Merge branch 'master' into SPARK-4705

Conflicts:
	core/src/main/scala/org/apache/spark/SparkContext.scala

R/pkg/DESCRIPTION

@@ -17,19 +17,19 @@ License: Apache License (== 2.0)
 Collate:
     'generics.R'
     'jobj.R'
-    'SQLTypes.R'
     'RDD.R'
     'pairRDD.R'
+    'SQLTypes.R'
     'column.R'
     'group.R'
     'DataFrame.R'
     'SQLContext.R'
+    'backend.R'
     'broadcast.R'
+    'client.R'
     'context.R'
     'deserialize.R'
     'serialize.R'
     'sparkR.R'
-    'backend.R'
-    'client.R'
     'utils.R'
     'zzz.R'

R/pkg/R/DataFrame.R

@@ -17,7 +17,7 @@
 
 # DataFrame.R - DataFrame class and methods implemented in S4 OO classes
 
-#' @include jobj.R SQLTypes.R RDD.R pairRDD.R column.R group.R
+#' @include generics.R jobj.R SQLTypes.R RDD.R pairRDD.R column.R group.R
 NULL
 
 setOldClass("jobj")

R/pkg/R/RDD.R

@@ -85,7 +85,7 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val)
 
   if (!inherits(prev, "PipelinedRDD") || !isPipelinable(prev)) {
     # This transformation is the first in its stage:
-    .Object@func <- func
+    .Object@func <- cleanClosure(func)
     .Object@prev_jrdd <- getJRDD(prev)
     .Object@env$prev_serializedMode <- prev@env$serializedMode
     # NOTE: We use prev_serializedMode to track the serialization mode of prev_JRDD
@@ -94,7 +94,7 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val)
     pipelinedFunc <- function(split, iterator) {
       func(split, prev@func(split, iterator))
     }
-    .Object@func <- pipelinedFunc
+    .Object@func <- cleanClosure(pipelinedFunc)
     .Object@prev_jrdd <- prev@prev_jrdd # maintain the pipeline
     # Get the serialization mode of the parent RDD
     .Object@env$prev_serializedMode <- prev@env$prev_serializedMode
@@ -144,17 +144,13 @@ setMethod("getJRDD", signature(rdd = "PipelinedRDD"),
               return(rdd@env$jrdd_val)
             }
 
-            computeFunc <- function(split, part) {
-              rdd@func(split, part)
-            }
-
             packageNamesArr <- serialize(.sparkREnv[[".packages"]],
                                          connection = NULL)
 
             broadcastArr <- lapply(ls(.broadcastNames),
                                    function(name) { get(name, .broadcastNames) })
 
-            serializedFuncArr <- serialize(computeFunc, connection = NULL)
+            serializedFuncArr <- serialize(rdd@func, connection = NULL)
 
             prev_jrdd <- rdd@prev_jrdd
 
@@ -279,7 +275,7 @@ setMethod("unpersist",
 #' @examples
 #'\dontrun{
 #' sc <- sparkR.init()
-#' setCheckpointDir(sc, "checkpoints")
+#' setCheckpointDir(sc, "checkpoint")
 #' rdd <- parallelize(sc, 1:10, 2L)
 #' checkpoint(rdd)
 #'}
@@ -551,11 +547,7 @@ setMethod("mapPartitions",
 setMethod("lapplyPartitionsWithIndex",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
-            FUN <- cleanClosure(FUN)
-            closureCapturingFunc <- function(split, part) {
-              FUN(split, part)
-            }
-            PipelinedRDD(X, closureCapturingFunc)
+            PipelinedRDD(X, FUN)
           })
 
 #' @rdname lapplyPartitionsWithIndex

R/pkg/R/column.R

@@ -17,7 +17,7 @@
 
 # Column Class
 
-#' @include generics.R jobj.R
+#' @include generics.R jobj.R SQLTypes.R
 NULL
 
 setOldClass("jobj")

R/pkg/R/context.R

@@ -216,7 +216,7 @@ broadcast <- function(sc, object) {
 #' @examples
 #'\dontrun{
 #' sc <- sparkR.init()
-#' setCheckpointDir(sc, "~/checkpoints")
+#' setCheckpointDir(sc, "~/checkpoint")
 #' rdd <- parallelize(sc, 1:2, 2L)
 #' checkpoint(rdd)
 #'}

R/pkg/R/group.R

@@ -17,6 +17,9 @@
 
 # group.R - GroupedData class and methods implemented in S4 OO classes
 
+#' @include generics.R jobj.R SQLTypes.R column.R
+NULL
+
 setOldClass("jobj")
 
 #' @title S4 class that represents a GroupedData

R/pkg/R/jobj.R

@@ -18,6 +18,9 @@
 # References to objects that exist on the JVM backend
 # are maintained using the jobj. 
 
+#' @include generics.R
+NULL
+
 # Maintain a reference count of Java object references
 # This allows us to GC the java object when it is safe
 .validJobjs <- new.env(parent = emptyenv())

R/pkg/R/pairRDD.R

@@ -16,6 +16,8 @@
 #
 
 # Operations supported on RDDs contains pairs (i.e key, value)
+#' @include generics.R jobj.R RDD.R
+NULL
 
 ############ Actions and Transformations ############
 
@@ -694,10 +696,6 @@ setMethod("cogroup",
             for (i in 1:rddsLen) {
               rdds[[i]] <- lapply(rdds[[i]], 
                                   function(x) { list(x[[1]], list(i, x[[2]])) })
-              # TODO(hao): As issue [SparkR-142] mentions, the right value of i
-              # will not be captured into UDF if getJRDD is not invoked.
-              # It should be resolved together with that issue.
-              getJRDD(rdds[[i]])  # Capture the closure.
             }
             union.rdd <- Reduce(unionRDD, rdds)
             group.func <- function(vlist) {

R/pkg/inst/tests/test_rdd.R

@@ -141,7 +141,8 @@ test_that("PipelinedRDD support actions: cache(), persist(), unpersist(), checkp
   unpersist(rdd2)
   expect_false(rdd2@env$isCached)
 
-  setCheckpointDir(sc, "checkpoints")
+  tempDir <- tempfile(pattern = "checkpoint")
+  setCheckpointDir(sc, tempDir)
   checkpoint(rdd2)
   expect_true(rdd2@env$isCheckpointed)
 
@@ -152,7 +153,7 @@ test_that("PipelinedRDD support actions: cache(), persist(), unpersist(), checkp
   # make sure the data is collectable
   collect(rdd2)
 
-  unlink("checkpoints")
+  unlink(tempDir)
 })
 
 test_that("reduce on RDD", {

bin/pyspark

@@ -89,6 +89,7 @@ export PYTHONSTARTUP="$SPARK_HOME/python/pyspark/shell.py"
 if [[ -n "$SPARK_TESTING" ]]; then
   unset YARN_CONF_DIR
   unset HADOOP_CONF_DIR
+  export PYTHONHASHSEED=0
   if [[ -n "$PYSPARK_DOC_TEST" ]]; then
     exec "$PYSPARK_DRIVER_PYTHON" -m doctest $1
   else

bin/spark-class

@@ -82,13 +82,22 @@ if [ $(command -v "$JAR_CMD") ] ; then
   fi
 fi
 
+LAUNCH_CLASSPATH="$SPARK_ASSEMBLY_JAR"
+
+# Add the launcher build dir to the classpath if requested.
+if [ -n "$SPARK_PREPEND_CLASSES" ]; then
+  LAUNCH_CLASSPATH="$SPARK_HOME/launcher/target/scala-$SPARK_SCALA_VERSION/classes:$LAUNCH_CLASSPATH"
+fi
+
+export _SPARK_ASSEMBLY="$SPARK_ASSEMBLY_JAR"
+
 # The launcher library will print arguments separated by a NULL character, to allow arguments with
 # characters that would be otherwise interpreted by the shell. Read that in a while loop, populating
 # an array that will be used to exec the final command.
 CMD=()
 while IFS= read -d '' -r ARG; do
   CMD+=("$ARG")
-done < <("$RUNNER" -cp "$SPARK_ASSEMBLY_JAR" org.apache.spark.launcher.Main "$@")
+done < <("$RUNNER" -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@")
 
 if [ "${CMD[0]}" = "usage" ]; then
   "${CMD[@]}"

bin/spark-class2.cmd

@@ -46,13 +46,22 @@ if "%SPARK_ASSEMBLY_JAR%"=="0" (
   exit /b 1
 )
 
+set LAUNCH_CLASSPATH=%SPARK_ASSEMBLY_JAR%
+
+rem Add the launcher build dir to the classpath if requested.
+if not "x%SPARK_PREPEND_CLASSES%"=="x" (
+  set LAUNCH_CLASSPATH=%SPARK_HOME%\launcher\target\scala-%SPARK_SCALA_VERSION%\classes;%LAUNCH_CLASSPATH%
+)
+
+set _SPARK_ASSEMBLY=%SPARK_ASSEMBLY_JAR%
+
 rem Figure out where java is.
 set RUNNER=java
 if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java
 
 rem The launcher library prints the command to be executed in a single line suitable for being
 rem executed by the batch interpreter. So read all the output of the launcher into a variable.
-for /f "tokens=*" %%i in ('cmd /C ""%RUNNER%" -cp %SPARK_ASSEMBLY_JAR% org.apache.spark.launcher.Main %*"') do (
+for /f "tokens=*" %%i in ('cmd /C ""%RUNNER%" -cp %LAUNCH_CLASSPATH% org.apache.spark.launcher.Main %*"') do (
   set SPARK_CMD=%%i
 )
 %SPARK_CMD%

bin/spark-submit

@@ -19,6 +19,9 @@
 
 SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
 
+# disable randomized hash for string in Python 3.3+
+export PYTHONHASHSEED=0
+
 # Only define a usage function if an upstream script hasn't done so.
 if ! type -t usage >/dev/null 2>&1; then
   usage() {

bin/spark-submit2.cmd

@@ -20,6 +20,9 @@ rem
 rem This is the entry point for running Spark submit. To avoid polluting the
 rem environment, it just launches a new cmd to do the real work.
 
+rem disable randomized hash for string in Python 3.3+
+set PYTHONHASHSEED=0
+
 set CLASS=org.apache.spark.deploy.SparkSubmit
 call %~dp0spark-class2.cmd %CLASS% %*
 set SPARK_ERROR_LEVEL=%ERRORLEVEL%

core/src/main/resources/org/apache/spark/ui/static/webui.css

@@ -85,17 +85,13 @@ table.sortable td {
   filter: progid:dximagetransform.microsoft.gradient(startColorstr='#FFA4EDFF', endColorstr='#FF94DDFF', GradientType=0);
 }
 
-span.kill-link {
+a.kill-link {
   margin-right: 2px;
   margin-left: 20px;
   color: gray;
   float: right;
 }
 
-span.kill-link a {
-  color: gray;
-}
-
 span.expand-details {
   font-size: 10pt;
   cursor: pointer;