[SPARK-1308] [PySpark] Add partitions() method to PySpark RDDs

.rat-excludes

@@ -11,6 +11,7 @@ RELEASE
 control
 docs
 fairscheduler.xml.template
+spark-defaults.conf.template
 log4j.properties
 log4j.properties.template
 metrics.properties.template
@@ -39,3 +40,6 @@ work
 .*\.q
 golden
 test.out/*
+.*iml
+service.properties
+db.lck

README.md

@@ -10,20 +10,33 @@ guide, on the project webpage at <http://spark.apache.org/documentation.html>.
 This README file only contains basic setup instructions.
 
 
-## Building
+## Building Spark
 
-Spark requires Scala 2.10. The project is built using Simple Build Tool (SBT),
-which can be obtained [here](http://www.scala-sbt.org). If SBT is installed we
-will use the system version of sbt otherwise we will attempt to download it
-automatically. To build Spark and its example programs, run:
+Spark is built on Scala 2.10. To build Spark and its example programs, run:
 
     ./sbt/sbt assembly
 
-Once you've built Spark, the easiest way to start using it is the shell:
+## Interactive Scala Shell
+
+The easiest way to start using Spark is through the Scala shell:
 
     ./bin/spark-shell
 
-Or, for the Python API, the Python shell (`./bin/pyspark`).
+Try the following command, which should return 1000:
+
+    scala> sc.parallelize(1 to 1000).count()
+
+## Interactive Python Shell
+
+Alternatively, if you prefer Python, you can use the Python shell:
+
+    ./bin/pyspark
+
+And run the following command, which should also return 1000:
+
+    >>> sc.parallelize(range(1000)).count()
+
+## Example Programs
 
 Spark also comes with several sample programs in the `examples` directory.
 To run one of them, use `./bin/run-example <class> <params>`. For example:
@@ -38,13 +51,13 @@ All of the Spark samples take a `<master>` parameter that is the cluster URL
 to connect to. This can be a mesos:// or spark:// URL, or "local" to run
 locally with one thread, or "local[N]" to run locally with N threads.
 
-## Running tests
+## Running Tests
 
-Testing first requires [Building](#building) Spark. Once Spark is built, tests
+Testing first requires [building Spark](#building-spark). Once Spark is built, tests
 can be run using:
 
-`./sbt/sbt test`
- 
+    ./sbt/sbt test
+
 ## A Note About Hadoop Versions
 
 Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported

assembly/pom.xml

@@ -163,6 +163,16 @@
         </dependency>
       </dependencies>
     </profile>
+    <profile>
+      <id>hive</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.spark</groupId>
+          <artifactId>spark-hive_${scala.binary.version}</artifactId>
+          <version>${project.version}</version>
+        </dependency>
+      </dependencies>
+    </profile>
     <profile>
       <id>spark-ganglia-lgpl</id>
       <dependencies>
@@ -208,7 +218,7 @@
           <plugin>
             <groupId>org.codehaus.mojo</groupId>
             <artifactId>buildnumber-maven-plugin</artifactId>
-            <version>1.1</version>
+            <version>1.2</version>
             <executions>
               <execution>
                 <phase>validate</phase>

bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala

@@ -220,27 +220,31 @@ object Bagel extends Logging {
    */
   private def comp[K: Manifest, V <: Vertex, M <: Message[K], C](
     sc: SparkContext,
-    grouped: RDD[(K, (Seq[C], Seq[V]))],
+    grouped: RDD[(K, (Iterable[C], Iterable[V]))],
     compute: (V, Option[C]) => (V, Array[M]),
     storageLevel: StorageLevel
   ): (RDD[(K, (V, Array[M]))], Int, Int) = {
     var numMsgs = sc.accumulator(0)
     var numActiveVerts = sc.accumulator(0)
-    val processed = grouped.flatMapValues {
-      case (_, vs) if vs.size == 0 => None
-      case (c, vs) =>
+    val processed = grouped.mapValues(x => (x._1.iterator, x._2.iterator))
+      .flatMapValues {
+      case (_, vs) if !vs.hasNext => None
+      case (c, vs) => {
         val (newVert, newMsgs) =
-          compute(vs(0), c match {
-            case Seq(comb) => Some(comb)
-            case Seq() => None
-          })
+          compute(vs.next,
+            c.hasNext match {
+              case true => Some(c.next)
+              case false => None
+            }
+          )
 
         numMsgs += newMsgs.size
         if (newVert.active) {
           numActiveVerts += 1
         }
 
         Some((newVert, newMsgs))
+      }
     }.persist(storageLevel)
 
     // Force evaluation of processed RDD for accurate performance measurements

bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala

@@ -24,13 +24,15 @@ import org.scalatest.time.SpanSugar._
 import org.apache.spark._
 import org.apache.spark.storage.StorageLevel
 
+import scala.language.postfixOps
+
 class TestVertex(val active: Boolean, val age: Int) extends Vertex with Serializable
 class TestMessage(val targetId: String) extends Message[String] with Serializable
 
 class BagelSuite extends FunSuite with Assertions with BeforeAndAfter with Timeouts {
-  
+
   var sc: SparkContext = _
-  
+
   after {
     if (sc != null) {
       sc.stop()

bin/compute-classpath.sh

@@ -30,21 +30,7 @@ FWDIR="$(cd `dirname $0`/..; pwd)"
 # Build up classpath
 CLASSPATH="$SPARK_CLASSPATH:$FWDIR/conf"
 
-# Support for interacting with Hive.  Since hive pulls in a lot of dependencies that might break
-# existing Spark applications, it is not included in the standard spark assembly.  Instead, we only
-# include it in the classpath if the user has explicitly requested it by running "sbt hive/assembly"
-# Hopefully we will find a way to avoid uber-jars entirely and deploy only the needed packages in
-# the future.
-if [ -f "$FWDIR"/sql/hive/target/scala-$SCALA_VERSION/spark-hive-assembly-*.jar ]; then
-
-  # Datanucleus jars do not work if only included in the uberjar as plugin.xml metadata is lost.
-  DATANUCLEUSJARS=$(JARS=("$FWDIR/lib_managed/jars"/datanucleus-*.jar); IFS=:; echo "${JARS[*]}")
-  CLASSPATH=$CLASSPATH:$DATANUCLEUSJARS
-
-  ASSEMBLY_DIR="$FWDIR/sql/hive/target/scala-$SCALA_VERSION/"
-else
-  ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SCALA_VERSION/"
-fi
+ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SCALA_VERSION"
 
 # First check if we have a dependencies jar. If so, include binary classes with the deps jar
 if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
@@ -59,7 +45,7 @@ if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
 
-  DEPS_ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark*-assembly*hadoop*-deps.jar`
+  DEPS_ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar`
   CLASSPATH="$CLASSPATH:$DEPS_ASSEMBLY_JAR"
 else
   # Else use spark-assembly jar from either RELEASE or assembly directory
@@ -71,6 +57,23 @@ else
   CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR"
 fi
 
+# When Hive support is needed, Datanucleus jars must be included on the classpath.
+# Datanucleus jars do not work if only included in the  uber jar as plugin.xml metadata is lost.
+# Both sbt and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is
+# built with Hive, so first check if the datanucleus jars exist, and then ensure the current Spark
+# assembly is built for Hive, before actually populating the CLASSPATH with the jars.
+# Note that this check order is faster (by up to half a second) in the case where Hive is not used.
+num_datanucleus_jars=$(ls "$FWDIR"/lib_managed/jars/ 2>/dev/null | grep "datanucleus-.*\\.jar" | wc -l)
+if [ $num_datanucleus_jars -gt 0 ]; then
+  AN_ASSEMBLY_JAR=${ASSEMBLY_JAR:-$DEPS_ASSEMBLY_JAR}
+  num_hive_files=$(jar tvf "$AN_ASSEMBLY_JAR" org/apache/hadoop/hive/ql/exec 2>/dev/null | wc -l)
+  if [ $num_hive_files -gt 0 ]; then
+    echo "Spark assembly has been built with Hive, including Datanucleus jars on classpath" 1>&2
+    DATANUCLEUSJARS=$(echo "$FWDIR/lib_managed/jars"/datanucleus-*.jar | tr " " :)
+    CLASSPATH=$CLASSPATH:$DATANUCLEUSJARS
+  fi
+fi
+
 # Add test classes if we're running from SBT or Maven with SPARK_TESTING set to 1
 if [[ $SPARK_TESTING == 1 ]]; then
   CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SCALA_VERSION/test-classes"

bin/load-spark-env.sh

@@ -30,6 +30,9 @@ if [ -z "$SPARK_ENV_LOADED" ]; then
   use_conf_dir=${SPARK_CONF_DIR:-"$parent_dir/conf"}
 
   if [ -f "${use_conf_dir}/spark-env.sh" ]; then
+    # Promote all variable declarations to environment (exported) variables
+    set -a
     . "${use_conf_dir}/spark-env.sh"
+    set +a
   fi
 fi

bin/pyspark

@@ -55,7 +55,8 @@ if [ -n "$IPYTHON_OPTS" ]; then
   IPYTHON=1
 fi
 
-if [[ "$IPYTHON" = "1" ]] ; then
+# Only use ipython if no command line arguments were provided [SPARK-1134]
+if [[ "$IPYTHON" = "1" && $# = 0 ]] ; then
   exec ipython $IPYTHON_OPTS
 else
   exec "$PYSPARK_PYTHON" "$@"

bin/run-example

@@ -75,7 +75,6 @@ fi
 
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
 JAVA_OPTS="$SPARK_JAVA_OPTS"
-JAVA_OPTS="$JAVA_OPTS -Djava.library.path=$SPARK_LIBRARY_PATH"
 # Load extra JAVA_OPTS from conf/java-opts, if it exists
 if [ -e "$FWDIR/conf/java-opts" ] ; then
   JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`"

bin/spark-class

@@ -47,9 +47,9 @@ DEFAULT_MEM=${SPARK_MEM:-512m}
 
 SPARK_DAEMON_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS -Dspark.akka.logLifecycleEvents=true"
 
-# Add java opts and memory settings for master, worker, executors, and repl.
+# Add java opts and memory settings for master, worker, history server, executors, and repl.
 case "$1" in
-  # Master and Worker use SPARK_DAEMON_JAVA_OPTS (and specific opts) + SPARK_DAEMON_MEMORY.
+  # Master, Worker, and HistoryServer use SPARK_DAEMON_JAVA_OPTS (and specific opts) + SPARK_DAEMON_MEMORY.
   'org.apache.spark.deploy.master.Master')
     OUR_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS $SPARK_MASTER_OPTS"
     OUR_JAVA_MEM=${SPARK_DAEMON_MEMORY:-$DEFAULT_MEM}
@@ -58,6 +58,10 @@ case "$1" in
     OUR_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS $SPARK_WORKER_OPTS"
     OUR_JAVA_MEM=${SPARK_DAEMON_MEMORY:-$DEFAULT_MEM}
     ;;
+  'org.apache.spark.deploy.history.HistoryServer')
+    OUR_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS $SPARK_HISTORY_OPTS"
+    OUR_JAVA_MEM=${SPARK_DAEMON_MEMORY:-$DEFAULT_MEM}
+    ;;
 
   # Executors use SPARK_JAVA_OPTS + SPARK_EXECUTOR_MEMORY.
   'org.apache.spark.executor.CoarseGrainedExecutorBackend')
@@ -94,7 +98,7 @@ fi
 
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
 JAVA_OPTS="$OUR_JAVA_OPTS"
-JAVA_OPTS="$JAVA_OPTS -Djava.library.path=$SPARK_LIBRARY_PATH"
+JAVA_OPTS="$JAVA_OPTS -Djava.library.path=$_SPARK_LIBRARY_PATH"
 JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM"
 # Load extra JAVA_OPTS from conf/java-opts, if it exists
 if [ -e "$FWDIR/conf/java-opts" ] ; then
@@ -154,5 +158,3 @@ if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then
 fi
 
 exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"
-
-

bin/spark-class2.cmd

@@ -45,14 +45,17 @@ if "x%OUR_JAVA_MEM%"=="x" set OUR_JAVA_MEM=512m
 
 set SPARK_DAEMON_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% -Dspark.akka.logLifecycleEvents=true
 
-rem Add java opts and memory settings for master, worker, executors, and repl.
-rem Master and Worker use SPARK_DAEMON_JAVA_OPTS (and specific opts) + SPARK_DAEMON_MEMORY.
+rem Add java opts and memory settings for master, worker, history server, executors, and repl.
+rem Master, Worker and HistoryServer use SPARK_DAEMON_JAVA_OPTS (and specific opts) + SPARK_DAEMON_MEMORY.
 if "%1"=="org.apache.spark.deploy.master.Master" (
   set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_MASTER_OPTS%
   if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY%
 ) else if "%1"=="org.apache.spark.deploy.worker.Worker" (
   set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_WORKER_OPTS%
   if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY%
+) else if "%1"=="org.apache.spark.deploy.history.HistoryServer" (
+  set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_HISTORY_OPTS%
+  if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY%
 
 rem Executors use SPARK_JAVA_OPTS + SPARK_EXECUTOR_MEMORY.
 ) else if "%1"=="org.apache.spark.executor.CoarseGrainedExecutorBackend" (

bin/spark-shell

@@ -34,7 +34,7 @@ set -o posix
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
 SPARK_REPL_OPTS="${SPARK_REPL_OPTS:-""}"
-DEFAULT_MASTER="local"
+DEFAULT_MASTER="local[*]"
 MASTER=${MASTER:-""}
 
 info_log=0
@@ -64,7 +64,7 @@ ${txtbld}OPTIONS${txtrst}:
                               is followed by m for megabytes or g for gigabytes, e.g. "1g".
     -dm --driver-memory     : The memory used by the Spark Shell, the number is followed 
                               by m for megabytes or g for gigabytes, e.g. "1g".
-    -m  --master            : A full string that describes the Spark Master, defaults to "local"
+    -m  --master            : A full string that describes the Spark Master, defaults to "local[*]"
                               e.g. "spark://localhost:7077".
     --log-conf              : Enables logging of the supplied SparkConf as INFO at start of the
                               Spark Context.
@@ -127,7 +127,7 @@ function set_spark_log_conf(){
 
 function set_spark_master(){
     if ! [[ "$1" =~ $ARG_FLAG_PATTERN ]]; then
-        MASTER="$1"
+        export MASTER="$1"
     else
         out_error "wrong format for $2"
     fi
@@ -145,7 +145,7 @@ function resolve_spark_master(){
     fi
 
     if [ -z "$MASTER" ]; then
-        MASTER="$DEFAULT_MASTER"
+        export MASTER="$DEFAULT_MASTER"
     fi
 
 }

bin/spark-submit

@@ -25,8 +25,13 @@ while (($#)); do
     DEPLOY_MODE=$2
   elif [ $1 = "--driver-memory" ]; then
     DRIVER_MEMORY=$2
+  elif [ $1 = "--driver-library-path" ]; then
+    export _SPARK_LIBRARY_PATH=$2
+  elif [ $1 = "--driver-class-path" ]; then
+    export SPARK_CLASSPATH="$SPARK_CLASSPATH:$2"
+  elif [ $1 = "--driver-java-options" ]; then
+    export SPARK_JAVA_OPTS="$SPARK_JAVA_OPTS $2"
   fi
-
   shift
 done
 

conf/spark-defaults.conf.template

@@ -0,0 +1,7 @@
+# Default system properties included when running spark-submit.
+# This is useful for setting default environmental settings.
+
+# Example:
+# spark.master 	          spark://master:7077
+# spark.eventLog.enabled  true
+# spark.eventLog.dir      hdfs://namenode:8021/directory