitioning. + // + // Note that we don't perform local sort if the new partitioning has only 1 partition, under + // that case all output rows go to the same partition. + val newRdd = if (SQLConf.get.sortBeforeRepartition && + newPartitioning.numPartitions > 1 && + newPartitioning.isInstanceOf[RoundRobinPartitioning]) { rdd.mapPartitionsInternal { iter => + val recordComparatorSupplier = new Supplier[RecordComparator] { + override def get: RecordComparator = new RecordBinaryComparator() + } + // The comparator for comparing row hashcode, which should always be Integer. + val prefixComparator = PrefixComparators.LONG + val canUseRadixSort = SparkEnv.get.conf.get(SQLConf.RADIX_SORT_ENABLED) + // The prefix computer generates row hashcode as the prefix, so we may decrease the + // probability that the prefixes are equal when input rows choose column values from a + // limited range. + val prefixComputer = new UnsafeExternalRowSorter.PrefixComputer { + private val result = new UnsafeExternalRowSorter.PrefixComputer.Prefix + override def computePrefix(row: InternalRow): + UnsafeExternalRowSorter.PrefixComputer.Prefix = { + // The hashcode generated from the binary form of a [[UnsafeRow]] should not be null. + result.isNull = false + result.value = row.hashCode() + result + } + } + val pageSize = SparkEnv.get.memoryManager.pageSizeBytes + + val sorter = UnsafeExternalRowSorter.createWithRecordComparator( + StructType.fromAttributes(outputAttributes), + recordComparatorSupplier, + prefixComparator, + prefixComputer, + pageSize, + canUseRadixSort) + sorter.sort(iter.asInstanceOf[Iterator[UnsafeRow]]) + } + } else { + rdd + } + + if (needToCopyObjectsBeforeShuffle(part)) { + newRdd.mapPartitionsInternal { iter => val getPartitionKey = getPartitionKeyExtractor() iter.map { row => (part.getPartition(getPartitionKey(row)), row.copy()) } } } else { - rdd.mapPartitionsInternal { iter => + newRdd.mapPartitionsInternal { iter => val getPartitionKey = getPartitionKeyExtractor() val mutablePair = new MutablePair[Int, InternalRow]() iter.map { row => mutablePair.update(part.getPartition(getPartitionKey(row)), row) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala index 1465346eb802d..20ce01f4ce8cc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala @@ -557,7 +557,7 @@ private[execution] final class LongToUnsafeRowMap(val mm: TaskMemoryManager, cap def append(key: Long, row: UnsafeRow): Unit = { val sizeInBytes = row.getSizeInBytes if (sizeInBytes >= (1 << SIZE_BITS)) { - sys.error("Does not support row that is larger than 256M") + throw new UnsupportedOperationException("Does not support row that is larger than 256M") } if (key < minKey) { @@ -567,19 +567,7 @@ private[execution] final class LongToUnsafeRowMap(val mm: TaskMemoryManager, cap maxKey = key } - // There is 8 bytes for the pointer to next value - if (cursor + 8 + row.getSizeInBytes > page.length * 8L + Platform.LONG_ARRAY_OFFSET) { - val used = page.length - if (used >= (1 << 30)) { - sys.error("Can not build a HashedRelation that is larger than 8G") - } - ensureAcquireMemory(used * 8L * 2) - val newPage = new Array[Long](used * 2) - Platform.copyMemory(page, Platform.LONG_ARRAY_OFFSET, newPage, Platform.LONG_ARRAY_OFFSET, - cursor - Platform.LONG_ARRAY_OFFSET) - page = newPage - freeMemory(used * 8L) - } + grow(row.getSizeInBytes) // copy the bytes of UnsafeRow val offset = cursor @@ -615,7 +603,8 @@ private[execution] final class LongToUnsafeRowMap(val mm: TaskMemoryManager, cap growArray() } else if (numKeys > array.length / 2 * 0.75) { // The fill ratio should be less than 0.75 - sys.error("Cannot build HashedRelation with more than 1/3 billions unique keys") + throw new UnsupportedOperationException( + "Cannot build HashedRelation with more than 1/3 billions unique keys") } } } else { @@ -626,6 +615,25 @@ private[execution] final class LongToUnsafeRowMap(val mm: TaskMemoryManager, cap } } + private def grow(inputRowSize: Int): Unit = { + // There is 8 bytes for the pointer to next value + val neededNumWords = (cursor - Platform.LONG_ARRAY_OFFSET + 8 + inputRowSize + 7) / 8 + if (neededNumWords > page.length) { + if (neededNumWords > (1 << 30)) { + throw new UnsupportedOperationException( + "Can not build a HashedRelation that is larger than 8G") + } + val newNumWords = math.max(neededNumWords, math.min(page.length * 2, 1 << 30)) + ensureAcquireMemory(newNumWords * 8L) + val newPage = new Array[Long](newNumWords.toInt) + Platform.copyMemory(page, Platform.LONG_ARRAY_OFFSET, newPage, Platform.LONG_ARRAY_OFFSET, + cursor - Platform.LONG_ARRAY_OFFSET) + val used = page.length + page = newPage + freeMemory(used * 8L) + } + } + private def growArray(): Unit = { var old_array = array val n = array.length diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoinExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoinExec.scala index 66e8031bb5191..897a4dae39f32 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoinExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoinExec.scala @@ -46,7 +46,7 @@ case class ShuffledHashJoinExec( "avgHashProbe" -> SQLMetrics.createAverageMetric(sparkContext, "avg hash probe")) override def requiredChildDistribution: Seq[Distribution] = - ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil + HashClusteredDistribution(leftKeys) :: HashClusteredDistribution(rightKeys) :: Nil private def buildHashedRelation(iter: Iterator[InternalRow]): HashedRelation = { val buildDataSize = longMetric("buildDataSize") diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala index 94405410cce90..2de2f30eb05d3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala @@ -78,7 +78,7 @@ case class SortMergeJoinExec( } override def requiredChildDistribution: Seq[Distribution] = - ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil + HashClusteredDistribution(leftKeys) :: HashClusteredDistribution(rightKeys) :: Nil override def outputOrdering: Seq[SortOrder] = joinType match { // For inner join, orders of both sides keys should be kept. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala index d1bd8a7076863..03d1bbf2ab882 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala @@ -456,7 +456,7 @@ case class CoGroupExec( right: SparkPlan) extends BinaryExecNode with ObjectProducerExec { override def requiredChildDistribution: Seq[Distribution] = - ClusteredDistribution(leftGroup) :: ClusteredDistribution(rightGroup) :: Nil + HashClusteredDistribution(leftGroup) :: HashClusteredDistribution(rightGroup) :: Nil override def requiredChildOrdering: Seq[Seq[SortOrder]] = leftGroup.map(SortOrder(_, Ascending)) :: rightGroup.map(SortOrder(_, Ascending)) :: Nil diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowEvalPythonExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowEvalPythonExec.scala index c06bc7b66ff39..c4de214679ae4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowEvalPythonExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowEvalPythonExec.scala @@ -74,15 +74,14 @@ case class ArrowEvalPythonExec(udfs: Seq[PythonUDF], output: Seq[Attribute], chi schema: StructType, context: TaskContext): Iterator[InternalRow] = { - val schemaOut = StructType.fromAttributes(output.drop(child.output.length).zipWithIndex - .map { case (attr, i) => attr.withName(s"_$i") }) + val outputTypes = output.drop(child.output.length).map(_.dataType) // DO NOT use iter.grouped(). See BatchIterator. val batchIter = if (batchSize > 0) new BatchIterator(iter, batchSize) else Iterator(iter) val columnarBatchIter = new ArrowPythonRunner( funcs, bufferSize, reuseWorker, - PythonEvalType.SQL_PANDAS_SCALAR_UDF, argOffsets, schema, + PythonEvalType.SQL_SCALAR_PANDAS_UDF, argOffsets, schema, sessionLocalTimeZone, pandasRespectSessionTimeZone) .compute(batchIter, context.partitionId(), context) @@ -90,8 +89,9 @@ case class ArrowEvalPythonExec(udfs: Seq[PythonUDF], output: Seq[Attribute], chi private var currentIter = if (columnarBatchIter.hasNext) { val batch = columnarBatchIter.next() - assert(schemaOut.equals(batch.schema), - s"Invalid schema from pandas_udf: expected $schemaOut, got ${batch.schema}") + val actualDataTypes = (0 until batch.numCols()).map(i => batch.column(i).dataType()) + assert(outputTypes == actualDataTypes, "Invalid schema from pandas_udf: " + + s"expected ${outputTypes.mkString(", ")}, got ${actualDataTypes.mkString(", ")}") batch.rowIterator.asScala } else { Iterator.empty diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowPythonRunner.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowPythonRunner.scala index 5cc8ed3535654..01e19bddbfb66 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowPythonRunner.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowPythonRunner.scala @@ -30,8 +30,8 @@ import org.apache.spark._ import org.apache.spark.api.python._ import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.execution.arrow.{ArrowUtils, ArrowWriter} -import org.apache.spark.sql.execution.vectorized.{ArrowColumnVector, ColumnarBatch, ColumnVector} import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.{ArrowColumnVector, ColumnarBatch, ColumnVector} import org.apache.spark.util.Utils /** @@ -70,19 +70,13 @@ class ArrowPythonRunner( val arrowSchema = ArrowUtils.toArrowSchema(schema, timeZoneId) val allocator = ArrowUtils.rootAllocator.newChildAllocator( s"stdout writer for $pythonExec", 0, Long.MaxValue) - val root = VectorSchemaRoot.create(arrowSchema, allocator) - val arrowWriter = ArrowWriter.create(root) - - context.addTaskCompletionListener { _ => - root.close() - allocator.close() - } - - val writer = new ArrowStreamWriter(root, null, dataOut) - writer.start() Utils.tryWithSafeFinally { + val arrowWriter = ArrowWriter.create(root) + val writer = new ArrowStreamWriter(root, null, dataOut) + writer.start() + while (inputIterator.hasNext) { val nextBatch = inputIterator.next() @@ -94,8 +88,21 @@ class ArrowPythonRunner( writer.writeBatch() arrowWriter.reset() } - } { + // end writes footer to the output stream and doesn't clean any resources. + // It could throw exception if the output stream is closed, so it should be + // in the try block. writer.end() + } { + // If we close root and allocator in TaskCompletionListener, there could be a race + // condition where the writer thread keeps writing to the VectorSchemaRoot while + // it's being closed by the TaskCompletion listener. + // Closing root and allocator here is cleaner because root and allocator is owned + // by the writer thread and is only visible to the writer thread. + // + // If the writer thread is interrupted by TaskCompletionListener, it should either + // (1) in the try block, in which case it will get an InterruptedException when + // performing io, and goes into the finally block or (2) in the finally block, + // in which case it will ignore the interruption and close the resources. root.close() allocator.close() } @@ -138,7 +145,7 @@ class ArrowPythonRunner( if (reader != null && batchLoaded) { batchLoaded = reader.loadNextBatch() if (batchLoaded) { - val batch = new ColumnarBatch(schema, vectors, root.getRowCount) + val batch = new ColumnarBatch(vectors) batch.setNumRows(root.getRowCount) batch } else { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/BatchEvalPythonExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/BatchEvalPythonExec.scala index 26ee25f633ea4..f4d83e8dc7c2b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/BatchEvalPythonExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/BatchEvalPythonExec.scala @@ -79,16 +79,19 @@ case class BatchEvalPythonExec(udfs: Seq[PythonUDF], output: Seq[Attribute], chi } else { StructType(udfs.map(u => StructField("", u.dataType, u.nullable))) } + + val fromJava = EvaluatePython.makeFromJava(resultType) + outputIterator.flatMap { pickedResult => val unpickledBatch = unpickle.loads(pickedResult) unpickledBatch.asInstanceOf[java.util.ArrayList[Any]].asScala }.map { result => if (udfs.length == 1) { // fast path for single UDF - mutableRow(0) = EvaluatePython.fromJava(result, resultType) + mutableRow(0) = fromJava(result) mutableRow } else { - EvaluatePython.fromJava(result, resultType).asInstanceOf[InternalRow] + fromJava(result).asInstanceOf[InternalRow] } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala index 9bbfa6018ba77..520afad287648 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala @@ -83,82 +83,134 @@ object EvaluatePython { } /** - * Converts `obj` to the type specified by the data type, or returns null if the type of obj is - * unexpected. Because Python doesn't enforce the type. + * Make a converter that converts `obj` to the type specified by the data type, or returns + * null if the type of obj is unexpected. Because Python doesn't enforce the type. */ - def fromJava(obj: Any, dataType: DataType): Any = (obj, dataType) match { - case (null, _) => null - - case (c: Boolean, BooleanType) => c + def makeFromJava(dataType: DataType): Any => Any = dataType match { + case BooleanType => (obj: Any) => nullSafeConvert(obj) { + case b: Boolean => b + } - case (c: Byte, ByteType) => c - case (c: Short, ByteType) => c.toByte - case (c: Int, ByteType) => c.toByte - case (c: Long, ByteType) => c.toByte + case ByteType => (obj: Any) => nullSafeConvert(obj) { + case c: Byte => c + case c: Short => c.toByte + case c: Int => c.toByte + case c: Long => c.toByte + } - case (c: Byte, ShortType) => c.toShort - case (c: Short, ShortType) => c - case (c: Int, ShortType) => c.toShort - case (c: Long, ShortType) => c.toShort + case ShortType => (obj: Any) => nullSafeConvert(obj) { + case c: Byte => c.toShort + case c: Short => c + case c: Int => c.toShort + case c: Long => c.toShort + } - case (c: Byte, IntegerType) => c.toInt - case (c: Short, IntegerType) => c.toInt - case (c: Int, IntegerType) => c - case (c: Long, IntegerType) => c.toInt + case IntegerType => (obj: Any) => nullSafeConvert(obj) { + case c: Byte => c.toInt + case c: Short => c.toInt + case c: Int => c + case c: Long => c.toInt + } - case (c: Byte, LongType) => c.toLong - case (c: Short, LongType) => c.toLong - case (c: Int, LongType) => c.toLong - case (c: Long, LongType) => c + case LongType => (obj: Any) => nullSafeConvert(obj) { + case c: Byte => c.toLong + case c: Short => c.toLong + case c: Int => c.toLong + case c: Long => c + } - case (c: Float, FloatType) => c - case (c: Double, FloatType) => c.toFloat + case FloatType => (obj: Any) => nullSafeConvert(obj) { + case c: Float => c + case c: Double => c.toFloat + } - case (c: Float, DoubleType) => c.toDouble - case (c: Double, DoubleType) => c + case DoubleType => (obj: Any) => nullSafeConvert(obj) { + case c: Float => c.toDouble + case c: Double => c + } - case (c: java.math.BigDecimal, dt: DecimalType) => Decimal(c, dt.precision, dt.scale) + case dt: DecimalType => (obj: Any) => nullSafeConvert(obj) { + case c: java.math.BigDecimal => Decimal(c, dt.precision, dt.scale) + } - case (c: Int, DateType) => c + case DateType => (obj: Any) => nullSafeConvert(obj) { + case c: Int => c + } - case (c: Long, TimestampType) => c - // Py4J serializes values between MIN_INT and MAX_INT as Ints, not Longs - case (c: Int, TimestampType) => c.toLong + case TimestampType => (obj: Any) => nullSafeConvert(obj) { + case c: Long => c + // Py4J serializes values between MIN_INT and MAX_INT as Ints, not Longs + case c: Int => c.toLong + } - case (c, StringType) => UTF8String.fromString(c.toString) + case StringType => (obj: Any) => nullSafeConvert(obj) { + case _ => UTF8String.fromString(obj.toString) + } - case (c: String, BinaryType) => c.getBytes(StandardCharsets.UTF_8) - case (c, BinaryType) if c.getClass.isArray && c.getClass.getComponentType.getName == "byte" => c + case BinaryType => (obj: Any) => nullSafeConvert(obj) { + case c: String => c.getBytes(StandardCharsets.UTF_8) + case c if c.getClass.isArray && c.getClass.getComponentType.getName == "byte" => c + } - case (c: java.util.List[_], ArrayType(elementType, _)) => - new GenericArrayData(c.asScala.map { e => fromJava(e, elementType)}.toArray) + case ArrayType(elementType, _) => + val elementFromJava = makeFromJava(elementType) - case (c, ArrayType(elementType, _)) if c.getClass.isArray => - new GenericArrayData(c.asInstanceOf[Array[_]].map(e => fromJava(e, elementType))) + (obj: Any) => nullSafeConvert(obj) { + case c: java.util.List[_] => + new GenericArrayData(c.asScala.map { e => elementFromJava(e) }.toArray) + case c if c.getClass.isArray => + new GenericArrayData(c.asInstanceOf[Array[_]].map(e => elementFromJava(e))) + } - case (javaMap: java.util.Map[_, _], MapType(keyType, valueType, _)) => - ArrayBasedMapData( - javaMap, - (key: Any) => fromJava(key, keyType), - (value: Any) => fromJava(value, valueType)) + case MapType(keyType, valueType, _) => + val keyFromJava = makeFromJava(keyType) + val valueFromJava = makeFromJava(valueType) + + (obj: Any) => nullSafeConvert(obj) { + case javaMap: java.util.Map[_, _] => + ArrayBasedMapData( + javaMap, + (key: Any) => keyFromJava(key), + (value: Any) => valueFromJava(value)) + } - case (c, StructType(fields)) if c.getClass.isArray => - val array = c.asInstanceOf[Array[_]] - if (array.length != fields.length) { - throw new IllegalStateException( - s"Input row doesn't have expected number of values required by the schema. " + - s"${fields.length} fields are required while ${array.length} values are provided." - ) + case StructType(fields) => + val fieldsFromJava = fields.map(f => makeFromJava(f.dataType)).toArray + + (obj: Any) => nullSafeConvert(obj) { + case c if c.getClass.isArray => + val array = c.asInstanceOf[Array[_]] + if (array.length != fields.length) { + throw new IllegalStateException( + s"Input row doesn't have expected number of values required by the schema. " + + s"${fields.length} fields are required while ${array.length} values are provided." + ) + } + + val row = new GenericInternalRow(fields.length) + var i = 0 + while (i < fields.length) { + row(i) = fieldsFromJava(i)(array(i)) + i += 1 + } + row } - new GenericInternalRow(array.zip(fields).map { - case (e, f) => fromJava(e, f.dataType) - }) - case (_, udt: UserDefinedType[_]) => fromJava(obj, udt.sqlType) + case udt: UserDefinedType[_] => makeFromJava(udt.sqlType) + + case other => (obj: Any) => nullSafeConvert(other)(PartialFunction.empty) + } - // all other unexpected type should be null, or we will have runtime exception - // TODO(davies): we could improve this by try to cast the object to expected type - case (c, _) => null + private def nullSafeConvert(input: Any)(f: PartialFunction[Any, Any]): Any = { + if (input == null) { + null + } else { + f.applyOrElse(input, { + // all other unexpected type should be null, or we will have runtime exception + // TODO(davies): we could improve this by try to cast the object to expected type + _: Any => null + }) + } } private val module = "pyspark.sql.types" diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ExtractPythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ExtractPythonUDFs.scala index 2f53fe788c7d0..78521526476b9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ExtractPythonUDFs.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ExtractPythonUDFs.scala @@ -30,7 +30,7 @@ import org.apache.spark.sql.execution.{FilterExec, ProjectExec, SparkPlan} /** * Extracts all the Python UDFs in logical aggregate, which depends on aggregate expression or - * grouping key, evaluate them after aggregate. + * grouping key, or doesn't depend on any above expressions, evaluate them after aggregate. */ object ExtractPythonUDFFromAggregate extends Rule[LogicalPlan] { @@ -44,7 +44,8 @@ object ExtractPythonUDFFromAggregate extends Rule[LogicalPlan] { private def hasPythonUdfOverAggregate(expr: Expression, agg: Aggregate): Boolean = { expr.find { - e => e.isInstanceOf[PythonUDF] && e.find(belongAggregate(_, agg)).isDefined + e => e.isInstanceOf[PythonUDF] && + (e.references.isEmpty || e.find(belongAggregate(_, agg)).isDefined) }.isDefined } @@ -151,7 +152,7 @@ object ExtractPythonUDFs extends Rule[SparkPlan] with PredicateHelper { if (validUdfs.nonEmpty) { require(validUdfs.forall(udf => udf.evalType == PythonEvalType.SQL_BATCHED_UDF || - udf.evalType == PythonEvalType.SQL_PANDAS_SCALAR_UDF + udf.evalType == PythonEvalType.SQL_SCALAR_PANDAS_UDF ), "Can only extract scalar vectorized udf or sql batch udf") val resultAttrs = udfs.zipWithIndex.map { case (u, i) => @@ -159,7 +160,7 @@ object ExtractPythonUDFs extends Rule[SparkPlan] with PredicateHelper { } val evaluation = validUdfs.partition( - _.evalType == PythonEvalType.SQL_PANDAS_SCALAR_UDF + _.evalType == PythonEvalType.SQL_SCALAR_PANDAS_UDF ) match { case (vectorizedUdfs, plainUdfs) if plainUdfs.isEmpty => ArrowEvalPythonExec(vectorizedUdfs, child.output ++ resultAttrs, child) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/FlatMapGroupsInPandasExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/FlatMapGroupsInPandasExec.scala index 59db66bd7adf1..c798fe5a92c54 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/FlatMapGroupsInPandasExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/FlatMapGroupsInPandasExec.scala @@ -96,7 +96,7 @@ case class FlatMapGroupsInPandasExec( val columnarBatchIter = new ArrowPythonRunner( chainedFunc, bufferSize, reuseWorker, - PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF, argOffsets, schema, + PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF, argOffsets, schema, sessionLocalTimeZone, pandasRespectSessionTimeZone) .compute(grouped, context.partitionId(), context) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSink.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSink.scala index 2715fa93d0e98..b3d12f67b5d63 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSink.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSink.scala @@ -26,7 +26,8 @@ import org.apache.spark.internal.Logging import org.apache.spark.internal.io.FileCommitProtocol import org.apache.spark.sql.{DataFrame, SparkSession} import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.execution.datasources.{FileFormat, FileFormatWriter} +import org.apache.spark.sql.execution.datasources.{BasicWriteJobStatsTracker, FileFormat, FileFormatWriter} +import org.apache.spark.util.SerializableConfiguration object FileStreamSink extends Logging { // The name of the subdirectory that is used to store metadata about which files are valid. @@ -42,9 +43,11 @@ object FileStreamSink extends Logging { try { val hdfsPath = new Path(singlePath) val fs = hdfsPath.getFileSystem(hadoopConf) - val metadataPath = new Path(hdfsPath, metadataDir) - val res = fs.exists(metadataPath) - res + if (fs.isDirectory(hdfsPath)) { + fs.exists(new Path(hdfsPath, metadataDir)) + } else { + false + } } catch { case NonFatal(e) => logWarning(s"Error while looking for metadata directory.") @@ -95,6 +98,11 @@ class FileStreamSink( new FileStreamSinkLog(FileStreamSinkLog.VERSION, sparkSession, logPath.toUri.toString) private val hadoopConf = sparkSession.sessionState.newHadoopConf() + private def basicWriteJobStatsTracker: BasicWriteJobStatsTracker = { + val serializableHadoopConf = new SerializableConfiguration(hadoopConf) + new BasicWriteJobStatsTracker(serializableHadoopConf, BasicWriteJobStatsTracker.metrics) + } + override def addBatch(batchId: Long, data: DataFrame): Unit = { if (batchId <= fileLog.getLatest().map(_._1).getOrElse(-1L)) { logInfo(s"Skipping already committed batch $batchId") @@ -129,7 +137,7 @@ class FileStreamSink( hadoopConf = hadoopConf, partitionColumns = partitionColumns, bucketSpec = None, - statsTrackers = Nil, + statsTrackers = Seq(basicWriteJobStatsTracker), options = options) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala index 0debd7db84757..8c016abc5b643 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala @@ -166,7 +166,7 @@ class FileStreamSource( val newDataSource = DataSource( sparkSession, - paths = files.map(_.path), + paths = files.map(f => new Path(new URI(f.path)).toString), userSpecifiedSchema = Some(schema), partitionColumns = partitionColumns, className = fileFormatClassName, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/HDFSMetadataLog.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/HDFSMetadataLog.scala index 6e8154d58d4c6..00bc215a5dc8c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/HDFSMetadataLog.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/HDFSMetadataLog.scala @@ -330,7 +330,7 @@ object HDFSMetadataLog { /** A simple trait to abstract out the file management operations needed by HDFSMetadataLog. */ trait FileManager { - /** List the files in a path that matches a filter. */ + /** List the files in a path that match a filter. */ def list(path: Path, filter: PathFilter): Array[FileStatus] /** Make directory at the give path and all its parent directories as needed. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala index a10ed5f2df1b5..1a83c884d55bd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala @@ -62,7 +62,7 @@ class IncrementalExecution( StreamingDeduplicationStrategy :: Nil } - private val numStateStores = offsetSeqMetadata.conf.get(SQLConf.SHUFFLE_PARTITIONS.key) + private[sql] val numStateStores = offsetSeqMetadata.conf.get(SQLConf.SHUFFLE_PARTITIONS.key) .map(SQLConf.SHUFFLE_PARTITIONS.valueConverter) .getOrElse(sparkSession.sessionState.conf.numShufflePartitions) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MetricsReporter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MetricsReporter.scala index b84e6ce64c611..66b11ecddf233 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MetricsReporter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MetricsReporter.scala @@ -17,15 +17,11 @@ package org.apache.spark.sql.execution.streaming -import java.{util => ju} - -import scala.collection.mutable - import com.codahale.metrics.{Gauge, MetricRegistry} import org.apache.spark.internal.Logging import org.apache.spark.metrics.source.{Source => CodahaleSource} -import org.apache.spark.util.Clock +import org.apache.spark.sql.streaming.StreamingQueryProgress /** * Serves metrics from a [[org.apache.spark.sql.streaming.StreamingQuery]] to @@ -39,14 +35,17 @@ class MetricsReporter( // Metric names should not have . in them, so that all the metrics of a query are identified // together in Ganglia as a single metric group - registerGauge("inputRate-total", () => stream.lastProgress.inputRowsPerSecond) - registerGauge("processingRate-total", () => stream.lastProgress.processedRowsPerSecond) - registerGauge("latency", () => stream.lastProgress.durationMs.get("triggerExecution").longValue()) - - private def registerGauge[T](name: String, f: () => T)(implicit num: Numeric[T]): Unit = { + registerGauge("inputRate-total", _.inputRowsPerSecond, 0.0) + registerGauge("processingRate-total", _.processedRowsPerSecond, 0.0) + registerGauge("latency", _.durationMs.get("triggerExecution").longValue(), 0L) + + private def registerGauge[T]( + name: String, + f: StreamingQueryProgress => T, + default: T): Unit = { synchronized { metricRegistry.register(name, new Gauge[T] { - override def getValue: T = f() + override def getValue: T = Option(stream.lastProgress).map(f).getOrElse(default) }) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala index 9a7a13fcc5806..6a264ad708dae 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala @@ -17,14 +17,21 @@ package org.apache.spark.sql.execution.streaming +import java.util.Optional + +import scala.collection.JavaConverters._ import scala.collection.mutable.{ArrayBuffer, Map => MutableMap} import org.apache.spark.sql.{Dataset, SparkSession} import org.apache.spark.sql.catalyst.encoders.RowEncoder -import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, CurrentBatchTimestamp, CurrentDate, CurrentTimestamp} -import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} +import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, AttributeMap, CurrentBatchTimestamp, CurrentDate, CurrentTimestamp} +import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan, Project} import org.apache.spark.sql.execution.SQLExecution -import org.apache.spark.sql.sources.v2.streaming.MicroBatchReadSupport +import org.apache.spark.sql.execution.datasources.v2.{StreamingDataSourceV2Relation, WriteToDataSourceV2} +import org.apache.spark.sql.execution.streaming.sources.{InternalRowMicroBatchWriter, MicroBatchWriter} +import org.apache.spark.sql.sources.v2.{DataSourceOptions, MicroBatchReadSupport, StreamWriteSupport} +import org.apache.spark.sql.sources.v2.reader.streaming.{MicroBatchReader, Offset => OffsetV2} +import org.apache.spark.sql.sources.v2.writer.SupportsWriteInternalRow import org.apache.spark.sql.streaming.{OutputMode, ProcessingTime, Trigger} import org.apache.spark.util.{Clock, Utils} @@ -33,10 +40,11 @@ class MicroBatchExecution( name: String, checkpointRoot: String, analyzedPlan: LogicalPlan, - sink: Sink, + sink: BaseStreamingSink, trigger: Trigger, triggerClock: Clock, outputMode: OutputMode, + extraOptions: Map[String, String], deleteCheckpointOnStop: Boolean) extends StreamExecution( sparkSession, name, checkpointRoot, analyzedPlan, sink, @@ -57,6 +65,13 @@ class MicroBatchExecution( var nextSourceId = 0L val toExecutionRelationMap = MutableMap[StreamingRelation, StreamingExecutionRelation]() val v2ToExecutionRelationMap = MutableMap[StreamingRelationV2, StreamingExecutionRelation]() + // We transform each distinct streaming relation into a StreamingExecutionRelation, keeping a + // map as we go to ensure each identical relation gets the same StreamingExecutionRelation + // object. For each microbatch, the StreamingExecutionRelation will be replaced with a logical + // plan for the data within that batch. + // Note that we have to use the previous `output` as attributes in StreamingExecutionRelation, + // since the existing logical plan has already used those attributes. The per-microbatch + // transformation is responsible for replacing attributes with their final values. val _logicalPlan = analyzedPlan.transform { case streamingRelation@StreamingRelation(dataSource, _, output) => toExecutionRelationMap.getOrElseUpdate(streamingRelation, { @@ -64,19 +79,29 @@ class MicroBatchExecution( val metadataPath = s"$resolvedCheckpointRoot/sources/$nextSourceId" val source = dataSource.createSource(metadataPath) nextSourceId += 1 - // We still need to use the previous `output` instead of `source.schema` as attributes in - // "df.logicalPlan" has already used attributes of the previous `output`. StreamingExecutionRelation(source, output)(sparkSession) }) - case s @ StreamingRelationV2(v2DataSource, _, _, output, v1DataSource) - if !v2DataSource.isInstanceOf[MicroBatchReadSupport] => + case s @ StreamingRelationV2(source: MicroBatchReadSupport, _, options, output, _) => + v2ToExecutionRelationMap.getOrElseUpdate(s, { + // Materialize source to avoid creating it in every batch + val metadataPath = s"$resolvedCheckpointRoot/sources/$nextSourceId" + val reader = source.createMicroBatchReader( + Optional.empty(), // user specified schema + metadataPath, + new DataSourceOptions(options.asJava)) + nextSourceId += 1 + StreamingExecutionRelation(reader, output)(sparkSession) + }) + case s @ StreamingRelationV2(_, sourceName, _, output, v1Relation) => v2ToExecutionRelationMap.getOrElseUpdate(s, { // Materialize source to avoid creating it in every batch val metadataPath = s"$resolvedCheckpointRoot/sources/$nextSourceId" - val source = v1DataSource.createSource(metadataPath) + if (v1Relation.isEmpty) { + throw new UnsupportedOperationException( + s"Data source $sourceName does not support microbatch processing.") + } + val source = v1Relation.get.dataSource.createSource(metadataPath) nextSourceId += 1 - // We still need to use the previous `output` instead of `source.schema` as attributes in - // "df.logicalPlan" has already used attributes of the previous `output`. StreamingExecutionRelation(source, output)(sparkSession) }) } @@ -187,12 +212,11 @@ class MicroBatchExecution( * batch will be executed before getOffset is called again. */ availableOffsets.foreach { case (source: Source, end: Offset) => - if (committedOffsets.get(source).map(_ != end).getOrElse(true)) { - val start = committedOffsets.get(source) - source.getBatch(start, end) - } + val start = committedOffsets.get(source) + source.getBatch(start, end) case nonV1Tuple => - throw new IllegalStateException(s"Unexpected V2 source in $nonV1Tuple") + // The V2 API does not have the same edge case requiring getBatch to be called + // here, so we do nothing here. } currentBatchId = latestCommittedBatchId + 1 committedOffsets ++= availableOffsets @@ -236,14 +260,27 @@ class MicroBatchExecution( val hasNewData = { awaitProgressLock.lock() try { - val latestOffsets: Map[Source, Option[Offset]] = uniqueSources.map { + // Generate a map from each unique source to the next available offset. + val latestOffsets: Map[BaseStreamingSource, Option[Offset]] = uniqueSources.map { case s: Source => updateStatusMessage(s"Getting offsets from $s") reportTimeTaken("getOffset") { (s, s.getOffset) } + case s: MicroBatchReader => + updateStatusMessage(s"Getting offsets from $s") + reportTimeTaken("getOffset") { + // Once v1 streaming source execution is gone, we can refactor this away. + // For now, we set the range here to get the source to infer the available end offset, + // get that offset, and then set the range again when we later execute. + s.setOffsetRange( + toJava(availableOffsets.get(s).map(off => s.deserializeOffset(off.json))), + Optional.empty()) + + (s, Some(s.getEndOffset)) + } }.toMap - availableOffsets ++= latestOffsets.filter { case (s, o) => o.nonEmpty }.mapValues(_.get) + availableOffsets ++= latestOffsets.filter { case (_, o) => o.nonEmpty }.mapValues(_.get) if (dataAvailable) { true @@ -317,6 +354,8 @@ class MicroBatchExecution( if (prevBatchOff.isDefined) { prevBatchOff.get.toStreamProgress(sources).foreach { case (src: Source, off) => src.commit(off) + case (reader: MicroBatchReader, off) => + reader.commit(reader.deserializeOffset(off.json)) } } else { throw new IllegalStateException(s"batch $currentBatchId doesn't exist") @@ -357,33 +396,39 @@ class MicroBatchExecution( s"DataFrame returned by getBatch from $source did not have isStreaming=true\n" + s"${batch.queryExecution.logical}") logDebug(s"Retrieving data from $source: $current -> $available") - Some(source -> batch) + Some(source -> batch.logicalPlan) + case (reader: MicroBatchReader, available) + if committedOffsets.get(reader).map(_ != available).getOrElse(true) => + val current = committedOffsets.get(reader).map(off => reader.deserializeOffset(off.json)) + reader.setOffsetRange( + toJava(current), + Optional.of(available.asInstanceOf[OffsetV2])) + logDebug(s"Retrieving data from $reader: $current -> $available") + Some(reader -> + new StreamingDataSourceV2Relation(reader.readSchema().toAttributes, reader)) case _ => None } } - // A list of attributes that will need to be updated. - val replacements = new ArrayBuffer[(Attribute, Attribute)] // Replace sources in the logical plan with data that has arrived since the last batch. - val withNewSources = logicalPlan transform { + val newBatchesPlan = logicalPlan transform { case StreamingExecutionRelation(source, output) => - newData.get(source).map { data => - val newPlan = data.logicalPlan - assert(output.size == newPlan.output.size, + newData.get(source).map { dataPlan => + assert(output.size == dataPlan.output.size, s"Invalid batch: ${Utils.truncatedString(output, ",")} != " + - s"${Utils.truncatedString(newPlan.output, ",")}") - replacements ++= output.zip(newPlan.output) - newPlan + s"${Utils.truncatedString(dataPlan.output, ",")}") + + val aliases = output.zip(dataPlan.output).map { case (to, from) => + Alias(from, to.name)(exprId = to.exprId, explicitMetadata = Some(from.metadata)) + } + Project(aliases, dataPlan) }.getOrElse { LocalRelation(output, isStreaming = true) } } // Rewire the plan to use the new attributes that were returned by the source. - val replacementMap = AttributeMap(replacements) - val triggerLogicalPlan = withNewSources transformAllExpressions { - case a: Attribute if replacementMap.contains(a) => - replacementMap(a).withMetadata(a.metadata) + val newAttributePlan = newBatchesPlan transformAllExpressions { case ct: CurrentTimestamp => CurrentBatchTimestamp(offsetSeqMetadata.batchTimestampMs, ct.dataType) @@ -392,6 +437,23 @@ class MicroBatchExecution( cd.dataType, cd.timeZoneId) } + val triggerLogicalPlan = sink match { + case _: Sink => newAttributePlan + case s: StreamWriteSupport => + val writer = s.createStreamWriter( + s"$runId", + newAttributePlan.schema, + outputMode, + new DataSourceOptions(extraOptions.asJava)) + if (writer.isInstanceOf[SupportsWriteInternalRow]) { + WriteToDataSourceV2( + new InternalRowMicroBatchWriter(currentBatchId, writer), newAttributePlan) + } else { + WriteToDataSourceV2(new MicroBatchWriter(currentBatchId, writer), newAttributePlan) + } + case _ => throw new IllegalArgumentException(s"unknown sink type for $sink") + } + reportTimeTaken("queryPlanning") { lastExecution = new IncrementalExecution( sparkSessionToRunBatch, @@ -409,7 +471,12 @@ class MicroBatchExecution( reportTimeTaken("addBatch") { SQLExecution.withNewExecutionId(sparkSessionToRunBatch, lastExecution) { - sink.addBatch(currentBatchId, nextBatch) + sink match { + case s: Sink => s.addBatch(currentBatchId, nextBatch) + case _: StreamWriteSupport => + // This doesn't accumulate any data - it just forces execution of the microbatch writer. + nextBatch.collect() + } } } @@ -421,4 +488,8 @@ class MicroBatchExecution( awaitProgressLock.unlock() } } + + private def toJava(scalaOption: Option[OffsetV2]): Optional[OffsetV2] = { + Optional.ofNullable(scalaOption.orNull) + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/Offset.java b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/Offset.java index 80aa5505db991..43ad4b3384ec3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/Offset.java +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/Offset.java @@ -19,8 +19,8 @@ /** * This is an internal, deprecated interface. New source implementations should use the - * org.apache.spark.sql.sources.v2.reader.Offset class, which is the one that will be supported - * in the long term. + * org.apache.spark.sql.sources.v2.reader.streaming.Offset class, which is the one that will be + * supported in the long term. * * This class will be removed in a future release. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/OffsetSeq.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/OffsetSeq.scala index a1b63a6de3823..73945b39b8967 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/OffsetSeq.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/OffsetSeq.scala @@ -32,7 +32,7 @@ import org.apache.spark.sql.internal.SQLConf.{SHUFFLE_PARTITIONS, STATE_STORE_PR case class OffsetSeq(offsets: Seq[Option[Offset]], metadata: Option[OffsetSeqMetadata] = None) { /** - * Unpacks an offset into [[StreamProgress]] by associating each offset with the order list of + * Unpacks an offset into [[StreamProgress]] by associating each offset with the ordered list of * sources. * * This method is typically used to associate a serialized offset with actual sources (which diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/OffsetSeqLog.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/OffsetSeqLog.scala index e3f4abcf9f1dc..2c8d7c7b0f3c5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/OffsetSeqLog.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/OffsetSeqLog.scala @@ -28,7 +28,7 @@ import org.apache.spark.sql.SparkSession /** * This class is used to log offsets to persistent files in HDFS. * Each file corresponds to a specific batch of offsets. The file - * format contain a version string in the first line, followed + * format contains a version string in the first line, followed * by a the JSON string representation of the offsets separated * by a newline character. If a source offset is missing, then * that line will contain a string value defined in the diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/ProgressReporter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/ProgressReporter.scala index 1c9043613cb69..d1e5be9c12762 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/ProgressReporter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/ProgressReporter.scala @@ -53,7 +53,7 @@ trait ProgressReporter extends Logging { protected def triggerClock: Clock protected def logicalPlan: LogicalPlan protected def lastExecution: QueryExecution - protected def newData: Map[BaseStreamingSource, DataFrame] + protected def newData: Map[BaseStreamingSource, LogicalPlan] protected def availableOffsets: StreamProgress protected def committedOffsets: StreamProgress protected def sources: Seq[BaseStreamingSource] @@ -225,8 +225,8 @@ trait ProgressReporter extends Logging { // // 3. For each source, we sum the metrics of the associated execution plan leaves. // - val logicalPlanLeafToSource = newData.flatMap { case (source, df) => - df.logicalPlan.collectLeaves().map { leaf => leaf -> source } + val logicalPlanLeafToSource = newData.flatMap { case (source, logicalPlan) => + logicalPlan.collectLeaves().map { leaf => leaf -> source } } val allLogicalPlanLeaves = lastExecution.logical.collectLeaves() // includes non-streaming val allExecPlanLeaves = lastExecution.executedPlan.collectLeaves() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateSourceProvider.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateSourceProvider.scala index d02cf882b61ac..649fbbfa184ec 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateSourceProvider.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateSourceProvider.scala @@ -29,12 +29,10 @@ import org.apache.spark.network.util.JavaUtils import org.apache.spark.sql.{AnalysisException, DataFrame, SQLContext} import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils} -import org.apache.spark.sql.execution.streaming.continuous.ContinuousRateStreamReader -import org.apache.spark.sql.execution.streaming.sources.RateStreamV2Reader +import org.apache.spark.sql.execution.streaming.continuous.RateStreamContinuousReader import org.apache.spark.sql.sources.{DataSourceRegister, StreamSourceProvider} import org.apache.spark.sql.sources.v2._ -import org.apache.spark.sql.sources.v2.streaming.ContinuousReadSupport -import org.apache.spark.sql.sources.v2.streaming.reader.ContinuousReader +import org.apache.spark.sql.sources.v2.reader.streaming.ContinuousReader import org.apache.spark.sql.types._ import org.apache.spark.util.{ManualClock, SystemClock} @@ -111,8 +109,8 @@ class RateSourceProvider extends StreamSourceProvider with DataSourceRegister override def createContinuousReader( schema: Optional[StructType], checkpointLocation: String, - options: DataSourceV2Options): ContinuousReader = { - new ContinuousRateStreamReader(options) + options: DataSourceOptions): ContinuousReader = { + new RateStreamContinuousReader(options) } override def shortName(): String = "rate" diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateStreamOffset.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateStreamOffset.scala index 261d69bbd9843..02fed50485b94 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateStreamOffset.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateStreamOffset.scala @@ -23,7 +23,7 @@ import org.json4s.jackson.Serialization import org.apache.spark.sql.sources.v2 case class RateStreamOffset(partitionToValueAndRunTimeMs: Map[Int, ValueRunTimeMsPair]) - extends v2.streaming.reader.Offset { + extends v2.reader.streaming.Offset { implicit val defaultFormats: DefaultFormats = DefaultFormats override val json = Serialization.write(partitionToValueAndRunTimeMs) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala index 3e76bf7b7ca8f..3fc8c7887896a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala @@ -163,7 +163,7 @@ abstract class StreamExecution( var lastExecution: IncrementalExecution = _ /** Holds the most recent input data for each source. */ - protected var newData: Map[BaseStreamingSource, DataFrame] = _ + protected var newData: Map[BaseStreamingSource, LogicalPlan] = _ @volatile protected var streamDeathCause: StreamingQueryException = null @@ -356,25 +356,7 @@ abstract class StreamExecution( private def isInterruptedByStop(e: Throwable): Boolean = { if (state.get == TERMINATED) { - e match { - // InterruptedIOException - thrown when an I/O operation is interrupted - // ClosedByInterruptException - thrown when an I/O operation upon a channel is interrupted - case _: InterruptedException | _: InterruptedIOException | _: ClosedByInterruptException => - true - // The cause of the following exceptions may be one of the above exceptions: - // - // UncheckedIOException - thrown by codes that cannot throw a checked IOException, such as - // BiFunction.apply - // ExecutionException - thrown by codes running in a thread pool and these codes throw an - // exception - // UncheckedExecutionException - thrown by codes that cannot throw a checked - // ExecutionException, such as BiFunction.apply - case e2 @ (_: UncheckedIOException | _: ExecutionException | _: UncheckedExecutionException) - if e2.getCause != null => - isInterruptedByStop(e2.getCause) - case _ => - false - } + StreamExecution.isInterruptionException(e) } else { false } @@ -418,11 +400,17 @@ abstract class StreamExecution( * Blocks the current thread until processing for data from the given `source` has reached at * least the given `Offset`. This method is intended for use primarily when writing tests. */ - private[sql] def awaitOffset(source: Source, newOffset: Offset): Unit = { + private[sql] def awaitOffset(sourceIndex: Int, newOffset: Offset): Unit = { assertAwaitThread() def notDone = { val localCommittedOffsets = committedOffsets - !localCommittedOffsets.contains(source) || localCommittedOffsets(source) != newOffset + if (sources == null) { + // sources might not be initialized yet + false + } else { + val source = sources(sourceIndex) + !localCommittedOffsets.contains(source) || localCommittedOffsets(source) != newOffset + } } while (notDone) { @@ -436,7 +424,7 @@ abstract class StreamExecution( awaitProgressLock.unlock() } } - logDebug(s"Unblocked at $newOffset for $source") + logDebug(s"Unblocked at $newOffset for ${sources(sourceIndex)}") } /** A flag to indicate that a batch has completed with no new data available. */ @@ -559,6 +547,26 @@ abstract class StreamExecution( object StreamExecution { val QUERY_ID_KEY = "sql.streaming.queryId" + + def isInterruptionException(e: Throwable): Boolean = e match { + // InterruptedIOException - thrown when an I/O operation is interrupted + // ClosedByInterruptException - thrown when an I/O operation upon a channel is interrupted + case _: InterruptedException | _: InterruptedIOException | _: ClosedByInterruptException => + true + // The cause of the following exceptions may be one of the above exceptions: + // + // UncheckedIOException - thrown by codes that cannot throw a checked IOException, such as + // BiFunction.apply + // ExecutionException - thrown by codes running in a thread pool and these codes throw an + // exception + // UncheckedExecutionException - thrown by codes that cannot throw a checked + // ExecutionException, such as BiFunction.apply + case e2 @ (_: UncheckedIOException | _: ExecutionException | _: UncheckedExecutionException) + if e2.getCause != null => + isInterruptionException(e2.getCause) + case _ => + false + } } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingQueryWrapper.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingQueryWrapper.scala index 020c9cb4a7304..3f2cdadfbaeee 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingQueryWrapper.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingQueryWrapper.scala @@ -22,7 +22,7 @@ import org.apache.spark.sql.SparkSession import org.apache.spark.sql.streaming.{StreamingQuery, StreamingQueryException, StreamingQueryProgress, StreamingQueryStatus} /** - * Wrap non-serializable StreamExecution to make the query serializable as it's easy to for it to + * Wrap non-serializable StreamExecution to make the query serializable as it's easy for it to * get captured with normal usage. It's safe to capture the query but not use it in executors. * However, if the user tries to call its methods, it will throw `IllegalStateException`. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingRelation.scala index a9d50e3a112e7..24195b5657e8a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingRelation.scala @@ -20,13 +20,12 @@ package org.apache.spark.sql.execution.streaming import org.apache.spark.rdd.RDD import org.apache.spark.sql.SparkSession import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation import org.apache.spark.sql.catalyst.expressions.Attribute -import org.apache.spark.sql.catalyst.plans.logical.LeafNode -import org.apache.spark.sql.catalyst.plans.logical.Statistics +import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics} import org.apache.spark.sql.execution.LeafExecNode import org.apache.spark.sql.execution.datasources.DataSource -import org.apache.spark.sql.sources.v2.DataSourceV2 -import org.apache.spark.sql.sources.v2.streaming.ContinuousReadSupport +import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceV2} object StreamingRelation { def apply(dataSource: DataSource): StreamingRelation = { @@ -43,7 +42,7 @@ object StreamingRelation { * passing to [[StreamExecution]] to run a query. */ case class StreamingRelation(dataSource: DataSource, sourceName: String, output: Seq[Attribute]) - extends LeafNode { + extends LeafNode with MultiInstanceRelation { override def isStreaming: Boolean = true override def toString: String = sourceName @@ -54,6 +53,8 @@ case class StreamingRelation(dataSource: DataSource, sourceName: String, output: override def computeStats(): Statistics = Statistics( sizeInBytes = BigInt(dataSource.sparkSession.sessionState.conf.defaultSizeInBytes) ) + + override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance())) } /** @@ -61,10 +62,11 @@ case class StreamingRelation(dataSource: DataSource, sourceName: String, output: * [[org.apache.spark.sql.catalyst.plans.logical.LogicalPlan]]. */ case class StreamingExecutionRelation( - source: Source, + source: BaseStreamingSource, output: Seq[Attribute])(session: SparkSession) - extends LeafNode { + extends LeafNode with MultiInstanceRelation { + override def otherCopyArgs: Seq[AnyRef] = session :: Nil override def isStreaming: Boolean = true override def toString: String = source.toString @@ -75,6 +77,8 @@ case class StreamingExecutionRelation( override def computeStats(): Statistics = Statistics( sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes) ) + + override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))(session) } // We have to pack in the V1 data source as a shim, for the case when a source implements @@ -92,14 +96,17 @@ case class StreamingRelationV2( sourceName: String, extraOptions: Map[String, String], output: Seq[Attribute], - v1DataSource: DataSource)(session: SparkSession) - extends LeafNode { + v1Relation: Option[StreamingRelation])(session: SparkSession) + extends LeafNode with MultiInstanceRelation { + override def otherCopyArgs: Seq[AnyRef] = session :: Nil override def isStreaming: Boolean = true override def toString: String = sourceName override def computeStats(): Statistics = Statistics( sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes) ) + + override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))(session) } /** @@ -109,8 +116,9 @@ case class ContinuousExecutionRelation( source: ContinuousReadSupport, extraOptions: Map[String, String], output: Seq[Attribute])(session: SparkSession) - extends LeafNode { + extends LeafNode with MultiInstanceRelation { + override def otherCopyArgs: Seq[AnyRef] = session :: Nil override def isStreaming: Boolean = true override def toString: String = source.toString @@ -121,6 +129,8 @@ case class ContinuousExecutionRelation( override def computeStats(): Statistics = Statistics( sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes) ) + + override def newInstance(): LogicalPlan = this.copy(output = output.map(_.newInstance()))(session) } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala index c351f658cb955..1402eba8bbad2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala @@ -167,7 +167,8 @@ case class StreamingSymmetricHashJoinExec( val nullRight = new GenericInternalRow(right.output.map(_.withNullability(true)).length) override def requiredChildDistribution: Seq[Distribution] = - ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil + HashClusteredDistribution(leftKeys, stateInfo.map(_.numPartitions)) :: + HashClusteredDistribution(rightKeys, stateInfo.map(_.numPartitions)) :: Nil override def output: Seq[Attribute] = joinType match { case _: InnerLike => left.output ++ right.output diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/Triggers.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/Triggers.scala index 271bc4da99c08..19e3e55cb2829 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/Triggers.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/Triggers.scala @@ -21,7 +21,7 @@ import org.apache.spark.annotation.{Experimental, InterfaceStability} import org.apache.spark.sql.streaming.Trigger /** - * A [[Trigger]] that process only one batch of data in a streaming query then terminates + * A [[Trigger]] that processes only one batch of data in a streaming query then terminates * the query. */ @Experimental diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/console.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/console.scala index 71eaabe273fea..cfba1001c6de0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/console.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/console.scala @@ -17,58 +17,30 @@ package org.apache.spark.sql.execution.streaming -import org.apache.spark.internal.Logging -import org.apache.spark.sql.{DataFrame, SaveMode, SQLContext} -import org.apache.spark.sql.execution.SQLExecution -import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, StreamSinkProvider} +import org.apache.spark.sql._ +import org.apache.spark.sql.execution.streaming.sources.ConsoleWriter +import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister} +import org.apache.spark.sql.sources.v2.{DataSourceOptions, DataSourceV2, StreamWriteSupport} +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter import org.apache.spark.sql.streaming.OutputMode import org.apache.spark.sql.types.StructType -class ConsoleSink(options: Map[String, String]) extends Sink with Logging { - // Number of rows to display, by default 20 rows - private val numRowsToShow = options.get("numRows").map(_.toInt).getOrElse(20) - - // Truncate the displayed data if it is too long, by default it is true - private val isTruncated = options.get("truncate").map(_.toBoolean).getOrElse(true) - - // Track the batch id - private var lastBatchId = -1L - - override def addBatch(batchId: Long, data: DataFrame): Unit = synchronized { - val batchIdStr = if (batchId <= lastBatchId) { - s"Rerun batch: $batchId" - } else { - lastBatchId = batchId - s"Batch: $batchId" - } - - // scalastyle:off println - println("-------------------------------------------") - println(batchIdStr) - println("-------------------------------------------") - // scalastyle:off println - data.sparkSession.createDataFrame( - data.sparkSession.sparkContext.parallelize(data.collect()), data.schema) - .show(numRowsToShow, isTruncated) - } - - override def toString(): String = s"ConsoleSink[numRows=$numRowsToShow, truncate=$isTruncated]" -} - case class ConsoleRelation(override val sqlContext: SQLContext, data: DataFrame) extends BaseRelation { override def schema: StructType = data.schema } -class ConsoleSinkProvider extends StreamSinkProvider +class ConsoleSinkProvider extends DataSourceV2 + with StreamWriteSupport with DataSourceRegister with CreatableRelationProvider { - def createSink( - sqlContext: SQLContext, - parameters: Map[String, String], - partitionColumns: Seq[String], - outputMode: OutputMode): Sink = { - new ConsoleSink(parameters) + + override def createStreamWriter( + queryId: String, + schema: StructType, + mode: OutputMode, + options: DataSourceOptions): StreamWriter = { + new ConsoleWriter(schema, options) } def createRelation( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousDataSourceRDDIter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousDataSourceRDDIter.scala index d79e4bd65f563..cf02c0dda25d7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousDataSourceRDDIter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousDataSourceRDDIter.scala @@ -18,43 +18,45 @@ package org.apache.spark.sql.execution.streaming.continuous import java.util.concurrent.{ArrayBlockingQueue, BlockingQueue, TimeUnit} -import java.util.concurrent.atomic.{AtomicBoolean, AtomicLong} +import java.util.concurrent.atomic.AtomicBoolean import scala.collection.JavaConverters._ import org.apache.spark._ import org.apache.spark.internal.Logging import org.apache.spark.rdd.RDD -import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.sql.{Row, SQLContext} import org.apache.spark.sql.catalyst.expressions.UnsafeRow import org.apache.spark.sql.execution.datasources.v2.{DataSourceRDDPartition, RowToUnsafeDataReader} -import org.apache.spark.sql.execution.streaming._ -import org.apache.spark.sql.execution.streaming.continuous._ import org.apache.spark.sql.sources.v2.reader._ -import org.apache.spark.sql.sources.v2.streaming.reader.{ContinuousDataReader, PartitionOffset} -import org.apache.spark.sql.streaming.ProcessingTime -import org.apache.spark.util.{SystemClock, ThreadUtils} +import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousDataReader, PartitionOffset} +import org.apache.spark.util.ThreadUtils class ContinuousDataSourceRDD( sc: SparkContext, sqlContext: SQLContext, - @transient private val readTasks: java.util.List[ReadTask[UnsafeRow]]) + @transient private val readerFactories: java.util.List[DataReaderFactory[UnsafeRow]]) extends RDD[UnsafeRow](sc, Nil) { private val dataQueueSize = sqlContext.conf.continuousStreamingExecutorQueueSize private val epochPollIntervalMs = sqlContext.conf.continuousStreamingExecutorPollIntervalMs override protected def getPartitions: Array[Partition] = { - readTasks.asScala.zipWithIndex.map { - case (readTask, index) => new DataSourceRDDPartition(index, readTask) + readerFactories.asScala.zipWithIndex.map { + case (readerFactory, index) => new DataSourceRDDPartition(index, readerFactory) }.toArray } override def compute(split: Partition, context: TaskContext): Iterator[UnsafeRow] = { - val reader = split.asInstanceOf[DataSourceRDDPartition].readTask.createDataReader() + // If attempt number isn't 0, this is a task retry, which we don't support. + if (context.attemptNumber() != 0) { + throw new ContinuousTaskRetryException() + } + + val reader = split.asInstanceOf[DataSourceRDDPartition[UnsafeRow]] + .readerFactory.createDataReader() - val runId = context.getLocalProperty(ContinuousExecution.RUN_ID_KEY) + val coordinatorId = context.getLocalProperty(ContinuousExecution.EPOCH_COORDINATOR_ID_KEY) // This queue contains two types of messages: // * (null, null) representing an epoch boundary. @@ -63,7 +65,7 @@ class ContinuousDataSourceRDD( val epochPollFailed = new AtomicBoolean(false) val epochPollExecutor = ThreadUtils.newDaemonSingleThreadScheduledExecutor( - s"epoch-poll--${runId}--${context.partitionId()}") + s"epoch-poll--$coordinatorId--${context.partitionId()}") val epochPollRunnable = new EpochPollRunnable(queue, context, epochPollFailed) epochPollExecutor.scheduleWithFixedDelay( epochPollRunnable, 0, epochPollIntervalMs, TimeUnit.MILLISECONDS) @@ -77,12 +79,11 @@ class ContinuousDataSourceRDD( dataReaderThread.start() context.addTaskCompletionListener(_ => { - reader.close() dataReaderThread.interrupt() epochPollExecutor.shutdown() }) - val epochEndpoint = EpochCoordinatorRef.get(runId, SparkEnv.get) + val epochEndpoint = EpochCoordinatorRef.get(coordinatorId, SparkEnv.get) new Iterator[UnsafeRow] { private val POLL_TIMEOUT_MS = 1000 @@ -132,7 +133,7 @@ class ContinuousDataSourceRDD( } override def getPreferredLocations(split: Partition): Seq[String] = { - split.asInstanceOf[DataSourceRDDPartition].readTask.preferredLocations() + split.asInstanceOf[DataSourceRDDPartition[UnsafeRow]].readerFactory.preferredLocations() } } @@ -146,7 +147,7 @@ class EpochPollRunnable( private[continuous] var failureReason: Throwable = _ private val epochEndpoint = EpochCoordinatorRef.get( - context.getLocalProperty(ContinuousExecution.RUN_ID_KEY), SparkEnv.get) + context.getLocalProperty(ContinuousExecution.EPOCH_COORDINATOR_ID_KEY), SparkEnv.get) private var currentEpoch = context.getLocalProperty(ContinuousExecution.START_EPOCH_KEY).toLong override def run(): Unit = { @@ -173,10 +174,11 @@ class DataReaderThread( failedFlag: AtomicBoolean) extends Thread( s"continuous-reader--${context.partitionId()}--" + - s"${context.getLocalProperty(ContinuousExecution.RUN_ID_KEY)}") { + s"${context.getLocalProperty(ContinuousExecution.EPOCH_COORDINATOR_ID_KEY)}") { private[continuous] var failureReason: Throwable = _ override def run(): Unit = { + TaskContext.setTaskContext(context) val baseReader = ContinuousDataSourceRDD.getBaseReader(reader) try { while (!context.isInterrupted && !context.isCompleted()) { @@ -201,6 +203,8 @@ class DataReaderThread( failedFlag.set(true) // Don't rethrow the exception in this thread. It's not needed, and the default Spark // exception handler will kill the executor. + } finally { + reader.close() } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala index 2843ab13bde2b..c3294d64b10cd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala @@ -17,22 +17,22 @@ package org.apache.spark.sql.execution.streaming.continuous +import java.util.UUID import java.util.concurrent.TimeUnit +import java.util.function.UnaryOperator import scala.collection.JavaConverters._ import scala.collection.mutable.{ArrayBuffer, Map => MutableMap} import org.apache.spark.SparkEnv -import org.apache.spark.sql.{AnalysisException, SparkSession} -import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, CurrentBatchTimestamp, CurrentDate, CurrentTimestamp} +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, CurrentDate, CurrentTimestamp} import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.SQLExecution -import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2Relation, WriteToDataSourceV2} +import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2Relation, StreamingDataSourceV2Relation, WriteToDataSourceV2} import org.apache.spark.sql.execution.streaming.{ContinuousExecutionRelation, StreamingRelationV2, _} -import org.apache.spark.sql.sources.v2.DataSourceV2Options -import org.apache.spark.sql.sources.v2.streaming.{ContinuousReadSupport, ContinuousWriteSupport} -import org.apache.spark.sql.sources.v2.streaming.reader.{ContinuousReader, Offset, PartitionOffset} -import org.apache.spark.sql.sources.v2.streaming.writer.ContinuousWriter +import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceOptions, StreamWriteSupport} +import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousReader, PartitionOffset} import org.apache.spark.sql.streaming.{OutputMode, ProcessingTime, Trigger} import org.apache.spark.sql.types.StructType import org.apache.spark.util.{Clock, Utils} @@ -42,7 +42,7 @@ class ContinuousExecution( name: String, checkpointRoot: String, analyzedPlan: LogicalPlan, - sink: ContinuousWriteSupport, + sink: StreamWriteSupport, trigger: Trigger, triggerClock: Clock, outputMode: OutputMode, @@ -52,13 +52,13 @@ class ContinuousExecution( sparkSession, name, checkpointRoot, analyzedPlan, sink, trigger, triggerClock, outputMode, deleteCheckpointOnStop) { - @volatile protected var continuousSources: Seq[ContinuousReader] = Seq.empty + @volatile protected var continuousSources: Seq[ContinuousReader] = Seq() override protected def sources: Seq[BaseStreamingSource] = continuousSources - override lazy val logicalPlan: LogicalPlan = { - assert(queryExecutionThread eq Thread.currentThread, - "logicalPlan must be initialized in StreamExecutionThread " + - s"but the current thread was ${Thread.currentThread}") + // For use only in test harnesses. + private[sql] var currentEpochCoordinatorId: String = _ + + override val logicalPlan: LogicalPlan = { val toExecutionRelationMap = MutableMap[StreamingRelationV2, ContinuousExecutionRelation]() analyzedPlan.transform { case r @ StreamingRelationV2( @@ -67,7 +67,7 @@ class ContinuousExecution( ContinuousExecutionRelation(source, extraReaderOptions, output)(sparkSession) }) case StreamingRelationV2(_, sourceName, _, _, _) => - throw new AnalysisException( + throw new UnsupportedOperationException( s"Data source $sourceName does not support continuous processing.") } } @@ -78,15 +78,17 @@ class ContinuousExecution( } override protected def runActivatedStream(sparkSessionForStream: SparkSession): Unit = { - do { - try { - runContinuous(sparkSessionForStream) - } catch { - case _: InterruptedException if state.get().equals(RECONFIGURING) => - // swallow exception and run again - state.set(ACTIVE) + val stateUpdate = new UnaryOperator[State] { + override def apply(s: State) = s match { + // If we ended the query to reconfigure, reset the state to active. + case RECONFIGURING => ACTIVE + case _ => s } - } while (state.get() == ACTIVE) + } + + do { + runContinuous(sparkSessionForStream) + } while (state.updateAndGet(stateUpdate) == ACTIVE) } /** @@ -120,12 +122,16 @@ class ContinuousExecution( } committedOffsets = nextOffsets.toStreamProgress(sources) - // Forcibly align commit and offset logs by slicing off any spurious offset logs from - // a previous run. We can't allow commits to an epoch that a previous run reached but - // this run has not. - offsetLog.purgeAfter(latestEpochId) + // Get to an epoch ID that has definitely never been sent to a sink before. Since sink + // commit happens between offset log write and commit log write, this means an epoch ID + // which is not in the offset log. + val (latestOffsetEpoch, _) = offsetLog.getLatest().getOrElse { + throw new IllegalStateException( + s"Offset log had no latest element. This shouldn't be possible because nextOffsets is" + + s"an element.") + } + currentBatchId = latestOffsetEpoch + 1 - currentBatchId = latestEpochId + 1 logDebug(s"Resuming at epoch $currentBatchId with committed offsets $committedOffsets") nextOffsets case None => @@ -153,7 +159,7 @@ class ContinuousExecution( dataSource.createContinuousReader( java.util.Optional.empty[StructType](), metadataPath, - new DataSourceV2Options(extraReaderOptions.asJava)) + new DataSourceOptions(extraReaderOptions.asJava)) } uniqueSources = continuousSources.distinct @@ -173,8 +179,8 @@ class ContinuousExecution( val loggedOffset = offsets.offsets(0) val realOffset = loggedOffset.map(off => reader.deserializeOffset(off.json)) - reader.setOffset(java.util.Optional.ofNullable(realOffset.orNull)) - DataSourceV2Relation(newOutput, reader) + reader.setStartOffset(java.util.Optional.ofNullable(realOffset.orNull)) + new StreamingDataSourceV2Relation(newOutput, reader) } // Rewire the plan to use the new attributes that were returned by the source. @@ -187,12 +193,12 @@ class ContinuousExecution( "CurrentTimestamp and CurrentDate not yet supported for continuous processing") } - val writer = sink.createContinuousWriter( + val writer = sink.createStreamWriter( s"$runId", triggerLogicalPlan.schema, outputMode, - new DataSourceV2Options(extraOptions.asJava)) - val withSink = WriteToDataSourceV2(writer.get(), triggerLogicalPlan) + new DataSourceOptions(extraOptions.asJava)) + val withSink = WriteToDataSourceV2(writer, triggerLogicalPlan) val reader = withSink.collect { case DataSourceV2Relation(_, r: ContinuousReader) => r @@ -210,28 +216,30 @@ class ContinuousExecution( lastExecution.executedPlan // Force the lazy generation of execution plan } - sparkSession.sparkContext.setLocalProperty( + sparkSessionForQuery.sparkContext.setLocalProperty( ContinuousExecution.START_EPOCH_KEY, currentBatchId.toString) - sparkSession.sparkContext.setLocalProperty( - ContinuousExecution.RUN_ID_KEY, runId.toString) + // Add another random ID on top of the run ID, to distinguish epoch coordinators across + // reconfigurations. + val epochCoordinatorId = s"$runId--${UUID.randomUUID}" + currentEpochCoordinatorId = epochCoordinatorId + sparkSessionForQuery.sparkContext.setLocalProperty( + ContinuousExecution.EPOCH_COORDINATOR_ID_KEY, epochCoordinatorId) // Use the parent Spark session for the endpoint since it's where this query ID is registered. val epochEndpoint = EpochCoordinatorRef.create( - writer.get(), reader, this, currentBatchId, sparkSession, SparkEnv.get) + writer, reader, this, epochCoordinatorId, currentBatchId, sparkSession, SparkEnv.get) val epochUpdateThread = new Thread(new Runnable { override def run: Unit = { try { triggerExecutor.execute(() => { startTrigger() - if (reader.needsReconfiguration()) { - state.set(RECONFIGURING) + if (reader.needsReconfiguration() && state.compareAndSet(ACTIVE, RECONFIGURING)) { stopSources() if (queryExecutionThread.isAlive) { sparkSession.sparkContext.cancelJobGroup(runId.toString) queryExecutionThread.interrupt() - // No need to join - this thread is about to end anyway. } false } else if (isActive) { @@ -259,6 +267,7 @@ class ContinuousExecution( sparkSessionForQuery, lastExecution)(lastExecution.toRdd) } } finally { + epochEndpoint.askSync[Unit](StopContinuousExecutionWrites) SparkEnv.get.rpcEnv.stop(epochEndpoint) epochUpdateThread.interrupt() @@ -273,17 +282,22 @@ class ContinuousExecution( epoch: Long, reader: ContinuousReader, partitionOffsets: Seq[PartitionOffset]): Unit = { assert(continuousSources.length == 1, "only one continuous source supported currently") - if (partitionOffsets.contains(null)) { - // If any offset is null, that means the corresponding partition hasn't seen any data yet, so - // there's nothing meaningful to add to the offset log. - } val globalOffset = reader.mergeOffsets(partitionOffsets.toArray) - synchronized { - if (queryExecutionThread.isAlive) { - offsetLog.add(epoch, OffsetSeq.fill(globalOffset)) - } else { - return - } + val oldOffset = synchronized { + offsetLog.add(epoch, OffsetSeq.fill(globalOffset)) + offsetLog.get(epoch - 1) + } + + // If offset hasn't changed since last epoch, there's been no new data. + if (oldOffset.contains(OffsetSeq.fill(globalOffset))) { + noNewData = true + } + + awaitProgressLock.lock() + try { + awaitProgressLockCondition.signalAll() + } finally { + awaitProgressLock.unlock() } } @@ -346,5 +360,5 @@ class ContinuousExecution( object ContinuousExecution { val START_EPOCH_KEY = "__continuous_start_epoch" - val RUN_ID_KEY = "__run_id" + val EPOCH_COORDINATOR_ID_KEY = "__epoch_coordinator_id" } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousRateStreamSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousRateStreamSource.scala index c9aa78a5a2e28..b63d8d3e20650 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousRateStreamSource.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousRateStreamSource.scala @@ -23,19 +23,18 @@ import org.json4s.DefaultFormats import org.json4s.jackson.Serialization import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.expressions.UnsafeRow import org.apache.spark.sql.catalyst.util.DateTimeUtils import org.apache.spark.sql.execution.streaming.{RateSourceProvider, RateStreamOffset, ValueRunTimeMsPair} import org.apache.spark.sql.execution.streaming.sources.RateStreamSourceV2 -import org.apache.spark.sql.sources.v2.{DataSourceV2, DataSourceV2Options} +import org.apache.spark.sql.sources.v2.DataSourceOptions import org.apache.spark.sql.sources.v2.reader._ -import org.apache.spark.sql.sources.v2.streaming.reader.{ContinuousDataReader, ContinuousReader, Offset, PartitionOffset} -import org.apache.spark.sql.types.{LongType, StructField, StructType, TimestampType} +import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousDataReader, ContinuousReader, Offset, PartitionOffset} +import org.apache.spark.sql.types.StructType -case class ContinuousRateStreamPartitionOffset( +case class RateStreamPartitionOffset( partition: Int, currentValue: Long, currentTimeMs: Long) extends PartitionOffset -class ContinuousRateStreamReader(options: DataSourceV2Options) +class RateStreamContinuousReader(options: DataSourceOptions) extends ContinuousReader { implicit val defaultFormats: DefaultFormats = DefaultFormats @@ -48,7 +47,7 @@ class ContinuousRateStreamReader(options: DataSourceV2Options) override def mergeOffsets(offsets: Array[PartitionOffset]): Offset = { assert(offsets.length == numPartitions) val tuples = offsets.map { - case ContinuousRateStreamPartitionOffset(i, currVal, nextRead) => + case RateStreamPartitionOffset(i, currVal, nextRead) => (i, ValueRunTimeMsPair(currVal, nextRead)) } RateStreamOffset(Map(tuples: _*)) @@ -62,13 +61,13 @@ class ContinuousRateStreamReader(options: DataSourceV2Options) private var offset: Offset = _ - override def setOffset(offset: java.util.Optional[Offset]): Unit = { + override def setStartOffset(offset: java.util.Optional[Offset]): Unit = { this.offset = offset.orElse(RateStreamSourceV2.createInitialOffset(numPartitions, creationTime)) } override def getStartOffset(): Offset = offset - override def createReadTasks(): java.util.List[ReadTask[Row]] = { + override def createDataReaderFactories(): java.util.List[DataReaderFactory[Row]] = { val partitionStartMap = offset match { case off: RateStreamOffset => off.partitionToValueAndRunTimeMs case off => @@ -86,13 +85,13 @@ class ContinuousRateStreamReader(options: DataSourceV2Options) val start = partitionStartMap(i) // Have each partition advance by numPartitions each row, with starting points staggered // by their partition index. - RateStreamReadTask( + RateStreamContinuousDataReaderFactory( start.value, start.runTimeMs, i, numPartitions, perPartitionRate) - .asInstanceOf[ReadTask[Row]] + .asInstanceOf[DataReaderFactory[Row]] }.asJava } @@ -101,18 +100,19 @@ class ContinuousRateStreamReader(options: DataSourceV2Options) } -case class RateStreamReadTask( +case class RateStreamContinuousDataReaderFactory( startValue: Long, startTimeMs: Long, partitionIndex: Int, increment: Long, rowsPerSecond: Double) - extends ReadTask[Row] { + extends DataReaderFactory[Row] { override def createDataReader(): DataReader[Row] = - new RateStreamDataReader(startValue, startTimeMs, partitionIndex, increment, rowsPerSecond) + new RateStreamContinuousDataReader( + startValue, startTimeMs, partitionIndex, increment, rowsPerSecond) } -class RateStreamDataReader( +class RateStreamContinuousDataReader( startValue: Long, startTimeMs: Long, partitionIndex: Int, @@ -151,5 +151,5 @@ class RateStreamDataReader( override def close(): Unit = {} override def getOffset(): PartitionOffset = - ContinuousRateStreamPartitionOffset(partitionIndex, currentValue, nextReadTime) + RateStreamPartitionOffset(partitionIndex, currentValue, nextReadTime) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousTaskRetryException.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousTaskRetryException.scala new file mode 100644 index 0000000000000..e0a6f6dd50bb3 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousTaskRetryException.scala @@ -0,0 +1,26 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.streaming.continuous + +import org.apache.spark.SparkException + +/** + * An exception thrown when a continuous processing task runs with a nonzero attempt ID. + */ +class ContinuousTaskRetryException + extends SparkException("Continuous execution does not support task retry", null) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/EpochCoordinator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/EpochCoordinator.scala index 98017c3ac6a33..cc6808065c0cd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/EpochCoordinator.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/EpochCoordinator.scala @@ -17,18 +17,15 @@ package org.apache.spark.sql.execution.streaming.continuous -import java.util.concurrent.atomic.AtomicLong - import scala.collection.mutable import org.apache.spark.SparkEnv import org.apache.spark.internal.Logging import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint} import org.apache.spark.sql.SparkSession -import org.apache.spark.sql.execution.streaming.StreamingQueryWrapper -import org.apache.spark.sql.sources.v2.streaming.reader.{ContinuousReader, PartitionOffset} -import org.apache.spark.sql.sources.v2.streaming.writer.ContinuousWriter +import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousReader, PartitionOffset} import org.apache.spark.sql.sources.v2.writer.WriterCommitMessage +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter import org.apache.spark.util.RpcUtils private[continuous] sealed trait EpochCoordinatorMessage extends Serializable @@ -39,6 +36,15 @@ private[continuous] sealed trait EpochCoordinatorMessage extends Serializable */ private[sql] case object IncrementAndGetEpoch extends EpochCoordinatorMessage +/** + * The RpcEndpoint stop() will wait to clear out the message queue before terminating the + * object. This can lead to a race condition where the query restarts at epoch n, a new + * EpochCoordinator starts at epoch n, and then the old epoch coordinator commits epoch n + 1. + * The framework doesn't provide a handle to wait on the message queue, so we use a synchronous + * message to stop any writes to the ContinuousExecution object. + */ +private[sql] case object StopContinuousExecutionWrites extends EpochCoordinatorMessage + // Init messages /** * Set the reader and writer partition counts. Tasks may not be started until the coordinator @@ -70,27 +76,28 @@ private[sql] case class ReportPartitionOffset( /** Helper object used to create reference to [[EpochCoordinator]]. */ private[sql] object EpochCoordinatorRef extends Logging { - private def endpointName(runId: String) = s"EpochCoordinator-$runId" + private def endpointName(id: String) = s"EpochCoordinator-$id" /** * Create a reference to a new [[EpochCoordinator]]. */ def create( - writer: ContinuousWriter, + writer: StreamWriter, reader: ContinuousReader, query: ContinuousExecution, + epochCoordinatorId: String, startEpoch: Long, session: SparkSession, env: SparkEnv): RpcEndpointRef = synchronized { val coordinator = new EpochCoordinator( writer, reader, query, startEpoch, session, env.rpcEnv) - val ref = env.rpcEnv.setupEndpoint(endpointName(query.runId.toString()), coordinator) + val ref = env.rpcEnv.setupEndpoint(endpointName(epochCoordinatorId), coordinator) logInfo("Registered EpochCoordinator endpoint") ref } - def get(runId: String, env: SparkEnv): RpcEndpointRef = synchronized { - val rpcEndpointRef = RpcUtils.makeDriverRef(endpointName(runId), env.conf, env.rpcEnv) + def get(id: String, env: SparkEnv): RpcEndpointRef = synchronized { + val rpcEndpointRef = RpcUtils.makeDriverRef(endpointName(id), env.conf, env.rpcEnv) logDebug("Retrieved existing EpochCoordinator endpoint") rpcEndpointRef } @@ -108,7 +115,7 @@ private[sql] object EpochCoordinatorRef extends Logging { * have both committed and reported an end offset for a given epoch. */ private[continuous] class EpochCoordinator( - writer: ContinuousWriter, + writer: StreamWriter, reader: ContinuousReader, query: ContinuousExecution, startEpoch: Long, @@ -116,6 +123,8 @@ private[continuous] class EpochCoordinator( override val rpcEnv: RpcEnv) extends ThreadSafeRpcEndpoint with Logging { + private var queryWritesStopped: Boolean = false + private var numReaderPartitions: Int = _ private var numWriterPartitions: Int = _ @@ -147,12 +156,16 @@ private[continuous] class EpochCoordinator( partitionCommits.remove(k) } for (k <- partitionOffsets.keys.filter { case (e, _) => e < epoch }) { - partitionCommits.remove(k) + partitionOffsets.remove(k) } } } override def receive: PartialFunction[Any, Unit] = { + // If we just drop these messages, we won't do any writes to the query. The lame duck tasks + // won't shed errors or anything. + case _ if queryWritesStopped => () + case CommitPartitionEpoch(partitionId, epoch, message) => logDebug(s"Got commit from partition $partitionId at epoch $epoch: $message") if (!partitionCommits.isDefinedAt((epoch, partitionId))) { @@ -188,5 +201,9 @@ private[continuous] class EpochCoordinator( case SetWriterPartitions(numPartitions) => numWriterPartitions = numPartitions context.reply(()) + + case StopContinuousExecutionWrites => + queryWritesStopped = true + context.reply(()) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala index 3041d4d703cb4..509a69dd922fb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala @@ -119,9 +119,15 @@ case class MemoryStream[A : Encoder](id: Int, sqlContext: SQLContext) val newBlocks = synchronized { val sliceStart = startOrdinal - lastOffsetCommitted.offset.toInt - 1 val sliceEnd = endOrdinal - lastOffsetCommitted.offset.toInt - 1 + assert(sliceStart <= sliceEnd, s"sliceStart: $sliceStart sliceEnd: $sliceEnd") batches.slice(sliceStart, sliceEnd) } + if (newBlocks.isEmpty) { + return sqlContext.internalCreateDataFrame( + sqlContext.sparkContext.emptyRDD, schema, isStreaming = true) + } + logDebug(generateDebugString(newBlocks, startOrdinal, endOrdinal)) newBlocks diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/ConsoleWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/ConsoleWriter.scala new file mode 100644 index 0000000000000..d276403190b3c --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/ConsoleWriter.scala @@ -0,0 +1,73 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.streaming.sources + +import scala.collection.JavaConverters._ + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.{Row, SparkSession} +import org.apache.spark.sql.sources.v2.DataSourceOptions +import org.apache.spark.sql.sources.v2.writer.{DataWriterFactory, WriterCommitMessage} +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter +import org.apache.spark.sql.types.StructType + +/** Common methods used to create writes for the the console sink */ +class ConsoleWriter(schema: StructType, options: DataSourceOptions) + extends StreamWriter with Logging { + + // Number of rows to display, by default 20 rows + protected val numRowsToShow = options.getInt("numRows", 20) + + // Truncate the displayed data if it is too long, by default it is true + protected val isTruncated = options.getBoolean("truncate", true) + + assert(SparkSession.getActiveSession.isDefined) + protected val spark = SparkSession.getActiveSession.get + + def createWriterFactory(): DataWriterFactory[Row] = PackedRowWriterFactory + + override def commit(epochId: Long, messages: Array[WriterCommitMessage]): Unit = { + // We have to print a "Batch" label for the epoch for compatibility with the pre-data source V2 + // behavior. + printRows(messages, schema, s"Batch: $epochId") + } + + def abort(epochId: Long, messages: Array[WriterCommitMessage]): Unit = {} + + protected def printRows( + commitMessages: Array[WriterCommitMessage], + schema: StructType, + printMessage: String): Unit = { + val rows = commitMessages.collect { + case PackedRowCommitMessage(rs) => rs + }.flatten + + // scalastyle:off println + println("-------------------------------------------") + println(printMessage) + println("-------------------------------------------") + // scalastyle:off println + spark + .createDataFrame(rows.toList.asJava, schema) + .show(numRowsToShow, isTruncated) + } + + override def toString(): String = { + s"ConsoleWriter[numRows=$numRowsToShow, truncate=$isTruncated]" + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/MicroBatchWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/MicroBatchWriter.scala new file mode 100644 index 0000000000000..56f7ff25cbed0 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/MicroBatchWriter.scala @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.streaming.sources + +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.sources.v2.writer.{DataSourceWriter, DataWriterFactory, SupportsWriteInternalRow, WriterCommitMessage} +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter + +/** + * A [[DataSourceWriter]] used to hook V2 stream writers into a microbatch plan. It implements + * the non-streaming interface, forwarding the batch ID determined at construction to a wrapped + * streaming writer. + */ +class MicroBatchWriter(batchId: Long, writer: StreamWriter) extends DataSourceWriter { + override def commit(messages: Array[WriterCommitMessage]): Unit = { + writer.commit(batchId, messages) + } + + override def abort(messages: Array[WriterCommitMessage]): Unit = writer.abort(batchId, messages) + + override def createWriterFactory(): DataWriterFactory[Row] = writer.createWriterFactory() +} + +class InternalRowMicroBatchWriter(batchId: Long, writer: StreamWriter) + extends DataSourceWriter with SupportsWriteInternalRow { + override def commit(messages: Array[WriterCommitMessage]): Unit = { + writer.commit(batchId, messages) + } + + override def abort(messages: Array[WriterCommitMessage]): Unit = writer.abort(batchId, messages) + + override def createInternalRowWriterFactory(): DataWriterFactory[InternalRow] = + writer match { + case w: SupportsWriteInternalRow => w.createInternalRowWriterFactory() + case _ => throw new IllegalStateException( + "InternalRowMicroBatchWriter should only be created with base writer support") + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/PackedRowWriterFactory.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/PackedRowWriterFactory.scala new file mode 100644 index 0000000000000..248295e401a0d --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/PackedRowWriterFactory.scala @@ -0,0 +1,60 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.streaming.sources + +import scala.collection.mutable + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.Row +import org.apache.spark.sql.sources.v2.writer.{DataSourceWriter, DataWriter, DataWriterFactory, WriterCommitMessage} + +/** + * A simple [[DataWriterFactory]] whose tasks just pack rows into the commit message for delivery + * to a [[DataSourceWriter]] on the driver. + * + * Note that, because it sends all rows to the driver, this factory will generally be unsuitable + * for production-quality sinks. It's intended for use in tests. + */ +case object PackedRowWriterFactory extends DataWriterFactory[Row] { + def createDataWriter(partitionId: Int, attemptNumber: Int): DataWriter[Row] = { + new PackedRowDataWriter() + } +} + +/** + * Commit message for a [[PackedRowDataWriter]], containing all the rows written in the most + * recent interval. + */ +case class PackedRowCommitMessage(rows: Array[Row]) extends WriterCommitMessage + +/** + * A simple [[DataWriter]] that just sends all the rows it's received as a commit message. + */ +class PackedRowDataWriter() extends DataWriter[Row] with Logging { + private val data = mutable.Buffer[Row]() + + override def write(row: Row): Unit = data.append(row) + + override def commit(): PackedRowCommitMessage = { + val msg = PackedRowCommitMessage(data.toArray) + data.clear() + msg + } + + override def abort(): Unit = data.clear() +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/RateStreamSourceV2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/RateStreamSourceV2.scala index 97bada08bcd2b..6dcd331138fd8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/RateStreamSourceV2.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/RateStreamSourceV2.scala @@ -28,17 +28,37 @@ import org.json4s.jackson.Serialization import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.util.DateTimeUtils import org.apache.spark.sql.execution.streaming.{RateStreamOffset, ValueRunTimeMsPair} -import org.apache.spark.sql.sources.v2.DataSourceV2Options +import org.apache.spark.sql.sources.DataSourceRegister +import org.apache.spark.sql.sources.v2.{DataSourceOptions, DataSourceV2, MicroBatchReadSupport} import org.apache.spark.sql.sources.v2.reader._ -import org.apache.spark.sql.sources.v2.streaming.reader.{MicroBatchReader, Offset} +import org.apache.spark.sql.sources.v2.reader.streaming.{MicroBatchReader, Offset} import org.apache.spark.sql.types.{LongType, StructField, StructType, TimestampType} -import org.apache.spark.util.SystemClock +import org.apache.spark.util.{ManualClock, SystemClock} -class RateStreamV2Reader(options: DataSourceV2Options) +/** + * This is a temporary register as we build out v2 migration. Microbatch read support should + * be implemented in the same register as v1. + */ +class RateSourceProviderV2 extends DataSourceV2 with MicroBatchReadSupport with DataSourceRegister { + override def createMicroBatchReader( + schema: Optional[StructType], + checkpointLocation: String, + options: DataSourceOptions): MicroBatchReader = { + new RateStreamMicroBatchReader(options) + } + + override def shortName(): String = "ratev2" +} + +class RateStreamMicroBatchReader(options: DataSourceOptions) extends MicroBatchReader { implicit val defaultFormats: DefaultFormats = DefaultFormats - val clock = new SystemClock + val clock = { + // The option to use a manual clock is provided only for unit testing purposes. + if (options.get("useManualClock").orElse("false").toBoolean) new ManualClock + else new SystemClock + } private val numPartitions = options.get(RateStreamSourceV2.NUM_PARTITIONS).orElse("5").toInt @@ -102,7 +122,7 @@ class RateStreamV2Reader(options: DataSourceV2Options) RateStreamOffset(Serialization.read[Map[Int, ValueRunTimeMsPair]](json)) } - override def createReadTasks(): java.util.List[ReadTask[Row]] = { + override def createDataReaderFactories(): java.util.List[DataReaderFactory[Row]] = { val startMap = start.partitionToValueAndRunTimeMs val endMap = end.partitionToValueAndRunTimeMs endMap.keys.toSeq.map { part => @@ -111,14 +131,14 @@ class RateStreamV2Reader(options: DataSourceV2Options) val packedRows = mutable.ListBuffer[(Long, Long)]() var outVal = startVal + numPartitions - var outTimeMs = startTimeMs + msPerPartitionBetweenRows + var outTimeMs = startTimeMs while (outVal <= endVal) { packedRows.append((outTimeMs, outVal)) outVal += numPartitions outTimeMs += msPerPartitionBetweenRows } - RateStreamBatchTask(packedRows).asInstanceOf[ReadTask[Row]] + RateStreamBatchTask(packedRows).asInstanceOf[DataReaderFactory[Row]] }.toList.asJava } @@ -126,7 +146,7 @@ class RateStreamV2Reader(options: DataSourceV2Options) override def stop(): Unit = {} } -case class RateStreamBatchTask(vals: Seq[(Long, Long)]) extends ReadTask[Row] { +case class RateStreamBatchTask(vals: Seq[(Long, Long)]) extends DataReaderFactory[Row] { override def createDataReader(): DataReader[Row] = new RateStreamBatchReader(vals) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/memoryV2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/memoryV2.scala index da7c31cf62428..f960208155e3b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/memoryV2.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/memoryV2.scala @@ -29,10 +29,9 @@ import org.apache.spark.sql.catalyst.expressions.Attribute import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, Statistics} import org.apache.spark.sql.catalyst.streaming.InternalOutputModes.{Append, Complete, Update} import org.apache.spark.sql.execution.streaming.Sink -import org.apache.spark.sql.sources.v2.{DataSourceV2, DataSourceV2Options} -import org.apache.spark.sql.sources.v2.streaming.{ContinuousWriteSupport, MicroBatchWriteSupport} -import org.apache.spark.sql.sources.v2.streaming.writer.ContinuousWriter +import org.apache.spark.sql.sources.v2.{DataSourceOptions, DataSourceV2, StreamWriteSupport} import org.apache.spark.sql.sources.v2.writer._ +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter import org.apache.spark.sql.streaming.OutputMode import org.apache.spark.sql.types.StructType @@ -40,24 +39,13 @@ import org.apache.spark.sql.types.StructType * A sink that stores the results in memory. This [[Sink]] is primarily intended for use in unit * tests and does not provide durability. */ -class MemorySinkV2 extends DataSourceV2 - with MicroBatchWriteSupport with ContinuousWriteSupport with Logging { - - override def createMicroBatchWriter( +class MemorySinkV2 extends DataSourceV2 with StreamWriteSupport with Logging { + override def createStreamWriter( queryId: String, - batchId: Long, schema: StructType, mode: OutputMode, - options: DataSourceV2Options): java.util.Optional[DataSourceV2Writer] = { - java.util.Optional.of(new MemoryWriter(this, batchId, mode)) - } - - override def createContinuousWriter( - queryId: String, - schema: StructType, - mode: OutputMode, - options: DataSourceV2Options): java.util.Optional[ContinuousWriter] = { - java.util.Optional.of(new ContinuousMemoryWriter(this, mode)) + options: DataSourceOptions): StreamWriter = { + new MemoryStreamWriter(this, mode) } private case class AddedData(batchId: Long, data: Array[Row]) @@ -125,7 +113,7 @@ class MemorySinkV2 extends DataSourceV2 case class MemoryWriterCommitMessage(partition: Int, data: Seq[Row]) extends WriterCommitMessage {} class MemoryWriter(sink: MemorySinkV2, batchId: Long, outputMode: OutputMode) - extends DataSourceV2Writer with Logging { + extends DataSourceWriter with Logging { override def createWriterFactory: MemoryWriterFactory = MemoryWriterFactory(outputMode) @@ -141,8 +129,8 @@ class MemoryWriter(sink: MemorySinkV2, batchId: Long, outputMode: OutputMode) } } -class ContinuousMemoryWriter(val sink: MemorySinkV2, outputMode: OutputMode) - extends ContinuousWriter { +class MemoryStreamWriter(val sink: MemorySinkV2, outputMode: OutputMode) + extends StreamWriter { override def createWriterFactory: MemoryWriterFactory = MemoryWriterFactory(outputMode) @@ -153,7 +141,7 @@ class ContinuousMemoryWriter(val sink: MemorySinkV2, outputMode: OutputMode) sink.write(epochId, outputMode, newRows) } - override def abort(messages: Array[WriterCommitMessage]): Unit = { + override def abort(epochId: Long, messages: Array[WriterCommitMessage]): Unit = { // Don't accept any of the new input. } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala index 6fe632f958ffc..d1d9f95cb0977 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala @@ -94,7 +94,7 @@ trait StateStore { def abort(): Unit /** - * Return an iterator containing all the key-value pairs in the SateStore. Implementations must + * Return an iterator containing all the key-value pairs in the StateStore. Implementations must * ensure that updates (puts, removes) can be made while iterating over this iterator. */ def iterator(): Iterator[UnsafeRowPair] diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala index b9b07a2e688f9..c9354ac0ec78a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala @@ -340,37 +340,35 @@ case class StateStoreSaveExec( // Update and output modified rows from the StateStore. case Some(Update) => - val updatesStartTimeNs = System.nanoTime - - new Iterator[InternalRow] { - + new NextIterator[InternalRow] { // Filter late date using watermark if specified private[this] val baseIterator = watermarkPredicateForData match { case Some(predicate) => iter.filter((row: InternalRow) => !predicate.eval(row)) case None => iter } + private val updatesStartTimeNs = System.nanoTime - override def hasNext: Boolean = { - if (!baseIterator.hasNext) { - allUpdatesTimeMs += NANOSECONDS.toMillis(System.nanoTime - updatesStartTimeNs) - - // Remove old aggregates if watermark specified - allRemovalsTimeMs += timeTakenMs { removeKeysOlderThanWatermark(store) } - commitTimeMs += timeTakenMs { store.commit() } - setStoreMetrics(store) - false + override protected def getNext(): InternalRow = { + if (baseIterator.hasNext) { + val row = baseIterator.next().asInstanceOf[UnsafeRow] + val key = getKey(row) + store.put(key, row) + numOutputRows += 1 + numUpdatedStateRows += 1 + row } else { - true + finished = true + null } } - override def next(): InternalRow = { - val row = baseIterator.next().asInstanceOf[UnsafeRow] - val key = getKey(row) - store.put(key, row) - numOutputRows += 1 - numUpdatedStateRows += 1 - row + override protected def close(): Unit = { + allUpdatesTimeMs += NANOSECONDS.toMillis(System.nanoTime - updatesStartTimeNs) + + // Remove old aggregates if watermark specified + allRemovalsTimeMs += timeTakenMs { removeKeysOlderThanWatermark(store) } + commitTimeMs += timeTakenMs { store.commit() } + setStoreMetrics(store) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala index 7019d98e1619f..582528777f90e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala @@ -39,7 +39,8 @@ private[ui] class AllExecutionsPage(parent: SQLTab) extends WebUIPage("") with L val failed = new mutable.ArrayBuffer[SQLExecutionUIData]() sqlStore.executionsList().foreach { e => - val isRunning = e.jobs.exists { case (_, status) => status == JobExecutionStatus.RUNNING } + val isRunning = e.completionTime.isEmpty || + e.jobs.exists { case (_, status) => status == JobExecutionStatus.RUNNING } val isFailed = e.jobs.exists { case (_, status) => status == JobExecutionStatus.FAILED } if (isRunning) { running += e @@ -179,7 +180,7 @@ private[ui] abstract class ExecutionTable( } private def descriptionCell(execution: SQLExecutionUIData): Seq[Node] = { - val details = if (execution.details.nonEmpty) { + val details = if (execution.details != null && execution.details.nonEmpty) { +details ++ @@ -190,8 +191,10 @@ private[ui] abstract class ExecutionTable( Nil } - val desc = { + val desc = if (execution.description != null && execution.description.nonEmpty) { {execution.description} + } else { + {execution.executionId} }
    {desc} {details}
    diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala index f29e135ac357f..e0554f0c4d337 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala @@ -80,7 +80,7 @@ class ExecutionPage(parent: SQLTab) extends WebUIPage("execution") with Logging planVisualization(metrics, graph) ++ physicalPlanDescription(executionUIData.physicalPlanDescription) }.getOrElse { -
    No information to display for Plan {executionId}
    +
    No information to display for query {executionId}
    } UIUtils.headerSparkPage(s"Details for Query $executionId", content, parent, Some(5000)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListener.scala index d8adbe7bee13e..d254af400a7cf 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListener.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListener.scala @@ -88,7 +88,7 @@ class SQLAppStatusListener( exec.jobs = exec.jobs + (jobId -> JobExecutionStatus.RUNNING) exec.stages ++= event.stageIds.toSet - update(exec) + update(exec, force = true) } override def onStageSubmitted(event: SparkListenerStageSubmitted): Unit = { @@ -99,7 +99,7 @@ class SQLAppStatusListener( // Reset the metrics tracking object for the new attempt. Option(stageMetrics.get(event.stageInfo.stageId)).foreach { metrics => metrics.taskMetrics.clear() - metrics.attemptId = event.stageInfo.attemptId + metrics.attemptId = event.stageInfo.attemptNumber } } @@ -289,7 +289,7 @@ class SQLAppStatusListener( private def onDriverAccumUpdates(event: SparkListenerDriverAccumUpdates): Unit = { val SparkListenerDriverAccumUpdates(executionId, accumUpdates) = event Option(liveExecutions.get(executionId)).foreach { exec => - exec.driverAccumUpdates = accumUpdates.toMap + exec.driverAccumUpdates = exec.driverAccumUpdates ++ accumUpdates update(exec) } } @@ -308,11 +308,13 @@ class SQLAppStatusListener( }) } - private def update(exec: LiveExecutionData): Unit = { + private def update(exec: LiveExecutionData, force: Boolean = false): Unit = { val now = System.nanoTime() if (exec.endEvents >= exec.jobs.size + 1) { exec.write(kvstore, now) liveExecutions.remove(exec.executionId) + } else if (force) { + exec.write(kvstore, now) } else if (liveUpdatePeriodNs >= 0) { if (now - exec.lastWriteTime > liveUpdatePeriodNs) { exec.write(kvstore, now) @@ -332,9 +334,12 @@ class SQLAppStatusListener( return } - val toDelete = KVUtils.viewToSeq(kvstore.view(classOf[SQLExecutionUIData]), - countToDelete.toInt) { e => e.completionTime.isDefined } - toDelete.foreach { e => kvstore.delete(e.getClass(), e.executionId) } + val view = kvstore.view(classOf[SQLExecutionUIData]).index("completionTime").first(0L) + val toDelete = KVUtils.viewToSeq(view, countToDelete.toInt)(_.completionTime.isDefined) + toDelete.foreach { e => + kvstore.delete(e.getClass(), e.executionId) + kvstore.delete(classOf[SparkPlanGraphWrapper], e.executionId) + } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusStore.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusStore.scala index 910f2e52fdbb3..241001a857c8f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusStore.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusStore.scala @@ -23,11 +23,12 @@ import java.util.Date import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer +import com.fasterxml.jackson.annotation.JsonIgnore import com.fasterxml.jackson.databind.annotation.JsonDeserialize import org.apache.spark.JobExecutionStatus import org.apache.spark.status.KVUtils.KVIndexParam -import org.apache.spark.util.kvstore.KVStore +import org.apache.spark.util.kvstore.{KVIndex, KVStore} /** * Provides a view of a KVStore with methods that make it easy to query SQL-specific state. There's @@ -53,6 +54,10 @@ class SQLAppStatusStore( store.count(classOf[SQLExecutionUIData]) } + def planGraphCount(): Long = { + store.count(classOf[SparkPlanGraphWrapper]) + } + def executionMetrics(executionId: Long): Map[Long, String] = { def metricsFromStore(): Option[Map[Long, String]] = { val exec = store.read(classOf[SQLExecutionUIData], executionId) @@ -90,7 +95,11 @@ class SQLExecutionUIData( * from the SQL listener instance. */ @JsonDeserialize(keyAs = classOf[JLong]) - val metricValues: Map[Long, String]) + val metricValues: Map[Long, String]) { + + @JsonIgnore @KVIndex("completionTime") + private def completionTimeIndex: Long = completionTime.map(_.getTime).getOrElse(-1L) +} class SparkPlanGraphWrapper( @KVIndexParam val executionId: Long, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowExec.scala index 800a2ea3f3996..626f39d9e95cc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowExec.scala @@ -112,9 +112,11 @@ case class WindowExec( * * @param frame to evaluate. This can either be a Row or Range frame. * @param bound with respect to the row. + * @param timeZone the session local timezone for time related calculations. * @return a bound ordering object. */ - private[this] def createBoundOrdering(frame: FrameType, bound: Expression): BoundOrdering = { + private[this] def createBoundOrdering( + frame: FrameType, bound: Expression, timeZone: String): BoundOrdering = { (frame, bound) match { case (RowFrame, CurrentRow) => RowBoundOrdering(0) @@ -144,7 +146,7 @@ case class WindowExec( val boundExpr = (expr.dataType, boundOffset.dataType) match { case (DateType, IntegerType) => DateAdd(expr, boundOffset) case (TimestampType, CalendarIntervalType) => - TimeAdd(expr, boundOffset, Some(conf.sessionLocalTimeZone)) + TimeAdd(expr, boundOffset, Some(timeZone)) case (a, b) if a== b => Add(expr, boundOffset) } val bound = newMutableProjection(boundExpr :: Nil, child.output) @@ -197,6 +199,7 @@ case class WindowExec( // Map the groups to a (unbound) expression and frame factory pair. var numExpressions = 0 + val timeZone = conf.sessionLocalTimeZone framedFunctions.toSeq.map { case (key, (expressions, functionSeq)) => val ordinal = numExpressions @@ -237,7 +240,7 @@ case class WindowExec( new UnboundedPrecedingWindowFunctionFrame( target, processor, - createBoundOrdering(frameType, upper)) + createBoundOrdering(frameType, upper, timeZone)) } // Shrinking Frame. @@ -246,7 +249,7 @@ case class WindowExec( new UnboundedFollowingWindowFunctionFrame( target, processor, - createBoundOrdering(frameType, lower)) + createBoundOrdering(frameType, lower, timeZone)) } // Moving Frame. @@ -255,8 +258,8 @@ case class WindowExec( new SlidingWindowFunctionFrame( target, processor, - createBoundOrdering(frameType, lower), - createBoundOrdering(frameType, upper)) + createBoundOrdering(frameType, lower, timeZone), + createBoundOrdering(frameType, upper, timeZone)) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/UserDefinedFunction.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/UserDefinedFunction.scala index 03b654f830520..bdc4bb4422ae7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/UserDefinedFunction.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/UserDefinedFunction.scala @@ -27,8 +27,8 @@ import org.apache.spark.sql.types.DataType * * As an example: * {{{ - * // Defined a UDF that returns true or false based on some numeric score. - * val predict = udf((score: Double) => if (score > 0.5) true else false) + * // Define a UDF that returns true or false based on some numeric score. + * val predict = udf((score: Double) => score > 0.5) * * // Projects a column that adds a prediction column based on the score column. * df.select( predict(df("score")) ) @@ -66,6 +66,7 @@ case class UserDefinedFunction protected[sql] ( * * @since 1.3.0 */ + @scala.annotation.varargs def apply(exprs: Column*): Column = { Column(ScalaUDF( f, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 530a525a01dec..e31a9c8e3af34 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -24,6 +24,7 @@ import scala.util.Try import scala.util.control.NonFatal import org.apache.spark.annotation.InterfaceStability +import org.apache.spark.sql.api.java._ import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.analysis.{Star, UnresolvedFunction} import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder @@ -32,7 +33,6 @@ import org.apache.spark.sql.catalyst.expressions.aggregate._ import org.apache.spark.sql.catalyst.plans.logical.{HintInfo, ResolvedHint} import org.apache.spark.sql.execution.SparkSqlParser import org.apache.spark.sql.expressions.UserDefinedFunction -import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.types._ import org.apache.spark.util.Utils @@ -1313,8 +1313,7 @@ object functions { ////////////////////////////////////////////////////////////////////////////////////////////// /** - * Computes the cosine inverse of the given value; the returned angle is in the range - * 0.0 through pi. + * @return inverse cosine of `e` in radians, as if computed by `java.lang.Math.acos` * * @group math_funcs * @since 1.4.0 @@ -1322,8 +1321,7 @@ object functions { def acos(e: Column): Column = withExpr { Acos(e.expr) } /** - * Computes the cosine inverse of the given column; the returned angle is in the range - * 0.0 through pi. + * @return inverse cosine of `columnName`, as if computed by `java.lang.Math.acos` * * @group math_funcs * @since 1.4.0 @@ -1331,8 +1329,7 @@ object functions { def acos(columnName: String): Column = acos(Column(columnName)) /** - * Computes the sine inverse of the given value; the returned angle is in the range - * -pi/2 through pi/2. + * @return inverse sine of `e` in radians, as if computed by `java.lang.Math.asin` * * @group math_funcs * @since 1.4.0 @@ -1340,8 +1337,7 @@ object functions { def asin(e: Column): Column = withExpr { Asin(e.expr) } /** - * Computes the sine inverse of the given column; the returned angle is in the range - * -pi/2 through pi/2. + * @return inverse sine of `columnName`, as if computed by `java.lang.Math.asin` * * @group math_funcs * @since 1.4.0 @@ -1349,8 +1345,7 @@ object functions { def asin(columnName: String): Column = asin(Column(columnName)) /** - * Computes the tangent inverse of the given column; the returned angle is in the range - * -pi/2 through pi/2 + * @return inverse tangent of `e`, as if computed by `java.lang.Math.atan` * * @group math_funcs * @since 1.4.0 @@ -1358,8 +1353,7 @@ object functions { def atan(e: Column): Column = withExpr { Atan(e.expr) } /** - * Computes the tangent inverse of the given column; the returned angle is in the range - * -pi/2 through pi/2 + * @return inverse tangent of `columnName`, as if computed by `java.lang.Math.atan` * * @group math_funcs * @since 1.4.0 @@ -1367,77 +1361,117 @@ object functions { def atan(columnName: String): Column = atan(Column(columnName)) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). Units in radians. + * @param y coordinate on y-axis + * @param x coordinate on x-axis + * @return the theta component of the point + * (r, theta) + * in polar coordinates that corresponds to the point + * (x, y) in Cartesian coordinates, + * as if computed by `java.lang.Math.atan2` * * @group math_funcs * @since 1.4.0 */ - def atan2(l: Column, r: Column): Column = withExpr { Atan2(l.expr, r.expr) } + def atan2(y: Column, x: Column): Column = withExpr { Atan2(y.expr, x.expr) } /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). + * @param y coordinate on y-axis + * @param xName coordinate on x-axis + * @return the theta component of the point + * (r, theta) + * in polar coordinates that corresponds to the point + * (x, y) in Cartesian coordinates, + * as if computed by `java.lang.Math.atan2` * * @group math_funcs * @since 1.4.0 */ - def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName)) + def atan2(y: Column, xName: String): Column = atan2(y, Column(xName)) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). + * @param yName coordinate on y-axis + * @param x coordinate on x-axis + * @return the theta component of the point + * (r, theta) + * in polar coordinates that corresponds to the point + * (x, y) in Cartesian coordinates, + * as if computed by `java.lang.Math.atan2` * * @group math_funcs * @since 1.4.0 */ - def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r) + def atan2(yName: String, x: Column): Column = atan2(Column(yName), x) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). + * @param yName coordinate on y-axis + * @param xName coordinate on x-axis + * @return the theta component of the point + * (r, theta) + * in polar coordinates that corresponds to the point + * (x, y) in Cartesian coordinates, + * as if computed by `java.lang.Math.atan2` * * @group math_funcs * @since 1.4.0 */ - def atan2(leftName: String, rightName: String): Column = - atan2(Column(leftName), Column(rightName)) + def atan2(yName: String, xName: String): Column = + atan2(Column(yName), Column(xName)) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). + * @param y coordinate on y-axis + * @param xValue coordinate on x-axis + * @return the theta component of the point + * (r, theta) + * in polar coordinates that corresponds to the point + * (x, y) in Cartesian coordinates, + * as if computed by `java.lang.Math.atan2` * * @group math_funcs * @since 1.4.0 */ - def atan2(l: Column, r: Double): Column = atan2(l, lit(r)) + def atan2(y: Column, xValue: Double): Column = atan2(y, lit(xValue)) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). + * @param yName coordinate on y-axis + * @param xValue coordinate on x-axis + * @return the theta component of the point + * (r, theta) + * in polar coordinates that corresponds to the point + * (x, y) in Cartesian coordinates, + * as if computed by `java.lang.Math.atan2` * * @group math_funcs * @since 1.4.0 */ - def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r) + def atan2(yName: String, xValue: Double): Column = atan2(Column(yName), xValue) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). + * @param yValue coordinate on y-axis + * @param x coordinate on x-axis + * @return the theta component of the point + * (r, theta) + * in polar coordinates that corresponds to the point + * (x, y) in Cartesian coordinates, + * as if computed by `java.lang.Math.atan2` * * @group math_funcs * @since 1.4.0 */ - def atan2(l: Double, r: Column): Column = atan2(lit(l), r) + def atan2(yValue: Double, x: Column): Column = atan2(lit(yValue), x) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). + * @param yValue coordinate on y-axis + * @param xName coordinate on x-axis + * @return the theta component of the point + * (r, theta) + * in polar coordinates that corresponds to the point + * (x, y) in Cartesian coordinates, + * as if computed by `java.lang.Math.atan2` * * @group math_funcs * @since 1.4.0 */ - def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName)) + def atan2(yValue: Double, xName: String): Column = atan2(yValue, Column(xName)) /** * An expression that returns the string representation of the binary value of the given long @@ -1500,7 +1534,8 @@ object functions { } /** - * Computes the cosine of the given value. Units in radians. + * @param e angle in radians + * @return cosine of the angle, as if computed by `java.lang.Math.cos` * * @group math_funcs * @since 1.4.0 @@ -1508,7 +1543,8 @@ object functions { def cos(e: Column): Column = withExpr { Cos(e.expr) } /** - * Computes the cosine of the given column. + * @param columnName angle in radians + * @return cosine of the angle, as if computed by `java.lang.Math.cos` * * @group math_funcs * @since 1.4.0 @@ -1516,7 +1552,8 @@ object functions { def cos(columnName: String): Column = cos(Column(columnName)) /** - * Computes the hyperbolic cosine of the given value. + * @param e hyperbolic angle + * @return hyperbolic cosine of the angle, as if computed by `java.lang.Math.cosh` * * @group math_funcs * @since 1.4.0 @@ -1524,7 +1561,8 @@ object functions { def cosh(e: Column): Column = withExpr { Cosh(e.expr) } /** - * Computes the hyperbolic cosine of the given column. + * @param columnName hyperbolic angle + * @return hyperbolic cosine of the angle, as if computed by `java.lang.Math.cosh` * * @group math_funcs * @since 1.4.0 @@ -1967,7 +2005,8 @@ object functions { def signum(columnName: String): Column = signum(Column(columnName)) /** - * Computes the sine of the given value. Units in radians. + * @param e angle in radians + * @return sine of the angle, as if computed by `java.lang.Math.sin` * * @group math_funcs * @since 1.4.0 @@ -1975,7 +2014,8 @@ object functions { def sin(e: Column): Column = withExpr { Sin(e.expr) } /** - * Computes the sine of the given column. + * @param columnName angle in radians + * @return sine of the angle, as if computed by `java.lang.Math.sin` * * @group math_funcs * @since 1.4.0 @@ -1983,7 +2023,8 @@ object functions { def sin(columnName: String): Column = sin(Column(columnName)) /** - * Computes the hyperbolic sine of the given value. + * @param e hyperbolic angle + * @return hyperbolic sine of the given value, as if computed by `java.lang.Math.sinh` * * @group math_funcs * @since 1.4.0 @@ -1991,7 +2032,8 @@ object functions { def sinh(e: Column): Column = withExpr { Sinh(e.expr) } /** - * Computes the hyperbolic sine of the given column. + * @param columnName hyperbolic angle + * @return hyperbolic sine of the given value, as if computed by `java.lang.Math.sinh` * * @group math_funcs * @since 1.4.0 @@ -1999,7 +2041,8 @@ object functions { def sinh(columnName: String): Column = sinh(Column(columnName)) /** - * Computes the tangent of the given value. Units in radians. + * @param e angle in radians + * @return tangent of the given value, as if computed by `java.lang.Math.tan` * * @group math_funcs * @since 1.4.0 @@ -2007,7 +2050,8 @@ object functions { def tan(e: Column): Column = withExpr { Tan(e.expr) } /** - * Computes the tangent of the given column. + * @param columnName angle in radians + * @return tangent of the given value, as if computed by `java.lang.Math.tan` * * @group math_funcs * @since 1.4.0 @@ -2015,7 +2059,8 @@ object functions { def tan(columnName: String): Column = tan(Column(columnName)) /** - * Computes the hyperbolic tangent of the given value. + * @param e hyperbolic angle + * @return hyperbolic tangent of the given value, as if computed by `java.lang.Math.tanh` * * @group math_funcs * @since 1.4.0 @@ -2023,7 +2068,8 @@ object functions { def tanh(e: Column): Column = withExpr { Tanh(e.expr) } /** - * Computes the hyperbolic tangent of the given column. + * @param columnName hyperbolic angle + * @return hyperbolic tangent of the given value, as if computed by `java.lang.Math.tanh` * * @group math_funcs * @since 1.4.0 @@ -2047,6 +2093,9 @@ object functions { /** * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. * + * @param e angle in radians + * @return angle in degrees, as if computed by `java.lang.Math.toDegrees` + * * @group math_funcs * @since 2.1.0 */ @@ -2055,6 +2104,9 @@ object functions { /** * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. * + * @param columnName angle in radians + * @return angle in degrees, as if computed by `java.lang.Math.toDegrees` + * * @group math_funcs * @since 2.1.0 */ @@ -2077,6 +2129,9 @@ object functions { /** * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. * + * @param e angle in degrees + * @return angle in radians, as if computed by `java.lang.Math.toRadians` + * * @group math_funcs * @since 2.1.0 */ @@ -2085,6 +2140,9 @@ object functions { /** * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. * + * @param columnName angle in degrees + * @return angle in radians, as if computed by `java.lang.Math.toRadians` + * * @group math_funcs * @since 2.1.0 */ @@ -2267,7 +2325,9 @@ object functions { } /** - * Computes the length of a given string or binary column. + * Computes the character length of a given string or number of bytes of a binary string. + * The length of character strings include the trailing spaces. The length of binary strings + * includes binary zeros. * * @group string_funcs * @since 1.5.0 @@ -2871,7 +2931,7 @@ object functions { * or equal to the `windowDuration`. Check * `org.apache.spark.unsafe.types.CalendarInterval` for valid duration * identifiers. This duration is likewise absolute, and does not vary - * according to a calendar. + * according to a calendar. * @param startTime The offset with respect to 1970-01-01 00:00:00 UTC with which to start * window intervals. For example, in order to have hourly tumbling windows that * start 15 minutes past the hour, e.g. 12:15-13:15, 13:15-14:15... provide @@ -2927,7 +2987,7 @@ object functions { * or equal to the `windowDuration`. Check * `org.apache.spark.unsafe.types.CalendarInterval` for valid duration * identifiers. This duration is likewise absolute, and does not vary - * according to a calendar. + * according to a calendar. * * @group datetime_funcs * @since 2.0.0 @@ -3072,7 +3132,7 @@ object functions { * @since 2.2.0 */ def from_json(e: Column, schema: DataType, options: Map[String, String]): Column = withExpr { - JsonToStructs(schema, options, e.expr) + new JsonToStructs(schema, options, e.expr) } /** @@ -3254,42 +3314,66 @@ object functions { */ def map_values(e: Column): Column = withExpr { MapValues(e.expr) } - ////////////////////////////////////////////////////////////////////////////////////////////// - ////////////////////////////////////////////////////////////////////////////////////////////// - // scalastyle:off line.size.limit // scalastyle:off parameter.number /* Use the following code to generate: - (0 to 10).map { x => + + (0 to 10).foreach { x => val types = (1 to x).foldRight("RT")((i, s) => {s"A$i, $s"}) val typeTags = (1 to x).map(i => s"A$i: TypeTag").foldLeft("RT: TypeTag")(_ + ", " + _) val inputTypes = (1 to x).foldRight("Nil")((i, s) => {s"ScalaReflection.schemaFor(typeTag[A$i]).dataType :: $s"}) println(s""" - /** - * Defines a deterministic user-defined function of ${x} arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. - * - * @group udf_funcs - * @since 1.3.0 - */ - def udf[$typeTags](f: Function$x[$types]): UserDefinedFunction = { - val ScalaReflection.Schema(dataType, nullable) = ScalaReflection.schemaFor[RT] - val inputTypes = Try($inputTypes).toOption - val udf = UserDefinedFunction(f, dataType, inputTypes) - if (nullable) udf else udf.asNonNullable() - }""") + |/** + | * Defines a Scala closure of $x arguments as user-defined function (UDF). + | * The data types are automatically inferred based on the Scala closure's + | * signature. By default the returned UDF is deterministic. To change it to + | * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. + | * + | * @group udf_funcs + | * @since 1.3.0 + | */ + |def udf[$typeTags](f: Function$x[$types]): UserDefinedFunction = { + | val ScalaReflection.Schema(dataType, nullable) = ScalaReflection.schemaFor[RT] + | val inputTypes = Try($inputTypes).toOption + | val udf = UserDefinedFunction(f, dataType, inputTypes) + | if (nullable) udf else udf.asNonNullable() + |}""".stripMargin) + } + + (0 to 10).foreach { i => + val extTypeArgs = (0 to i).map(_ => "_").mkString(", ") + val anyTypeArgs = (0 to i).map(_ => "Any").mkString(", ") + val anyCast = s".asInstanceOf[UDF$i[$anyTypeArgs]]" + val anyParams = (1 to i).map(_ => "_: Any").mkString(", ") + val funcCall = if (i == 0) "() => func" else "func" + println(s""" + |/** + | * Defines a Java UDF$i instance as user-defined function (UDF). + | * The caller must specify the output data type, and there is no automatic input type coercion. + | * By default the returned UDF is deterministic. To change it to nondeterministic, call the + | * API `UserDefinedFunction.asNondeterministic()`. + | * + | * @group udf_funcs + | * @since 2.3.0 + | */ + |def udf(f: UDF$i[$extTypeArgs], returnType: DataType): UserDefinedFunction = { + | val func = f$anyCast.call($anyParams) + | UserDefinedFunction($funcCall, returnType, inputTypes = None) + |}""".stripMargin) } */ + ////////////////////////////////////////////////////////////////////////////////////////////// + // Scala UDF functions + ////////////////////////////////////////////////////////////////////////////////////////////// + /** - * Defines a deterministic user-defined function of 0 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 0 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3302,10 +3386,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 1 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 1 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3318,10 +3402,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 2 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 2 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3334,10 +3418,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 3 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 3 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3350,10 +3434,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 4 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 4 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3366,10 +3450,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 5 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 5 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3382,10 +3466,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 6 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 6 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3398,10 +3482,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 7 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 7 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3414,10 +3498,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 8 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 8 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3430,10 +3514,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 9 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 9 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3446,10 +3530,10 @@ object functions { } /** - * Defines a deterministic user-defined function of 10 arguments as user-defined - * function (UDF). The data types are automatically inferred based on the function's - * signature. To change a UDF to nondeterministic, call the API - * `UserDefinedFunction.asNondeterministic()`. + * Defines a Scala closure of 10 arguments as user-defined function (UDF). + * The data types are automatically inferred based on the Scala closure's + * signature. By default the returned UDF is deterministic. To change it to + * nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. * * @group udf_funcs * @since 1.3.0 @@ -3461,13 +3545,172 @@ object functions { if (nullable) udf else udf.asNonNullable() } + ////////////////////////////////////////////////////////////////////////////////////////////// + // Java UDF functions + ////////////////////////////////////////////////////////////////////////////////////////////// + + /** + * Defines a Java UDF0 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF0[_], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF0[Any]].call() + UserDefinedFunction(() => func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF1 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF1[_, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF1[Any, Any]].call(_: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF2 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF2[_, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF2[Any, Any, Any]].call(_: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF3 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF3[_, _, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF3[Any, Any, Any, Any]].call(_: Any, _: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF4 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF4[_, _, _, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF4[Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF5 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF5[_, _, _, _, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF5[Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF6 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF6[_, _, _, _, _, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF6[Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF7 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF7[_, _, _, _, _, _, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF7[Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF8 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF8[_, _, _, _, _, _, _, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF8[Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF9 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF9[_, _, _, _, _, _, _, _, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF9[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + + /** + * Defines a Java UDF10 instance as user-defined function (UDF). + * The caller must specify the output data type, and there is no automatic input type coercion. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. + * + * @group udf_funcs + * @since 2.3.0 + */ + def udf(f: UDF10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType): UserDefinedFunction = { + val func = f.asInstanceOf[UDF10[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any) + UserDefinedFunction(func, returnType, inputTypes = None) + } + // scalastyle:on parameter.number // scalastyle:on line.size.limit /** * Defines a deterministic user-defined function (UDF) using a Scala closure. For this variant, * the caller must specify the output data type, and there is no automatic input type coercion. - * To change a UDF to nondeterministic, call the API `UserDefinedFunction.asNondeterministic()`. + * By default the returned UDF is deterministic. To change it to nondeterministic, call the + * API `UserDefinedFunction.asNondeterministic()`. * * @param f A closure in Scala * @param dataType The output data type of the UDF diff --git a/sql/core/src/main/scala/org/apache/spark/sql/internal/BaseSessionStateBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/internal/BaseSessionStateBuilder.scala index 2867b4cd7da5e..007f8760edf82 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/internal/BaseSessionStateBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/internal/BaseSessionStateBuilder.scala @@ -206,7 +206,7 @@ abstract class BaseSessionStateBuilder( /** * Logical query plan optimizer. * - * Note: this depends on the `conf`, `catalog` and `experimentalMethods` fields. + * Note: this depends on `catalog` and `experimentalMethods` fields. */ protected def optimizer: Optimizer = { new SparkOptimizer(catalog, experimentalMethods) { @@ -263,7 +263,7 @@ abstract class BaseSessionStateBuilder( * An interface to register custom [[org.apache.spark.sql.util.QueryExecutionListener]]s * that listen for execution metrics. * - * This gets cloned from parent if available, otherwise is a new instance is created. + * This gets cloned from parent if available, otherwise a new instance is created. */ protected def listenerManager: ExecutionListenerManager = { parentState.map(_.listenerManager.clone()).getOrElse( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/internal/HiveSerDe.scala b/sql/core/src/main/scala/org/apache/spark/sql/internal/HiveSerDe.scala index b9515ec7bca2a..eca612f06f9bb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/internal/HiveSerDe.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/internal/HiveSerDe.scala @@ -31,7 +31,8 @@ object HiveSerDe { "sequencefile" -> HiveSerDe( inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"), - outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat")), + outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat"), + serde = Option("org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe")), "rcfile" -> HiveSerDe( @@ -54,7 +55,8 @@ object HiveSerDe { "textfile" -> HiveSerDe( inputFormat = Option("org.apache.hadoop.mapred.TextInputFormat"), - outputFormat = Option("org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat")), + outputFormat = Option("org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat"), + serde = Option("org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe")), "avro" -> HiveSerDe( @@ -73,6 +75,7 @@ object HiveSerDe { val key = source.toLowerCase(Locale.ROOT) match { case s if s.startsWith("org.apache.spark.sql.parquet") => "parquet" case s if s.startsWith("org.apache.spark.sql.orc") => "orc" + case s if s.startsWith("org.apache.spark.sql.hive.orc") => "orc" case s if s.equals("orcfile") => "orc" case s if s.equals("parquetfile") => "parquet" case s if s.equals("avrofile") => "avro" diff --git a/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala index 2e92beecf2c17..61e22fac854f9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.streaming -import java.util.Locale +import java.util.{Locale, Optional} import scala.collection.JavaConverters._ @@ -27,8 +27,8 @@ import org.apache.spark.sql.{AnalysisException, DataFrame, Dataset, SparkSession import org.apache.spark.sql.execution.command.DDLUtils import org.apache.spark.sql.execution.datasources.DataSource import org.apache.spark.sql.execution.streaming.{StreamingRelation, StreamingRelationV2} -import org.apache.spark.sql.sources.v2.DataSourceV2Options -import org.apache.spark.sql.sources.v2.streaming.ContinuousReadSupport +import org.apache.spark.sql.sources.StreamSourceProvider +import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceOptions, MicroBatchReadSupport} import org.apache.spark.sql.types.StructType import org.apache.spark.util.Utils @@ -117,7 +117,7 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo * You can set the following option(s): *
      *
    • `timeZone` (default session local timezone): sets the string that indicates a timezone - * to be used to parse timestamps in the JSON/CSV datasources or partition values.
      • + * to be used to parse timestamps in the JSON/CSV data sources or partition values. *
    * * @since 2.0.0 @@ -128,12 +128,12 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo } /** - * Adds input options for the underlying data source. + * (Java-specific) Adds input options for the underlying data source. * * You can set the following option(s): *
      *
    • `timeZone` (default session local timezone): sets the string that indicates a timezone - * to be used to parse timestamps in the JSON/CSV datasources or partition values.
      • + * to be used to parse timestamps in the JSON/CSV data sources or partition values. *
    * * @since 2.0.0 @@ -157,7 +157,7 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo } val ds = DataSource.lookupDataSource(source, sparkSession.sqlContext.conf).newInstance() - val options = new DataSourceV2Options(extraOptions.asJava) + val options = new DataSourceOptions(extraOptions.asJava) // We need to generate the V1 data source so we can pass it to the V2 relation as a shim. // We can't be sure at this point whether we'll actually want to use V2, since we don't know the // writer or whether the query is continuous. @@ -166,19 +166,31 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo userSpecifiedSchema = userSpecifiedSchema, className = source, options = extraOptions.toMap) + val v1Relation = ds match { + case _: StreamSourceProvider => Some(StreamingRelation(v1DataSource)) + case _ => None + } ds match { + case s: MicroBatchReadSupport => + val tempReader = s.createMicroBatchReader( + Optional.ofNullable(userSpecifiedSchema.orNull), + Utils.createTempDir(namePrefix = s"temporaryReader").getCanonicalPath, + options) + Dataset.ofRows( + sparkSession, + StreamingRelationV2( + s, source, extraOptions.toMap, + tempReader.readSchema().toAttributes, v1Relation)(sparkSession)) case s: ContinuousReadSupport => val tempReader = s.createContinuousReader( - java.util.Optional.ofNullable(userSpecifiedSchema.orNull), + Optional.ofNullable(userSpecifiedSchema.orNull), Utils.createTempDir(namePrefix = s"temporaryReader").getCanonicalPath, options) - // Generate the V1 node to catch errors thrown within generation. - StreamingRelation(v1DataSource) Dataset.ofRows( sparkSession, StreamingRelationV2( s, source, extraOptions.toMap, - tempReader.readSchema().toAttributes, v1DataSource)(sparkSession)) + tempReader.readSchema().toAttributes, v1Relation)(sparkSession)) case _ => // Code path for data source v1. Dataset.ofRows(sparkSession, StreamingRelation(v1DataSource)) @@ -224,12 +236,12 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo *
  • `mode` (default `PERMISSIVE`): allows a mode for dealing with corrupt records * during parsing. *
      - *
    • `PERMISSIVE` : sets other fields to `null` when it meets a corrupted record, and puts - * the malformed string into a field configured by `columnNameOfCorruptRecord`. To keep - * corrupt records, an user can set a string type field named `columnNameOfCorruptRecord` - * in an user-defined schema. If a schema does not have the field, it drops corrupt records - * during parsing. When inferring a schema, it implicitly adds a `columnNameOfCorruptRecord` - * field in an output schema.
      • + *
    • `PERMISSIVE` : when it meets a corrupted record, puts the malformed string into a + * field configured by `columnNameOfCorruptRecord`, and sets other fields to `null`. To + * keep corrupt records, an user can set a string type field named + * `columnNameOfCorruptRecord` in an user-defined schema. If a schema does not have the + * field, it drops corrupt records during parsing. When inferring a schema, it implicitly + * adds a `columnNameOfCorruptRecord` field in an output schema.
      • *
    • `DROPMALFORMED` : ignores the whole corrupted records.
      • *
    • `FAILFAST` : throws an exception when it meets corrupted records.
      • *
    @@ -304,12 +316,14 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo *
  • `mode` (default `PERMISSIVE`): allows a mode for dealing with corrupt records * during parsing. It supports the following case-insensitive modes. *
      - *
    • `PERMISSIVE` : sets other fields to `null` when it meets a corrupted record, and puts - * the malformed string into a field configured by `columnNameOfCorruptRecord`. To keep + *
    • `PERMISSIVE` : when it meets a corrupted record, puts the malformed string into a + * field configured by `columnNameOfCorruptRecord`, and sets other fields to `null`. To keep * corrupt records, an user can set a string type field named `columnNameOfCorruptRecord` * in an user-defined schema. If a schema does not have the field, it drops corrupt records - * during parsing. When a length of parsed CSV tokens is shorter than an expected length - * of a schema, it sets `null` for extra fields.
      • + * during parsing. A record with less/more tokens than schema is not a corrupted record to + * CSV. When it meets a record having fewer tokens than the length of the schema, sets + * `null` to extra fields. When the record has more tokens than the length of the schema, + * it drops extra tokens. *
    • `DROPMALFORMED` : ignores the whole corrupted records.
      • *
    • `FAILFAST` : throws an exception when it meets corrupted records.
      • *
    diff --git a/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala index db588ae282f38..2fc903168cfa0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala @@ -29,6 +29,7 @@ import org.apache.spark.sql.execution.datasources.DataSource import org.apache.spark.sql.execution.streaming._ import org.apache.spark.sql.execution.streaming.continuous.ContinuousTrigger import org.apache.spark.sql.execution.streaming.sources.{MemoryPlanV2, MemorySinkV2} +import org.apache.spark.sql.sources.v2.StreamWriteSupport /** * Interface used to write a streaming `Dataset` to external storage systems (e.g. file systems, @@ -279,18 +280,25 @@ final class DataStreamWriter[T] private[sql](ds: Dataset[T]) { useTempCheckpointLocation = true, trigger = trigger) } else { - val dataSource = - DataSource( - df.sparkSession, - className = source, - options = extraOptions.toMap, - partitionColumns = normalizedParCols.getOrElse(Nil)) + val ds = DataSource.lookupDataSource(source, df.sparkSession.sessionState.conf) + val disabledSources = df.sparkSession.sqlContext.conf.disabledV2StreamingWriters.split(",") + val sink = ds.newInstance() match { + case w: StreamWriteSupport if !disabledSources.contains(w.getClass.getCanonicalName) => w + case _ => + val ds = DataSource( + df.sparkSession, + className = source, + options = extraOptions.toMap, + partitionColumns = normalizedParCols.getOrElse(Nil)) + ds.createSink(outputMode) + } + df.sparkSession.sessionState.streamingQueryManager.startQuery( extraOptions.get("queryName"), extraOptions.get("checkpointLocation"), df, extraOptions.toMap, - dataSource.createSink(outputMode), + sink, outputMode, useTempCheckpointLocation = source == "console", recoverFromCheckpointLocation = true, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryManager.scala b/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryManager.scala index b508f4406138f..7cefd03e43bc3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryManager.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryManager.scala @@ -29,10 +29,10 @@ import org.apache.spark.internal.Logging import org.apache.spark.sql.{AnalysisException, DataFrame, SparkSession} import org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker import org.apache.spark.sql.execution.streaming._ -import org.apache.spark.sql.execution.streaming.continuous.ContinuousExecution +import org.apache.spark.sql.execution.streaming.continuous.{ContinuousExecution, ContinuousTrigger} import org.apache.spark.sql.execution.streaming.state.StateStoreCoordinatorRef import org.apache.spark.sql.internal.SQLConf -import org.apache.spark.sql.sources.v2.streaming.ContinuousWriteSupport +import org.apache.spark.sql.sources.v2.StreamWriteSupport import org.apache.spark.util.{Clock, SystemClock, Utils} /** @@ -240,26 +240,27 @@ class StreamingQueryManager private[sql] (sparkSession: SparkSession) extends Lo "is not supported in streaming DataFrames/Datasets and will be disabled.") } - sink match { - case v1Sink: Sink => - new StreamingQueryWrapper(new MicroBatchExecution( + (sink, trigger) match { + case (v2Sink: StreamWriteSupport, trigger: ContinuousTrigger) => + UnsupportedOperationChecker.checkForContinuous(analyzedPlan, outputMode) + new StreamingQueryWrapper(new ContinuousExecution( sparkSession, userSpecifiedName.orNull, checkpointLocation, analyzedPlan, - v1Sink, + v2Sink, trigger, triggerClock, outputMode, + extraOptions, deleteCheckpointOnStop)) - case v2Sink: ContinuousWriteSupport => - UnsupportedOperationChecker.checkForContinuous(analyzedPlan, outputMode) - new StreamingQueryWrapper(new ContinuousExecution( + case _ => + new StreamingQueryWrapper(new MicroBatchExecution( sparkSession, userSpecifiedName.orNull, checkpointLocation, analyzedPlan, - v2Sink, + sink, trigger, triggerClock, outputMode, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala b/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala index a73e4272950a4..8bab7e1c58762 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala @@ -34,6 +34,8 @@ private[sql] class ExamplePoint(val x: Double, val y: Double) extends Serializab case that: ExamplePoint => this.x == that.x && this.y == that.y case _ => false } + + override def toString(): String = s"($x, $y)" } /** diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index b007093dad84b..69a2904f5f3fe 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -36,6 +36,7 @@ import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.expressions.UserDefinedFunction; import org.apache.spark.sql.test.TestSparkSession; import org.apache.spark.sql.types.*; import org.apache.spark.util.sketch.BloomFilter; @@ -455,4 +456,15 @@ public void testCircularReferenceBean() { CircularReference1Bean bean = new CircularReference1Bean(); spark.createDataFrame(Arrays.asList(bean), CircularReference1Bean.class); } + + @Test + public void testUDF() { + UserDefinedFunction foo = udf((Integer i, String s) -> i.toString() + s, DataTypes.StringType); + Dataset df = spark.table("testData").select(foo.apply(col("key"), col("value"))); + String[] result = df.collectAsList().stream().map(row -> row.getString(0)) + .toArray(String[]::new); + String[] expected = spark.table("testData").collectAsList().stream() + .map(row -> row.get(0).toString() + row.getString(1)).toArray(String[]::new); + Assert.assertArrayEquals(expected, result); + } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDAFSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDAFSuite.java index ddbaa45a483cb..08dc129f27a0c 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDAFSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDAFSuite.java @@ -46,7 +46,7 @@ public void tearDown() { @SuppressWarnings("unchecked") @Test public void udf1Test() { - spark.range(1, 10).toDF("value").registerTempTable("df"); + spark.range(1, 10).toDF("value").createOrReplaceTempView("df"); spark.udf().registerJavaUDAF("myDoubleAvg", MyDoubleAvg.class.getName()); Row result = spark.sql("SELECT myDoubleAvg(value) as my_avg from df").head(); Assert.assertEquals(105.0, result.getDouble(0), 1.0e-6); diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaAdvancedDataSourceV2.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaAdvancedDataSourceV2.java index 1cfdc08217e6e..172e5d5eebcbe 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaAdvancedDataSourceV2.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaAdvancedDataSourceV2.java @@ -24,19 +24,20 @@ import org.apache.spark.sql.catalyst.expressions.GenericRow; import org.apache.spark.sql.sources.Filter; import org.apache.spark.sql.sources.GreaterThan; +import org.apache.spark.sql.sources.v2.DataSourceOptions; import org.apache.spark.sql.sources.v2.DataSourceV2; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; import org.apache.spark.sql.sources.v2.ReadSupport; import org.apache.spark.sql.sources.v2.reader.*; import org.apache.spark.sql.types.StructType; public class JavaAdvancedDataSourceV2 implements DataSourceV2, ReadSupport { - class Reader implements DataSourceV2Reader, SupportsPushDownRequiredColumns, + public class Reader implements DataSourceReader, SupportsPushDownRequiredColumns, SupportsPushDownFilters { - private StructType requiredSchema = new StructType().add("i", "int").add("j", "int"); - private Filter[] filters = new Filter[0]; + // Exposed for testing. + public StructType requiredSchema = new StructType().add("i", "int").add("j", "int"); + public Filter[] filters = new Filter[0]; @Override public StructType readSchema() { @@ -50,8 +51,26 @@ public void pruneColumns(StructType requiredSchema) { @Override public Filter[] pushFilters(Filter[] filters) { - this.filters = filters; - return new Filter[0]; + Filter[] supported = Arrays.stream(filters).filter(f -> { + if (f instanceof GreaterThan) { + GreaterThan gt = (GreaterThan) f; + return gt.attribute().equals("i") && gt.value() instanceof Integer; + } else { + return false; + } + }).toArray(Filter[]::new); + + Filter[] unsupported = Arrays.stream(filters).filter(f -> { + if (f instanceof GreaterThan) { + GreaterThan gt = (GreaterThan) f; + return !gt.attribute().equals("i") || !(gt.value() instanceof Integer); + } else { + return true; + } + }).toArray(Filter[]::new); + + this.filters = supported; + return unsupported; } @Override @@ -60,8 +79,8 @@ public Filter[] pushedFilters() { } @Override - public List> createReadTasks() { - List> res = new ArrayList<>(); + public List> createDataReaderFactories() { + List> res = new ArrayList<>(); Integer lowerBound = null; for (Filter filter : filters) { @@ -75,25 +94,25 @@ public List> createReadTasks() { } if (lowerBound == null) { - res.add(new JavaAdvancedReadTask(0, 5, requiredSchema)); - res.add(new JavaAdvancedReadTask(5, 10, requiredSchema)); + res.add(new JavaAdvancedDataReaderFactory(0, 5, requiredSchema)); + res.add(new JavaAdvancedDataReaderFactory(5, 10, requiredSchema)); } else if (lowerBound < 4) { - res.add(new JavaAdvancedReadTask(lowerBound + 1, 5, requiredSchema)); - res.add(new JavaAdvancedReadTask(5, 10, requiredSchema)); + res.add(new JavaAdvancedDataReaderFactory(lowerBound + 1, 5, requiredSchema)); + res.add(new JavaAdvancedDataReaderFactory(5, 10, requiredSchema)); } else if (lowerBound < 9) { - res.add(new JavaAdvancedReadTask(lowerBound + 1, 10, requiredSchema)); + res.add(new JavaAdvancedDataReaderFactory(lowerBound + 1, 10, requiredSchema)); } return res; } } - static class JavaAdvancedReadTask implements ReadTask, DataReader { + static class JavaAdvancedDataReaderFactory implements DataReaderFactory, DataReader { private int start; private int end; private StructType requiredSchema; - JavaAdvancedReadTask(int start, int end, StructType requiredSchema) { + JavaAdvancedDataReaderFactory(int start, int end, StructType requiredSchema) { this.start = start; this.end = end; this.requiredSchema = requiredSchema; @@ -101,7 +120,7 @@ static class JavaAdvancedReadTask implements ReadTask, DataReader { @Override public DataReader createDataReader() { - return new JavaAdvancedReadTask(start - 1, end, requiredSchema); + return new JavaAdvancedDataReaderFactory(start - 1, end, requiredSchema); } @Override @@ -131,7 +150,7 @@ public void close() throws IOException { @Override - public DataSourceV2Reader createReader(DataSourceV2Options options) { + public DataSourceReader createReader(DataSourceOptions options) { return new Reader(); } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaBatchDataSourceV2.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaBatchDataSourceV2.java new file mode 100644 index 0000000000000..c55093768105b --- /dev/null +++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaBatchDataSourceV2.java @@ -0,0 +1,114 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package test.org.apache.spark.sql.sources.v2; + +import java.io.IOException; +import java.util.List; + +import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector; +import org.apache.spark.sql.sources.v2.DataSourceOptions; +import org.apache.spark.sql.sources.v2.DataSourceV2; +import org.apache.spark.sql.sources.v2.ReadSupport; +import org.apache.spark.sql.sources.v2.reader.*; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.sql.vectorized.ColumnVector; +import org.apache.spark.sql.vectorized.ColumnarBatch; + + +public class JavaBatchDataSourceV2 implements DataSourceV2, ReadSupport { + + class Reader implements DataSourceReader, SupportsScanColumnarBatch { + private final StructType schema = new StructType().add("i", "int").add("j", "int"); + + @Override + public StructType readSchema() { + return schema; + } + + @Override + public List> createBatchDataReaderFactories() { + return java.util.Arrays.asList( + new JavaBatchDataReaderFactory(0, 50), new JavaBatchDataReaderFactory(50, 90)); + } + } + + static class JavaBatchDataReaderFactory + implements DataReaderFactory, DataReader { + private int start; + private int end; + + private static final int BATCH_SIZE = 20; + + private OnHeapColumnVector i; + private OnHeapColumnVector j; + private ColumnarBatch batch; + + JavaBatchDataReaderFactory(int start, int end) { + this.start = start; + this.end = end; + } + + @Override + public DataReader createDataReader() { + this.i = new OnHeapColumnVector(BATCH_SIZE, DataTypes.IntegerType); + this.j = new OnHeapColumnVector(BATCH_SIZE, DataTypes.IntegerType); + ColumnVector[] vectors = new ColumnVector[2]; + vectors[0] = i; + vectors[1] = j; + this.batch = new ColumnarBatch(vectors); + return this; + } + + @Override + public boolean next() { + i.reset(); + j.reset(); + int count = 0; + while (start < end && count < BATCH_SIZE) { + i.putInt(count, start); + j.putInt(count, -start); + start += 1; + count += 1; + } + + if (count == 0) { + return false; + } else { + batch.setNumRows(count); + return true; + } + } + + @Override + public ColumnarBatch get() { + return batch; + } + + @Override + public void close() throws IOException { + batch.close(); + } + } + + + @Override + public DataSourceReader createReader(DataSourceOptions options) { + return new Reader(); + } +} diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaPartitionAwareDataSource.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaPartitionAwareDataSource.java new file mode 100644 index 0000000000000..32fad59b97ff6 --- /dev/null +++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaPartitionAwareDataSource.java @@ -0,0 +1,113 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package test.org.apache.spark.sql.sources.v2; + +import java.io.IOException; +import java.util.Arrays; +import java.util.List; + +import org.apache.spark.sql.Row; +import org.apache.spark.sql.catalyst.expressions.GenericRow; +import org.apache.spark.sql.sources.v2.DataSourceOptions; +import org.apache.spark.sql.sources.v2.DataSourceV2; +import org.apache.spark.sql.sources.v2.ReadSupport; +import org.apache.spark.sql.sources.v2.reader.*; +import org.apache.spark.sql.sources.v2.reader.partitioning.ClusteredDistribution; +import org.apache.spark.sql.sources.v2.reader.partitioning.Distribution; +import org.apache.spark.sql.sources.v2.reader.partitioning.Partitioning; +import org.apache.spark.sql.types.StructType; + +public class JavaPartitionAwareDataSource implements DataSourceV2, ReadSupport { + + class Reader implements DataSourceReader, SupportsReportPartitioning { + private final StructType schema = new StructType().add("a", "int").add("b", "int"); + + @Override + public StructType readSchema() { + return schema; + } + + @Override + public List> createDataReaderFactories() { + return java.util.Arrays.asList( + new SpecificDataReaderFactory(new int[]{1, 1, 3}, new int[]{4, 4, 6}), + new SpecificDataReaderFactory(new int[]{2, 4, 4}, new int[]{6, 2, 2})); + } + + @Override + public Partitioning outputPartitioning() { + return new MyPartitioning(); + } + } + + static class MyPartitioning implements Partitioning { + + @Override + public int numPartitions() { + return 2; + } + + @Override + public boolean satisfy(Distribution distribution) { + if (distribution instanceof ClusteredDistribution) { + String[] clusteredCols = ((ClusteredDistribution) distribution).clusteredColumns; + return Arrays.asList(clusteredCols).contains("a"); + } + + return false; + } + } + + static class SpecificDataReaderFactory implements DataReaderFactory, DataReader { + private int[] i; + private int[] j; + private int current = -1; + + SpecificDataReaderFactory(int[] i, int[] j) { + assert i.length == j.length; + this.i = i; + this.j = j; + } + + @Override + public boolean next() throws IOException { + current += 1; + return current < i.length; + } + + @Override + public Row get() { + return new GenericRow(new Object[] {i[current], j[current]}); + } + + @Override + public void close() throws IOException { + + } + + @Override + public DataReader createDataReader() { + return this; + } + } + + @Override + public DataSourceReader createReader(DataSourceOptions options) { + return new Reader(); + } +} diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaSchemaRequiredDataSource.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaSchemaRequiredDataSource.java index a174bd8092cbd..048d078dfaac4 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaSchemaRequiredDataSource.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaSchemaRequiredDataSource.java @@ -20,16 +20,16 @@ import java.util.List; import org.apache.spark.sql.Row; +import org.apache.spark.sql.sources.v2.DataSourceOptions; import org.apache.spark.sql.sources.v2.DataSourceV2; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; import org.apache.spark.sql.sources.v2.ReadSupportWithSchema; -import org.apache.spark.sql.sources.v2.reader.DataSourceV2Reader; -import org.apache.spark.sql.sources.v2.reader.ReadTask; +import org.apache.spark.sql.sources.v2.reader.DataSourceReader; +import org.apache.spark.sql.sources.v2.reader.DataReaderFactory; import org.apache.spark.sql.types.StructType; public class JavaSchemaRequiredDataSource implements DataSourceV2, ReadSupportWithSchema { - class Reader implements DataSourceV2Reader { + class Reader implements DataSourceReader { private final StructType schema; Reader(StructType schema) { @@ -42,13 +42,13 @@ public StructType readSchema() { } @Override - public List> createReadTasks() { + public List> createDataReaderFactories() { return java.util.Collections.emptyList(); } } @Override - public DataSourceV2Reader createReader(StructType schema, DataSourceV2Options options) { + public DataSourceReader createReader(StructType schema, DataSourceOptions options) { return new Reader(schema); } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaSimpleDataSourceV2.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaSimpleDataSourceV2.java index 2d458b7f7e906..96f55b8a76811 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaSimpleDataSourceV2.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaSimpleDataSourceV2.java @@ -23,16 +23,16 @@ import org.apache.spark.sql.Row; import org.apache.spark.sql.catalyst.expressions.GenericRow; import org.apache.spark.sql.sources.v2.DataSourceV2; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; +import org.apache.spark.sql.sources.v2.DataSourceOptions; import org.apache.spark.sql.sources.v2.ReadSupport; import org.apache.spark.sql.sources.v2.reader.DataReader; -import org.apache.spark.sql.sources.v2.reader.ReadTask; -import org.apache.spark.sql.sources.v2.reader.DataSourceV2Reader; +import org.apache.spark.sql.sources.v2.reader.DataReaderFactory; +import org.apache.spark.sql.sources.v2.reader.DataSourceReader; import org.apache.spark.sql.types.StructType; public class JavaSimpleDataSourceV2 implements DataSourceV2, ReadSupport { - class Reader implements DataSourceV2Reader { + class Reader implements DataSourceReader { private final StructType schema = new StructType().add("i", "int").add("j", "int"); @Override @@ -41,25 +41,25 @@ public StructType readSchema() { } @Override - public List> createReadTasks() { + public List> createDataReaderFactories() { return java.util.Arrays.asList( - new JavaSimpleReadTask(0, 5), - new JavaSimpleReadTask(5, 10)); + new JavaSimpleDataReaderFactory(0, 5), + new JavaSimpleDataReaderFactory(5, 10)); } } - static class JavaSimpleReadTask implements ReadTask, DataReader { + static class JavaSimpleDataReaderFactory implements DataReaderFactory, DataReader { private int start; private int end; - JavaSimpleReadTask(int start, int end) { + JavaSimpleDataReaderFactory(int start, int end) { this.start = start; this.end = end; } @Override public DataReader createDataReader() { - return new JavaSimpleReadTask(start - 1, end); + return new JavaSimpleDataReaderFactory(start - 1, end); } @Override @@ -80,7 +80,7 @@ public void close() throws IOException { } @Override - public DataSourceV2Reader createReader(DataSourceV2Options options) { + public DataSourceReader createReader(DataSourceOptions options) { return new Reader(); } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaUnsafeRowDataSourceV2.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaUnsafeRowDataSourceV2.java index f6aa00869a681..c3916e0b370b5 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaUnsafeRowDataSourceV2.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/v2/JavaUnsafeRowDataSourceV2.java @@ -21,15 +21,15 @@ import java.util.List; import org.apache.spark.sql.catalyst.expressions.UnsafeRow; +import org.apache.spark.sql.sources.v2.DataSourceOptions; import org.apache.spark.sql.sources.v2.DataSourceV2; -import org.apache.spark.sql.sources.v2.DataSourceV2Options; import org.apache.spark.sql.sources.v2.ReadSupport; import org.apache.spark.sql.sources.v2.reader.*; import org.apache.spark.sql.types.StructType; public class JavaUnsafeRowDataSourceV2 implements DataSourceV2, ReadSupport { - class Reader implements DataSourceV2Reader, SupportsScanUnsafeRow { + class Reader implements DataSourceReader, SupportsScanUnsafeRow { private final StructType schema = new StructType().add("i", "int").add("j", "int"); @Override @@ -38,19 +38,20 @@ public StructType readSchema() { } @Override - public List> createUnsafeRowReadTasks() { + public List> createUnsafeRowReaderFactories() { return java.util.Arrays.asList( - new JavaUnsafeRowReadTask(0, 5), - new JavaUnsafeRowReadTask(5, 10)); + new JavaUnsafeRowDataReaderFactory(0, 5), + new JavaUnsafeRowDataReaderFactory(5, 10)); } } - static class JavaUnsafeRowReadTask implements ReadTask, DataReader { + static class JavaUnsafeRowDataReaderFactory + implements DataReaderFactory, DataReader { private int start; private int end; private UnsafeRow row; - JavaUnsafeRowReadTask(int start, int end) { + JavaUnsafeRowDataReaderFactory(int start, int end) { this.start = start; this.end = end; this.row = new UnsafeRow(2); @@ -59,7 +60,7 @@ static class JavaUnsafeRowReadTask implements ReadTask, DataReader createDataReader() { - return new JavaUnsafeRowReadTask(start - 1, end); + return new JavaUnsafeRowDataReaderFactory(start - 1, end); } @Override @@ -82,7 +83,7 @@ public void close() throws IOException { } @Override - public DataSourceV2Reader createReader(DataSourceV2Options options) { + public DataSourceReader createReader(DataSourceOptions options) { return new Reader(); } } diff --git a/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister index c6973bf41d34b..46b38bed1c0fb 100644 --- a/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister +++ b/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister @@ -5,3 +5,10 @@ org.apache.spark.sql.sources.FakeSourceFour org.apache.fakesource.FakeExternalSourceOne org.apache.fakesource.FakeExternalSourceTwo org.apache.fakesource.FakeExternalSourceThree +org.apache.spark.sql.streaming.sources.FakeReadMicroBatchOnly +org.apache.spark.sql.streaming.sources.FakeReadContinuousOnly +org.apache.spark.sql.streaming.sources.FakeReadBothModes +org.apache.spark.sql.streaming.sources.FakeReadNeitherMode +org.apache.spark.sql.streaming.sources.FakeWrite +org.apache.spark.sql.streaming.sources.FakeNoWrite +org.apache.spark.sql.streaming.sources.FakeWriteV1Fallback diff --git a/sql/core/src/test/resources/sql-tests/inputs/change-column.sql b/sql/core/src/test/resources/sql-tests/inputs/change-column.sql index ad0f885f63d3d..2909024e4c9f7 100644 --- a/sql/core/src/test/resources/sql-tests/inputs/change-column.sql +++ b/sql/core/src/test/resources/sql-tests/inputs/change-column.sql @@ -49,6 +49,7 @@ ALTER TABLE global_temp.global_temp_view CHANGE a a INT COMMENT 'this is column -- Change column in partition spec (not supported yet) CREATE TABLE partition_table(a INT, b STRING, c INT, d STRING) USING parquet PARTITIONED BY (c, d); ALTER TABLE partition_table PARTITION (c = 1) CHANGE COLUMN a new_a INT; +ALTER TABLE partition_table CHANGE COLUMN c c INT COMMENT 'this is column C'; -- DROP TEST TABLE DROP TABLE test_change; diff --git a/sql/core/src/test/resources/sql-tests/inputs/group-by.sql b/sql/core/src/test/resources/sql-tests/inputs/group-by.sql index 1e1384549a410..2c18d6aaabdba 100644 --- a/sql/core/src/test/resources/sql-tests/inputs/group-by.sql +++ b/sql/core/src/test/resources/sql-tests/inputs/group-by.sql @@ -60,3 +60,16 @@ SELECT a, COUNT(1) FROM testData WHERE false GROUP BY a; -- Aggregate with empty input and empty GroupBy expressions. SELECT COUNT(1) FROM testData WHERE false; SELECT 1 FROM (SELECT COUNT(1) FROM testData WHERE false) t; + +-- Aggregate with empty GroupBy expressions and filter on top +SELECT 1 from ( + SELECT 1 AS z, + MIN(a.x) + FROM (select 1 as x) a + WHERE false +) b +where b.z != b.z; + +-- SPARK-24369 multiple distinct aggregations having the same argument set +SELECT corr(DISTINCT x, y), corr(DISTINCT y, x), count(*) + FROM (VALUES (1, 1), (2, 2), (2, 2)) t(x, y); diff --git a/sql/core/src/test/resources/sql-tests/inputs/literals.sql b/sql/core/src/test/resources/sql-tests/inputs/literals.sql index 37b4b7606d12b..a743cf1ec2cde 100644 --- a/sql/core/src/test/resources/sql-tests/inputs/literals.sql +++ b/sql/core/src/test/resources/sql-tests/inputs/literals.sql @@ -105,3 +105,6 @@ select X'XuZ'; -- Hive literal_double test. SELECT 3.14, -3.14, 3.14e8, 3.14e-8, -3.14e8, -3.14e-8, 3.14e+8, 3.14E8, 3.14E-8; + +-- map + interval test +select map(1, interval 1 day, 2, interval 3 week); diff --git a/sql/core/src/test/resources/sql-tests/inputs/subquery/scalar-subquery/scalar-subquery-predicate.sql b/sql/core/src/test/resources/sql-tests/inputs/subquery/scalar-subquery/scalar-subquery-predicate.sql index fb0d07fbdace7..1661209093fc4 100644 --- a/sql/core/src/test/resources/sql-tests/inputs/subquery/scalar-subquery/scalar-subquery-predicate.sql +++ b/sql/core/src/test/resources/sql-tests/inputs/subquery/scalar-subquery/scalar-subquery-predicate.sql @@ -173,6 +173,16 @@ WHERE t1a = (SELECT max(t2a) HAVING count(*) >= 0) OR t1i > '2014-12-31'; +-- TC 02.03.01 +SELECT t1a +FROM t1 +WHERE t1a = (SELECT max(t2a) + FROM t2 + WHERE t2c = t1c + GROUP BY t2c + HAVING count(*) >= 1) +OR t1i > '2014-12-31'; + -- TC 02.04 -- t1 on the right of an outer join -- can be reduced to inner join diff --git a/sql/core/src/test/resources/sql-tests/inputs/typeCoercion/native/decimalArithmeticOperations.sql b/sql/core/src/test/resources/sql-tests/inputs/typeCoercion/native/decimalArithmeticOperations.sql index c8e108ac2c45e..28a0e20c0f495 100644 --- a/sql/core/src/test/resources/sql-tests/inputs/typeCoercion/native/decimalArithmeticOperations.sql +++ b/sql/core/src/test/resources/sql-tests/inputs/typeCoercion/native/decimalArithmeticOperations.sql @@ -22,12 +22,65 @@ select a / b from t; select a % b from t; select pmod(a, b) from t; +-- tests for decimals handling in operations +create table decimals_test(id int, a decimal(38,18), b decimal(38,18)) using parquet; + +insert into decimals_test values(1, 100.0, 999.0), (2, 12345.123, 12345.123), + (3, 0.1234567891011, 1234.1), (4, 123456789123456789.0, 1.123456789123456789); + +-- test decimal operations +select id, a+b, a-b, a*b, a/b from decimals_test order by id; + +-- test operations between decimals and constants +select id, a*10, b/10 from decimals_test order by id; + +-- test operations on constants +select 10.3 * 3.0; +select 10.3000 * 3.0; +select 10.30000 * 30.0; +select 10.300000000000000000 * 3.000000000000000000; +select 10.300000000000000000 * 3.0000000000000000000; +select 2.35E10 * 1.0; + +-- arithmetic operations causing an overflow return NULL +select (5e36 + 0.1) + 5e36; +select (-4e36 - 0.1) - 7e36; +select 12345678901234567890.0 * 12345678901234567890.0; +select 1e35 / 0.1; +select 1.2345678901234567890E30 * 1.2345678901234567890E25; + +-- arithmetic operations causing a precision loss are truncated +select 12345678912345678912345678912.1234567 + 9999999999999999999999999999999.12345; +select 123456789123456789.1234567890 * 1.123456789123456789; +select 12345678912345.123456789123 / 0.000000012345678; + +-- return NULL instead of rounding, according to old Spark versions' behavior +set spark.sql.decimalOperations.allowPrecisionLoss=false; + +-- test decimal operations +select id, a+b, a-b, a*b, a/b from decimals_test order by id; + +-- test operations between decimals and constants +select id, a*10, b/10 from decimals_test order by id; + +-- test operations on constants +select 10.3 * 3.0; +select 10.3000 * 3.0; +select 10.30000 * 30.0; +select 10.300000000000000000 * 3.000000000000000000; +select 10.300000000000000000 * 3.0000000000000000000; +select 2.35E10 * 1.0; + -- arithmetic operations causing an overflow return NULL select (5e36 + 0.1) + 5e36; select (-4e36 - 0.1) - 7e36; select 12345678901234567890.0 * 12345678901234567890.0; select 1e35 / 0.1; +select 1.2345678901234567890E30 * 1.2345678901234567890E25; -- arithmetic operations causing a precision loss return NULL +select 12345678912345678912345678912.1234567 + 9999999999999999999999999999999.12345; select 123456789123456789.1234567890 * 1.123456789123456789; -select 0.001 / 9876543210987654321098765432109876543.2 +select 12345678912345.123456789123 / 0.000000012345678; + +drop table decimals_test; diff --git a/sql/core/src/test/resources/sql-tests/inputs/typeCoercion/native/elt.sql b/sql/core/src/test/resources/sql-tests/inputs/typeCoercion/native/elt.sql new file mode 100644 index 0000000000000..717616f91db05 --- /dev/null +++ b/sql/core/src/test/resources/sql-tests/inputs/typeCoercion/native/elt.sql @@ -0,0 +1,44 @@ +-- Mixed inputs (output type is string) +SELECT elt(2, col1, col2, col3, col4, col5) col +FROM ( + SELECT + 'prefix_' col1, + id col2, + string(id + 1) col3, + encode(string(id + 2), 'utf-8') col4, + CAST(id AS DOUBLE) col5 + FROM range(10) +); + +SELECT elt(3, col1, col2, col3, col4) col +FROM ( + SELECT + string(id) col1, + string(id + 1) col2, + encode(string(id + 2), 'utf-8') col3, + encode(string(id + 3), 'utf-8') col4 + FROM range(10) +); + +-- turn on eltOutputAsString +set spark.sql.function.eltOutputAsString=true; + +SELECT elt(1, col1, col2) col +FROM ( + SELECT + encode(string(id), 'utf-8') col1, + encode(string(id + 1), 'utf-8') col2 + FROM range(10) +); + +-- turn off eltOutputAsString +set spark.sql.function.eltOutputAsString=false; + +-- Elt binary inputs (output type is binary) +SELECT elt(2, col1, col2) col +FROM ( + SELECT + encode(string(id), 'utf-8') col1, + encode(string(id + 1), 'utf-8') col2 + FROM range(10) +); diff --git a/sql/core/src/test/resources/sql-tests/results/change-column.sql.out b/sql/core/src/test/resources/sql-tests/results/change-column.sql.out index ba8bc936f0c79..ff1ecbcc44c23 100644 --- a/sql/core/src/test/resources/sql-tests/results/change-column.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/change-column.sql.out @@ -1,5 +1,5 @@ -- Automatically generated by SQLQueryTestSuite --- Number of queries: 32 +-- Number of queries: 33 -- !query 0 @@ -154,7 +154,7 @@ ALTER TABLE test_change CHANGE invalid_col invalid_col INT struct<> -- !query 15 output org.apache.spark.sql.AnalysisException -Invalid column reference 'invalid_col', table schema is 'StructType(StructField(a,IntegerType,true), StructField(b,StringType,true), StructField(c,IntegerType,true))'; +Can't find column `invalid_col` given table data columns [`a`, `b`, `c`]; -- !query 16 @@ -291,16 +291,25 @@ ALTER TABLE partition_table PARTITION (c = 1) CHANGE COLUMN a new_a INT -- !query 30 -DROP TABLE test_change +ALTER TABLE partition_table CHANGE COLUMN c c INT COMMENT 'this is column C' -- !query 30 schema struct<> -- !query 30 output - +org.apache.spark.sql.AnalysisException +Can't find column `c` given table data columns [`a`, `b`]; -- !query 31 -DROP TABLE partition_table +DROP TABLE test_change -- !query 31 schema struct<> -- !query 31 output + + +-- !query 32 +DROP TABLE partition_table +-- !query 32 schema +struct<> +-- !query 32 output + diff --git a/sql/core/src/test/resources/sql-tests/results/columnresolution-negative.sql.out b/sql/core/src/test/resources/sql-tests/results/columnresolution-negative.sql.out index b5a4f5c2bf654..539f673c9d679 100644 --- a/sql/core/src/test/resources/sql-tests/results/columnresolution-negative.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/columnresolution-negative.sql.out @@ -195,7 +195,7 @@ SELECT t1.x.y.* FROM t1 struct<> -- !query 22 output org.apache.spark.sql.AnalysisException -cannot resolve 't1.x.y.*' give input columns 'i1'; +cannot resolve 't1.x.y.*' given input columns 'i1'; -- !query 23 diff --git a/sql/core/src/test/resources/sql-tests/results/columnresolution-views.sql.out b/sql/core/src/test/resources/sql-tests/results/columnresolution-views.sql.out index 7c451c2aa5b5c..2092119600954 100644 --- a/sql/core/src/test/resources/sql-tests/results/columnresolution-views.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/columnresolution-views.sql.out @@ -88,7 +88,7 @@ SELECT global_temp.view1.* FROM global_temp.view1 struct<> -- !query 10 output org.apache.spark.sql.AnalysisException -cannot resolve 'global_temp.view1.*' give input columns 'i1'; +cannot resolve 'global_temp.view1.*' given input columns 'i1'; -- !query 11 diff --git a/sql/core/src/test/resources/sql-tests/results/columnresolution.sql.out b/sql/core/src/test/resources/sql-tests/results/columnresolution.sql.out index d3ca4443cce55..e10f516ad6e5b 100644 --- a/sql/core/src/test/resources/sql-tests/results/columnresolution.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/columnresolution.sql.out @@ -179,7 +179,7 @@ SELECT mydb1.t1.* FROM mydb1.t1 struct<> -- !query 21 output org.apache.spark.sql.AnalysisException -cannot resolve 'mydb1.t1.*' give input columns 'i1'; +cannot resolve 'mydb1.t1.*' given input columns 'i1'; -- !query 22 @@ -212,7 +212,7 @@ SELECT mydb1.t1.* FROM mydb1.t1 struct<> -- !query 25 output org.apache.spark.sql.AnalysisException -cannot resolve 'mydb1.t1.*' give input columns 'i1'; +cannot resolve 'mydb1.t1.*' given input columns 'i1'; -- !query 26 @@ -420,7 +420,7 @@ SELECT mydb1.t5.* FROM mydb1.t5 struct<> -- !query 50 output org.apache.spark.sql.AnalysisException -cannot resolve 'mydb1.t5.*' give input columns 'i1, t5'; +cannot resolve 'mydb1.t5.*' given input columns 'i1, t5'; -- !query 51 diff --git a/sql/core/src/test/resources/sql-tests/results/group-by.sql.out b/sql/core/src/test/resources/sql-tests/results/group-by.sql.out index 986bb01c13fe4..581aa1754ce14 100644 --- a/sql/core/src/test/resources/sql-tests/results/group-by.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/group-by.sql.out @@ -1,5 +1,5 @@ -- Automatically generated by SQLQueryTestSuite --- Number of queries: 25 +-- Number of queries: 27 -- !query 0 @@ -227,3 +227,26 @@ SELECT 1 FROM (SELECT COUNT(1) FROM testData WHERE false) t struct<1:int> -- !query 24 output 1 + + +-- !query 25 +SELECT 1 from ( + SELECT 1 AS z, + MIN(a.x) + FROM (select 1 as x) a + WHERE false +) b +where b.z != b.z +-- !query 25 schema +struct<1:int> +-- !query 25 output + + + +-- !query 26 +SELECT corr(DISTINCT x, y), corr(DISTINCT y, x), count(*) + FROM (VALUES (1, 1), (2, 2), (2, 2)) t(x, y) +-- !query 26 schema +struct +-- !query 26 output +1.0 1.0 3 diff --git a/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out b/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out index d9dc728a18e8d..14a69128ffb41 100644 --- a/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out @@ -129,7 +129,7 @@ select to_json() struct<> -- !query 12 output org.apache.spark.sql.AnalysisException -Invalid number of arguments for function to_json; line 1 pos 7 +Invalid number of arguments for function to_json. Expected: one of 1 and 2; Found: 0; line 1 pos 7 -- !query 13 @@ -225,7 +225,7 @@ select from_json() struct<> -- !query 21 output org.apache.spark.sql.AnalysisException -Invalid number of arguments for function from_json; line 1 pos 7 +Invalid number of arguments for function from_json. Expected: one of 2 and 3; Found: 0; line 1 pos 7 -- !query 22 diff --git a/sql/core/src/test/resources/sql-tests/results/literals.sql.out b/sql/core/src/test/resources/sql-tests/results/literals.sql.out index 95d4413148f64..b8c91dc8b59a4 100644 --- a/sql/core/src/test/resources/sql-tests/results/literals.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/literals.sql.out @@ -1,5 +1,5 @@ -- Automatically generated by SQLQueryTestSuite --- Number of queries: 43 +-- Number of queries: 44 -- !query 0 @@ -323,19 +323,17 @@ select timestamp '2016-33-11 20:54:00.000' -- !query 34 select interval 13.123456789 seconds, interval -13.123456789 second -- !query 34 schema -struct<> +struct -- !query 34 output -scala.MatchError -(interval 13 seconds 123 milliseconds 456 microseconds,CalendarIntervalType) (of class scala.Tuple2) +interval 13 seconds 123 milliseconds 456 microseconds interval -12 seconds -876 milliseconds -544 microseconds -- !query 35 select interval 1 year 2 month 3 week 4 day 5 hour 6 minute 7 seconds 8 millisecond, 9 microsecond -- !query 35 schema -struct<> +struct -- !query 35 output -scala.MatchError -(interval 1 years 2 months 3 weeks 4 days 5 hours 6 minutes 7 seconds 8 milliseconds,CalendarIntervalType) (of class scala.Tuple2) +interval 1 years 2 months 3 weeks 4 days 5 hours 6 minutes 7 seconds 8 milliseconds 9 -- !query 36 @@ -416,3 +414,11 @@ SELECT 3.14, -3.14, 3.14e8, 3.14e-8, -3.14e8, -3.14e-8, 3.14e+8, 3.14E8, 3.14E-8 struct<3.14:decimal(3,2),-3.14:decimal(3,2),3.14E+8:decimal(3,-6),3.14E-8:decimal(10,10),-3.14E+8:decimal(3,-6),-3.14E-8:decimal(10,10),3.14E+8:decimal(3,-6),3.14E+8:decimal(3,-6),3.14E-8:decimal(10,10)> -- !query 42 output 3.14 -3.14 314000000 0.0000000314 -314000000 -0.0000000314 314000000 314000000 0.0000000314 + + +-- !query 43 +select map(1, interval 1 day, 2, interval 3 week) +-- !query 43 schema +struct> +-- !query 43 output +{1:interval 1 days,2:interval 3 weeks} diff --git a/sql/core/src/test/resources/sql-tests/results/operators.sql.out b/sql/core/src/test/resources/sql-tests/results/operators.sql.out index 237b618a8b904..840655b7a6447 100644 --- a/sql/core/src/test/resources/sql-tests/results/operators.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/operators.sql.out @@ -425,7 +425,7 @@ struct<(7 % 2):int,(7 % 0):int,(0 % 2):int,(7 % CAST(NULL AS INT)):int,(CAST(NUL -- !query 51 select BIT_LENGTH('abc') -- !query 51 schema -struct +struct -- !query 51 output 24 @@ -449,7 +449,7 @@ struct -- !query 54 select OCTET_LENGTH('abc') -- !query 54 schema -struct +struct -- !query 54 output 3 diff --git a/sql/core/src/test/resources/sql-tests/results/subquery/scalar-subquery/scalar-subquery-predicate.sql.out b/sql/core/src/test/resources/sql-tests/results/subquery/scalar-subquery/scalar-subquery-predicate.sql.out index 8b29300e71f90..dd82efba0dde1 100644 --- a/sql/core/src/test/resources/sql-tests/results/subquery/scalar-subquery/scalar-subquery-predicate.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/subquery/scalar-subquery/scalar-subquery-predicate.sql.out @@ -1,5 +1,5 @@ -- Automatically generated by SQLQueryTestSuite --- Number of queries: 26 +-- Number of queries: 27 -- !query 0 @@ -293,6 +293,22 @@ val1d -- !query 19 +SELECT t1a +FROM t1 +WHERE t1a = (SELECT max(t2a) + FROM t2 + WHERE t2c = t1c + GROUP BY t2c + HAVING count(*) >= 1) +OR t1i > '2014-12-31' +-- !query 19 schema +struct +-- !query 19 output +val1c +val1d + + +-- !query 20 SELECT count(t1a) FROM t1 RIGHT JOIN t2 ON t1d = t2d @@ -300,13 +316,13 @@ WHERE t1a < (SELECT max(t2a) FROM t2 WHERE t2c = t1c GROUP BY t2c) --- !query 19 schema +-- !query 20 schema struct --- !query 19 output +-- !query 20 output 7 --- !query 20 +-- !query 21 SELECT t1a FROM t1 WHERE t1b <= (SELECT max(t2b) @@ -317,14 +333,14 @@ AND t1b >= (SELECT min(t2b) FROM t2 WHERE t2c = t1c GROUP BY t2c) --- !query 20 schema +-- !query 21 schema struct --- !query 20 output +-- !query 21 output val1b val1c --- !query 21 +-- !query 22 SELECT t1a FROM t1 WHERE t1a <= (SELECT max(t2a) @@ -338,14 +354,14 @@ WHERE t1a >= (SELECT min(t2a) FROM t2 WHERE t2c = t1c GROUP BY t2c) --- !query 21 schema +-- !query 22 schema struct --- !query 21 output +-- !query 22 output val1b val1c --- !query 22 +-- !query 23 SELECT t1a FROM t1 WHERE t1a <= (SELECT max(t2a) @@ -359,9 +375,9 @@ WHERE t1a >= (SELECT min(t2a) FROM t2 WHERE t2c = t1c GROUP BY t2c) --- !query 22 schema +-- !query 23 schema struct --- !query 22 output +-- !query 23 output val1a val1a val1b @@ -372,7 +388,7 @@ val1d val1d --- !query 23 +-- !query 24 SELECT t1a FROM t1 WHERE t1a <= (SELECT max(t2a) @@ -386,16 +402,16 @@ WHERE t1a >= (SELECT min(t2a) FROM t2 WHERE t2c = t1c GROUP BY t2c) --- !query 23 schema +-- !query 24 schema struct --- !query 23 output +-- !query 24 output val1a val1b val1c val1d --- !query 24 +-- !query 25 SELECT t1a FROM t1 WHERE t1a <= (SELECT max(t2a) @@ -409,13 +425,13 @@ WHERE t1a >= (SELECT min(t2a) FROM t2 WHERE t2c = t1c GROUP BY t2c) --- !query 24 schema +-- !query 25 schema struct --- !query 24 output +-- !query 25 output val1a --- !query 25 +-- !query 26 SELECT t1a FROM t1 GROUP BY t1a, t1c @@ -423,8 +439,8 @@ HAVING max(t1b) <= (SELECT max(t2b) FROM t2 WHERE t2c = t1c GROUP BY t2c) --- !query 25 schema +-- !query 26 schema struct --- !query 25 output +-- !query 26 output val1b val1c diff --git a/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/decimalArithmeticOperations.sql.out b/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/decimalArithmeticOperations.sql.out index ce02f6adc456c..cbf44548b3cce 100644 --- a/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/decimalArithmeticOperations.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/decimalArithmeticOperations.sql.out @@ -1,5 +1,5 @@ -- Automatically generated by SQLQueryTestSuite --- Number of queries: 10 +-- Number of queries: 40 -- !query 0 @@ -35,48 +35,301 @@ NULL -- !query 4 -select (5e36 + 0.1) + 5e36 +create table decimals_test(id int, a decimal(38,18), b decimal(38,18)) using parquet -- !query 4 schema -struct<(CAST((CAST(5E+36 AS DECIMAL(38,1)) + CAST(0.1 AS DECIMAL(38,1))) AS DECIMAL(38,1)) + CAST(5E+36 AS DECIMAL(38,1))):decimal(38,1)> +struct<> -- !query 4 output -NULL + -- !query 5 -select (-4e36 - 0.1) - 7e36 +insert into decimals_test values(1, 100.0, 999.0), (2, 12345.123, 12345.123), + (3, 0.1234567891011, 1234.1), (4, 123456789123456789.0, 1.123456789123456789) -- !query 5 schema -struct<(CAST((CAST(-4E+36 AS DECIMAL(38,1)) - CAST(0.1 AS DECIMAL(38,1))) AS DECIMAL(38,1)) - CAST(7E+36 AS DECIMAL(38,1))):decimal(38,1)> +struct<> -- !query 5 output -NULL + -- !query 6 -select 12345678901234567890.0 * 12345678901234567890.0 +select id, a+b, a-b, a*b, a/b from decimals_test order by id -- !query 6 schema -struct<(12345678901234567890.0 * 12345678901234567890.0):decimal(38,2)> +struct -- !query 6 output -NULL +1 1099 -899 99900 0.1001 +2 24690.246 0 152402061.885129 1 +3 1234.2234567891011 -1233.9765432108989 152.358023 0.0001 +4 123456789123456790.12345678912345679 123456789123456787.87654321087654321 138698367904130467.515623 109890109097814272.043109 -- !query 7 -select 1e35 / 0.1 +select id, a*10, b/10 from decimals_test order by id -- !query 7 schema -struct<(CAST(1E+35 AS DECIMAL(37,1)) / CAST(0.1 AS DECIMAL(37,1))):decimal(38,3)> +struct -- !query 7 output -NULL +1 1000 99.9 +2 123451.23 1234.5123 +3 1.234567891011 123.41 +4 1234567891234567890 0.112345678912345679 -- !query 8 -select 123456789123456789.1234567890 * 1.123456789123456789 +select 10.3 * 3.0 -- !query 8 schema -struct<(CAST(123456789123456789.1234567890 AS DECIMAL(36,18)) * CAST(1.123456789123456789 AS DECIMAL(36,18))):decimal(38,28)> +struct<(CAST(10.3 AS DECIMAL(3,1)) * CAST(3.0 AS DECIMAL(3,1))):decimal(6,2)> -- !query 8 output -NULL +30.9 -- !query 9 -select 0.001 / 9876543210987654321098765432109876543.2 +select 10.3000 * 3.0 -- !query 9 schema -struct<(CAST(0.001 AS DECIMAL(38,3)) / CAST(9876543210987654321098765432109876543.2 AS DECIMAL(38,3))):decimal(38,37)> +struct<(CAST(10.3000 AS DECIMAL(6,4)) * CAST(3.0 AS DECIMAL(6,4))):decimal(9,5)> -- !query 9 output +30.9 + + +-- !query 10 +select 10.30000 * 30.0 +-- !query 10 schema +struct<(CAST(10.30000 AS DECIMAL(7,5)) * CAST(30.0 AS DECIMAL(7,5))):decimal(11,6)> +-- !query 10 output +309 + + +-- !query 11 +select 10.300000000000000000 * 3.000000000000000000 +-- !query 11 schema +struct<(CAST(10.300000000000000000 AS DECIMAL(20,18)) * CAST(3.000000000000000000 AS DECIMAL(20,18))):decimal(38,34)> +-- !query 11 output +30.9 + + +-- !query 12 +select 10.300000000000000000 * 3.0000000000000000000 +-- !query 12 schema +struct<(CAST(10.300000000000000000 AS DECIMAL(21,19)) * CAST(3.0000000000000000000 AS DECIMAL(21,19))):decimal(38,34)> +-- !query 12 output +30.9 + + +-- !query 13 +select 2.35E10 * 1.0 +-- !query 13 schema +struct<(CAST(2.35E+10 AS DECIMAL(12,1)) * CAST(1.0 AS DECIMAL(12,1))):decimal(6,-7)> +-- !query 13 output +23500000000 + + +-- !query 14 +select (5e36 + 0.1) + 5e36 +-- !query 14 schema +struct<(CAST((CAST(5E+36 AS DECIMAL(38,1)) + CAST(0.1 AS DECIMAL(38,1))) AS DECIMAL(38,1)) + CAST(5E+36 AS DECIMAL(38,1))):decimal(38,1)> +-- !query 14 output +NULL + + +-- !query 15 +select (-4e36 - 0.1) - 7e36 +-- !query 15 schema +struct<(CAST((CAST(-4E+36 AS DECIMAL(38,1)) - CAST(0.1 AS DECIMAL(38,1))) AS DECIMAL(38,1)) - CAST(7E+36 AS DECIMAL(38,1))):decimal(38,1)> +-- !query 15 output +NULL + + +-- !query 16 +select 12345678901234567890.0 * 12345678901234567890.0 +-- !query 16 schema +struct<(12345678901234567890.0 * 12345678901234567890.0):decimal(38,2)> +-- !query 16 output +NULL + + +-- !query 17 +select 1e35 / 0.1 +-- !query 17 schema +struct<(CAST(1E+35 AS DECIMAL(37,1)) / CAST(0.1 AS DECIMAL(37,1))):decimal(38,6)> +-- !query 17 output +NULL + + +-- !query 18 +select 1.2345678901234567890E30 * 1.2345678901234567890E25 +-- !query 18 schema +struct<(CAST(1.2345678901234567890E+30 AS DECIMAL(25,-6)) * CAST(1.2345678901234567890E+25 AS DECIMAL(25,-6))):decimal(38,-17)> +-- !query 18 output +NULL + + +-- !query 19 +select 12345678912345678912345678912.1234567 + 9999999999999999999999999999999.12345 +-- !query 19 schema +struct<(CAST(12345678912345678912345678912.1234567 AS DECIMAL(38,6)) + CAST(9999999999999999999999999999999.12345 AS DECIMAL(38,6))):decimal(38,6)> +-- !query 19 output +10012345678912345678912345678911.246907 + + +-- !query 20 +select 123456789123456789.1234567890 * 1.123456789123456789 +-- !query 20 schema +struct<(CAST(123456789123456789.1234567890 AS DECIMAL(36,18)) * CAST(1.123456789123456789 AS DECIMAL(36,18))):decimal(38,18)> +-- !query 20 output +138698367904130467.654320988515622621 + + +-- !query 21 +select 12345678912345.123456789123 / 0.000000012345678 +-- !query 21 schema +struct<(CAST(12345678912345.123456789123 AS DECIMAL(29,15)) / CAST(1.2345678E-8 AS DECIMAL(29,15))):decimal(38,9)> +-- !query 21 output +1000000073899961059796.725866332 + + +-- !query 22 +set spark.sql.decimalOperations.allowPrecisionLoss=false +-- !query 22 schema +struct +-- !query 22 output +spark.sql.decimalOperations.allowPrecisionLoss false + + +-- !query 23 +select id, a+b, a-b, a*b, a/b from decimals_test order by id +-- !query 23 schema +struct +-- !query 23 output +1 1099 -899 NULL 0.1001001001001001 +2 24690.246 0 NULL 1 +3 1234.2234567891011 -1233.9765432108989 NULL 0.000100037913541123 +4 123456789123456790.123456789123456789 123456789123456787.876543210876543211 NULL 109890109097814272.043109406191131436 + + +-- !query 24 +select id, a*10, b/10 from decimals_test order by id +-- !query 24 schema +struct +-- !query 24 output +1 1000 99.9 +2 123451.23 1234.5123 +3 1.234567891011 123.41 +4 1234567891234567890 0.1123456789123456789 + + +-- !query 25 +select 10.3 * 3.0 +-- !query 25 schema +struct<(CAST(10.3 AS DECIMAL(3,1)) * CAST(3.0 AS DECIMAL(3,1))):decimal(6,2)> +-- !query 25 output +30.9 + + +-- !query 26 +select 10.3000 * 3.0 +-- !query 26 schema +struct<(CAST(10.3000 AS DECIMAL(6,4)) * CAST(3.0 AS DECIMAL(6,4))):decimal(9,5)> +-- !query 26 output +30.9 + + +-- !query 27 +select 10.30000 * 30.0 +-- !query 27 schema +struct<(CAST(10.30000 AS DECIMAL(7,5)) * CAST(30.0 AS DECIMAL(7,5))):decimal(11,6)> +-- !query 27 output +309 + + +-- !query 28 +select 10.300000000000000000 * 3.000000000000000000 +-- !query 28 schema +struct<(CAST(10.300000000000000000 AS DECIMAL(20,18)) * CAST(3.000000000000000000 AS DECIMAL(20,18))):decimal(38,36)> +-- !query 28 output +30.9 + + +-- !query 29 +select 10.300000000000000000 * 3.0000000000000000000 +-- !query 29 schema +struct<(CAST(10.300000000000000000 AS DECIMAL(21,19)) * CAST(3.0000000000000000000 AS DECIMAL(21,19))):decimal(38,37)> +-- !query 29 output +NULL + + +-- !query 30 +select 2.35E10 * 1.0 +-- !query 30 schema +struct<(CAST(2.35E+10 AS DECIMAL(12,1)) * CAST(1.0 AS DECIMAL(12,1))):decimal(6,-7)> +-- !query 30 output +23500000000 + + +-- !query 31 +select (5e36 + 0.1) + 5e36 +-- !query 31 schema +struct<(CAST((CAST(5E+36 AS DECIMAL(38,1)) + CAST(0.1 AS DECIMAL(38,1))) AS DECIMAL(38,1)) + CAST(5E+36 AS DECIMAL(38,1))):decimal(38,1)> +-- !query 31 output +NULL + + +-- !query 32 +select (-4e36 - 0.1) - 7e36 +-- !query 32 schema +struct<(CAST((CAST(-4E+36 AS DECIMAL(38,1)) - CAST(0.1 AS DECIMAL(38,1))) AS DECIMAL(38,1)) - CAST(7E+36 AS DECIMAL(38,1))):decimal(38,1)> +-- !query 32 output NULL + + +-- !query 33 +select 12345678901234567890.0 * 12345678901234567890.0 +-- !query 33 schema +struct<(12345678901234567890.0 * 12345678901234567890.0):decimal(38,2)> +-- !query 33 output +NULL + + +-- !query 34 +select 1e35 / 0.1 +-- !query 34 schema +struct<(CAST(1E+35 AS DECIMAL(37,1)) / CAST(0.1 AS DECIMAL(37,1))):decimal(38,3)> +-- !query 34 output +NULL + + +-- !query 35 +select 1.2345678901234567890E30 * 1.2345678901234567890E25 +-- !query 35 schema +struct<(CAST(1.2345678901234567890E+30 AS DECIMAL(25,-6)) * CAST(1.2345678901234567890E+25 AS DECIMAL(25,-6))):decimal(38,-17)> +-- !query 35 output +NULL + + +-- !query 36 +select 12345678912345678912345678912.1234567 + 9999999999999999999999999999999.12345 +-- !query 36 schema +struct<(CAST(12345678912345678912345678912.1234567 AS DECIMAL(38,7)) + CAST(9999999999999999999999999999999.12345 AS DECIMAL(38,7))):decimal(38,7)> +-- !query 36 output +NULL + + +-- !query 37 +select 123456789123456789.1234567890 * 1.123456789123456789 +-- !query 37 schema +struct<(CAST(123456789123456789.1234567890 AS DECIMAL(36,18)) * CAST(1.123456789123456789 AS DECIMAL(36,18))):decimal(38,28)> +-- !query 37 output +NULL + + +-- !query 38 +select 12345678912345.123456789123 / 0.000000012345678 +-- !query 38 schema +struct<(CAST(12345678912345.123456789123 AS DECIMAL(29,15)) / CAST(1.2345678E-8 AS DECIMAL(29,15))):decimal(38,18)> +-- !query 38 output +NULL + + +-- !query 39 +drop table decimals_test +-- !query 39 schema +struct<> +-- !query 39 output + diff --git a/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/decimalPrecision.sql.out b/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/decimalPrecision.sql.out index ebc8201ed5a1d..6ee7f59d69877 100644 --- a/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/decimalPrecision.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/decimalPrecision.sql.out @@ -2329,7 +2329,7 @@ struct<(CAST(CAST(CAST(1 AS BIGINT) AS DECIMAL(20,0)) AS DECIMAL(20,0)) / CAST(C -- !query 280 SELECT cast(1 as bigint) / cast(1 as decimal(20, 0)) FROM t -- !query 280 schema -struct<(CAST(CAST(1 AS BIGINT) AS DECIMAL(20,0)) / CAST(1 AS DECIMAL(20,0))):decimal(38,19)> +struct<(CAST(CAST(1 AS BIGINT) AS DECIMAL(20,0)) / CAST(1 AS DECIMAL(20,0))):decimal(38,18)> -- !query 280 output 1 @@ -2661,7 +2661,7 @@ struct<(CAST(CAST(1 AS DECIMAL(10,0)) AS DECIMAL(20,0)) / CAST(CAST(CAST(1 AS BI -- !query 320 SELECT cast(1 as decimal(20, 0)) / cast(1 as bigint) FROM t -- !query 320 schema -struct<(CAST(1 AS DECIMAL(20,0)) / CAST(CAST(1 AS BIGINT) AS DECIMAL(20,0))):decimal(38,19)> +struct<(CAST(1 AS DECIMAL(20,0)) / CAST(CAST(1 AS BIGINT) AS DECIMAL(20,0))):decimal(38,18)> -- !query 320 output 1 diff --git a/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/elt.sql.out b/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/elt.sql.out new file mode 100644 index 0000000000000..b62e1b6826045 --- /dev/null +++ b/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/elt.sql.out @@ -0,0 +1,115 @@ +-- Automatically generated by SQLQueryTestSuite +-- Number of queries: 6 + + +-- !query 0 +SELECT elt(2, col1, col2, col3, col4, col5) col +FROM ( + SELECT + 'prefix_' col1, + id col2, + string(id + 1) col3, + encode(string(id + 2), 'utf-8') col4, + CAST(id AS DOUBLE) col5 + FROM range(10) +) +-- !query 0 schema +struct +-- !query 0 output +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 + + +-- !query 1 +SELECT elt(3, col1, col2, col3, col4) col +FROM ( + SELECT + string(id) col1, + string(id + 1) col2, + encode(string(id + 2), 'utf-8') col3, + encode(string(id + 3), 'utf-8') col4 + FROM range(10) +) +-- !query 1 schema +struct +-- !query 1 output +10 +11 +2 +3 +4 +5 +6 +7 +8 +9 + + +-- !query 2 +set spark.sql.function.eltOutputAsString=true +-- !query 2 schema +struct +-- !query 2 output +spark.sql.function.eltOutputAsString true + + +-- !query 3 +SELECT elt(1, col1, col2) col +FROM ( + SELECT + encode(string(id), 'utf-8') col1, + encode(string(id + 1), 'utf-8') col2 + FROM range(10) +) +-- !query 3 schema +struct +-- !query 3 output +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 + + +-- !query 4 +set spark.sql.function.eltOutputAsString=false +-- !query 4 schema +struct +-- !query 4 output +spark.sql.function.eltOutputAsString false + + +-- !query 5 +SELECT elt(2, col1, col2) col +FROM ( + SELECT + encode(string(id), 'utf-8') col1, + encode(string(id + 1), 'utf-8') col2 + FROM range(10) +) +-- !query 5 schema +struct +-- !query 5 output +1 +10 +2 +3 +4 +5 +6 +7 +8 +9 diff --git a/sql/core/src/test/resources/test-data/parquet-1217.parquet b/sql/core/src/test/resources/test-data/parquet-1217.parquet new file mode 100644 index 0000000000000..eb2dc4f799070 Binary files /dev/null and b/sql/core/src/test/resources/test-data/parquet-1217.parquet differ diff --git a/sql/core/src/test/scala/org/apache/spark/sql/BenchmarkQueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/BenchmarkQueryTest.scala index 7037749f14478..e51aad021fcbf 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/BenchmarkQueryTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/BenchmarkQueryTest.scala @@ -46,7 +46,7 @@ abstract class BenchmarkQueryTest extends QueryTest with SharedSQLContext with B override def beforeAll() { super.beforeAll() - RuleExecutor.resetTime() + RuleExecutor.resetMetrics() } protected def checkGeneratedCode(plan: SparkPlan): Unit = { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala index 1e52445f28fc1..669e5f2bf4e65 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -21,8 +21,6 @@ import scala.collection.mutable.HashSet import scala.concurrent.duration._ import scala.language.postfixOps -import org.scalatest.concurrent.Eventually._ - import org.apache.spark.CleanerListener import org.apache.spark.sql.catalyst.TableIdentifier import org.apache.spark.sql.catalyst.expressions.SubqueryExpression @@ -30,6 +28,7 @@ import org.apache.spark.sql.execution.{RDDScanExec, SparkPlan} import org.apache.spark.sql.execution.columnar._ import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec import org.apache.spark.sql.functions._ +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils} import org.apache.spark.storage.{RDDBlockId, StorageLevel} import org.apache.spark.util.{AccumulatorContext, Utils} @@ -368,12 +367,12 @@ class CachedTableSuite extends QueryTest with SQLTestUtils with SharedSQLContext val toBeCleanedAccIds = new HashSet[Long] val accId1 = spark.table("t1").queryExecution.withCachedData.collect { - case i: InMemoryRelation => i.batchStats.id + case i: InMemoryRelation => i.sizeInBytesStats.id }.head toBeCleanedAccIds += accId1 val accId2 = spark.table("t1").queryExecution.withCachedData.collect { - case i: InMemoryRelation => i.batchStats.id + case i: InMemoryRelation => i.sizeInBytesStats.id }.head toBeCleanedAccIds += accId2 @@ -782,4 +781,17 @@ class CachedTableSuite extends QueryTest with SQLTestUtils with SharedSQLContext assert(getNumInMemoryRelations(cachedDs2) == 1) } } + + test("SPARK-23312: vectorized cache reader can be disabled") { + Seq(true, false).foreach { vectorized => + withSQLConf(SQLConf.CACHE_VECTORIZED_READER_ENABLED.key -> vectorized.toString) { + val df = spark.range(10).cache() + df.queryExecution.executedPlan.foreach { + case i: InMemoryTableScanExec => + assert(i.supportsBatch == vectorized && i.supportCodegen == vectorized) + case _ => + } + } + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala index 06848e4d2b297..e7776e36702ad 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala @@ -19,6 +19,8 @@ package org.apache.spark.sql import scala.util.Random +import org.apache.spark.sql.catalyst.expressions.{Alias, Literal} +import org.apache.spark.sql.catalyst.expressions.aggregate.Count import org.apache.spark.sql.execution.WholeStageCodegenExec import org.apache.spark.sql.execution.aggregate.{HashAggregateExec, ObjectHashAggregateExec, SortAggregateExec} import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec @@ -27,7 +29,7 @@ import org.apache.spark.sql.functions._ import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.SharedSQLContext import org.apache.spark.sql.test.SQLTestData.DecimalData -import org.apache.spark.sql.types.{Decimal, DecimalType} +import org.apache.spark.sql.types.DecimalType case class Fact(date: Int, hour: Int, minute: Int, room_name: String, temp: Double) @@ -456,7 +458,6 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext { test("null moments") { val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b") - checkAnswer( emptyTableData.agg(variance('a), var_samp('a), var_pop('a), skewness('a), kurtosis('a)), Row(null, null, null, null, null)) @@ -666,4 +667,23 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext { assert(exchangePlans.length == 1) } } + + Seq(true, false).foreach { codegen => + test("SPARK-22951: dropDuplicates on empty dataFrames should produce correct aggregate " + + s"results when codegen is enabled: $codegen") { + withSQLConf((SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, codegen.toString)) { + // explicit global aggregations + val emptyAgg = Map.empty[String, String] + checkAnswer(spark.emptyDataFrame.agg(emptyAgg), Seq(Row())) + checkAnswer(spark.emptyDataFrame.groupBy().agg(emptyAgg), Seq(Row())) + checkAnswer(spark.emptyDataFrame.groupBy().agg(count("*")), Seq(Row(0))) + checkAnswer(spark.emptyDataFrame.dropDuplicates().agg(emptyAgg), Seq(Row())) + checkAnswer(spark.emptyDataFrame.dropDuplicates().groupBy().agg(emptyAgg), Seq(Row())) + checkAnswer(spark.emptyDataFrame.dropDuplicates().groupBy().agg(count("*")), Seq(Row(0))) + + // global aggregation is converted to grouping aggregation: + assert(spark.emptyDataFrame.dropDuplicates().count() == 0) + } + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala index aef0d7f3e425b..0d9eeabb397a1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala @@ -21,6 +21,7 @@ import org.apache.spark.sql.catalyst.plans.{Inner, LeftOuter, RightOuter} import org.apache.spark.sql.catalyst.plans.logical.Join import org.apache.spark.sql.execution.joins.BroadcastHashJoinExec import org.apache.spark.sql.functions._ +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.SharedSQLContext class DataFrameJoinSuite extends QueryTest with SharedSQLContext { @@ -274,4 +275,16 @@ class DataFrameJoinSuite extends QueryTest with SharedSQLContext { checkAnswer(innerJoin, Row(1) :: Nil) } + test("SPARK-23087: don't throw Analysis Exception in CheckCartesianProduct when join condition " + + "is false or null") { + withSQLConf(SQLConf.CROSS_JOINS_ENABLED.key -> "false") { + val df = spark.range(10) + val dfNull = spark.range(10).select(lit(null).as("b")) + df.join(dfNull, $"id" === $"b", "left").queryExecution.optimizedPlan + + val dfOne = df.select(lit(1).as("a")) + val dfTwo = spark.range(10).select(lit(2).as("b")) + dfOne.join(dfTwo, $"a" === $"b", "left").queryExecution.optimizedPlan + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameRangeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameRangeSuite.scala index 45afbd29d1907..b0b46640ff317 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameRangeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameRangeSuite.scala @@ -23,8 +23,8 @@ import scala.util.Random import org.scalatest.concurrent.Eventually -import org.apache.spark.{SparkException, TaskContext} -import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart} +import org.apache.spark.SparkException +import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskStart} import org.apache.spark.sql.functions._ import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.SharedSQLContext @@ -153,23 +153,17 @@ class DataFrameRangeSuite extends QueryTest with SharedSQLContext with Eventuall test("Cancelling stage in a query with Range.") { val listener = new SparkListener { - override def onJobStart(jobStart: SparkListenerJobStart): Unit = { - eventually(timeout(10.seconds)) { - assert(DataFrameRangeSuite.stageToKill > 0) - } - sparkContext.cancelStage(DataFrameRangeSuite.stageToKill) + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = { + sparkContext.cancelStage(taskStart.stageId) } } sparkContext.addSparkListener(listener) for (codegen <- Seq(true, false)) { withSQLConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> codegen.toString()) { - DataFrameRangeSuite.stageToKill = -1 val ex = intercept[SparkException] { - spark.range(1000000000L).map { x => - DataFrameRangeSuite.stageToKill = TaskContext.get().stageId() - x - }.toDF("id").agg(sum("id")).collect() + spark.range(0, 100000000000L, 1, 1) + .toDF("id").agg(sum("id")).collect() } ex.getCause() match { case null => @@ -180,10 +174,13 @@ class DataFrameRangeSuite extends QueryTest with SharedSQLContext with Eventuall fail("Expected the cause to be SparkException, got " + cause.toString() + " instead.") } } + // Wait until all ListenerBus events consumed to make sure cancelStage called for all stages + sparkContext.listenerBus.waitUntilEmpty(20.seconds.toMillis) eventually(timeout(20.seconds)) { assert(sparkContext.statusTracker.getExecutorInfos.map(_.numRunningTasks()).sum == 0) } } + sparkContext.removeSparkListener(listener) } test("SPARK-20430 Initialize Range parameters in a driver side") { @@ -203,7 +200,3 @@ class DataFrameRangeSuite extends QueryTest with SharedSQLContext with Eventuall } } } - -object DataFrameRangeSuite { - @volatile var stageToKill = -1 -} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala index 46b21c3b64a2e..8eae35325faea 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala @@ -154,24 +154,24 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext { val Array(d1, d2) = df.stat.approxQuantile("doubles", Array(q1, q2), epsilon) val Array(s1, s2) = df.stat.approxQuantile("singles", Array(q1, q2), epsilon) - val error_single = 2 * 1000 * epsilon - val error_double = 2 * 2000 * epsilon + val errorSingle = 1000 * epsilon + val errorDouble = 2.0 * errorSingle - assert(math.abs(single1 - q1 * n) < error_single) - assert(math.abs(double2 - 2 * q2 * n) < error_double) - assert(math.abs(s1 - q1 * n) < error_single) - assert(math.abs(s2 - q2 * n) < error_single) - assert(math.abs(d1 - 2 * q1 * n) < error_double) - assert(math.abs(d2 - 2 * q2 * n) < error_double) + assert(math.abs(single1 - q1 * n) <= errorSingle) + assert(math.abs(double2 - 2 * q2 * n) <= errorDouble) + assert(math.abs(s1 - q1 * n) <= errorSingle) + assert(math.abs(s2 - q2 * n) <= errorSingle) + assert(math.abs(d1 - 2 * q1 * n) <= errorDouble) + assert(math.abs(d2 - 2 * q2 * n) <= errorDouble) // Multiple columns val Array(Array(ms1, ms2), Array(md1, md2)) = df.stat.approxQuantile(Array("singles", "doubles"), Array(q1, q2), epsilon) - assert(math.abs(ms1 - q1 * n) < error_single) - assert(math.abs(ms2 - q2 * n) < error_single) - assert(math.abs(md1 - 2 * q1 * n) < error_double) - assert(math.abs(md2 - 2 * q2 * n) < error_double) + assert(math.abs(ms1 - q1 * n) <= errorSingle) + assert(math.abs(ms2 - q2 * n) <= errorSingle) + assert(math.abs(md1 - 2 * q1 * n) <= errorDouble) + assert(math.abs(md2 - 2 * q2 * n) <= errorDouble) } // quantile should be in the range [0.0, 1.0] @@ -260,6 +260,14 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext { assert(res2(1).isEmpty) } + // SPARK-22957: check for 32bit overflow when computing rank. + // ignored - takes 4 minutes to run. + ignore("approx quantile 4: test for Int overflow") { + val res = spark.range(3000000000L).stat.approxQuantile("id", Array(0.8, 0.9), 0.05) + assert(res(0) > 2200000000.0) + assert(res(1) > 2200000000.0) + } + test("crosstab") { withSQLConf(SQLConf.SUPPORT_QUOTED_REGEX_COLUMN_NAME.key -> "false") { val rng = new Random() diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 5e4c1a6a484fb..fd624ad8ef68d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -28,6 +28,7 @@ import org.scalatest.Matchers._ import org.apache.spark.SparkException import org.apache.spark.sql.catalyst.TableIdentifier +import org.apache.spark.sql.catalyst.expressions.Uuid import org.apache.spark.sql.catalyst.plans.logical.{Filter, OneRowRelation, Union} import org.apache.spark.sql.execution.{FilterExec, QueryExecution, WholeStageCodegenExec} import org.apache.spark.sql.execution.aggregate.HashAggregateExec @@ -589,6 +590,14 @@ class DataFrameSuite extends QueryTest with SharedSQLContext { Nil) } + test("SPARK-23274: except between two projects without references used in filter") { + val df = Seq((1, 2, 4), (1, 3, 5), (2, 2, 3), (2, 4, 5)).toDF("a", "b", "c") + val df1 = df.filter($"a" === 1) + val df2 = df.filter($"a" === 2) + checkAnswer(df1.select("b").except(df2.select("b")), Row(3) :: Nil) + checkAnswer(df1.select("b").except(df2.select("c")), Row(2) :: Nil) + } + test("except distinct - SQL compliance") { val df_left = Seq(1, 2, 2, 3, 3, 4).toDF("id") val df_right = Seq(1, 3).toDF("id") @@ -1255,6 +1264,34 @@ class DataFrameSuite extends QueryTest with SharedSQLContext { assert(testData.select($"*").showString(1, vertical = true) === expectedAnswer) } + test("SPARK-23023 Cast rows to strings in showString") { + val df1 = Seq(Seq(1, 2, 3, 4)).toDF("a") + assert(df1.showString(10) === + s"""+------------+ + || a| + |+------------+ + ||[1, 2, 3, 4]| + |+------------+ + |""".stripMargin) + val df2 = Seq(Map(1 -> "a", 2 -> "b")).toDF("a") + assert(df2.showString(10) === + s"""+----------------+ + || a| + |+----------------+ + ||[1 -> a, 2 -> b]| + |+----------------+ + |""".stripMargin) + val df3 = Seq(((1, "a"), 0), ((2, "b"), 0)).toDF("a", "b") + assert(df3.showString(10) === + s"""+------+---+ + || a| b| + |+------+---+ + ||[1, a]| 0| + ||[2, b]| 0| + |+------+---+ + |""".stripMargin) + } + test("SPARK-7327 show with empty dataFrame") { val expectedAnswer = """+---+-----+ ||key|value| @@ -2228,4 +2265,14 @@ class DataFrameSuite extends QueryTest with SharedSQLContext { checkAnswer(df, Row(0, 10) :: Nil) assert(df.queryExecution.executedPlan.isInstanceOf[WholeStageCodegenExec]) } + + test("Uuid expressions should produce same results at retries in the same DataFrame") { + val df = spark.range(1).select($"id", new Column(Uuid())) + checkAnswer(df, df.collect()) + } + + test("SPARK-24313: access map with binary keys") { + val mapWithBinaryKey = map(lit(Array[Byte](1.toByte)), lit(1)) + checkAnswer(spark.range(1).select(mapWithBinaryKey.getItem(Array[Byte](1.toByte))), Row(1)) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameWindowFramesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameWindowFramesSuite.scala new file mode 100644 index 0000000000000..2a0b2b85e10a9 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameWindowFramesSuite.scala @@ -0,0 +1,419 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import java.sql.{Date, Timestamp} + +import org.apache.spark.sql.expressions.Window +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.test.SharedSQLContext +import org.apache.spark.unsafe.types.CalendarInterval + +/** + * Window frame testing for DataFrame API. + */ +class DataFrameWindowFramesSuite extends QueryTest with SharedSQLContext { + import testImplicits._ + + test("lead/lag with empty data frame") { + val df = Seq.empty[(Int, String)].toDF("key", "value") + val window = Window.partitionBy($"key").orderBy($"value") + + checkAnswer( + df.select( + lead("value", 1).over(window), + lag("value", 1).over(window)), + Nil) + } + + test("lead/lag with positive offset") { + val df = Seq((1, "1"), (2, "2"), (1, "3"), (2, "4")).toDF("key", "value") + val window = Window.partitionBy($"key").orderBy($"value") + + checkAnswer( + df.select( + $"key", + lead("value", 1).over(window), + lag("value", 1).over(window)), + Row(1, "3", null) :: Row(1, null, "1") :: Row(2, "4", null) :: Row(2, null, "2") :: Nil) + } + + test("reverse lead/lag with positive offset") { + val df = Seq((1, "1"), (2, "2"), (1, "3"), (2, "4")).toDF("key", "value") + val window = Window.partitionBy($"key").orderBy($"value".desc) + + checkAnswer( + df.select( + $"key", + lead("value", 1).over(window), + lag("value", 1).over(window)), + Row(1, "1", null) :: Row(1, null, "3") :: Row(2, "2", null) :: Row(2, null, "4") :: Nil) + } + + test("lead/lag with negative offset") { + val df = Seq((1, "1"), (2, "2"), (1, "3"), (2, "4")).toDF("key", "value") + val window = Window.partitionBy($"key").orderBy($"value") + + checkAnswer( + df.select( + $"key", + lead("value", -1).over(window), + lag("value", -1).over(window)), + Row(1, null, "3") :: Row(1, "1", null) :: Row(2, null, "4") :: Row(2, "2", null) :: Nil) + } + + test("reverse lead/lag with negative offset") { + val df = Seq((1, "1"), (2, "2"), (1, "3"), (2, "4")).toDF("key", "value") + val window = Window.partitionBy($"key").orderBy($"value".desc) + + checkAnswer( + df.select( + $"key", + lead("value", -1).over(window), + lag("value", -1).over(window)), + Row(1, null, "1") :: Row(1, "3", null) :: Row(2, null, "2") :: Row(2, "4", null) :: Nil) + } + + test("lead/lag with default value") { + val default = "n/a" + val df = Seq((1, "1"), (2, "2"), (1, "3"), (2, "4"), (2, "5")).toDF("key", "value") + val window = Window.partitionBy($"key").orderBy($"value") + + checkAnswer( + df.select( + $"key", + lead("value", 2, default).over(window), + lag("value", 2, default).over(window), + lead("value", -2, default).over(window), + lag("value", -2, default).over(window)), + Row(1, default, default, default, default) :: Row(1, default, default, default, default) :: + Row(2, "5", default, default, "5") :: Row(2, default, "2", "2", default) :: + Row(2, default, default, default, default) :: Nil) + } + + test("rows/range between with empty data frame") { + val df = Seq.empty[(String, Int)].toDF("key", "value") + val window = Window.partitionBy($"key").orderBy($"value") + + checkAnswer( + df.select( + 'key, + first("value").over( + window.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)), + first("value").over( + window.rangeBetween(Window.unboundedPreceding, Window.unboundedFollowing))), + Nil) + } + + test("rows between should accept int/long values as boundary") { + val df = Seq((1L, "1"), (1L, "1"), (2147483650L, "1"), (3L, "2"), (2L, "1"), (2147483650L, "2")) + .toDF("key", "value") + + checkAnswer( + df.select( + $"key", + count("key").over( + Window.partitionBy($"value").orderBy($"key").rowsBetween(0, 2147483647))), + Seq(Row(1, 3), Row(1, 4), Row(2, 2), Row(3, 2), Row(2147483650L, 1), Row(2147483650L, 1)) + ) + + val e = intercept[AnalysisException]( + df.select( + $"key", + count("key").over( + Window.partitionBy($"value").orderBy($"key").rowsBetween(0, 2147483648L)))) + assert(e.message.contains("Boundary end is not a valid integer: 2147483648")) + } + + test("range between should accept at most one ORDER BY expression when unbounded") { + val df = Seq((1, 1)).toDF("key", "value") + val window = Window.orderBy($"key", $"value") + + checkAnswer( + df.select( + $"key", + min("key").over( + window.rangeBetween(Window.unboundedPreceding, Window.unboundedFollowing))), + Seq(Row(1, 1)) + ) + + val e1 = intercept[AnalysisException]( + df.select( + min("key").over(window.rangeBetween(Window.unboundedPreceding, 1)))) + assert(e1.message.contains("A range window frame with value boundaries cannot be used in a " + + "window specification with multiple order by expressions")) + + val e2 = intercept[AnalysisException]( + df.select( + min("key").over(window.rangeBetween(-1, Window.unboundedFollowing)))) + assert(e2.message.contains("A range window frame with value boundaries cannot be used in a " + + "window specification with multiple order by expressions")) + + val e3 = intercept[AnalysisException]( + df.select( + min("key").over(window.rangeBetween(-1, 1)))) + assert(e3.message.contains("A range window frame with value boundaries cannot be used in a " + + "window specification with multiple order by expressions")) + } + + test("range between should accept numeric values only when bounded") { + val df = Seq("non_numeric").toDF("value") + val window = Window.orderBy($"value") + + checkAnswer( + df.select( + $"value", + min("value").over( + window.rangeBetween(Window.unboundedPreceding, Window.unboundedFollowing))), + Row("non_numeric", "non_numeric") :: Nil) + + val e1 = intercept[AnalysisException]( + df.select( + min("value").over(window.rangeBetween(Window.unboundedPreceding, 1)))) + assert(e1.message.contains("The data type of the upper bound 'string' " + + "does not match the expected data type")) + + val e2 = intercept[AnalysisException]( + df.select( + min("value").over(window.rangeBetween(-1, Window.unboundedFollowing)))) + assert(e2.message.contains("The data type of the lower bound 'string' " + + "does not match the expected data type")) + + val e3 = intercept[AnalysisException]( + df.select( + min("value").over(window.rangeBetween(-1, 1)))) + assert(e3.message.contains("The data type of the lower bound 'string' " + + "does not match the expected data type")) + } + + test("range between should accept int/long values as boundary") { + val df = Seq((1L, "1"), (1L, "1"), (2147483650L, "1"), (3L, "2"), (2L, "1"), (2147483650L, "2")) + .toDF("key", "value") + + checkAnswer( + df.select( + $"key", + count("key").over( + Window.partitionBy($"value").orderBy($"key").rangeBetween(0, 2147483648L))), + Seq(Row(1, 3), Row(1, 3), Row(2, 2), Row(3, 2), Row(2147483650L, 1), Row(2147483650L, 1)) + ) + checkAnswer( + df.select( + $"key", + count("key").over( + Window.partitionBy($"value").orderBy($"key").rangeBetween(-2147483649L, 0))), + Seq(Row(1, 2), Row(1, 2), Row(2, 3), Row(2147483650L, 2), Row(2147483650L, 4), Row(3, 1)) + ) + + def dt(date: String): Date = Date.valueOf(date) + + val df2 = Seq((dt("2017-08-01"), "1"), (dt("2017-08-01"), "1"), (dt("2020-12-31"), "1"), + (dt("2017-08-03"), "2"), (dt("2017-08-02"), "1"), (dt("2020-12-31"), "2")) + .toDF("key", "value") + val window = Window.partitionBy($"value").orderBy($"key").rangeBetween(lit(0), lit(2)) + + checkAnswer( + df2.select( + $"key", + count("key").over(window)), + Seq(Row(dt("2017-08-01"), 3), Row(dt("2017-08-01"), 3), Row(dt("2020-12-31"), 1), + Row(dt("2017-08-03"), 1), Row(dt("2017-08-02"), 1), Row(dt("2020-12-31"), 1)) + ) + } + + test("range between should accept double values as boundary") { + val df = Seq((1.0D, "1"), (1.0D, "1"), (100.001D, "1"), (3.3D, "2"), (2.02D, "1"), + (100.001D, "2")).toDF("key", "value") + val window = Window.partitionBy($"value").orderBy($"key").rangeBetween(currentRow, lit(2.5D)) + + checkAnswer( + df.select( + $"key", + count("key").over(window)), + Seq(Row(1.0, 3), Row(1.0, 3), Row(100.001, 1), Row(3.3, 1), Row(2.02, 1), Row(100.001, 1)) + ) + } + + test("range between should accept interval values as boundary") { + def ts(timestamp: Long): Timestamp = new Timestamp(timestamp * 1000) + + val df = Seq((ts(1501545600), "1"), (ts(1501545600), "1"), (ts(1609372800), "1"), + (ts(1503000000), "2"), (ts(1502000000), "1"), (ts(1609372800), "2")) + .toDF("key", "value") + val window = Window.partitionBy($"value").orderBy($"key") + .rangeBetween(currentRow, lit(CalendarInterval.fromString("interval 23 days 4 hours"))) + + checkAnswer( + df.select( + $"key", + count("key").over(window)), + Seq(Row(ts(1501545600), 3), Row(ts(1501545600), 3), Row(ts(1609372800), 1), + Row(ts(1503000000), 1), Row(ts(1502000000), 1), Row(ts(1609372800), 1)) + ) + } + + test("unbounded rows/range between with aggregation") { + val df = Seq(("one", 1), ("two", 2), ("one", 3), ("two", 4)).toDF("key", "value") + val window = Window.partitionBy($"key").orderBy($"value") + + checkAnswer( + df.select( + 'key, + sum("value").over(window. + rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)), + sum("value").over(window. + rangeBetween(Window.unboundedPreceding, Window.unboundedFollowing))), + Row("one", 4, 4) :: Row("one", 4, 4) :: Row("two", 6, 6) :: Row("two", 6, 6) :: Nil) + } + + test("unbounded preceding/following rows between with aggregation") { + val df = Seq((1, "1"), (2, "2"), (2, "3"), (1, "3"), (3, "2"), (4, "3")).toDF("key", "value") + val window = Window.partitionBy($"value").orderBy($"key") + + checkAnswer( + df.select( + $"key", + last("key").over( + window.rowsBetween(Window.currentRow, Window.unboundedFollowing)), + last("key").over( + window.rowsBetween(Window.unboundedPreceding, Window.currentRow))), + Row(1, 1, 1) :: Row(2, 3, 2) :: Row(3, 3, 3) :: Row(1, 4, 1) :: Row(2, 4, 2) :: + Row(4, 4, 4) :: Nil) + } + + test("reverse unbounded preceding/following rows between with aggregation") { + val df = Seq((1, "1"), (2, "2"), (2, "3"), (1, "3"), (3, "2"), (4, "3")).toDF("key", "value") + val window = Window.partitionBy($"value").orderBy($"key".desc) + + checkAnswer( + df.select( + $"key", + last("key").over( + window.rowsBetween(Window.currentRow, Window.unboundedFollowing)), + last("key").over( + window.rowsBetween(Window.unboundedPreceding, Window.currentRow))), + Row(1, 1, 1) :: Row(3, 2, 3) :: Row(2, 2, 2) :: Row(4, 1, 4) :: Row(2, 1, 2) :: + Row(1, 1, 1) :: Nil) + } + + test("unbounded preceding/following range between with aggregation") { + val df = Seq((5, "1"), (5, "2"), (4, "2"), (6, "2"), (3, "1"), (2, "2")).toDF("key", "value") + val window = Window.partitionBy("value").orderBy("key") + + checkAnswer( + df.select( + $"key", + avg("key").over(window.rangeBetween(Window.unboundedPreceding, 1)) + .as("avg_key1"), + avg("key").over(window.rangeBetween(Window.currentRow, Window.unboundedFollowing)) + .as("avg_key2")), + Row(3, 3.0d, 4.0d) :: Row(5, 4.0d, 5.0d) :: Row(2, 2.0d, 17.0d / 4.0d) :: + Row(4, 11.0d / 3.0d, 5.0d) :: Row(5, 17.0d / 4.0d, 11.0d / 2.0d) :: + Row(6, 17.0d / 4.0d, 6.0d) :: Nil) + } + + // This is here to illustrate the fact that reverse order also reverses offsets. + test("reverse preceding/following range between with aggregation") { + val df = Seq(1, 2, 4, 3, 2, 1).toDF("value") + val window = Window.orderBy($"value".desc) + + checkAnswer( + df.select( + $"value", + sum($"value").over(window.rangeBetween(Window.unboundedPreceding, 1)), + sum($"value").over(window.rangeBetween(1, Window.unboundedFollowing))), + Row(1, 13, null) :: Row(2, 13, 2) :: Row(4, 7, 9) :: Row(3, 11, 6) :: + Row(2, 13, 2) :: Row(1, 13, null) :: Nil) + } + + test("sliding rows between with aggregation") { + val df = Seq((1, "1"), (2, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + val window = Window.partitionBy($"value").orderBy($"key").rowsBetween(-1, 2) + + checkAnswer( + df.select( + $"key", + avg("key").over(window)), + Row(1, 4.0d / 3.0d) :: Row(1, 4.0d / 3.0d) :: Row(2, 3.0d / 2.0d) :: Row(2, 2.0d) :: + Row(2, 2.0d) :: Nil) + } + + test("reverse sliding rows between with aggregation") { + val df = Seq((1, "1"), (2, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") + val window = Window.partitionBy($"value").orderBy($"key".desc).rowsBetween(-1, 2) + + checkAnswer( + df.select( + $"key", + avg("key").over(window)), + Row(1, 1.0d) :: Row(1, 4.0d / 3.0d) :: Row(2, 4.0d / 3.0d) :: Row(2, 2.0d) :: + Row(2, 2.0d) :: Nil) + } + + test("sliding range between with aggregation") { + val df = Seq((1, "1"), (1, "1"), (3, "1"), (2, "2"), (2, "1"), (2, "2")).toDF("key", "value") + val window = Window.partitionBy($"value").orderBy($"key").rangeBetween(-1, 1) + + checkAnswer( + df.select( + $"key", + avg("key").over(window)), + Row(1, 4.0d / 3.0d) :: Row(1, 4.0d / 3.0d) :: Row(2, 7.0d / 4.0d) :: Row(3, 5.0d / 2.0d) :: + Row(2, 2.0d) :: Row(2, 2.0d) :: Nil) + } + + test("reverse sliding range between with aggregation") { + val df = Seq( + (1, "Thin", "Cell Phone", 6000), + (2, "Normal", "Tablet", 1500), + (3, "Mini", "Tablet", 5500), + (4, "Ultra thin", "Cell Phone", 5500), + (5, "Very thin", "Cell Phone", 6000), + (6, "Big", "Tablet", 2500), + (7, "Bendable", "Cell Phone", 3000), + (8, "Foldable", "Cell Phone", 3000), + (9, "Pro", "Tablet", 4500), + (10, "Pro2", "Tablet", 6500)). + toDF("id", "product", "category", "revenue") + val window = Window.partitionBy($"category").orderBy($"revenue".desc). + rangeBetween(-2000L, 1000L) + + checkAnswer( + df.select( + $"id", + avg($"revenue").over(window).cast("int")), + Row(1, 5833) :: Row(2, 2000) :: Row(3, 5500) :: + Row(4, 5833) :: Row(5, 5833) :: Row(6, 2833) :: + Row(7, 3000) :: Row(8, 3000) :: Row(9, 5500) :: + Row(10, 6000) :: Nil) + } + + test("SPARK-24033: Analysis Failure of OffsetWindowFunction") { + val ds = Seq((1, 1), (1, 2), (1, 3), (2, 1), (2, 2)).toDF("n", "i") + val res = + Row(1, 1, null) :: Row (1, 2, 1) :: Row(1, 3, 2) :: Row(2, 1, null) :: Row(2, 2, 1) :: Nil + checkAnswer( + ds.withColumn("m", + lead("i", -1).over(Window.partitionBy("n").orderBy("i").rowsBetween(-1, -1))), + res) + checkAnswer( + ds.withColumn("m", + lag("i", 1).over(Window.partitionBy("n").orderBy("i").rowsBetween(-1, -1))), + res) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameWindowFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameWindowFunctionsSuite.scala index 01c988ecc3726..281147835abde 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameWindowFunctionsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameWindowFunctionsSuite.scala @@ -55,56 +55,6 @@ class DataFrameWindowFunctionsSuite extends QueryTest with SharedSQLContext { Row(1, "1") :: Row(2, "2") :: Row(null, null) :: Row(null, null) :: Nil) } - test("Window.rowsBetween") { - val df = Seq(("one", 1), ("two", 2)).toDF("key", "value") - // Running (cumulative) sum - checkAnswer( - df.select('key, sum("value").over( - Window.rowsBetween(Window.unboundedPreceding, Window.currentRow))), - Row("one", 1) :: Row("two", 3) :: Nil - ) - } - - test("lead") { - val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") - df.createOrReplaceTempView("window_table") - - checkAnswer( - df.select( - lead("value", 1).over(Window.partitionBy($"key").orderBy($"value"))), - Row("1") :: Row(null) :: Row("2") :: Row(null) :: Nil) - } - - test("lag") { - val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") - df.createOrReplaceTempView("window_table") - - checkAnswer( - df.select( - lag("value", 1).over(Window.partitionBy($"key").orderBy($"value"))), - Row(null) :: Row("1") :: Row(null) :: Row("2") :: Nil) - } - - test("lead with default value") { - val df = Seq((1, "1"), (1, "1"), (2, "2"), (1, "1"), - (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - lead("value", 2, "n/a").over(Window.partitionBy("key").orderBy("value"))), - Seq(Row("1"), Row("1"), Row("n/a"), Row("n/a"), Row("2"), Row("n/a"), Row("n/a"))) - } - - test("lag with default value") { - val df = Seq((1, "1"), (1, "1"), (2, "2"), (1, "1"), - (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - lag("value", 2, "n/a").over(Window.partitionBy($"key").orderBy($"value"))), - Seq(Row("n/a"), Row("n/a"), Row("1"), Row("1"), Row("n/a"), Row("n/a"), Row("2"))) - } - test("rank functions in unspecific window") { val df = Seq((1, "1"), (2, "2"), (1, "2"), (2, "2")).toDF("key", "value") df.createOrReplaceTempView("window_table") @@ -136,199 +86,6 @@ class DataFrameWindowFunctionsSuite extends QueryTest with SharedSQLContext { assert(e.message.contains("requires window to be ordered")) } - test("aggregation and rows between") { - val df = Seq((1, "1"), (2, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - avg("key").over(Window.partitionBy($"value").orderBy($"key").rowsBetween(-1, 2))), - Seq(Row(4.0d / 3.0d), Row(4.0d / 3.0d), Row(3.0d / 2.0d), Row(2.0d), Row(2.0d))) - } - - test("aggregation and range between") { - val df = Seq((1, "1"), (1, "1"), (3, "1"), (2, "2"), (2, "1"), (2, "2")).toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - avg("key").over(Window.partitionBy($"value").orderBy($"key").rangeBetween(-1, 1))), - Seq(Row(4.0d / 3.0d), Row(4.0d / 3.0d), Row(7.0d / 4.0d), Row(5.0d / 2.0d), - Row(2.0d), Row(2.0d))) - } - - test("row between should accept integer values as boundary") { - val df = Seq((1L, "1"), (1L, "1"), (2147483650L, "1"), - (3L, "2"), (2L, "1"), (2147483650L, "2")) - .toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - $"key", - count("key").over( - Window.partitionBy($"value").orderBy($"key").rowsBetween(0, 2147483647))), - Seq(Row(1, 3), Row(1, 4), Row(2, 2), Row(3, 2), Row(2147483650L, 1), Row(2147483650L, 1)) - ) - - val e = intercept[AnalysisException]( - df.select( - $"key", - count("key").over( - Window.partitionBy($"value").orderBy($"key").rowsBetween(0, 2147483648L)))) - assert(e.message.contains("Boundary end is not a valid integer: 2147483648")) - } - - test("range between should accept int/long values as boundary") { - val df = Seq((1L, "1"), (1L, "1"), (2147483650L, "1"), - (3L, "2"), (2L, "1"), (2147483650L, "2")) - .toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - $"key", - count("key").over( - Window.partitionBy($"value").orderBy($"key").rangeBetween(0, 2147483648L))), - Seq(Row(1, 3), Row(1, 3), Row(2, 2), Row(3, 2), Row(2147483650L, 1), Row(2147483650L, 1)) - ) - checkAnswer( - df.select( - $"key", - count("key").over( - Window.partitionBy($"value").orderBy($"key").rangeBetween(-2147483649L, 0))), - Seq(Row(1, 2), Row(1, 2), Row(2, 3), Row(2147483650L, 2), Row(2147483650L, 4), Row(3, 1)) - ) - - def dt(date: String): Date = Date.valueOf(date) - - val df2 = Seq((dt("2017-08-01"), "1"), (dt("2017-08-01"), "1"), (dt("2020-12-31"), "1"), - (dt("2017-08-03"), "2"), (dt("2017-08-02"), "1"), (dt("2020-12-31"), "2")) - .toDF("key", "value") - checkAnswer( - df2.select( - $"key", - count("key").over( - Window.partitionBy($"value").orderBy($"key").rangeBetween(lit(0), lit(2)))), - Seq(Row(dt("2017-08-01"), 3), Row(dt("2017-08-01"), 3), Row(dt("2020-12-31"), 1), - Row(dt("2017-08-03"), 1), Row(dt("2017-08-02"), 1), Row(dt("2020-12-31"), 1)) - ) - } - - test("range between should accept double values as boundary") { - val df = Seq((1.0D, "1"), (1.0D, "1"), (100.001D, "1"), - (3.3D, "2"), (2.02D, "1"), (100.001D, "2")) - .toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - $"key", - count("key").over( - Window.partitionBy($"value").orderBy($"key") - .rangeBetween(currentRow, lit(2.5D)))), - Seq(Row(1.0, 3), Row(1.0, 3), Row(100.001, 1), Row(3.3, 1), Row(2.02, 1), Row(100.001, 1)) - ) - } - - test("range between should accept interval values as boundary") { - def ts(timestamp: Long): Timestamp = new Timestamp(timestamp * 1000) - - val df = Seq((ts(1501545600), "1"), (ts(1501545600), "1"), (ts(1609372800), "1"), - (ts(1503000000), "2"), (ts(1502000000), "1"), (ts(1609372800), "2")) - .toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - $"key", - count("key").over( - Window.partitionBy($"value").orderBy($"key") - .rangeBetween(currentRow, - lit(CalendarInterval.fromString("interval 23 days 4 hours"))))), - Seq(Row(ts(1501545600), 3), Row(ts(1501545600), 3), Row(ts(1609372800), 1), - Row(ts(1503000000), 1), Row(ts(1502000000), 1), Row(ts(1609372800), 1)) - ) - } - - test("aggregation and rows between with unbounded") { - val df = Seq((1, "1"), (2, "2"), (2, "3"), (1, "3"), (3, "2"), (4, "3")).toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - $"key", - last("key").over( - Window.partitionBy($"value").orderBy($"key") - .rowsBetween(Window.currentRow, Window.unboundedFollowing)), - last("key").over( - Window.partitionBy($"value").orderBy($"key") - .rowsBetween(Window.unboundedPreceding, Window.currentRow)), - last("key").over(Window.partitionBy($"value").orderBy($"key").rowsBetween(-1, 1))), - Seq(Row(1, 1, 1, 1), Row(2, 3, 2, 3), Row(3, 3, 3, 3), Row(1, 4, 1, 2), Row(2, 4, 2, 4), - Row(4, 4, 4, 4))) - } - - test("aggregation and range between with unbounded") { - val df = Seq((5, "1"), (5, "2"), (4, "2"), (6, "2"), (3, "1"), (2, "2")).toDF("key", "value") - df.createOrReplaceTempView("window_table") - checkAnswer( - df.select( - $"key", - last("value").over( - Window.partitionBy($"value").orderBy($"key").rangeBetween(-2, -1)) - .equalTo("2") - .as("last_v"), - avg("key").over(Window.partitionBy("value").orderBy("key").rangeBetween(Long.MinValue, 1)) - .as("avg_key1"), - avg("key").over(Window.partitionBy("value").orderBy("key").rangeBetween(0, Long.MaxValue)) - .as("avg_key2"), - avg("key").over(Window.partitionBy("value").orderBy("key").rangeBetween(-1, 0)) - .as("avg_key3") - ), - Seq(Row(3, null, 3.0d, 4.0d, 3.0d), - Row(5, false, 4.0d, 5.0d, 5.0d), - Row(2, null, 2.0d, 17.0d / 4.0d, 2.0d), - Row(4, true, 11.0d / 3.0d, 5.0d, 4.0d), - Row(5, true, 17.0d / 4.0d, 11.0d / 2.0d, 4.5d), - Row(6, true, 17.0d / 4.0d, 6.0d, 11.0d / 2.0d))) - } - - test("reverse sliding range frame") { - val df = Seq( - (1, "Thin", "Cell Phone", 6000), - (2, "Normal", "Tablet", 1500), - (3, "Mini", "Tablet", 5500), - (4, "Ultra thin", "Cell Phone", 5500), - (5, "Very thin", "Cell Phone", 6000), - (6, "Big", "Tablet", 2500), - (7, "Bendable", "Cell Phone", 3000), - (8, "Foldable", "Cell Phone", 3000), - (9, "Pro", "Tablet", 4500), - (10, "Pro2", "Tablet", 6500)). - toDF("id", "product", "category", "revenue") - val window = Window. - partitionBy($"category"). - orderBy($"revenue".desc). - rangeBetween(-2000L, 1000L) - checkAnswer( - df.select( - $"id", - avg($"revenue").over(window).cast("int")), - Row(1, 5833) :: Row(2, 2000) :: Row(3, 5500) :: - Row(4, 5833) :: Row(5, 5833) :: Row(6, 2833) :: - Row(7, 3000) :: Row(8, 3000) :: Row(9, 5500) :: - Row(10, 6000) :: Nil) - } - - // This is here to illustrate the fact that reverse order also reverses offsets. - test("reverse unbounded range frame") { - val df = Seq(1, 2, 4, 3, 2, 1). - map(Tuple1.apply). - toDF("value") - val window = Window.orderBy($"value".desc) - checkAnswer( - df.select( - $"value", - sum($"value").over(window.rangeBetween(Long.MinValue, 1)), - sum($"value").over(window.rangeBetween(1, Long.MaxValue))), - Row(1, 13, null) :: Row(2, 13, 2) :: Row(4, 7, 9) :: - Row(3, 11, 6) :: Row(2, 13, 2) :: Row(1, 13, null) :: Nil) - } - test("statistical functions") { val df = Seq(("a", 1), ("a", 1), ("a", 2), ("a", 2), ("b", 4), ("b", 3), ("b", 2)). toDF("key", "value") diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala index d535896723bd5..e0f4d2ba685e1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala @@ -958,12 +958,12 @@ class DatasetSuite extends QueryTest with SharedSQLContext { ).toDS() val expected = - """+-------+ - || f| - |+-------+ - ||[foo,1]| - ||[bar,2]| - |+-------+ + """+--------+ + || f| + |+--------+ + ||[foo, 1]| + ||[bar, 2]| + |+--------+ |""".stripMargin checkShowString(ds, expected) @@ -1441,8 +1441,27 @@ class DatasetSuite extends QueryTest with SharedSQLContext { assert(e.getCause.isInstanceOf[NullPointerException]) } } + + test("SPARK-23025: Add support for null type in scala reflection") { + val data = Seq(("a", null)) + checkDataset(data.toDS(), data: _*) + } + + test("SPARK-23614: Union produces incorrect results when caching is used") { + val cached = spark.createDataset(Seq(TestDataUnion(1, 2, 3), TestDataUnion(4, 5, 6))).cache() + val group1 = cached.groupBy("x").agg(min(col("y")) as "value") + val group2 = cached.groupBy("x").agg(min(col("z")) as "value") + checkAnswer(group1.union(group2), Row(4, 5) :: Row(1, 2) :: Row(4, 6) :: Row(1, 3) :: Nil) + } + + test("SPARK-23835: null primitive data type should throw NullPointerException") { + val ds = Seq[(Option[Int], Option[Int])]((Some(1), None)).toDS() + intercept[NullPointerException](ds.as[(Int, Int)].collect()) + } } +case class TestDataUnion(x: Int, y: Int, z: Int) + case class SingleData(id: Int) case class DoubleData(id: Int, val1: String) case class TripleData(id: Int, val1: String, val2: Long) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala new file mode 100644 index 0000000000000..b5d4c558f0d3e --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala @@ -0,0 +1,159 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import java.io.FileNotFoundException + +import org.apache.hadoop.fs.Path +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.SparkException +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SharedSQLContext + + +class FileBasedDataSourceSuite extends QueryTest with SharedSQLContext with BeforeAndAfterAll { + import testImplicits._ + + override def beforeAll(): Unit = { + super.beforeAll() + spark.sessionState.conf.setConf(SQLConf.ORC_IMPLEMENTATION, "native") + } + + override def afterAll(): Unit = { + try { + spark.sessionState.conf.unsetConf(SQLConf.ORC_IMPLEMENTATION) + } finally { + super.afterAll() + } + } + + private val allFileBasedDataSources = Seq("orc", "parquet", "csv", "json", "text") + private val nameWithSpecialChars = "sp&cial%c hars" + + allFileBasedDataSources.foreach { format => + test(s"Writing empty datasets should not fail - $format") { + withTempPath { dir => + Seq("str").toDS().limit(0).write.format(format).save(dir.getCanonicalPath) + } + } + } + + // `TEXT` data source always has a single column whose name is `value`. + allFileBasedDataSources.filterNot(_ == "text").foreach { format => + test(s"SPARK-23072 Write and read back unicode column names - $format") { + withTempPath { path => + val dir = path.getCanonicalPath + + // scalastyle:off nonascii + val df = Seq("a").toDF("한글") + // scalastyle:on nonascii + + df.write.format(format).option("header", "true").save(dir) + val answerDf = spark.read.format(format).option("header", "true").load(dir) + + assert(df.schema.sameType(answerDf.schema)) + checkAnswer(df, answerDf) + } + } + } + + // Only ORC/Parquet support this. `CSV` and `JSON` returns an empty schema. + // `TEXT` data source always has a single column whose name is `value`. + Seq("orc", "parquet").foreach { format => + test(s"SPARK-15474 Write and read back non-empty schema with empty dataframe - $format") { + withTempPath { file => + val path = file.getCanonicalPath + val emptyDf = Seq((true, 1, "str")).toDF().limit(0) + emptyDf.write.format(format).save(path) + + val df = spark.read.format(format).load(path) + assert(df.schema.sameType(emptyDf.schema)) + checkAnswer(df, emptyDf) + } + } + } + + allFileBasedDataSources.foreach { format => + test(s"SPARK-22146 read files containing special characters using $format") { + withTempDir { dir => + val tmpFile = s"$dir/$nameWithSpecialChars" + spark.createDataset(Seq("a", "b")).write.format(format).save(tmpFile) + val fileContent = spark.read.format(format).load(tmpFile) + checkAnswer(fileContent, Seq(Row("a"), Row("b"))) + } + } + } + + // Separate test case for formats that support multiLine as an option. + Seq("json", "csv").foreach { format => + test("SPARK-23148 read files containing special characters " + + s"using $format with multiline enabled") { + withTempDir { dir => + val tmpFile = s"$dir/$nameWithSpecialChars" + spark.createDataset(Seq("a", "b")).write.format(format).save(tmpFile) + val reader = spark.read.format(format).option("multiLine", true) + val fileContent = reader.load(tmpFile) + checkAnswer(fileContent, Seq(Row("a"), Row("b"))) + } + } + } + + allFileBasedDataSources.foreach { format => + testQuietly(s"Enabling/disabling ignoreMissingFiles using $format") { + def testIgnoreMissingFiles(): Unit = { + withTempDir { dir => + val basePath = dir.getCanonicalPath + + Seq("0").toDF("a").write.format(format).save(new Path(basePath, "first").toString) + Seq("1").toDF("a").write.format(format).save(new Path(basePath, "second").toString) + + val thirdPath = new Path(basePath, "third") + val fs = thirdPath.getFileSystem(spark.sparkContext.hadoopConfiguration) + Seq("2").toDF("a").write.format(format).save(thirdPath.toString) + val files = fs.listStatus(thirdPath).filter(_.isFile).map(_.getPath) + + val df = spark.read.format(format).load( + new Path(basePath, "first").toString, + new Path(basePath, "second").toString, + new Path(basePath, "third").toString) + + // Make sure all data files are deleted and can't be opened. + files.foreach(f => fs.delete(f, false)) + assert(fs.delete(thirdPath, true)) + for (f <- files) { + intercept[FileNotFoundException](fs.open(f)) + } + + checkAnswer(df, Seq(Row("0"), Row("1"))) + } + } + + withSQLConf(SQLConf.IGNORE_MISSING_FILES.key -> "true") { + testIgnoreMissingFiles() + } + + withSQLConf(SQLConf.IGNORE_MISSING_FILES.key -> "false") { + val exception = intercept[SparkException] { + testIgnoreMissingFiles() + } + assert(exception.getMessage().contains("does not exist")) + } + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/FilterPushdownBenchmark.scala b/sql/core/src/test/scala/org/apache/spark/sql/FilterPushdownBenchmark.scala new file mode 100644 index 0000000000000..c6dd7dadc9d93 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/FilterPushdownBenchmark.scala @@ -0,0 +1,243 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import java.io.File + +import scala.util.{Random, Try} + +import org.apache.spark.SparkConf +import org.apache.spark.sql.functions.monotonically_increasing_id +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.util.{Benchmark, Utils} + + +/** + * Benchmark to measure read performance with Filter pushdown. + */ +object FilterPushdownBenchmark { + val conf = new SparkConf() + conf.set("orc.compression", "snappy") + conf.set("spark.sql.parquet.compression.codec", "snappy") + + private val spark = SparkSession.builder() + .master("local[1]") + .appName("FilterPushdownBenchmark") + .config(conf) + .getOrCreate() + + def withTempPath(f: File => Unit): Unit = { + val path = Utils.createTempDir() + path.delete() + try f(path) finally Utils.deleteRecursively(path) + } + + def withTempTable(tableNames: String*)(f: => Unit): Unit = { + try f finally tableNames.foreach(spark.catalog.dropTempView) + } + + def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = { + val (keys, values) = pairs.unzip + val currentValues = keys.map(key => Try(spark.conf.get(key)).toOption) + (keys, values).zipped.foreach(spark.conf.set) + try f finally { + keys.zip(currentValues).foreach { + case (key, Some(value)) => spark.conf.set(key, value) + case (key, None) => spark.conf.unset(key) + } + } + } + + private def prepareTable(dir: File, numRows: Int, width: Int): Unit = { + import spark.implicits._ + val selectExpr = (1 to width).map(i => s"CAST(value AS STRING) c$i") + val df = spark.range(numRows).map(_ => Random.nextLong).selectExpr(selectExpr: _*) + .withColumn("id", monotonically_increasing_id()) + + val dirORC = dir.getCanonicalPath + "/orc" + val dirParquet = dir.getCanonicalPath + "/parquet" + + df.write.mode("overwrite").orc(dirORC) + df.write.mode("overwrite").parquet(dirParquet) + + spark.read.orc(dirORC).createOrReplaceTempView("orcTable") + spark.read.parquet(dirParquet).createOrReplaceTempView("parquetTable") + } + + def filterPushDownBenchmark( + values: Int, + title: String, + whereExpr: String, + selectExpr: String = "*"): Unit = { + val benchmark = new Benchmark(title, values, minNumIters = 5) + + Seq(false, true).foreach { pushDownEnabled => + val name = s"Parquet Vectorized ${if (pushDownEnabled) s"(Pushdown)" else ""}" + benchmark.addCase(name) { _ => + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> s"$pushDownEnabled") { + spark.sql(s"SELECT $selectExpr FROM parquetTable WHERE $whereExpr").collect() + } + } + } + + Seq(false, true).foreach { pushDownEnabled => + val name = s"Native ORC Vectorized ${if (pushDownEnabled) s"(Pushdown)" else ""}" + benchmark.addCase(name) { _ => + withSQLConf(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key -> s"$pushDownEnabled") { + spark.sql(s"SELECT $selectExpr FROM orcTable WHERE $whereExpr").collect() + } + } + } + + /* + Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.2 + Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz + + Select 0 row (id IS NULL): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 7882 / 7957 2.0 501.1 1.0X + Parquet Vectorized (Pushdown) 55 / 60 285.2 3.5 142.9X + Native ORC Vectorized 5592 / 5627 2.8 355.5 1.4X + Native ORC Vectorized (Pushdown) 66 / 70 237.2 4.2 118.9X + + Select 0 row (7864320 < id < 7864320): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 7884 / 7909 2.0 501.2 1.0X + Parquet Vectorized (Pushdown) 739 / 752 21.3 47.0 10.7X + Native ORC Vectorized 5614 / 5646 2.8 356.9 1.4X + Native ORC Vectorized (Pushdown) 81 / 83 195.2 5.1 97.8X + + Select 1 row (id = 7864320): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 7905 / 8027 2.0 502.6 1.0X + Parquet Vectorized (Pushdown) 740 / 766 21.2 47.1 10.7X + Native ORC Vectorized 5684 / 5738 2.8 361.4 1.4X + Native ORC Vectorized (Pushdown) 78 / 81 202.4 4.9 101.7X + + Select 1 row (id <=> 7864320): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 7928 / 7993 2.0 504.1 1.0X + Parquet Vectorized (Pushdown) 747 / 772 21.0 47.5 10.6X + Native ORC Vectorized 5728 / 5753 2.7 364.2 1.4X + Native ORC Vectorized (Pushdown) 76 / 78 207.9 4.8 104.8X + + Select 1 row (7864320 <= id <= 7864320):Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 7939 / 8021 2.0 504.8 1.0X + Parquet Vectorized (Pushdown) 746 / 770 21.1 47.4 10.6X + Native ORC Vectorized 5690 / 5734 2.8 361.7 1.4X + Native ORC Vectorized (Pushdown) 76 / 79 206.7 4.8 104.3X + + Select 1 row (7864319 < id < 7864321): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 7972 / 8019 2.0 506.9 1.0X + Parquet Vectorized (Pushdown) 742 / 764 21.2 47.2 10.7X + Native ORC Vectorized 5704 / 5743 2.8 362.6 1.4X + Native ORC Vectorized (Pushdown) 76 / 78 207.9 4.8 105.4X + + Select 10% rows (id < 1572864): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 8733 / 8808 1.8 555.2 1.0X + Parquet Vectorized (Pushdown) 2213 / 2267 7.1 140.7 3.9X + Native ORC Vectorized 6420 / 6463 2.4 408.2 1.4X + Native ORC Vectorized (Pushdown) 1313 / 1331 12.0 83.5 6.7X + + Select 50% rows (id < 7864320): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 11518 / 11591 1.4 732.3 1.0X + Parquet Vectorized (Pushdown) 7962 / 7991 2.0 506.2 1.4X + Native ORC Vectorized 8927 / 8985 1.8 567.6 1.3X + Native ORC Vectorized (Pushdown) 6102 / 6160 2.6 387.9 1.9X + + Select 90% rows (id < 14155776): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 14255 / 14389 1.1 906.3 1.0X + Parquet Vectorized (Pushdown) 13564 / 13594 1.2 862.4 1.1X + Native ORC Vectorized 11442 / 11608 1.4 727.5 1.2X + Native ORC Vectorized (Pushdown) 10991 / 11029 1.4 698.8 1.3X + + Select all rows (id IS NOT NULL): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 14917 / 14938 1.1 948.4 1.0X + Parquet Vectorized (Pushdown) 14910 / 14964 1.1 948.0 1.0X + Native ORC Vectorized 11986 / 12069 1.3 762.0 1.2X + Native ORC Vectorized (Pushdown) 12037 / 12123 1.3 765.3 1.2X + + Select all rows (id > -1): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 14951 / 14976 1.1 950.6 1.0X + Parquet Vectorized (Pushdown) 14934 / 15016 1.1 949.5 1.0X + Native ORC Vectorized 12000 / 12156 1.3 763.0 1.2X + Native ORC Vectorized (Pushdown) 12079 / 12113 1.3 767.9 1.2X + + Select all rows (id != -1): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ----------------------------------------------------------------------------------------------- + Parquet Vectorized 14930 / 14972 1.1 949.3 1.0X + Parquet Vectorized (Pushdown) 15015 / 15047 1.0 954.6 1.0X + Native ORC Vectorized 12090 / 12259 1.3 768.7 1.2X + Native ORC Vectorized (Pushdown) 12021 / 12096 1.3 764.2 1.2X + */ + benchmark.run() + } + + def main(args: Array[String]): Unit = { + val numRows = 1024 * 1024 * 15 + val width = 5 + val mid = numRows / 2 + + withTempPath { dir => + withTempTable("orcTable", "patquetTable") { + prepareTable(dir, numRows, width) + + Seq("id IS NULL", s"$mid < id AND id < $mid").foreach { whereExpr => + val title = s"Select 0 row ($whereExpr)".replace("id AND id", "id") + filterPushDownBenchmark(numRows, title, whereExpr) + } + + Seq( + s"id = $mid", + s"id <=> $mid", + s"$mid <= id AND id <= $mid", + s"${mid - 1} < id AND id < ${mid + 1}" + ).foreach { whereExpr => + val title = s"Select 1 row ($whereExpr)".replace("id AND id", "id") + filterPushDownBenchmark(numRows, title, whereExpr) + } + + val selectExpr = (1 to width).map(i => s"MAX(c$i)").mkString("", ",", ", MAX(id)") + + Seq(10, 50, 90).foreach { percent => + filterPushDownBenchmark( + numRows, + s"Select $percent% rows (id < ${numRows * percent / 100})", + s"id < ${numRows * percent / 100}", + selectExpr + ) + } + + Seq("id IS NOT NULL", "id > -1", "id != -1").foreach { whereExpr => + filterPushDownBenchmark( + numRows, + s"Select all rows ($whereExpr)", + whereExpr, + selectExpr) + } + } + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/GroupedDatasetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/GroupedDatasetSuite.scala new file mode 100644 index 0000000000000..218a1b7248f12 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/GroupedDatasetSuite.scala @@ -0,0 +1,96 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.api.python.PythonEvalType +import org.apache.spark.sql.catalyst.plans.logical.AnalysisBarrier +import org.apache.spark.sql.execution.python.PythonUDF +import org.apache.spark.sql.functions.udf +import org.apache.spark.sql.test.SharedSQLContext +import org.apache.spark.sql.types.{LongType, StructField, StructType} + +class GroupedDatasetSuite extends QueryTest with SharedSQLContext { + import testImplicits._ + + private val scalaUDF = udf((x: Long) => { x + 1 }) + private lazy val datasetWithUDF = spark.range(1).toDF("s").select($"s", scalaUDF($"s")) + + private def assertContainsAnalysisBarrier(ds: Dataset[_], atLevel: Int = 1): Unit = { + assert(atLevel >= 0) + var children = Seq(ds.queryExecution.logical) + (1 to atLevel).foreach { _ => + children = children.flatMap(_.children) + } + val barriers = children.collect { + case ab: AnalysisBarrier => ab + } + assert(barriers.nonEmpty, s"Plan does not contain AnalysisBarrier at level $atLevel:\n" + + ds.queryExecution.logical) + } + + test("SPARK-24373: avoid running Analyzer rules twice on RelationalGroupedDataset") { + val groupByDataset = datasetWithUDF.groupBy() + val rollupDataset = datasetWithUDF.rollup("s") + val cubeDataset = datasetWithUDF.cube("s") + val pivotDataset = datasetWithUDF.groupBy().pivot("s", Seq(1, 2)) + datasetWithUDF.cache() + Seq(groupByDataset, rollupDataset, cubeDataset, pivotDataset).foreach { rgDS => + val df = rgDS.count() + assertContainsAnalysisBarrier(df) + assertCached(df) + } + + val flatMapGroupsInRDF = datasetWithUDF.groupBy().flatMapGroupsInR( + Array.emptyByteArray, + Array.emptyByteArray, + Array.empty, + StructType(Seq(StructField("s", LongType)))) + val flatMapGroupsInPandasDF = datasetWithUDF.groupBy().flatMapGroupsInPandas(PythonUDF( + "pyUDF", + null, + StructType(Seq(StructField("s", LongType))), + Seq.empty, + PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF, + true)) + Seq(flatMapGroupsInRDF, flatMapGroupsInPandasDF).foreach { df => + assertContainsAnalysisBarrier(df, 2) + assertCached(df) + } + datasetWithUDF.unpersist(true) + } + + test("SPARK-24373: avoid running Analyzer rules twice on KeyValueGroupedDataset") { + val kvDasaset = datasetWithUDF.groupByKey(_.getLong(0)) + datasetWithUDF.cache() + val mapValuesKVDataset = kvDasaset.mapValues(_.getLong(0)).reduceGroups(_ + _) + val keysKVDataset = kvDasaset.keys + val flatMapGroupsKVDataset = kvDasaset.flatMapGroups((k, _) => Seq(k)) + val aggKVDataset = kvDasaset.count() + val otherKVDataset = spark.range(1).groupByKey(_ + 1) + val cogroupKVDataset = kvDasaset.cogroup(otherKVDataset)((k, _, _) => Seq(k)) + Seq((mapValuesKVDataset, 1), + (keysKVDataset, 2), + (flatMapGroupsKVDataset, 2), + (aggKVDataset, 1), + (cogroupKVDataset, 2)).foreach { case (df, analysisBarrierDepth) => + assertContainsAnalysisBarrier(df, analysisBarrierDepth) + assertCached(df) + } + datasetWithUDF.unpersist(true) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala index 771e1186e63ab..44767dfc92497 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala @@ -239,7 +239,7 @@ class JoinSuite extends QueryTest with SharedSQLContext { Row(2, 2, 1, null) :: Row(2, 2, 2, 2) :: Nil) } - assert(e.getMessage.contains("Detected cartesian product for INNER join " + + assert(e.getMessage.contains("Detected implicit cartesian product for INNER join " + "between logical plans")) } } @@ -611,7 +611,7 @@ class JoinSuite extends QueryTest with SharedSQLContext { val e = intercept[Exception] { checkAnswer(sql(query), Nil); } - assert(e.getMessage.contains("Detected cartesian product")) + assert(e.getMessage.contains("Detected implicit cartesian product")) } cartesianQueries.foreach(checkCartesianDetection) @@ -882,4 +882,15 @@ class JoinSuite extends QueryTest with SharedSQLContext { checkAnswer(df, Row(3, 8, 7, 2) :: Row(3, 8, 4, 2) :: Nil) } } + + test("SPARK-24495: Join may return wrong result when having duplicated equal-join keys") { + withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1", + SQLConf.CONSTRAINT_PROPAGATION_ENABLED.key -> "false", + SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") { + val df1 = spark.range(0, 100, 1, 2) + val df2 = spark.range(100).select($"id".as("b1"), (- $"id").as("b2")) + val res = df1.join(df2, $"id" === $"b1" && $"id" === $"b2").select($"b1", $"b2", $"id") + checkAnswer(res, Row(0, 0, 0)) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/LocalSparkSession.scala b/sql/core/src/test/scala/org/apache/spark/sql/LocalSparkSession.scala index d66a6902b0510..cbef1c7828319 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/LocalSparkSession.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/LocalSparkSession.scala @@ -30,11 +30,15 @@ trait LocalSparkSession extends BeforeAndAfterEach with BeforeAndAfterAll { self override def beforeAll() { super.beforeAll() InternalLoggerFactory.setDefaultFactory(Slf4JLoggerFactory.INSTANCE) + SparkSession.clearActiveSession() + SparkSession.clearDefaultSession() } override def afterEach() { try { resetSparkContext() + SparkSession.clearActiveSession() + SparkSession.clearDefaultSession() } finally { super.afterEach() } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 5e077285ade55..bc57efeef69c1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -18,7 +18,6 @@ package org.apache.spark.sql import java.io.File -import java.math.MathContext import java.net.{MalformedURLException, URL} import java.sql.Timestamp import java.util.concurrent.atomic.AtomicBoolean @@ -28,8 +27,6 @@ import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart} import org.apache.spark.sql.catalyst.util.StringUtils import org.apache.spark.sql.execution.aggregate import org.apache.spark.sql.execution.aggregate.{HashAggregateExec, SortAggregateExec} -import org.apache.spark.sql.execution.datasources.{HadoopFsRelation, LogicalRelation} -import org.apache.spark.sql.execution.datasources.orc.OrcFileFormat import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, CartesianProductExec, SortMergeJoinExec} import org.apache.spark.sql.functions._ import org.apache.spark.sql.internal.SQLConf @@ -1519,24 +1516,6 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext { } } - test("decimal precision with multiply/division") { - checkAnswer(sql("select 10.3 * 3.0"), Row(BigDecimal("30.90"))) - checkAnswer(sql("select 10.3000 * 3.0"), Row(BigDecimal("30.90000"))) - checkAnswer(sql("select 10.30000 * 30.0"), Row(BigDecimal("309.000000"))) - checkAnswer(sql("select 10.300000000000000000 * 3.000000000000000000"), - Row(BigDecimal("30.900000000000000000000000000000000000", new MathContext(38)))) - checkAnswer(sql("select 10.300000000000000000 * 3.0000000000000000000"), - Row(null)) - - checkAnswer(sql("select 10.3 / 3.0"), Row(BigDecimal("3.433333"))) - checkAnswer(sql("select 10.3000 / 3.0"), Row(BigDecimal("3.4333333"))) - checkAnswer(sql("select 10.30000 / 30.0"), Row(BigDecimal("0.343333333"))) - checkAnswer(sql("select 10.300000000000000000 / 3.00000000000000000"), - Row(BigDecimal("3.433333333333333333333333333", new MathContext(38)))) - checkAnswer(sql("select 10.3000000000000000000 / 3.00000000000000000"), - Row(BigDecimal("3.4333333333333333333333333333", new MathContext(38)))) - } - test("SPARK-10215 Div of Decimal returns null") { val d = Decimal(1.12321).toBigDecimal val df = Seq((d, 1)).toDF("a", "b") @@ -1636,6 +1615,46 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext { } } + test("SPARK-23281: verify the correctness of sort direction on composite order by clause") { + withTempView("src") { + Seq[(Integer, Integer)]( + (1, 1), + (1, 3), + (2, 3), + (3, 3), + (4, null), + (5, null) + ).toDF("key", "value").createOrReplaceTempView("src") + + checkAnswer(sql( + """ + |SELECT MAX(value) as value, key as col2 + |FROM src + |GROUP BY key + |ORDER BY value desc, key + """.stripMargin), + Seq(Row(3, 1), Row(3, 2), Row(3, 3), Row(null, 4), Row(null, 5))) + + checkAnswer(sql( + """ + |SELECT MAX(value) as value, key as col2 + |FROM src + |GROUP BY key + |ORDER BY value desc, key desc + """.stripMargin), + Seq(Row(3, 3), Row(3, 2), Row(3, 1), Row(null, 5), Row(null, 4))) + + checkAnswer(sql( + """ + |SELECT MAX(value) as value, key as col2 + |FROM src + |GROUP BY key + |ORDER BY value asc, key desc + """.stripMargin), + Seq(Row(null, 5), Row(null, 4), Row(3, 3), Row(3, 2), Row(3, 1))) + } + } + test("run sql directly on files") { val df = spark.range(100).toDF() withTempPath(f => { @@ -1916,12 +1935,12 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext { var e = intercept[AnalysisException] { sql("SELECT a.* FROM temp_table_no_cols a") }.getMessage - assert(e.contains("cannot resolve 'a.*' give input columns ''")) + assert(e.contains("cannot resolve 'a.*' given input columns ''")) e = intercept[AnalysisException] { dfNoCols.select($"b.*") }.getMessage - assert(e.contains("cannot resolve 'b.*' give input columns ''")) + assert(e.contains("cannot resolve 'b.*' given input columns ''")) } } @@ -2129,7 +2148,8 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext { test("data source table created in InMemoryCatalog should be able to read/write") { withTable("tbl") { - sql("CREATE TABLE tbl(i INT, j STRING) USING parquet") + val provider = spark.sessionState.conf.defaultDataSourceName + sql(s"CREATE TABLE tbl(i INT, j STRING) USING $provider") checkAnswer(sql("SELECT i, j FROM tbl"), Nil) Seq(1 -> "a", 2 -> "b").toDF("i", "j").write.mode("overwrite").insertInto("tbl") @@ -2453,9 +2473,9 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext { test("SPARK-16975: Column-partition path starting '_' should be handled correctly") { withTempDir { dir => - val parquetDir = new File(dir, "parquet").getCanonicalPath - spark.range(10).withColumn("_col", $"id").write.partitionBy("_col").save(parquetDir) - spark.read.parquet(parquetDir) + val dataDir = new File(dir, "data").getCanonicalPath + spark.range(10).withColumn("_col", $"id").write.partitionBy("_col").save(dataDir) + spark.read.load(dataDir) } } @@ -2700,11 +2720,6 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext { } } - test("SPARK-21743: top-most limit should not cause memory leak") { - // In unit test, Spark will fail the query if memory leak detected. - spark.range(100).groupBy("id").count().limit(1).collect() - } - test("SPARK-21652: rule confliction of InferFiltersFromConstraints and ConstantPropagation") { withTempView("t1", "t2") { Seq((1, 1)).toDF("col1", "col2").createOrReplaceTempView("t1") @@ -2719,6 +2734,17 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext { } } + test("SPARK-23079: constraints should be inferred correctly with aliases") { + withTable("t") { + spark.range(5).write.saveAsTable("t") + val t = spark.read.table("t") + val left = t.withColumn("xid", $"id" + lit(1)).as("x") + val right = t.withColumnRenamed("id", "xid").as("y") + val df = left.join(right, "xid").filter("id = 3").toDF() + checkAnswer(df, Row(4, 3)) + } + } + test("SRARK-22266: the same aggregate function was calculated multiple times") { val query = "SELECT a, max(b+1), max(b+1) + 1 FROM testData2 GROUP BY a" val df = sql(query) @@ -2759,20 +2785,4 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext { } } } - - // Only New OrcFileFormat supports this - Seq(classOf[org.apache.spark.sql.execution.datasources.orc.OrcFileFormat].getCanonicalName, - "parquet").foreach { format => - test(s"SPARK-15474 Write and read back non-emtpy schema with empty dataframe - $format") { - withTempPath { file => - val path = file.getCanonicalPath - val emptyDf = Seq((true, 1, "str")).toDF.limit(0) - emptyDf.write.format(format).save(path) - - val df = spark.read.format(format).load(path) - assert(df.schema.sameType(emptyDf.schema)) - checkAnswer(df, emptyDf) - } - } - } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQueryTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQueryTestSuite.scala index e3901af4b9988..beac9699585d5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQueryTestSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQueryTestSuite.scala @@ -230,7 +230,8 @@ class SQLQueryTestSuite extends QueryTest with SharedSQLContext { .replaceAll("Location.*/sql/core/", s"Location ${notIncludedMsg}sql/core/") .replaceAll("Created By.*", s"Created By $notIncludedMsg") .replaceAll("Created Time.*", s"Created Time $notIncludedMsg") - .replaceAll("Last Access.*", s"Last Access $notIncludedMsg")) + .replaceAll("Last Access.*", s"Last Access $notIncludedMsg") + .replaceAll("\\*\\(\\d+\\) ", "*")) // remove the WholeStageCodegen codegenStageIds // If the output is not pre-sorted, sort it. if (isSorted(df.queryExecution.analyzed)) (schema, answer) else (schema, answer.sorted) @@ -291,7 +292,7 @@ class SQLQueryTestSuite extends QueryTest with SharedSQLContext { TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles")) // Add Locale setting Locale.setDefault(Locale.US) - RuleExecutor.resetTime() + RuleExecutor.resetMetrics() } override def afterAll(): Unit = { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SessionStateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SessionStateSuite.scala index c01666770720c..77710fd277808 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SessionStateSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SessionStateSuite.scala @@ -17,8 +17,6 @@ package org.apache.spark.sql -import org.scalatest.BeforeAndAfterAll -import org.scalatest.BeforeAndAfterEach import scala.collection.mutable.ArrayBuffer import org.apache.spark.SparkFunSuite @@ -28,8 +26,7 @@ import org.apache.spark.sql.catalyst.rules.Rule import org.apache.spark.sql.execution.QueryExecution import org.apache.spark.sql.util.QueryExecutionListener -class SessionStateSuite extends SparkFunSuite - with BeforeAndAfterEach with BeforeAndAfterAll { +class SessionStateSuite extends SparkFunSuite { /** * A shared SparkSession for all tests in this suite. Make sure you reset any changes to this @@ -46,6 +43,8 @@ class SessionStateSuite extends SparkFunSuite if (activeSession != null) { activeSession.stop() activeSession = null + SparkSession.clearActiveSession() + SparkSession.clearDefaultSession() } super.afterAll() } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SparkSessionBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SparkSessionBuilderSuite.scala index c0301f2ce2d66..4c560d4b032d0 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SparkSessionBuilderSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SparkSessionBuilderSuite.scala @@ -50,6 +50,14 @@ class SparkSessionBuilderSuite extends SparkFunSuite with BeforeAndAfterEach { assert(SparkSession.builder().getOrCreate() == session) } + test("sets default and active session") { + assert(SparkSession.getDefaultSession == None) + assert(SparkSession.getActiveSession == None) + val session = SparkSession.builder().master("local").getOrCreate() + assert(SparkSession.getDefaultSession == Some(session)) + assert(SparkSession.getActiveSession == Some(session)) + } + test("config options are propagated to existing SparkSession") { val session1 = SparkSession.builder().master("local").config("spark-config1", "a").getOrCreate() assert(session1.conf.get("spark-config1") == "a") diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SubquerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SubquerySuite.scala index 8673dc14f7597..acef62d81ee12 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SubquerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SubquerySuite.scala @@ -950,4 +950,24 @@ class SubquerySuite extends QueryTest with SharedSQLContext { assert(join.duplicateResolved) assert(optimizedPlan.resolved) } + + test("SPARK-23316: AnalysisException after max iteration reached for IN query") { + // before the fix this would throw AnalysisException + spark.range(10).where("(id,id) in (select id, null from range(3))").count + } + + test("SPARK-24085 scalar subquery in partitioning expression") { + withTable("parquet_part") { + Seq("1" -> "a", "2" -> "a", "3" -> "b", "4" -> "b") + .toDF("id_value", "id_type") + .write + .mode(SaveMode.Overwrite) + .partitionBy("id_type") + .format("parquet") + .saveAsTable("parquet_part") + checkAnswer( + sql("SELECT * FROM parquet_part WHERE id_type = (SELECT 'b')"), + Row("3", "b") :: Row("4", "b") :: Nil) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestQueryExecutionListener.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestQueryExecutionListener.scala new file mode 100644 index 0000000000000..d2a6358ee822b --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/TestQueryExecutionListener.scala @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import java.util.concurrent.atomic.AtomicBoolean + +import org.apache.spark.sql.execution.QueryExecution +import org.apache.spark.sql.util.QueryExecutionListener + + +class TestQueryExecutionListener extends QueryExecutionListener { + override def onSuccess(funcName: String, qe: QueryExecution, durationNs: Long): Unit = { + OnSuccessCall.isOnSuccessCalled.set(true) + } + + override def onFailure(funcName: String, qe: QueryExecution, exception: Exception): Unit = { } +} + +/** + * This has a variable to check if `onSuccess` is actually called or not. Currently, this is for + * the test case in PySpark. See SPARK-23942. + */ +object OnSuccessCall { + val isOnSuccessCalled = new AtomicBoolean(false) + + def isCalled(): Boolean = isOnSuccessCalled.get() + + def clear(): Unit = isOnSuccessCalled.set(false) +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala index 7f1c009ca6e7a..af6a10b425b9f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala @@ -17,12 +17,13 @@ package org.apache.spark.sql +import org.apache.spark.sql.api.java._ import org.apache.spark.sql.catalyst.plans.logical.Project import org.apache.spark.sql.execution.command.ExplainCommand -import org.apache.spark.sql.functions.{col, udf} +import org.apache.spark.sql.functions.udf import org.apache.spark.sql.test.SharedSQLContext import org.apache.spark.sql.test.SQLTestData._ -import org.apache.spark.sql.types.DataTypes +import org.apache.spark.sql.types.{DataTypes, DoubleType} private case class FunctionResult(f1: String, f2: String) @@ -79,7 +80,7 @@ class UDFSuite extends QueryTest with SharedSQLContext { val e = intercept[AnalysisException] { df.selectExpr("substr('abcd', 2, 3, 4)") } - assert(e.getMessage.contains("Invalid number of arguments for function substr")) + assert(e.getMessage.contains("Invalid number of arguments for function substr. Expected:")) } test("error reporting for incorrect number of arguments - udf") { @@ -88,7 +89,7 @@ class UDFSuite extends QueryTest with SharedSQLContext { spark.udf.register("foo", (_: String).length) df.selectExpr("foo(2, 3, 4)") } - assert(e.getMessage.contains("Invalid number of arguments for function foo")) + assert(e.getMessage.contains("Invalid number of arguments for function foo. Expected:")) } test("error reporting for undefined functions") { @@ -128,6 +129,13 @@ class UDFSuite extends QueryTest with SharedSQLContext { val df2 = testData.select(bar()) assert(df2.logicalPlan.asInstanceOf[Project].projectList.forall(!_.deterministic)) assert(df2.head().getDouble(0) >= 0.0) + + val javaUdf = udf(new UDF0[Double] { + override def call(): Double = Math.random() + }, DoubleType).asNondeterministic() + val df3 = testData.select(javaUdf()) + assert(df3.logicalPlan.asInstanceOf[Project].projectList.forall(!_.deterministic)) + assert(df3.head().getDouble(0) >= 0.0) } test("TwoArgument UDF") { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala index a08433ba794d9..cc8b600efa46a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala @@ -21,7 +21,7 @@ import scala.beans.{BeanInfo, BeanProperty} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow} -import org.apache.spark.sql.catalyst.expressions.GenericInternalRow +import org.apache.spark.sql.catalyst.expressions.{Cast, ExpressionEvalHelper, GenericInternalRow, Literal} import org.apache.spark.sql.catalyst.util.{ArrayData, GenericArrayData} import org.apache.spark.sql.execution.datasources.parquet.ParquetTest import org.apache.spark.sql.functions._ @@ -44,6 +44,8 @@ object UDT { case v: MyDenseVector => java.util.Arrays.equals(this.data, v.data) case _ => false } + + override def toString: String = data.mkString("(", ", ", ")") } private[sql] class MyDenseVectorUDT extends UserDefinedType[MyDenseVector] { @@ -143,7 +145,8 @@ private[spark] class ExampleSubTypeUDT extends UserDefinedType[IExampleSubType] override def userClass: Class[IExampleSubType] = classOf[IExampleSubType] } -class UserDefinedTypeSuite extends QueryTest with SharedSQLContext with ParquetTest { +class UserDefinedTypeSuite extends QueryTest with SharedSQLContext with ParquetTest + with ExpressionEvalHelper { import testImplicits._ private lazy val pointsRDD = Seq( @@ -304,4 +307,12 @@ class UserDefinedTypeSuite extends QueryTest with SharedSQLContext with ParquetT pointsRDD.except(pointsRDD2), Seq(Row(0.0, new UDT.MyDenseVector(Array(0.2, 2.0))))) } + + test("SPARK-23054 Cast UserDefinedType to string") { + val udt = new UDT.MyDenseVectorUDT() + val vector = new UDT.MyDenseVector(Array(1.0, 3.0, 5.0, 7.0, 9.0)) + val data = udt.serialize(vector) + val ret = Cast(Literal(data, udt), StringType, None) + checkEvaluation(ret, "(1.0, 3.0, 5.0, 7.0, 9.0)") + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala index aac8d56ba6201..bde2de5b39fd7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala @@ -17,11 +17,14 @@ package org.apache.spark.sql.execution -import org.apache.spark.sql.Row +import scala.util.Random + +import org.apache.spark.sql.{Dataset, Row} import org.apache.spark.sql.catalyst.expressions.{Alias, Literal} import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, IdentityBroadcastMode, SinglePartition} import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec, ReusedExchangeExec, ShuffleExchangeExec} import org.apache.spark.sql.execution.joins.HashedRelationBroadcastMode +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.SharedSQLContext class ExchangeSuite extends SparkPlanTest with SharedSQLContext { @@ -101,4 +104,32 @@ class ExchangeSuite extends SparkPlanTest with SharedSQLContext { assert(exchange4.sameResult(exchange5)) assert(exchange5 sameResult exchange4) } + + test("SPARK-23207: Make repartition() generate consistent output") { + def assertConsistency(ds: Dataset[java.lang.Long]): Unit = { + ds.persist() + + val exchange = ds.mapPartitions { iter => + Random.shuffle(iter) + }.repartition(111) + val exchange2 = ds.repartition(111) + + assert(exchange.rdd.collectPartitions() === exchange2.rdd.collectPartitions()) + } + + withSQLConf(SQLConf.SORT_BEFORE_REPARTITION.key -> "true") { + // repartition() should generate consistent output. + assertConsistency(spark.range(10000)) + + // case when input contains duplicated rows. + assertConsistency(spark.range(10000).map(i => Random.nextInt(1000).toLong)) + } + } + + test("SPARK-23614: Fix incorrect reuse exchange when caching is used") { + val cached = spark.createDataset(Seq((1, 2, 3), (4, 5, 6))).cache() + val projection1 = cached.select("_1", "_2").queryExecution.executedPlan + val projection2 = cached.select("_1", "_3").queryExecution.executedPlan + assert(!projection1.sameResult(projection2)) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/GlobalTempViewSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/GlobalTempViewSuite.scala index cc943e0356f2a..dcc6fa6403f31 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/GlobalTempViewSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/GlobalTempViewSuite.scala @@ -36,7 +36,7 @@ class GlobalTempViewSuite extends QueryTest with SharedSQLContext { test("basic semantic") { val expectedErrorMsg = "not found" - try { + withGlobalTempView("src") { sql("CREATE GLOBAL TEMP VIEW src AS SELECT 1, 'a'") // If there is no database in table name, we should try local temp view first, if not found, @@ -79,19 +79,15 @@ class GlobalTempViewSuite extends QueryTest with SharedSQLContext { // We can also use Dataset API to replace global temp view Seq(2 -> "b").toDF("i", "j").createOrReplaceGlobalTempView("src") checkAnswer(spark.table(s"$globalTempDB.src"), Row(2, "b")) - } finally { - spark.catalog.dropGlobalTempView("src") } } test("global temp view is shared among all sessions") { - try { + withGlobalTempView("src") { sql("CREATE GLOBAL TEMP VIEW src AS SELECT 1, 2") checkAnswer(spark.table(s"$globalTempDB.src"), Row(1, 2)) val newSession = spark.newSession() checkAnswer(newSession.table(s"$globalTempDB.src"), Row(1, 2)) - } finally { - spark.catalog.dropGlobalTempView("src") } } @@ -105,27 +101,25 @@ class GlobalTempViewSuite extends QueryTest with SharedSQLContext { test("CREATE GLOBAL TEMP VIEW USING") { withTempPath { path => - try { + withGlobalTempView("src") { Seq(1 -> "a").toDF("i", "j").write.parquet(path.getAbsolutePath) sql(s"CREATE GLOBAL TEMP VIEW src USING parquet OPTIONS (PATH '${path.toURI}')") checkAnswer(spark.table(s"$globalTempDB.src"), Row(1, "a")) sql(s"INSERT INTO $globalTempDB.src SELECT 2, 'b'") checkAnswer(spark.table(s"$globalTempDB.src"), Row(1, "a") :: Row(2, "b") :: Nil) - } finally { - spark.catalog.dropGlobalTempView("src") } } } test("CREATE TABLE LIKE should work for global temp view") { - try { - sql("CREATE GLOBAL TEMP VIEW src AS SELECT 1 AS a, '2' AS b") - sql(s"CREATE TABLE cloned LIKE $globalTempDB.src") - val tableMeta = spark.sessionState.catalog.getTableMetadata(TableIdentifier("cloned")) - assert(tableMeta.schema == new StructType().add("a", "int", false).add("b", "string", false)) - } finally { - spark.catalog.dropGlobalTempView("src") - sql("DROP TABLE default.cloned") + withTable("cloned") { + withGlobalTempView("src") { + sql("CREATE GLOBAL TEMP VIEW src AS SELECT 1 AS a, '2' AS b") + sql(s"CREATE TABLE cloned LIKE $globalTempDB.src") + val tableMeta = spark.sessionState.catalog.getTableMetadata(TableIdentifier("cloned")) + assert(tableMeta.schema == new StructType() + .add("a", "int", false).add("b", "string", false)) + } } } @@ -146,26 +140,25 @@ class GlobalTempViewSuite extends QueryTest with SharedSQLContext { } test("should lookup global temp view if and only if global temp db is specified") { - try { - sql("CREATE GLOBAL TEMP VIEW same_name AS SELECT 3, 4") - sql("CREATE TEMP VIEW same_name AS SELECT 1, 2") + withTempView("same_name") { + withGlobalTempView("same_name") { + sql("CREATE GLOBAL TEMP VIEW same_name AS SELECT 3, 4") + sql("CREATE TEMP VIEW same_name AS SELECT 1, 2") - checkAnswer(sql("SELECT * FROM same_name"), Row(1, 2)) + checkAnswer(sql("SELECT * FROM same_name"), Row(1, 2)) - // we never lookup global temp views if database is not specified in table name - spark.catalog.dropTempView("same_name") - intercept[AnalysisException](sql("SELECT * FROM same_name")) + // we never lookup global temp views if database is not specified in table name + spark.catalog.dropTempView("same_name") + intercept[AnalysisException](sql("SELECT * FROM same_name")) - // Use qualified name to lookup a global temp view. - checkAnswer(sql(s"SELECT * FROM $globalTempDB.same_name"), Row(3, 4)) - } finally { - spark.catalog.dropTempView("same_name") - spark.catalog.dropGlobalTempView("same_name") + // Use qualified name to lookup a global temp view. + checkAnswer(sql(s"SELECT * FROM $globalTempDB.same_name"), Row(3, 4)) + } } } test("public Catalog should recognize global temp view") { - try { + withGlobalTempView("src") { sql("CREATE GLOBAL TEMP VIEW src AS SELECT 1, 2") assert(spark.catalog.tableExists(globalTempDB, "src")) @@ -175,8 +168,6 @@ class GlobalTempViewSuite extends QueryTest with SharedSQLContext { description = null, tableType = "TEMPORARY", isTemporary = true).toString) - } finally { - spark.catalog.dropGlobalTempView("src") } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuerySuite.scala index 78c1e5dae566d..a543eb8351656 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/OptimizeMetadataOnlyQuerySuite.scala @@ -17,9 +17,12 @@ package org.apache.spark.sql.execution +import java.io.File + import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.plans.logical.LocalRelation import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.internal.SQLConf.OPTIMIZER_METADATA_ONLY import org.apache.spark.sql.test.SharedSQLContext class OptimizeMetadataOnlyQuerySuite extends QueryTest with SharedSQLContext { @@ -125,4 +128,23 @@ class OptimizeMetadataOnlyQuerySuite extends QueryTest with SharedSQLContext { sql("SELECT COUNT(DISTINCT p) FROM t_1000").collect() } } + + test("Incorrect result caused by the rule OptimizeMetadataOnlyQuery") { + withSQLConf(OPTIMIZER_METADATA_ONLY.key -> "true") { + withTempPath { path => + val tablePath = new File(s"${path.getCanonicalPath}/cOl3=c/cOl1=a/cOl5=e") + Seq(("a", "b", "c", "d", "e")).toDF("cOl1", "cOl2", "cOl3", "cOl4", "cOl5") + .write.json(tablePath.getCanonicalPath) + + val df = spark.read.json(path.getCanonicalPath).select("CoL1", "CoL5", "CoL3").distinct() + checkAnswer(df, Row("a", "e", "c")) + + val localRelation = df.queryExecution.optimizedPlan.collectFirst { + case l: LocalRelation => l + } + assert(localRelation.nonEmpty, "expect to see a LocalRelation") + assert(localRelation.get.output.map(_.name) == Seq("cOl3", "cOl1", "cOl5")) + } + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala index b50642d275ba8..4d3b2f6883e05 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala @@ -69,6 +69,27 @@ class PlannerSuite extends SharedSQLContext { testPartialAggregationPlan(query) } + test("mixed aggregates with same distinct columns") { + def assertNoExpand(plan: SparkPlan): Unit = { + assert(plan.collect { case e: ExpandExec => e }.isEmpty) + } + + withTempView("v") { + Seq((1, 1.0, 1.0), (1, 2.0, 2.0)).toDF("i", "j", "k").createTempView("v") + // one distinct column + val query1 = sql("SELECT sum(DISTINCT j), max(DISTINCT j) FROM v GROUP BY i") + assertNoExpand(query1.queryExecution.executedPlan) + + // 2 distinct columns + val query2 = sql("SELECT corr(DISTINCT j, k), count(DISTINCT j, k) FROM v GROUP BY i") + assertNoExpand(query2.queryExecution.executedPlan) + + // 2 distinct columns with different order + val query3 = sql("SELECT corr(DISTINCT j, k), count(DISTINCT k, j) FROM v GROUP BY i") + assertNoExpand(query3.queryExecution.executedPlan) + } + } + test("sizeInBytes estimation of limit operator for broadcast hash join optimization") { def checkPlan(fieldTypes: Seq[DataType]): Unit = { withTempView("testLimit") { @@ -260,11 +281,16 @@ class PlannerSuite extends SharedSQLContext { // do they satisfy the distribution requirements? As a result, we need at least four test cases. private def assertDistributionRequirementsAreSatisfied(outputPlan: SparkPlan): Unit = { - if (outputPlan.children.length > 1 - && outputPlan.requiredChildDistribution.toSet != Set(UnspecifiedDistribution)) { - val childPartitionings = outputPlan.children.map(_.outputPartitioning) - if (!Partitioning.allCompatible(childPartitionings)) { - fail(s"Partitionings are not compatible: $childPartitionings") + if (outputPlan.children.length > 1) { + val childPartitionings = outputPlan.children.zip(outputPlan.requiredChildDistribution) + .filter { + case (_, UnspecifiedDistribution) => false + case (_, _: BroadcastDistribution) => false + case _ => true + }.map(_._1.outputPartitioning) + + if (childPartitionings.map(_.numPartitions).toSet.size > 1) { + fail(s"Partitionings doesn't have same number of partitions: $childPartitionings") } } outputPlan.children.zip(outputPlan.requiredChildDistribution).foreach { @@ -274,40 +300,7 @@ class PlannerSuite extends SharedSQLContext { } } - test("EnsureRequirements with incompatible child partitionings which satisfy distribution") { - // Consider an operator that requires inputs that are clustered by two expressions (e.g. - // sort merge join where there are multiple columns in the equi-join condition) - val clusteringA = Literal(1) :: Nil - val clusteringB = Literal(2) :: Nil - val distribution = ClusteredDistribution(clusteringA ++ clusteringB) - // Say that the left and right inputs are each partitioned by _one_ of the two join columns: - val leftPartitioning = HashPartitioning(clusteringA, 1) - val rightPartitioning = HashPartitioning(clusteringB, 1) - // Individually, each input's partitioning satisfies the clustering distribution: - assert(leftPartitioning.satisfies(distribution)) - assert(rightPartitioning.satisfies(distribution)) - // However, these partitionings are not compatible with each other, so we still need to - // repartition both inputs prior to performing the join: - assert(!leftPartitioning.compatibleWith(rightPartitioning)) - assert(!rightPartitioning.compatibleWith(leftPartitioning)) - val inputPlan = DummySparkPlan( - children = Seq( - DummySparkPlan(outputPartitioning = leftPartitioning), - DummySparkPlan(outputPartitioning = rightPartitioning) - ), - requiredChildDistribution = Seq(distribution, distribution), - requiredChildOrdering = Seq(Seq.empty, Seq.empty) - ) - val outputPlan = EnsureRequirements(spark.sessionState.conf).apply(inputPlan) - assertDistributionRequirementsAreSatisfied(outputPlan) - if (outputPlan.collect { case e: ShuffleExchangeExec => true }.isEmpty) { - fail(s"Exchange should have been added:\n$outputPlan") - } - } - test("EnsureRequirements with child partitionings with different numbers of output partitions") { - // This is similar to the previous test, except it checks that partitionings are not compatible - // unless they produce the same number of partitions. val clustering = Literal(1) :: Nil val distribution = ClusteredDistribution(clustering) val inputPlan = DummySparkPlan( @@ -386,18 +379,15 @@ class PlannerSuite extends SharedSQLContext { } } - test("EnsureRequirements eliminates Exchange if child has Exchange with same partitioning") { + test("EnsureRequirements eliminates Exchange if child has same partitioning") { val distribution = ClusteredDistribution(Literal(1) :: Nil) - val finalPartitioning = HashPartitioning(Literal(1) :: Nil, 5) - val childPartitioning = HashPartitioning(Literal(2) :: Nil, 5) - assert(!childPartitioning.satisfies(distribution)) - val inputPlan = ShuffleExchangeExec(finalPartitioning, - DummySparkPlan( - children = DummySparkPlan(outputPartitioning = childPartitioning) :: Nil, - requiredChildDistribution = Seq(distribution), - requiredChildOrdering = Seq(Seq.empty)), - None) + val partitioning = HashPartitioning(Literal(1) :: Nil, 5) + assert(partitioning.satisfies(distribution)) + val inputPlan = ShuffleExchangeExec( + partitioning, + DummySparkPlan(outputPartitioning = partitioning), + None) val outputPlan = EnsureRequirements(spark.sessionState.conf).apply(inputPlan) assertDistributionRequirementsAreSatisfied(outputPlan) if (outputPlan.collect { case e: ShuffleExchangeExec => true }.size == 2) { @@ -407,17 +397,13 @@ class PlannerSuite extends SharedSQLContext { test("EnsureRequirements does not eliminate Exchange with different partitioning") { val distribution = ClusteredDistribution(Literal(1) :: Nil) - // Number of partitions differ - val finalPartitioning = HashPartitioning(Literal(1) :: Nil, 8) - val childPartitioning = HashPartitioning(Literal(2) :: Nil, 5) - assert(!childPartitioning.satisfies(distribution)) - val inputPlan = ShuffleExchangeExec(finalPartitioning, - DummySparkPlan( - children = DummySparkPlan(outputPartitioning = childPartitioning) :: Nil, - requiredChildDistribution = Seq(distribution), - requiredChildOrdering = Seq(Seq.empty)), - None) + val partitioning = HashPartitioning(Literal(2) :: Nil, 5) + assert(!partitioning.satisfies(distribution)) + val inputPlan = ShuffleExchangeExec( + partitioning, + DummySparkPlan(outputPartitioning = partitioning), + None) val outputPlan = EnsureRequirements(spark.sessionState.conf).apply(inputPlan) assertDistributionRequirementsAreSatisfied(outputPlan) if (outputPlan.collect { case e: ShuffleExchangeExec => true }.size == 1) { @@ -643,6 +629,23 @@ class PlannerSuite extends SharedSQLContext { requiredOrdering = Seq(orderingA, orderingB), shouldHaveSort = true) } + + test("SPARK-24495: EnsureRequirements can return wrong plan when reusing the same key in join") { + val plan1 = DummySparkPlan(outputOrdering = Seq(orderingA), + outputPartitioning = HashPartitioning(exprA :: exprA :: Nil, 5)) + val plan2 = DummySparkPlan(outputOrdering = Seq(orderingB), + outputPartitioning = HashPartitioning(exprB :: Nil, 5)) + val smjExec = SortMergeJoinExec( + exprA :: exprA :: Nil, exprB :: exprC :: Nil, Inner, None, plan1, plan2) + + val outputPlan = EnsureRequirements(spark.sessionState.conf).apply(smjExec) + outputPlan match { + case SortMergeJoinExec(leftKeys, rightKeys, _, _, _, _) => + assert(leftKeys == Seq(exprA, exprA)) + assert(rightKeys == Seq(exprB, exprC)) + case _ => fail() + } + } } // Used for unit-testing EnsureRequirements diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SQLViewSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SQLViewSuite.scala index 08a4a21b20f61..ce8fde28a941c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SQLViewSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SQLViewSuite.scala @@ -69,21 +69,25 @@ abstract class SQLViewSuite extends QueryTest with SQLTestUtils { } test("create a permanent view on a temp view") { - withView("jtv1", "temp_jtv1", "global_temp_jtv1") { - sql("CREATE TEMPORARY VIEW temp_jtv1 AS SELECT * FROM jt WHERE id > 3") - var e = intercept[AnalysisException] { - sql("CREATE VIEW jtv1 AS SELECT * FROM temp_jtv1 WHERE id < 6") - }.getMessage - assert(e.contains("Not allowed to create a permanent view `jtv1` by " + - "referencing a temporary view `temp_jtv1`")) - - val globalTempDB = spark.sharedState.globalTempViewManager.database - sql("CREATE GLOBAL TEMP VIEW global_temp_jtv1 AS SELECT * FROM jt WHERE id > 0") - e = intercept[AnalysisException] { - sql(s"CREATE VIEW jtv1 AS SELECT * FROM $globalTempDB.global_temp_jtv1 WHERE id < 6") - }.getMessage - assert(e.contains(s"Not allowed to create a permanent view `jtv1` by referencing " + - s"a temporary view `global_temp`.`global_temp_jtv1`")) + withView("jtv1") { + withTempView("temp_jtv1") { + withGlobalTempView("global_temp_jtv1") { + sql("CREATE TEMPORARY VIEW temp_jtv1 AS SELECT * FROM jt WHERE id > 3") + var e = intercept[AnalysisException] { + sql("CREATE VIEW jtv1 AS SELECT * FROM temp_jtv1 WHERE id < 6") + }.getMessage + assert(e.contains("Not allowed to create a permanent view `jtv1` by " + + "referencing a temporary view `temp_jtv1`")) + + val globalTempDB = spark.sharedState.globalTempViewManager.database + sql("CREATE GLOBAL TEMP VIEW global_temp_jtv1 AS SELECT * FROM jt WHERE id > 0") + e = intercept[AnalysisException] { + sql(s"CREATE VIEW jtv1 AS SELECT * FROM $globalTempDB.global_temp_jtv1 WHERE id < 6") + }.getMessage + assert(e.contains(s"Not allowed to create a permanent view `jtv1` by referencing " + + s"a temporary view `global_temp`.`global_temp_jtv1`")) + } + } } } @@ -289,7 +293,7 @@ abstract class SQLViewSuite extends QueryTest with SQLTestUtils { sql("CREATE TEMPORARY VIEW testView AS SELECT id FROM jt") } - assert(e.message.contains("Temporary table") && e.message.contains("already exists")) + assert(e.message.contains("Temporary view") && e.message.contains("already exists")) } } @@ -659,7 +663,7 @@ abstract class SQLViewSuite extends QueryTest with SQLTestUtils { }.getMessage assert(e.contains("The depth of view `default`.`view0` exceeds the maximum view " + "resolution depth (10). Analysis is aborted to avoid errors. Increase the value " + - "of spark.sql.view.maxNestedViewDepth to work aroud this.")) + "of spark.sql.view.maxNestedViewDepth to work around this.")) } val e = intercept[IllegalArgumentException] { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala index 232c1beae7998..3e31d22e15c0e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala @@ -70,6 +70,7 @@ class UnsafeFixedWidthAggregationMapSuite TaskContext.setTaskContext(new TaskContextImpl( stageId = 0, + stageAttemptNumber = 0, partitionId = 0, taskAttemptId = Random.nextInt(10000), attemptNumber = 0, diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala index 604502f2a57d0..bf588d3bb7841 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala @@ -29,6 +29,7 @@ import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow} import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, UnsafeProjection, UnsafeRow} import org.apache.spark.sql.test.SharedSQLContext import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.map.BytesToBytesMap /** * Test suite for [[UnsafeKVExternalSorter]], with randomly generated test data. @@ -116,6 +117,7 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite with SharedSQLContext { val taskMemMgr = new TaskMemoryManager(memoryManager, 0) TaskContext.setTaskContext(new TaskContextImpl( stageId = 0, + stageAttemptNumber = 0, partitionId = 0, taskAttemptId = 98456, attemptNumber = 0, @@ -204,4 +206,42 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite with SharedSQLContext { spill = true ) } + + test("SPARK-23376: Create UnsafeKVExternalSorter with BytesToByteMap having duplicated keys") { + val memoryManager = new TestMemoryManager(new SparkConf()) + val taskMemoryManager = new TaskMemoryManager(memoryManager, 0) + val map = new BytesToBytesMap(taskMemoryManager, 64, taskMemoryManager.pageSizeBytes()) + + // Key/value are a unsafe rows with a single int column + val schema = new StructType().add("i", IntegerType) + val key = new UnsafeRow(1) + key.pointTo(new Array[Byte](32), 32) + key.setInt(0, 1) + val value = new UnsafeRow(1) + value.pointTo(new Array[Byte](32), 32) + value.setInt(0, 2) + + for (_ <- 1 to 65) { + val loc = map.lookup(key.getBaseObject, key.getBaseOffset, key.getSizeInBytes) + loc.append( + key.getBaseObject, key.getBaseOffset, key.getSizeInBytes, + value.getBaseObject, value.getBaseOffset, value.getSizeInBytes) + } + + // Make sure we can successfully create a UnsafeKVExternalSorter with a `BytesToBytesMap` + // which has duplicated keys and the number of entries exceeds its capacity. + try { + TaskContext.setTaskContext(new TaskContextImpl(0, 0, 0, 0, 0, taskMemoryManager, null, null)) + new UnsafeKVExternalSorter( + schema, + schema, + sparkContext.env.blockManager, + sparkContext.env.serializerManager, + taskMemoryManager.pageSizeBytes(), + Int.MaxValue, + map) + } finally { + TaskContext.unset() + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala index dff88ce7f1b9a..d305ce3e698ae 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala @@ -21,15 +21,13 @@ import java.io.{ByteArrayInputStream, ByteArrayOutputStream, File} import java.util.Properties import org.apache.spark._ -import org.apache.spark.executor.TaskMetrics import org.apache.spark.memory.TaskMemoryManager import org.apache.spark.rdd.RDD -import org.apache.spark.sql.Row +import org.apache.spark.sql.{LocalSparkSession, Row, SparkSession} import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow} import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, UnsafeRow} import org.apache.spark.sql.types._ import org.apache.spark.storage.ShuffleBlockId -import org.apache.spark.util.Utils import org.apache.spark.util.collection.ExternalSorter /** @@ -43,7 +41,7 @@ class ClosableByteArrayInputStream(buf: Array[Byte]) extends ByteArrayInputStrea } } -class UnsafeRowSerializerSuite extends SparkFunSuite with LocalSparkContext { +class UnsafeRowSerializerSuite extends SparkFunSuite with LocalSparkSession { private def toUnsafeRow(row: Row, schema: Array[DataType]): UnsafeRow = { val converter = unsafeRowConverter(schema) @@ -58,7 +56,7 @@ class UnsafeRowSerializerSuite extends SparkFunSuite with LocalSparkContext { } test("toUnsafeRow() test helper method") { - // This currently doesnt work because the generic getter throws an exception. + // This currently doesn't work because the generic getter throws an exception. val row = Row("Hello", 123) val unsafeRow = toUnsafeRow(row, Array(StringType, IntegerType)) assert(row.getString(0) === unsafeRow.getUTF8String(0).toString) @@ -97,59 +95,43 @@ class UnsafeRowSerializerSuite extends SparkFunSuite with LocalSparkContext { } test("SPARK-10466: external sorter spilling with unsafe row serializer") { - var sc: SparkContext = null - var outputFile: File = null - val oldEnv = SparkEnv.get // save the old SparkEnv, as it will be overwritten - Utils.tryWithSafeFinally { - val conf = new SparkConf() - .set("spark.shuffle.spill.initialMemoryThreshold", "1") - .set("spark.shuffle.sort.bypassMergeThreshold", "0") - .set("spark.testing.memory", "80000") - - sc = new SparkContext("local", "test", conf) - outputFile = File.createTempFile("test-unsafe-row-serializer-spill", "") - // prepare data - val converter = unsafeRowConverter(Array(IntegerType)) - val data = (1 to 10000).iterator.map { i => - (i, converter(Row(i))) - } - val taskMemoryManager = new TaskMemoryManager(sc.env.memoryManager, 0) - val taskContext = new TaskContextImpl(0, 0, 0, 0, taskMemoryManager, new Properties, null) - - val sorter = new ExternalSorter[Int, UnsafeRow, UnsafeRow]( - taskContext, - partitioner = Some(new HashPartitioner(10)), - serializer = new UnsafeRowSerializer(numFields = 1)) - - // Ensure we spilled something and have to merge them later - assert(sorter.numSpills === 0) - sorter.insertAll(data) - assert(sorter.numSpills > 0) + val conf = new SparkConf() + .set("spark.shuffle.spill.initialMemoryThreshold", "1") + .set("spark.shuffle.sort.bypassMergeThreshold", "0") + .set("spark.testing.memory", "80000") + spark = SparkSession.builder().master("local").appName("test").config(conf).getOrCreate() + val outputFile = File.createTempFile("test-unsafe-row-serializer-spill", "") + outputFile.deleteOnExit() + // prepare data + val converter = unsafeRowConverter(Array(IntegerType)) + val data = (1 to 10000).iterator.map { i => + (i, converter(Row(i))) + } + val taskMemoryManager = new TaskMemoryManager(spark.sparkContext.env.memoryManager, 0) + val taskContext = new TaskContextImpl(0, 0, 0, 0, 0, taskMemoryManager, new Properties, null) - // Merging spilled files should not throw assertion error - sorter.writePartitionedFile(ShuffleBlockId(0, 0, 0), outputFile) - } { - // Clean up - if (sc != null) { - sc.stop() - } + val sorter = new ExternalSorter[Int, UnsafeRow, UnsafeRow]( + taskContext, + partitioner = Some(new HashPartitioner(10)), + serializer = new UnsafeRowSerializer(numFields = 1)) - // restore the spark env - SparkEnv.set(oldEnv) + // Ensure we spilled something and have to merge them later + assert(sorter.numSpills === 0) + sorter.insertAll(data) + assert(sorter.numSpills > 0) - if (outputFile != null) { - outputFile.delete() - } - } + // Merging spilled files should not throw assertion error + sorter.writePartitionedFile(ShuffleBlockId(0, 0, 0), outputFile) } test("SPARK-10403: unsafe row serializer with SortShuffleManager") { val conf = new SparkConf().set("spark.shuffle.manager", "sort") - sc = new SparkContext("local", "test", conf) + spark = SparkSession.builder().master("local").appName("test").config(conf).getOrCreate() val row = Row("Hello", 123) val unsafeRow = toUnsafeRow(row, Array(StringType, IntegerType)) - val rowsRDD = sc.parallelize(Seq((0, unsafeRow), (1, unsafeRow), (0, unsafeRow))) - .asInstanceOf[RDD[Product2[Int, InternalRow]]] + val rowsRDD = spark.sparkContext.parallelize( + Seq((0, unsafeRow), (1, unsafeRow), (0, unsafeRow)) + ).asInstanceOf[RDD[Product2[Int, InternalRow]]] val dependency = new ShuffleDependency[Int, InternalRow, InternalRow]( rowsRDD, diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/WholeStageCodegenSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/WholeStageCodegenSuite.scala index bc05dca578c47..9180a22c260f1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/WholeStageCodegenSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/WholeStageCodegenSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.execution +import org.apache.spark.metrics.source.CodegenMetrics import org.apache.spark.sql.{QueryTest, Row, SaveMode} import org.apache.spark.sql.catalyst.expressions.codegen.{CodeAndComment, CodeGenerator} import org.apache.spark.sql.execution.aggregate.HashAggregateExec @@ -24,13 +25,15 @@ import org.apache.spark.sql.execution.columnar.InMemoryTableScanExec import org.apache.spark.sql.execution.joins.BroadcastHashJoinExec import org.apache.spark.sql.execution.joins.SortMergeJoinExec import org.apache.spark.sql.expressions.scalalang.typed -import org.apache.spark.sql.functions.{avg, broadcast, col, max} +import org.apache.spark.sql.functions.{avg, broadcast, col, lit, max} import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.SharedSQLContext import org.apache.spark.sql.types.{IntegerType, StringType, StructType} class WholeStageCodegenSuite extends QueryTest with SharedSQLContext { + import testImplicits._ + test("range/filter should be combined") { val df = spark.range(10).filter("id = 1").selectExpr("id + 1") val plan = df.queryExecution.executedPlan @@ -121,31 +124,23 @@ class WholeStageCodegenSuite extends QueryTest with SharedSQLContext { test("cache for primitive type should be in WholeStageCodegen with InMemoryTableScanExec") { import testImplicits._ - val dsInt = spark.range(3).cache - dsInt.count + val dsInt = spark.range(3).cache() + dsInt.count() val dsIntFilter = dsInt.filter(_ > 0) val planInt = dsIntFilter.queryExecution.executedPlan - assert(planInt.find(p => - p.isInstanceOf[WholeStageCodegenExec] && - p.asInstanceOf[WholeStageCodegenExec].child.isInstanceOf[FilterExec] && - p.asInstanceOf[WholeStageCodegenExec].child.asInstanceOf[FilterExec].child - .isInstanceOf[InMemoryTableScanExec] && - p.asInstanceOf[WholeStageCodegenExec].child.asInstanceOf[FilterExec].child - .asInstanceOf[InMemoryTableScanExec].supportCodegen).isDefined - ) + assert(planInt.collect { + case WholeStageCodegenExec(FilterExec(_, i: InMemoryTableScanExec)) if i.supportsBatch => () + }.length == 1) assert(dsIntFilter.collect() === Array(1, 2)) // cache for string type is not supported for InMemoryTableScanExec - val dsString = spark.range(3).map(_.toString).cache - dsString.count + val dsString = spark.range(3).map(_.toString).cache() + dsString.count() val dsStringFilter = dsString.filter(_ == "1") val planString = dsStringFilter.queryExecution.executedPlan - assert(planString.find(p => - p.isInstanceOf[WholeStageCodegenExec] && - p.asInstanceOf[WholeStageCodegenExec].child.isInstanceOf[FilterExec] && - !p.asInstanceOf[WholeStageCodegenExec].child.asInstanceOf[FilterExec].child - .isInstanceOf[InMemoryTableScanExec]).isDefined - ) + assert(planString.collect { + case i: InMemoryTableScanExec if !i.supportsBatch => () + }.length == 1) assert(dsStringFilter.collect() === Array("1")) } @@ -209,16 +204,16 @@ class WholeStageCodegenSuite extends QueryTest with SharedSQLContext { wholeStageCodeGenExec.get.asInstanceOf[WholeStageCodegenExec].doCodeGen()._2 } - test("SPARK-21871 check if we can get large code size when compiling too long functions") { + ignore("SPARK-21871 check if we can get large code size when compiling too long functions") { val codeWithShortFunctions = genGroupByCode(3) val (_, maxCodeSize1) = CodeGenerator.compile(codeWithShortFunctions) assert(maxCodeSize1 < SQLConf.WHOLESTAGE_HUGE_METHOD_LIMIT.defaultValue.get) - val codeWithLongFunctions = genGroupByCode(20) + val codeWithLongFunctions = genGroupByCode(50) val (_, maxCodeSize2) = CodeGenerator.compile(codeWithLongFunctions) assert(maxCodeSize2 > SQLConf.WHOLESTAGE_HUGE_METHOD_LIMIT.defaultValue.get) } - test("bytecode of batch file scan exceeds the limit of WHOLESTAGE_HUGE_METHOD_LIMIT") { + ignore("bytecode of batch file scan exceeds the limit of WHOLESTAGE_HUGE_METHOD_LIMIT") { import testImplicits._ withTempPath { dir => val path = dir.getCanonicalPath @@ -236,4 +231,92 @@ class WholeStageCodegenSuite extends QueryTest with SharedSQLContext { } } } + + test("Control splitting consume function by operators with config") { + import testImplicits._ + val df = spark.range(10).select(Seq.tabulate(2) {i => ('id + i).as(s"c$i")} : _*) + + Seq(true, false).foreach { config => + withSQLConf(SQLConf.WHOLESTAGE_SPLIT_CONSUME_FUNC_BY_OPERATOR.key -> s"$config") { + val plan = df.queryExecution.executedPlan + val wholeStageCodeGenExec = plan.find(p => p match { + case wp: WholeStageCodegenExec => true + case _ => false + }) + assert(wholeStageCodeGenExec.isDefined) + val code = wholeStageCodeGenExec.get.asInstanceOf[WholeStageCodegenExec].doCodeGen()._2 + assert(code.body.contains("project_doConsume") == config) + } + } + } + + test("Skip splitting consume function when parameter number exceeds JVM limit") { + // since every field is nullable we have 2 params for each input column (one for the value + // and one for the isNull variable) + Seq((128, false), (127, true)).foreach { case (columnNum, hasSplit) => + withTempPath { dir => + val path = dir.getCanonicalPath + spark.range(10).select(Seq.tabulate(columnNum) {i => lit(i).as(s"c$i")} : _*) + .write.mode(SaveMode.Overwrite).parquet(path) + + withSQLConf(SQLConf.WHOLESTAGE_MAX_NUM_FIELDS.key -> "255", + SQLConf.WHOLESTAGE_SPLIT_CONSUME_FUNC_BY_OPERATOR.key -> "true") { + val projection = Seq.tabulate(columnNum)(i => s"c$i + c$i as newC$i") + val df = spark.read.parquet(path).selectExpr(projection: _*) + + val plan = df.queryExecution.executedPlan + val wholeStageCodeGenExec = plan.find { + case _: WholeStageCodegenExec => true + case _ => false + } + assert(wholeStageCodeGenExec.isDefined) + val code = wholeStageCodeGenExec.get.asInstanceOf[WholeStageCodegenExec].doCodeGen()._2 + assert(code.body.contains("project_doConsume") == hasSplit) + } + } + } + } + + test("codegen stage IDs should be preserved in transformations after CollapseCodegenStages") { + // test case adapted from DataFrameSuite to trigger ReuseExchange + withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "2") { + val df = spark.range(100) + val join = df.join(df, "id") + val plan = join.queryExecution.executedPlan + assert(!plan.find(p => + p.isInstanceOf[WholeStageCodegenExec] && + p.asInstanceOf[WholeStageCodegenExec].codegenStageId == 0).isDefined, + "codegen stage IDs should be preserved through ReuseExchange") + checkAnswer(join, df.toDF) + } + } + + test("including codegen stage ID in generated class name should not regress codegen caching") { + import testImplicits._ + + withSQLConf(SQLConf.WHOLESTAGE_CODEGEN_USE_ID_IN_CLASS_NAME.key -> "true") { + val bytecodeSizeHisto = CodegenMetrics.METRIC_GENERATED_METHOD_BYTECODE_SIZE + + // the same query run twice should hit the codegen cache + spark.range(3).select('id + 2).collect + val after1 = bytecodeSizeHisto.getCount + spark.range(3).select('id + 2).collect + val after2 = bytecodeSizeHisto.getCount // same query shape as above, deliberately + // bytecodeSizeHisto's count is always monotonically increasing if new compilation to + // bytecode had occurred. If the count stayed the same that means we've got a cache hit. + assert(after1 == after2, "Should hit codegen cache. No new compilation to bytecode expected") + + // a different query can result in codegen cache miss, that's by design + } + } + + ignore("SPARK-23598: Codegen working for lots of aggregation operations without runtime errors") { + withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") { + var df = Seq((8, "bat"), (15, "mouse"), (5, "horse")).toDF("age", "name") + for (i <- 0 until 70) { + df = df.groupBy("name").agg(avg("age").alias("age")) + } + assert(df.limit(1).collect() === Array(Row("bat", 8.0))) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationStoreSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationStoreSuite.scala index 10f1ee279bedf..3fad7dfddadcc 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationStoreSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationStoreSuite.scala @@ -35,7 +35,8 @@ class SortBasedAggregationStoreSuite extends SparkFunSuite with LocalSparkConte val conf = new SparkConf() sc = new SparkContext("local[2, 4]", "test", conf) val taskManager = new TaskMemoryManager(new TestMemoryManager(conf), 0) - TaskContext.setTaskContext(new TaskContextImpl(0, 0, 0, 0, taskManager, new Properties, null)) + TaskContext.setTaskContext( + new TaskContextImpl(0, 0, 0, 0, 0, taskManager, new Properties, null)) } override def afterAll(): Unit = TaskContext.unset() diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/arrow/ArrowWriterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/arrow/ArrowWriterSuite.scala index 508c116aae92e..92506032ab2e5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/arrow/ArrowWriterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/arrow/ArrowWriterSuite.scala @@ -20,8 +20,8 @@ package org.apache.spark.sql.execution.arrow import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.util.ArrayData -import org.apache.spark.sql.execution.vectorized.ArrowColumnVector import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.ArrowColumnVector import org.apache.spark.unsafe.types.UTF8String class ArrowWriterSuite extends SparkFunSuite { @@ -217,21 +217,21 @@ class ArrowWriterSuite extends SparkFunSuite { val reader = new ArrowColumnVector(writer.root.getFieldVectors().get(0)) - val struct0 = reader.getStruct(0, 2) + val struct0 = reader.getStruct(0) assert(struct0.getInt(0) === 1) assert(struct0.getUTF8String(1) === UTF8String.fromString("str1")) - val struct1 = reader.getStruct(1, 2) + val struct1 = reader.getStruct(1) assert(struct1.isNullAt(0)) assert(struct1.isNullAt(1)) assert(reader.isNullAt(2)) - val struct3 = reader.getStruct(3, 2) + val struct3 = reader.getStruct(3) assert(struct3.getInt(0) === 4) assert(struct3.isNullAt(1)) - val struct4 = reader.getStruct(4, 2) + val struct4 = reader.getStruct(4) assert(struct4.isNullAt(0)) assert(struct4.getUTF8String(1) === UTF8String.fromString("str5")) @@ -252,15 +252,15 @@ class ArrowWriterSuite extends SparkFunSuite { val reader = new ArrowColumnVector(writer.root.getFieldVectors().get(0)) - val struct00 = reader.getStruct(0, 1).getStruct(0, 2) + val struct00 = reader.getStruct(0).getStruct(0, 2) assert(struct00.getInt(0) === 1) assert(struct00.getUTF8String(1) === UTF8String.fromString("str1")) - val struct10 = reader.getStruct(1, 1).getStruct(0, 2) + val struct10 = reader.getStruct(1).getStruct(0, 2) assert(struct10.isNullAt(0)) assert(struct10.isNullAt(1)) - val struct2 = reader.getStruct(2, 1) + val struct2 = reader.getStruct(2) assert(struct2.isNullAt(0)) assert(reader.isNullAt(3)) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/columnar/InMemoryColumnarQuerySuite.scala index ff7c5e58e9863..26b63e8e8490f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/columnar/InMemoryColumnarQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/columnar/InMemoryColumnarQuerySuite.scala @@ -336,7 +336,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext { checkAnswer(cached, expectedAnswer) // Check that the right size was calculated. - assert(cached.batchStats.value === expectedAnswer.size * INT.defaultSize) + assert(cached.sizeInBytesStats.value === expectedAnswer.size * INT.defaultSize) } test("access primitive-type columns in CachedBatch without whole stage codegen") { @@ -477,7 +477,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext { assert(planBeforeFilter.head.isInstanceOf[InMemoryTableScanExec]) val execPlan = if (enabled == "true") { - WholeStageCodegenExec(planBeforeFilter.head) + WholeStageCodegenExec(planBeforeFilter.head)(codegenStageId = 0) } else { planBeforeFilter.head } @@ -487,7 +487,10 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext { } test("SPARK-22673: InMemoryRelation should utilize existing stats of the plan to be cached") { - withSQLConf("spark.sql.cbo.enabled" -> "true") { + // This test case depends on the size of parquet in statistics. + withSQLConf( + SQLConf.CBO_ENABLED.key -> "true", + SQLConf.DEFAULT_DATA_SOURCE_NAME.key -> "parquet") { withTempPath { workDir => withTable("table1") { val workDirPath = workDir.getAbsolutePath diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/command/CommandUtilsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/command/CommandUtilsSuite.scala new file mode 100644 index 0000000000000..f3e15189a6418 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/command/CommandUtilsSuite.scala @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.command + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.catalog.CatalogStatistics + +class CommandUtilsSuite extends SparkFunSuite { + + test("Check if compareAndGetNewStats returns correct results") { + val oldStats1 = CatalogStatistics(sizeInBytes = 10, rowCount = Some(100)) + val newStats1 = CommandUtils.compareAndGetNewStats( + Some(oldStats1), newTotalSize = 10, newRowCount = Some(100)) + assert(newStats1.isEmpty) + val newStats2 = CommandUtils.compareAndGetNewStats( + Some(oldStats1), newTotalSize = -1, newRowCount = None) + assert(newStats2.isEmpty) + val newStats3 = CommandUtils.compareAndGetNewStats( + Some(oldStats1), newTotalSize = 20, newRowCount = Some(-1)) + assert(newStats3.isDefined) + newStats3.foreach { stat => + assert(stat.sizeInBytes === 20) + assert(stat.rowCount.isEmpty) + } + val newStats4 = CommandUtils.compareAndGetNewStats( + Some(oldStats1), newTotalSize = -1, newRowCount = Some(200)) + assert(newStats4.isDefined) + newStats4.foreach { stat => + assert(stat.sizeInBytes === 10) + assert(stat.rowCount.isDefined && stat.rowCount.get === 200) + } + } + + test("Check if compareAndGetNewStats can handle large values") { + // Tests for large values + val oldStats2 = CatalogStatistics(sizeInBytes = BigInt(Long.MaxValue) * 2) + val newStats5 = CommandUtils.compareAndGetNewStats( + Some(oldStats2), newTotalSize = BigInt(Long.MaxValue) * 2, None) + assert(newStats5.isEmpty) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLParserSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLParserSuite.scala index eb7c33590b602..e0ccae15f1d05 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLParserSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLParserSuite.scala @@ -54,6 +54,13 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { } } + private def intercept(sqlCommand: String, messages: String*): Unit = { + val e = intercept[ParseException](parser.parsePlan(sqlCommand)).getMessage + messages.foreach { message => + assert(e.contains(message)) + } + } + private def parseAs[T: ClassTag](query: String): T = { parser.parsePlan(query) match { case t: T => t @@ -229,7 +236,7 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { Seq( FunctionResource(FunctionResourceType.fromString("jar"), "/path/to/jar1"), FunctionResource(FunctionResourceType.fromString("jar"), "/path/to/jar2")), - isTemp = true, ifNotExists = false, replace = false) + isTemp = true, ignoreIfExists = false, replace = false) val expected2 = CreateFunctionCommand( Some("hello"), "world", @@ -237,7 +244,7 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { Seq( FunctionResource(FunctionResourceType.fromString("archive"), "/path/to/archive"), FunctionResource(FunctionResourceType.fromString("file"), "/path/to/file")), - isTemp = false, ifNotExists = false, replace = false) + isTemp = false, ignoreIfExists = false, replace = false) val expected3 = CreateFunctionCommand( None, "helloworld3", @@ -245,7 +252,7 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { Seq( FunctionResource(FunctionResourceType.fromString("jar"), "/path/to/jar1"), FunctionResource(FunctionResourceType.fromString("jar"), "/path/to/jar2")), - isTemp = true, ifNotExists = false, replace = true) + isTemp = true, ignoreIfExists = false, replace = true) val expected4 = CreateFunctionCommand( Some("hello"), "world1", @@ -253,7 +260,7 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { Seq( FunctionResource(FunctionResourceType.fromString("archive"), "/path/to/archive"), FunctionResource(FunctionResourceType.fromString("file"), "/path/to/file")), - isTemp = false, ifNotExists = false, replace = true) + isTemp = false, ignoreIfExists = false, replace = true) val expected5 = CreateFunctionCommand( Some("hello"), "world2", @@ -261,7 +268,7 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { Seq( FunctionResource(FunctionResourceType.fromString("archive"), "/path/to/archive"), FunctionResource(FunctionResourceType.fromString("file"), "/path/to/file")), - isTemp = false, ifNotExists = true, replace = false) + isTemp = false, ignoreIfExists = true, replace = false) comparePlans(parsed1, expected1) comparePlans(parsed2, expected2) comparePlans(parsed3, expected3) @@ -494,6 +501,37 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { } } + test("Duplicate clauses - create table") { + def createTableHeader(duplicateClause: String, isNative: Boolean): String = { + val fileFormat = if (isNative) "USING parquet" else "STORED AS parquet" + s"CREATE TABLE my_tab(a INT, b STRING) $fileFormat $duplicateClause $duplicateClause" + } + + Seq(true, false).foreach { isNative => + intercept(createTableHeader("TBLPROPERTIES('test' = 'test2')", isNative), + "Found duplicate clauses: TBLPROPERTIES") + intercept(createTableHeader("LOCATION '/tmp/file'", isNative), + "Found duplicate clauses: LOCATION") + intercept(createTableHeader("COMMENT 'a table'", isNative), + "Found duplicate clauses: COMMENT") + intercept(createTableHeader("CLUSTERED BY(b) INTO 256 BUCKETS", isNative), + "Found duplicate clauses: CLUSTERED BY") + } + + // Only for native data source tables + intercept(createTableHeader("PARTITIONED BY (b)", isNative = true), + "Found duplicate clauses: PARTITIONED BY") + + // Only for Hive serde tables + intercept(createTableHeader("PARTITIONED BY (k int)", isNative = false), + "Found duplicate clauses: PARTITIONED BY") + intercept(createTableHeader("STORED AS parquet", isNative = false), + "Found duplicate clauses: STORED AS/BY") + intercept( + createTableHeader("ROW FORMAT SERDE 'parquet.hive.serde.ParquetHiveSerDe'", isNative = false), + "Found duplicate clauses: ROW FORMAT") + } + test("create table - with location") { val v1 = "CREATE TABLE my_tab(a INT, b STRING) USING parquet LOCATION '/tmp/file'" @@ -1153,38 +1191,119 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { } } + test("Test CTAS against data source tables") { + val s1 = + """ + |CREATE TABLE IF NOT EXISTS mydb.page_view + |USING parquet + |COMMENT 'This is the staging page view table' + |LOCATION '/user/external/page_view' + |TBLPROPERTIES ('p1'='v1', 'p2'='v2') + |AS SELECT * FROM src + """.stripMargin + + val s2 = + """ + |CREATE TABLE IF NOT EXISTS mydb.page_view + |USING parquet + |LOCATION '/user/external/page_view' + |COMMENT 'This is the staging page view table' + |TBLPROPERTIES ('p1'='v1', 'p2'='v2') + |AS SELECT * FROM src + """.stripMargin + + val s3 = + """ + |CREATE TABLE IF NOT EXISTS mydb.page_view + |USING parquet + |COMMENT 'This is the staging page view table' + |LOCATION '/user/external/page_view' + |TBLPROPERTIES ('p1'='v1', 'p2'='v2') + |AS SELECT * FROM src + """.stripMargin + + checkParsing(s1) + checkParsing(s2) + checkParsing(s3) + + def checkParsing(sql: String): Unit = { + val (desc, exists) = extractTableDesc(sql) + assert(exists) + assert(desc.identifier.database == Some("mydb")) + assert(desc.identifier.table == "page_view") + assert(desc.storage.locationUri == Some(new URI("/user/external/page_view"))) + assert(desc.schema.isEmpty) // will be populated later when the table is actually created + assert(desc.comment == Some("This is the staging page view table")) + assert(desc.viewText.isEmpty) + assert(desc.viewDefaultDatabase.isEmpty) + assert(desc.viewQueryColumnNames.isEmpty) + assert(desc.partitionColumnNames.isEmpty) + assert(desc.provider == Some("parquet")) + assert(desc.properties == Map("p1" -> "v1", "p2" -> "v2")) + } + } + test("Test CTAS #1") { val s1 = - """CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view + """ + |CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view |COMMENT 'This is the staging page view table' |STORED AS RCFILE |LOCATION '/user/external/page_view' |TBLPROPERTIES ('p1'='v1', 'p2'='v2') - |AS SELECT * FROM src""".stripMargin + |AS SELECT * FROM src + """.stripMargin - val (desc, exists) = extractTableDesc(s1) - assert(exists) - assert(desc.identifier.database == Some("mydb")) - assert(desc.identifier.table == "page_view") - assert(desc.tableType == CatalogTableType.EXTERNAL) - assert(desc.storage.locationUri == Some(new URI("/user/external/page_view"))) - assert(desc.schema.isEmpty) // will be populated later when the table is actually created - assert(desc.comment == Some("This is the staging page view table")) - // TODO will be SQLText - assert(desc.viewText.isEmpty) - assert(desc.viewDefaultDatabase.isEmpty) - assert(desc.viewQueryColumnNames.isEmpty) - assert(desc.partitionColumnNames.isEmpty) - assert(desc.storage.inputFormat == Some("org.apache.hadoop.hive.ql.io.RCFileInputFormat")) - assert(desc.storage.outputFormat == Some("org.apache.hadoop.hive.ql.io.RCFileOutputFormat")) - assert(desc.storage.serde == - Some("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe")) - assert(desc.properties == Map("p1" -> "v1", "p2" -> "v2")) + val s2 = + """ + |CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view + |STORED AS RCFILE + |COMMENT 'This is the staging page view table' + |TBLPROPERTIES ('p1'='v1', 'p2'='v2') + |LOCATION '/user/external/page_view' + |AS SELECT * FROM src + """.stripMargin + + val s3 = + """ + |CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view + |TBLPROPERTIES ('p1'='v1', 'p2'='v2') + |LOCATION '/user/external/page_view' + |STORED AS RCFILE + |COMMENT 'This is the staging page view table' + |AS SELECT * FROM src + """.stripMargin + + checkParsing(s1) + checkParsing(s2) + checkParsing(s3) + + def checkParsing(sql: String): Unit = { + val (desc, exists) = extractTableDesc(sql) + assert(exists) + assert(desc.identifier.database == Some("mydb")) + assert(desc.identifier.table == "page_view") + assert(desc.tableType == CatalogTableType.EXTERNAL) + assert(desc.storage.locationUri == Some(new URI("/user/external/page_view"))) + assert(desc.schema.isEmpty) // will be populated later when the table is actually created + assert(desc.comment == Some("This is the staging page view table")) + // TODO will be SQLText + assert(desc.viewText.isEmpty) + assert(desc.viewDefaultDatabase.isEmpty) + assert(desc.viewQueryColumnNames.isEmpty) + assert(desc.partitionColumnNames.isEmpty) + assert(desc.storage.inputFormat == Some("org.apache.hadoop.hive.ql.io.RCFileInputFormat")) + assert(desc.storage.outputFormat == Some("org.apache.hadoop.hive.ql.io.RCFileOutputFormat")) + assert(desc.storage.serde == + Some("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe")) + assert(desc.properties == Map("p1" -> "v1", "p2" -> "v2")) + } } test("Test CTAS #2") { - val s2 = - """CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view + val s1 = + """ + |CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view |COMMENT 'This is the staging page view table' |ROW FORMAT SERDE 'parquet.hive.serde.ParquetHiveSerDe' | STORED AS @@ -1192,26 +1311,45 @@ class DDLParserSuite extends PlanTest with SharedSQLContext { | OUTPUTFORMAT 'parquet.hive.DeprecatedParquetOutputFormat' |LOCATION '/user/external/page_view' |TBLPROPERTIES ('p1'='v1', 'p2'='v2') - |AS SELECT * FROM src""".stripMargin + |AS SELECT * FROM src + """.stripMargin - val (desc, exists) = extractTableDesc(s2) - assert(exists) - assert(desc.identifier.database == Some("mydb")) - assert(desc.identifier.table == "page_view") - assert(desc.tableType == CatalogTableType.EXTERNAL) - assert(desc.storage.locationUri == Some(new URI("/user/external/page_view"))) - assert(desc.schema.isEmpty) // will be populated later when the table is actually created - // TODO will be SQLText - assert(desc.comment == Some("This is the staging page view table")) - assert(desc.viewText.isEmpty) - assert(desc.viewDefaultDatabase.isEmpty) - assert(desc.viewQueryColumnNames.isEmpty) - assert(desc.partitionColumnNames.isEmpty) - assert(desc.storage.properties == Map()) - assert(desc.storage.inputFormat == Some("parquet.hive.DeprecatedParquetInputFormat")) - assert(desc.storage.outputFormat == Some("parquet.hive.DeprecatedParquetOutputFormat")) - assert(desc.storage.serde == Some("parquet.hive.serde.ParquetHiveSerDe")) - assert(desc.properties == Map("p1" -> "v1", "p2" -> "v2")) + val s2 = + """ + |CREATE EXTERNAL TABLE IF NOT EXISTS mydb.page_view + |LOCATION '/user/external/page_view' + |TBLPROPERTIES ('p1'='v1', 'p2'='v2') + |ROW FORMAT SERDE 'parquet.hive.serde.ParquetHiveSerDe' + | STORED AS + | INPUTFORMAT 'parquet.hive.DeprecatedParquetInputFormat' + | OUTPUTFORMAT 'parquet.hive.DeprecatedParquetOutputFormat' + |COMMENT 'This is the staging page view table' + |AS SELECT * FROM src + """.stripMargin + + checkParsing(s1) + checkParsing(s2) + + def checkParsing(sql: String): Unit = { + val (desc, exists) = extractTableDesc(sql) + assert(exists) + assert(desc.identifier.database == Some("mydb")) + assert(desc.identifier.table == "page_view") + assert(desc.tableType == CatalogTableType.EXTERNAL) + assert(desc.storage.locationUri == Some(new URI("/user/external/page_view"))) + assert(desc.schema.isEmpty) // will be populated later when the table is actually created + // TODO will be SQLText + assert(desc.comment == Some("This is the staging page view table")) + assert(desc.viewText.isEmpty) + assert(desc.viewDefaultDatabase.isEmpty) + assert(desc.viewQueryColumnNames.isEmpty) + assert(desc.partitionColumnNames.isEmpty) + assert(desc.storage.properties == Map()) + assert(desc.storage.inputFormat == Some("parquet.hive.DeprecatedParquetInputFormat")) + assert(desc.storage.outputFormat == Some("parquet.hive.DeprecatedParquetOutputFormat")) + assert(desc.storage.serde == Some("parquet.hive.serde.ParquetHiveSerDe")) + assert(desc.properties == Map("p1" -> "v1", "p2" -> "v2")) + } } test("Test CTAS #3") { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLSuite.scala index fdb9b2f51f9cb..d360367d0c52b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLSuite.scala @@ -37,6 +37,8 @@ import org.apache.spark.util.Utils class InMemoryCatalogedDDLSuite extends DDLSuite with SharedSQLContext with BeforeAndAfterEach { + import testImplicits._ + override def afterEach(): Unit = { try { // drop all databases, tables and functions after each test @@ -132,6 +134,37 @@ class InMemoryCatalogedDDLSuite extends DDLSuite with SharedSQLContext with Befo checkAnswer(spark.table("t"), Row(Row("a", 1)) :: Nil) } } + + // TODO: This test is copied from HiveDDLSuite, unify it later. + test("SPARK-23348: append data to data source table with saveAsTable") { + withTable("t", "t1") { + Seq(1 -> "a").toDF("i", "j").write.saveAsTable("t") + checkAnswer(spark.table("t"), Row(1, "a")) + + sql("INSERT INTO t SELECT 2, 'b'") + checkAnswer(spark.table("t"), Row(1, "a") :: Row(2, "b") :: Nil) + + Seq(3 -> "c").toDF("i", "j").write.mode("append").saveAsTable("t") + checkAnswer(spark.table("t"), Row(1, "a") :: Row(2, "b") :: Row(3, "c") :: Nil) + + Seq("c" -> 3).toDF("i", "j").write.mode("append").saveAsTable("t") + checkAnswer(spark.table("t"), Row(1, "a") :: Row(2, "b") :: Row(3, "c") + :: Row(null, "3") :: Nil) + + Seq(4 -> "d").toDF("i", "j").write.saveAsTable("t1") + + val e = intercept[AnalysisException] { + val format = if (spark.sessionState.conf.defaultDataSourceName.equalsIgnoreCase("json")) { + "orc" + } else { + "json" + } + Seq(5 -> "e").toDF("i", "j").write.mode("append").format(format).saveAsTable("t1") + } + assert(e.message.contains("The format of the existing table default.t1 is ")) + assert(e.message.contains("It doesn't match the specified format")) + } + } } abstract class DDLSuite extends QueryTest with SQLTestUtils { @@ -508,6 +541,35 @@ abstract class DDLSuite extends QueryTest with SQLTestUtils { } } + test("create table - append to a non-partitioned table created with different paths") { + import testImplicits._ + withTempDir { dir1 => + withTempDir { dir2 => + withTable("path_test") { + Seq(1L -> "a").toDF("v1", "v2") + .write + .mode(SaveMode.Append) + .format("json") + .option("path", dir1.getCanonicalPath) + .saveAsTable("path_test") + + val ex = intercept[AnalysisException] { + Seq((3L, "c")).toDF("v1", "v2") + .write + .mode(SaveMode.Append) + .format("json") + .option("path", dir2.getCanonicalPath) + .saveAsTable("path_test") + }.getMessage + assert(ex.contains("The location of the existing table `default`.`path_test`")) + + checkAnswer( + spark.table("path_test"), Row(1L, "a") :: Nil) + } + } + } + } + test("Refresh table after changing the data source table partitioning") { import testImplicits._ @@ -835,6 +897,31 @@ abstract class DDLSuite extends QueryTest with SQLTestUtils { } } + test("rename temporary view - destination table with database name,with:CREATE TEMPORARY view") { + withTempView("view1") { + sql( + """ + |CREATE TEMPORARY VIEW view1 + |USING org.apache.spark.sql.sources.DDLScanSource + |OPTIONS ( + | From '1', + | To '10', + | Table 'test1' + |) + """.stripMargin) + + val e = intercept[AnalysisException] { + sql("ALTER TABLE view1 RENAME TO default.tab2") + } + assert(e.getMessage.contains( + "RENAME TEMPORARY VIEW from '`view1`' to '`default`.`tab2`': " + + "cannot specify database name 'default' in the destination table")) + + val catalog = spark.sessionState.catalog + assert(catalog.listTables("default") == Seq(TableIdentifier("view1"))) + } + } + test("rename temporary view") { withTempView("tab1", "tab2") { spark.range(10).createOrReplaceTempView("tab1") @@ -883,6 +970,42 @@ abstract class DDLSuite extends QueryTest with SQLTestUtils { } } + test("rename temporary view - destination table already exists, with: CREATE TEMPORARY view") { + withTempView("view1", "view2") { + sql( + """ + |CREATE TEMPORARY VIEW view1 + |USING org.apache.spark.sql.sources.DDLScanSource + |OPTIONS ( + | From '1', + | To '10', + | Table 'test1' + |) + """.stripMargin) + + sql( + """ + |CREATE TEMPORARY VIEW view2 + |USING org.apache.spark.sql.sources.DDLScanSource + |OPTIONS ( + | From '1', + | To '10', + | Table 'test1' + |) + """.stripMargin) + + val e = intercept[AnalysisException] { + sql("ALTER TABLE view1 RENAME TO view2") + } + assert(e.getMessage.contains( + "RENAME TEMPORARY VIEW from '`view1`' to '`view2`': destination table already exists")) + + val catalog = spark.sessionState.catalog + assert(catalog.listTables("default") == + Seq(TableIdentifier("view1"), TableIdentifier("view2"))) + } + } + test("alter table: bucketing is not supported") { val catalog = spark.sessionState.catalog val tableIdent = TableIdentifier("tab1", Some("dbx")) @@ -991,6 +1114,10 @@ abstract class DDLSuite extends QueryTest with SQLTestUtils { sql("SHOW DATABASES LIKE '*db1A'"), Row("showdb1a") :: Nil) + checkAnswer( + sql("SHOW DATABASES '*db1A'"), + Row("showdb1a") :: Nil) + checkAnswer( sql("SHOW DATABASES LIKE 'showdb1A'"), Row("showdb1a") :: Nil) @@ -1475,6 +1602,7 @@ abstract class DDLSuite extends QueryTest with SQLTestUtils { // Ensure that change column will preserve other metadata fields. sql("ALTER TABLE dbx.tab1 CHANGE COLUMN col1 col1 INT COMMENT 'this is col1'") assert(getMetadata("col1").getString("key") == "value") + assert(getMetadata("col1").getString("comment") == "this is col1") } test("drop build-in function") { @@ -1724,12 +1852,22 @@ abstract class DDLSuite extends QueryTest with SQLTestUtils { } test("block creating duplicate temp table") { - withView("t_temp") { + withTempView("t_temp") { sql("CREATE TEMPORARY VIEW t_temp AS SELECT 1, 2") val e = intercept[TempTableAlreadyExistsException] { sql("CREATE TEMPORARY TABLE t_temp (c3 int, c4 string) USING JSON") }.getMessage - assert(e.contains("Temporary table 't_temp' already exists")) + assert(e.contains("Temporary view 't_temp' already exists")) + } + } + + test("block creating duplicate temp view") { + withTempView("t_temp") { + sql("CREATE TEMPORARY VIEW t_temp AS SELECT 1, 2") + val e = intercept[TempTableAlreadyExistsException] { + sql("CREATE TEMPORARY VIEW t_temp (c3 int, c4 string) USING JSON") + }.getMessage + assert(e.contains("Temporary view 't_temp' already exists")) } } @@ -1971,8 +2109,8 @@ abstract class DDLSuite extends QueryTest with SQLTestUtils { s""" |CREATE TABLE t(a int, b int, c int, d int) |USING parquet - |PARTITIONED BY(a, b) |LOCATION "${dir.toURI}" + |PARTITIONED BY(a, b) """.stripMargin) spark.sql("INSERT INTO TABLE t PARTITION(a=1, b=2) SELECT 3, 4") checkAnswer(spark.table("t"), Row(3, 4, 1, 2) :: Nil) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelationSuite.scala index caf03885e3873..c1f2c18d1417d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelationSuite.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.datasources import java.io.{File, FilenameFilter} import org.apache.spark.sql.QueryTest +import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, SortMergeJoinExec} import org.apache.spark.sql.test.SharedSQLContext class HadoopFsRelationSuite extends QueryTest with SharedSQLContext { @@ -39,4 +40,44 @@ class HadoopFsRelationSuite extends QueryTest with SharedSQLContext { assert(df.queryExecution.logical.stats.sizeInBytes === BigInt(totalSize)) } } + + test("SPARK-22790: spark.sql.sources.compressionFactor takes effect") { + import testImplicits._ + Seq(1.0, 0.5).foreach { compressionFactor => + withSQLConf("spark.sql.sources.fileCompressionFactor" -> compressionFactor.toString, + "spark.sql.autoBroadcastJoinThreshold" -> "400") { + withTempPath { workDir => + // the file size is 740 bytes + val workDirPath = workDir.getAbsolutePath + val data1 = Seq(100, 200, 300, 400).toDF("count") + data1.write.parquet(workDirPath + "/data1") + val df1FromFile = spark.read.parquet(workDirPath + "/data1") + val data2 = Seq(100, 200, 300, 400).toDF("count") + data2.write.parquet(workDirPath + "/data2") + val df2FromFile = spark.read.parquet(workDirPath + "/data2") + val joinedDF = df1FromFile.join(df2FromFile, Seq("count")) + if (compressionFactor == 0.5) { + val bJoinExec = joinedDF.queryExecution.executedPlan.collect { + case bJoin: BroadcastHashJoinExec => bJoin + } + assert(bJoinExec.nonEmpty) + val smJoinExec = joinedDF.queryExecution.executedPlan.collect { + case smJoin: SortMergeJoinExec => smJoin + } + assert(smJoinExec.isEmpty) + } else { + // compressionFactor is 1.0 + val bJoinExec = joinedDF.queryExecution.executedPlan.collect { + case bJoin: BroadcastHashJoinExec => bJoin + } + assert(bJoinExec.isEmpty) + val smJoinExec = joinedDF.queryExecution.executedPlan.collect { + case smJoin: SortMergeJoinExec => smJoin + } + assert(smJoinExec.nonEmpty) + } + } + } + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/SaveIntoDataSourceCommandSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/SaveIntoDataSourceCommandSuite.scala index 4b3ca8e60cab6..a1da3ec43eae3 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/SaveIntoDataSourceCommandSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/SaveIntoDataSourceCommandSuite.scala @@ -23,9 +23,6 @@ import org.apache.spark.sql.test.SharedSQLContext class SaveIntoDataSourceCommandSuite extends SharedSQLContext { - override protected def sparkConf: SparkConf = super.sparkConf - .set("spark.redaction.regex", "(?i)password|url") - test("simpleString is redacted") { val URL = "connection.url" val PASS = "123" diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala index e00e057a18cc6..f58c331f33ca8 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala @@ -31,6 +31,7 @@ import org.apache.orc.OrcConf.COMPRESS import org.apache.orc.mapred.OrcStruct import org.apache.orc.mapreduce.OrcInputFormat +import org.apache.spark.SparkException import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.TableIdentifier import org.apache.spark.sql.execution.datasources.{HadoopFsRelation, LogicalRelation, RecordReaderIterator} @@ -531,6 +532,52 @@ abstract class OrcQueryTest extends OrcTest { val df = spark.read.orc(path1.getCanonicalPath, path2.getCanonicalPath) assert(df.count() == 20) } + + test("Enabling/disabling ignoreCorruptFiles") { + def testIgnoreCorruptFiles(): Unit = { + withTempDir { dir => + val basePath = dir.getCanonicalPath + spark.range(1).toDF("a").write.orc(new Path(basePath, "first").toString) + spark.range(1, 2).toDF("a").write.orc(new Path(basePath, "second").toString) + spark.range(2, 3).toDF("a").write.json(new Path(basePath, "third").toString) + val df = spark.read.orc( + new Path(basePath, "first").toString, + new Path(basePath, "second").toString, + new Path(basePath, "third").toString) + checkAnswer(df, Seq(Row(0), Row(1))) + } + } + + def testIgnoreCorruptFilesWithoutSchemaInfer(): Unit = { + withTempDir { dir => + val basePath = dir.getCanonicalPath + spark.range(1).toDF("a").write.orc(new Path(basePath, "first").toString) + spark.range(1, 2).toDF("a").write.orc(new Path(basePath, "second").toString) + spark.range(2, 3).toDF("a").write.json(new Path(basePath, "third").toString) + val df = spark.read.schema("a long").orc( + new Path(basePath, "first").toString, + new Path(basePath, "second").toString, + new Path(basePath, "third").toString) + checkAnswer(df, Seq(Row(0), Row(1))) + } + } + + withSQLConf(SQLConf.IGNORE_CORRUPT_FILES.key -> "true") { + testIgnoreCorruptFiles() + testIgnoreCorruptFilesWithoutSchemaInfer() + } + + withSQLConf(SQLConf.IGNORE_CORRUPT_FILES.key -> "false") { + val m1 = intercept[SparkException] { + testIgnoreCorruptFiles() + }.getMessage + assert(m1.contains("Could not read footer for file")) + val m2 = intercept[SparkException] { + testIgnoreCorruptFilesWithoutSchemaInfer() + }.getMessage + assert(m2.contains("Malformed ORC file")) + } + } } class OrcQuerySuite extends OrcQueryTest with SharedSQLContext { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcSourceSuite.scala index 6f5f2fd795f74..9a20e271c11e8 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcSourceSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcSourceSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution.datasources.orc import java.io.File +import java.sql.Timestamp import java.util.Locale import org.apache.orc.OrcConf.COMPRESS @@ -160,6 +161,23 @@ abstract class OrcSuite extends OrcTest with BeforeAndAfterAll { } } } + + test("SPARK-23340 Empty float/double array columns raise EOFException") { + Seq(Seq(Array.empty[Float]).toDF(), Seq(Array.empty[Double]).toDF()).foreach { df => + withTempPath { path => + df.write.format("orc").save(path.getCanonicalPath) + checkAnswer(spark.read.orc(path.getCanonicalPath), df) + } + } + } + + test("SPARK-24322 Fix incorrect workaround for bug in java.sql.Timestamp") { + withTempPath { path => + val ts = Timestamp.valueOf("1900-05-05 12:34:56.000789") + Seq(ts).toDF.write.orc(path.getCanonicalPath) + checkAnswer(spark.read.orc(path.getCanonicalPath), Row(ts)) + } + } } class OrcSourceSuite extends OrcSuite with SharedSQLContext { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompressionCodecPrecedenceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompressionCodecPrecedenceSuite.scala new file mode 100644 index 0000000000000..ed8fd2b453456 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompressionCodecPrecedenceSuite.scala @@ -0,0 +1,122 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.datasources.parquet + +import java.io.File + +import scala.collection.JavaConverters._ + +import org.apache.hadoop.fs.Path +import org.apache.parquet.hadoop.ParquetOutputFormat + +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SharedSQLContext + +class ParquetCompressionCodecPrecedenceSuite extends ParquetTest with SharedSQLContext { + test("Test `spark.sql.parquet.compression.codec` config") { + Seq("NONE", "UNCOMPRESSED", "SNAPPY", "GZIP", "LZO").foreach { c => + withSQLConf(SQLConf.PARQUET_COMPRESSION.key -> c) { + val expected = if (c == "NONE") "UNCOMPRESSED" else c + val option = new ParquetOptions(Map.empty[String, String], spark.sessionState.conf) + assert(option.compressionCodecClassName == expected) + } + } + } + + test("[SPARK-21786] Test Acquiring 'compressionCodecClassName' for parquet in right order.") { + // When "compression" is configured, it should be the first choice. + withSQLConf(SQLConf.PARQUET_COMPRESSION.key -> "snappy") { + val props = Map("compression" -> "uncompressed", ParquetOutputFormat.COMPRESSION -> "gzip") + val option = new ParquetOptions(props, spark.sessionState.conf) + assert(option.compressionCodecClassName == "UNCOMPRESSED") + } + + // When "compression" is not configured, "parquet.compression" should be the preferred choice. + withSQLConf(SQLConf.PARQUET_COMPRESSION.key -> "snappy") { + val props = Map(ParquetOutputFormat.COMPRESSION -> "gzip") + val option = new ParquetOptions(props, spark.sessionState.conf) + assert(option.compressionCodecClassName == "GZIP") + } + + // When both "compression" and "parquet.compression" are not configured, + // spark.sql.parquet.compression.codec should be the right choice. + withSQLConf(SQLConf.PARQUET_COMPRESSION.key -> "snappy") { + val props = Map.empty[String, String] + val option = new ParquetOptions(props, spark.sessionState.conf) + assert(option.compressionCodecClassName == "SNAPPY") + } + } + + private def getTableCompressionCodec(path: String): Seq[String] = { + val hadoopConf = spark.sessionState.newHadoopConf() + val codecs = for { + footer <- readAllFootersWithoutSummaryFiles(new Path(path), hadoopConf) + block <- footer.getParquetMetadata.getBlocks.asScala + column <- block.getColumns.asScala + } yield column.getCodec.name() + codecs.distinct + } + + private def createTableWithCompression( + tableName: String, + isPartitioned: Boolean, + compressionCodec: String, + rootDir: File): Unit = { + val options = + s""" + |OPTIONS('path'='${rootDir.toURI.toString.stripSuffix("/")}/$tableName', + |'parquet.compression'='$compressionCodec') + """.stripMargin + val partitionCreate = if (isPartitioned) "PARTITIONED BY (p)" else "" + sql( + s""" + |CREATE TABLE $tableName USING Parquet $options $partitionCreate + |AS SELECT 1 AS col1, 2 AS p + """.stripMargin) + } + + private def checkCompressionCodec(compressionCodec: String, isPartitioned: Boolean): Unit = { + withTempDir { tmpDir => + val tempTableName = "TempParquetTable" + withTable(tempTableName) { + createTableWithCompression(tempTableName, isPartitioned, compressionCodec, tmpDir) + val partitionPath = if (isPartitioned) "p=2" else "" + val path = s"${tmpDir.getPath.stripSuffix("/")}/$tempTableName/$partitionPath" + val realCompressionCodecs = getTableCompressionCodec(path) + assert(realCompressionCodecs.forall(_ == compressionCodec)) + } + } + } + + test("Create parquet table with compression") { + Seq(true, false).foreach { isPartitioned => + Seq("UNCOMPRESSED", "SNAPPY", "GZIP").foreach { compressionCodec => + checkCompressionCodec(compressionCodec, isPartitioned) + } + } + } + + test("Create table with unknown compression") { + Seq(true, false).foreach { isPartitioned => + val exception = intercept[IllegalArgumentException] { + checkCompressionCodec("aa", isPartitioned) + } + assert(exception.getMessage.contains("Codec [aa] is not available")) + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala index 33801954ebd51..f8d04b593f3d0 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala @@ -602,6 +602,18 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex } } } + + test("SPARK-23852: Broken Parquet push-down for partially-written stats") { + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") { + // parquet-1217.parquet contains a single column with values -1, 0, 1, 2 and null. + // The row-group statistics include null counts, but not min and max values, which + // triggers PARQUET-1217. + val df = readResourceParquetFile("test-data/parquet-1217.parquet") + + // Will return 0 rows if PARQUET-1217 is not fixed. + assert(df.where("col > 0").count() === 2) + } + } } class NumRowGroupsAcc extends AccumulatorV2[Integer, Integer] { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala index 44a8b25c61dfb..e4e0e6e68403e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala @@ -43,6 +43,7 @@ import org.apache.spark.sql.catalyst.{InternalRow, ScalaReflection} import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow, UnsafeRow} import org.apache.spark.sql.catalyst.util.DateTimeUtils import org.apache.spark.sql.execution.datasources.SQLHadoopMapReduceCommitProtocol +import org.apache.spark.sql.functions._ import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.SharedSQLContext import org.apache.spark.sql.types._ @@ -662,7 +663,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext { val v = (row.getInt(0), row.getString(1)) result += v } - assert(data == result) + assert(data.toSet == result.toSet) } finally { reader.close() } @@ -678,7 +679,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext { val row = reader.getCurrentValue.asInstanceOf[InternalRow] result += row.getString(0) } - assert(data.map(_._2) == result) + assert(data.map(_._2).toSet == result.toSet) } finally { reader.close() } @@ -695,7 +696,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext { val v = (row.getString(0), row.getInt(1)) result += v } - assert(data.map { x => (x._2, x._1) } == result) + assert(data.map { x => (x._2, x._1) }.toSet == result.toSet) } finally { reader.close() } @@ -771,6 +772,24 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext { assert(option.compressionCodecClassName == "UNCOMPRESSED") } } + + test("SPARK-23173 Writing a file with data converted from JSON with and incorrect user schema") { + withTempPath { file => + val jsonData = + """{ + | "a": 1, + | "c": "foo" + |} + |""".stripMargin + val jsonSchema = new StructType() + .add("a", LongType, nullable = false) + .add("b", StringType, nullable = false) + .add("c", StringType, nullable = false) + spark.range(1).select(from_json(lit(jsonData), jsonSchema) as "input") + .write.parquet(file.getAbsolutePath) + checkAnswer(spark.read.parquet(file.getAbsolutePath), Seq(Row(Row(1, null, "foo")))) + } + } } class JobCommitFailureParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala index d4902641e335f..e887c9734a8b8 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala @@ -57,6 +57,16 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest with Sha val timeZone = TimeZone.getDefault() val timeZoneId = timeZone.getID + protected override def beforeAll(): Unit = { + super.beforeAll() + spark.conf.set(SQLConf.DEFAULT_DATA_SOURCE_NAME.key, "parquet") + } + + protected override def afterAll(): Unit = { + spark.conf.unset(SQLConf.DEFAULT_DATA_SOURCE_NAME.key) + super.afterAll() + } + test("column type inference") { def check(raw: String, literal: Literal, timeZone: TimeZone = timeZone): Unit = { assert(inferPartitionColumnValue(raw, true, timeZone) === literal) @@ -1120,4 +1130,18 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest with Sha Row(3, BigDecimal("2" * 30)) :: Nil) } } + + test("SPARK-23436: invalid Dates should be inferred as String in partition inference") { + withTempPath { path => + val data = Seq(("1", "2018-01", "2018-01-01-04", "test")) + .toDF("id", "date_month", "date_hour", "data") + + data.write.partitionBy("date_month", "date_hour").parquet(path.getAbsolutePath) + val input = spark.read.parquet(path.getAbsolutePath).select("id", + "date_month", "date_hour", "data") + + assert(input.schema.sameType(input.schema)) + checkAnswer(input, data) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala index 4c8c9ef6e0432..5b680bf3e2138 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala @@ -320,54 +320,38 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext new Path(basePath, "first").toString, new Path(basePath, "second").toString, new Path(basePath, "third").toString) - checkAnswer( - df, - Seq(Row(0), Row(1))) - } - } - - withSQLConf(SQLConf.IGNORE_CORRUPT_FILES.key -> "true") { - testIgnoreCorruptFiles() - } - - withSQLConf(SQLConf.IGNORE_CORRUPT_FILES.key -> "false") { - val exception = intercept[SparkException] { - testIgnoreCorruptFiles() + checkAnswer(df, Seq(Row(0), Row(1))) } - assert(exception.getMessage().contains("is not a Parquet file")) } - } - testQuietly("Enabling/disabling ignoreMissingFiles") { - def testIgnoreMissingFiles(): Unit = { + def testIgnoreCorruptFilesWithoutSchemaInfer(): Unit = { withTempDir { dir => val basePath = dir.getCanonicalPath spark.range(1).toDF("a").write.parquet(new Path(basePath, "first").toString) spark.range(1, 2).toDF("a").write.parquet(new Path(basePath, "second").toString) - val thirdPath = new Path(basePath, "third") - spark.range(2, 3).toDF("a").write.parquet(thirdPath.toString) - val df = spark.read.parquet( + spark.range(2, 3).toDF("a").write.json(new Path(basePath, "third").toString) + val df = spark.read.schema("a long").parquet( new Path(basePath, "first").toString, new Path(basePath, "second").toString, new Path(basePath, "third").toString) - - val fs = thirdPath.getFileSystem(spark.sparkContext.hadoopConfiguration) - fs.delete(thirdPath, true) - checkAnswer( - df, - Seq(Row(0), Row(1))) + checkAnswer(df, Seq(Row(0), Row(1))) } } - withSQLConf(SQLConf.IGNORE_MISSING_FILES.key -> "true") { - testIgnoreMissingFiles() + withSQLConf(SQLConf.IGNORE_CORRUPT_FILES.key -> "true") { + testIgnoreCorruptFiles() + testIgnoreCorruptFilesWithoutSchemaInfer() } - withSQLConf(SQLConf.IGNORE_MISSING_FILES.key -> "false") { + withSQLConf(SQLConf.IGNORE_CORRUPT_FILES.key -> "false") { val exception = intercept[SparkException] { - testIgnoreMissingFiles() + testIgnoreCorruptFiles() + } + assert(exception.getMessage().contains("is not a Parquet file")) + val exception2 = intercept[SparkException] { + testIgnoreCorruptFilesWithoutSchemaInfer() } - assert(exception.getMessage().contains("does not exist")) + assert(exception2.getMessage().contains("is not a Parquet file")) } } @@ -895,6 +879,18 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext } } } + + test("SPARK-24230: filter row group using dictionary") { + withSQLConf(("parquet.filter.dictionary.enabled", "true")) { + // create a table with values from 0, 2, ..., 18 that will be dictionary-encoded + withParquetTable((0 until 100).map(i => ((i * 2) % 20, s"data-$i")), "t") { + // search for a key that is not present so the dictionary filter eliminates all row groups + // Fails without SPARK-24230: + // java.io.IOException: expecting more rows but reached last block. Read 0 out of 50 + checkAnswer(sql("SELECT _2 FROM t WHERE t._1 = 5"), Seq.empty) + } + } + } } object TestingUDT { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala index 2cd2a600f2b97..9d3dfae348beb 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala @@ -20,10 +20,13 @@ package org.apache.spark.sql.execution.datasources.parquet import scala.reflect.ClassTag import scala.reflect.runtime.universe.TypeTag +import org.apache.parquet.io.ParquetDecodingException import org.apache.parquet.schema.{MessageType, MessageTypeParser} import org.apache.spark.SparkException import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.execution.QueryExecutionException +import org.apache.spark.sql.execution.datasources.SchemaColumnConvertNotSupportedException import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.SharedSQLContext import org.apache.spark.sql.types._ @@ -382,6 +385,58 @@ class ParquetSchemaSuite extends ParquetSchemaTest { } } + // ======================================= + // Tests for parquet schema mismatch error + // ======================================= + def testSchemaMismatch(path: String, vectorizedReaderEnabled: Boolean): SparkException = { + import testImplicits._ + + var e: SparkException = null + withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> vectorizedReaderEnabled.toString) { + // Create two parquet files with different schemas in the same folder + Seq(("bcd", 2)).toDF("a", "b").coalesce(1).write.mode("overwrite").parquet(s"$path/parquet") + Seq((1, "abc")).toDF("a", "b").coalesce(1).write.mode("append").parquet(s"$path/parquet") + + e = intercept[SparkException] { + spark.read.parquet(s"$path/parquet").collect() + } + } + e + } + + test("schema mismatch failure error message for parquet reader") { + withTempPath { dir => + val e = testSchemaMismatch(dir.getCanonicalPath, vectorizedReaderEnabled = false) + val expectedMessage = "Encounter error while reading parquet files. " + + "One possible cause: Parquet column cannot be converted in the corresponding " + + "files. Details:" + assert(e.getCause.isInstanceOf[QueryExecutionException]) + assert(e.getCause.getCause.isInstanceOf[ParquetDecodingException]) + assert(e.getCause.getMessage.startsWith(expectedMessage)) + } + } + + test("schema mismatch failure error message for parquet vectorized reader") { + withTempPath { dir => + val e = testSchemaMismatch(dir.getCanonicalPath, vectorizedReaderEnabled = true) + assert(e.getCause.isInstanceOf[QueryExecutionException]) + assert(e.getCause.getCause.isInstanceOf[SchemaColumnConvertNotSupportedException]) + + // Check if the physical type is reporting correctly + val errMsg = e.getCause.getMessage + assert(errMsg.startsWith("Parquet column cannot be converted in file")) + val file = errMsg.substring("Parquet column cannot be converted in file ".length, + errMsg.indexOf(". ")) + val col = spark.read.parquet(file).schema.fields.filter(_.name.equals("a")) + assert(col.length == 1) + if (col(0).dataType == StringType) { + assert(errMsg.contains("Column: [a], Expected: IntegerType, Found: BINARY")) + } else { + assert(errMsg.endsWith("Column: [a], Expected: StringType, Found: INT32")) + } + } + } + // ======================================================= // Tests for converting Parquet LIST to Catalyst ArrayType // ======================================================= diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/text/WholeTextFileSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/text/WholeTextFileSuite.scala index 8bd736bee69de..fff0f82f9bc2b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/text/WholeTextFileSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/text/WholeTextFileSuite.scala @@ -95,7 +95,7 @@ class WholeTextFileSuite extends QueryTest with SharedSQLContext { df1.write.option("compression", "gzip").mode("overwrite").text(path) // On reading through wholetext mode, one file will be read as a single row, i.e. not // delimited by "next line" character. - val expected = Row(Range(0, 1000).mkString("", "\n", "\n")) + val expected = Row(df1.collect().map(_.getString(0)).mkString("", "\n", "\n")) Seq(10, 100, 1000).foreach { bytes => withSQLConf(SQLConf.FILES_MAX_PARTITION_BYTES.key -> bytes.toString) { val df2 = spark.read.option("wholetext", "true").format("text").load(path) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala index 6da46ea3480b3..bcdee792f4c70 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala @@ -22,7 +22,8 @@ import scala.reflect.ClassTag import org.apache.spark.AccumulatorSuite import org.apache.spark.sql.{Dataset, QueryTest, Row, SparkSession} import org.apache.spark.sql.catalyst.expressions.{BitwiseAnd, BitwiseOr, Cast, Literal, ShiftLeft} -import org.apache.spark.sql.execution.{BinaryExecNode, SparkPlan, WholeStageCodegenExec} +import org.apache.spark.sql.execution.{SparkPlan, WholeStageCodegenExec} +import org.apache.spark.sql.execution.columnar.InMemoryTableScanExec import org.apache.spark.sql.execution.exchange.EnsureRequirements import org.apache.spark.sql.functions._ import org.apache.spark.sql.internal.SQLConf @@ -70,8 +71,8 @@ class BroadcastJoinSuite extends QueryTest with SQLTestUtils { private def testBroadcastJoin[T: ClassTag]( joinType: String, forceBroadcast: Boolean = false): SparkPlan = { - val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value") - val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value") + val df1 = Seq((1, "4"), (2, "2")).toDF("key", "value") + val df2 = Seq((1, "1"), (2, "2")).toDF("key", "value") // Comparison at the end is for broadcast left semi join val joinExpression = df1("key") === df2("key") && df1("value") > df2("value") @@ -109,17 +110,58 @@ class BroadcastJoinSuite extends QueryTest with SQLTestUtils { } } + test("SPARK-23192: broadcast hint should be retained after using the cached data") { + withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") { + try { + val df1 = Seq((1, "4"), (2, "2")).toDF("key", "value") + val df2 = Seq((1, "1"), (2, "2")).toDF("key", "value") + df2.cache() + val df3 = df1.join(broadcast(df2), Seq("key"), "inner") + val numBroadCastHashJoin = df3.queryExecution.executedPlan.collect { + case b: BroadcastHashJoinExec => b + }.size + assert(numBroadCastHashJoin === 1) + } finally { + spark.catalog.clearCache() + } + } + } + + test("SPARK-23214: cached data should not carry extra hint info") { + withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") { + try { + val df1 = Seq((1, "4"), (2, "2")).toDF("key", "value") + val df2 = Seq((1, "1"), (2, "2")).toDF("key", "value") + broadcast(df2).cache() + + val df3 = df1.join(df2, Seq("key"), "inner") + val numCachedPlan = df3.queryExecution.executedPlan.collect { + case i: InMemoryTableScanExec => i + }.size + // df2 should be cached. + assert(numCachedPlan === 1) + + val numBroadCastHashJoin = df3.queryExecution.executedPlan.collect { + case b: BroadcastHashJoinExec => b + }.size + // df2 should not be broadcasted. + assert(numBroadCastHashJoin === 0) + } finally { + spark.catalog.clearCache() + } + } + } + test("broadcast hint isn't propagated after a join") { withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") { - val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value") - val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value") + val df1 = Seq((1, "4"), (2, "2")).toDF("key", "value") + val df2 = Seq((1, "1"), (2, "2")).toDF("key", "value") val df3 = df1.join(broadcast(df2), Seq("key"), "inner").drop(df2("key")) - val df4 = spark.createDataFrame(Seq((1, "5"), (2, "5"))).toDF("key", "value") + val df4 = Seq((1, "5"), (2, "5")).toDF("key", "value") val df5 = df4.join(df3, Seq("key"), "inner") - val plan = - EnsureRequirements(spark.sessionState.conf).apply(df5.queryExecution.sparkPlan) + val plan = EnsureRequirements(spark.sessionState.conf).apply(df5.queryExecution.sparkPlan) assert(plan.collect { case p: BroadcastHashJoinExec => p }.size === 1) assert(plan.collect { case p: SortMergeJoinExec => p }.size === 1) @@ -127,30 +169,30 @@ class BroadcastJoinSuite extends QueryTest with SQLTestUtils { } private def assertBroadcastJoin(df : Dataset[Row]) : Unit = { - val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value") + val df1 = Seq((1, "4"), (2, "2")).toDF("key", "value") val joined = df1.join(df, Seq("key"), "inner") - val plan = - EnsureRequirements(spark.sessionState.conf).apply(joined.queryExecution.sparkPlan) + val plan = EnsureRequirements(spark.sessionState.conf).apply(joined.queryExecution.sparkPlan) assert(plan.collect { case p: BroadcastHashJoinExec => p }.size === 1) } test("broadcast hint programming API") { withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") { - val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"), (3, "2"))).toDF("key", "value") + val df2 = Seq((1, "1"), (2, "2"), (3, "2")).toDF("key", "value") val broadcasted = broadcast(df2) - val df3 = spark.createDataFrame(Seq((2, "2"), (3, "3"))).toDF("key", "value") - - val cases = Seq(broadcasted.limit(2), - broadcasted.filter("value < 10"), - broadcasted.sample(true, 0.5), - broadcasted.distinct(), - broadcasted.groupBy("value").agg(min($"key").as("key")), - // except and intersect are semi/anti-joins which won't return more data then - // their left argument, so the broadcast hint should be propagated here - broadcasted.except(df3), - broadcasted.intersect(df3)) + val df3 = Seq((2, "2"), (3, "3")).toDF("key", "value") + + val cases = Seq( + broadcasted.limit(2), + broadcasted.filter("value < 10"), + broadcasted.sample(true, 0.5), + broadcasted.distinct(), + broadcasted.groupBy("value").agg(min($"key").as("key")), + // except and intersect are semi/anti-joins which won't return more data then + // their left argument, so the broadcast hint should be propagated here + broadcasted.except(df3), + broadcasted.intersect(df3)) cases.foreach(assertBroadcastJoin) } @@ -227,9 +269,8 @@ class BroadcastJoinSuite extends QueryTest with SQLTestUtils { test("Shouldn't change broadcast join buildSide if user clearly specified") { withTempView("t1", "t2") { - spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value").createTempView("t1") - spark.createDataFrame(Seq((1, "1"), (2, "12.3"), (2, "123"))).toDF("key", "value") - .createTempView("t2") + Seq((1, "4"), (2, "2")).toDF("key", "value").createTempView("t1") + Seq((1, "1"), (2, "12.3"), (2, "123")).toDF("key", "value").createTempView("t2") val t1Size = spark.table("t1").queryExecution.analyzed.children.head.stats.sizeInBytes val t2Size = spark.table("t2").queryExecution.analyzed.children.head.stats.sizeInBytes @@ -279,9 +320,8 @@ class BroadcastJoinSuite extends QueryTest with SQLTestUtils { test("Shouldn't bias towards build right if user didn't specify") { withTempView("t1", "t2") { - spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value").createTempView("t1") - spark.createDataFrame(Seq((1, "1"), (2, "12.3"), (2, "123"))).toDF("key", "value") - .createTempView("t2") + Seq((1, "4"), (2, "2")).toDF("key", "value").createTempView("t1") + Seq((1, "1"), (2, "12.3"), (2, "123")).toDF("key", "value").createTempView("t2") val t1Size = spark.table("t1").queryExecution.analyzed.children.head.stats.sizeInBytes val t2Size = spark.table("t2").queryExecution.analyzed.children.head.stats.sizeInBytes @@ -318,7 +358,7 @@ class BroadcastJoinSuite extends QueryTest with SQLTestUtils { case b: BroadcastNestedLoopJoinExec => assert(b.getClass.getSimpleName === joinMethod) assert(b.buildSide === buildSide) - case b: BroadcastNestedLoopJoinExec => + case b: BroadcastHashJoinExec => assert(b.getClass.getSimpleName === joinMethod) assert(b.buildSide === buildSide) case w: WholeStageCodegenExec => diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala index 51f8c3325fdff..037cc2e3ccad7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala @@ -28,7 +28,7 @@ import org.apache.spark.serializer.KryoSerializer import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.test.SharedSQLContext -import org.apache.spark.sql.types.{IntegerType, LongType, StringType, StructField, StructType} +import org.apache.spark.sql.types._ import org.apache.spark.unsafe.map.BytesToBytesMap import org.apache.spark.unsafe.types.UTF8String import org.apache.spark.util.collection.CompactBuffer @@ -254,6 +254,30 @@ class HashedRelationSuite extends SparkFunSuite with SharedSQLContext { map.free() } + test("SPARK-24257: insert big values into LongToUnsafeRowMap") { + val taskMemoryManager = new TaskMemoryManager( + new StaticMemoryManager( + new SparkConf().set(MEMORY_OFFHEAP_ENABLED.key, "false"), + Long.MaxValue, + Long.MaxValue, + 1), + 0) + val unsafeProj = UnsafeProjection.create(Array[DataType](StringType)) + val map = new LongToUnsafeRowMap(taskMemoryManager, 1) + + val key = 0L + // the page array is initialized with length 1 << 17 (1M bytes), + // so here we need a value larger than 1 << 18 (2M bytes), to trigger the bug + val bigStr = UTF8String.fromString("x" * (1 << 19)) + + map.append(key, unsafeProj(InternalRow(bigStr))) + map.optimize() + + val resultRow = new UnsafeRow(1) + assert(map.getValue(key, resultRow).getUTF8String(0) === bigStr) + map.free() + } + test("Spark-14521") { val ser = new KryoSerializer( (new SparkConf).set("spark.kryo.referenceTracking", "false")).newInstance() diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsTestUtils.scala index 122d28798136f..e22bbb642642a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsTestUtils.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsTestUtils.scala @@ -34,6 +34,7 @@ trait SQLMetricsTestUtils extends SQLTestUtils { import testImplicits._ protected def currentExecutionIds(): Set[Long] = { + spark.sparkContext.listenerBus.waitUntilEmpty(10000) statusStore.executionsList.map(_.executionId).toSet } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/ForeachSinkSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/ForeachSinkSuite.scala index 9137d650e906b..41434e6d8b974 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/ForeachSinkSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/ForeachSinkSuite.scala @@ -52,13 +52,13 @@ class ForeachSinkSuite extends StreamTest with SharedSQLContext with BeforeAndAf var expectedEventsForPartition0 = Seq( ForeachSinkSuite.Open(partition = 0, version = 0), - ForeachSinkSuite.Process(value = 1), + ForeachSinkSuite.Process(value = 2), ForeachSinkSuite.Process(value = 3), ForeachSinkSuite.Close(None) ) var expectedEventsForPartition1 = Seq( ForeachSinkSuite.Open(partition = 1, version = 0), - ForeachSinkSuite.Process(value = 2), + ForeachSinkSuite.Process(value = 1), ForeachSinkSuite.Process(value = 4), ForeachSinkSuite.Close(None) ) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/MemorySinkV2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/MemorySinkV2Suite.scala index 00d4f0b8503d8..9be22d94b5654 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/MemorySinkV2Suite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/MemorySinkV2Suite.scala @@ -40,7 +40,7 @@ class MemorySinkV2Suite extends StreamTest with BeforeAndAfter { test("continuous writer") { val sink = new MemorySinkV2 - val writer = new ContinuousMemoryWriter(sink, OutputMode.Append()) + val writer = new MemoryStreamWriter(sink, OutputMode.Append()) writer.commit(0, Array( MemoryWriterCommitMessage(0, Seq(Row(1), Row(2))), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/RateSourceV2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/RateSourceV2Suite.scala index e11705a227f48..983ba1668f58f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/RateSourceV2Suite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/RateSourceV2Suite.scala @@ -18,28 +18,72 @@ package org.apache.spark.sql.execution.streaming import java.util.Optional +import java.util.concurrent.TimeUnit import scala.collection.JavaConverters._ import org.apache.spark.sql.Row import org.apache.spark.sql.execution.datasources.DataSource import org.apache.spark.sql.execution.streaming.continuous._ -import org.apache.spark.sql.execution.streaming.sources.{RateStreamBatchTask, RateStreamSourceV2, RateStreamV2Reader} -import org.apache.spark.sql.sources.v2.DataSourceV2Options -import org.apache.spark.sql.sources.v2.streaming.ContinuousReadSupport +import org.apache.spark.sql.execution.streaming.sources.{RateStreamBatchTask, RateStreamMicroBatchReader, RateStreamSourceV2} +import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, MicroBatchReadSupport} +import org.apache.spark.sql.sources.v2.DataSourceOptions import org.apache.spark.sql.streaming.StreamTest +import org.apache.spark.util.ManualClock class RateSourceV2Suite extends StreamTest { + import testImplicits._ + + case class AdvanceRateManualClock(seconds: Long) extends AddData { + override def addData(query: Option[StreamExecution]): (BaseStreamingSource, Offset) = { + assert(query.nonEmpty) + val rateSource = query.get.logicalPlan.collect { + case StreamingExecutionRelation(source: RateStreamMicroBatchReader, _) => source + }.head + rateSource.clock.asInstanceOf[ManualClock].advance(TimeUnit.SECONDS.toMillis(seconds)) + rateSource.setOffsetRange(Optional.empty(), Optional.empty()) + (rateSource, rateSource.getEndOffset()) + } + } + + test("microbatch in registry") { + DataSource.lookupDataSource("ratev2", spark.sqlContext.conf).newInstance() match { + case ds: MicroBatchReadSupport => + val reader = ds.createMicroBatchReader(Optional.empty(), "", DataSourceOptions.empty()) + assert(reader.isInstanceOf[RateStreamMicroBatchReader]) + case _ => + throw new IllegalStateException("Could not find v2 read support for rate") + } + } + + test("basic microbatch execution") { + val input = spark.readStream + .format("rateV2") + .option("numPartitions", "1") + .option("rowsPerSecond", "10") + .option("useManualClock", "true") + .load() + testStream(input, useV2Sink = true)( + AdvanceRateManualClock(seconds = 1), + CheckLastBatch((0 until 10).map(v => new java.sql.Timestamp(v * 100L) -> v): _*), + StopStream, + StartStream(), + // Advance 2 seconds because creating a new RateSource will also create a new ManualClock + AdvanceRateManualClock(seconds = 2), + CheckLastBatch((10 until 20).map(v => new java.sql.Timestamp(v * 100L) -> v): _*) + ) + } + test("microbatch - numPartitions propagated") { - val reader = new RateStreamV2Reader( - new DataSourceV2Options(Map("numPartitions" -> "11", "rowsPerSecond" -> "33").asJava)) + val reader = new RateStreamMicroBatchReader( + new DataSourceOptions(Map("numPartitions" -> "11", "rowsPerSecond" -> "33").asJava)) reader.setOffsetRange(Optional.empty(), Optional.empty()) - val tasks = reader.createReadTasks() + val tasks = reader.createDataReaderFactories() assert(tasks.size == 11) } test("microbatch - set offset") { - val reader = new RateStreamV2Reader(DataSourceV2Options.empty()) + val reader = new RateStreamMicroBatchReader(DataSourceOptions.empty()) val startOffset = RateStreamOffset(Map((0, ValueRunTimeMsPair(0, 1000)))) val endOffset = RateStreamOffset(Map((0, ValueRunTimeMsPair(0, 2000)))) reader.setOffsetRange(Optional.of(startOffset), Optional.of(endOffset)) @@ -48,8 +92,8 @@ class RateSourceV2Suite extends StreamTest { } test("microbatch - infer offsets") { - val reader = new RateStreamV2Reader( - new DataSourceV2Options(Map("numPartitions" -> "1", "rowsPerSecond" -> "100").asJava)) + val reader = new RateStreamMicroBatchReader( + new DataSourceOptions(Map("numPartitions" -> "1", "rowsPerSecond" -> "100").asJava)) reader.clock.waitTillTime(reader.clock.getTimeMillis() + 100) reader.setOffsetRange(Optional.empty(), Optional.empty()) reader.getStartOffset() match { @@ -69,19 +113,19 @@ class RateSourceV2Suite extends StreamTest { } test("microbatch - predetermined batch size") { - val reader = new RateStreamV2Reader( - new DataSourceV2Options(Map("numPartitions" -> "1", "rowsPerSecond" -> "20").asJava)) + val reader = new RateStreamMicroBatchReader( + new DataSourceOptions(Map("numPartitions" -> "1", "rowsPerSecond" -> "20").asJava)) val startOffset = RateStreamOffset(Map((0, ValueRunTimeMsPair(0, 1000)))) val endOffset = RateStreamOffset(Map((0, ValueRunTimeMsPair(20, 2000)))) reader.setOffsetRange(Optional.of(startOffset), Optional.of(endOffset)) - val tasks = reader.createReadTasks() + val tasks = reader.createDataReaderFactories() assert(tasks.size == 1) assert(tasks.get(0).asInstanceOf[RateStreamBatchTask].vals.size == 20) } test("microbatch - data read") { - val reader = new RateStreamV2Reader( - new DataSourceV2Options(Map("numPartitions" -> "11", "rowsPerSecond" -> "33").asJava)) + val reader = new RateStreamMicroBatchReader( + new DataSourceOptions(Map("numPartitions" -> "11", "rowsPerSecond" -> "33").asJava)) val startOffset = RateStreamSourceV2.createInitialOffset(11, reader.creationTimeMs) val endOffset = RateStreamOffset(startOffset.partitionToValueAndRunTimeMs.toSeq.map { case (part, ValueRunTimeMsPair(currentVal, currentReadTime)) => @@ -89,7 +133,7 @@ class RateSourceV2Suite extends StreamTest { }.toMap) reader.setOffsetRange(Optional.of(startOffset), Optional.of(endOffset)) - val tasks = reader.createReadTasks() + val tasks = reader.createDataReaderFactories() assert(tasks.size == 11) val readData = tasks.asScala @@ -106,33 +150,33 @@ class RateSourceV2Suite extends StreamTest { test("continuous in registry") { DataSource.lookupDataSource("rate", spark.sqlContext.conf).newInstance() match { case ds: ContinuousReadSupport => - val reader = ds.createContinuousReader(Optional.empty(), "", DataSourceV2Options.empty()) - assert(reader.isInstanceOf[ContinuousRateStreamReader]) + val reader = ds.createContinuousReader(Optional.empty(), "", DataSourceOptions.empty()) + assert(reader.isInstanceOf[RateStreamContinuousReader]) case _ => throw new IllegalStateException("Could not find v2 read support for rate") } } test("continuous data") { - val reader = new ContinuousRateStreamReader( - new DataSourceV2Options(Map("numPartitions" -> "2", "rowsPerSecond" -> "20").asJava)) - reader.setOffset(Optional.empty()) - val tasks = reader.createReadTasks() + val reader = new RateStreamContinuousReader( + new DataSourceOptions(Map("numPartitions" -> "2", "rowsPerSecond" -> "20").asJava)) + reader.setStartOffset(Optional.empty()) + val tasks = reader.createDataReaderFactories() assert(tasks.size == 2) val data = scala.collection.mutable.ListBuffer[Row]() tasks.asScala.foreach { - case t: RateStreamReadTask => + case t: RateStreamContinuousDataReaderFactory => val startTimeMs = reader.getStartOffset() .asInstanceOf[RateStreamOffset] .partitionToValueAndRunTimeMs(t.partitionIndex) .runTimeMs - val r = t.createDataReader().asInstanceOf[RateStreamDataReader] + val r = t.createDataReader().asInstanceOf[RateStreamContinuousDataReader] for (rowIndex <- 0 to 9) { r.next() data.append(r.get()) assert(r.getOffset() == - ContinuousRateStreamPartitionOffset( + RateStreamPartitionOffset( t.partitionIndex, t.partitionIndex + rowIndex * 2, startTimeMs + (rowIndex + 1) * 100)) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ConsoleWriterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ConsoleWriterSuite.scala new file mode 100644 index 0000000000000..55acf2ba28d2f --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ConsoleWriterSuite.scala @@ -0,0 +1,153 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.streaming.sources + +import java.io.ByteArrayOutputStream + +import org.scalatest.time.SpanSugar._ + +import org.apache.spark.sql.execution.streaming.MemoryStream +import org.apache.spark.sql.streaming.{StreamTest, Trigger} + +class ConsoleWriterSuite extends StreamTest { + import testImplicits._ + + test("microbatch - default") { + val input = MemoryStream[Int] + + val captured = new ByteArrayOutputStream() + Console.withOut(captured) { + val query = input.toDF().writeStream.format("console").start() + try { + input.addData(1, 2, 3) + query.processAllAvailable() + input.addData(4, 5, 6) + query.processAllAvailable() + input.addData() + query.processAllAvailable() + } finally { + query.stop() + } + } + + assert(captured.toString() == + """------------------------------------------- + |Batch: 0 + |------------------------------------------- + |+-----+ + ||value| + |+-----+ + || 1| + || 2| + || 3| + |+-----+ + | + |------------------------------------------- + |Batch: 1 + |------------------------------------------- + |+-----+ + ||value| + |+-----+ + || 4| + || 5| + || 6| + |+-----+ + | + |------------------------------------------- + |Batch: 2 + |------------------------------------------- + |+-----+ + ||value| + |+-----+ + |+-----+ + | + |""".stripMargin) + } + + test("microbatch - with numRows") { + val input = MemoryStream[Int] + + val captured = new ByteArrayOutputStream() + Console.withOut(captured) { + val query = input.toDF().writeStream.format("console").option("NUMROWS", 2).start() + try { + input.addData(1, 2, 3) + query.processAllAvailable() + } finally { + query.stop() + } + } + + assert(captured.toString() == + """------------------------------------------- + |Batch: 0 + |------------------------------------------- + |+-----+ + ||value| + |+-----+ + || 1| + || 2| + |+-----+ + |only showing top 2 rows + | + |""".stripMargin) + } + + test("microbatch - truncation") { + val input = MemoryStream[String] + + val captured = new ByteArrayOutputStream() + Console.withOut(captured) { + val query = input.toDF().writeStream.format("console").option("TRUNCATE", true).start() + try { + input.addData("123456789012345678901234567890") + query.processAllAvailable() + } finally { + query.stop() + } + } + + assert(captured.toString() == + """------------------------------------------- + |Batch: 0 + |------------------------------------------- + |+--------------------+ + || value| + |+--------------------+ + ||12345678901234567...| + |+--------------------+ + | + |""".stripMargin) + } + + test("continuous - default") { + val captured = new ByteArrayOutputStream() + Console.withOut(captured) { + val input = spark.readStream + .format("rate") + .option("numPartitions", "1") + .option("rowsPerSecond", "5") + .load() + .select('value) + + val query = input.writeStream.format("console").trigger(Trigger.Continuous(200)).start() + assert(query.isActive) + query.stop() + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/state/StateStoreRDDSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/state/StateStoreRDDSuite.scala index 65b39f0fbd73d..579a364ebc3e5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/state/StateStoreRDDSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/state/StateStoreRDDSuite.scala @@ -55,7 +55,7 @@ class StateStoreRDDSuite extends SparkFunSuite with BeforeAndAfter with BeforeAn test("versioning and immutability") { withSparkSession(SparkSession.builder.config(sparkConf).getOrCreate()) { spark => - val path = Utils.createDirectory(tempDir, Random.nextString(10)).toString + val path = Utils.createDirectory(tempDir, Random.nextFloat.toString).toString val rdd1 = makeRDD(spark.sparkContext, Seq("a", "b", "a")).mapPartitionsWithStateStore( spark.sqlContext, operatorStateInfo(path, version = 0), keySchema, valueSchema, None)( increment) @@ -73,7 +73,7 @@ class StateStoreRDDSuite extends SparkFunSuite with BeforeAndAfter with BeforeAn } test("recovering from files") { - val path = Utils.createDirectory(tempDir, Random.nextString(10)).toString + val path = Utils.createDirectory(tempDir, Random.nextFloat.toString).toString def makeStoreRDD( spark: SparkSession, @@ -101,7 +101,7 @@ class StateStoreRDDSuite extends SparkFunSuite with BeforeAndAfter with BeforeAn test("usage with iterators - only gets and only puts") { withSparkSession(SparkSession.builder.config(sparkConf).getOrCreate()) { spark => implicit val sqlContext = spark.sqlContext - val path = Utils.createDirectory(tempDir, Random.nextString(10)).toString + val path = Utils.createDirectory(tempDir, Random.nextFloat.toString).toString val opId = 0 // Returns an iterator of the incremented value made into the store @@ -149,7 +149,7 @@ class StateStoreRDDSuite extends SparkFunSuite with BeforeAndAfter with BeforeAn quietly { val queryRunId = UUID.randomUUID val opId = 0 - val path = Utils.createDirectory(tempDir, Random.nextString(10)).toString + val path = Utils.createDirectory(tempDir, Random.nextFloat.toString).toString withSparkSession(SparkSession.builder.config(sparkConf).getOrCreate()) { spark => implicit val sqlContext = spark.sqlContext @@ -189,7 +189,7 @@ class StateStoreRDDSuite extends SparkFunSuite with BeforeAndAfter with BeforeAn .config(sparkConf.setMaster("local-cluster[2, 1, 1024]")) .getOrCreate()) { spark => implicit val sqlContext = spark.sqlContext - val path = Utils.createDirectory(tempDir, Random.nextString(10)).toString + val path = Utils.createDirectory(tempDir, Random.nextFloat.toString).toString val opId = 0 val rdd1 = makeRDD(spark.sparkContext, Seq("a", "b", "a")).mapPartitionsWithStateStore( sqlContext, operatorStateInfo(path, version = 0), keySchema, valueSchema, None)(increment) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListenerSuite.scala index 7d84f45d36bee..02df45d1b7989 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListenerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListenerSuite.scala @@ -35,6 +35,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LocalRelation import org.apache.spark.sql.catalyst.util.quietly import org.apache.spark.sql.execution.{LeafExecNode, QueryExecution, SparkPlanInfo, SQLExecution} import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics} +import org.apache.spark.sql.internal.StaticSQLConf.UI_RETAINED_EXECUTIONS import org.apache.spark.sql.test.SharedSQLContext import org.apache.spark.status.ElementTrackingStore import org.apache.spark.status.config._ @@ -442,7 +443,8 @@ class SQLAppStatusListenerSuite extends SparkFunSuite with SharedSQLContext with val oldCount = statusStore.executionsList().size val expectedAccumValue = 12345 - val physicalPlan = MyPlan(sqlContext.sparkContext, expectedAccumValue) + val expectedAccumValue2 = 54321 + val physicalPlan = MyPlan(sqlContext.sparkContext, expectedAccumValue, expectedAccumValue2) val dummyQueryExecution = new QueryExecution(spark, LocalRelation()) { override lazy val sparkPlan = physicalPlan override lazy val executedPlan = physicalPlan @@ -465,10 +467,14 @@ class SQLAppStatusListenerSuite extends SparkFunSuite with SharedSQLContext with val execId = statusStore.executionsList().last.executionId val metrics = statusStore.executionMetrics(execId) val driverMetric = physicalPlan.metrics("dummy") + val driverMetric2 = physicalPlan.metrics("dummy2") val expectedValue = SQLMetrics.stringValue(driverMetric.metricType, Seq(expectedAccumValue)) + val expectedValue2 = SQLMetrics.stringValue(driverMetric2.metricType, Seq(expectedAccumValue2)) assert(metrics.contains(driverMetric.id)) assert(metrics(driverMetric.id) === expectedValue) + assert(metrics.contains(driverMetric2.id)) + assert(metrics(driverMetric2.id) === expectedValue2) } test("roundtripping SparkListenerDriverAccumUpdates through JsonProtocol (SPARK-18462)") { @@ -510,6 +516,50 @@ class SQLAppStatusListenerSuite extends SparkFunSuite with SharedSQLContext with } } + test("eviction should respect execution completion time") { + val conf = sparkContext.conf.clone().set(UI_RETAINED_EXECUTIONS.key, "2") + val store = new ElementTrackingStore(new InMemoryStore, conf) + val listener = new SQLAppStatusListener(conf, store, live = true) + val statusStore = new SQLAppStatusStore(store, Some(listener)) + + var time = 0 + val df = createTestDataFrame + // Start execution 1 and execution 2 + time += 1 + listener.onOtherEvent(SparkListenerSQLExecutionStart( + 1, + "test", + "test", + df.queryExecution.toString, + SparkPlanInfo.fromSparkPlan(df.queryExecution.executedPlan), + time)) + time += 1 + listener.onOtherEvent(SparkListenerSQLExecutionStart( + 2, + "test", + "test", + df.queryExecution.toString, + SparkPlanInfo.fromSparkPlan(df.queryExecution.executedPlan), + time)) + + // Stop execution 2 before execution 1 + time += 1 + listener.onOtherEvent(SparkListenerSQLExecutionEnd(2, time)) + time += 1 + listener.onOtherEvent(SparkListenerSQLExecutionEnd(1, time)) + + // Start execution 3 and execution 2 should be evicted. + time += 1 + listener.onOtherEvent(SparkListenerSQLExecutionStart( + 3, + "test", + "test", + df.queryExecution.toString, + SparkPlanInfo.fromSparkPlan(df.queryExecution.executedPlan), + time)) + assert(statusStore.executionsCount === 2) + assert(statusStore.execution(2) === None) + } } @@ -517,20 +567,31 @@ class SQLAppStatusListenerSuite extends SparkFunSuite with SharedSQLContext with * A dummy [[org.apache.spark.sql.execution.SparkPlan]] that updates a [[SQLMetrics]] * on the driver. */ -private case class MyPlan(sc: SparkContext, expectedValue: Long) extends LeafExecNode { +private case class MyPlan(sc: SparkContext, expectedValue: Long, expectedValue2: Long) + extends LeafExecNode { + override def sparkContext: SparkContext = sc override def output: Seq[Attribute] = Seq() override val metrics: Map[String, SQLMetric] = Map( - "dummy" -> SQLMetrics.createMetric(sc, "dummy")) + "dummy" -> SQLMetrics.createMetric(sc, "dummy"), + "dummy2" -> SQLMetrics.createMetric(sc, "dummy2")) override def doExecute(): RDD[InternalRow] = { longMetric("dummy") += expectedValue + longMetric("dummy2") += expectedValue2 + + // postDriverMetricUpdates may happen multiple time in a query. + // (normally from different operators, but for the sake of testing, from one operator) + SQLMetrics.postDriverMetricUpdates( + sc, + sc.getLocalProperty(SQLExecution.EXECUTION_ID_KEY), + Seq(metrics("dummy"))) SQLMetrics.postDriverMetricUpdates( sc, sc.getLocalProperty(SQLExecution.EXECUTION_ID_KEY), - metrics.values.toSeq) + Seq(metrics("dummy2"))) sc.emptyRDD } } @@ -566,6 +627,7 @@ class SQLAppStatusListenerMemoryLeakSuite extends SparkFunSuite { sc.listenerBus.waitUntilEmpty(10000) val statusStore = spark.sharedState.statusStore assert(statusStore.executionsCount() <= 50) + assert(statusStore.planGraphCount() <= 50) // No live data should be left behind after all executions end. assert(statusStore.listener.get.noLiveData()) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ArrowColumnVectorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ArrowColumnVectorSuite.scala index 03490ad15a655..b55489cb2678a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ArrowColumnVectorSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ArrowColumnVectorSuite.scala @@ -23,6 +23,7 @@ import org.apache.arrow.vector.complex._ import org.apache.spark.SparkFunSuite import org.apache.spark.sql.execution.arrow.ArrowUtils import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.ArrowColumnVector import org.apache.spark.unsafe.types.UTF8String class ArrowColumnVectorSuite extends SparkFunSuite { @@ -41,6 +42,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === BooleanType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -68,6 +70,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === ByteType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -95,6 +98,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === ShortType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -122,6 +126,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === IntegerType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -149,6 +154,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === LongType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -176,6 +182,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === FloatType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -203,6 +210,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === DoubleType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -231,6 +239,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === StringType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -257,6 +266,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === BinaryType) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) (0 until 10).foreach { i => @@ -299,6 +309,7 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === ArrayType(IntegerType)) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) val array0 = columnVector.getArray(0) @@ -321,6 +332,43 @@ class ArrowColumnVectorSuite extends SparkFunSuite { allocator.close() } + test("non nullable struct") { + val allocator = ArrowUtils.rootAllocator.newChildAllocator("struct", 0, Long.MaxValue) + val schema = new StructType().add("int", IntegerType).add("long", LongType) + val vector = ArrowUtils.toArrowField("struct", schema, nullable = false, null) + .createVector(allocator).asInstanceOf[NullableMapVector] + + vector.allocateNew() + val intVector = vector.getChildByOrdinal(0).asInstanceOf[IntVector] + val longVector = vector.getChildByOrdinal(1).asInstanceOf[BigIntVector] + + vector.setIndexDefined(0) + intVector.setSafe(0, 1) + longVector.setSafe(0, 1L) + + vector.setIndexDefined(1) + intVector.setSafe(1, 2) + longVector.setNull(1) + + vector.setValueCount(2) + + val columnVector = new ArrowColumnVector(vector) + assert(columnVector.dataType === schema) + assert(!columnVector.hasNull) + assert(columnVector.numNulls === 0) + + val row0 = columnVector.getStruct(0) + assert(row0.getInt(0) === 1) + assert(row0.getLong(1) === 1L) + + val row1 = columnVector.getStruct(1) + assert(row1.getInt(0) === 2) + assert(row1.isNullAt(1)) + + columnVector.close() + allocator.close() + } + test("struct") { val allocator = ArrowUtils.rootAllocator.newChildAllocator("struct", 0, Long.MaxValue) val schema = new StructType().add("int", IntegerType).add("long", LongType) @@ -359,23 +407,24 @@ class ArrowColumnVectorSuite extends SparkFunSuite { val columnVector = new ArrowColumnVector(vector) assert(columnVector.dataType === schema) + assert(columnVector.hasNull) assert(columnVector.numNulls === 1) - val row0 = columnVector.getStruct(0, 2) + val row0 = columnVector.getStruct(0) assert(row0.getInt(0) === 1) assert(row0.getLong(1) === 1L) - val row1 = columnVector.getStruct(1, 2) + val row1 = columnVector.getStruct(1) assert(row1.getInt(0) === 2) assert(row1.isNullAt(1)) - val row2 = columnVector.getStruct(2, 2) + val row2 = columnVector.getStruct(2) assert(row2.isNullAt(0)) assert(row2.getLong(1) === 3L) assert(columnVector.isNullAt(3)) - val row4 = columnVector.getStruct(4, 2) + val row4 = columnVector.getStruct(4) assert(row4.getInt(0) === 5) assert(row4.getLong(1) === 5L) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnVectorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnVectorSuite.scala index 54b31cee031f6..2d1ad4b456783 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnVectorSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnVectorSuite.scala @@ -21,10 +21,10 @@ import org.scalatest.BeforeAndAfterEach import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.expressions.SpecificInternalRow -import org.apache.spark.sql.catalyst.util.ArrayData import org.apache.spark.sql.execution.columnar.ColumnAccessor import org.apache.spark.sql.execution.columnar.compression.ColumnBuilderHelper import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.ColumnarArray import org.apache.spark.unsafe.types.UTF8String class ColumnVectorSuite extends SparkFunSuite with BeforeAndAfterEach { @@ -199,17 +199,17 @@ class ColumnVectorSuite extends SparkFunSuite with BeforeAndAfterEach { val structType: StructType = new StructType().add("int", IntegerType).add("double", DoubleType) testVectors("struct", 10, structType) { testVector => - val c1 = testVector.getChildColumn(0) - val c2 = testVector.getChildColumn(1) + val c1 = testVector.getChild(0) + val c2 = testVector.getChild(1) c1.putInt(0, 123) c2.putDouble(0, 3.45) c1.putInt(1, 456) c2.putDouble(1, 5.67) - assert(testVector.getStruct(0, structType.length).get(0, IntegerType) === 123) - assert(testVector.getStruct(0, structType.length).get(1, DoubleType) === 3.45) - assert(testVector.getStruct(1, structType.length).get(0, IntegerType) === 456) - assert(testVector.getStruct(1, structType.length).get(1, DoubleType) === 5.67) + assert(testVector.getStruct(0).get(0, IntegerType) === 123) + assert(testVector.getStruct(0).get(1, DoubleType) === 3.45) + assert(testVector.getStruct(1).get(0, IntegerType) === 456) + assert(testVector.getStruct(1).get(1, DoubleType) === 5.67) } test("[SPARK-22092] off-heap column vector reallocation corrupts array data") { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnarBatchBenchmark.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnarBatchBenchmark.scala index 38ea2e47fdef8..1f31aa45a1220 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnarBatchBenchmark.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnarBatchBenchmark.scala @@ -14,7 +14,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package org.apache.spark.sql.execution.datasources.parquet +package org.apache.spark.sql.execution.vectorized import java.nio.ByteBuffer import java.nio.charset.StandardCharsets @@ -23,8 +23,6 @@ import scala.util.Random import org.apache.spark.memory.MemoryMode import org.apache.spark.sql.catalyst.expressions.UnsafeRow -import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector -import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector import org.apache.spark.sql.types.{ArrayType, BinaryType, IntegerType} import org.apache.spark.unsafe.Platform import org.apache.spark.util.Benchmark @@ -268,17 +266,17 @@ object ColumnarBatchBenchmark { Int Read/Write: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ - Java Array 177 / 181 1856.4 0.5 1.0X - ByteBuffer Unsafe 318 / 322 1032.0 1.0 0.6X - ByteBuffer API 1411 / 1418 232.2 4.3 0.1X - DirectByteBuffer 467 / 474 701.8 1.4 0.4X - Unsafe Buffer 178 / 185 1843.6 0.5 1.0X - Column(on heap) 178 / 184 1840.8 0.5 1.0X - Column(off heap) 341 / 344 961.8 1.0 0.5X - Column(off heap direct) 178 / 184 1845.4 0.5 1.0X - UnsafeRow (on heap) 378 / 389 866.3 1.2 0.5X - UnsafeRow (off heap) 393 / 402 834.0 1.2 0.4X - Column On Heap Append 309 / 318 1059.1 0.9 0.6X + Java Array 177 / 183 1851.1 0.5 1.0X + ByteBuffer Unsafe 314 / 330 1043.7 1.0 0.6X + ByteBuffer API 1298 / 1307 252.4 4.0 0.1X + DirectByteBuffer 465 / 483 704.2 1.4 0.4X + Unsafe Buffer 179 / 183 1835.5 0.5 1.0X + Column(on heap) 181 / 186 1815.2 0.6 1.0X + Column(off heap) 344 / 349 951.7 1.1 0.5X + Column(off heap direct) 178 / 186 1838.6 0.5 1.0X + UnsafeRow (on heap) 388 / 394 844.8 1.2 0.5X + UnsafeRow (off heap) 400 / 403 819.4 1.2 0.4X + Column On Heap Append 315 / 325 1041.8 1.0 0.6X */ val benchmark = new Benchmark("Int Read/Write", count * iters) benchmark.addCase("Java Array")(javaArray) @@ -337,8 +335,8 @@ object ColumnarBatchBenchmark { Boolean Read/Write: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ - Bitset 726 / 727 462.4 2.2 1.0X - Byte Array 530 / 542 632.7 1.6 1.4X + Bitset 741 / 747 452.6 2.2 1.0X + Byte Array 531 / 542 631.6 1.6 1.4X */ benchmark.run() } @@ -394,8 +392,8 @@ object ColumnarBatchBenchmark { String Read/Write: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ - On Heap 332 / 338 49.3 20.3 1.0X - Off Heap 466 / 467 35.2 28.4 0.7X + On Heap 351 / 362 46.6 21.4 1.0X + Off Heap 456 / 466 35.9 27.8 0.8X */ val benchmark = new Benchmark("String Read/Write", count * iters) benchmark.addCase("On Heap")(column(MemoryMode.ON_HEAP)) @@ -434,7 +432,6 @@ object ColumnarBatchBenchmark { } def readArrays(onHeap: Boolean): Unit = { - System.gc() val vector = if (onHeap) onHeapVector else offHeapVector var sum = 0L @@ -448,7 +445,6 @@ object ColumnarBatchBenchmark { } def readArrayElements(onHeap: Boolean): Unit = { - System.gc() val vector = if (onHeap) onHeapVector else offHeapVector var sum = 0L @@ -479,10 +475,10 @@ object ColumnarBatchBenchmark { Array Vector Read: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ - On Heap Read Size Only 415 / 422 394.7 2.5 1.0X - Off Heap Read Size Only 394 / 402 415.9 2.4 1.1X - On Heap Read Elements 2558 / 2593 64.0 15.6 0.2X - Off Heap Read Elements 3316 / 3317 49.4 20.2 0.1X + On Heap Read Size Only 426 / 437 384.9 2.6 1.0X + Off Heap Read Size Only 406 / 421 404.0 2.5 1.0X + On Heap Read Elements 2636 / 2642 62.2 16.1 0.2X + Off Heap Read Elements 3770 / 3774 43.5 23.0 0.1X */ benchmark.run } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnarBatchSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnarBatchSuite.scala index 7848ebdcab6d0..f57f07b498261 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnarBatchSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/vectorized/ColumnarBatchSuite.scala @@ -33,6 +33,7 @@ import org.apache.spark.sql.{RandomDataGenerator, Row} import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.execution.arrow.ArrowUtils import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.{ArrowColumnVector, ColumnarBatch, ColumnVector} import org.apache.spark.unsafe.Platform import org.apache.spark.unsafe.types.CalendarInterval @@ -65,22 +66,27 @@ class ColumnarBatchSuite extends SparkFunSuite { column => val reference = mutable.ArrayBuffer.empty[Boolean] var idx = 0 + assert(!column.hasNull) assert(column.numNulls() == 0) column.appendNotNull() reference += false + assert(!column.hasNull) assert(column.numNulls() == 0) column.appendNotNulls(3) (1 to 3).foreach(_ => reference += false) + assert(!column.hasNull) assert(column.numNulls() == 0) column.appendNull() reference += true + assert(column.hasNull) assert(column.numNulls() == 1) column.appendNulls(3) (1 to 3).foreach(_ => reference += true) + assert(column.hasNull) assert(column.numNulls() == 4) idx = column.elementsAppended @@ -88,11 +94,13 @@ class ColumnarBatchSuite extends SparkFunSuite { column.putNotNull(idx) reference += false idx += 1 + assert(column.hasNull) assert(column.numNulls() == 4) column.putNull(idx) reference += true idx += 1 + assert(column.hasNull) assert(column.numNulls() == 5) column.putNulls(idx, 3) @@ -100,6 +108,7 @@ class ColumnarBatchSuite extends SparkFunSuite { reference += true reference += true idx += 3 + assert(column.hasNull) assert(column.numNulls() == 8) column.putNotNulls(idx, 4) @@ -108,6 +117,7 @@ class ColumnarBatchSuite extends SparkFunSuite { reference += false reference += false idx += 4 + assert(column.hasNull) assert(column.numNulls() == 8) reference.zipWithIndex.foreach { v => @@ -562,7 +572,7 @@ class ColumnarBatchSuite extends SparkFunSuite { } } - testVector("String APIs", 6, StringType) { + testVector("String APIs", 7, StringType) { column => val reference = mutable.ArrayBuffer.empty[String] @@ -609,6 +619,10 @@ class ColumnarBatchSuite extends SparkFunSuite { idx += 1 assert(column.arrayData().elementsAppended == 17 + (s + s).length) + column.putNull(idx) + assert(column.getUTF8String(idx) == null) + idx += 1 + reference.zipWithIndex.foreach { v => val errMsg = "VectorType=" + column.getClass.getSimpleName assert(v._1.length == column.getArrayLength(v._2), errMsg) @@ -619,6 +633,40 @@ class ColumnarBatchSuite extends SparkFunSuite { assert(column.arrayData().elementsAppended == 0) } + testVector("CalendarInterval APIs", 4, CalendarIntervalType) { + column => + val reference = mutable.ArrayBuffer.empty[CalendarInterval] + + val months = column.getChild(0) + val microseconds = column.getChild(1) + assert(months.dataType() == IntegerType) + assert(microseconds.dataType() == LongType) + + months.putInt(0, 1) + microseconds.putLong(0, 100) + reference += new CalendarInterval(1, 100) + + months.putInt(1, 0) + microseconds.putLong(1, 2000) + reference += new CalendarInterval(0, 2000) + + column.putNull(2) + assert(column.getInterval(2) == null) + reference += null + + months.putInt(3, 20) + microseconds.putLong(3, 0) + reference += new CalendarInterval(20, 0) + + reference.zipWithIndex.foreach { case (v, i) => + val errMsg = "VectorType=" + column.getClass.getSimpleName + assert(v == column.getInterval(i), errMsg) + if (v == null) assert(column.isNullAt(i), errMsg) + } + + column.close() + } + testVector("Int Array", 10, new ArrayType(IntegerType, true)) { column => @@ -630,35 +678,38 @@ class ColumnarBatchSuite extends SparkFunSuite { i += 1 } - // Populate it with arrays [0], [1, 2], [], [3, 4, 5] + // Populate it with arrays [0], [1, 2], null, [], [3, 4, 5] column.putArray(0, 0, 1) column.putArray(1, 1, 2) - column.putArray(2, 2, 0) - column.putArray(3, 3, 3) + column.putNull(2) + column.putArray(3, 3, 0) + column.putArray(4, 3, 3) + + assert(column.getArray(0).numElements == 1) + assert(column.getArray(1).numElements == 2) + assert(column.isNullAt(2)) + assert(column.getArray(2) == null) + assert(column.getArray(3).numElements == 0) + assert(column.getArray(4).numElements == 3) val a1 = ColumnVectorUtils.toJavaIntArray(column.getArray(0)) val a2 = ColumnVectorUtils.toJavaIntArray(column.getArray(1)) - val a3 = ColumnVectorUtils.toJavaIntArray(column.getArray(2)) - val a4 = ColumnVectorUtils.toJavaIntArray(column.getArray(3)) + val a3 = ColumnVectorUtils.toJavaIntArray(column.getArray(3)) + val a4 = ColumnVectorUtils.toJavaIntArray(column.getArray(4)) assert(a1 === Array(0)) assert(a2 === Array(1, 2)) assert(a3 === Array.empty[Int]) assert(a4 === Array(3, 4, 5)) - // Verify the ArrayData APIs - assert(column.getArray(0).numElements() == 1) + // Verify the ArrayData get APIs assert(column.getArray(0).getInt(0) == 0) - assert(column.getArray(1).numElements() == 2) assert(column.getArray(1).getInt(0) == 1) assert(column.getArray(1).getInt(1) == 2) - assert(column.getArray(2).numElements() == 0) - - assert(column.getArray(3).numElements() == 3) - assert(column.getArray(3).getInt(0) == 3) - assert(column.getArray(3).getInt(1) == 4) - assert(column.getArray(3).getInt(2) == 5) + assert(column.getArray(4).getInt(0) == 3) + assert(column.getArray(4).getInt(1) == 4) + assert(column.getArray(4).getInt(2) == 5) // Add a longer array which requires resizing column.reset() @@ -668,8 +719,7 @@ class ColumnarBatchSuite extends SparkFunSuite { assert(data.capacity == array.length * 2) data.putInts(0, array.length, array, 0) column.putArray(0, 0, array.length) - assert(ColumnVectorUtils.toJavaIntArray(column.getArray(0)) - === array) + assert(ColumnVectorUtils.toJavaIntArray(column.getArray(0)) === array) } test("toArray for primitive types") { @@ -727,25 +777,70 @@ class ColumnarBatchSuite extends SparkFunSuite { } } + test("Int Map") { + (MemoryMode.ON_HEAP :: MemoryMode.OFF_HEAP :: Nil).foreach { memMode => + val column = allocate(10, new MapType(IntegerType, IntegerType, false), memMode) + (0 to 1).foreach { colIndex => + val data = column.getChild(colIndex) + (0 to 5).foreach {i => + data.putInt(i, i * (colIndex + 1)) + } + } + + // Populate it with maps [0->0], [1->2, 2->4], null, [], [3->6, 4->8, 5->10] + column.putArray(0, 0, 1) + column.putArray(1, 1, 2) + column.putNull(2) + assert(column.getMap(2) == null) + column.putArray(3, 3, 0) + column.putArray(4, 3, 3) + + assert(column.getMap(0).numElements == 1) + assert(column.getMap(1).numElements == 2) + assert(column.isNullAt(2)) + assert(column.getMap(3).numElements == 0) + assert(column.getMap(4).numElements == 3) + + val a1 = ColumnVectorUtils.toJavaIntMap(column.getMap(0)) + val a2 = ColumnVectorUtils.toJavaIntMap(column.getMap(1)) + val a4 = ColumnVectorUtils.toJavaIntMap(column.getMap(3)) + val a5 = ColumnVectorUtils.toJavaIntMap(column.getMap(4)) + + assert(a1.asScala == Map(0 -> 0)) + assert(a2.asScala == Map(1 -> 2, 2 -> 4)) + assert(a4.asScala == Map()) + assert(a5.asScala == Map(3 -> 6, 4 -> 8, 5 -> 10)) + + column.close() + } + } + testVector( "Struct Column", 10, new StructType().add("int", IntegerType).add("double", DoubleType)) { column => - val c1 = column.getChildColumn(0) - val c2 = column.getChildColumn(1) + val c1 = column.getChild(0) + val c2 = column.getChild(1) assert(c1.dataType() == IntegerType) assert(c2.dataType() == DoubleType) c1.putInt(0, 123) c2.putDouble(0, 3.45) - c1.putInt(1, 456) - c2.putDouble(1, 5.67) + + column.putNull(1) + assert(column.getStruct(1) == null) + + c1.putInt(2, 456) + c2.putDouble(2, 5.67) val s = column.getStruct(0) assert(s.getInt(0) == 123) assert(s.getDouble(1) == 3.45) - val s2 = column.getStruct(1) + assert(column.isNullAt(1)) + assert(column.getStruct(1) == null) + + val s2 = column.getStruct(2) assert(s2.getInt(0) == 456) assert(s2.getDouble(1) == 5.67) } @@ -786,8 +881,8 @@ class ColumnarBatchSuite extends SparkFunSuite { 10, new ArrayType(structType, true)) { column => val data = column.arrayData() - val c0 = data.getChildColumn(0) - val c1 = data.getChildColumn(1) + val c0 = data.getChild(0) + val c1 = data.getChild(1) // Structs in child column: (0, 0), (1, 10), (2, 20), (3, 30), (4, 40), (5, 50) (0 until 6).foreach { i => c0.putInt(i, i) @@ -814,8 +909,8 @@ class ColumnarBatchSuite extends SparkFunSuite { new StructType() .add("int", IntegerType) .add("array", new ArrayType(IntegerType, true))) { column => - val c0 = column.getChildColumn(0) - val c1 = column.getChildColumn(1) + val c0 = column.getChild(0) + val c1 = column.getChild(1) c0.putInt(0, 0) c0.putInt(1, 1) c0.putInt(2, 2) @@ -843,13 +938,13 @@ class ColumnarBatchSuite extends SparkFunSuite { "Nest Struct in Struct", 10, new StructType().add("int", IntegerType).add("struct", subSchema)) { column => - val c0 = column.getChildColumn(0) - val c1 = column.getChildColumn(1) + val c0 = column.getChild(0) + val c1 = column.getChild(1) c0.putInt(0, 0) c0.putInt(1, 1) c0.putInt(2, 2) - val c1c0 = c1.getChildColumn(0) - val c1c1 = c1.getChildColumn(1) + val c1c0 = c1.getChild(0) + val c1c1 = c1.getChild(1) // Structs in c1: (7, 70), (8, 80), (9, 90) c1c0.putInt(0, 7) c1c0.putInt(1, 8) @@ -874,14 +969,13 @@ class ColumnarBatchSuite extends SparkFunSuite { .add("intCol2", IntegerType) .add("string", BinaryType) - val capacity = ColumnarBatch.DEFAULT_BATCH_SIZE + val capacity = 4 * 1024 val columns = schema.fields.map { field => allocate(capacity, field.dataType, memMode) } - val batch = new ColumnarBatch(schema, columns.toArray, ColumnarBatch.DEFAULT_BATCH_SIZE) + val batch = new ColumnarBatch(columns.toArray) assert(batch.numCols() == 4) assert(batch.numRows() == 0) - assert(batch.capacity() > 0) assert(batch.rowIterator().hasNext == false) // Add a row [1, 1.1, NULL] @@ -918,10 +1012,7 @@ class ColumnarBatchSuite extends SparkFunSuite { assert(it.hasNext == false) // Reset and add 3 rows - batch.reset() - assert(batch.numRows() == 0) - assert(batch.rowIterator().hasNext == false) - + columns.foreach(_.reset()) // Add rows [NULL, 2.2, 2, "abc"], [3, NULL, 3, ""], [4, 4.4, 4, "world] columns(0).putNull(0) columns(1).putDouble(0, 2.2) @@ -1155,7 +1246,7 @@ class ColumnarBatchSuite extends SparkFunSuite { val columnVectors = Seq(new ArrowColumnVector(vector1), new ArrowColumnVector(vector2)) val schema = StructType(Seq(StructField("int1", IntegerType), StructField("int2", IntegerType))) - val batch = new ColumnarBatch(schema, columnVectors.toArray[ColumnVector], 11) + val batch = new ColumnarBatch(columnVectors.toArray) batch.setNumRows(11) assert(batch.numCols() == 2) @@ -1178,4 +1269,75 @@ class ColumnarBatchSuite extends SparkFunSuite { batch.close() allocator.close() } + + testVector("Decimal API", 4, DecimalType.IntDecimal) { + column => + + val reference = mutable.ArrayBuffer.empty[Decimal] + + var idx = 0 + column.putDecimal(idx, new Decimal().set(10), 10) + reference += new Decimal().set(10) + idx += 1 + + column.putDecimal(idx, new Decimal().set(20), 10) + reference += new Decimal().set(20) + idx += 1 + + column.putNull(idx) + assert(column.getDecimal(idx, 10, 0) == null) + reference += null + idx += 1 + + column.putDecimal(idx, new Decimal().set(30), 10) + reference += new Decimal().set(30) + + reference.zipWithIndex.foreach { case (v, i) => + val errMsg = "VectorType=" + column.getClass.getSimpleName + assert(v == column.getDecimal(i, 10, 0), errMsg) + if (v == null) assert(column.isNullAt(i), errMsg) + } + + column.close() + } + + testVector("Binary APIs", 4, BinaryType) { + column => + + val reference = mutable.ArrayBuffer.empty[String] + var idx = 0 + column.putByteArray(idx, "Hello".getBytes(StandardCharsets.UTF_8)) + reference += "Hello" + idx += 1 + + column.putByteArray(idx, "World".getBytes(StandardCharsets.UTF_8)) + reference += "World" + idx += 1 + + column.putNull(idx) + reference += null + idx += 1 + + column.putByteArray(idx, "abc".getBytes(StandardCharsets.UTF_8)) + reference += "abc" + + reference.zipWithIndex.foreach { case (v, i) => + val errMsg = "VectorType=" + column.getClass.getSimpleName + if (v != null) { + assert(v == new String(column.getBinary(i)), errMsg) + } else { + assert(column.isNullAt(i), errMsg) + assert(column.getBinary(i) == null, errMsg) + } + } + + column.close() + } + + testVector("WritableColumnVector.reserve(): requested capacity is negative", 1024, ByteType) { + column => + val ex = intercept[RuntimeException] { column.reserve(-1) } + assert(ex.getMessage.contains( + "Cannot reserve additional contiguous bytes in the vectorized reader (integer overflow)")) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index cb2df0ac54f4c..5238adce4a699 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -1168,4 +1168,26 @@ class JDBCSuite extends SparkFunSuite val df3 = sql("SELECT * FROM test_sessionInitStatement") assert(df3.collect() === Array(Row(21519, 1234))) } + + test("jdbc data source shouldn't have unnecessary metadata in its schema") { + val schema = StructType(Seq( + StructField("NAME", StringType, true), StructField("THEID", IntegerType, true))) + + val df = spark.read.format("jdbc") + .option("Url", urlWithUserAndPass) + .option("DbTaBle", "TEST.PEOPLE") + .load() + assert(df.schema === schema) + + withTempView("people_view") { + sql( + s""" + |CREATE TEMPORARY VIEW people_view + |USING org.apache.spark.sql.jdbc + |OPTIONS (uRl '$url', DbTaBlE 'TEST.PEOPLE', User 'testUser', PassWord 'testPass') + """.stripMargin.replaceAll("\n", " ")) + + assert(sql("select * from people_view").schema === schema) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala index 8b7e2e5f45946..438d5d8176b8b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala @@ -20,10 +20,36 @@ package org.apache.spark.sql.sources import java.io.File import org.apache.spark.SparkException -import org.apache.spark.sql.{AnalysisException, Row} +import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.TableIdentifier +import org.apache.spark.sql.catalyst.catalog.{CatalogStorageFormat, CatalogTable, CatalogTableType} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.internal.SQLConf.PartitionOverwriteMode import org.apache.spark.sql.test.SharedSQLContext +import org.apache.spark.sql.types._ import org.apache.spark.util.Utils +class SimpleInsertSource extends SchemaRelationProvider { + override def createRelation( + sqlContext: SQLContext, + parameters: Map[String, String], + schema: StructType): BaseRelation = { + SimpleInsert(schema)(sqlContext.sparkSession) + } +} + +case class SimpleInsert(userSpecifiedSchema: StructType)(@transient val sparkSession: SparkSession) + extends BaseRelation with InsertableRelation { + + override def sqlContext: SQLContext = sparkSession.sqlContext + + override def schema: StructType = userSpecifiedSchema + + override def insert(input: DataFrame, overwrite: Boolean): Unit = { + input.collect + } +} + class InsertSuite extends DataSourceTest with SharedSQLContext { import testImplicits._ @@ -442,4 +468,105 @@ class InsertSuite extends DataSourceTest with SharedSQLContext { assert(e.contains("Only Data Sources providing FileFormat are supported")) } } + + test("SPARK-20236: dynamic partition overwrite without catalog table") { + withSQLConf(SQLConf.PARTITION_OVERWRITE_MODE.key -> PartitionOverwriteMode.DYNAMIC.toString) { + withTempPath { path => + Seq((1, 1, 1)).toDF("i", "part1", "part2") + .write.partitionBy("part1", "part2").parquet(path.getAbsolutePath) + checkAnswer(spark.read.parquet(path.getAbsolutePath), Row(1, 1, 1)) + + Seq((2, 1, 1)).toDF("i", "part1", "part2") + .write.partitionBy("part1", "part2").mode("overwrite").parquet(path.getAbsolutePath) + checkAnswer(spark.read.parquet(path.getAbsolutePath), Row(2, 1, 1)) + + Seq((2, 2, 2)).toDF("i", "part1", "part2") + .write.partitionBy("part1", "part2").mode("overwrite").parquet(path.getAbsolutePath) + checkAnswer(spark.read.parquet(path.getAbsolutePath), Row(2, 1, 1) :: Row(2, 2, 2) :: Nil) + } + } + } + + test("SPARK-20236: dynamic partition overwrite") { + withSQLConf(SQLConf.PARTITION_OVERWRITE_MODE.key -> PartitionOverwriteMode.DYNAMIC.toString) { + withTable("t") { + sql( + """ + |create table t(i int, part1 int, part2 int) using parquet + |partitioned by (part1, part2) + """.stripMargin) + + sql("insert into t partition(part1=1, part2=1) select 1") + checkAnswer(spark.table("t"), Row(1, 1, 1)) + + sql("insert overwrite table t partition(part1=1, part2=1) select 2") + checkAnswer(spark.table("t"), Row(2, 1, 1)) + + sql("insert overwrite table t partition(part1=2, part2) select 2, 2") + checkAnswer(spark.table("t"), Row(2, 1, 1) :: Row(2, 2, 2) :: Nil) + + sql("insert overwrite table t partition(part1=1, part2=2) select 3") + checkAnswer(spark.table("t"), Row(2, 1, 1) :: Row(2, 2, 2) :: Row(3, 1, 2) :: Nil) + + sql("insert overwrite table t partition(part1=1, part2) select 4, 1") + checkAnswer(spark.table("t"), Row(4, 1, 1) :: Row(2, 2, 2) :: Row(3, 1, 2) :: Nil) + } + } + } + + test("SPARK-20236: dynamic partition overwrite with customer partition path") { + withSQLConf(SQLConf.PARTITION_OVERWRITE_MODE.key -> PartitionOverwriteMode.DYNAMIC.toString) { + withTable("t") { + sql( + """ + |create table t(i int, part1 int, part2 int) using parquet + |partitioned by (part1, part2) + """.stripMargin) + + val path1 = Utils.createTempDir() + sql(s"alter table t add partition(part1=1, part2=1) location '$path1'") + sql(s"insert into t partition(part1=1, part2=1) select 1") + checkAnswer(spark.table("t"), Row(1, 1, 1)) + + sql("insert overwrite table t partition(part1=1, part2=1) select 2") + checkAnswer(spark.table("t"), Row(2, 1, 1)) + + sql("insert overwrite table t partition(part1=2, part2) select 2, 2") + checkAnswer(spark.table("t"), Row(2, 1, 1) :: Row(2, 2, 2) :: Nil) + + val path2 = Utils.createTempDir() + sql(s"alter table t add partition(part1=1, part2=2) location '$path2'") + sql("insert overwrite table t partition(part1=1, part2=2) select 3") + checkAnswer(spark.table("t"), Row(2, 1, 1) :: Row(2, 2, 2) :: Row(3, 1, 2) :: Nil) + + sql("insert overwrite table t partition(part1=1, part2) select 4, 1") + checkAnswer(spark.table("t"), Row(4, 1, 1) :: Row(2, 2, 2) :: Row(3, 1, 2) :: Nil) + } + } + } + + test("SPARK-24583 Wrong schema type in InsertIntoDataSourceCommand") { + withTable("test_table") { + val schema = new StructType() + .add("i", LongType, false) + .add("s", StringType, false) + val newTable = CatalogTable( + identifier = TableIdentifier("test_table", None), + tableType = CatalogTableType.EXTERNAL, + storage = CatalogStorageFormat( + locationUri = None, + inputFormat = None, + outputFormat = None, + serde = None, + compressed = false, + properties = Map.empty), + schema = schema, + provider = Some(classOf[SimpleInsertSource].getName)) + + spark.sessionState.catalog.createTable(newTable, false) + + sql("INSERT INTO TABLE test_table SELECT 1, 'a'") + sql("INSERT INTO TABLE test_table SELECT 2, null") + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceV2OptionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceOptionsSuite.scala similarity index 80% rename from sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceV2OptionsSuite.scala rename to sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceOptionsSuite.scala index 90d92864b26fa..31dfc55b23361 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceV2OptionsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceOptionsSuite.scala @@ -22,24 +22,24 @@ import scala.collection.JavaConverters._ import org.apache.spark.SparkFunSuite /** - * A simple test suite to verify `DataSourceV2Options`. + * A simple test suite to verify `DataSourceOptions`. */ -class DataSourceV2OptionsSuite extends SparkFunSuite { +class DataSourceOptionsSuite extends SparkFunSuite { test("key is case-insensitive") { - val options = new DataSourceV2Options(Map("foo" -> "bar").asJava) + val options = new DataSourceOptions(Map("foo" -> "bar").asJava) assert(options.get("foo").get() == "bar") assert(options.get("FoO").get() == "bar") assert(!options.get("abc").isPresent) } test("value is case-sensitive") { - val options = new DataSourceV2Options(Map("foo" -> "bAr").asJava) + val options = new DataSourceOptions(Map("foo" -> "bAr").asJava) assert(options.get("foo").get == "bAr") } test("getInt") { - val options = new DataSourceV2Options(Map("numFOo" -> "1", "foo" -> "bar").asJava) + val options = new DataSourceOptions(Map("numFOo" -> "1", "foo" -> "bar").asJava) assert(options.getInt("numFOO", 10) == 1) assert(options.getInt("numFOO2", 10) == 10) @@ -49,7 +49,7 @@ class DataSourceV2OptionsSuite extends SparkFunSuite { } test("getBoolean") { - val options = new DataSourceV2Options( + val options = new DataSourceOptions( Map("isFoo" -> "true", "isFOO2" -> "false", "foo" -> "bar").asJava) assert(options.getBoolean("isFoo", false)) assert(!options.getBoolean("isFoo2", true)) @@ -59,7 +59,7 @@ class DataSourceV2OptionsSuite extends SparkFunSuite { } test("getLong") { - val options = new DataSourceV2Options(Map("numFoo" -> "9223372036854775807", + val options = new DataSourceOptions(Map("numFoo" -> "9223372036854775807", "foo" -> "bar").asJava) assert(options.getLong("numFOO", 0L) == 9223372036854775807L) assert(options.getLong("numFoo2", -1L) == -1L) @@ -70,7 +70,7 @@ class DataSourceV2OptionsSuite extends SparkFunSuite { } test("getDouble") { - val options = new DataSourceV2Options(Map("numFoo" -> "922337.1", + val options = new DataSourceOptions(Map("numFoo" -> "922337.1", "foo" -> "bar").asJava) assert(options.getDouble("numFOO", 0d) == 922337.1d) assert(options.getDouble("numFoo2", -1.02d) == -1.02d) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceV2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceV2Suite.scala index ab37e4984bd1f..6ad0e5f79bc40 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceV2Suite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/DataSourceV2Suite.scala @@ -22,12 +22,18 @@ import java.util.{ArrayList, List => JList} import test.org.apache.spark.sql.sources.v2._ import org.apache.spark.SparkException -import org.apache.spark.sql.{AnalysisException, QueryTest, Row} +import org.apache.spark.sql.{AnalysisException, DataFrame, QueryTest, Row} import org.apache.spark.sql.catalyst.expressions.UnsafeRow +import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2Relation, DataSourceV2ScanExec} +import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec +import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector +import org.apache.spark.sql.functions._ import org.apache.spark.sql.sources.{Filter, GreaterThan} import org.apache.spark.sql.sources.v2.reader._ +import org.apache.spark.sql.sources.v2.reader.partitioning.{ClusteredDistribution, Distribution, Partitioning} import org.apache.spark.sql.test.SharedSQLContext -import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.types.{IntegerType, StructType} +import org.apache.spark.sql.vectorized.ColumnarBatch class DataSourceV2Suite extends QueryTest with SharedSQLContext { import testImplicits._ @@ -44,19 +50,77 @@ class DataSourceV2Suite extends QueryTest with SharedSQLContext { } test("advanced implementation") { + def getReader(query: DataFrame): AdvancedDataSourceV2#Reader = { + query.queryExecution.executedPlan.collect { + case d: DataSourceV2ScanExec => d.reader.asInstanceOf[AdvancedDataSourceV2#Reader] + }.head + } + + def getJavaReader(query: DataFrame): JavaAdvancedDataSourceV2#Reader = { + query.queryExecution.executedPlan.collect { + case d: DataSourceV2ScanExec => d.reader.asInstanceOf[JavaAdvancedDataSourceV2#Reader] + }.head + } + Seq(classOf[AdvancedDataSourceV2], classOf[JavaAdvancedDataSourceV2]).foreach { cls => withClue(cls.getName) { val df = spark.read.format(cls.getName).load() checkAnswer(df, (0 until 10).map(i => Row(i, -i))) - checkAnswer(df.select('j), (0 until 10).map(i => Row(-i))) - checkAnswer(df.filter('i > 3), (4 until 10).map(i => Row(i, -i))) - checkAnswer(df.select('j).filter('i > 6), (7 until 10).map(i => Row(-i))) - checkAnswer(df.select('i).filter('i > 10), Nil) + + val q1 = df.select('j) + checkAnswer(q1, (0 until 10).map(i => Row(-i))) + if (cls == classOf[AdvancedDataSourceV2]) { + val reader = getReader(q1) + assert(reader.filters.isEmpty) + assert(reader.requiredSchema.fieldNames === Seq("j")) + } else { + val reader = getJavaReader(q1) + assert(reader.filters.isEmpty) + assert(reader.requiredSchema.fieldNames === Seq("j")) + } + + val q2 = df.filter('i > 3) + checkAnswer(q2, (4 until 10).map(i => Row(i, -i))) + if (cls == classOf[AdvancedDataSourceV2]) { + val reader = getReader(q2) + assert(reader.filters.flatMap(_.references).toSet == Set("i")) + assert(reader.requiredSchema.fieldNames === Seq("i", "j")) + } else { + val reader = getJavaReader(q2) + assert(reader.filters.flatMap(_.references).toSet == Set("i")) + assert(reader.requiredSchema.fieldNames === Seq("i", "j")) + } + + val q3 = df.select('i).filter('i > 6) + checkAnswer(q3, (7 until 10).map(i => Row(i))) + if (cls == classOf[AdvancedDataSourceV2]) { + val reader = getReader(q3) + assert(reader.filters.flatMap(_.references).toSet == Set("i")) + assert(reader.requiredSchema.fieldNames === Seq("i")) + } else { + val reader = getJavaReader(q3) + assert(reader.filters.flatMap(_.references).toSet == Set("i")) + assert(reader.requiredSchema.fieldNames === Seq("i")) + } + + val q4 = df.select('j).filter('j < -10) + checkAnswer(q4, Nil) + if (cls == classOf[AdvancedDataSourceV2]) { + val reader = getReader(q4) + // 'j < 10 is not supported by the testing data source. + assert(reader.filters.isEmpty) + assert(reader.requiredSchema.fieldNames === Seq("j")) + } else { + val reader = getJavaReader(q4) + // 'j < 10 is not supported by the testing data source. + assert(reader.filters.isEmpty) + assert(reader.requiredSchema.fieldNames === Seq("j")) + } } } } - test("unsafe row implementation") { + test("unsafe row scan implementation") { Seq(classOf[UnsafeRowDataSourceV2], classOf[JavaUnsafeRowDataSourceV2]).foreach { cls => withClue(cls.getName) { val df = spark.read.format(cls.getName).load() @@ -67,6 +131,17 @@ class DataSourceV2Suite extends QueryTest with SharedSQLContext { } } + test("columnar batch scan implementation") { + Seq(classOf[BatchDataSourceV2], classOf[JavaBatchDataSourceV2]).foreach { cls => + withClue(cls.getName) { + val df = spark.read.format(cls.getName).load() + checkAnswer(df, (0 until 90).map(i => Row(i, -i))) + checkAnswer(df.select('j), (0 until 90).map(i => Row(-i))) + checkAnswer(df.filter('i > 50), (51 until 90).map(i => Row(i, -i))) + } + } + } + test("schema required data source") { Seq(classOf[SchemaRequiredDataSource], classOf[JavaSchemaRequiredDataSource]).foreach { cls => withClue(cls.getName) { @@ -82,6 +157,40 @@ class DataSourceV2Suite extends QueryTest with SharedSQLContext { } } + test("partitioning reporting") { + import org.apache.spark.sql.functions.{count, sum} + Seq(classOf[PartitionAwareDataSource], classOf[JavaPartitionAwareDataSource]).foreach { cls => + withClue(cls.getName) { + val df = spark.read.format(cls.getName).load() + checkAnswer(df, Seq(Row(1, 4), Row(1, 4), Row(3, 6), Row(2, 6), Row(4, 2), Row(4, 2))) + + val groupByColA = df.groupBy('a).agg(sum('b)) + checkAnswer(groupByColA, Seq(Row(1, 8), Row(2, 6), Row(3, 6), Row(4, 4))) + assert(groupByColA.queryExecution.executedPlan.collectFirst { + case e: ShuffleExchangeExec => e + }.isEmpty) + + val groupByColAB = df.groupBy('a, 'b).agg(count("*")) + checkAnswer(groupByColAB, Seq(Row(1, 4, 2), Row(2, 6, 1), Row(3, 6, 1), Row(4, 2, 2))) + assert(groupByColAB.queryExecution.executedPlan.collectFirst { + case e: ShuffleExchangeExec => e + }.isEmpty) + + val groupByColB = df.groupBy('b).agg(sum('a)) + checkAnswer(groupByColB, Seq(Row(2, 8), Row(4, 2), Row(6, 5))) + assert(groupByColB.queryExecution.executedPlan.collectFirst { + case e: ShuffleExchangeExec => e + }.isDefined) + + val groupByAPlusB = df.groupBy('a + 'b).agg(count("*")) + checkAnswer(groupByAPlusB, Seq(Row(5, 2), Row(6, 2), Row(8, 1), Row(9, 1))) + assert(groupByAPlusB.queryExecution.executedPlan.collectFirst { + case e: ShuffleExchangeExec => e + }.isDefined) + } + } + } + test("simple writable data source") { // TODO: java implementation. Seq(classOf[SimpleWritableDataSource]).foreach { cls => @@ -149,25 +258,84 @@ class DataSourceV2Suite extends QueryTest with SharedSQLContext { } } } + + test("SPARK-23293: data source v2 self join") { + val df = spark.read.format(classOf[SimpleDataSourceV2].getName).load() + val df2 = df.select(($"i" + 1).as("k"), $"j") + checkAnswer(df.join(df2, "j"), (0 until 10).map(i => Row(-i, i, i + 1))) + } + + test("SPARK-23301: column pruning with arbitrary expressions") { + def getReader(query: DataFrame): AdvancedDataSourceV2#Reader = { + query.queryExecution.executedPlan.collect { + case d: DataSourceV2ScanExec => d.reader.asInstanceOf[AdvancedDataSourceV2#Reader] + }.head + } + + val df = spark.read.format(classOf[AdvancedDataSourceV2].getName).load() + + val q1 = df.select('i + 1) + checkAnswer(q1, (1 until 11).map(i => Row(i))) + val reader1 = getReader(q1) + assert(reader1.requiredSchema.fieldNames === Seq("i")) + + val q2 = df.select(lit(1)) + checkAnswer(q2, (0 until 10).map(i => Row(1))) + val reader2 = getReader(q2) + assert(reader2.requiredSchema.isEmpty) + + // 'j === 1 can't be pushed down, but we should still be able do column pruning + val q3 = df.filter('j === -1).select('j * 2) + checkAnswer(q3, Row(-2)) + val reader3 = getReader(q3) + assert(reader3.filters.isEmpty) + assert(reader3.requiredSchema.fieldNames === Seq("j")) + + // column pruning should work with other operators. + val q4 = df.sort('i).limit(1).select('i + 1) + checkAnswer(q4, Row(1)) + val reader4 = getReader(q4) + assert(reader4.requiredSchema.fieldNames === Seq("i")) + } + + test("SPARK-23315: get output from canonicalized data source v2 related plans") { + def checkCanonicalizedOutput(df: DataFrame, numOutput: Int): Unit = { + val logical = df.queryExecution.optimizedPlan.collect { + case d: DataSourceV2Relation => d + }.head + assert(logical.canonicalized.output.length == numOutput) + + val physical = df.queryExecution.executedPlan.collect { + case d: DataSourceV2ScanExec => d + }.head + assert(physical.canonicalized.output.length == numOutput) + } + + val df = spark.read.format(classOf[AdvancedDataSourceV2].getName).load() + checkCanonicalizedOutput(df, 2) + checkCanonicalizedOutput(df.select('i), 1) + } } class SimpleDataSourceV2 extends DataSourceV2 with ReadSupport { - class Reader extends DataSourceV2Reader { + class Reader extends DataSourceReader { override def readSchema(): StructType = new StructType().add("i", "int").add("j", "int") - override def createReadTasks(): JList[ReadTask[Row]] = { - java.util.Arrays.asList(new SimpleReadTask(0, 5), new SimpleReadTask(5, 10)) + override def createDataReaderFactories(): JList[DataReaderFactory[Row]] = { + java.util.Arrays.asList(new SimpleDataReaderFactory(0, 5), new SimpleDataReaderFactory(5, 10)) } } - override def createReader(options: DataSourceV2Options): DataSourceV2Reader = new Reader + override def createReader(options: DataSourceOptions): DataSourceReader = new Reader } -class SimpleReadTask(start: Int, end: Int) extends ReadTask[Row] with DataReader[Row] { +class SimpleDataReaderFactory(start: Int, end: Int) + extends DataReaderFactory[Row] + with DataReader[Row] { private var current = start - 1 - override def createDataReader(): DataReader[Row] = new SimpleReadTask(start, end) + override def createDataReader(): DataReader[Row] = new SimpleDataReaderFactory(start, end) override def next(): Boolean = { current += 1 @@ -183,7 +351,7 @@ class SimpleReadTask(start: Int, end: Int) extends ReadTask[Row] with DataReader class AdvancedDataSourceV2 extends DataSourceV2 with ReadSupport { - class Reader extends DataSourceV2Reader + class Reader extends DataSourceReader with SupportsPushDownRequiredColumns with SupportsPushDownFilters { var requiredSchema = new StructType().add("i", "int").add("j", "int") @@ -194,8 +362,12 @@ class AdvancedDataSourceV2 extends DataSourceV2 with ReadSupport { } override def pushFilters(filters: Array[Filter]): Array[Filter] = { - this.filters = filters - Array.empty + val (supported, unsupported) = filters.partition { + case GreaterThan("i", _: Int) => true + case _ => false + } + this.filters = supported + unsupported } override def pushedFilters(): Array[Filter] = filters @@ -204,37 +376,37 @@ class AdvancedDataSourceV2 extends DataSourceV2 with ReadSupport { requiredSchema } - override def createReadTasks(): JList[ReadTask[Row]] = { + override def createDataReaderFactories(): JList[DataReaderFactory[Row]] = { val lowerBound = filters.collect { case GreaterThan("i", v: Int) => v }.headOption - val res = new ArrayList[ReadTask[Row]] + val res = new ArrayList[DataReaderFactory[Row]] if (lowerBound.isEmpty) { - res.add(new AdvancedReadTask(0, 5, requiredSchema)) - res.add(new AdvancedReadTask(5, 10, requiredSchema)) + res.add(new AdvancedDataReaderFactory(0, 5, requiredSchema)) + res.add(new AdvancedDataReaderFactory(5, 10, requiredSchema)) } else if (lowerBound.get < 4) { - res.add(new AdvancedReadTask(lowerBound.get + 1, 5, requiredSchema)) - res.add(new AdvancedReadTask(5, 10, requiredSchema)) + res.add(new AdvancedDataReaderFactory(lowerBound.get + 1, 5, requiredSchema)) + res.add(new AdvancedDataReaderFactory(5, 10, requiredSchema)) } else if (lowerBound.get < 9) { - res.add(new AdvancedReadTask(lowerBound.get + 1, 10, requiredSchema)) + res.add(new AdvancedDataReaderFactory(lowerBound.get + 1, 10, requiredSchema)) } res } } - override def createReader(options: DataSourceV2Options): DataSourceV2Reader = new Reader + override def createReader(options: DataSourceOptions): DataSourceReader = new Reader } -class AdvancedReadTask(start: Int, end: Int, requiredSchema: StructType) - extends ReadTask[Row] with DataReader[Row] { +class AdvancedDataReaderFactory(start: Int, end: Int, requiredSchema: StructType) + extends DataReaderFactory[Row] with DataReader[Row] { private var current = start - 1 override def createDataReader(): DataReader[Row] = { - new AdvancedReadTask(start, end, requiredSchema) + new AdvancedDataReaderFactory(start, end, requiredSchema) } override def close(): Unit = {} @@ -256,26 +428,27 @@ class AdvancedReadTask(start: Int, end: Int, requiredSchema: StructType) class UnsafeRowDataSourceV2 extends DataSourceV2 with ReadSupport { - class Reader extends DataSourceV2Reader with SupportsScanUnsafeRow { + class Reader extends DataSourceReader with SupportsScanUnsafeRow { override def readSchema(): StructType = new StructType().add("i", "int").add("j", "int") - override def createUnsafeRowReadTasks(): JList[ReadTask[UnsafeRow]] = { - java.util.Arrays.asList(new UnsafeRowReadTask(0, 5), new UnsafeRowReadTask(5, 10)) + override def createUnsafeRowReaderFactories(): JList[DataReaderFactory[UnsafeRow]] = { + java.util.Arrays.asList(new UnsafeRowDataReaderFactory(0, 5), + new UnsafeRowDataReaderFactory(5, 10)) } } - override def createReader(options: DataSourceV2Options): DataSourceV2Reader = new Reader + override def createReader(options: DataSourceOptions): DataSourceReader = new Reader } -class UnsafeRowReadTask(start: Int, end: Int) - extends ReadTask[UnsafeRow] with DataReader[UnsafeRow] { +class UnsafeRowDataReaderFactory(start: Int, end: Int) + extends DataReaderFactory[UnsafeRow] with DataReader[UnsafeRow] { private val row = new UnsafeRow(2) row.pointTo(new Array[Byte](8 * 3), 8 * 3) private var current = start - 1 - override def createDataReader(): DataReader[UnsafeRow] = new UnsafeRowReadTask(start, end) + override def createDataReader(): DataReader[UnsafeRow] = this override def next(): Boolean = { current += 1 @@ -292,11 +465,109 @@ class UnsafeRowReadTask(start: Int, end: Int) class SchemaRequiredDataSource extends DataSourceV2 with ReadSupportWithSchema { - class Reader(val readSchema: StructType) extends DataSourceV2Reader { - override def createReadTasks(): JList[ReadTask[Row]] = + class Reader(val readSchema: StructType) extends DataSourceReader { + override def createDataReaderFactories(): JList[DataReaderFactory[Row]] = java.util.Collections.emptyList() } - override def createReader(schema: StructType, options: DataSourceV2Options): DataSourceV2Reader = + override def createReader(schema: StructType, options: DataSourceOptions): DataSourceReader = new Reader(schema) } + +class BatchDataSourceV2 extends DataSourceV2 with ReadSupport { + + class Reader extends DataSourceReader with SupportsScanColumnarBatch { + override def readSchema(): StructType = new StructType().add("i", "int").add("j", "int") + + override def createBatchDataReaderFactories(): JList[DataReaderFactory[ColumnarBatch]] = { + java.util.Arrays.asList(new BatchDataReaderFactory(0, 50), new BatchDataReaderFactory(50, 90)) + } + } + + override def createReader(options: DataSourceOptions): DataSourceReader = new Reader +} + +class BatchDataReaderFactory(start: Int, end: Int) + extends DataReaderFactory[ColumnarBatch] with DataReader[ColumnarBatch] { + + private final val BATCH_SIZE = 20 + private lazy val i = new OnHeapColumnVector(BATCH_SIZE, IntegerType) + private lazy val j = new OnHeapColumnVector(BATCH_SIZE, IntegerType) + private lazy val batch = new ColumnarBatch(Array(i, j)) + + private var current = start + + override def createDataReader(): DataReader[ColumnarBatch] = this + + override def next(): Boolean = { + i.reset() + j.reset() + + var count = 0 + while (current < end && count < BATCH_SIZE) { + i.putInt(count, current) + j.putInt(count, -current) + current += 1 + count += 1 + } + + if (count == 0) { + false + } else { + batch.setNumRows(count) + true + } + } + + override def get(): ColumnarBatch = { + batch + } + + override def close(): Unit = batch.close() +} + +class PartitionAwareDataSource extends DataSourceV2 with ReadSupport { + + class Reader extends DataSourceReader with SupportsReportPartitioning { + override def readSchema(): StructType = new StructType().add("a", "int").add("b", "int") + + override def createDataReaderFactories(): JList[DataReaderFactory[Row]] = { + // Note that we don't have same value of column `a` across partitions. + java.util.Arrays.asList( + new SpecificDataReaderFactory(Array(1, 1, 3), Array(4, 4, 6)), + new SpecificDataReaderFactory(Array(2, 4, 4), Array(6, 2, 2))) + } + + override def outputPartitioning(): Partitioning = new MyPartitioning + } + + class MyPartitioning extends Partitioning { + override def numPartitions(): Int = 2 + + override def satisfy(distribution: Distribution): Boolean = distribution match { + case c: ClusteredDistribution => c.clusteredColumns.contains("a") + case _ => false + } + } + + override def createReader(options: DataSourceOptions): DataSourceReader = new Reader +} + +class SpecificDataReaderFactory(i: Array[Int], j: Array[Int]) + extends DataReaderFactory[Row] + with DataReader[Row] { + assert(i.length == j.length) + + private var current = -1 + + override def createDataReader(): DataReader[Row] = this + + override def next(): Boolean = { + current += 1 + current < i.length + } + + override def get(): Row = Row(i(current), j(current)) + + override def close(): Unit = {} +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/SimpleWritableDataSource.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/SimpleWritableDataSource.scala index cd7252eb2e3d6..a131b16953e3b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/SimpleWritableDataSource.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/v2/SimpleWritableDataSource.scala @@ -28,7 +28,7 @@ import org.apache.hadoop.fs.{FileSystem, FSDataInputStream, Path} import org.apache.spark.SparkContext import org.apache.spark.sql.{Row, SaveMode} import org.apache.spark.sql.catalyst.InternalRow -import org.apache.spark.sql.sources.v2.reader.{DataReader, DataSourceV2Reader, ReadTask} +import org.apache.spark.sql.sources.v2.reader.{DataReader, DataReaderFactory, DataSourceReader} import org.apache.spark.sql.sources.v2.writer._ import org.apache.spark.sql.types.{DataType, StructType} import org.apache.spark.util.SerializableConfiguration @@ -42,10 +42,10 @@ class SimpleWritableDataSource extends DataSourceV2 with ReadSupport with WriteS private val schema = new StructType().add("i", "long").add("j", "long") - class Reader(path: String, conf: Configuration) extends DataSourceV2Reader { + class Reader(path: String, conf: Configuration) extends DataSourceReader { override def readSchema(): StructType = schema - override def createReadTasks(): JList[ReadTask[Row]] = { + override def createDataReaderFactories(): JList[DataReaderFactory[Row]] = { val dataPath = new Path(path) val fs = dataPath.getFileSystem(conf) if (fs.exists(dataPath)) { @@ -54,7 +54,9 @@ class SimpleWritableDataSource extends DataSourceV2 with ReadSupport with WriteS name.startsWith("_") || name.startsWith(".") }.map { f => val serializableConf = new SerializableConfiguration(conf) - new SimpleCSVReadTask(f.getPath.toUri.toString, serializableConf): ReadTask[Row] + new SimpleCSVDataReaderFactory( + f.getPath.toUri.toString, + serializableConf): DataReaderFactory[Row] }.toList.asJava } else { Collections.emptyList() @@ -62,7 +64,7 @@ class SimpleWritableDataSource extends DataSourceV2 with ReadSupport with WriteS } } - class Writer(jobId: String, path: String, conf: Configuration) extends DataSourceV2Writer { + class Writer(jobId: String, path: String, conf: Configuration) extends DataSourceWriter { override def createWriterFactory(): DataWriterFactory[Row] = { new SimpleCSVDataWriterFactory(path, jobId, new SerializableConfiguration(conf)) } @@ -102,7 +104,7 @@ class SimpleWritableDataSource extends DataSourceV2 with ReadSupport with WriteS } } - override def createReader(options: DataSourceV2Options): DataSourceV2Reader = { + override def createReader(options: DataSourceOptions): DataSourceReader = { val path = new Path(options.get("path").get()) val conf = SparkContext.getActive.get.hadoopConfiguration new Reader(path.toUri.toString, conf) @@ -112,7 +114,7 @@ class SimpleWritableDataSource extends DataSourceV2 with ReadSupport with WriteS jobId: String, schema: StructType, mode: SaveMode, - options: DataSourceV2Options): Optional[DataSourceV2Writer] = { + options: DataSourceOptions): Optional[DataSourceWriter] = { assert(DataType.equalsStructurally(schema.asNullable, this.schema.asNullable)) assert(!SparkContext.getActive.get.conf.getBoolean("spark.speculation", false)) @@ -139,7 +141,7 @@ class SimpleWritableDataSource extends DataSourceV2 with ReadSupport with WriteS } private def createWriter( - jobId: String, path: Path, conf: Configuration, internal: Boolean): DataSourceV2Writer = { + jobId: String, path: Path, conf: Configuration, internal: Boolean): DataSourceWriter = { val pathStr = path.toUri.toString if (internal) { new InternalRowWriter(jobId, pathStr, conf) @@ -149,8 +151,8 @@ class SimpleWritableDataSource extends DataSourceV2 with ReadSupport with WriteS } } -class SimpleCSVReadTask(path: String, conf: SerializableConfiguration) - extends ReadTask[Row] with DataReader[Row] { +class SimpleCSVDataReaderFactory(path: String, conf: SerializableConfiguration) + extends DataReaderFactory[Row] with DataReader[Row] { @transient private var lines: Iterator[String] = _ @transient private var currentLine: String = _ diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/DeduplicateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/DeduplicateSuite.scala index caf2bab8a5859..0088b64d6195e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/DeduplicateSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/DeduplicateSuite.scala @@ -25,9 +25,7 @@ import org.apache.spark.sql.execution.streaming.{MemoryStream, StreamingDeduplic import org.apache.spark.sql.execution.streaming.state.StateStore import org.apache.spark.sql.functions._ -class DeduplicateSuite extends StateStoreMetricsTest - with BeforeAndAfterAll - with StatefulOperatorTest { +class DeduplicateSuite extends StateStoreMetricsTest with BeforeAndAfterAll { import testImplicits._ @@ -44,8 +42,6 @@ class DeduplicateSuite extends StateStoreMetricsTest AddData(inputData, "a"), CheckLastBatch("a"), assertNumStateRows(total = 1, updated = 1), - AssertOnQuery(sq => - checkChildOutputHashPartitioning[StreamingDeduplicateExec](sq, Seq("value"))), AddData(inputData, "a"), CheckLastBatch(), assertNumStateRows(total = 1, updated = 0), @@ -63,8 +59,6 @@ class DeduplicateSuite extends StateStoreMetricsTest AddData(inputData, "a" -> 1), CheckLastBatch("a" -> 1), assertNumStateRows(total = 1, updated = 1), - AssertOnQuery(sq => - checkChildOutputHashPartitioning[StreamingDeduplicateExec](sq, Seq("_1"))), AddData(inputData, "a" -> 2), // Dropped CheckLastBatch(), assertNumStateRows(total = 1, updated = 0), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSinkSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSinkSuite.scala index 8c4e1fd00b0a2..50bdbcb4186ad 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSinkSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSinkSuite.scala @@ -21,6 +21,7 @@ import java.util.Locale import org.apache.hadoop.fs.Path +import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskEnd} import org.apache.spark.sql.{AnalysisException, DataFrame} import org.apache.spark.sql.execution.DataSourceScanExec import org.apache.spark.sql.execution.datasources._ @@ -33,6 +34,19 @@ import org.apache.spark.util.Utils class FileStreamSinkSuite extends StreamTest { import testImplicits._ + override def beforeAll(): Unit = { + super.beforeAll() + spark.sessionState.conf.setConf(SQLConf.ORC_IMPLEMENTATION, "native") + } + + override def afterAll(): Unit = { + try { + spark.sessionState.conf.unsetConf(SQLConf.ORC_IMPLEMENTATION) + } finally { + super.afterAll() + } + } + test("unpartitioned writing and batch reading") { val inputData = MemoryStream[Int] val df = inputData.toDF() @@ -392,4 +406,55 @@ class FileStreamSinkSuite extends StreamTest { } } } + + test("SPARK-23288 writing and checking output metrics") { + Seq("parquet", "orc", "text", "json").foreach { format => + val inputData = MemoryStream[String] + val df = inputData.toDF() + + withTempDir { outputDir => + withTempDir { checkpointDir => + + var query: StreamingQuery = null + + var numTasks = 0 + var recordsWritten: Long = 0L + var bytesWritten: Long = 0L + try { + spark.sparkContext.addSparkListener(new SparkListener() { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) { + val outputMetrics = taskEnd.taskMetrics.outputMetrics + recordsWritten += outputMetrics.recordsWritten + bytesWritten += outputMetrics.bytesWritten + numTasks += 1 + } + }) + + query = + df.writeStream + .option("checkpointLocation", checkpointDir.getCanonicalPath) + .format(format) + .start(outputDir.getCanonicalPath) + + inputData.addData("1", "2", "3") + inputData.addData("4", "5") + + failAfter(streamingTimeout) { + query.processAllAvailable() + } + spark.sparkContext.listenerBus.waitUntilEmpty(streamingTimeout.toMillis) + + assert(numTasks > 0) + assert(recordsWritten === 5) + // This is heavily file type/version specific but should be filled + assert(bytesWritten > 0) + } finally { + if (query != null) { + query.stop() + } + } + } + } + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala index 39bb572740617..d4bd9c7987f2d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala @@ -74,11 +74,11 @@ abstract class FileStreamSourceTest protected def addData(source: FileStreamSource): Unit } - case class AddTextFileData(content: String, src: File, tmp: File) + case class AddTextFileData(content: String, src: File, tmp: File, tmpFilePrefix: String = "text") extends AddFileData { override def addData(source: FileStreamSource): Unit = { - val tempFile = Utils.tempFileWith(new File(tmp, "text")) + val tempFile = Utils.tempFileWith(new File(tmp, tmpFilePrefix)) val finalFile = new File(src, tempFile.getName) src.mkdirs() require(stringToFile(tempFile, content).renameTo(finalFile)) @@ -207,6 +207,19 @@ class FileStreamSourceSuite extends FileStreamSourceTest { .collect { case s @ StreamingRelation(dataSource, _, _) => s.schema }.head } + override def beforeAll(): Unit = { + super.beforeAll() + spark.sessionState.conf.setConf(SQLConf.ORC_IMPLEMENTATION, "native") + } + + override def afterAll(): Unit = { + try { + spark.sessionState.conf.unsetConf(SQLConf.ORC_IMPLEMENTATION) + } finally { + super.afterAll() + } + } + // ============= Basic parameter exists tests ================ test("FileStreamSource schema: no path") { @@ -408,6 +421,52 @@ class FileStreamSourceSuite extends FileStreamSourceTest { } } + test("SPARK-21996 read from text files -- file name has space") { + withTempDirs { case (src, tmp) => + val textStream = createFileStream("text", src.getCanonicalPath) + val filtered = textStream.filter($"value" contains "keep") + + testStream(filtered)( + AddTextFileData("drop1\nkeep2\nkeep3", src, tmp, "text text"), + CheckAnswer("keep2", "keep3") + ) + } + } + + test("SPARK-21996 read from text files generated by file sink -- file name has space") { + val testTableName = "FileStreamSourceTest" + withTable(testTableName) { + withTempDirs { case (src, checkpoint) => + val output = new File(src, "text text") + val inputData = MemoryStream[String] + val ds = inputData.toDS() + + val query = ds.writeStream + .option("checkpointLocation", checkpoint.getCanonicalPath) + .format("text") + .start(output.getCanonicalPath) + + try { + inputData.addData("foo") + failAfter(streamingTimeout) { + query.processAllAvailable() + } + } finally { + query.stop() + } + + val df2 = spark.readStream.format("text").load(output.getCanonicalPath) + val query2 = df2.writeStream.format("memory").queryName(testTableName).start() + try { + query2.processAllAvailable() + checkDatasetUnorderly(spark.table(testTableName).as[String], "foo") + } finally { + query2.stop() + } + } + } + } + test("read from textfile") { withTempDirs { case (src, tmp) => val textStream = spark.readStream.textFile(src.getCanonicalPath) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/FlatMapGroupsWithStateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/FlatMapGroupsWithStateSuite.scala index de2b51678cea6..b1416bff87ee7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/FlatMapGroupsWithStateSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/FlatMapGroupsWithStateSuite.scala @@ -42,8 +42,7 @@ case class RunningCount(count: Long) case class Result(key: Long, count: Int) class FlatMapGroupsWithStateSuite extends StateStoreMetricsTest - with BeforeAndAfterAll - with StatefulOperatorTest { + with BeforeAndAfterAll { import testImplicits._ import GroupStateImpl._ @@ -618,8 +617,6 @@ class FlatMapGroupsWithStateSuite extends StateStoreMetricsTest AddData(inputData, "a"), CheckLastBatch(("a", "1")), assertNumStateRows(total = 1, updated = 1), - AssertOnQuery(sq => checkChildOutputHashPartitioning[FlatMapGroupsWithStateExec]( - sq, Seq("value"))), AddData(inputData, "a", "b"), CheckLastBatch(("a", "2"), ("b", "1")), assertNumStateRows(total = 2, updated = 2), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StatefulOperatorTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StatefulOperatorTest.scala deleted file mode 100644 index 45142278993bb..0000000000000 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StatefulOperatorTest.scala +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.streaming - -import org.apache.spark.sql.catalyst.plans.physical._ -import org.apache.spark.sql.execution.streaming._ - -trait StatefulOperatorTest { - /** - * Check that the output partitioning of a child operator of a Stateful operator satisfies the - * distribution that we expect for our Stateful operator. - */ - protected def checkChildOutputHashPartitioning[T <: StatefulOperator]( - sq: StreamingQuery, - colNames: Seq[String]): Boolean = { - val attr = sq.asInstanceOf[StreamExecution].lastExecution.analyzed.output - val partitions = sq.sparkSession.sessionState.conf.numShufflePartitions - val groupingAttr = attr.filter(a => colNames.contains(a.name)) - checkChildOutputPartitioning(sq, HashPartitioning(groupingAttr, partitions)) - } - - /** - * Check that the output partitioning of a child operator of a Stateful operator satisfies the - * distribution that we expect for our Stateful operator. - */ - protected def checkChildOutputPartitioning[T <: StatefulOperator]( - sq: StreamingQuery, - expectedPartitioning: Partitioning): Boolean = { - val operator = sq.asInstanceOf[StreamExecution].lastExecution - .executedPlan.collect { case p: T => p } - operator.head.children.forall( - _.outputPartitioning.numPartitions == expectedPartitioning.numPartitions) - } -} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala index 4b7f0fbe97d4e..c2620d197b832 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala @@ -37,9 +37,11 @@ import org.apache.spark.SparkEnv import org.apache.spark.sql.{Dataset, Encoder, QueryTest, Row} import org.apache.spark.sql.catalyst.encoders.{encoderFor, ExpressionEncoder, RowEncoder} import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.plans.physical.AllTuples import org.apache.spark.sql.catalyst.util._ +import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation import org.apache.spark.sql.execution.streaming._ -import org.apache.spark.sql.execution.streaming.continuous.{ContinuousExecution, EpochCoordinatorRef, IncrementAndGetEpoch} +import org.apache.spark.sql.execution.streaming.continuous.{ContinuousExecution, ContinuousTrigger, EpochCoordinatorRef, IncrementAndGetEpoch} import org.apache.spark.sql.execution.streaming.sources.MemorySinkV2 import org.apache.spark.sql.execution.streaming.state.StateStore import org.apache.spark.sql.streaming.StreamingQueryListener._ @@ -80,6 +82,9 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be StateStore.stop() // stop the state store maintenance thread and unload store providers } + protected val defaultTrigger = Trigger.ProcessingTime(0) + protected val defaultUseV2Sink = false + /** How long to wait for an active stream to catch up when checking a result. */ val streamingTimeout = 10.seconds @@ -105,7 +110,7 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be * the active query, and then return the source object the data was added, as well as the * offset of added data. */ - def addData(query: Option[StreamExecution]): (Source, Offset) + def addData(query: Option[StreamExecution]): (BaseStreamingSource, Offset) } /** A trait that can be extended when testing a source. */ @@ -189,7 +194,7 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be /** Starts the stream, resuming if data has already been processed. It must not be running. */ case class StartStream( - trigger: Trigger = Trigger.ProcessingTime(0), + trigger: Trigger = defaultTrigger, triggerClock: Clock = new SystemClock, additionalConfs: Map[String, String] = Map.empty, checkpointLocation: String = null) @@ -259,7 +264,7 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be def apply(): AssertOnQuery = Execute { case s: ContinuousExecution => - val newEpoch = EpochCoordinatorRef.get(s.runId.toString, SparkEnv.get) + val newEpoch = EpochCoordinatorRef.get(s.currentEpochCoordinatorId, SparkEnv.get) .askSync[Long](IncrementAndGetEpoch) s.awaitEpoch(newEpoch - 1) case _ => throw new IllegalStateException("microbatch cannot increment epoch") @@ -276,7 +281,7 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be def testStream( _stream: Dataset[_], outputMode: OutputMode = OutputMode.Append, - useV2Sink: Boolean = false)(actions: StreamAction*): Unit = synchronized { + useV2Sink: Boolean = defaultUseV2Sink)(actions: StreamAction*): Unit = synchronized { import org.apache.spark.sql.streaming.util.StreamManualClock // `synchronized` is added to prevent the user from calling multiple `testStream`s concurrently @@ -403,18 +408,29 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be def fetchStreamAnswer(currentStream: StreamExecution, lastOnly: Boolean) = { verify(currentStream != null, "stream not running") - // Get the map of source index to the current source objects - val indexToSource = currentStream - .logicalPlan - .collect { case StreamingExecutionRelation(s, _) => s } - .zipWithIndex - .map(_.swap) - .toMap // Block until all data added has been processed for all the source awaiting.foreach { case (sourceIndex, offset) => failAfter(streamingTimeout) { - currentStream.awaitOffset(indexToSource(sourceIndex), offset) + currentStream.awaitOffset(sourceIndex, offset) + } + } + + val lastExecution = currentStream.lastExecution + if (currentStream.isInstanceOf[MicroBatchExecution] && lastExecution != null) { + // Verify if stateful operators have correct metadata and distribution + // This can often catch hard to debug errors when developing stateful operators + lastExecution.executedPlan.collect { case s: StatefulOperator => s }.foreach { s => + assert(s.stateInfo.map(_.numPartitions).contains(lastExecution.numStateStores)) + s.requiredChildDistribution.foreach { d => + withClue(s"$s specifies incorrect # partitions in requiredChildDistribution $d") { + assert(d.requiredNumPartitions.isDefined) + assert(d.requiredNumPartitions.get >= 1) + if (d != AllTuples) { + assert(d.requiredNumPartitions.get == s.stateInfo.get.numPartitions) + } + } + } } } @@ -473,6 +489,12 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be // after starting the query. try { currentStream.awaitInitialization(streamingTimeout.toMillis) + currentStream match { + case s: ContinuousExecution => eventually("IncrementalExecution was not created") { + assert(s.lastExecution != null) + } + case _ => + } } catch { case _: StreamingQueryException => // Ignore the exception. `StopStream` or `ExpectFailure` will catch it as well. @@ -600,7 +622,10 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be def findSourceIndex(plan: LogicalPlan): Option[Int] = { plan - .collect { case StreamingExecutionRelation(s, _) => s } + .collect { + case StreamingExecutionRelation(s, _) => s + case DataSourceV2Relation(_, r) => r + } .zipWithIndex .find(_._1 == source) .map(_._2) @@ -613,9 +638,13 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be findSourceIndex(query.logicalPlan) }.orElse { findSourceIndex(stream.logicalPlan) + }.orElse { + queryToUse.flatMap { q => + findSourceIndex(q.lastExecution.logical) + } }.getOrElse { throw new IllegalArgumentException( - "Could find index of the source to which data was added") + "Could not find index of the source to which data was added") } // Store the expected offset of added data to wait for it later @@ -635,7 +664,10 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be } case CheckAnswerRowsContains(expectedAnswer, lastOnly) => - val sparkAnswer = fetchStreamAnswer(currentStream, lastOnly) + val sparkAnswer = currentStream match { + case null => fetchStreamAnswer(lastStream, lastOnly) + case s => fetchStreamAnswer(s, lastOnly) + } QueryTest.includesRows(expectedAnswer, sparkAnswer).foreach { error => failTest(error) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingAggregationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingAggregationSuite.scala index 97e065193fd05..382da13430781 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingAggregationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingAggregationSuite.scala @@ -44,7 +44,7 @@ object FailureSingleton { } class StreamingAggregationSuite extends StateStoreMetricsTest - with BeforeAndAfterAll with Assertions with StatefulOperatorTest { + with BeforeAndAfterAll with Assertions { override def afterAll(): Unit = { super.afterAll() @@ -281,8 +281,6 @@ class StreamingAggregationSuite extends StateStoreMetricsTest AddData(inputData, 0L, 5L, 5L, 10L), AdvanceManualClock(10 * 1000), CheckLastBatch((0L, 1), (5L, 2), (10L, 1)), - AssertOnQuery(sq => - checkChildOutputHashPartitioning[StateStoreRestoreExec](sq, Seq("value"))), // advance clock to 20 seconds, should retain keys >= 10 AddData(inputData, 15L, 15L, 20L), @@ -538,6 +536,31 @@ class StreamingAggregationSuite extends StateStoreMetricsTest ) } + test("SPARK-23004: Ensure that TypedImperativeAggregate functions do not throw errors") { + // See the JIRA SPARK-23004 for more details. In short, this test reproduces the error + // by ensuring the following. + // - A streaming query with a streaming aggregation. + // - Aggregation function 'collect_list' that is a subclass of TypedImperativeAggregate. + // - Post shuffle partition has exactly 128 records (i.e. the threshold at which + // ObjectHashAggregateExec falls back to sort-based aggregation). This is done by having a + // micro-batch with 128 records that shuffle to a single partition. + // This test throws the exact error reported in SPARK-23004 without the corresponding fix. + withSQLConf("spark.sql.shuffle.partitions" -> "1") { + val input = MemoryStream[Int] + val df = input.toDF().toDF("value") + .selectExpr("value as group", "value") + .groupBy("group") + .agg(collect_list("value")) + testStream(df, outputMode = OutputMode.Update)( + AddData(input, (1 to spark.sqlContext.conf.objectAggSortBasedFallbackThreshold): _*), + AssertOnQuery { q => + q.processAllAvailable() + true + } + ) + } + } + /** Add blocks of data to the `BlockRDDBackedSource`. */ case class AddBlockData(source: BlockRDDBackedSource, data: Seq[Int]*) extends AddData { override def addData(query: Option[StreamExecution]): (Source, Offset) = { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingJoinSuite.scala index 54eb863dacc83..4af6dc378d998 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingJoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingJoinSuite.scala @@ -28,7 +28,9 @@ import org.apache.spark.sql.{AnalysisException, DataFrame, Row, SparkSession} import org.apache.spark.sql.catalyst.analysis.StreamingJoinHelper import org.apache.spark.sql.catalyst.expressions.{AttributeReference, AttributeSet, Literal} import org.apache.spark.sql.catalyst.plans.logical.{EventTimeWatermark, Filter} -import org.apache.spark.sql.execution.LogicalRDD +import org.apache.spark.sql.catalyst.trees.TreeNode +import org.apache.spark.sql.execution.{FileSourceScanExec, LogicalRDD} +import org.apache.spark.sql.execution.datasources.LogicalRelation import org.apache.spark.sql.execution.streaming.{MemoryStream, StatefulOperatorStateInfo, StreamingSymmetricHashJoinHelper} import org.apache.spark.sql.execution.streaming.state.{StateStore, StateStoreProviderId} import org.apache.spark.sql.functions._ @@ -323,6 +325,27 @@ class StreamingInnerJoinSuite extends StreamTest with StateStoreMetricsTest with assert(e.toString.contains("Stream stream joins without equality predicate is not supported")) } + test("stream stream self join") { + val input = MemoryStream[Int] + val df = input.toDF + val join = + df.select('value % 5 as "key", 'value).join( + df.select('value % 5 as "key", 'value), "key") + + testStream(join)( + AddData(input, 1, 2), + CheckAnswer((1, 1, 1), (2, 2, 2)), + StopStream, + StartStream(), + AddData(input, 3, 6), + /* + (1, 1) (1, 1) + (2, 2) x (2, 2) = (1, 1, 1), (1, 1, 6), (2, 2, 2), (1, 6, 1), (1, 6, 6) + (1, 6) (1, 6) + */ + CheckAnswer((3, 3, 3), (1, 1, 1), (1, 1, 6), (2, 2, 2), (1, 6, 1), (1, 6, 6))) + } + test("locality preferences of StateStoreAwareZippedRDD") { import StreamingSymmetricHashJoinHelper._ @@ -383,6 +406,20 @@ class StreamingInnerJoinSuite extends StreamTest with StateStoreMetricsTest with AddData(input3, 5, 10), CheckLastBatch((5, 10, 5, 15, 5, 25))) } + + test("streaming join should require HashClusteredDistribution from children") { + val input1 = MemoryStream[Int] + val input2 = MemoryStream[Int] + + val df1 = input1.toDF.select('value as 'a, 'value * 2 as 'b) + val df2 = input2.toDF.select('value as 'a, 'value * 2 as 'b).repartition('b) + val joined = df1.join(df2, Seq("a", "b")).select('a) + + testStream(joined)( + AddData(input1, 1.to(1000): _*), + AddData(input2, 1.to(1000): _*), + CheckAnswer(1.to(1000): _*)) + } } class StreamingOuterJoinSuite extends StreamTest with StateStoreMetricsTest with BeforeAndAfter { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryListenerSuite.scala index 9ff02dee288fb..79d65192a14aa 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryListenerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryListenerSuite.scala @@ -174,6 +174,31 @@ class StreamingQueryListenerSuite extends StreamTest with BeforeAndAfter { } } + test("continuous processing listeners should receive QueryTerminatedEvent") { + val df = spark.readStream.format("rate").load() + val listeners = (1 to 5).map(_ => new EventCollector) + try { + listeners.foreach(listener => spark.streams.addListener(listener)) + testStream(df, OutputMode.Append, useV2Sink = true)( + StartStream(Trigger.Continuous(1000)), + StopStream, + AssertOnQuery { query => + eventually(Timeout(streamingTimeout)) { + listeners.foreach(listener => assert(listener.terminationEvent !== null)) + listeners.foreach(listener => assert(listener.terminationEvent.id === query.id)) + listeners.foreach(listener => assert(listener.terminationEvent.runId === query.runId)) + listeners.foreach(listener => assert(listener.terminationEvent.exception === None)) + } + listeners.foreach(listener => listener.checkAsyncErrors()) + listeners.foreach(listener => listener.reset()) + true + } + ) + } finally { + listeners.foreach(spark.streams.removeListener) + } + } + test("adding and removing listener") { def isListenerActive(listener: EventCollector): Boolean = { listener.reset() diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQuerySuite.scala index 2fa4595dab376..e3429b58dceec 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQuerySuite.scala @@ -424,6 +424,29 @@ class StreamingQuerySuite extends StreamTest with BeforeAndAfter with Logging wi } } + test("SPARK-22975: MetricsReporter defaults when there was no progress reported") { + withSQLConf("spark.sql.streaming.metricsEnabled" -> "true") { + BlockingSource.latch = new CountDownLatch(1) + withTempDir { tempDir => + val sq = spark.readStream + .format("org.apache.spark.sql.streaming.util.BlockingSource") + .load() + .writeStream + .format("org.apache.spark.sql.streaming.util.BlockingSource") + .option("checkpointLocation", tempDir.toString) + .start() + .asInstanceOf[StreamingQueryWrapper] + .streamingQuery + + val gauges = sq.streamMetrics.metricRegistry.getGauges + assert(gauges.get("latency").getValue.asInstanceOf[Long] == 0) + assert(gauges.get("processingRate-total").getValue.asInstanceOf[Double] == 0.0) + assert(gauges.get("inputRate-total").getValue.asInstanceOf[Double] == 0.0) + sq.stop() + } + } + } + test("input row calculation with mixed batch and streaming sources") { val streamingTriggerDF = spark.createDataset(1 to 10).toDF val streamingInputDF = createSingleTriggerStreamingDF(streamingTriggerDF).toDF("value") @@ -509,22 +532,22 @@ class StreamingQuerySuite extends StreamTest with BeforeAndAfter with Logging wi .start() } - val input = MemoryStream[Int] - val q1 = startQuery(input.toDS, "stream_serializable_test_1") - val q2 = startQuery(input.toDS.map { i => + val input = MemoryStream[Int] :: MemoryStream[Int] :: MemoryStream[Int] :: Nil + val q1 = startQuery(input(0).toDS, "stream_serializable_test_1") + val q2 = startQuery(input(1).toDS.map { i => // Emulate that `StreamingQuery` get captured with normal usage unintentionally. // It should not fail the query. q1 i }, "stream_serializable_test_2") - val q3 = startQuery(input.toDS.map { i => + val q3 = startQuery(input(2).toDS.map { i => // Emulate that `StreamingQuery` is used in executors. We should fail the query with a clear // error message. q1.explain() i }, "stream_serializable_test_3") try { - input.addData(1) + input.foreach(_.addData(1)) // q2 should not fail since it doesn't use `q1` in the closure q2.processAllAvailable() @@ -664,6 +687,21 @@ class StreamingQuerySuite extends StreamTest with BeforeAndAfter with Logging wi CheckLastBatch(("A", 1))) } + test("StreamingRelationV2/StreamingExecutionRelation/ContinuousExecutionRelation.toJSON " + + "should not fail") { + val df = spark.readStream.format("rate").load() + assert(df.logicalPlan.toJSON.contains("StreamingRelationV2")) + + testStream(df)( + AssertOnQuery(_.logicalPlan.toJSON.contains("StreamingExecutionRelation")) + ) + + testStream(df, useV2Sink = true)( + StartStream(trigger = Trigger.Continuous(100)), + AssertOnQuery(_.logicalPlan.toJSON.contains("ContinuousExecutionRelation")) + ) + } + /** Create a streaming DF that only execute one batch in which it returns the given static DF */ private def createSingleTriggerStreamingDF(triggerDF: DataFrame): DataFrame = { require(!triggerDF.isStreaming) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala index eda0d8ad48313..95406b3a8b468 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala @@ -17,36 +17,18 @@ package org.apache.spark.sql.streaming.continuous -import java.io.{File, InterruptedIOException, IOException, UncheckedIOException} -import java.nio.channels.ClosedByInterruptException -import java.util.concurrent.{CountDownLatch, ExecutionException, TimeoutException, TimeUnit} +import java.util.UUID -import scala.reflect.ClassTag -import scala.util.control.ControlThrowable - -import com.google.common.util.concurrent.UncheckedExecutionException -import org.apache.commons.io.FileUtils -import org.apache.hadoop.conf.Configuration - -import org.apache.spark.{SparkContext, SparkEnv} -import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart} +import org.apache.spark.{SparkContext, SparkEnv, SparkException} +import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart, SparkListenerTaskStart} import org.apache.spark.sql._ -import org.apache.spark.sql.catalyst.plans.logical.Range -import org.apache.spark.sql.catalyst.streaming.InternalOutputModes -import org.apache.spark.sql.execution.command.ExplainCommand import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2ScanExec, WriteToDataSourceV2Exec} import org.apache.spark.sql.execution.streaming._ import org.apache.spark.sql.execution.streaming.continuous._ import org.apache.spark.sql.execution.streaming.sources.MemorySinkV2 -import org.apache.spark.sql.execution.streaming.state.{StateStore, StateStoreConf, StateStoreId, StateStoreProvider} import org.apache.spark.sql.functions._ -import org.apache.spark.sql.internal.SQLConf -import org.apache.spark.sql.sources.StreamSourceProvider import org.apache.spark.sql.streaming.{StreamTest, Trigger} -import org.apache.spark.sql.streaming.util.StreamManualClock import org.apache.spark.sql.test.TestSparkSession -import org.apache.spark.sql.types._ -import org.apache.spark.util.Utils class ContinuousSuiteBase extends StreamTest { // We need more than the default local[2] to be able to schedule all partitions simultaneously. @@ -61,7 +43,7 @@ class ContinuousSuiteBase extends StreamTest { case s: ContinuousExecution => assert(numTriggers >= 2, "must wait for at least 2 triggers to ensure query is initialized") val reader = s.lastExecution.executedPlan.collectFirst { - case DataSourceV2ScanExec(_, r: ContinuousRateStreamReader) => r + case DataSourceV2ScanExec(_, r: RateStreamContinuousReader) => r }.get val deltaMs = numTriggers * 1000 + 300 @@ -192,6 +174,25 @@ class ContinuousSuite extends ContinuousSuiteBase { "Continuous processing does not support current time operations.")) } + test("subquery alias") { + val df = spark.readStream + .format("rate") + .option("numPartitions", "5") + .option("rowsPerSecond", "5") + .load() + .createOrReplaceTempView("rate") + val test = spark.sql("select value from rate where value > 5") + + testStream(test, useV2Sink = true)( + StartStream(longContinuousTrigger), + AwaitEpoch(0), + Execute(waitForRateSourceTriggers(_, 2)), + IncrementEpoch(), + Execute(waitForRateSourceTriggers(_, 4)), + IncrementEpoch(), + CheckAnswerRowsContains(scala.Range(6, 20).map(Row(_)))) + } + test("repeatedly restart") { val df = spark.readStream .format("rate") @@ -219,6 +220,41 @@ class ContinuousSuite extends ContinuousSuiteBase { StopStream) } + test("task failure kills the query") { + val df = spark.readStream + .format("rate") + .option("numPartitions", "5") + .option("rowsPerSecond", "5") + .load() + .select('value) + + // Get an arbitrary task from this query to kill. It doesn't matter which one. + var taskId: Long = -1 + val listener = new SparkListener() { + override def onTaskStart(start: SparkListenerTaskStart): Unit = { + taskId = start.taskInfo.taskId + } + } + spark.sparkContext.addSparkListener(listener) + try { + testStream(df, useV2Sink = true)( + StartStream(Trigger.Continuous(100)), + Execute(waitForRateSourceTriggers(_, 2)), + Execute { _ => + // Wait until a task is started, then kill its first attempt. + eventually(timeout(streamingTimeout)) { + assert(taskId != -1) + } + spark.sparkContext.killTaskAttempt(taskId) + }, + ExpectFailure[SparkException] { e => + e.getCause != null && e.getCause.getCause.isInstanceOf[ContinuousTaskRetryException] + }) + } finally { + spark.sparkContext.removeSparkListener(listener) + } + } + test("query without test harness") { val df = spark.readStream .format("rate") @@ -258,13 +294,9 @@ class ContinuousStressSuite extends ContinuousSuiteBase { AwaitEpoch(0), Execute(waitForRateSourceTriggers(_, 201)), IncrementEpoch(), - Execute { query => - val data = query.sink.asInstanceOf[MemorySinkV2].allData - val vals = data.map(_.getLong(0)).toSet - assert(scala.Range(0, 25000).forall { i => - vals.contains(i) - }) - }) + StopStream, + CheckAnswerRowsContains(scala.Range(0, 25000).map(Row(_))) + ) } test("automatic epoch advancement") { @@ -280,6 +312,7 @@ class ContinuousStressSuite extends ContinuousSuiteBase { AwaitEpoch(0), Execute(waitForRateSourceTriggers(_, 201)), IncrementEpoch(), + StopStream, CheckAnswerRowsContains(scala.Range(0, 25000).map(Row(_)))) } @@ -311,6 +344,7 @@ class ContinuousStressSuite extends ContinuousSuiteBase { StopStream, StartStream(Trigger.Continuous(2012)), AwaitEpoch(50), + StopStream, CheckAnswerRowsContains(scala.Range(0, 25000).map(Row(_)))) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/sources/StreamingDataSourceV2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/sources/StreamingDataSourceV2Suite.scala new file mode 100644 index 0000000000000..af4618bed5456 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/sources/StreamingDataSourceV2Suite.scala @@ -0,0 +1,254 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.streaming.sources + +import java.util.Optional + +import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.apache.spark.sql.execution.datasources.DataSource +import org.apache.spark.sql.execution.streaming.{RateStreamOffset, Sink, StreamingQueryWrapper} +import org.apache.spark.sql.execution.streaming.continuous.ContinuousTrigger +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.sources.{DataSourceRegister, StreamSinkProvider} +import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceOptions, MicroBatchReadSupport, StreamWriteSupport} +import org.apache.spark.sql.sources.v2.reader.DataReaderFactory +import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousReader, MicroBatchReader, Offset, PartitionOffset} +import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter +import org.apache.spark.sql.streaming.{OutputMode, StreamTest, Trigger} +import org.apache.spark.sql.types.StructType +import org.apache.spark.util.Utils + +case class FakeReader() extends MicroBatchReader with ContinuousReader { + def setOffsetRange(start: Optional[Offset], end: Optional[Offset]): Unit = {} + def getStartOffset: Offset = RateStreamOffset(Map()) + def getEndOffset: Offset = RateStreamOffset(Map()) + def deserializeOffset(json: String): Offset = RateStreamOffset(Map()) + def commit(end: Offset): Unit = {} + def readSchema(): StructType = StructType(Seq()) + def stop(): Unit = {} + def mergeOffsets(offsets: Array[PartitionOffset]): Offset = RateStreamOffset(Map()) + def setStartOffset(start: Optional[Offset]): Unit = {} + + def createDataReaderFactories(): java.util.ArrayList[DataReaderFactory[Row]] = { + throw new IllegalStateException("fake source - cannot actually read") + } +} + +trait FakeMicroBatchReadSupport extends MicroBatchReadSupport { + override def createMicroBatchReader( + schema: Optional[StructType], + checkpointLocation: String, + options: DataSourceOptions): MicroBatchReader = FakeReader() +} + +trait FakeContinuousReadSupport extends ContinuousReadSupport { + override def createContinuousReader( + schema: Optional[StructType], + checkpointLocation: String, + options: DataSourceOptions): ContinuousReader = FakeReader() +} + +trait FakeStreamWriteSupport extends StreamWriteSupport { + override def createStreamWriter( + queryId: String, + schema: StructType, + mode: OutputMode, + options: DataSourceOptions): StreamWriter = { + throw new IllegalStateException("fake sink - cannot actually write") + } +} + +class FakeReadMicroBatchOnly extends DataSourceRegister with FakeMicroBatchReadSupport { + override def shortName(): String = "fake-read-microbatch-only" +} + +class FakeReadContinuousOnly extends DataSourceRegister with FakeContinuousReadSupport { + override def shortName(): String = "fake-read-continuous-only" +} + +class FakeReadBothModes extends DataSourceRegister + with FakeMicroBatchReadSupport with FakeContinuousReadSupport { + override def shortName(): String = "fake-read-microbatch-continuous" +} + +class FakeReadNeitherMode extends DataSourceRegister { + override def shortName(): String = "fake-read-neither-mode" +} + +class FakeWrite extends DataSourceRegister with FakeStreamWriteSupport { + override def shortName(): String = "fake-write-microbatch-continuous" +} + +class FakeNoWrite extends DataSourceRegister { + override def shortName(): String = "fake-write-neither-mode" +} + + +case class FakeWriteV1FallbackException() extends Exception + +class FakeSink extends Sink { + override def addBatch(batchId: Long, data: DataFrame): Unit = {} +} + +class FakeWriteV1Fallback extends DataSourceRegister + with FakeStreamWriteSupport with StreamSinkProvider { + + override def createSink( + sqlContext: SQLContext, + parameters: Map[String, String], + partitionColumns: Seq[String], + outputMode: OutputMode): Sink = { + new FakeSink() + } + + override def shortName(): String = "fake-write-v1-fallback" +} + + +class StreamingDataSourceV2Suite extends StreamTest { + + override def beforeAll(): Unit = { + super.beforeAll() + val fakeCheckpoint = Utils.createTempDir() + spark.conf.set("spark.sql.streaming.checkpointLocation", fakeCheckpoint.getCanonicalPath) + } + + val readFormats = Seq( + "fake-read-microbatch-only", + "fake-read-continuous-only", + "fake-read-microbatch-continuous", + "fake-read-neither-mode") + val writeFormats = Seq( + "fake-write-microbatch-continuous", + "fake-write-neither-mode") + val triggers = Seq( + Trigger.Once(), + Trigger.ProcessingTime(1000), + Trigger.Continuous(1000)) + + private def testPositiveCase(readFormat: String, writeFormat: String, trigger: Trigger) = { + val query = spark.readStream + .format(readFormat) + .load() + .writeStream + .format(writeFormat) + .trigger(trigger) + .start() + query.stop() + query + } + + private def testNegativeCase( + readFormat: String, + writeFormat: String, + trigger: Trigger, + errorMsg: String) = { + val ex = intercept[UnsupportedOperationException] { + testPositiveCase(readFormat, writeFormat, trigger) + } + assert(ex.getMessage.contains(errorMsg)) + } + + private def testPostCreationNegativeCase( + readFormat: String, + writeFormat: String, + trigger: Trigger, + errorMsg: String) = { + val query = spark.readStream + .format(readFormat) + .load() + .writeStream + .format(writeFormat) + .trigger(trigger) + .start() + + eventually(timeout(streamingTimeout)) { + assert(query.exception.isDefined) + assert(query.exception.get.cause != null) + assert(query.exception.get.cause.getMessage.contains(errorMsg)) + } + } + + test("disabled v2 write") { + // Ensure the V2 path works normally and generates a V2 sink.. + val v2Query = testPositiveCase( + "fake-read-microbatch-continuous", "fake-write-v1-fallback", Trigger.Once()) + assert(v2Query.asInstanceOf[StreamingQueryWrapper].streamingQuery.sink + .isInstanceOf[FakeWriteV1Fallback]) + + // Ensure we create a V1 sink with the config. Note the config is a comma separated + // list, including other fake entries. + val fullSinkName = "org.apache.spark.sql.streaming.sources.FakeWriteV1Fallback" + withSQLConf(SQLConf.DISABLED_V2_STREAMING_WRITERS.key -> s"a,b,c,test,$fullSinkName,d,e") { + val v1Query = testPositiveCase( + "fake-read-microbatch-continuous", "fake-write-v1-fallback", Trigger.Once()) + assert(v1Query.asInstanceOf[StreamingQueryWrapper].streamingQuery.sink + .isInstanceOf[FakeSink]) + } + } + + // Get a list of (read, write, trigger) tuples for test cases. + val cases = readFormats.flatMap { read => + writeFormats.flatMap { write => + triggers.map(t => (write, t)) + }.map { + case (write, t) => (read, write, t) + } + } + + for ((read, write, trigger) <- cases) { + testQuietly(s"stream with read format $read, write format $write, trigger $trigger") { + val readSource = DataSource.lookupDataSource(read, spark.sqlContext.conf).newInstance() + val writeSource = DataSource.lookupDataSource(write, spark.sqlContext.conf).newInstance() + (readSource, writeSource, trigger) match { + // Valid microbatch queries. + case (_: MicroBatchReadSupport, _: StreamWriteSupport, t) + if !t.isInstanceOf[ContinuousTrigger] => + testPositiveCase(read, write, trigger) + + // Valid continuous queries. + case (_: ContinuousReadSupport, _: StreamWriteSupport, _: ContinuousTrigger) => + testPositiveCase(read, write, trigger) + + // Invalid - can't read at all + case (r, _, _) + if !r.isInstanceOf[MicroBatchReadSupport] + && !r.isInstanceOf[ContinuousReadSupport] => + testNegativeCase(read, write, trigger, + s"Data source $read does not support streamed reading") + + // Invalid - can't write + case (_, w, _) if !w.isInstanceOf[StreamWriteSupport] => + testNegativeCase(read, write, trigger, + s"Data source $write does not support streamed writing") + + // Invalid - trigger is continuous but reader is not + case (r, _: StreamWriteSupport, _: ContinuousTrigger) + if !r.isInstanceOf[ContinuousReadSupport] => + testNegativeCase(read, write, trigger, + s"Data source $read does not support continuous processing") + + // Invalid - trigger is microbatch but reader is not + case (r, _, t) + if !r.isInstanceOf[MicroBatchReadSupport] && !t.isInstanceOf[ContinuousTrigger] => + testPostCreationNegativeCase(read, write, trigger, + s"Data source $read does not support microbatch processing") + } + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/test/DataStreamReaderWriterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/test/DataStreamReaderWriterSuite.scala index aa163d2211c38..8212fb912ec57 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/test/DataStreamReaderWriterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/test/DataStreamReaderWriterSuite.scala @@ -422,21 +422,6 @@ class DataStreamReaderWriterSuite extends StreamTest with BeforeAndAfter { } } - test("ConsoleSink can be correctly loaded") { - LastOptions.clear() - val df = spark.readStream - .format("org.apache.spark.sql.streaming.test") - .load() - - val sq = df.writeStream - .format("console") - .option("checkpointLocation", newMetadataDir) - .trigger(ProcessingTime(2.seconds)) - .start() - - sq.awaitTermination(2000L) - } - test("prevent all column partitioning") { withTempDir { dir => val path = dir.getCanonicalPath @@ -450,16 +435,6 @@ class DataStreamReaderWriterSuite extends StreamTest with BeforeAndAfter { } } - test("ConsoleSink should not require checkpointLocation") { - LastOptions.clear() - val df = spark.readStream - .format("org.apache.spark.sql.streaming.test") - .load() - - val sq = df.writeStream.format("console").start() - sq.stop() - } - private def testMemorySinkCheckpointRecovery(chkLoc: String, provideInWriter: Boolean): Unit = { import testImplicits._ val ms = new MemoryStream[Int](0, sqlContext) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/DataFrameReaderWriterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/DataFrameReaderWriterSuite.scala index 8c9bb7d56a35f..b3147b0a11478 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/test/DataFrameReaderWriterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/test/DataFrameReaderWriterSuite.scala @@ -563,7 +563,8 @@ class DataFrameReaderWriterSuite extends QueryTest with SharedSQLContext with Be "and a same-name temp view exist") { withTable("same_name") { withTempView("same_name") { - sql("CREATE TABLE same_name(id LONG) USING parquet") + val format = spark.sessionState.conf.defaultDataSourceName + sql(s"CREATE TABLE same_name(id LONG) USING $format") spark.range(10).createTempView("same_name") spark.range(20).write.mode(SaveMode.Append).saveAsTable("same_name") checkAnswer(spark.table("same_name"), spark.range(10).toDF()) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala index 904f9f2ad0b22..bc4a120f7042f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala @@ -254,13 +254,26 @@ private[sql] trait SQLTestUtilsBase } /** - * Drops temporary table `tableName` after calling `f`. + * Drops temporary view `viewNames` after calling `f`. */ - protected def withTempView(tableNames: String*)(f: => Unit): Unit = { + protected def withTempView(viewNames: String*)(f: => Unit): Unit = { try f finally { // If the test failed part way, we don't want to mask the failure by failing to remove - // temp tables that never got created. - try tableNames.foreach(spark.catalog.dropTempView) catch { + // temp views that never got created. + try viewNames.foreach(spark.catalog.dropTempView) catch { + case _: NoSuchTableException => + } + } + } + + /** + * Drops global temporary view `viewNames` after calling `f`. + */ + protected def withGlobalTempView(viewNames: String*)(f: => Unit): Unit = { + try f finally { + // If the test failed part way, we don't want to mask the failure by failing to remove + // global temp views that never got created. + try viewNames.foreach(spark.catalog.dropGlobalTempView) catch { case _: NoSuchTableException => } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSparkSession.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSparkSession.scala index 0b4629a51b425..8968dbf36d507 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSparkSession.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSparkSession.scala @@ -60,6 +60,7 @@ trait SharedSparkSession protected implicit def sqlContext: SQLContext = _spark.sqlContext protected def createSparkSession: TestSparkSession = { + SparkSession.cleanupAnyExistingSession() new TestSparkSession(sparkConf) } @@ -92,11 +93,22 @@ trait SharedSparkSession * Stop the underlying [[org.apache.spark.SparkContext]], if any. */ protected override def afterAll(): Unit = { - super.afterAll() - if (_spark != null) { - _spark.sessionState.catalog.reset() - _spark.stop() - _spark = null + try { + super.afterAll() + } finally { + try { + if (_spark != null) { + try { + _spark.sessionState.catalog.reset() + } finally { + _spark.stop() + _spark = null + } + } + } finally { + SparkSession.clearActiveSession() + SparkSession.clearDefaultSession() + } } } @@ -111,7 +123,7 @@ trait SharedSparkSession spark.sharedState.cacheManager.clearCache() // files can be closed from other threads, so wait a bit // normally this doesn't take more than 1s - eventually(timeout(10.seconds)) { + eventually(timeout(10.seconds), interval(2.seconds)) { DebugFilesystem.assertNoOpenStreams() } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala index 4286e8a6ca2c8..17603deacdcdd 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala @@ -34,6 +34,9 @@ private[spark] class TestSparkSession(sc: SparkContext) extends SparkSession(sc) this(new SparkConf) } + SparkSession.setDefaultSession(this) + SparkSession.setActiveSession(this) + @transient override lazy val sessionState: SessionState = { new TestSQLSessionStateBuilder(this, None).build() diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/CachedKafkaConsumerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSparkSessionSuite.scala similarity index 59% rename from external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/CachedKafkaConsumerSuite.scala rename to sql/core/src/test/scala/org/apache/spark/sql/test/TestSparkSessionSuite.scala index 7aa7dd096c07b..4019c6888da98 100644 --- a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/CachedKafkaConsumerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSparkSessionSuite.scala @@ -15,20 +15,15 @@ * limitations under the License. */ -package org.apache.spark.sql.kafka010 +package org.apache.spark.sql.test -import org.scalatest.PrivateMethodTester +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.SparkSession -import org.apache.spark.sql.test.SharedSQLContext - -class CachedKafkaConsumerSuite extends SharedSQLContext with PrivateMethodTester { - - test("SPARK-19886: Report error cause correctly in reportDataLoss") { - val cause = new Exception("D'oh!") - val reportDataLoss = PrivateMethod[Unit]('reportDataLoss0) - val e = intercept[IllegalStateException] { - CachedKafkaConsumer.invokePrivate(reportDataLoss(true, "message", cause)) - } - assert(e.getCause === cause) +class TestSparkSessionSuite extends SparkFunSuite { + test("default session is set in constructor") { + val session = new TestSparkSession() + assert(SparkSession.getDefaultSession.contains(session)) + session.stop() } } diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml index 3135a8a275dae..8a0c6fba66cda 100644 --- a/sql/hive-thriftserver/pom.xml +++ b/sql/hive-thriftserver/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java b/sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java index 47bfaa86021d6..fc818bc69c761 100644 --- a/sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java +++ b/sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java @@ -44,7 +44,7 @@ import org.apache.hadoop.hive.ql.history.HiveHistory; import org.apache.hadoop.hive.ql.metadata.Hive; import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.hive.ql.processors.SetProcessor; +import org.apache.hadoop.hive.ql.parse.VariableSubstitution; import org.apache.hadoop.hive.ql.session.SessionState; import org.apache.hadoop.hive.shims.ShimLoader; import org.apache.hive.common.util.HiveVersionInfo; @@ -71,6 +71,12 @@ import org.apache.hive.service.cli.thrift.TProtocolVersion; import org.apache.hive.service.server.ThreadWithGarbageCleanup; +import static org.apache.hadoop.hive.conf.SystemVariables.ENV_PREFIX; +import static org.apache.hadoop.hive.conf.SystemVariables.HIVECONF_PREFIX; +import static org.apache.hadoop.hive.conf.SystemVariables.HIVEVAR_PREFIX; +import static org.apache.hadoop.hive.conf.SystemVariables.METACONF_PREFIX; +import static org.apache.hadoop.hive.conf.SystemVariables.SYSTEM_PREFIX; + /** * HiveSession * @@ -209,7 +215,7 @@ private void configureSession(Map sessionConfMap) throws HiveSQL String key = entry.getKey(); if (key.startsWith("set:")) { try { - SetProcessor.setVariable(key.substring(4), entry.getValue()); + setVariable(key.substring(4), entry.getValue()); } catch (Exception e) { throw new HiveSQLException(e); } @@ -221,8 +227,84 @@ private void configureSession(Map sessionConfMap) throws HiveSQL } } + // Copy from org.apache.hadoop.hive.ql.processors.SetProcessor, only change: + // setConf(varname, propName, varvalue, true) when varname.startsWith(HIVECONF_PREFIX) + public static int setVariable(String varname, String varvalue) throws Exception { + SessionState ss = SessionState.get(); + if (varvalue.contains("\n")){ + ss.err.println("Warning: Value had a \\n character in it."); + } + varname = varname.trim(); + if (varname.startsWith(ENV_PREFIX)){ + ss.err.println("env:* variables can not be set."); + return 1; + } else if (varname.startsWith(SYSTEM_PREFIX)){ + String propName = varname.substring(SYSTEM_PREFIX.length()); + System.getProperties().setProperty(propName, + new VariableSubstitution().substitute(ss.getConf(),varvalue)); + } else if (varname.startsWith(HIVECONF_PREFIX)){ + String propName = varname.substring(HIVECONF_PREFIX.length()); + setConf(varname, propName, varvalue, true); + } else if (varname.startsWith(HIVEVAR_PREFIX)) { + String propName = varname.substring(HIVEVAR_PREFIX.length()); + ss.getHiveVariables().put(propName, + new VariableSubstitution().substitute(ss.getConf(),varvalue)); + } else if (varname.startsWith(METACONF_PREFIX)) { + String propName = varname.substring(METACONF_PREFIX.length()); + Hive hive = Hive.get(ss.getConf()); + hive.setMetaConf(propName, new VariableSubstitution().substitute(ss.getConf(), varvalue)); + } else { + setConf(varname, varname, varvalue, true); + } + return 0; + } + + // returns non-null string for validation fail + private static void setConf(String varname, String key, String varvalue, boolean register) + throws IllegalArgumentException { + HiveConf conf = SessionState.get().getConf(); + String value = new VariableSubstitution().substitute(conf, varvalue); + if (conf.getBoolVar(HiveConf.ConfVars.HIVECONFVALIDATION)) { + HiveConf.ConfVars confVars = HiveConf.getConfVars(key); + if (confVars != null) { + if (!confVars.isType(value)) { + StringBuilder message = new StringBuilder(); + message.append("'SET ").append(varname).append('=').append(varvalue); + message.append("' FAILED because ").append(key).append(" expects "); + message.append(confVars.typeString()).append(" type value."); + throw new IllegalArgumentException(message.toString()); + } + String fail = confVars.validate(value); + if (fail != null) { + StringBuilder message = new StringBuilder(); + message.append("'SET ").append(varname).append('=').append(varvalue); + message.append("' FAILED in validation : ").append(fail).append('.'); + throw new IllegalArgumentException(message.toString()); + } + } else if (key.startsWith("hive.")) { + throw new IllegalArgumentException("hive configuration " + key + " does not exists."); + } + } + conf.verifyAndSet(key, value); + if (register) { + SessionState.get().getOverriddenConfigurations().put(key, value); + } + } + @Override public void setOperationLogSessionDir(File operationLogRootDir) { + if (!operationLogRootDir.exists()) { + LOG.warn("The operation log root directory is removed, recreating: " + + operationLogRootDir.getAbsolutePath()); + if (!operationLogRootDir.mkdirs()) { + LOG.warn("Unable to create operation log root directory: " + + operationLogRootDir.getAbsolutePath()); + } + } + if (!operationLogRootDir.canWrite()) { + LOG.warn("The operation log root directory is not writable: " + + operationLogRootDir.getAbsolutePath()); + } sessionLogDir = new File(operationLogRootDir, sessionHandle.getHandleIdentifier().toString()); isOperationLogEnabled = true; if (!sessionLogDir.exists()) { diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala index 664bc20601eaa..3cfc81b8a9579 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala @@ -102,7 +102,7 @@ private[hive] class SparkExecuteStatementOperation( to += from.getAs[Timestamp](ordinal) case BinaryType => to += from.getAs[Array[Byte]](ordinal) - case _: ArrayType | _: StructType | _: MapType => + case _: ArrayType | _: StructType | _: MapType | _: UserDefinedType[_] => val hiveString = HiveUtils.toHiveString((from.get(ordinal), dataTypes(ordinal))) to += hiveString } diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala index 832a15d09599f..084f8200102ba 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala @@ -34,11 +34,13 @@ import org.apache.hadoop.hive.ql.Driver import org.apache.hadoop.hive.ql.exec.Utilities import org.apache.hadoop.hive.ql.processors._ import org.apache.hadoop.hive.ql.session.SessionState +import org.apache.hadoop.security.{Credentials, UserGroupInformation} import org.apache.log4j.{Level, Logger} import org.apache.thrift.transport.TSocket import org.apache.spark.SparkConf import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.deploy.security.HiveDelegationTokenProvider import org.apache.spark.internal.Logging import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.hive.HiveUtils @@ -121,6 +123,13 @@ private[hive] object SparkSQLCLIDriver extends Logging { } } + val tokenProvider = new HiveDelegationTokenProvider() + if (tokenProvider.delegationTokensRequired(sparkConf, hadoopConf)) { + val credentials = new Credentials() + tokenProvider.obtainDelegationTokens(hadoopConf, sparkConf, credentials) + UserGroupInformation.getCurrentUser.addCredentials(credentials) + } + SessionState.start(sessionState) // Clean up after we exit diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala index 6b19f971b73bb..cbd75ad12d430 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala @@ -50,8 +50,7 @@ private[hive] object SparkSQLEnv extends Logging { sqlContext = sparkSession.sqlContext val metadataHive = sparkSession - .sharedState.externalCatalog.asInstanceOf[HiveExternalCatalog] - .client.newSession() + .sharedState.externalCatalog.asInstanceOf[HiveExternalCatalog].client metadataHive.setOut(new PrintStream(System.out, true, "UTF-8")) metadataHive.setInfo(new PrintStream(System.err, true, "UTF-8")) metadataHive.setError(new PrintStream(System.err, true, "UTF-8")) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala index 48c0ebef3e0ce..2958b771f3648 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala @@ -40,22 +40,8 @@ private[hive] class SparkSQLSessionManager(hiveServer: HiveServer2, sqlContext: private lazy val sparkSqlOperationManager = new SparkSQLOperationManager() override def init(hiveConf: HiveConf) { - setSuperField(this, "hiveConf", hiveConf) - - // Create operation log root directory, if operation logging is enabled - if (hiveConf.getBoolVar(ConfVars.HIVE_SERVER2_LOGGING_OPERATION_ENABLED)) { - invoke(classOf[SessionManager], this, "initOperationLogRootDir") - } - - val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS) - setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize)) - getAncestorField[Log](this, 3, "LOG").info( - s"HiveServer2: Async execution pool size $backgroundPoolSize") - setSuperField(this, "operationManager", sparkSqlOperationManager) - addService(sparkSqlOperationManager) - - initCompositeService(hiveConf) + super.init(hiveConf) } override def openSession( diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala index a0e5012633f5e..bf7c01f60fb5c 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala @@ -28,6 +28,7 @@ import org.apache.spark.internal.Logging import org.apache.spark.sql.SQLContext import org.apache.spark.sql.hive.HiveUtils import org.apache.spark.sql.hive.thriftserver.{ReflectionUtils, SparkExecuteStatementOperation} +import org.apache.spark.sql.internal.SQLConf /** * Executes queries using Spark SQL, and maintains a list of handles to active queries. @@ -50,6 +51,9 @@ private[thriftserver] class SparkSQLOperationManager() require(sqlContext != null, s"Session handle: ${parentSession.getSessionHandle} has not been" + s" initialized or had already closed.") val conf = sqlContext.sessionState.conf + val hiveSessionState = parentSession.getSessionState + setConfMap(conf, hiveSessionState.getOverriddenConfigurations) + setConfMap(conf, hiveSessionState.getHiveVariables) val runInBackground = async && conf.getConf(HiveUtils.HIVE_THRIFT_SERVER_ASYNC) val operation = new SparkExecuteStatementOperation(parentSession, statement, confOverlay, runInBackground)(sqlContext, sessionToActivePool) @@ -58,4 +62,12 @@ private[thriftserver] class SparkSQLOperationManager() s"runInBackground=$runInBackground") operation } + + def setConfMap(conf: SQLConf, confMap: java.util.Map[String, String]): Unit = { + val iterator = confMap.entrySet().iterator() + while (iterator.hasNext) { + val kv = iterator.next() + conf.setConfString(kv.getKey, kv.getValue) + } + } } diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala index 7289da71a3365..496f8c82a6c61 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala @@ -135,6 +135,22 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { } } + test("Support beeline --hiveconf and --hivevar") { + withJdbcStatement() { statement => + executeTest(hiveConfList) + executeTest(hiveVarList) + def executeTest(hiveList: String): Unit = { + hiveList.split(";").foreach{ m => + val kv = m.split("=") + // select "${a}"; ---> avalue + val resultSet = statement.executeQuery("select \"${" + kv(0) + "}\"") + resultSet.next() + assert(resultSet.getString(1) === kv(1)) + } + } + } + } + test("JDBC query execution") { withJdbcStatement("test") { statement => val queries = Seq( @@ -740,10 +756,11 @@ abstract class HiveThriftJdbcTest extends HiveThriftServer2Test { s"""jdbc:hive2://localhost:$serverPort/ |default? |hive.server2.transport.mode=http; - |hive.server2.thrift.http.path=cliservice + |hive.server2.thrift.http.path=cliservice; + |${hiveConfList}#${hiveVarList} """.stripMargin.split("\n").mkString.trim } else { - s"jdbc:hive2://localhost:$serverPort/" + s"jdbc:hive2://localhost:$serverPort/?${hiveConfList}#${hiveVarList}" } def withMultipleConnectionJdbcStatement(tableNames: String*)(fs: (Statement => Unit)*) { @@ -779,6 +796,8 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl private var listeningPort: Int = _ protected def serverPort: Int = listeningPort + protected val hiveConfList = "a=avalue;b=bvalue" + protected val hiveVarList = "c=cvalue;d=dvalue" protected def user = System.getProperty("user.name") protected var warehousePath: File = _ diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index 45791c69b4cb7..cebaad5b4ad9b 100644 --- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -62,7 +62,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { // Fix session local timezone to America/Los_Angeles for those timezone sensitive tests // (timestamp_*) TestHive.setConf(SQLConf.SESSION_LOCAL_TIMEZONE, "America/Los_Angeles") - RuleExecutor.resetTime() + RuleExecutor.resetMetrics() } override def afterAll() { diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index 66fad85ea0263..2eeff747a68c6 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -22,7 +22,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../../pom.xml diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala index 632e3e0c4c3f9..3b8a8ca301c27 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala @@ -109,8 +109,8 @@ private[spark] class HiveExternalCatalog(conf: SparkConf, hadoopConf: Configurat } /** - * Get the raw table metadata from hive metastore directly. The raw table metadata may contains - * special data source properties and should not be exposed outside of `HiveExternalCatalog`. We + * Get the raw table metadata from hive metastore directly. The raw table metadata may contain + * special data source properties that should not be exposed outside of `HiveExternalCatalog`. We * should interpret these special data source properties and restore the original table metadata * before returning it. */ diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveSessionStateBuilder.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveSessionStateBuilder.scala index 92cb4ef11c9e3..12c74368dd184 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveSessionStateBuilder.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveSessionStateBuilder.scala @@ -42,7 +42,7 @@ class HiveSessionStateBuilder(session: SparkSession, parentState: Option[Session * Create a Hive aware resource loader. */ override protected lazy val resourceLoader: HiveSessionResourceLoader = { - val client: HiveClient = externalCatalog.client.newSession() + val client: HiveClient = externalCatalog.client new HiveSessionResourceLoader(session, client) } @@ -96,22 +96,7 @@ class HiveSessionStateBuilder(session: SparkSession, parentState: Option[Session override val sparkSession: SparkSession = session override def extraPlanningStrategies: Seq[Strategy] = - super.extraPlanningStrategies ++ customPlanningStrategies - - override def strategies: Seq[Strategy] = { - experimentalMethods.extraStrategies ++ - extraPlanningStrategies ++ Seq( - FileSourceStrategy, - DataSourceStrategy(conf), - SpecialLimits, - InMemoryScans, - HiveTableScans, - Scripts, - Aggregation, - JoinSelection, - BasicOperators - ) - } + super.extraPlanningStrategies ++ customPlanningStrategies ++ Seq(HiveTableScans, Scripts) } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala index ab857b9055720..8df05cbb20361 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala @@ -157,7 +157,7 @@ object HiveAnalysis extends Rule[LogicalPlan] { case CreateTable(tableDesc, mode, Some(query)) if DDLUtils.isHiveTable(tableDesc) => DDLUtils.checkDataColNames(tableDesc) - CreateHiveTableAsSelectCommand(tableDesc, query, mode) + CreateHiveTableAsSelectCommand(tableDesc, query, query.output, mode) case InsertIntoDir(isLocal, storage, provider, child, overwrite) if DDLUtils.isHiveTable(provider) => diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala index c489690af8cd1..c448c5a9821be 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala @@ -47,7 +47,7 @@ import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.internal.SQLConf._ import org.apache.spark.sql.internal.StaticSQLConf.{CATALOG_IMPLEMENTATION, WAREHOUSE_PATH} import org.apache.spark.sql.types._ -import org.apache.spark.util.Utils +import org.apache.spark.util.{ChildFirstURLClassLoader, Utils} private[spark] object HiveUtils extends Logging { @@ -109,7 +109,7 @@ private[spark] object HiveUtils extends Logging { .doc("When set to true, the built-in ORC reader and writer are used to process " + "ORC tables created by using the HiveQL syntax, instead of Hive serde.") .booleanConf - .createWithDefault(true) + .createWithDefault(false) val HIVE_METASTORE_SHARED_PREFIXES = buildConf("spark.sql.hive.metastore.sharedPrefixes") .doc("A comma separated list of class prefixes that should be loaded using the classloader " + @@ -304,7 +304,7 @@ private[spark] object HiveUtils extends Logging { throw new IllegalArgumentException( "Builtin jars can only be used when hive execution version == hive metastore version. " + s"Execution: $builtinHiveVersion != Metastore: $hiveMetastoreVersion. " + - "Specify a vaild path to the correct hive jars using $HIVE_METASTORE_JARS " + + s"Specify a valid path to the correct hive jars using ${HIVE_METASTORE_JARS.key} " + s"or change ${HIVE_METASTORE_VERSION.key} to $builtinHiveVersion.") } @@ -312,6 +312,8 @@ private[spark] object HiveUtils extends Logging { // starting from the given classLoader. def allJars(classLoader: ClassLoader): Array[URL] = classLoader match { case null => Array.empty[URL] + case childFirst: ChildFirstURLClassLoader => + childFirst.getURLs() ++ allJars(Utils.getSparkClassLoader) case urlClassLoader: URLClassLoader => urlClassLoader.getURLs ++ allJars(urlClassLoader.getParent) case other => allJars(other.getParent) @@ -322,7 +324,7 @@ private[spark] object HiveUtils extends Logging { if (jars.length == 0) { throw new IllegalArgumentException( "Unable to locate hive jars to connect to metastore. " + - "Please set spark.sql.hive.metastore.jars.") + s"Please set ${HIVE_METASTORE_JARS.key}.") } logInfo( @@ -458,6 +460,7 @@ private[spark] object HiveUtils extends Logging { case (decimal: java.math.BigDecimal, DecimalType()) => // Hive strips trailing zeros so use its toString HiveDecimal.create(decimal).toString + case (other, _ : UserDefinedType[_]) => other.toString case (other, tpe) if primitiveTypes contains tpe => other.toString } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala index cc8907a0bbc93..b5444a4217924 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala @@ -381,7 +381,7 @@ private[hive] object HadoopTableReader extends HiveInspectors with Logging { val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map { case (attr, ordinal) => soi.getStructFieldRef(attr.name) -> ordinal - }.unzip + }.toArray.unzip /** * Builds specific unwrappers ahead of time according to object inspector diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala index 7b7f4e0f10210..2e6628a7eafda 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala @@ -330,7 +330,7 @@ private[hive] class HiveClientImpl( Option(client.getDatabase(dbName)).map { d => CatalogDatabase( name = d.getName, - description = d.getDescription, + description = Option(d.getDescription).getOrElse(""), locationUri = CatalogUtils.stringToURI(d.getLocationUri), properties = Option(d.getParameters).map(_.asScala.toMap).orNull) }.getOrElse(throw new NoSuchDatabaseException(dbName)) @@ -824,19 +824,19 @@ private[hive] class HiveClientImpl( def reset(): Unit = withHiveState { client.getAllTables("default").asScala.foreach { t => - logDebug(s"Deleting table $t") - val table = client.getTable("default", t) - client.getIndexes("default", t, 255).asScala.foreach { index => - shim.dropIndex(client, "default", t, index.getIndexName) - } - if (!table.isIndexTable) { - client.dropTable("default", t) - } + logDebug(s"Deleting table $t") + val table = client.getTable("default", t) + client.getIndexes("default", t, 255).asScala.foreach { index => + shim.dropIndex(client, "default", t, index.getIndexName) } - client.getAllDatabases.asScala.filterNot(_ == "default").foreach { db => - logDebug(s"Dropping Database: $db") - client.dropDatabase(db, true, false, true) + if (!table.isIndexTable) { + client.dropTable("default", t) } + } + client.getAllDatabases.asScala.filterNot(_ == "default").foreach { db => + logDebug(s"Dropping Database: $db") + client.dropDatabase(db, true, false, true) + } } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala index 1eac70dbf19cd..60fe31f3542a5 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala @@ -343,7 +343,7 @@ private[client] class Shim_v0_12 extends Shim with Logging { } override def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long = { - conf.getIntVar(HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY) * 1000 + conf.getIntVar(HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY) * 1000L } override def loadPartition( diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateHiveTableAsSelectCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateHiveTableAsSelectCommand.scala index 65e8b4e3c725c..1e801fe1845c4 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateHiveTableAsSelectCommand.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateHiveTableAsSelectCommand.scala @@ -20,10 +20,11 @@ package org.apache.spark.sql.hive.execution import scala.util.control.NonFatal import org.apache.spark.sql.{AnalysisException, Row, SaveMode, SparkSession} -import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation import org.apache.spark.sql.catalyst.catalog.CatalogTable -import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoTable, LogicalPlan} -import org.apache.spark.sql.execution.command.RunnableCommand +import org.apache.spark.sql.catalyst.expressions.Attribute +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.execution.SparkPlan +import org.apache.spark.sql.execution.command.DataWritingCommand /** @@ -36,15 +37,15 @@ import org.apache.spark.sql.execution.command.RunnableCommand case class CreateHiveTableAsSelectCommand( tableDesc: CatalogTable, query: LogicalPlan, + outputColumns: Seq[Attribute], mode: SaveMode) - extends RunnableCommand { + extends DataWritingCommand { private val tableIdentifier = tableDesc.identifier - override def innerChildren: Seq[LogicalPlan] = Seq(query) - - override def run(sparkSession: SparkSession): Seq[Row] = { - if (sparkSession.sessionState.catalog.tableExists(tableIdentifier)) { + override def run(sparkSession: SparkSession, child: SparkPlan): Seq[Row] = { + val catalog = sparkSession.sessionState.catalog + if (catalog.tableExists(tableIdentifier)) { assert(mode != SaveMode.Overwrite, s"Expect the table $tableIdentifier has been dropped when the save mode is Overwrite") @@ -56,34 +57,36 @@ case class CreateHiveTableAsSelectCommand( return Seq.empty } - sparkSession.sessionState.executePlan( - InsertIntoTable( - UnresolvedRelation(tableIdentifier), - Map(), - query, - overwrite = false, - ifPartitionNotExists = false)).toRdd + InsertIntoHiveTable( + tableDesc, + Map.empty, + query, + overwrite = false, + ifPartitionNotExists = false, + outputColumns = outputColumns).run(sparkSession, child) } else { // TODO ideally, we should get the output data ready first and then // add the relation into catalog, just in case of failure occurs while data // processing. assert(tableDesc.schema.isEmpty) - sparkSession.sessionState.catalog.createTable( - tableDesc.copy(schema = query.schema), ignoreIfExists = false) + catalog.createTable(tableDesc.copy(schema = query.schema), ignoreIfExists = false) try { - sparkSession.sessionState.executePlan( - InsertIntoTable( - UnresolvedRelation(tableIdentifier), - Map(), - query, - overwrite = true, - ifPartitionNotExists = false)).toRdd + // Read back the metadata of the table which was created just now. + val createdTableMeta = catalog.getTableMetadata(tableDesc.identifier) + // For CTAS, there is no static partition values to insert. + val partition = createdTableMeta.partitionColumnNames.map(_ -> None).toMap + InsertIntoHiveTable( + createdTableMeta, + partition, + query, + overwrite = true, + ifPartitionNotExists = false, + outputColumns = outputColumns).run(sparkSession, child) } catch { case NonFatal(e) => // drop the created table. - sparkSession.sessionState.catalog.dropTable(tableIdentifier, ignoreIfNotExists = true, - purge = false) + catalog.dropTable(tableIdentifier, ignoreIfNotExists = true, purge = false) throw e } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveOptions.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveOptions.scala index 5c515515b9b9c..802ddafdbee4d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveOptions.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveOptions.scala @@ -19,7 +19,16 @@ package org.apache.spark.sql.hive.execution import java.util.Locale +import scala.collection.JavaConverters._ + +import org.apache.hadoop.hive.ql.plan.TableDesc +import org.apache.orc.OrcConf.COMPRESS +import org.apache.parquet.hadoop.ParquetOutputFormat + import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap +import org.apache.spark.sql.execution.datasources.orc.OrcOptions +import org.apache.spark.sql.execution.datasources.parquet.ParquetOptions +import org.apache.spark.sql.internal.SQLConf /** * Options for the Hive data source. Note that rule `DetermineHiveSerde` will extract Hive @@ -102,4 +111,17 @@ object HiveOptions { "collectionDelim" -> "colelction.delim", "mapkeyDelim" -> "mapkey.delim", "lineDelim" -> "line.delim").map { case (k, v) => k.toLowerCase(Locale.ROOT) -> v } + + def getHiveWriteCompression(tableInfo: TableDesc, sqlConf: SQLConf): Option[(String, String)] = { + val tableProps = tableInfo.getProperties.asScala.toMap + tableInfo.getOutputFileFormatClassName.toLowerCase(Locale.ROOT) match { + case formatName if formatName.endsWith("parquetoutputformat") => + val compressionCodec = new ParquetOptions(tableProps, sqlConf).compressionCodecClassName + Option((ParquetOutputFormat.COMPRESSION, compressionCodec)) + case formatName if formatName.endsWith("orcoutputformat") => + val compressionCodec = new OrcOptions(tableProps, sqlConf).compressionCodec + Option((COMPRESS.getAttribute, compressionCodec)) + case _ => None + } + } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala index 3ce5b8469d6fc..02a60f16b3b3a 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala @@ -172,7 +172,7 @@ case class InsertIntoHiveTable( val enforceBucketingConfig = "hive.enforce.bucketing" val enforceSortingConfig = "hive.enforce.sorting" - val message = s"Output Hive table ${table.identifier} is bucketed but Spark" + + val message = s"Output Hive table ${table.identifier} is bucketed but Spark " + "currently does NOT populate bucketed output which is compatible with Hive." if (hadoopConf.get(enforceBucketingConfig, "true").toBoolean || diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/SaveAsHiveFile.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/SaveAsHiveFile.scala index 9a6607f2f2c6c..e484356906e87 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/SaveAsHiveFile.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/SaveAsHiveFile.scala @@ -55,18 +55,28 @@ private[hive] trait SaveAsHiveFile extends DataWritingCommand { customPartitionLocations: Map[TablePartitionSpec, String] = Map.empty, partitionAttributes: Seq[Attribute] = Nil): Set[String] = { - val isCompressed = hadoopConf.get("hive.exec.compress.output", "false").toBoolean + val isCompressed = + fileSinkConf.getTableInfo.getOutputFileFormatClassName.toLowerCase(Locale.ROOT) match { + case formatName if formatName.endsWith("orcoutputformat") => + // For ORC,"mapreduce.output.fileoutputformat.compress", + // "mapreduce.output.fileoutputformat.compress.codec", and + // "mapreduce.output.fileoutputformat.compress.type" + // have no impact because it uses table properties to store compression information. + false + case _ => hadoopConf.get("hive.exec.compress.output", "false").toBoolean + } + if (isCompressed) { - // Please note that isCompressed, "mapreduce.output.fileoutputformat.compress", - // "mapreduce.output.fileoutputformat.compress.codec", and - // "mapreduce.output.fileoutputformat.compress.type" - // have no impact on ORC because it uses table properties to store compression information. hadoopConf.set("mapreduce.output.fileoutputformat.compress", "true") fileSinkConf.setCompressed(true) fileSinkConf.setCompressCodec(hadoopConf .get("mapreduce.output.fileoutputformat.compress.codec")) fileSinkConf.setCompressType(hadoopConf .get("mapreduce.output.fileoutputformat.compress.type")) + } else { + // Set compression by priority + HiveOptions.getHiveWriteCompression(fileSinkConf.getTableInfo, sparkSession.sessionState.conf) + .foreach { case (compression, codec) => hadoopConf.set(compression, codec) } } val committer = FileCommitProtocol.instantiate( diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileFormat.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileFormat.scala index 95741c7b30289..237ed9bc05988 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileFormat.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileFormat.scala @@ -59,9 +59,11 @@ class OrcFileFormat extends FileFormat with DataSourceRegister with Serializable sparkSession: SparkSession, options: Map[String, String], files: Seq[FileStatus]): Option[StructType] = { + val ignoreCorruptFiles = sparkSession.sessionState.conf.ignoreCorruptFiles OrcFileOperator.readSchema( files.map(_.getPath.toString), - Some(sparkSession.sessionState.newHadoopConf()) + Some(sparkSession.sessionState.newHadoopConf()), + ignoreCorruptFiles ) } @@ -129,6 +131,7 @@ class OrcFileFormat extends FileFormat with DataSourceRegister with Serializable val broadcastedHadoopConf = sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf)) + val ignoreCorruptFiles = sparkSession.sessionState.conf.ignoreCorruptFiles (file: PartitionedFile) => { val conf = broadcastedHadoopConf.value.value @@ -138,7 +141,8 @@ class OrcFileFormat extends FileFormat with DataSourceRegister with Serializable // SPARK-8501: Empty ORC files always have an empty schema stored in their footer. In this // case, `OrcFileOperator.readSchema` returns `None`, and we can't read the underlying file // using the given physical schema. Instead, we simply return an empty iterator. - val isEmptyFile = OrcFileOperator.readSchema(Seq(filePath.toString), Some(conf)).isEmpty + val isEmptyFile = + OrcFileOperator.readSchema(Seq(filePath.toString), Some(conf), ignoreCorruptFiles).isEmpty if (isEmptyFile) { Iterator.empty } else { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala index 5a3fcd7a759c0..80e44ca504356 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala @@ -17,11 +17,14 @@ package org.apache.spark.sql.hive.orc +import java.io.IOException + import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.hive.ql.io.orc.{OrcFile, Reader} import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector +import org.apache.spark.SparkException import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.internal.Logging import org.apache.spark.sql.catalyst.parser.CatalystSqlParser @@ -46,7 +49,10 @@ private[hive] object OrcFileOperator extends Logging { * create the result reader from that file. If no such file is found, it returns `None`. * @todo Needs to consider all files when schema evolution is taken into account. */ - def getFileReader(basePath: String, config: Option[Configuration] = None): Option[Reader] = { + def getFileReader(basePath: String, + config: Option[Configuration] = None, + ignoreCorruptFiles: Boolean = false) + : Option[Reader] = { def isWithNonEmptySchema(path: Path, reader: Reader): Boolean = { reader.getObjectInspector match { case oi: StructObjectInspector if oi.getAllStructFieldRefs.size() == 0 => @@ -65,16 +71,28 @@ private[hive] object OrcFileOperator extends Logging { } listOrcFiles(basePath, conf).iterator.map { path => - path -> OrcFile.createReader(fs, path) + val reader = try { + Some(OrcFile.createReader(fs, path)) + } catch { + case e: IOException => + if (ignoreCorruptFiles) { + logWarning(s"Skipped the footer in the corrupted file: $path", e) + None + } else { + throw new SparkException(s"Could not read footer for file: $path", e) + } + } + path -> reader }.collectFirst { - case (path, reader) if isWithNonEmptySchema(path, reader) => reader + case (path, Some(reader)) if isWithNonEmptySchema(path, reader) => reader } } - def readSchema(paths: Seq[String], conf: Option[Configuration]): Option[StructType] = { + def readSchema(paths: Seq[String], conf: Option[Configuration], ignoreCorruptFiles: Boolean) + : Option[StructType] = { // Take the first file where we can open a valid reader if we can find one. Otherwise just // return None to indicate we can't infer the schema. - paths.flatMap(getFileReader(_, conf)).headOption.map { reader => + paths.flatMap(getFileReader(_, conf, ignoreCorruptFiles)).headOption.map { reader => val readerInspector = reader.getObjectInspector.asInstanceOf[StructObjectInspector] val schema = readerInspector.getTypeName logDebug(s"Reading schema from file $paths, got Hive schema string: $schema") diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index b6be00dbb3a73..533e7a0e022cb 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -159,6 +159,10 @@ private[hive] class TestHiveSparkSession( private val loadTestTables: Boolean) extends SparkSession(sc) with Logging { self => + // TODO(SPARK-23826): TestHiveSparkSession should set default session the same way as + // TestSparkSession, but doing this the same way breaks many tests in the package. We need + // to investigate and find a different strategy. + def this(sc: SparkContext, loadTestTables: Boolean) { this( sc, @@ -175,12 +179,21 @@ private[hive] class TestHiveSparkSession( loadTestTables) } + SparkSession.setDefaultSession(this) + SparkSession.setActiveSession(this) + { // set the metastore temporary configuration val metastoreTempConf = HiveUtils.newTemporaryConfiguration(useInMemoryDerby = false) ++ Map( ConfVars.METASTORE_INTEGER_JDO_PUSHDOWN.varname -> "true", // scratch directory used by Hive's metastore client ConfVars.SCRATCHDIR.varname -> TestHiveContext.makeScratchDir().toURI.toString, - ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY.varname -> "1") + ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY.varname -> "1") ++ + // After session cloning, the JDBC connect string for a JDBC metastore should not be changed. + existingSharedState.map { state => + val connKey = + state.sparkContext.hadoopConfiguration.get(ConfVars.METASTORECONNECTURLKEY.varname) + ConfVars.METASTORECONNECTURLKEY.varname -> connKey + } metastoreTempConf.foreach { case (k, v) => sc.hadoopConfiguration.set(k, v) @@ -486,8 +499,7 @@ private[hive] class TestHiveSparkSession( protected val originalUDFs: JavaSet[String] = FunctionRegistry.getFunctionNames /** - * Resets the test instance by deleting any tables that have been created. - * TODO: also clear out UDFs, views, etc. + * Resets the test instance by deleting any table, view, temp view, and UDF that have been created */ def reset() { try { @@ -525,8 +537,6 @@ private[hive] class TestHiveSparkSession( // For some reason, RESET does not reset the following variables... // https://issues.apache.org/jira/browse/HIVE-9004 metadataHive.runSqlHive("set hive.table.parameters.default=") - metadataHive.runSqlHive("set datanucleus.cache.collections=true") - metadataHive.runSqlHive("set datanucleus.cache.collections.lazy=true") // Lots of tests fail if we do not change the partition whitelist from the default. metadataHive.runSqlHive("set hive.metastore.partition.name.whitelist.pattern=.*") @@ -570,7 +580,7 @@ private[hive] class TestHiveQueryExecution( logDebug(s"Query references test tables: ${referencedTestTables.mkString(", ")}") referencedTestTables.foreach(sparkSession.loadTestTable) // Proceed with analysis. - sparkSession.sessionState.analyzer.execute(logical) + sparkSession.sessionState.analyzer.executeAndCheck(logical) } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CompressionCodecSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CompressionCodecSuite.scala new file mode 100644 index 0000000000000..d10a6f25c64fc --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CompressionCodecSuite.scala @@ -0,0 +1,353 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import java.io.File + +import scala.collection.JavaConverters._ + +import org.apache.hadoop.fs.Path +import org.apache.orc.OrcConf.COMPRESS +import org.apache.parquet.hadoop.ParquetOutputFormat +import org.scalatest.BeforeAndAfterAll + +import org.apache.spark.sql.execution.datasources.orc.OrcOptions +import org.apache.spark.sql.execution.datasources.parquet.{ParquetOptions, ParquetTest} +import org.apache.spark.sql.hive.orc.OrcFileOperator +import org.apache.spark.sql.hive.test.TestHiveSingleton +import org.apache.spark.sql.internal.SQLConf + +class CompressionCodecSuite extends TestHiveSingleton with ParquetTest with BeforeAndAfterAll { + import spark.implicits._ + + override def beforeAll(): Unit = { + super.beforeAll() + (0 until maxRecordNum).toDF("a").createOrReplaceTempView("table_source") + } + + override def afterAll(): Unit = { + try { + spark.catalog.dropTempView("table_source") + } finally { + super.afterAll() + } + } + + private val maxRecordNum = 50 + + private def getConvertMetastoreConfName(format: String): String = format.toLowerCase match { + case "parquet" => HiveUtils.CONVERT_METASTORE_PARQUET.key + case "orc" => HiveUtils.CONVERT_METASTORE_ORC.key + } + + private def getSparkCompressionConfName(format: String): String = format.toLowerCase match { + case "parquet" => SQLConf.PARQUET_COMPRESSION.key + case "orc" => SQLConf.ORC_COMPRESSION.key + } + + private def getHiveCompressPropName(format: String): String = format.toLowerCase match { + case "parquet" => ParquetOutputFormat.COMPRESSION + case "orc" => COMPRESS.getAttribute + } + + private def normalizeCodecName(format: String, name: String): String = { + format.toLowerCase match { + case "parquet" => ParquetOptions.getParquetCompressionCodecName(name) + case "orc" => OrcOptions.getORCCompressionCodecName(name) + } + } + + private def getTableCompressionCodec(path: String, format: String): Seq[String] = { + val hadoopConf = spark.sessionState.newHadoopConf() + val codecs = format.toLowerCase match { + case "parquet" => for { + footer <- readAllFootersWithoutSummaryFiles(new Path(path), hadoopConf) + block <- footer.getParquetMetadata.getBlocks.asScala + column <- block.getColumns.asScala + } yield column.getCodec.name() + case "orc" => new File(path).listFiles().filter { file => + file.isFile && !file.getName.endsWith(".crc") && file.getName != "_SUCCESS" + }.map { orcFile => + OrcFileOperator.getFileReader(orcFile.toPath.toString).get.getCompression.toString + }.toSeq + } + codecs.distinct + } + + private def createTable( + rootDir: File, + tableName: String, + isPartitioned: Boolean, + format: String, + compressionCodec: Option[String]): Unit = { + val tblProperties = compressionCodec match { + case Some(prop) => s"TBLPROPERTIES('${getHiveCompressPropName(format)}'='$prop')" + case _ => "" + } + val partitionCreate = if (isPartitioned) "PARTITIONED BY (p string)" else "" + sql( + s""" + |CREATE TABLE $tableName(a int) + |$partitionCreate + |STORED AS $format + |LOCATION '${rootDir.toURI.toString.stripSuffix("/")}/$tableName' + |$tblProperties + """.stripMargin) + } + + private def writeDataToTable( + tableName: String, + partitionValue: Option[String]): Unit = { + val partitionInsert = partitionValue.map(p => s"partition (p='$p')").mkString + sql( + s""" + |INSERT INTO TABLE $tableName + |$partitionInsert + |SELECT * FROM table_source + """.stripMargin) + } + + private def writeDateToTableUsingCTAS( + rootDir: File, + tableName: String, + partitionValue: Option[String], + format: String, + compressionCodec: Option[String]): Unit = { + val partitionCreate = partitionValue.map(p => s"PARTITIONED BY (p)").mkString + val compressionOption = compressionCodec.map { codec => + s",'${getHiveCompressPropName(format)}'='$codec'" + }.mkString + val partitionSelect = partitionValue.map(p => s",'$p' AS p").mkString + sql( + s""" + |CREATE TABLE $tableName + |USING $format + |OPTIONS('path'='${rootDir.toURI.toString.stripSuffix("/")}/$tableName' $compressionOption) + |$partitionCreate + |AS SELECT * $partitionSelect FROM table_source + """.stripMargin) + } + + private def getPreparedTablePath( + tmpDir: File, + tableName: String, + isPartitioned: Boolean, + format: String, + compressionCodec: Option[String], + usingCTAS: Boolean): String = { + val partitionValue = if (isPartitioned) Some("test") else None + if (usingCTAS) { + writeDateToTableUsingCTAS(tmpDir, tableName, partitionValue, format, compressionCodec) + } else { + createTable(tmpDir, tableName, isPartitioned, format, compressionCodec) + writeDataToTable(tableName, partitionValue) + } + getTablePartitionPath(tmpDir, tableName, partitionValue) + } + + private def getTableSize(path: String): Long = { + val dir = new File(path) + val files = dir.listFiles().filter(_.getName.startsWith("part-")) + files.map(_.length()).sum + } + + private def getTablePartitionPath( + dir: File, + tableName: String, + partitionValue: Option[String]) = { + val partitionPath = partitionValue.map(p => s"p=$p").mkString + s"${dir.getPath.stripSuffix("/")}/$tableName/$partitionPath" + } + + private def getUncompressedDataSizeByFormat( + format: String, isPartitioned: Boolean, usingCTAS: Boolean): Long = { + var totalSize = 0L + val tableName = s"tbl_$format" + val codecName = normalizeCodecName(format, "uncompressed") + withSQLConf(getSparkCompressionConfName(format) -> codecName) { + withTempDir { tmpDir => + withTable(tableName) { + val compressionCodec = Option(codecName) + val path = getPreparedTablePath( + tmpDir, tableName, isPartitioned, format, compressionCodec, usingCTAS) + totalSize = getTableSize(path) + } + } + } + assert(totalSize > 0L) + totalSize + } + + private def checkCompressionCodecForTable( + format: String, + isPartitioned: Boolean, + compressionCodec: Option[String], + usingCTAS: Boolean) + (assertion: (String, Long) => Unit): Unit = { + val tableName = + if (usingCTAS) s"tbl_$format$isPartitioned" else s"tbl_$format${isPartitioned}_CAST" + withTempDir { tmpDir => + withTable(tableName) { + val path = getPreparedTablePath( + tmpDir, tableName, isPartitioned, format, compressionCodec, usingCTAS) + val relCompressionCodecs = getTableCompressionCodec(path, format) + assert(relCompressionCodecs.length == 1) + val tableSize = getTableSize(path) + assertion(relCompressionCodecs.head, tableSize) + } + } + } + + private def checkTableCompressionCodecForCodecs( + format: String, + isPartitioned: Boolean, + convertMetastore: Boolean, + usingCTAS: Boolean, + compressionCodecs: List[String], + tableCompressionCodecs: List[String]) + (assertionCompressionCodec: (Option[String], String, String, Long) => Unit): Unit = { + withSQLConf(getConvertMetastoreConfName(format) -> convertMetastore.toString) { + tableCompressionCodecs.foreach { tableCompression => + compressionCodecs.foreach { sessionCompressionCodec => + withSQLConf(getSparkCompressionConfName(format) -> sessionCompressionCodec) { + // 'tableCompression = null' means no table-level compression + val compression = Option(tableCompression) + checkCompressionCodecForTable(format, isPartitioned, compression, usingCTAS) { + case (realCompressionCodec, tableSize) => + assertionCompressionCodec( + compression, sessionCompressionCodec, realCompressionCodec, tableSize) + } + } + } + } + } + } + + // When the amount of data is small, compressed data size may be larger than uncompressed one, + // so we just check the difference when compressionCodec is not NONE or UNCOMPRESSED. + private def checkTableSize( + format: String, + compressionCodec: String, + isPartitioned: Boolean, + convertMetastore: Boolean, + usingCTAS: Boolean, + tableSize: Long): Boolean = { + val uncompressedSize = getUncompressedDataSizeByFormat(format, isPartitioned, usingCTAS) + compressionCodec match { + case "UNCOMPRESSED" if format == "parquet" => tableSize == uncompressedSize + case "NONE" if format == "orc" => tableSize == uncompressedSize + case _ => tableSize != uncompressedSize + } + } + + def checkForTableWithCompressProp(format: String, compressCodecs: List[String]): Unit = { + Seq(true, false).foreach { isPartitioned => + Seq(true, false).foreach { convertMetastore => + // TODO: Also verify CTAS(usingCTAS=true) cases when the bug(SPARK-22926) is fixed. + Seq(false).foreach { usingCTAS => + checkTableCompressionCodecForCodecs( + format, + isPartitioned, + convertMetastore, + usingCTAS, + compressionCodecs = compressCodecs, + tableCompressionCodecs = compressCodecs) { + case (tableCodec, sessionCodec, realCodec, tableSize) => + // For non-partitioned table and when convertMetastore is true, Expect session-level + // take effect, and in other cases expect table-level take effect + // TODO: It should always be table-level taking effect when the bug(SPARK-22926) + // is fixed + val expectCodec = + if (convertMetastore && !isPartitioned) sessionCodec else tableCodec.get + assert(expectCodec == realCodec) + assert(checkTableSize( + format, expectCodec, isPartitioned, convertMetastore, usingCTAS, tableSize)) + } + } + } + } + } + + def checkForTableWithoutCompressProp(format: String, compressCodecs: List[String]): Unit = { + Seq(true, false).foreach { isPartitioned => + Seq(true, false).foreach { convertMetastore => + // TODO: Also verify CTAS(usingCTAS=true) cases when the bug(SPARK-22926) is fixed. + Seq(false).foreach { usingCTAS => + checkTableCompressionCodecForCodecs( + format, + isPartitioned, + convertMetastore, + usingCTAS, + compressionCodecs = compressCodecs, + tableCompressionCodecs = List(null)) { + case (tableCodec, sessionCodec, realCodec, tableSize) => + // Always expect session-level take effect + assert(sessionCodec == realCodec) + assert(checkTableSize( + format, sessionCodec, isPartitioned, convertMetastore, usingCTAS, tableSize)) + } + } + } + } + } + + test("both table-level and session-level compression are set") { + checkForTableWithCompressProp("parquet", List("UNCOMPRESSED", "SNAPPY", "GZIP")) + checkForTableWithCompressProp("orc", List("NONE", "SNAPPY", "ZLIB")) + } + + test("table-level compression is not set but session-level compressions is set ") { + checkForTableWithoutCompressProp("parquet", List("UNCOMPRESSED", "SNAPPY", "GZIP")) + checkForTableWithoutCompressProp("orc", List("NONE", "SNAPPY", "ZLIB")) + } + + def checkTableWriteWithCompressionCodecs(format: String, compressCodecs: List[String]): Unit = { + Seq(true, false).foreach { isPartitioned => + Seq(true, false).foreach { convertMetastore => + withTempDir { tmpDir => + val tableName = s"tbl_$format$isPartitioned" + createTable(tmpDir, tableName, isPartitioned, format, None) + withTable(tableName) { + compressCodecs.foreach { compressionCodec => + val partitionValue = if (isPartitioned) Some(compressionCodec) else None + withSQLConf(getConvertMetastoreConfName(format) -> convertMetastore.toString, + getSparkCompressionConfName(format) -> compressionCodec + ) { writeDataToTable(tableName, partitionValue) } + } + val tablePath = getTablePartitionPath(tmpDir, tableName, None) + val realCompressionCodecs = + if (isPartitioned) compressCodecs.flatMap { codec => + getTableCompressionCodec(s"$tablePath/p=$codec", format) + } else { + getTableCompressionCodec(tablePath, format) + } + + assert(realCompressionCodecs.distinct.sorted == compressCodecs.sorted) + val recordsNum = sql(s"SELECT * from $tableName").count() + assert(recordsNum == maxRecordNum * compressCodecs.length) + } + } + } + } + } + + test("test table containing mixed compression codec") { + checkTableWriteWithCompressionCodecs("parquet", List("UNCOMPRESSED", "SNAPPY", "GZIP")) + checkTableWriteWithCompressionCodecs("orc", List("NONE", "SNAPPY", "ZLIB")) + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogSuite.scala index 2e35fdeba464d..0a522b6a11c80 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogSuite.scala @@ -107,4 +107,10 @@ class HiveExternalCatalogSuite extends ExternalCatalogSuite { .filter(_.contains("Num Buckets")).head assert(bucketString.contains("10")) } + + test("SPARK-23001: NullPointerException when running desc database") { + val catalog = newBasicCatalog() + catalog.createDatabase(newDb("dbWithNullDesc").copy(description = null), ignoreIfExists = false) + assert(catalog.getDatabase("dbWithNullDesc").description == "") + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogVersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogVersionsSuite.scala index a3d5b941a6761..514921875f1f9 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogVersionsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogVersionsSuite.scala @@ -18,11 +18,14 @@ package org.apache.spark.sql.hive import java.io.File -import java.nio.file.Files +import java.nio.charset.StandardCharsets +import java.nio.file.{Files, Paths} import scala.sys.process._ -import org.apache.spark.TestUtils +import org.apache.hadoop.conf.Configuration + +import org.apache.spark.{SecurityManager, SparkConf, TestUtils} import org.apache.spark.sql.{QueryTest, Row, SparkSession} import org.apache.spark.sql.catalyst.TableIdentifier import org.apache.spark.sql.catalyst.catalog.CatalogTableType @@ -55,34 +58,67 @@ class HiveExternalCatalogVersionsSuite extends SparkSubmitTestUtils { private def tryDownloadSpark(version: String, path: String): Unit = { // Try mirrors a few times until one succeeds for (i <- 0 until 3) { + // we don't retry on a failure to get mirror url. If we can't get a mirror url, + // the test fails (getStringFromUrl will throw an exception) val preferredMirror = - Seq("wget", "https://www.apache.org/dyn/closer.lua?preferred=true", "-q", "-O", "-").!!.trim - val url = s"$preferredMirror/spark/spark-$version/spark-$version-bin-hadoop2.7.tgz" + getStringFromUrl("https://www.apache.org/dyn/closer.lua?preferred=true") + val filename = s"spark-$version-bin-hadoop2.7.tgz" + val url = s"$preferredMirror/spark/spark-$version/$filename" logInfo(s"Downloading Spark $version from $url") - if (Seq("wget", url, "-q", "-P", path).! == 0) { - return + try { + getFileFromUrl(url, path, filename) + val downloaded = new File(sparkTestingDir, filename).getCanonicalPath + val targetDir = new File(sparkTestingDir, s"spark-$version").getCanonicalPath + + Seq("mkdir", targetDir).! + val exitCode = Seq("tar", "-xzf", downloaded, "-C", targetDir, "--strip-components=1").! + Seq("rm", downloaded).! + + // For a corrupted file, `tar` returns non-zero values. However, we also need to check + // the extracted file because `tar` returns 0 for empty file. + val sparkSubmit = new File(sparkTestingDir, s"spark-$version/bin/spark-submit") + if (exitCode == 0 && sparkSubmit.exists()) { + return + } else { + Seq("rm", "-rf", targetDir).! + } + } catch { + case ex: Exception => logWarning(s"Failed to download Spark $version from $url", ex) } - logWarning(s"Failed to download Spark $version from $url") } fail(s"Unable to download Spark $version") } + private def genDataDir(name: String): String = { + new File(tmpDataDir, name).getCanonicalPath + } - private def downloadSpark(version: String): Unit = { - tryDownloadSpark(version, sparkTestingDir.getCanonicalPath) + private def getFileFromUrl(urlString: String, targetDir: String, filename: String): Unit = { + val conf = new SparkConf + // if the caller passes the name of an existing file, we want doFetchFile to write over it with + // the contents from the specified url. + conf.set("spark.files.overwrite", "true") + val securityManager = new SecurityManager(conf) + val hadoopConf = new Configuration - val downloaded = new File(sparkTestingDir, s"spark-$version-bin-hadoop2.7.tgz").getCanonicalPath - val targetDir = new File(sparkTestingDir, s"spark-$version").getCanonicalPath + val outDir = new File(targetDir) + if (!outDir.exists()) { + outDir.mkdirs() + } - Seq("mkdir", targetDir).! + // propagate exceptions up to the caller of getFileFromUrl + Utils.doFetchFile(urlString, outDir, filename, conf, securityManager, hadoopConf) + } - Seq("tar", "-xzf", downloaded, "-C", targetDir, "--strip-components=1").! + private def getStringFromUrl(urlString: String): String = { + val contentFile = File.createTempFile("string-", ".txt") + contentFile.deleteOnExit() - Seq("rm", downloaded).! - } + // exceptions will propagate to the caller of getStringFromUrl + getFileFromUrl(urlString, contentFile.getParent, contentFile.getName) - private def genDataDir(name: String): String = { - new File(tmpDataDir, name).getCanonicalPath + val contentPath = Paths.get(contentFile.toURI) + new String(Files.readAllBytes(contentPath), StandardCharsets.UTF_8) } override def beforeAll(): Unit = { @@ -125,7 +161,7 @@ class HiveExternalCatalogVersionsSuite extends SparkSubmitTestUtils { PROCESS_TABLES.testingVersions.zipWithIndex.foreach { case (version, index) => val sparkHome = new File(sparkTestingDir, s"spark-$version") if (!sparkHome.exists()) { - downloadSpark(version) + tryDownloadSpark(version, sparkTestingDir.getCanonicalPath) } val args = Seq( @@ -159,7 +195,7 @@ class HiveExternalCatalogVersionsSuite extends SparkSubmitTestUtils { object PROCESS_TABLES extends QueryTest with SQLTestUtils { // Tests the latest version of every release line. - val testingVersions = Seq("2.0.2", "2.1.2", "2.2.0") + val testingVersions = Seq("2.0.2", "2.1.2", "2.2.1", "2.3.1") protected var spark: SparkSession = _ @@ -213,7 +249,7 @@ object PROCESS_TABLES extends QueryTest with SQLTestUtils { // SPARK-22356: overlapped columns between data and partition schema in data source tables val tbl_with_col_overlap = s"tbl_with_col_overlap_$index" - // For Spark 2.2.0 and 2.1.x, the behavior is different from Spark 2.0. + // For Spark 2.2.0 and 2.1.x, the behavior is different from Spark 2.0, 2.2.1, 2.3+ if (testingVersions(index).startsWith("2.1") || testingVersions(index) == "2.2.0") { spark.sql("msck repair table " + tbl_with_col_overlap) assert(spark.table(tbl_with_col_overlap).columns === Array("i", "j", "p")) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala index 18137e7ea1d63..ba9b944e4a055 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala @@ -146,6 +146,12 @@ class DataSourceWithHiveMetastoreCatalogSuite 'id cast StringType as 'd2 ).coalesce(1) + override def beforeAll(): Unit = { + super.beforeAll() + sparkSession.sessionState.catalog.reset() + sparkSession.metadataHive.reset() + } + Seq( "parquet" -> (( "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat", diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSessionStateSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSessionStateSuite.scala index 958ad3e1c3ce8..3d1a0b054dc31 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSessionStateSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSessionStateSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.hive -import org.scalatest.BeforeAndAfterEach +import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.spark.sql._ import org.apache.spark.sql.hive.test.TestHiveSingleton @@ -25,8 +25,7 @@ import org.apache.spark.sql.hive.test.TestHiveSingleton /** * Run all tests from `SessionStateSuite` with a Hive based `SessionState`. */ -class HiveSessionStateSuite extends SessionStateSuite - with TestHiveSingleton with BeforeAndAfterEach { +class HiveSessionStateSuite extends SessionStateSuite with TestHiveSingleton { override def beforeAll(): Unit = { // Reuse the singleton session @@ -38,4 +37,15 @@ class HiveSessionStateSuite extends SessionStateSuite activeSession = null super.afterAll() } + + test("Clone then newSession") { + val sparkSession = hiveContext.sparkSession + val conf = sparkSession.sparkContext.hadoopConfiguration + val oldValue = conf.get(ConfVars.METASTORECONNECTURLKEY.varname) + sparkSession.cloneSession() + sparkSession.sharedState.externalCatalog.client.newSession() + val newValue = conf.get(ConfVars.METASTORECONNECTURLKEY.varname) + assert(oldValue == newValue, + "cloneSession and then newSession should not affect the Derby directory") + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveUtilsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveUtilsSuite.scala index fdbfcf1a68440..f2b75e4b23f02 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveUtilsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveUtilsSuite.scala @@ -17,11 +17,16 @@ package org.apache.spark.sql.hive +import java.net.URL + import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.spark.SparkConf +import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.sql.QueryTest import org.apache.spark.sql.hive.test.TestHiveSingleton -import org.apache.spark.sql.test.SQLTestUtils +import org.apache.spark.sql.test.{ExamplePoint, ExamplePointUDT, SQLTestUtils} +import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader} class HiveUtilsSuite extends QueryTest with SQLTestUtils with TestHiveSingleton { @@ -42,4 +47,25 @@ class HiveUtilsSuite extends QueryTest with SQLTestUtils with TestHiveSingleton assert(hiveConf("foo") === "bar") } } + + test("ChildFirstURLClassLoader's parent is null, get spark classloader instead") { + val conf = new SparkConf + val contextClassLoader = Thread.currentThread().getContextClassLoader + val loader = new ChildFirstURLClassLoader(Array(), contextClassLoader) + try { + Thread.currentThread().setContextClassLoader(loader) + HiveUtils.newClientForMetadata( + conf, + SparkHadoopUtil.newConfiguration(conf), + HiveUtils.newTemporaryConfiguration(useInMemoryDerby = true)) + } finally { + Thread.currentThread().setContextClassLoader(contextClassLoader) + } + } + + test("toHiveString correctly handles UDTs") { + val point = new ExamplePoint(50.0, 50.0) + val tpe = new ExamplePointUDT() + assert(HiveUtils.toHiveString((point, tpe)) === "(50.0, 50.0)") + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala index c8caba83bf365..d7e2b575da08e 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala @@ -23,14 +23,12 @@ import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.fs.Path -import org.apache.spark.SparkContext import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.TableIdentifier import org.apache.spark.sql.catalyst.catalog.{CatalogStorageFormat, CatalogTable, CatalogTableType} import org.apache.spark.sql.execution.command.CreateTableCommand import org.apache.spark.sql.execution.datasources.{HadoopFsRelation, LogicalRelation} import org.apache.spark.sql.hive.HiveExternalCatalog._ -import org.apache.spark.sql.hive.client.HiveClient import org.apache.spark.sql.hive.test.TestHiveSingleton import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.internal.StaticSQLConf._ @@ -593,7 +591,7 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv } test("Pre insert nullability check (ArrayType)") { - withTable("arrayInParquet") { + withTable("array") { { val df = (Tuple1(Seq(Int.box(1), null: Integer)) :: Nil).toDF("a") val expectedSchema = @@ -606,9 +604,8 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv assert(df.schema === expectedSchema) df.write - .format("parquet") .mode(SaveMode.Overwrite) - .saveAsTable("arrayInParquet") + .saveAsTable("array") } { @@ -623,25 +620,24 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv assert(df.schema === expectedSchema) df.write - .format("parquet") .mode(SaveMode.Append) - .insertInto("arrayInParquet") + .insertInto("array") } (Tuple1(Seq(4, 5)) :: Nil).toDF("a") .write .mode(SaveMode.Append) - .saveAsTable("arrayInParquet") // This one internally calls df2.insertInto. + .saveAsTable("array") // This one internally calls df2.insertInto. (Tuple1(Seq(Int.box(6), null: Integer)) :: Nil).toDF("a") .write .mode(SaveMode.Append) - .saveAsTable("arrayInParquet") + .saveAsTable("array") - sparkSession.catalog.refreshTable("arrayInParquet") + sparkSession.catalog.refreshTable("array") checkAnswer( - sql("SELECT a FROM arrayInParquet"), + sql("SELECT a FROM array"), Row(ArrayBuffer(1, null)) :: Row(ArrayBuffer(2, 3)) :: Row(ArrayBuffer(4, 5)) :: @@ -650,7 +646,7 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv } test("Pre insert nullability check (MapType)") { - withTable("mapInParquet") { + withTable("map") { { val df = (Tuple1(Map(1 -> (null: Integer))) :: Nil).toDF("a") val expectedSchema = @@ -663,9 +659,8 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv assert(df.schema === expectedSchema) df.write - .format("parquet") .mode(SaveMode.Overwrite) - .saveAsTable("mapInParquet") + .saveAsTable("map") } { @@ -680,27 +675,24 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv assert(df.schema === expectedSchema) df.write - .format("parquet") .mode(SaveMode.Append) - .insertInto("mapInParquet") + .insertInto("map") } (Tuple1(Map(4 -> 5)) :: Nil).toDF("a") .write - .format("parquet") .mode(SaveMode.Append) - .saveAsTable("mapInParquet") // This one internally calls df2.insertInto. + .saveAsTable("map") // This one internally calls df2.insertInto. (Tuple1(Map(6 -> null.asInstanceOf[Integer])) :: Nil).toDF("a") .write - .format("parquet") .mode(SaveMode.Append) - .saveAsTable("mapInParquet") + .saveAsTable("map") - sparkSession.catalog.refreshTable("mapInParquet") + sparkSession.catalog.refreshTable("map") checkAnswer( - sql("SELECT a FROM mapInParquet"), + sql("SELECT a FROM map"), Row(Map(1 -> null)) :: Row(Map(2 -> 3)) :: Row(Map(4 -> 5)) :: @@ -854,52 +846,52 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv (from to to).map(i => i -> s"str$i").toDF("c1", "c2") } - withTable("insertParquet") { - createDF(0, 9).write.format("parquet").saveAsTable("insertParquet") + withTable("t") { + createDF(0, 9).write.saveAsTable("t") checkAnswer( - sql("SELECT p.c1, p.c2 FROM insertParquet p WHERE p.c1 > 5"), + sql("SELECT p.c1, p.c2 FROM t p WHERE p.c1 > 5"), (6 to 9).map(i => Row(i, s"str$i"))) intercept[AnalysisException] { - createDF(10, 19).write.format("parquet").saveAsTable("insertParquet") + createDF(10, 19).write.saveAsTable("t") } - createDF(10, 19).write.mode(SaveMode.Append).format("parquet").saveAsTable("insertParquet") + createDF(10, 19).write.mode(SaveMode.Append).saveAsTable("t") checkAnswer( - sql("SELECT p.c1, p.c2 FROM insertParquet p WHERE p.c1 > 5"), + sql("SELECT p.c1, p.c2 FROM t p WHERE p.c1 > 5"), (6 to 19).map(i => Row(i, s"str$i"))) - createDF(20, 29).write.mode(SaveMode.Append).format("parquet").saveAsTable("insertParquet") + createDF(20, 29).write.mode(SaveMode.Append).saveAsTable("t") checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 25"), + sql("SELECT p.c1, c2 FROM t p WHERE p.c1 > 5 AND p.c1 < 25"), (6 to 24).map(i => Row(i, s"str$i"))) intercept[AnalysisException] { - createDF(30, 39).write.saveAsTable("insertParquet") + createDF(30, 39).write.saveAsTable("t") } - createDF(30, 39).write.mode(SaveMode.Append).saveAsTable("insertParquet") + createDF(30, 39).write.mode(SaveMode.Append).saveAsTable("t") checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 35"), + sql("SELECT p.c1, c2 FROM t p WHERE p.c1 > 5 AND p.c1 < 35"), (6 to 34).map(i => Row(i, s"str$i"))) - createDF(40, 49).write.mode(SaveMode.Append).insertInto("insertParquet") + createDF(40, 49).write.mode(SaveMode.Append).insertInto("t") checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 5 AND p.c1 < 45"), + sql("SELECT p.c1, c2 FROM t p WHERE p.c1 > 5 AND p.c1 < 45"), (6 to 44).map(i => Row(i, s"str$i"))) - createDF(50, 59).write.mode(SaveMode.Overwrite).saveAsTable("insertParquet") + createDF(50, 59).write.mode(SaveMode.Overwrite).saveAsTable("t") checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p WHERE p.c1 > 51 AND p.c1 < 55"), + sql("SELECT p.c1, c2 FROM t p WHERE p.c1 > 51 AND p.c1 < 55"), (52 to 54).map(i => Row(i, s"str$i"))) - createDF(60, 69).write.mode(SaveMode.Ignore).saveAsTable("insertParquet") + createDF(60, 69).write.mode(SaveMode.Ignore).saveAsTable("t") checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p"), + sql("SELECT p.c1, c2 FROM t p"), (50 to 59).map(i => Row(i, s"str$i"))) - createDF(70, 79).write.mode(SaveMode.Overwrite).insertInto("insertParquet") + createDF(70, 79).write.mode(SaveMode.Overwrite).insertInto("t") checkAnswer( - sql("SELECT p.c1, c2 FROM insertParquet p"), + sql("SELECT p.c1, c2 FROM t p"), (70 to 79).map(i => Row(i, s"str$i"))) } } @@ -1344,18 +1336,6 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv } } - Seq("orc", "parquet", "csv", "json", "text").foreach { format => - test(s"SPARK-22146: read files containing special characters using $format") { - val nameWithSpecialChars = s"sp&cial%chars" - withTempDir { dir => - val tmpFile = s"$dir/$nameWithSpecialChars" - spark.createDataset(Seq("a", "b")).write.format(format).save(tmpFile) - val fileContent = spark.read.format(format).load(tmpFile) - checkAnswer(fileContent, Seq(Row("a"), Row("b"))) - } - } - } - private def withDebugMode(f: => Unit): Unit = { val previousValue = sparkSession.sparkContext.conf.get(DEBUG_MODE) try { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/PartitionProviderCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/PartitionProviderCompatibilitySuite.scala index 9440a17677ebf..80afc9d8f44bc 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/PartitionProviderCompatibilitySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/PartitionProviderCompatibilitySuite.scala @@ -37,11 +37,11 @@ class PartitionProviderCompatibilitySuite spark.range(5).selectExpr("id as fieldOne", "id as partCol").write .partitionBy("partCol") .mode("overwrite") - .parquet(dir.getAbsolutePath) + .save(dir.getAbsolutePath) spark.sql(s""" |create table $tableName (fieldOne long, partCol int) - |using parquet + |using ${spark.sessionState.conf.defaultDataSourceName} |options (path "${dir.toURI}") |partitioned by (partCol)""".stripMargin) } @@ -358,7 +358,7 @@ class PartitionProviderCompatibilitySuite try { spark.sql(s""" |create table test (id long, P1 int, P2 int) - |using parquet + |using ${spark.sessionState.conf.defaultDataSourceName} |options (path "${base.toURI}") |partitioned by (P1, P2)""".stripMargin) spark.sql(s"alter table test add partition (P1=0, P2=0) location '${a.toURI}'") diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/PartitionedTablePerfStatsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/PartitionedTablePerfStatsSuite.scala index 54d3962a46b4d..1a86c604d5da3 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/PartitionedTablePerfStatsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/PartitionedTablePerfStatsSuite.scala @@ -417,7 +417,7 @@ class PartitionedTablePerfStatsSuite import spark.implicits._ Seq(1).toDF("a").write.mode("overwrite").save(dir.getAbsolutePath) HiveCatalogMetrics.reset() - spark.read.parquet(dir.getAbsolutePath) + spark.read.load(dir.getAbsolutePath) assert(HiveCatalogMetrics.METRIC_FILES_DISCOVERED.getCount() == 1) assert(HiveCatalogMetrics.METRIC_FILE_CACHE_HITS.getCount() == 1) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala index 94473a08dd317..e64389e56b5a1 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala @@ -163,6 +163,15 @@ class VersionsSuite extends SparkFunSuite with Logging { client.createDatabase(tempDB, ignoreIfExists = true) } + test(s"$version: createDatabase with null description") { + withTempDir { tmpDir => + val dbWithNullDesc = + CatalogDatabase("dbWithNullDesc", description = null, tmpDir.toURI, Map()) + client.createDatabase(dbWithNullDesc, ignoreIfExists = true) + assert(client.getDatabase("dbWithNullDesc").description == "") + } + } + test(s"$version: setCurrentDatabase") { client.setCurrentDatabase("default") } @@ -802,7 +811,7 @@ class VersionsSuite extends SparkFunSuite with Logging { test(s"$version: read avro file containing decimal") { val url = Thread.currentThread().getContextClassLoader.getResource("avroDecimal") - val location = new File(url.getFile) + val location = new File(url.getFile).toURI.toString val tableName = "tab1" val avroSchema = @@ -842,6 +851,8 @@ class VersionsSuite extends SparkFunSuite with Logging { } test(s"$version: SPARK-17920: Insert into/overwrite avro table") { + // skipped because it's failed in the condition on Windows + assume(!(Utils.isWindows && version == "0.12")) withTempDir { dir => val avroSchema = """ @@ -866,10 +877,10 @@ class VersionsSuite extends SparkFunSuite with Logging { val writer = new PrintWriter(schemaFile) writer.write(avroSchema) writer.close() - val schemaPath = schemaFile.getCanonicalPath + val schemaPath = schemaFile.toURI.toString val url = Thread.currentThread().getContextClassLoader.getResource("avroDecimal") - val srcLocation = new File(url.getFile).getCanonicalPath + val srcLocation = new File(url.getFile).toURI.toString val destTableName = "tab1" val srcTableName = "tab2" diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala index f2e0c695ca38b..db76ec9d084cb 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala @@ -875,12 +875,13 @@ class HiveDDLSuite test("desc table for Hive table - bucketed + sorted table") { withTable("tbl") { - sql(s""" - CREATE TABLE tbl (id int, name string) - PARTITIONED BY (ds string) - CLUSTERED BY(id) - SORTED BY(id, name) INTO 1024 BUCKETS - """) + sql( + s""" + |CREATE TABLE tbl (id int, name string) + |CLUSTERED BY(id) + |SORTED BY(id, name) INTO 1024 BUCKETS + |PARTITIONED BY (ds string) + """.stripMargin) val x = sql("DESC FORMATTED tbl").collect() assert(x.containsSlice( @@ -1657,8 +1658,8 @@ class HiveDDLSuite Seq(5 -> "e").toDF("i", "j") .write.format("hive").mode("append").saveAsTable("t1") } - assert(e.message.contains("The format of the existing table default.t1 is " + - "`ParquetFileFormat`. It doesn't match the specified format `HiveFileFormat`.")) + assert(e.message.contains("The format of the existing table default.t1 is ")) + assert(e.message.contains("It doesn't match the specified format `HiveFileFormat`.")) } } @@ -1708,11 +1709,12 @@ class HiveDDLSuite spark.sessionState.catalog.getTableMetadata(TableIdentifier(tblName)).schema.map(_.name) } + val provider = spark.sessionState.conf.defaultDataSourceName withTable("t", "t1", "t2", "t3", "t4", "t5", "t6") { - sql("CREATE TABLE t(a int, b int, c int, d int) USING parquet PARTITIONED BY (d, b)") + sql(s"CREATE TABLE t(a int, b int, c int, d int) USING $provider PARTITIONED BY (d, b)") assert(getTableColumns("t") == Seq("a", "c", "d", "b")) - sql("CREATE TABLE t1 USING parquet PARTITIONED BY (d, b) AS SELECT 1 a, 1 b, 1 c, 1 d") + sql(s"CREATE TABLE t1 USING $provider PARTITIONED BY (d, b) AS SELECT 1 a, 1 b, 1 c, 1 d") assert(getTableColumns("t1") == Seq("a", "c", "d", "b")) Seq((1, 1, 1, 1)).toDF("a", "b", "c", "d").write.partitionBy("d", "b").saveAsTable("t2") @@ -1722,7 +1724,7 @@ class HiveDDLSuite val dataPath = new File(new File(path, "d=1"), "b=1").getCanonicalPath Seq(1 -> 1).toDF("a", "c").write.save(dataPath) - sql(s"CREATE TABLE t3 USING parquet LOCATION '${path.toURI}'") + sql(s"CREATE TABLE t3 USING $provider LOCATION '${path.toURI}'") assert(getTableColumns("t3") == Seq("a", "c", "d", "b")) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala index dfabf1ec2a22a..5d56f89c2271c 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala @@ -128,40 +128,39 @@ class HiveExplainSuite extends QueryTest with SQLTestUtils with TestHiveSingleto "src") } - test("SPARK-17409: The EXPLAIN output of CTAS only shows the analyzed plan") { - withTempView("jt") { - val ds = (1 to 10).map(i => s"""{"a":$i, "b":"str$i"}""").toDS() - spark.read.json(ds).createOrReplaceTempView("jt") - val outputs = sql( - s""" - |EXPLAIN EXTENDED - |CREATE TABLE t1 - |AS - |SELECT * FROM jt - """.stripMargin).collect().map(_.mkString).mkString - - val shouldContain = - "== Parsed Logical Plan ==" :: "== Analyzed Logical Plan ==" :: "Subquery" :: - "== Optimized Logical Plan ==" :: "== Physical Plan ==" :: - "CreateHiveTableAsSelect" :: "InsertIntoHiveTable" :: "jt" :: Nil - for (key <- shouldContain) { - assert(outputs.contains(key), s"$key doesn't exist in result") - } - - val physicalIndex = outputs.indexOf("== Physical Plan ==") - assert(outputs.substring(physicalIndex).contains("Subquery"), - "Physical Plan should contain SubqueryAlias since the query should not be optimized") - } + test("explain output of physical plan should contain proper codegen stage ID") { + checkKeywordsExist(sql( + """ + |EXPLAIN SELECT t1.id AS a, t2.id AS b FROM + |(SELECT * FROM range(3)) t1 JOIN + |(SELECT * FROM range(10)) t2 ON t1.id == t2.id % 3 + """.stripMargin), + "== Physical Plan ==", + "*(2) Project ", + "+- *(2) BroadcastHashJoin ", + " :- BroadcastExchange ", + " : +- *(1) Range ", + " +- *(2) Range " + ) } test("EXPLAIN CODEGEN command") { - checkKeywordsExist(sql("EXPLAIN CODEGEN SELECT 1"), - "WholeStageCodegen", - "Generated code:", - "/* 001 */ public Object generate(Object[] references) {", - "/* 002 */ return new GeneratedIterator(references);", - "/* 003 */ }" - ) + // the generated class name in this test should stay in sync with + // org.apache.spark.sql.execution.WholeStageCodegenExec.generatedClassName() + for ((useIdInClassName, expectedClassName) <- Seq( + ("true", "GeneratedIteratorForCodegenStage1"), + ("false", "GeneratedIterator"))) { + withSQLConf( + SQLConf.WHOLESTAGE_CODEGEN_USE_ID_IN_CLASS_NAME.key -> useIdInClassName) { + checkKeywordsExist(sql("EXPLAIN CODEGEN SELECT 1"), + "WholeStageCodegen", + "Generated code:", + "/* 001 */ public Object generate(Object[] references) {", + s"/* 002 */ return new $expectedClassName(references);", + "/* 003 */ }" + ) + } + } checkKeywordsNotExist(sql("EXPLAIN CODEGEN SELECT 1"), "== Physical Plan ==" @@ -171,4 +170,21 @@ class HiveExplainSuite extends QueryTest with SQLTestUtils with TestHiveSingleto sql("EXPLAIN EXTENDED CODEGEN SELECT 1") } } + + test("SPARK-23021 AnalysisBarrier should not cut off explain output for parsed logical plans") { + val df = Seq((1, 1)).toDF("a", "b").groupBy("a").count().limit(1) + val outputStream = new java.io.ByteArrayOutputStream() + Console.withOut(outputStream) { + df.explain(true) + } + assert(outputStream.toString.replaceAll("""#\d+""", "#0").contains( + s"""== Parsed Logical Plan == + |GlobalLimit 1 + |+- LocalLimit 1 + | +- AnalysisBarrier + | +- Aggregate [a#0], [a#0, count(1) AS count#0L] + | +- Project [_1#0 AS a#0, _2#0 AS b#0] + | +- LocalRelation [_1#0, _2#0] + |""".stripMargin)) + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala index 1c9f00141ae1d..d7752e987cb4b 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala @@ -100,6 +100,25 @@ class HiveSerDeSuite extends HiveComparisonTest with PlanTest with BeforeAndAfte assert(output == Some("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat")) assert(serde == Some("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe")) } + + withSQLConf("hive.default.fileformat" -> "orc") { + val (desc, exists) = extractTableDesc( + "CREATE TABLE IF NOT EXISTS fileformat_test (id int) STORED AS textfile") + assert(exists) + assert(desc.storage.inputFormat == Some("org.apache.hadoop.mapred.TextInputFormat")) + assert(desc.storage.outputFormat == + Some("org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat")) + assert(desc.storage.serde == Some("org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe")) + } + + withSQLConf("hive.default.fileformat" -> "orc") { + val (desc, exists) = extractTableDesc( + "CREATE TABLE IF NOT EXISTS fileformat_test (id int) STORED AS sequencefile") + assert(exists) + assert(desc.storage.inputFormat == Some("org.apache.hadoop.mapred.SequenceFileInputFormat")) + assert(desc.storage.outputFormat == Some("org.apache.hadoop.mapred.SequenceFileOutputFormat")) + assert(desc.storage.serde == Some("org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe")) + } } test("create hive serde table with new syntax - basic") { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDAFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDAFSuite.scala index 8986fb58c6460..7402c9626873c 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDAFSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDAFSuite.scala @@ -49,8 +49,12 @@ class HiveUDAFSuite extends QueryTest with TestHiveSingleton with SQLTestUtils { } protected override def afterAll(): Unit = { - sql(s"DROP TEMPORARY FUNCTION IF EXISTS mock") - sql(s"DROP TEMPORARY FUNCTION IF EXISTS hive_max") + try { + sql(s"DROP TEMPORARY FUNCTION IF EXISTS mock") + sql(s"DROP TEMPORARY FUNCTION IF EXISTS hive_max") + } finally { + super.afterAll() + } } test("built-in Hive UDAF") { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/Hive_2_1_DDLSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/Hive_2_1_DDLSuite.scala index bc828877e35ec..eaedac1fa95d8 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/Hive_2_1_DDLSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/Hive_2_1_DDLSuite.scala @@ -74,7 +74,11 @@ class Hive_2_1_DDLSuite extends SparkFunSuite with TestHiveSingleton with Before } override def afterAll(): Unit = { - catalog = null + try { + catalog = null + } finally { + super.afterAll() + } } test("SPARK-21617: ALTER TABLE for non-compatible DataSource tables") { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ObjectHashAggregateSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ObjectHashAggregateSuite.scala index 9eaf44c043c71..8dbcd24cd78de 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ObjectHashAggregateSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ObjectHashAggregateSuite.scala @@ -47,7 +47,11 @@ class ObjectHashAggregateSuite } protected override def afterAll(): Unit = { - sql(s"DROP TEMPORARY FUNCTION IF EXISTS hive_max") + try { + sql(s"DROP TEMPORARY FUNCTION IF EXISTS hive_max") + } finally { + super.afterAll() + } } test("typed_count without grouping keys") { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 07ae3ae945848..081d854d771a7 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -461,75 +461,74 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton { } test("CTAS without serde without location") { - val originalConf = sessionState.conf.convertCTAS - - setConf(SQLConf.CONVERT_CTAS, true) - - val defaultDataSource = sessionState.conf.defaultDataSourceName - try { - sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value") - sql("CREATE TABLE IF NOT EXISTS ctas1 AS SELECT key k, value FROM src ORDER BY k, value") - val message = intercept[AnalysisException] { + withSQLConf(SQLConf.CONVERT_CTAS.key -> "true") { + val defaultDataSource = sessionState.conf.defaultDataSourceName + withTable("ctas1") { sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value") - }.getMessage - assert(message.contains("already exists")) - checkRelation("ctas1", true, defaultDataSource) - sql("DROP TABLE ctas1") + sql("CREATE TABLE IF NOT EXISTS ctas1 AS SELECT key k, value FROM src ORDER BY k, value") + val message = intercept[AnalysisException] { + sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value") + }.getMessage + assert(message.contains("already exists")) + checkRelation("ctas1", isDataSourceTable = true, defaultDataSource) + } // Specifying database name for query can be converted to data source write path // is not allowed right now. - sql("CREATE TABLE default.ctas1 AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", true, defaultDataSource) - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql("CREATE TABLE default.ctas1 AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", isDataSourceTable = true, defaultDataSource) + } - sql("CREATE TABLE ctas1 stored as textfile" + + withTable("ctas1") { + sql("CREATE TABLE ctas1 stored as textfile" + " AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", false, "text") - sql("DROP TABLE ctas1") + checkRelation("ctas1", isDataSourceTable = false, "text") + } - sql("CREATE TABLE ctas1 stored as sequencefile" + - " AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", false, "sequence") - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql("CREATE TABLE ctas1 stored as sequencefile" + + " AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", isDataSourceTable = false, "sequence") + } - sql("CREATE TABLE ctas1 stored as rcfile AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", false, "rcfile") - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql("CREATE TABLE ctas1 stored as rcfile AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", isDataSourceTable = false, "rcfile") + } - sql("CREATE TABLE ctas1 stored as orc AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", false, "orc") - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql("CREATE TABLE ctas1 stored as orc AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation("ctas1", isDataSourceTable = false, "orc") + } - sql("CREATE TABLE ctas1 stored as parquet AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", false, "parquet") - sql("DROP TABLE ctas1") - } finally { - setConf(SQLConf.CONVERT_CTAS, originalConf) - sql("DROP TABLE IF EXISTS ctas1") + withTable("ctas1") { + sql( + """ + |CREATE TABLE ctas1 stored as parquet + |AS SELECT key k, value FROM src ORDER BY k, value + """.stripMargin) + checkRelation("ctas1", isDataSourceTable = false, "parquet") + } } } test("CTAS with default fileformat") { val table = "ctas1" val ctas = s"CREATE TABLE IF NOT EXISTS $table SELECT key k, value FROM src" - withSQLConf(SQLConf.CONVERT_CTAS.key -> "true") { - withSQLConf("hive.default.fileformat" -> "textfile") { + Seq("orc", "parquet").foreach { dataSourceFormat => + withSQLConf( + SQLConf.CONVERT_CTAS.key -> "true", + SQLConf.DEFAULT_DATA_SOURCE_NAME.key -> dataSourceFormat, + "hive.default.fileformat" -> "textfile") { withTable(table) { sql(ctas) - // We should use parquet here as that is the default datasource fileformat. The default - // datasource file format is controlled by `spark.sql.sources.default` configuration. + // The default datasource file format is controlled by `spark.sql.sources.default`. // This testcase verifies that setting `hive.default.fileformat` has no impact on // the target table's fileformat in case of CTAS. - assert(sessionState.conf.defaultDataSourceName === "parquet") - checkRelation(tableName = table, isDataSourceTable = true, format = "parquet") + checkRelation(tableName = table, isDataSourceTable = true, format = dataSourceFormat) } } - withSQLConf("spark.sql.sources.default" -> "orc") { - withTable(table) { - sql(ctas) - checkRelation(tableName = table, isDataSourceTable = true, format = "orc") - } - } } } @@ -539,30 +538,40 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton { val defaultDataSource = sessionState.conf.defaultDataSourceName val tempLocation = dir.toURI.getPath.stripSuffix("/") - sql(s"CREATE TABLE ctas1 LOCATION 'file:$tempLocation/c1'" + - " AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", true, defaultDataSource, Some(s"file:$tempLocation/c1")) - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql(s"CREATE TABLE ctas1 LOCATION 'file:$tempLocation/c1'" + + " AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation( + "ctas1", isDataSourceTable = true, defaultDataSource, Some(s"file:$tempLocation/c1")) + } - sql(s"CREATE TABLE ctas1 LOCATION 'file:$tempLocation/c2'" + - " AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", true, defaultDataSource, Some(s"file:$tempLocation/c2")) - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql(s"CREATE TABLE ctas1 LOCATION 'file:$tempLocation/c2'" + + " AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation( + "ctas1", isDataSourceTable = true, defaultDataSource, Some(s"file:$tempLocation/c2")) + } - sql(s"CREATE TABLE ctas1 stored as textfile LOCATION 'file:$tempLocation/c3'" + - " AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", false, "text", Some(s"file:$tempLocation/c3")) - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql(s"CREATE TABLE ctas1 stored as textfile LOCATION 'file:$tempLocation/c3'" + + " AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation( + "ctas1", isDataSourceTable = false, "text", Some(s"file:$tempLocation/c3")) + } - sql(s"CREATE TABLE ctas1 stored as sequenceFile LOCATION 'file:$tempLocation/c4'" + - " AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", false, "sequence", Some(s"file:$tempLocation/c4")) - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql(s"CREATE TABLE ctas1 stored as sequenceFile LOCATION 'file:$tempLocation/c4'" + + " AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation( + "ctas1", isDataSourceTable = false, "sequence", Some(s"file:$tempLocation/c4")) + } - sql(s"CREATE TABLE ctas1 stored as rcfile LOCATION 'file:$tempLocation/c5'" + - " AS SELECT key k, value FROM src ORDER BY k, value") - checkRelation("ctas1", false, "rcfile", Some(s"file:$tempLocation/c5")) - sql("DROP TABLE ctas1") + withTable("ctas1") { + sql(s"CREATE TABLE ctas1 stored as rcfile LOCATION 'file:$tempLocation/c5'" + + " AS SELECT key k, value FROM src ORDER BY k, value") + checkRelation( + "ctas1", isDataSourceTable = false, "rcfile", Some(s"file:$tempLocation/c5")) + } } } } @@ -2146,11 +2155,34 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton { } } - Seq("orc", "parquet", "csv", "json", "text").foreach { format => - test(s"Writing empty datasets should not fail - $format") { - withTempDir { dir => - Seq("str").toDS.limit(0).write.format(format).save(dir.getCanonicalPath + "/tmp") + test("SPARK-24085 scalar subquery in partitioning expression") { + Seq("orc", "parquet").foreach { format => + Seq(true, false).foreach { isConverted => + withSQLConf( + HiveUtils.CONVERT_METASTORE_ORC.key -> s"$isConverted", + HiveUtils.CONVERT_METASTORE_PARQUET.key -> s"$isConverted", + "hive.exec.dynamic.partition.mode" -> "nonstrict") { + withTable(format) { + withTempPath { tempDir => + sql( + s""" + |CREATE TABLE ${format} (id_value string) + |PARTITIONED BY (id_type string) + |LOCATION '${tempDir.toURI}' + |STORED AS ${format} + """.stripMargin) + sql(s"insert into $format values ('1','a')") + sql(s"insert into $format values ('2','a')") + sql(s"insert into $format values ('3','b')") + sql(s"insert into $format values ('4','b')") + checkAnswer( + sql(s"SELECT * FROM $format WHERE id_type = (SELECT 'b')"), + Row("3", "b") :: Row("4", "b") :: Nil) + } + } + } } } } + } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/HiveOrcQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/HiveOrcQuerySuite.scala index 92b2f069cacd6..597b0f56a55e4 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/HiveOrcQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/HiveOrcQuerySuite.scala @@ -208,4 +208,14 @@ class HiveOrcQuerySuite extends OrcQueryTest with TestHiveSingleton { } } } + + test("SPARK-23340 Empty float/double array columns raise EOFException") { + withSQLConf(HiveUtils.CONVERT_METASTORE_ORC.key -> "false") { + withTable("spark_23340") { + sql("CREATE TABLE spark_23340(a array, b array) STORED AS ORC") + sql("INSERT INTO spark_23340 VALUES (array(), array())") + checkAnswer(spark.table("spark_23340"), Seq(Row(Array.empty[Float], Array.empty[Double]))) + } + } + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/HiveOrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/HiveOrcSourceSuite.scala index 17b7d8cfe127e..d556a030e2186 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/HiveOrcSourceSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/HiveOrcSourceSuite.scala @@ -20,8 +20,10 @@ package org.apache.spark.sql.hive.orc import java.io.File import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.TableIdentifier import org.apache.spark.sql.execution.datasources.orc.OrcSuite import org.apache.spark.sql.hive.test.TestHiveSingleton +import org.apache.spark.sql.internal.HiveSerDe import org.apache.spark.util.Utils class HiveOrcSourceSuite extends OrcSuite with TestHiveSingleton { @@ -62,6 +64,33 @@ class HiveOrcSourceSuite extends OrcSuite with TestHiveSingleton { """.stripMargin) } + test("SPARK-22972: hive orc source") { + val tableName = "normal_orc_as_source_hive" + withTable(tableName) { + sql( + s""" + |CREATE TABLE $tableName + |USING org.apache.spark.sql.hive.orc + |OPTIONS ( + | PATH '${new File(orcTableAsDir.getAbsolutePath).toURI}' + |) + """.stripMargin) + + val tableMetadata = spark.sessionState.catalog.getTableMetadata( + TableIdentifier(tableName)) + assert(tableMetadata.storage.inputFormat == + Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat")) + assert(tableMetadata.storage.outputFormat == + Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat")) + assert(tableMetadata.storage.serde == + Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde")) + assert(HiveSerDe.sourceToSerDe("org.apache.spark.sql.hive.orc") + .equals(HiveSerDe.sourceToSerDe("orc"))) + assert(HiveSerDe.sourceToSerDe("org.apache.spark.sql.orc") + .equals(HiveSerDe.sourceToSerDe("orc"))) + } + } + test("SPARK-19459/SPARK-18220: read char/varchar column written by Hive") { val location = Utils.createTempDir() val uri = location.toURI diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala index f87162f94c01a..ee421c150b644 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala @@ -33,11 +33,10 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest { override val dataSourceName: String = classOf[org.apache.spark.sql.execution.datasources.orc.OrcFileFormat].getCanonicalName - // ORC does not play well with NullType and UDT. + // ORC does not play well with NullType. override protected def supportsDataType(dataType: DataType): Boolean = dataType match { case _: NullType => false case _: CalendarIntervalType => false - case _: UserDefinedType[_] => false case _ => true } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala new file mode 100644 index 0000000000000..bf6efa7c4c08c --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala @@ -0,0 +1,501 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.orc + +import java.io.File + +import scala.util.{Random, Try} + +import org.apache.spark.SparkConf +import org.apache.spark.sql.{DataFrame, SparkSession} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types._ +import org.apache.spark.util.{Benchmark, Utils} + + +/** + * Benchmark to measure ORC read performance. + * + * This is in `sql/hive` module in order to compare `sql/core` and `sql/hive` ORC data sources. + */ +// scalastyle:off line.size.limit +object OrcReadBenchmark { + val conf = new SparkConf() + conf.set("orc.compression", "snappy") + + private val spark = SparkSession.builder() + .master("local[1]") + .appName("OrcReadBenchmark") + .config(conf) + .getOrCreate() + + // Set default configs. Individual cases will change them if necessary. + spark.conf.set(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key, "true") + + def withTempPath(f: File => Unit): Unit = { + val path = Utils.createTempDir() + path.delete() + try f(path) finally Utils.deleteRecursively(path) + } + + def withTempTable(tableNames: String*)(f: => Unit): Unit = { + try f finally tableNames.foreach(spark.catalog.dropTempView) + } + + def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = { + val (keys, values) = pairs.unzip + val currentValues = keys.map(key => Try(spark.conf.get(key)).toOption) + (keys, values).zipped.foreach(spark.conf.set) + try f finally { + keys.zip(currentValues).foreach { + case (key, Some(value)) => spark.conf.set(key, value) + case (key, None) => spark.conf.unset(key) + } + } + } + + private val NATIVE_ORC_FORMAT = classOf[org.apache.spark.sql.execution.datasources.orc.OrcFileFormat].getCanonicalName + private val HIVE_ORC_FORMAT = classOf[org.apache.spark.sql.hive.orc.OrcFileFormat].getCanonicalName + + private def prepareTable(dir: File, df: DataFrame, partition: Option[String] = None): Unit = { + val dirORC = dir.getCanonicalPath + + if (partition.isDefined) { + df.write.partitionBy(partition.get).orc(dirORC) + } else { + df.write.orc(dirORC) + } + + spark.read.format(NATIVE_ORC_FORMAT).load(dirORC).createOrReplaceTempView("nativeOrcTable") + spark.read.format(HIVE_ORC_FORMAT).load(dirORC).createOrReplaceTempView("hiveOrcTable") + } + + def numericScanBenchmark(values: Int, dataType: DataType): Unit = { + val benchmark = new Benchmark(s"SQL Single ${dataType.sql} Column Scan", values) + + withTempPath { dir => + withTempTable("t1", "nativeOrcTable", "hiveOrcTable") { + import spark.implicits._ + spark.range(values).map(_ => Random.nextLong).createOrReplaceTempView("t1") + + prepareTable(dir, spark.sql(s"SELECT CAST(value as ${dataType.sql}) id FROM t1")) + + benchmark.addCase("Native ORC MR") { _ => + withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") { + spark.sql("SELECT sum(id) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Native ORC Vectorized") { _ => + spark.sql("SELECT sum(id) FROM nativeOrcTable").collect() + } + + benchmark.addCase("Native ORC Vectorized with copy") { _ => + withSQLConf(SQLConf.ORC_COPY_BATCH_TO_SPARK.key -> "true") { + spark.sql("SELECT sum(id) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Hive built-in ORC") { _ => + spark.sql("SELECT sum(id) FROM hiveOrcTable").collect() + } + + /* + Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.13.1 + Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz + + SQL Single TINYINT Column Scan: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1135 / 1171 13.9 72.2 1.0X + Native ORC Vectorized 152 / 163 103.4 9.7 7.5X + Native ORC Vectorized with copy 149 / 162 105.4 9.5 7.6X + Hive built-in ORC 1380 / 1384 11.4 87.7 0.8X + + SQL Single SMALLINT Column Scan: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1182 / 1244 13.3 75.2 1.0X + Native ORC Vectorized 145 / 156 108.7 9.2 8.2X + Native ORC Vectorized with copy 148 / 158 106.4 9.4 8.0X + Hive built-in ORC 1591 / 1636 9.9 101.2 0.7X + + SQL Single INT Column Scan: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1271 / 1271 12.4 80.8 1.0X + Native ORC Vectorized 206 / 212 76.3 13.1 6.2X + Native ORC Vectorized with copy 200 / 213 78.8 12.7 6.4X + Hive built-in ORC 1776 / 1787 8.9 112.9 0.7X + + SQL Single BIGINT Column Scan: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1344 / 1355 11.7 85.4 1.0X + Native ORC Vectorized 258 / 268 61.0 16.4 5.2X + Native ORC Vectorized with copy 252 / 257 62.4 16.0 5.3X + Hive built-in ORC 1818 / 1823 8.7 115.6 0.7X + + SQL Single FLOAT Column Scan: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1333 / 1352 11.8 84.8 1.0X + Native ORC Vectorized 310 / 324 50.7 19.7 4.3X + Native ORC Vectorized with copy 312 / 320 50.4 19.9 4.3X + Hive built-in ORC 1904 / 1918 8.3 121.0 0.7X + + SQL Single DOUBLE Column Scan: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1408 / 1585 11.2 89.5 1.0X + Native ORC Vectorized 359 / 368 43.8 22.8 3.9X + Native ORC Vectorized with copy 364 / 371 43.2 23.2 3.9X + Hive built-in ORC 1881 / 1954 8.4 119.6 0.7X + */ + benchmark.run() + } + } + } + + def intStringScanBenchmark(values: Int): Unit = { + val benchmark = new Benchmark("Int and String Scan", values) + + withTempPath { dir => + withTempTable("t1", "nativeOrcTable", "hiveOrcTable") { + import spark.implicits._ + spark.range(values).map(_ => Random.nextLong).createOrReplaceTempView("t1") + + prepareTable( + dir, + spark.sql("SELECT CAST(value AS INT) AS c1, CAST(value as STRING) AS c2 FROM t1")) + + benchmark.addCase("Native ORC MR") { _ => + withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") { + spark.sql("SELECT sum(c1), sum(length(c2)) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Native ORC Vectorized") { _ => + spark.sql("SELECT sum(c1), sum(length(c2)) FROM nativeOrcTable").collect() + } + + benchmark.addCase("Native ORC Vectorized with copy") { _ => + withSQLConf(SQLConf.ORC_COPY_BATCH_TO_SPARK.key -> "true") { + spark.sql("SELECT sum(c1), sum(length(c2)) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Hive built-in ORC") { _ => + spark.sql("SELECT sum(c1), sum(length(c2)) FROM hiveOrcTable").collect() + } + + /* + Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.13.1 + Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz + + Int and String Scan: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 2566 / 2592 4.1 244.7 1.0X + Native ORC Vectorized 1098 / 1113 9.6 104.7 2.3X + Native ORC Vectorized with copy 1527 / 1593 6.9 145.6 1.7X + Hive built-in ORC 3561 / 3705 2.9 339.6 0.7X + */ + benchmark.run() + } + } + } + + def partitionTableScanBenchmark(values: Int): Unit = { + val benchmark = new Benchmark("Partitioned Table", values) + + withTempPath { dir => + withTempTable("t1", "nativeOrcTable", "hiveOrcTable") { + import spark.implicits._ + spark.range(values).map(_ => Random.nextLong).createOrReplaceTempView("t1") + + prepareTable(dir, spark.sql("SELECT value % 2 AS p, value AS id FROM t1"), Some("p")) + + benchmark.addCase("Data column - Native ORC MR") { _ => + withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") { + spark.sql("SELECT sum(id) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Data column - Native ORC Vectorized") { _ => + spark.sql("SELECT sum(id) FROM nativeOrcTable").collect() + } + + benchmark.addCase("Data column - Native ORC Vectorized with copy") { _ => + withSQLConf(SQLConf.ORC_COPY_BATCH_TO_SPARK.key -> "true") { + spark.sql("SELECT sum(id) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Data column - Hive built-in ORC") { _ => + spark.sql("SELECT sum(id) FROM hiveOrcTable").collect() + } + + benchmark.addCase("Partition column - Native ORC MR") { _ => + withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") { + spark.sql("SELECT sum(p) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Partition column - Native ORC Vectorized") { _ => + spark.sql("SELECT sum(p) FROM nativeOrcTable").collect() + } + + benchmark.addCase("Partition column - Native ORC Vectorized with copy") { _ => + withSQLConf(SQLConf.ORC_COPY_BATCH_TO_SPARK.key -> "true") { + spark.sql("SELECT sum(p) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Partition column - Hive built-in ORC") { _ => + spark.sql("SELECT sum(p) FROM hiveOrcTable").collect() + } + + benchmark.addCase("Both columns - Native ORC MR") { _ => + withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") { + spark.sql("SELECT sum(p), sum(id) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Both columns - Native ORC Vectorized") { _ => + spark.sql("SELECT sum(p), sum(id) FROM nativeOrcTable").collect() + } + + benchmark.addCase("Both column - Native ORC Vectorized with copy") { _ => + withSQLConf(SQLConf.ORC_COPY_BATCH_TO_SPARK.key -> "true") { + spark.sql("SELECT sum(p), sum(id) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Both columns - Hive built-in ORC") { _ => + spark.sql("SELECT sum(p), sum(id) FROM hiveOrcTable").collect() + } + + /* + Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.13.1 + Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz + + Partitioned Table: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Data only - Native ORC MR 1447 / 1457 10.9 92.0 1.0X + Data only - Native ORC Vectorized 256 / 266 61.4 16.3 5.6X + Data only - Native ORC Vectorized with copy 263 / 273 59.8 16.7 5.5X + Data only - Hive built-in ORC 1960 / 1988 8.0 124.6 0.7X + Partition only - Native ORC MR 1039 / 1043 15.1 66.0 1.4X + Partition only - Native ORC Vectorized 48 / 53 326.6 3.1 30.1X + Partition only - Native ORC Vectorized with copy 48 / 53 328.4 3.0 30.2X + Partition only - Hive built-in ORC 1234 / 1242 12.7 78.4 1.2X + Both columns - Native ORC MR 1465 / 1475 10.7 93.1 1.0X + Both columns - Native ORC Vectorized 292 / 301 53.9 18.6 5.0X + Both column - Native ORC Vectorized with copy 348 / 354 45.1 22.2 4.2X + Both columns - Hive built-in ORC 2051 / 2060 7.7 130.4 0.7X + */ + benchmark.run() + } + } + } + + def repeatedStringScanBenchmark(values: Int): Unit = { + val benchmark = new Benchmark("Repeated String", values) + + withTempPath { dir => + withTempTable("t1", "nativeOrcTable", "hiveOrcTable") { + spark.range(values).createOrReplaceTempView("t1") + + prepareTable(dir, spark.sql("SELECT CAST((id % 200) + 10000 as STRING) AS c1 FROM t1")) + + benchmark.addCase("Native ORC MR") { _ => + withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") { + spark.sql("SELECT sum(length(c1)) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Native ORC Vectorized") { _ => + spark.sql("SELECT sum(length(c1)) FROM nativeOrcTable").collect() + } + + benchmark.addCase("Native ORC Vectorized with copy") { _ => + withSQLConf(SQLConf.ORC_COPY_BATCH_TO_SPARK.key -> "true") { + spark.sql("SELECT sum(length(c1)) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Hive built-in ORC") { _ => + spark.sql("SELECT sum(length(c1)) FROM hiveOrcTable").collect() + } + + /* + Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.13.1 + Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz + + Repeated String: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1271 / 1278 8.3 121.2 1.0X + Native ORC Vectorized 200 / 212 52.4 19.1 6.4X + Native ORC Vectorized with copy 342 / 347 30.7 32.6 3.7X + Hive built-in ORC 1874 / 2105 5.6 178.7 0.7X + */ + benchmark.run() + } + } + } + + def stringWithNullsScanBenchmark(values: Int, fractionOfNulls: Double): Unit = { + withTempPath { dir => + withTempTable("t1", "nativeOrcTable", "hiveOrcTable") { + spark.range(values).createOrReplaceTempView("t1") + + prepareTable( + dir, + spark.sql( + s"SELECT IF(RAND(1) < $fractionOfNulls, NULL, CAST(id as STRING)) AS c1, " + + s"IF(RAND(2) < $fractionOfNulls, NULL, CAST(id as STRING)) AS c2 FROM t1")) + + val benchmark = new Benchmark(s"String with Nulls Scan ($fractionOfNulls%)", values) + + benchmark.addCase("Native ORC MR") { _ => + withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") { + spark.sql("SELECT SUM(LENGTH(c2)) FROM nativeOrcTable " + + "WHERE c1 IS NOT NULL AND c2 IS NOT NULL").collect() + } + } + + benchmark.addCase("Native ORC Vectorized") { _ => + spark.sql("SELECT SUM(LENGTH(c2)) FROM nativeOrcTable " + + "WHERE c1 IS NOT NULL AND c2 IS NOT NULL").collect() + } + + benchmark.addCase("Native ORC Vectorized with copy") { _ => + withSQLConf(SQLConf.ORC_COPY_BATCH_TO_SPARK.key -> "true") { + spark.sql("SELECT SUM(LENGTH(c2)) FROM nativeOrcTable " + + "WHERE c1 IS NOT NULL AND c2 IS NOT NULL").collect() + } + } + + benchmark.addCase("Hive built-in ORC") { _ => + spark.sql("SELECT SUM(LENGTH(c2)) FROM hiveOrcTable " + + "WHERE c1 IS NOT NULL AND c2 IS NOT NULL").collect() + } + + /* + Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.13.1 + Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz + + String with Nulls Scan (0.0%): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 2394 / 2886 4.4 228.3 1.0X + Native ORC Vectorized 699 / 729 15.0 66.7 3.4X + Native ORC Vectorized with copy 959 / 1025 10.9 91.5 2.5X + Hive built-in ORC 3899 / 3901 2.7 371.9 0.6X + + String with Nulls Scan (0.5%): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 2234 / 2255 4.7 213.1 1.0X + Native ORC Vectorized 854 / 869 12.3 81.4 2.6X + Native ORC Vectorized with copy 1099 / 1128 9.5 104.8 2.0X + Hive built-in ORC 2767 / 2793 3.8 263.9 0.8X + + String with Nulls Scan (0.95%): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1166 / 1202 9.0 111.2 1.0X + Native ORC Vectorized 338 / 345 31.1 32.2 3.5X + Native ORC Vectorized with copy 418 / 428 25.1 39.9 2.8X + Hive built-in ORC 1730 / 1761 6.1 164.9 0.7X + */ + benchmark.run() + } + } + } + + def columnsBenchmark(values: Int, width: Int): Unit = { + val benchmark = new Benchmark(s"Single Column Scan from $width columns", values) + + withTempPath { dir => + withTempTable("t1", "nativeOrcTable", "hiveOrcTable") { + import spark.implicits._ + val middle = width / 2 + val selectExpr = (1 to width).map(i => s"value as c$i") + spark.range(values).map(_ => Random.nextLong).toDF() + .selectExpr(selectExpr: _*).createOrReplaceTempView("t1") + + prepareTable(dir, spark.sql("SELECT * FROM t1")) + + benchmark.addCase("Native ORC MR") { _ => + withSQLConf(SQLConf.ORC_VECTORIZED_READER_ENABLED.key -> "false") { + spark.sql(s"SELECT sum(c$middle) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Native ORC Vectorized") { _ => + spark.sql(s"SELECT sum(c$middle) FROM nativeOrcTable").collect() + } + + benchmark.addCase("Native ORC Vectorized with copy") { _ => + withSQLConf(SQLConf.ORC_COPY_BATCH_TO_SPARK.key -> "true") { + spark.sql(s"SELECT sum(c$middle) FROM nativeOrcTable").collect() + } + } + + benchmark.addCase("Hive built-in ORC") { _ => + spark.sql(s"SELECT sum(c$middle) FROM hiveOrcTable").collect() + } + + /* + Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.13.1 + Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz + + Single Column Scan from 100 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 1050 / 1053 1.0 1001.1 1.0X + Native ORC Vectorized 95 / 101 11.0 90.9 11.0X + Native ORC Vectorized with copy 95 / 102 11.0 90.9 11.0X + Hive built-in ORC 348 / 358 3.0 331.8 3.0X + + Single Column Scan from 200 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 2099 / 2108 0.5 2002.1 1.0X + Native ORC Vectorized 179 / 187 5.8 171.1 11.7X + Native ORC Vectorized with copy 176 / 188 6.0 167.6 11.9X + Hive built-in ORC 562 / 581 1.9 535.9 3.7X + + Single Column Scan from 300 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + Native ORC MR 3221 / 3246 0.3 3071.4 1.0X + Native ORC Vectorized 312 / 322 3.4 298.0 10.3X + Native ORC Vectorized with copy 306 / 320 3.4 291.6 10.5X + Hive built-in ORC 815 / 824 1.3 777.3 4.0X + */ + benchmark.run() + } + } + } + + def main(args: Array[String]): Unit = { + Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType).foreach { dataType => + numericScanBenchmark(1024 * 1024 * 15, dataType) + } + intStringScanBenchmark(1024 * 1024 * 10) + partitionTableScanBenchmark(1024 * 1024 * 15) + repeatedStringScanBenchmark(1024 * 1024 * 10) + for (fractionOfNulls <- List(0.0, 0.50, 0.95)) { + stringWithNullsScanBenchmark(1024 * 1024 * 10, fractionOfNulls) + } + columnsBenchmark(1024 * 1024 * 1, 100) + columnsBenchmark(1024 * 1024 * 1, 200) + columnsBenchmark(1024 * 1024 * 1, 300) + } +} +// scalastyle:on line.size.limit diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala index 740e0837350cc..2327d83a1b4f6 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -180,15 +180,18 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest { } override def afterAll(): Unit = { - dropTables("partitioned_parquet", - "partitioned_parquet_with_key", - "partitioned_parquet_with_complextypes", - "partitioned_parquet_with_key_and_complextypes", - "normal_parquet", - "jt", - "jt_array", - "test_parquet") - super.afterAll() + try { + dropTables("partitioned_parquet", + "partitioned_parquet_with_key", + "partitioned_parquet_with_complextypes", + "partitioned_parquet_with_key_and_complextypes", + "normal_parquet", + "jt", + "jt_array", + "test_parquet") + } finally { + super.afterAll() + } } test(s"conversion is working") { @@ -931,11 +934,15 @@ abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with } override protected def afterAll(): Unit = { - partitionedTableDir.delete() - normalTableDir.delete() - partitionedTableDirWithKey.delete() - partitionedTableDirWithComplexTypes.delete() - partitionedTableDirWithKeyAndComplexTypes.delete() + try { + partitionedTableDir.delete() + normalTableDir.delete() + partitionedTableDirWithKey.delete() + partitionedTableDirWithComplexTypes.delete() + partitionedTableDirWithKeyAndComplexTypes.delete() + } finally { + super.afterAll() + } } /** diff --git a/streaming/pom.xml b/streaming/pom.xml index fea882ad11230..d16f48dec205f 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -21,7 +21,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala index 5d9a8ac0d9297..cf4324578ea87 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala @@ -112,10 +112,11 @@ private[streaming] class ReceivedBlockTracker( def allocateBlocksToBatch(batchTime: Time): Unit = synchronized { if (lastAllocatedBatchTime == null || batchTime > lastAllocatedBatchTime) { val streamIdToBlocks = streamIds.map { streamId => - (streamId, getReceivedBlockQueue(streamId).dequeueAll(x => true)) + (streamId, getReceivedBlockQueue(streamId).clone()) }.toMap val allocatedBlocks = AllocatedBlocks(streamIdToBlocks) if (writeToLog(BatchAllocationEvent(batchTime, allocatedBlocks))) { + streamIds.foreach(getReceivedBlockQueue(_).clear()) timeToAllocatedBlocks.put(batchTime, allocatedBlocks) lastAllocatedBatchTime = batchTime } else { @@ -193,12 +194,15 @@ private[streaming] class ReceivedBlockTracker( getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo } - // Insert the recovered block-to-batch allocations and clear the queue of received blocks - // (when the blocks were originally allocated to the batch, the queue must have been cleared). + // Insert the recovered block-to-batch allocations and removes them from queue of + // received blocks. def insertAllocatedBatch(batchTime: Time, allocatedBlocks: AllocatedBlocks) { logTrace(s"Recovery: Inserting allocated batch for time $batchTime to " + s"${allocatedBlocks.streamIdToAllocatedBlocks}") - streamIdToUnallocatedBlockQueues.values.foreach { _.clear() } + allocatedBlocks.streamIdToAllocatedBlocks.foreach { + case (streamId, allocatedBlocksInStream) => + getReceivedBlockQueue(streamId).dequeueAll(allocatedBlocksInStream.toSet) + } timeToAllocatedBlocks.put(batchTime, allocatedBlocks) lastAllocatedBatchTime = batchTime } @@ -227,7 +231,7 @@ private[streaming] class ReceivedBlockTracker( } /** Write an update to the tracker to the write ahead log */ - private def writeToLog(record: ReceivedBlockTrackerLogEvent): Boolean = { + private[streaming] def writeToLog(record: ReceivedBlockTrackerLogEvent): Boolean = { if (isWriteAheadLogEnabled) { logTrace(s"Writing record: $record") try { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala index ab7c8558321c8..2e8599026ea1d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala @@ -222,7 +222,7 @@ private[streaming] class FileBasedWriteAheadLog( pastLogs += LogInfo(currentLogWriterStartTime, currentLogWriterStopTime, _) } currentLogWriterStartTime = currentTime - currentLogWriterStopTime = currentTime + (rollingIntervalSecs * 1000) + currentLogWriterStopTime = currentTime + (rollingIntervalSecs * 1000L) val newLogPath = new Path(logDirectory, timeToLogFile(currentLogWriterStartTime, currentLogWriterStopTime)) currentLogPath = Some(newLogPath.toString) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala index 107c3f5dcc08d..fd7e00b1de25f 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala @@ -26,14 +26,16 @@ import scala.language.{implicitConversions, postfixOps} import scala.util.Random import org.apache.hadoop.conf.Configuration +import org.mockito.Matchers.any +import org.mockito.Mockito.{doThrow, reset, spy} import org.scalatest.{BeforeAndAfter, Matchers} import org.scalatest.concurrent.Eventually._ -import org.apache.spark.{SparkConf, SparkException, SparkFunSuite} +import org.apache.spark.{SparkConf, SparkFunSuite} import org.apache.spark.internal.Logging import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.receiver.BlockManagerBasedStoreResult -import org.apache.spark.streaming.scheduler._ +import org.apache.spark.streaming.scheduler.{AllocatedBlocks, _} import org.apache.spark.streaming.util._ import org.apache.spark.streaming.util.WriteAheadLogSuite._ import org.apache.spark.util.{Clock, ManualClock, SystemClock, Utils} @@ -94,6 +96,68 @@ class ReceivedBlockTrackerSuite receivedBlockTracker.getUnallocatedBlocks(streamId) shouldEqual blockInfos } + test("recovery with write ahead logs should remove only allocated blocks from received queue") { + val manualClock = new ManualClock + val batchTime = manualClock.getTimeMillis() + + val tracker1 = createTracker(clock = manualClock) + tracker1.isWriteAheadLogEnabled should be (true) + + val allocatedBlockInfos = generateBlockInfos() + val unallocatedBlockInfos = generateBlockInfos() + val receivedBlockInfos = allocatedBlockInfos ++ unallocatedBlockInfos + receivedBlockInfos.foreach { b => tracker1.writeToLog(BlockAdditionEvent(b)) } + val allocatedBlocks = AllocatedBlocks(Map(streamId -> allocatedBlockInfos)) + tracker1.writeToLog(BatchAllocationEvent(batchTime, allocatedBlocks)) + tracker1.stop() + + val tracker2 = createTracker(clock = manualClock, recoverFromWriteAheadLog = true) + tracker2.getBlocksOfBatch(batchTime) shouldEqual allocatedBlocks.streamIdToAllocatedBlocks + tracker2.getUnallocatedBlocks(streamId) shouldEqual unallocatedBlockInfos + tracker2.stop() + } + + test("block allocation to batch should not loose blocks from received queue") { + val tracker1 = spy(createTracker()) + tracker1.isWriteAheadLogEnabled should be (true) + tracker1.getUnallocatedBlocks(streamId) shouldEqual Seq.empty + + // Add blocks + val blockInfos = generateBlockInfos() + blockInfos.map(tracker1.addBlock) + tracker1.getUnallocatedBlocks(streamId) shouldEqual blockInfos + + // Try to allocate the blocks to a batch and verify that it's failing + // The blocks should stay in the received queue when WAL write failing + doThrow(new RuntimeException("Not able to write BatchAllocationEvent")) + .when(tracker1).writeToLog(any(classOf[BatchAllocationEvent])) + val errMsg = intercept[RuntimeException] { + tracker1.allocateBlocksToBatch(1) + } + assert(errMsg.getMessage === "Not able to write BatchAllocationEvent") + tracker1.getUnallocatedBlocks(streamId) shouldEqual blockInfos + tracker1.getBlocksOfBatch(1) shouldEqual Map.empty + tracker1.getBlocksOfBatchAndStream(1, streamId) shouldEqual Seq.empty + + // Allocate the blocks to a batch and verify that all of them have been allocated + reset(tracker1) + tracker1.allocateBlocksToBatch(2) + tracker1.getUnallocatedBlocks(streamId) shouldEqual Seq.empty + tracker1.hasUnallocatedReceivedBlocks should be (false) + tracker1.getBlocksOfBatch(2) shouldEqual Map(streamId -> blockInfos) + tracker1.getBlocksOfBatchAndStream(2, streamId) shouldEqual blockInfos + + tracker1.stop() + + // Recover from WAL to see the correctness + val tracker2 = createTracker(recoverFromWriteAheadLog = true) + tracker2.getUnallocatedBlocks(streamId) shouldEqual Seq.empty + tracker2.hasUnallocatedReceivedBlocks should be (false) + tracker2.getBlocksOfBatch(2) shouldEqual Map(streamId -> blockInfos) + tracker2.getBlocksOfBatchAndStream(2, streamId) shouldEqual blockInfos + tracker2.stop() + } + test("recovery and cleanup with write ahead logs") { val manualClock = new ManualClock // Set the time increment level to twice the rotation interval so that every increment creates @@ -291,7 +355,7 @@ class ReceivedBlockTrackerSuite recoverFromWriteAheadLog: Boolean = false, clock: Clock = new SystemClock): ReceivedBlockTracker = { val cpDirOption = if (setCheckpointDir) Some(checkpointDirectory.toString) else None - val tracker = new ReceivedBlockTracker( + var tracker = new ReceivedBlockTracker( conf, hadoopConf, Seq(streamId), clock, recoverFromWriteAheadLog, cpDirOption) allReceivedBlockTrackers += tracker tracker diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala index 145c48e5a9a72..fc6218a33f741 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala @@ -105,13 +105,13 @@ class ReceiverSuite extends TestSuiteBase with TimeLimits with Serializable { assert(executor.errors.head.eq(exception)) // Verify restarting actually stops and starts the receiver - receiver.restart("restarting", null, 100) - eventually(timeout(50 millis), interval(10 millis)) { + receiver.restart("restarting", null, 600) + eventually(timeout(300 millis), interval(10 millis)) { // receiver will be stopped async assert(receiver.isStopped) assert(receiver.onStopCalled) } - eventually(timeout(1000 millis), interval(100 millis)) { + eventually(timeout(1000 millis), interval(10 millis)) { // receiver will be started async assert(receiver.onStartCalled) assert(executor.isReceiverStarted) diff --git a/tools/pom.xml b/tools/pom.xml index 37427e8da62d8..f5927c5e41939 100644 --- a/tools/pom.xml +++ b/tools/pom.xml @@ -20,7 +20,7 @@ org.apache.spark spark-parent_2.11 - 2.3.0-SNAPSHOT + 2.3.2-SNAPSHOT ../pom.xml