Merge remote-tracking branch 'upstream/master' into ldaonline

s

Author: hhbyyh

assembly/pom.xml

@@ -39,7 +39,7 @@
     <deb.pkg.name>spark</deb.pkg.name>
     <deb.install.path>/usr/share/spark</deb.install.path>
     <deb.user>root</deb.user>
-    <deb.bin.filemode>744</deb.bin.filemode>
+    <deb.bin.filemode>755</deb.bin.filemode>
   </properties>
 
   <dependencies>
@@ -280,7 +280,7 @@
                         <user>${deb.user}</user>
                         <group>${deb.user}</group>
                         <prefix>${deb.install.path}/conf</prefix>
-                        <filemode>744</filemode>
+                        <filemode>${deb.bin.filemode}</filemode>
                       </mapper>
                     </data>
                     <data>
@@ -302,7 +302,7 @@
                         <user>${deb.user}</user>
                         <group>${deb.user}</group>
                         <prefix>${deb.install.path}/sbin</prefix>
-                        <filemode>744</filemode>
+                        <filemode>${deb.bin.filemode}</filemode>
                       </mapper>
                     </data>
                     <data>
@@ -313,7 +313,7 @@
                         <user>${deb.user}</user>
                         <group>${deb.user}</group>
                         <prefix>${deb.install.path}/python</prefix>
-                        <filemode>744</filemode>
+                        <filemode>${deb.bin.filemode}</filemode>
                       </mapper>
                     </data>
                   </dataSet>

bin/spark-shell.cmd

@@ -25,7 +25,7 @@ set ORIG_ARGS=%*
 rem Reset the values of all variables used
 set SPARK_SUBMIT_DEPLOY_MODE=client
 
-if not defined %SPARK_CONF_DIR% (
+if [%SPARK_CONF_DIR%] == [] (
   set SPARK_CONF_DIR=%SPARK_HOME%\conf
 )
 set SPARK_SUBMIT_PROPERTIES_FILE=%SPARK_CONF_DIR%\spark-defaults.conf

bin/spark-submit2.cmd

@@ -243,6 +243,14 @@
       <groupId>io.dropwizard.metrics</groupId>
       <artifactId>metrics-graphite</artifactId>
     </dependency>
+    <dependency>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-databind</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.fasterxml.jackson.module</groupId>
+      <artifactId>jackson-module-scala_2.10</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.derby</groupId>
       <artifactId>derby</artifactId>

core/pom.xml

@@ -103,6 +103,12 @@ span.expand-details {
   float: right;
 }
 
+span.rest-uri {
+  font-size: 10pt;
+  font-style: italic;
+  color: gray;
+}
+
 pre {
   font-size: 0.8em;
 }

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

@@ -47,10 +47,15 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
         val inputMetrics = blockResult.inputMetrics
         val existingMetrics = context.taskMetrics
           .getInputMetricsForReadMethod(inputMetrics.readMethod)
-        existingMetrics.addBytesRead(inputMetrics.bytesRead)
-
-        new InterruptibleIterator(context, blockResult.data.asInstanceOf[Iterator[T]])
+        existingMetrics.incBytesRead(inputMetrics.bytesRead)
 
+        val iter = blockResult.data.asInstanceOf[Iterator[T]]
+        new InterruptibleIterator[T](context, iter) {
+          override def next(): T = {
+            existingMetrics.incRecordsRead(1)
+            delegate.next()
+          }
+        }
       case None =>
         // Acquire a lock for loading this partition
         // If another thread already holds the lock, wait for it to finish return its results

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

@@ -76,15 +76,15 @@ private[spark] class ExecutorAllocationManager(
   private val maxNumExecutors = conf.getInt("spark.dynamicAllocation.maxExecutors",
     Integer.MAX_VALUE)
 
-  // How long there must be backlogged tasks for before an addition is triggered
+  // How long there must be backlogged tasks for before an addition is triggered (seconds)
   private val schedulerBacklogTimeout = conf.getLong(
-    "spark.dynamicAllocation.schedulerBacklogTimeout", 60)
+    "spark.dynamicAllocation.schedulerBacklogTimeout", 5)
 
   // Same as above, but used only after `schedulerBacklogTimeout` is exceeded
   private val sustainedSchedulerBacklogTimeout = conf.getLong(
     "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", schedulerBacklogTimeout)
 
-  // How long an executor must be idle for before it is removed
+  // How long an executor must be idle for before it is removed (seconds)
   private val executorIdleTimeout = conf.getLong(
     "spark.dynamicAllocation.executorIdleTimeout", 600)
 
@@ -486,8 +486,8 @@ private[spark] class ExecutorAllocationManager(
       }
     }
 
-    override def onBlockManagerAdded(blockManagerAdded: SparkListenerBlockManagerAdded): Unit = {
-      val executorId = blockManagerAdded.blockManagerId.executorId
+    override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit = {
+      val executorId = executorAdded.executorId
       if (executorId != SparkContext.DRIVER_IDENTIFIER) {
         // This guards against the race condition in which the `SparkListenerTaskStart`
         // event is posted before the `SparkListenerBlockManagerAdded` event, which is
@@ -498,9 +498,8 @@ private[spark] class ExecutorAllocationManager(
       }
     }
 
-    override def onBlockManagerRemoved(
-        blockManagerRemoved: SparkListenerBlockManagerRemoved): Unit = {
-      allocationManager.onExecutorRemoved(blockManagerRemoved.blockManagerId.executorId)
+    override def onExecutorRemoved(executorRemoved: SparkListenerExecutorRemoved): Unit = {
+      allocationManager.onExecutorRemoved(executorRemoved.executorId)
     }
 
     /**

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

@@ -288,7 +288,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   // the bound port to the cluster manager properly
   ui.foreach(_.bind())
 
-  /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
+  /**
+   * A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse.
+   *
+   * '''Note:''' As it will be reused in all Hadoop RDDs, it's better not to modify it unless you
+   * plan to set some global configurations for all Hadoop RDDs.
+   */
   val hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(conf)
 
   // Add each JAR given through the constructor
@@ -694,7 +699,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable),
    * using the older MapReduce API (`org.apache.hadoop.mapred`).
    *
-   * @param conf JobConf for setting up the dataset
+   * @param conf JobConf for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
    * @param inputFormatClass Class of the InputFormat
    * @param keyClass Class of the keys
    * @param valueClass Class of the values
@@ -830,6 +838,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * Get an RDD for a given Hadoop file with an arbitrary new API InputFormat
    * and extra configuration options to pass to the input format.
    *
+   * @param conf Configuration for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
+   * @param fClass Class of the InputFormat
+   * @param kClass Class of the keys
+   * @param vClass Class of the values
+   *
    * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each
    * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
    * operation will create many references to the same object.
@@ -2094,7 +2110,7 @@ object SparkContext extends Logging {
 
         val scheduler = new TaskSchedulerImpl(sc)
         val localCluster = new LocalSparkCluster(
-          numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt)
+          numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt, sc.conf)
         val masterUrls = localCluster.start()
         val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls)
         scheduler.initialize(backend)

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

@@ -43,11 +43,20 @@ private[spark] object TestUtils {
    * Note: if this is used during class loader tests, class names should be unique
    * in order to avoid interference between tests.
    */
-  def createJarWithClasses(classNames: Seq[String], value: String = ""): URL = {
+  def createJarWithClasses(
+      classNames: Seq[String],
+      toStringValue: String = "",
+      classNamesWithBase: Seq[(String, String)] = Seq(),
+      classpathUrls: Seq[URL] = Seq()): URL = {
     val tempDir = Utils.createTempDir()
-    val files = for (name <- classNames) yield createCompiledClass(name, tempDir, value)
+    val files1 = for (name <- classNames) yield {
+      createCompiledClass(name, tempDir, toStringValue, classpathUrls = classpathUrls) 
+    }
+    val files2 = for ((childName, baseName) <- classNamesWithBase) yield {
+      createCompiledClass(childName, tempDir, toStringValue, baseName, classpathUrls)
+    }
     val jarFile = new File(tempDir, "testJar-%s.jar".format(System.currentTimeMillis()))
-    createJar(files, jarFile)
+    createJar(files1 ++ files2, jarFile)
   }
 
 
@@ -85,15 +94,26 @@ private[spark] object TestUtils {
   }
 
   /** Creates a compiled class with the given name. Class file will be placed in destDir. */
-  def createCompiledClass(className: String, destDir: File, value: String = ""): File = {
+  def createCompiledClass(
+      className: String,
+      destDir: File,
+      toStringValue: String = "",
+      baseClass: String = null,
+      classpathUrls: Seq[URL] = Seq()): File = {
     val compiler = ToolProvider.getSystemJavaCompiler
+    val extendsText = Option(baseClass).map { c => s" extends ${c}" }.getOrElse("")
     val sourceFile = new JavaSourceFromString(className,
-      "public class " + className + " implements java.io.Serializable {" +
-      "  @Override public String toString() { return \"" + value + "\"; }}")
+      "public class " + className + extendsText + " implements java.io.Serializable {" +
+      "  @Override public String toString() { return \"" + toStringValue + "\"; }}")
 
     // Calling this outputs a class file in pwd. It's easier to just rename the file than
     // build a custom FileManager that controls the output location.
-    compiler.getTask(null, null, null, null, null, Seq(sourceFile)).call()
+    val options = if (classpathUrls.nonEmpty) {
+      Seq("-classpath", classpathUrls.map { _.getFile }.mkString(File.pathSeparator))
+    } else {
+      Seq()
+    }
+    compiler.getTask(null, null, null, options, null, Seq(sourceFile)).call()
 
     val fileName = className + ".class"
     val result = new File(fileName)

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

@@ -373,6 +373,15 @@ class JavaSparkContext(val sc: SparkContext)
    * other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable,
    * etc).
    *
+   * @param conf JobConf for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
+   * @param inputFormatClass Class of the InputFormat
+   * @param keyClass Class of the keys
+   * @param valueClass Class of the values
+   * @param minPartitions Minimum number of Hadoop Splits to generate.
+   *
    * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each
    * record, directly caching the returned RDD will create many references to the same object.
    * If you plan to directly cache Hadoop writable objects, you should first copy them using
@@ -395,6 +404,14 @@ class JavaSparkContext(val sc: SparkContext)
    * Get an RDD for a Hadoop-readable dataset from a Hadooop JobConf giving its InputFormat and any
    * other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable,
    *
+   * @param conf JobConf for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
+   * @param inputFormatClass Class of the InputFormat
+   * @param keyClass Class of the keys
+   * @param valueClass Class of the values
+   *
    * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each
    * record, directly caching the returned RDD will create many references to the same object.
    * If you plan to directly cache Hadoop writable objects, you should first copy them using
@@ -476,6 +493,14 @@ class JavaSparkContext(val sc: SparkContext)
    * Get an RDD for a given Hadoop file with an arbitrary new API InputFormat
    * and extra configuration options to pass to the input format.
    *
+   * @param conf Configuration for setting up the dataset. Note: This will be put into a Broadcast.
+   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
+   *             sure you won't modify the conf. A safe approach is always creating a new conf for
+   *             a new RDD.
+   * @param fClass Class of the InputFormat
+   * @param kClass Class of the keys
+   * @param vClass Class of the values
+   *
    * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each
    * record, directly caching the returned RDD will create many references to the same object.
    * If you plan to directly cache Hadoop writable objects, you should first copy them using
@@ -675,6 +700,9 @@ class JavaSparkContext(val sc: SparkContext)
 
   /**
    * Returns the Hadoop configuration used for the Hadoop code (e.g. file systems) we reuse.
+   *
+   * '''Note:''' As it will be reused in all Hadoop RDDs, it's better not to modify it unless you
+   * plan to set some global configurations for all Hadoop RDDs.
    */
   def hadoopConfiguration(): Configuration = {
     sc.hadoopConfiguration