Merge branch 'master' of https://github.com/apache/spark into format_pom

Author: witgo

assembly/pom.xml

@@ -33,7 +33,7 @@
 
   <properties>
     <spark.jar.dir>scala-${scala.binary.version}</spark.jar.dir>
-    <spark.jar.basename>${project.artifactId}-${project.version}-hadoop${hadoop.version}.jar</spark.jar.basename>
+    <spark.jar.basename>spark-assembly-${project.version}-hadoop${hadoop.version}.jar</spark.jar.basename>
     <spark.jar>${project.build.directory}/${spark.jar.dir}/${spark.jar.basename}</spark.jar>
     <deb.pkg.name>spark</deb.pkg.name>
     <deb.install.path>/usr/share/spark</deb.install.path>

bin/compute-classpath.sh

@@ -50,9 +50,9 @@ if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
 else
   # Else use spark-assembly jar from either RELEASE or assembly directory
   if [ -f "$FWDIR/RELEASE" ]; then
-    ASSEMBLY_JAR=`ls "$FWDIR"/jars/spark*-assembly*.jar`
+    ASSEMBLY_JAR=`ls "$FWDIR"/lib/spark-assembly*hadoop*.jar`
   else
-    ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark*-assembly*hadoop*.jar`
+    ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*.jar`
   fi
   CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR"
 fi

bin/run-example

@@ -40,12 +40,15 @@ fi
 # Figure out the JAR file that our examples were packaged into. This includes a bit of a hack
 # to avoid the -sources and -doc packages that are built by publish-local.
 EXAMPLES_DIR="$FWDIR"/examples
-SPARK_EXAMPLES_JAR=""
-if [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar ]; then
-  export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar`
+
+if [ -f "$FWDIR/RELEASE" ]; then
+  export SPARK_EXAMPLES_JAR=`ls "$FWDIR"/lib/spark-examples-*hadoop*.jar`
+elif [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.jar ]; then
+  export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.jar`
 fi
+
 if [[ -z $SPARK_EXAMPLES_JAR ]]; then
-  echo "Failed to find Spark examples assembly in $FWDIR/examples/target" >&2
+  echo "Failed to find Spark examples assembly in $FWDIR/lib or $FWDIR/examples/target" >&2
   echo "You need to build Spark with sbt/sbt assembly before running this program" >&2
   exit 1
 fi

conf/spark-env.sh.template

@@ -5,6 +5,7 @@
 
 # Options read when launching programs locally with 
 # ./bin/run-example or ./bin/spark-submit
+# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
 # - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
 # - SPARK_PUBLIC_DNS, to set the public dns name of the driver program
 # - SPARK_CLASSPATH, default classpath entries to append
@@ -17,6 +18,7 @@
 # - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos
 
 # Options read in YARN client mode
+# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
 # - SPARK_EXECUTOR_INSTANCES, Number of workers to start (Default: 2)
 # - SPARK_EXECUTOR_CORES, Number of cores for the workers (Default: 1).
 # - SPARK_EXECUTOR_MEMORY, Memory per Worker (e.g. 1000M, 2G) (Default: 1G)

core/pom.xml

@@ -247,7 +247,7 @@
     <dependency>
       <groupId>org.spark-project</groupId>
       <artifactId>pyrolite</artifactId>
-      <version>2.0</version>
+      <version>2.0.1</version>
     </dependency>
   </dependencies>
   <build>

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

@@ -216,10 +216,33 @@ class SparkContext(config: SparkConf) extends Logging {
   private[spark] val ui = new SparkUI(this)
   ui.bind()
 
+  /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
+  val hadoopConfiguration: Configuration = {
+    val env = SparkEnv.get
+    val hadoopConf = SparkHadoopUtil.get.newConfiguration()
+    // Explicitly check for S3 environment variables
+    if (System.getenv("AWS_ACCESS_KEY_ID") != null &&
+        System.getenv("AWS_SECRET_ACCESS_KEY") != null) {
+      hadoopConf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
+      hadoopConf.set("fs.s3n.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
+      hadoopConf.set("fs.s3.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
+      hadoopConf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
+    }
+    // Copy any "spark.hadoop.foo=bar" system properties into conf as "foo=bar"
+    conf.getAll.foreach { case (key, value) =>
+      if (key.startsWith("spark.hadoop.")) {
+        hadoopConf.set(key.substring("spark.hadoop.".length), value)
+      }
+    }
+    val bufferSize = conf.get("spark.buffer.size", "65536")
+    hadoopConf.set("io.file.buffer.size", bufferSize)
+    hadoopConf
+  }
+
   // Optionally log Spark events
   private[spark] val eventLogger: Option[EventLoggingListener] = {
     if (conf.getBoolean("spark.eventLog.enabled", false)) {
-      val logger = new EventLoggingListener(appName, conf)
+      val logger = new EventLoggingListener(appName, conf, hadoopConfiguration)
       logger.start()
       listenerBus.addListener(logger)
       Some(logger)
@@ -294,29 +317,6 @@ class SparkContext(config: SparkConf) extends Logging {
   postEnvironmentUpdate()
   postApplicationStart()
 
-  /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
-  val hadoopConfiguration: Configuration = {
-    val env = SparkEnv.get
-    val hadoopConf = SparkHadoopUtil.get.newConfiguration()
-    // Explicitly check for S3 environment variables
-    if (System.getenv("AWS_ACCESS_KEY_ID") != null &&
-        System.getenv("AWS_SECRET_ACCESS_KEY") != null) {
-      hadoopConf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
-      hadoopConf.set("fs.s3n.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
-      hadoopConf.set("fs.s3.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
-      hadoopConf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
-    }
-    // Copy any "spark.hadoop.foo=bar" system properties into conf as "foo=bar"
-    conf.getAll.foreach { case (key, value) =>
-      if (key.startsWith("spark.hadoop.")) {
-        hadoopConf.set(key.substring("spark.hadoop.".length), value)
-      }
-    }
-    val bufferSize = conf.get("spark.buffer.size", "65536")
-    hadoopConf.set("io.file.buffer.size", bufferSize)
-    hadoopConf
-  }
-
   private[spark] var checkpointDir: Option[String] = None
 
   // Thread Local variable that can be used by users to pass information down the stack
@@ -381,16 +381,27 @@ class SparkContext(config: SparkConf) extends Logging {
    * // In a separate thread:
    * sc.cancelJobGroup("some_job_to_cancel")
    * }}}
+   *
+   * If interruptOnCancel is set to true for the job group, then job cancellation will result
+   * in Thread.interrupt() being called on the job's executor threads. This is useful to help ensure
+   * that the tasks are actually stopped in a timely manner, but is off by default due to HDFS-1208,
+   * where HDFS may respond to Thread.interrupt() by marking nodes as dead.
    */
-  def setJobGroup(groupId: String, description: String) {
+  def setJobGroup(groupId: String, description: String, interruptOnCancel: Boolean = false) {
     setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, description)
     setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, groupId)
+    // Note: Specifying interruptOnCancel in setJobGroup (rather than cancelJobGroup) avoids
+    // changing several public APIs and allows Spark cancellations outside of the cancelJobGroup
+    // APIs to also take advantage of this property (e.g., internal job failures or canceling from
+    // JobProgressTab UI) on a per-job basis.
+    setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, interruptOnCancel.toString)
   }
 
   /** Clear the current thread's job group ID and its description. */
   def clearJobGroup() {
     setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, null)
     setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, null)
+    setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, null)
   }
 
   // Post init
@@ -1244,6 +1255,8 @@ object SparkContext extends Logging {
 
   private[spark] val SPARK_JOB_GROUP_ID = "spark.jobGroup.id"
 
+  private[spark] val SPARK_JOB_INTERRUPT_ON_CANCEL = "spark.job.interruptOnCancel"
+
   private[spark] val SPARK_UNKNOWN_USER = "<unknown>"
 
   implicit object DoubleAccumulatorParam extends AccumulatorParam[Double] {
@@ -1268,8 +1281,10 @@ object SparkContext extends Logging {
 
   // TODO: Add AccumulatorParams for other types, e.g. lists and strings
 
-  implicit def rddToPairRDDFunctions[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]) =
+  implicit def rddToPairRDDFunctions[K, V](rdd: RDD[(K, V)])
+      (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null) = {
     new PairRDDFunctions(rdd)
+  }
 
   implicit def rddToAsyncRDDActions[T: ClassTag](rdd: RDD[T]) = new AsyncRDDActions(rdd)
 

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

@@ -33,7 +33,7 @@ class TaskContext(
   val attemptId: Long,
   val runningLocally: Boolean = false,
   @volatile var interrupted: Boolean = false,
-  private[spark] val taskMetrics: TaskMetrics = TaskMetrics.empty()
+  private[spark] val taskMetrics: TaskMetrics = TaskMetrics.empty
 ) extends Serializable {
 
   @deprecated("use partitionId", "0.8.1")

core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala

@@ -24,6 +24,7 @@ import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collectio
 
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
+import scala.util.Try
 
 import net.razorvine.pickle.{Pickler, Unpickler}
 
@@ -89,16 +90,22 @@ private[spark] class PythonRDD[T: ClassTag](
           dataOut.flush()
           worker.shutdownOutput()
         } catch {
+
           case e: java.io.FileNotFoundException =>
             readerException = e
-            // Kill the Python worker process:
-            worker.shutdownOutput()
+            Try(worker.shutdownOutput()) // kill Python worker process
+
           case e: IOException =>
             // This can happen for legitimate reasons if the Python code stops returning data
-            // before we are done passing elements through, e.g., for take(). Just log a message
-            // to say it happened.
-            logInfo("stdin writer to Python finished early")
-            logDebug("stdin writer to Python finished early", e)
+            // before we are done passing elements through, e.g., for take(). Just log a message to
+            // say it happened (as it could also be hiding a real IOException from a data source).
+            logInfo("stdin writer to Python finished early (may not be an error)", e)
+
+          case e: Exception =>
+            // We must avoid throwing exceptions here, because the thread uncaught exception handler
+            // will kill the whole executor (see Executor).
+            readerException = e
+            Try(worker.shutdownOutput()) // kill Python worker process
         }
       }
     }.start()
@@ -152,7 +159,7 @@ private[spark] class PythonRDD[T: ClassTag](
               val exLength = stream.readInt()
               val obj = new Array[Byte](exLength)
               stream.readFully(obj)
-              throw new PythonException(new String(obj))
+              throw new PythonException(new String(obj), readerException)
             case SpecialLengths.END_OF_DATA_SECTION =>
               // We've finished the data section of the output, but we can still
               // read some accumulator updates:
@@ -162,15 +169,17 @@ private[spark] class PythonRDD[T: ClassTag](
                 val update = new Array[Byte](updateLen)
                 stream.readFully(update)
                 accumulator += Collections.singletonList(update)
-
               }
               Array.empty[Byte]
           }
         } catch {
-          case eof: EOFException => {
+          case e: Exception if readerException != null =>
+            logError("Python worker exited unexpectedly (crashed)", e)
+            logError("Python crash may have been caused by prior exception:", readerException)
+            throw readerException
+
+          case eof: EOFException =>
             throw new SparkException("Python worker exited unexpectedly (crashed)", eof)
-          }
-          case e: Throwable => throw e
         }
       }
 
@@ -185,7 +194,7 @@ private[spark] class PythonRDD[T: ClassTag](
 }
 
 /** Thrown for exceptions in user Python code. */
-private class PythonException(msg: String) extends Exception(msg)
+private class PythonException(msg: String, cause: Exception) extends RuntimeException(msg, cause)
 
 /**
  * Form an RDD[(Array[Byte], Array[Byte])] from key-value pairs returned from Python.

core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala

@@ -196,7 +196,7 @@ object SparkSubmit {
         childArgs ++= appArgs.childArgs
       } else if (clusterManager == YARN) {
         for (arg <- appArgs.childArgs) {
-          childArgs += ("--args", arg)
+          childArgs += ("--arg", arg)
         }
       }
     }

core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala

@@ -116,6 +116,15 @@ private[spark] class SparkSubmitArguments(args: Array[String]) {
     if (args.length == 0) printUsageAndExit(-1)
     if (primaryResource == null) SparkSubmit.printErrorAndExit("Must specify a primary resource")
     if (mainClass == null) SparkSubmit.printErrorAndExit("Must specify a main class with --class")
+
+    if (master.startsWith("yarn")) {
+      val hasHadoopEnv = sys.env.contains("HADOOP_CONF_DIR") || sys.env.contains("YARN_CONF_DIR")
+      val testing = sys.env.contains("SPARK_TESTING")
+      if (!hasHadoopEnv && !testing) {
+        throw new Exception(s"When running with master '$master' " +
+          "either HADOOP_CONF_DIR or YARN_CONF_DIR must be set in the environment.")
+      }
+    }
   }
 
   override def toString =  {