Merge pull request #116 from palantir/rk/merge-upstream

.gitignore

@@ -42,6 +42,7 @@ dependency-reduced-pom.xml
 derby.log
 dev/create-release/*final
 dev/create-release/*txt
+dev/pr-deps/
 dist/
 docs/_site
 docs/api

.travis.yml

@@ -28,7 +28,6 @@ dist: trusty
 # 2. Choose language and target JDKs for parallel builds.
 language: java
 jdk:
-  - oraclejdk7
   - oraclejdk8
 
 # 3. Setup cache directory for SBT and Maven.
@@ -44,7 +43,7 @@ notifications:
 # 5. Run maven install before running lint-java.
 install:
   - export MAVEN_SKIP_RC=1
-  - build/mvn -T 4 -q -DskipTests -Pmesos -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
+  - build/mvn -T 4 -q -DskipTests -Pmesos -Pyarn -Pkinesis-asl -Phive -Phive-thriftserver install
 
 # 6. Run lint-java.
 script:

R/WINDOWS.md

@@ -6,7 +6,7 @@ To build SparkR on Windows, the following steps are required
 include Rtools and R in `PATH`.
 
 2. Install
-[JDK7](http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.html) and set
+[JDK8](http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html) and set
 `JAVA_HOME` in the system environment variables.
 
 3. Download and install [Maven](http://maven.apache.org/download.html). Also include the `bin`
@@ -38,6 +38,6 @@ To run the SparkR unit tests on Windows, the following steps are required —ass
 
     ```
     R -e "install.packages('testthat', repos='http://cran.us.r-project.org')"
-    .\bin\spark-submit2.cmd --conf spark.hadoop.fs.default.name="file:///" R\pkg\tests\run-all.R
+    .\bin\spark-submit2.cmd --conf spark.hadoop.fs.defaultFS="file:///" R\pkg\tests\run-all.R
     ```
 

R/pkg/NAMESPACE

@@ -1,3 +1,20 @@
+#
+# 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.
+#
+
 # Imports from base R
 # Do not include stats:: "rpois", "runif" - causes error at runtime
 importFrom("methods", "setGeneric", "setMethod", "setOldClass")
@@ -47,7 +64,9 @@ exportMethods("glm",
               "spark.kstest",
               "spark.logit",
               "spark.randomForest",
-              "spark.gbt")
+              "spark.gbt",
+              "spark.bisectingKmeans",
+              "spark.svmLinear")
 
 # Job group lifecycle management methods
 export("setJobGroup",
@@ -63,6 +82,7 @@ exportMethods("arrange",
               "as.data.frame",
               "attach",
               "cache",
+              "coalesce",
               "collect",
               "colnames",
               "colnames<-",
@@ -94,6 +114,7 @@ exportMethods("arrange",
               "freqItems",
               "gapply",
               "gapplyCollect",
+              "getNumPartitions",
               "group_by",
               "groupBy",
               "head",
@@ -208,6 +229,7 @@ exportMethods("%in%",
               "floor",
               "format_number",
               "format_string",
+              "from_json",
               "from_unixtime",
               "from_utc_timestamp",
               "getField",
@@ -306,6 +328,8 @@ exportMethods("%in%",
               "toDegrees",
               "toRadians",
               "to_date",
+              "to_json",
+              "to_timestamp",
               "to_utc_timestamp",
               "translate",
               "trim",

R/pkg/R/DataFrame.R

@@ -280,7 +280,7 @@ setMethod("dtypes",
 
 #' Column Names of SparkDataFrame
 #'
-#' Return all column names as a list.
+#' Return a vector of column names.
 #'
 #' @param x a SparkDataFrame.
 #'
@@ -323,10 +323,8 @@ setMethod("names",
 setMethod("names<-",
           signature(x = "SparkDataFrame"),
           function(x, value) {
-            if (!is.null(value)) {
-              sdf <- callJMethod(x@sdf, "toDF", as.list(value))
-              dataFrame(sdf)
-            }
+            colnames(x) <- value
+            x
           })
 
 #' @rdname columns
@@ -340,7 +338,7 @@ setMethod("colnames",
           })
 
 #' @param value a character vector. Must have the same length as the number
-#'              of columns in the SparkDataFrame.
+#'              of columns to be renamed.
 #' @rdname columns
 #' @aliases colnames<-,SparkDataFrame-method
 #' @name colnames<-
@@ -417,7 +415,7 @@ setMethod("coltypes",
                   type <- PRIMITIVE_TYPES[[specialtype]]
                 }
               }
-              type
+              type[[1]]
             })
 
             # Find which types don't have mapping to R
@@ -680,14 +678,53 @@ setMethod("storageLevel",
             storageLevelToString(callJMethod(x@sdf, "storageLevel"))
           })
 
+#' Coalesce
+#'
+#' Returns a new SparkDataFrame that has exactly \code{numPartitions} partitions.
+#' This operation results in a narrow dependency, e.g. if you go from 1000 partitions to 100
+#' partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of
+#' the current partitions. If a larger number of partitions is requested, it will stay at the
+#' current number of partitions.
+#'
+#' However, if you're doing a drastic coalesce on a SparkDataFrame, e.g. to numPartitions = 1,
+#' this may result in your computation taking place on fewer nodes than
+#' you like (e.g. one node in the case of numPartitions = 1). To avoid this,
+#' call \code{repartition}. This will add a shuffle step, but means the
+#' current upstream partitions will be executed in parallel (per whatever
+#' the current partitioning is).
+#'
+#' @param numPartitions the number of partitions to use.
+#'
+#' @family SparkDataFrame functions
+#' @rdname coalesce
+#' @name coalesce
+#' @aliases coalesce,SparkDataFrame-method
+#' @seealso \link{repartition}
+#' @export
+#' @examples
+#'\dontrun{
+#' sparkR.session()
+#' path <- "path/to/file.json"
+#' df <- read.json(path)
+#' newDF <- coalesce(df, 1L)
+#'}
+#' @note coalesce(SparkDataFrame) since 2.1.1
+setMethod("coalesce",
+          signature(x = "SparkDataFrame"),
+          function(x, numPartitions) {
+            stopifnot(is.numeric(numPartitions))
+            sdf <- callJMethod(x@sdf, "coalesce", numToInt(numPartitions))
+            dataFrame(sdf)
+          })
+
 #' Repartition
 #'
 #' The following options for repartition are possible:
 #' \itemize{
-#'  \item{1.} {Return a new SparkDataFrame partitioned by
+#'  \item{1.} {Return a new SparkDataFrame that has exactly \code{numPartitions}.}
+#'  \item{2.} {Return a new SparkDataFrame hash partitioned by
 #'                      the given columns into \code{numPartitions}.}
-#'  \item{2.} {Return a new SparkDataFrame that has exactly \code{numPartitions}.}
-#'  \item{3.} {Return a new SparkDataFrame partitioned by the given column(s),
+#'  \item{3.} {Return a new SparkDataFrame hash partitioned by the given column(s),
 #'                      using \code{spark.sql.shuffle.partitions} as number of partitions.}
 #'}
 #' @param x a SparkDataFrame.
@@ -699,6 +736,7 @@ setMethod("storageLevel",
 #' @rdname repartition
 #' @name repartition
 #' @aliases repartition,SparkDataFrame-method
+#' @seealso \link{coalesce}
 #' @export
 #' @examples
 #'\dontrun{
@@ -1138,6 +1176,7 @@ setMethod("collect",
                   if (!is.null(PRIMITIVE_TYPES[[colType]]) && colType != "binary") {
                     vec <- do.call(c, col)
                     stopifnot(class(vec) != "list")
+                    class(vec) <- PRIMITIVE_TYPES[[colType]]
                     df[[colIndex]] <- vec
                   } else {
                     df[[colIndex]] <- col
@@ -1765,6 +1804,10 @@ setClassUnion("numericOrcharacter", c("numeric", "character"))
 #' @note [[ since 1.4.0
 setMethod("[[", signature(x = "SparkDataFrame", i = "numericOrcharacter"),
           function(x, i) {
+            if (length(i) > 1) {
+              warning("Subset index has length > 1. Only the first index is used.")
+              i <- i[1]
+            }
             if (is.numeric(i)) {
               cols <- columns(x)
               i <- cols[[i]]
@@ -1778,6 +1821,10 @@ setMethod("[[", signature(x = "SparkDataFrame", i = "numericOrcharacter"),
 #' @note [[<- since 2.1.1
 setMethod("[[<-", signature(x = "SparkDataFrame", i = "numericOrcharacter"),
           function(x, i, value) {
+            if (length(i) > 1) {
+              warning("Subset index has length > 1. Only the first index is used.")
+              i <- i[1]
+            }
             if (is.numeric(i)) {
               cols <- columns(x)
               i <- cols[[i]]
@@ -1831,6 +1878,8 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #' Return subsets of SparkDataFrame according to given conditions
 #' @param x a SparkDataFrame.
 #' @param i,subset (Optional) a logical expression to filter on rows.
+#'                 For extract operator [[ and replacement operator [[<-, the indexing parameter for
+#'                 a single Column.
 #' @param j,select expression for the single Column or a list of columns to select from the SparkDataFrame.
 #' @param drop if TRUE, a Column will be returned if the resulting dataset has only one column.
 #'             Otherwise, a SparkDataFrame will always be returned.
@@ -1841,6 +1890,7 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #' @export
 #' @family SparkDataFrame functions
 #' @aliases subset,SparkDataFrame-method
+#' @seealso \link{withColumn}
 #' @rdname subset
 #' @name subset
 #' @family subsetting functions
@@ -1858,6 +1908,10 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #'   subset(df, df$age %in% c(19, 30), 1:2)
 #'   subset(df, df$age %in% c(19), select = c(1,2))
 #'   subset(df, select = c(1,2))
+#'   # Columns can be selected and set
+#'   df[["age"]] <- 23
+#'   df[[1]] <- df$age
+#'   df[[2]] <- NULL # drop column
 #' }
 #' @note subset since 1.5.0
 setMethod("subset", signature(x = "SparkDataFrame"),
@@ -1982,7 +2036,7 @@ setMethod("selectExpr",
 #' @aliases withColumn,SparkDataFrame,character-method
 #' @rdname withColumn
 #' @name withColumn
-#' @seealso \link{rename} \link{mutate}
+#' @seealso \link{rename} \link{mutate} \link{subset}
 #' @export
 #' @examples
 #'\dontrun{
@@ -1993,6 +2047,10 @@ setMethod("selectExpr",
 #' # Replace an existing column
 #' newDF2 <- withColumn(newDF, "newCol", newDF$col1)
 #' newDF3 <- withColumn(newDF, "newCol", 42)
+#' # Use extract operator to set an existing or new column
+#' df[["age"]] <- 23
+#' df[[2]] <- df$col1
+#' df[[2]] <- NULL # drop column
 #' }
 #' @note withColumn since 1.4.0
 setMethod("withColumn",
@@ -3428,3 +3486,26 @@ setMethod("randomSplit",
             }
             sapply(sdfs, dataFrame)
           })
+
+#' getNumPartitions
+#'
+#' Return the number of partitions
+#'
+#' @param x A SparkDataFrame
+#' @family SparkDataFrame functions
+#' @aliases getNumPartitions,SparkDataFrame-method
+#' @rdname getNumPartitions
+#' @name getNumPartitions
+#' @export
+#' @examples
+#'\dontrun{
+#' sparkR.session()
+#' df <- createDataFrame(cars, numPartitions = 2)
+#' getNumPartitions(df)
+#' }
+#' @note getNumPartitions since 2.1.1
+setMethod("getNumPartitions",
+          signature(x = "SparkDataFrame"),
+          function(x) {
+            callJMethod(callJMethod(x@sdf, "rdd"), "getNumPartitions")
+          })