Merge pull request #2 from markhamstra/master-csd

Catching up with Apache and reverting the Zookeeper version

Author: jhartlaub

README.md

@@ -69,7 +69,7 @@ described below.
 
 When developing a Spark application, specify the Hadoop version by adding the
 "hadoop-client" artifact to your project's dependencies. For example, if you're
-using Hadoop 1.0.1 and build your application using SBT, add this entry to
+using Hadoop 1.2.1 and build your application using SBT, add this entry to
 `libraryDependencies`:
 
     "org.apache.hadoop" % "hadoop-client" % "1.2.1"

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

@@ -24,7 +24,7 @@ import java.util.concurrent.atomic.AtomicInteger
 
 import scala.collection.Map
 import scala.collection.generic.Growable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashMap
 
@@ -51,7 +51,7 @@ import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFor
 
 import org.apache.mesos.MesosNativeLibrary
 
-import org.apache.spark.deploy.LocalSparkCluster
+import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil}
 import org.apache.spark.partial.{ApproximateEvaluator, PartialResult}
 import org.apache.spark.rdd._
 import org.apache.spark.scheduler._
@@ -248,7 +248,7 @@ class SparkContext(
   /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
   val hadoopConfiguration = {
     val env = SparkEnv.get
-    val conf = env.hadoop.newConfiguration()
+    val conf = SparkHadoopUtil.get.newConfiguration()
     // Explicitly check for S3 environment variables
     if (System.getenv("AWS_ACCESS_KEY_ID") != null &&
         System.getenv("AWS_SECRET_ACCESS_KEY") != null) {
@@ -258,8 +258,10 @@ class SparkContext(
       conf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
     }
     // Copy any "spark.hadoop.foo=bar" system properties into conf as "foo=bar"
-    for (key <- System.getProperties.toMap[String, String].keys if key.startsWith("spark.hadoop.")) {
-      conf.set(key.substring("spark.hadoop.".length), System.getProperty(key))
+    Utils.getSystemProperties.foreach { case (key, value) =>
+      if (key.startsWith("spark.hadoop.")) {
+        conf.set(key.substring("spark.hadoop.".length), value)
+      }
     }
     val bufferSize = System.getProperty("spark.buffer.size", "65536")
     conf.set("io.file.buffer.size", bufferSize)
@@ -382,7 +384,7 @@ class SparkContext(
       minSplits: Int = defaultMinSplits
       ): RDD[(K, V)] = {
     // Add necessary security credentials to the JobConf before broadcasting it.
-    SparkEnv.get.hadoop.addCredentials(conf)
+    SparkHadoopUtil.get.addCredentials(conf)
     new HadoopRDD(this, conf, inputFormatClass, keyClass, valueClass, minSplits)
   }
 
@@ -592,7 +594,8 @@ class SparkContext(
     val uri = new URI(path)
     val key = uri.getScheme match {
       case null | "file" => env.httpFileServer.addFile(new File(uri.getPath))
-      case _ => path
+      case "local"       => "file:" + uri.getPath
+      case _             => path
     }
     addedFiles(key) = System.currentTimeMillis
 
@@ -686,7 +689,7 @@ class SparkContext(
   /**
    * Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.
    * The `path` passed can be either a local file, a file in HDFS (or other Hadoop-supported
-   * filesystems), or an HTTP, HTTPS or FTP URI.
+   * filesystems), an HTTP, HTTPS or FTP URI, or local:/path for a file on every worker node.
    */
   def addJar(path: String) {
     if (path == null) {
@@ -699,8 +702,9 @@ class SparkContext(
       } else {
         val uri = new URI(path)
         key = uri.getScheme match {
+          // A JAR file which exists only on the driver node
           case null | "file" =>
-            if (env.hadoop.isYarnMode()) {
+            if (SparkHadoopUtil.get.isYarnMode()) {
               // In order for this to work on yarn the user must specify the --addjars option to
               // the client to upload the file into the distributed cache to make it show up in the
               // current working directory.
@@ -716,6 +720,9 @@ class SparkContext(
             } else {
               env.httpFileServer.addJar(new File(uri.getPath))
             }
+          // A JAR file which exists locally on every worker node
+          case "local" =>
+            "file:" + uri.getPath
           case _ =>
             path
         }
@@ -935,9 +942,8 @@ class SparkContext(
    * prevent accidental overriding of checkpoint files in the existing directory.
    */
   def setCheckpointDir(dir: String, useExisting: Boolean = false) {
-    val env = SparkEnv.get
     val path = new Path(dir)
-    val fs = path.getFileSystem(env.hadoop.newConfiguration())
+    val fs = path.getFileSystem(SparkHadoopUtil.get.newConfiguration())
     if (!useExisting) {
       if (fs.exists(path)) {
         throw new Exception("Checkpoint directory '" + path + "' already exists.")

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

@@ -25,13 +25,13 @@ import akka.remote.RemoteActorRefProvider
 
 import org.apache.spark.broadcast.BroadcastManager
 import org.apache.spark.metrics.MetricsSystem
-import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.storage.{BlockManagerMasterActor, BlockManager, BlockManagerMaster}
 import org.apache.spark.network.ConnectionManager
 import org.apache.spark.serializer.{Serializer, SerializerManager}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.api.python.PythonWorkerFactory
 
+import com.google.common.collect.MapMaker
 
 /**
  * Holds all the runtime environment objects for a running Spark instance (either master or worker),
@@ -58,18 +58,9 @@ class SparkEnv (
 
   private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]()
 
-  val hadoop = {
-    val yarnMode = java.lang.Boolean.valueOf(System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE")))
-    if(yarnMode) {
-      try {
-        Class.forName("org.apache.spark.deploy.yarn.YarnSparkHadoopUtil").newInstance.asInstanceOf[SparkHadoopUtil]
-      } catch {
-        case th: Throwable => throw new SparkException("Unable to load YARN support", th)
-      }
-    } else {
-      new SparkHadoopUtil
-    }
-  }
+  // A general, soft-reference map for metadata needed during HadoopRDD split computation
+  // (e.g., HadoopFileRDD uses this to cache JobConfs and InputFormats).
+  private[spark] val hadoopJobMetadata = new MapMaker().softValues().makeMap[String, Any]()
 
   def stop() {
     pythonWorkers.foreach { case(key, worker) => worker.stop() }

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

@@ -622,4 +622,15 @@ object JavaPairRDD {
     new JavaPairRDD[K, V](rdd)
 
   implicit def toRDD[K, V](rdd: JavaPairRDD[K, V]): RDD[(K, V)] = rdd.rdd
+
+
+  /** Convert a JavaRDD of key-value pairs to JavaPairRDD. */
+  def fromJavaRDD[K, V](rdd: JavaRDD[(K, V)]): JavaPairRDD[K, V] = {
+    implicit val cmk: ClassManifest[K] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]]
+    implicit val cmv: ClassManifest[V] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V]]
+    new JavaPairRDD[K, V](rdd.rdd)
+  }
+
 }

core/src/main/scala/org/apache/spark/api/java/function/Function3.java

@@ -19,7 +19,6 @@
 
 import scala.reflect.ClassManifest;
 import scala.reflect.ClassManifest$;
-import scala.runtime.AbstractFunction3;
 
 import java.io.Serializable;
 
@@ -35,4 +34,3 @@ public ClassManifest<R> returnType() {
     return (ClassManifest<R>) ClassManifest$.MODULE$.fromClass(Object.class);
   }
 }
-

core/src/main/scala/org/apache/spark/api/java/function/WrappedFunction3.scala

@@ -24,7 +24,8 @@ import scala.runtime.AbstractFunction3
  * apply() method as call() and declare that it can throw Exception (since AbstractFunction3.apply
  * isn't marked to allow that).
  */
-private[spark] abstract class WrappedFunction3[T1, T2, T3, R] extends AbstractFunction3[T1, T2, T3, R] {
+private[spark] abstract class WrappedFunction3[T1, T2, T3, R]
+  extends AbstractFunction3[T1, T2, T3, R] {
   @throws(classOf[Exception])
   def call(t1: T1, t2: T2, t3: T3): R
 

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

@@ -20,17 +20,13 @@ package org.apache.spark.deploy
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.mapred.JobConf
 
-import com.google.common.collect.MapMaker
-
+import org.apache.spark.SparkException
 
 /**
  * Contains util methods to interact with Hadoop from Spark.
  */
 private[spark]
 class SparkHadoopUtil {
-  // A general, soft-reference map for metadata needed during HadoopRDD split computation
-  // (e.g., HadoopFileRDD uses this to cache JobConfs and InputFormats).
-  private[spark] val hadoopJobMetadata = new MapMaker().softValues().makeMap[String, Any]()
 
   /**
    * Return an appropriate (subclass) of Configuration. Creating config can initializes some Hadoop
@@ -45,5 +41,23 @@ class SparkHadoopUtil {
   def addCredentials(conf: JobConf) {}
 
   def isYarnMode(): Boolean = { false }
-
+}
+  
+object SparkHadoopUtil {
+  private val hadoop = { 
+    val yarnMode = java.lang.Boolean.valueOf(System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE")))
+    if (yarnMode) {
+      try {
+        Class.forName("org.apache.spark.deploy.yarn.YarnSparkHadoopUtil").newInstance.asInstanceOf[SparkHadoopUtil]
+      } catch {
+       case th: Throwable => throw new SparkException("Unable to load YARN support", th)
+      }
+    } else {
+      new SparkHadoopUtil
+    }
+  }
+  
+  def get: SparkHadoopUtil = {
+    hadoop
+  }
 }

core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala

@@ -18,6 +18,7 @@
 package org.apache.spark.rdd
 
 import org.apache.spark._
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.hadoop.mapred.{FileInputFormat, SequenceFileInputFormat, JobConf, Reporter}
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io.{NullWritable, BytesWritable}
@@ -83,7 +84,7 @@ private[spark] object CheckpointRDD extends Logging {
   def writeToFile[T](path: String, blockSize: Int = -1)(ctx: TaskContext, iterator: Iterator[T]) {
     val env = SparkEnv.get
     val outputDir = new Path(path)
-    val fs = outputDir.getFileSystem(env.hadoop.newConfiguration())
+    val fs = outputDir.getFileSystem(SparkHadoopUtil.get.newConfiguration())
 
     val finalOutputName = splitIdToFile(ctx.partitionId)
     val finalOutputPath = new Path(outputDir, finalOutputName)
@@ -122,7 +123,7 @@ private[spark] object CheckpointRDD extends Logging {
 
   def readFromFile[T](path: Path, context: TaskContext): Iterator[T] = {
     val env = SparkEnv.get
-    val fs = path.getFileSystem(env.hadoop.newConfiguration())
+    val fs = path.getFileSystem(SparkHadoopUtil.get.newConfiguration())
     val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt
     val fileInputStream = fs.open(path, bufferSize)
     val serializer = env.serializer.newInstance()
@@ -145,7 +146,7 @@ private[spark] object CheckpointRDD extends Logging {
     val sc = new SparkContext(cluster, "CheckpointRDD Test")
     val rdd = sc.makeRDD(1 to 10, 10).flatMap(x => 1 to 10000)
     val path = new Path(hdfsPath, "temp")
-    val fs = path.getFileSystem(env.hadoop.newConfiguration())
+    val fs = path.getFileSystem(SparkHadoopUtil.get.newConfiguration())
     sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, 1024) _)
     val cpRDD = new CheckpointRDD[Int](sc, path.toString)
     assert(cpRDD.partitions.length == rdd.partitions.length, "Number of partitions is not the same")

core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala

@@ -29,6 +29,7 @@ import org.apache.hadoop.util.ReflectionUtils
 
 import org.apache.spark._
 import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.util.NextIterator
 import org.apache.hadoop.conf.{Configuration, Configurable}
 
@@ -198,10 +199,10 @@ private[spark] object HadoopRDD {
    * The three methods below are helpers for accessing the local map, a property of the SparkEnv of
    * the local process.
    */
-  def getCachedMetadata(key: String) = SparkEnv.get.hadoop.hadoopJobMetadata.get(key)
+  def getCachedMetadata(key: String) = SparkEnv.get.hadoopJobMetadata.get(key)
 
-  def containsCachedMetadata(key: String) = SparkEnv.get.hadoop.hadoopJobMetadata.containsKey(key)
+  def containsCachedMetadata(key: String) = SparkEnv.get.hadoopJobMetadata.containsKey(key)
 
   def putCachedMetadata(key: String, value: Any) =
-    SparkEnv.get.hadoop.hadoopJobMetadata.put(key, value)
+    SparkEnv.get.hadoopJobMetadata.put(key, value)
 }

core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala

@@ -420,15 +420,14 @@ class DAGScheduler(
       case ExecutorLost(execId) =>
         handleExecutorLost(execId)
 
-      case begin: BeginEvent =>
-        listenerBus.post(SparkListenerTaskStart(begin.task, begin.taskInfo))
+      case BeginEvent(task, taskInfo) =>
+        listenerBus.post(SparkListenerTaskStart(task, taskInfo))
 
-      case gettingResult: GettingResultEvent =>
-        listenerBus.post(SparkListenerTaskGettingResult(gettingResult.task, gettingResult.taskInfo))
+      case GettingResultEvent(task, taskInfo) =>
+        listenerBus.post(SparkListenerTaskGettingResult(task, taskInfo))
 
-      case completion: CompletionEvent =>
-        listenerBus.post(SparkListenerTaskEnd(
-          completion.task, completion.reason, completion.taskInfo, completion.taskMetrics))
+      case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
+        listenerBus.post(SparkListenerTaskEnd(task, reason, taskInfo, taskMetrics))
         handleTaskCompletion(completion)
 
       case TaskSetFailed(taskSet, reason) =>