merged master

.gitignore

@@ -49,3 +49,9 @@ unit-tests.log
 /lib/
 rat-results.txt
 scalastyle.txt
+
+# For Hive
+metastore_db/
+metastore/
+warehouse/
+TempStatsStore/

assembly/pom.xml

@@ -40,14 +40,6 @@
     <deb.user>root</deb.user>
   </properties>
 
-  <repositories>
-    <!-- A repository in the local filesystem for the Py4J JAR, which is not in Maven central -->
-    <repository>
-      <id>lib</id>
-      <url>file://${project.basedir}/lib</url>
-    </repository>
-  </repositories>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
@@ -84,11 +76,6 @@
       <artifactId>spark-sql_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
-    <dependency>
-      <groupId>net.sf.py4j</groupId>
-      <artifactId>py4j</artifactId>
-      <version>0.8.1</version>
-    </dependency>
   </dependencies>
 
   <build>

bin/compute-classpath.sh

@@ -32,6 +32,12 @@ CLASSPATH="$SPARK_CLASSPATH:$SPARK_SUBMIT_CLASSPATH:$FWDIR/conf"
 
 ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SCALA_VERSION"
 
+if [ -n "$JAVA_HOME" ]; then
+  JAR_CMD="$JAVA_HOME/bin/jar"
+else
+  JAR_CMD="jar"
+fi
+
 # First check if we have a dependencies jar. If so, include binary classes with the deps jar
 if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SCALA_VERSION/classes"
@@ -44,33 +50,50 @@ if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
+  CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
 
-  DEPS_ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar`
-  CLASSPATH="$CLASSPATH:$DEPS_ASSEMBLY_JAR"
+  ASSEMBLY_JAR=$(ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar 2>/dev/null)
 else
   # Else use spark-assembly jar from either RELEASE or assembly directory
   if [ -f "$FWDIR/RELEASE" ]; then
-    ASSEMBLY_JAR=`ls "$FWDIR"/lib/spark-assembly*hadoop*.jar`
+    ASSEMBLY_JAR=$(ls "$FWDIR"/lib/spark-assembly*hadoop*.jar 2>/dev/null)
   else
-    ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*.jar`
+    ASSEMBLY_JAR=$(ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*.jar 2>/dev/null)
   fi
-  CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR"
 fi
 
+# Verify that versions of java used to build the jars and run Spark are compatible
+jar_error_check=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" nonexistent/class/path 2>&1)
+if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then
+  echo "Loading Spark jar with '$JAR_CMD' failed. "
+  echo "This is likely because Spark was compiled with Java 7 and run "
+  echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark "
+  echo "or build Spark with Java 6."
+  exit 1
+fi
+
+CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR"
+
 # When Hive support is needed, Datanucleus jars must be included on the classpath.
-# Datanucleus jars do not work if only included in the  uber jar as plugin.xml metadata is lost.
+# Datanucleus jars do not work if only included in the uber jar as plugin.xml metadata is lost.
 # Both sbt and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is
 # built with Hive, so first check if the datanucleus jars exist, and then ensure the current Spark
 # assembly is built for Hive, before actually populating the CLASSPATH with the jars.
 # Note that this check order is faster (by up to half a second) in the case where Hive is not used.
-num_datanucleus_jars=$(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 [ $num_hive_files -gt 0 ]; then
+if [ -f "$FWDIR/RELEASE" ]; then
+  datanucleus_dir="$FWDIR"/lib
+else
+  datanucleus_dir="$FWDIR"/lib_managed/jars
+fi
+
+datanucleus_jars=$(find "$datanucleus_dir" 2>/dev/null | grep "datanucleus-.*\\.jar")
+datanucleus_jars=$(echo "$datanucleus_jars" | tr "\n" : | sed s/:$//g)
+
+if [ -n "$datanucleus_jars" ]; then
+  hive_files=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" org/apache/hadoop/hive/ql/exec 2>/dev/null)
+  if [ -n "$hive_files" ]; 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
+    CLASSPATH="$CLASSPATH:$datanucleus_jars"
   fi
 fi
 
@@ -90,10 +113,10 @@ fi
 # Add hadoop conf dir if given -- otherwise FileSystem.*, etc fail !
 # Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts
 # the configurtion files.
-if [ "x" != "x$HADOOP_CONF_DIR" ]; then
+if [ -n "$HADOOP_CONF_DIR" ]; then
   CLASSPATH="$CLASSPATH:$HADOOP_CONF_DIR"
 fi
-if [ "x" != "x$YARN_CONF_DIR" ]; then
+if [ -n "$YARN_CONF_DIR" ]; then
   CLASSPATH="$CLASSPATH:$YARN_CONF_DIR"
 fi
 

bin/pyspark

@@ -46,6 +46,7 @@ export PYSPARK_PYTHON
 
 # Add the PySpark classes to the Python path:
 export PYTHONPATH=$SPARK_HOME/python/:$PYTHONPATH
+export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH
 
 # Load the PySpark shell.py script when ./pyspark is used interactively:
 export OLD_PYTHONSTARTUP=$PYTHONSTARTUP

bin/pyspark2.cmd

@@ -45,6 +45,7 @@ rem Figure out which Python to use.
 if "x%PYSPARK_PYTHON%"=="x" set PYSPARK_PYTHON=python
 
 set PYTHONPATH=%FWDIR%python;%PYTHONPATH%
+set PYTHONPATH=%FWDIR%python\lib\py4j-0.8.1-src.zip;%PYTHONPATH%
 
 set OLD_PYTHONSTARTUP=%PYTHONSTARTUP%
 set PYTHONSTARTUP=%FWDIR%python\pyspark\shell.py

bin/spark-class

@@ -138,7 +138,14 @@ if [ -e "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar ]; then
 fi
 
 # Compute classpath using external script
-CLASSPATH=`$FWDIR/bin/compute-classpath.sh`
+classpath_output=$($FWDIR/bin/compute-classpath.sh)
+if [[ "$?" != "0" ]]; then
+  echo "$classpath_output"
+  exit 1
+else
+  CLASSPATH=$classpath_output
+fi
+
 if [[ "$1" =~ org.apache.spark.tools.* ]]; then
   CLASSPATH="$CLASSPATH:$SPARK_TOOLS_JAR"
 fi

bin/spark-submit

@@ -18,7 +18,7 @@
 #
 
 export SPARK_HOME="$(cd `dirname $0`/..; pwd)"
-ORIG_ARGS=$@
+ORIG_ARGS=("$@")
 
 while (($#)); do
   if [ "$1" = "--deploy-mode" ]; then
@@ -39,5 +39,5 @@ if [ ! -z $DRIVER_MEMORY ] && [ ! -z $DEPLOY_MODE ] && [ $DEPLOY_MODE = "client"
   export SPARK_MEM=$DRIVER_MEMORY
 fi
 
-$SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit $ORIG_ARGS
+$SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
 

core/pom.xml

@@ -38,12 +38,6 @@
     <dependency>
       <groupId>net.java.dev.jets3t</groupId>
       <artifactId>jets3t</artifactId>
-      <exclusions>
-        <exclusion>
-          <groupId>commons-logging</groupId>
-          <artifactId>commons-logging</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.apache.curator</groupId>
@@ -69,6 +63,10 @@
       <groupId>com.google.guava</groupId>
       <artifactId>guava</artifactId>
     </dependency>
+    <dependency>
+      <groupId>org.apache.commons</groupId>
+      <artifactId>commons-lang3</artifactId>
+    </dependency>
     <dependency>
       <groupId>com.google.code.findbugs</groupId>
       <artifactId>jsr305</artifactId>
@@ -249,6 +247,11 @@
       <artifactId>pyrolite</artifactId>
       <version>2.0.1</version>
     </dependency>
+    <dependency>
+      <groupId>net.sf.py4j</groupId>
+      <artifactId>py4j</artifactId>
+      <version>0.8.1</version>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
@@ -294,6 +297,48 @@
           </environmentVariables>
         </configuration>
       </plugin>
+      <!-- Unzip py4j so we can include its files in the jar -->
+      <plugin>
+        <groupId>org.codehaus.mojo</groupId>
+        <artifactId>exec-maven-plugin</artifactId>
+        <version>1.2.1</version>
+        <executions>
+          <execution>
+            <phase>generate-resources</phase>
+            <goals>
+              <goal>exec</goal>
+            </goals>
+          </execution>
+        </executions>
+        <configuration>
+          <executable>unzip</executable>
+          <workingDirectory>../python</workingDirectory>
+          <arguments>
+            <argument>-o</argument>
+            <argument>lib/py4j*.zip</argument>
+            <argument>-d</argument>
+            <argument>build</argument>
+          </arguments>
+        </configuration>
+      </plugin>
     </plugins>
+
+    <resources>
+      <resource>
+        <directory>src/main/resources</directory>
+      </resource>
+      <resource>
+        <directory>../python</directory>
+        <includes>
+          <include>pyspark/*.py</include>
+        </includes>
+      </resource>
+      <resource>
+        <directory>../python/build</directory>
+        <includes>
+          <include>py4j/*.py</include>
+        </includes>
+      </resource>
+    </resources>
   </build>
 </project>

core/src/main/scala/org/apache/spark/SecurityManager.scala

@@ -146,8 +146,9 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
   setViewAcls(defaultAclUsers, sparkConf.get("spark.ui.view.acls", ""))
 
   private val secretKey = generateSecretKey()
-  logInfo("SecurityManager, is authentication enabled: " + authOn +
-    " are ui acls enabled: " + uiAclsOn + " users with view permissions: " + viewAcls.toString())
+  logInfo("SecurityManager: authentication " + (if (authOn) "enabled" else "disabled") +
+    "; ui acls " + (if (uiAclsOn) "enabled" else "disabled") +
+    "; users with view permissions: " + viewAcls.toString())
 
   // Set our own authenticator to properly negotiate user/password for HTTP connections.
   // This is needed by the HTTP client fetching from the HttpServer. Put here so its

core/src/main/scala/org/apache/spark/SparkEnv.scala

@@ -17,6 +17,8 @@
 
 package org.apache.spark
 
+import java.io.File
+
 import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.concurrent.Await
@@ -156,13 +158,11 @@ object SparkEnv extends Logging {
       conf.set("spark.driver.port",  boundPort.toString)
     }
 
-    val classLoader = Thread.currentThread.getContextClassLoader
-
     // Create an instance of the class named by the given Java system property, or by
     // defaultClassName if the property is not set, and return it as a T
     def instantiateClass[T](propertyName: String, defaultClassName: String): T = {
       val name = conf.get(propertyName,  defaultClassName)
-      val cls = Class.forName(name, true, classLoader)
+      val cls = Class.forName(name, true, Utils.getContextOrSparkClassLoader)
       // First try with the constructor that takes SparkConf. If we can't find one,
       // use a no-arg constructor instead.
       try {
@@ -306,7 +306,7 @@ object SparkEnv extends Logging {
       k == "java.class.path"
     }.getOrElse(("", ""))
     val classPathEntries = classPathProperty._2
-      .split(conf.get("path.separator", ":"))
+      .split(File.pathSeparator)
       .filterNot(e => e.isEmpty)
       .map(e => (e, "System Classpath"))
     val addedJarsAndFiles = (addedJars ++ addedFiles).map((_, "Added By User"))

core/src/main/scala/org/apache/spark/TaskContext.scala

@@ -42,16 +42,21 @@ class TaskContext(
   // List of callback functions to execute when the task completes.
   @transient private val onCompleteCallbacks = new ArrayBuffer[() => Unit]
 
+  // Set to true when the task is completed, before the onCompleteCallbacks are executed.
+  @volatile var completed: Boolean = false
+
   /**
    * Add a callback function to be executed on task completion. An example use
    * is for HadoopRDD to register a callback to close the input stream.
+   * Will be called in any situation - success, failure, or cancellation.
    * @param f Callback function.
    */
   def addOnCompleteCallback(f: () => Unit) {
     onCompleteCallbacks += f
   }
 
   def executeOnCompleteCallbacks() {
+    completed = true
     // Process complete callbacks in the reverse order of registration
     onCompleteCallbacks.reverse.foreach{_()}
   }

core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.api.java
 
 import java.util.{Comparator, List => JList, Iterator => JIterator}
-import java.lang.{Iterable => JIterable}
+import java.lang.{Iterable => JIterable, Long => JLong}
 
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
@@ -75,11 +75,11 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Return a new RDD by applying a function to each partition of this RDD, while tracking the index
    * of the original partition.
    */
-  def mapPartitionsWithIndex[R: ClassTag](
+  def mapPartitionsWithIndex[R](
       f: JFunction2[java.lang.Integer, java.util.Iterator[T], java.util.Iterator[R]],
       preservesPartitioning: Boolean = false): JavaRDD[R] =
     new JavaRDD(rdd.mapPartitionsWithIndex(((a,b) => f(a,asJavaIterator(b))),
-        preservesPartitioning))
+        preservesPartitioning)(fakeClassTag))(fakeClassTag)
 
   /**
    * Return a new RDD by applying a function to all elements of this RDD.
@@ -264,6 +264,26 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
       rdd.zipPartitions(other.rdd)(fn)(other.classTag, fakeClassTag[V]))(fakeClassTag[V])
   }
 
+  /**
+   * Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k,
+   * 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method
+   * won't trigger a spark job, which is different from [[org.apache.spark.rdd.RDD#zipWithIndex]].
+   */
+  def zipWithUniqueId(): JavaPairRDD[T, JLong] = {
+    JavaPairRDD.fromRDD(rdd.zipWithUniqueId()).asInstanceOf[JavaPairRDD[T, JLong]]
+  }
+
+  /**
+   * Zips this RDD with its element indices. The ordering is first based on the partition index
+   * and then the ordering of items within each partition. So the first item in the first
+   * partition gets index 0, and the last item in the last partition receives the largest index.
+   * This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type.
+   * This method needs to trigger a spark job when this RDD contains more than one partitions.
+   */
+  def zipWithIndex(): JavaPairRDD[T, JLong] = {
+    JavaPairRDD.fromRDD(rdd.zipWithIndex()).asInstanceOf[JavaPairRDD[T, JLong]]
+  }
+
   // Actions (launch a job to return a value to the user program)
 
   /**