Merge remote-tracking branch 'apache-github/master' into standalone-driver

Conflicts:
	core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
	pom.xml

128 files changed, 2936 insertions, 1052 deletions

diff --git a/.gitignore b/.gitignore
index 399362f7d3..39635d7eef 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@
 *.iml
 *.iws
 .idea/
+sbt/*.jar
 .settings
 .cache
 /build/
diff --git a/README.md b/README.md
index 6daa4633ae..c840a68f76 100644
--- a/README.md
+++ b/README.md
@@ -13,9 +13,11 @@ This README file only contains basic setup instructions.
 ## Building
 
 Spark requires Scala 2.10. The project is built using Simple Build Tool (SBT),
-which can be obtained [here](http://www.scala-sbt.org). To build Spark and its example programs, run:
+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:
 
-    sbt assembly
+    ./sbt/sbt assembly
 
 Once you've built Spark, the easiest way to start using it is the shell:
 
@@ -38,10 +40,10 @@ locally with one thread, or "local[N]" to run locally with N threads.
 
 ## Running tests
 
-Testing first requires [Building](#Building) Spark. Once Spark is built, tests
+Testing first requires [Building](#building) Spark. Once Spark is built, tests
 can be run using:
 
-`sbt test`
+`./sbt/sbt test`
  
 ## A Note About Hadoop Versions
 
@@ -55,22 +57,22 @@ For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop
 versions without YARN, use:
 
     # Apache Hadoop 1.2.1
-    $ SPARK_HADOOP_VERSION=1.2.1 sbt assembly
+    $ SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly
 
     # Cloudera CDH 4.2.0 with MapReduce v1
-    $ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt assembly
+    $ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt/sbt assembly
 
 For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
 with YARN, also set `SPARK_YARN=true`:
 
     # Apache Hadoop 2.0.5-alpha
-    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt assembly
+    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
     # Cloudera CDH 4.2.0 with MapReduce v2
-    $ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt assembly
+    $ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt/sbt assembly
 
     # Apache Hadoop 2.2.X and newer
-    $ SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt assembly
+    $ SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt assembly
 
 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
diff --git a/assembly/pom.xml b/assembly/pom.xml
index 9b70812c64..54a25910ce 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -108,12 +108,12 @@
                 <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                   <resource>META-INF/services/org.apache.hadoop.fs.FileSystem</resource>
                 </transformer>
-              </transformers>
-              <transformers>
-                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
                 <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                   <resource>reference.conf</resource>
                 </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
+                  <resource>log4j.properties</resource>
+                </transformer>
               </transformers>
             </configuration>
           </execution>
diff --git a/bin/pyspark b/bin/pyspark
index f97dfa7e2f..d6810f4686 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -31,7 +31,7 @@ if [ ! -f "$FWDIR/RELEASE" ]; then
   ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*.jar >& /dev/null
   if [[ $? != 0 ]]; then
     echo "Failed to find Spark assembly in $FWDIR/assembly/target" >&2
-    echo "You need to build Spark with sbt assembly before running this program" >&2
+    echo "You need to build Spark with sbt/sbt assembly before running this program" >&2
     exit 1
   fi
 fi
diff --git a/bin/run-example b/bin/run-example
index dfb4bf7baf..2e9d51440b 100755
--- a/bin/run-example
+++ b/bin/run-example
@@ -45,20 +45,15 @@ fi
 EXAMPLES_DIR="$FWDIR"/examples
 SPARK_EXAMPLES_JAR=""
 if [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar ]; then
-  # Use the JAR from the SBT build
   export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar`
 fi
-if [ -e "$EXAMPLES_DIR"/target/spark-examples*[0-9Tg].jar ]; then
-  # Use the JAR from the Maven build
-  # TODO: this also needs to become an assembly!
-  export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/target/spark-examples*[0-9Tg].jar`
-fi
 if [[ -z $SPARK_EXAMPLES_JAR ]]; then
   echo "Failed to find Spark examples assembly in $FWDIR/examples/target" >&2
-  echo "You need to build Spark with sbt assembly before running this program" >&2
+  echo "You need to build Spark with sbt/sbt assembly before running this program" >&2
   exit 1
 fi
 
+
 # Since the examples JAR ideally shouldn't include spark-core (that dependency should be
 # "provided"), also add our standard Spark classpath, built using compute-classpath.sh.
 CLASSPATH=`$FWDIR/bin/compute-classpath.sh`
diff --git a/bin/spark-class b/bin/spark-class
index 49b0bef0bd..c4225a392d 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -104,7 +104,7 @@ if [ ! -f "$FWDIR/RELEASE" ]; then
   jars_list=$(ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/ | grep "spark-assembly.*hadoop.*.jar")
   if [ "$num_jars" -eq "0" ]; then
     echo "Failed to find Spark assembly in $FWDIR/assembly/target/scala-$SCALA_VERSION/" >&2
-    echo "You need to build Spark with 'sbt assembly' before running this program." >&2
+    echo "You need to build Spark with 'sbt/sbt assembly' before running this program." >&2
     exit 1
   fi
   if [ "$num_jars" -gt "1" ]; then
diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index 0a35ee7c79..e2071e2ade 100755
--- a/conf/spark-env.sh.template
+++ b/conf/spark-env.sh.template
@@ -18,4 +18,4 @@
 # - SPARK_WORKER_MEMORY, to set how much memory to use (e.g. 1000m, 2g)
 # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT
 # - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
-
+# - SPARK_WORKER_DIR, to set the working directory of worker processes
diff --git a/core/src/main/java/org/apache/spark/network/netty/FileClient.java b/core/src/main/java/org/apache/spark/network/netty/FileClient.java
index 46d61503bc..d2d778b756 100644
--- a/core/src/main/java/org/apache/spark/network/netty/FileClient.java
+++ b/core/src/main/java/org/apache/spark/network/netty/FileClient.java
@@ -31,7 +31,8 @@ import java.util.concurrent.TimeUnit;
 
 class FileClient {
 
-  private Logger LOG = LoggerFactory.getLogger(this.getClass().getName());
+  private static final Logger LOG = LoggerFactory.getLogger(FileClient.class.getName());
+
   private final FileClientHandler handler;
   private Channel channel = null;
   private Bootstrap bootstrap = null;
@@ -39,7 +40,7 @@ class FileClient {
   private final int connectTimeout;
   private final int sendTimeout = 60; // 1 min
 
-  public FileClient(FileClientHandler handler, int connectTimeout) {
+  FileClient(FileClientHandler handler, int connectTimeout) {
     this.handler = handler;
     this.connectTimeout = connectTimeout;
   }
diff --git a/core/src/main/java/org/apache/spark/network/netty/FileClientChannelInitializer.java b/core/src/main/java/org/apache/spark/network/netty/FileClientChannelInitializer.java
index fb61be1c12..264cf97d02 100644
--- a/core/src/main/java/org/apache/spark/network/netty/FileClientChannelInitializer.java
+++ b/core/src/main/java/org/apache/spark/network/netty/FileClientChannelInitializer.java
@@ -25,7 +25,7 @@ class FileClientChannelInitializer extends ChannelInitializer<SocketChannel> {
 
   private final FileClientHandler fhandler;
 
-  public FileClientChannelInitializer(FileClientHandler handler) {
+  FileClientChannelInitializer(FileClientHandler handler) {
     fhandler = handler;
   }
 
diff --git a/core/src/main/java/org/apache/spark/network/netty/FileServer.java b/core/src/main/java/org/apache/spark/network/netty/FileServer.java
index aea7534459..c93425e278 100644
--- a/core/src/main/java/org/apache/spark/network/netty/FileServer.java
+++ b/core/src/main/java/org/apache/spark/network/netty/FileServer.java
@@ -33,15 +33,14 @@ import org.slf4j.LoggerFactory;
  */
 class FileServer {
 
-  private Logger LOG = LoggerFactory.getLogger(this.getClass().getName());
+  private static final Logger LOG = LoggerFactory.getLogger(FileServer.class.getName());
 
   private EventLoopGroup bossGroup = null;
   private EventLoopGroup workerGroup = null;
   private ChannelFuture channelFuture = null;
   private int port = 0;
-  private Thread blockingThread = null;
 
-  public FileServer(PathResolver pResolver, int port) {
+  FileServer(PathResolver pResolver, int port) {
     InetSocketAddress addr = new InetSocketAddress(port);
 
     // Configure the server.
@@ -70,7 +69,8 @@ class FileServer {
    * Start the file server asynchronously in a new thread.
    */
   public void start() {
-    blockingThread = new Thread() {
+    Thread blockingThread = new Thread() {
+      @Override
       public void run() {
         try {
           channelFuture.channel().closeFuture().sync();
diff --git a/core/src/main/java/org/apache/spark/network/netty/FileServerChannelInitializer.java b/core/src/main/java/org/apache/spark/network/netty/FileServerChannelInitializer.java
index 3f15ff898f..46efec8f8d 100644
--- a/core/src/main/java/org/apache/spark/network/netty/FileServerChannelInitializer.java
+++ b/core/src/main/java/org/apache/spark/network/netty/FileServerChannelInitializer.java
@@ -25,9 +25,9 @@ import io.netty.handler.codec.string.StringDecoder;
 
 class FileServerChannelInitializer extends ChannelInitializer<SocketChannel> {
 
-  PathResolver pResolver;
+  private final PathResolver pResolver;
 
-  public FileServerChannelInitializer(PathResolver pResolver) {
+  FileServerChannelInitializer(PathResolver pResolver) {
     this.pResolver = pResolver;
   }
 
diff --git a/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java b/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java
index e2d9391b4c..3ac045f944 100644
--- a/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java
+++ b/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java
@@ -31,11 +31,11 @@ import org.slf4j.LoggerFactory;
 
 class FileServerHandler extends SimpleChannelInboundHandler<String> {
 
-  private Logger LOG = LoggerFactory.getLogger(this.getClass().getName());
+  private static final Logger LOG = LoggerFactory.getLogger(FileServerHandler.class.getName());
 
   private final PathResolver pResolver;
 
-  public FileServerHandler(PathResolver pResolver){
+  FileServerHandler(PathResolver pResolver){
     this.pResolver = pResolver;
   }
 
@@ -61,7 +61,7 @@ class FileServerHandler extends SimpleChannelInboundHandler<String> {
         ctx.flush();
         return;
       }
-      int len = new Long(length).intValue();
+      int len = (int) length;
       ctx.write((new FileHeader(len, blockId)).buffer());
       try {
         ctx.write(new DefaultFileRegion(new FileInputStream(file)
diff --git a/core/src/main/java/org/apache/spark/network/netty/PathResolver.java b/core/src/main/java/org/apache/spark/network/netty/PathResolver.java
index 9f7ced44cf..7ad8d03efb 100755
--- a/core/src/main/java/org/apache/spark/network/netty/PathResolver.java
+++ b/core/src/main/java/org/apache/spark/network/netty/PathResolver.java
@@ -1,26 +1,26 @@
-/*

- * Licensed to the Apache Software Foundation (ASF) under one or more

- * contributor license agreements.  See the NOTICE file distributed with

- * this work for additional information regarding copyright ownership.

- * The ASF licenses this file to You under the Apache License, Version 2.0

- * (the "License"); you may not use this file except in compliance with

- * the License.  You may obtain a copy of the License at

- *

- *    http://www.apache.org/licenses/LICENSE-2.0

- *

- * Unless required by applicable law or agreed to in writing, software

- * distributed under the License is distributed on an "AS IS" BASIS,

- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

- * See the License for the specific language governing permissions and

- * limitations under the License.

- */

-

-package org.apache.spark.network.netty;

-

-import org.apache.spark.storage.BlockId;

-import org.apache.spark.storage.FileSegment;

-

-public interface PathResolver {

-  /** Get the file segment in which the given block resides. */

-  public FileSegment getBlockLocation(BlockId blockId);

-}

+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty;
+
+import org.apache.spark.storage.BlockId;
+import org.apache.spark.storage.FileSegment;
+
+public interface PathResolver {
+  /** Get the file segment in which the given block resides. */
+  FileSegment getBlockLocation(BlockId blockId);
+}
diff --git a/core/src/main/resources/org/apache/spark/default-log4j.properties b/core/src/main/resources/org/apache/spark/log4j-defaults.properties
index d72dbadc39..d72dbadc39 100644
--- a/core/src/main/resources/org/apache/spark/default-log4j.properties
+++ b/core/src/main/resources/org/apache/spark/log4j-defaults.properties
diff --git a/core/src/main/scala/org/apache/spark/Logging.scala b/core/src/main/scala/org/apache/spark/Logging.scala
index d519fc5a29..4a34989e50 100644
--- a/core/src/main/scala/org/apache/spark/Logging.scala
+++ b/core/src/main/scala/org/apache/spark/Logging.scala
@@ -104,13 +104,15 @@ trait Logging {
     // If Log4j doesn't seem initialized, load a default properties file
     val log4jInitialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements
     if (!log4jInitialized) {
-      val defaultLogProps = "org/apache/spark/default-log4j.properties"
+      val defaultLogProps = "org/apache/spark/log4j-defaults.properties"
       val classLoader = this.getClass.getClassLoader
       Option(classLoader.getResource(defaultLogProps)) match {
-        case Some(url) => PropertyConfigurator.configure(url)
-        case None => System.err.println(s"Spark was unable to load $defaultLogProps")
+        case Some(url) => 
+          PropertyConfigurator.configure(url)
+          log.info(s"Using Spark's default log4j profile: $defaultLogProps")
+        case None => 
+          System.err.println(s"Spark was unable to load $defaultLogProps")
       }
-      log.info(s"Using Spark's default log4j profile: $defaultLogProps")
     }
     Logging.initialized = true
 
diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index cdae167aef..77b8ca1cce 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -55,7 +55,7 @@ private[spark] class MapOutputTracker(conf: SparkConf) extends Logging {
   private val timeout = AkkaUtils.askTimeout(conf)
 
   // Set to the MapOutputTrackerActor living on the driver
-  var trackerActor: Either[ActorRef, ActorSelection] = _
+  var trackerActor: ActorRef = _
 
   protected val mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]]
 
@@ -71,17 +71,7 @@ private[spark] class MapOutputTracker(conf: SparkConf) extends Logging {
   // throw a SparkException if this fails.
   private def askTracker(message: Any): Any = {
     try {
-      /*
-        The difference between ActorRef and ActorSelection is well explained here:
-        http://doc.akka.io/docs/akka/2.2.3/project/migration-guide-2.1.x-2.2.x.html#Use_actorSelection_instead_of_actorFor
-        In spark a map output tracker can be either started on Driver where it is created which
-        is an ActorRef or it can be on executor from where it is looked up which is an
-        actorSelection.
-       */
-      val future = trackerActor match {
-        case Left(a: ActorRef) => a.ask(message)(timeout)
-        case Right(b: ActorSelection) => b.ask(message)(timeout)
-      }
+      val future = trackerActor.ask(message)(timeout)
       Await.result(future, timeout)
     } catch {
       case e: Exception =>
diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 55f27033b5..2de32231e8 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -67,7 +67,7 @@ class SparkConf(loadDefaults: Boolean) extends Serializable with Cloneable with
 
   /** Set JAR files to distribute to the cluster. */
   def setJars(jars: Seq[String]): SparkConf = {
-    for (jar <- jars if (jar == null)) logWarning("null jar passed to SparkContext constructor") 
+    for (jar <- jars if (jar == null)) logWarning("null jar passed to SparkContext constructor")
     set("spark.jars", jars.filter(_ != null).mkString(","))
   }
 
@@ -165,6 +165,11 @@ class SparkConf(loadDefaults: Boolean) extends Serializable with Cloneable with
     getOption(key).map(_.toDouble).getOrElse(defaultValue)
   }
 
+  /** Get a parameter as a boolean, falling back to a default if not set */
+  def getBoolean(key: String, defaultValue: Boolean): Boolean = {
+    getOption(key).map(_.toBoolean).getOrElse(defaultValue)
+  }
+
   /** Get all executor environment variables set on this SparkConf */
   def getExecutorEnv: Seq[(String, String)] = {
     val prefix = "spark.executorEnv."
@@ -172,6 +177,9 @@ class SparkConf(loadDefaults: Boolean) extends Serializable with Cloneable with
           .map{case (k, v) => (k.substring(prefix.length), v)}
   }
 
+  /** Get all akka conf variables set on this SparkConf */
+  def getAkkaConf: Seq[(String, String)] =  getAll.filter {case (k, v) => k.startsWith("akka.")}
+
   /** Does the configuration contain a given parameter? */
   def contains(key: String): Boolean = settings.contains(key)
 
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index e80e43af6d..0e47f4e442 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -116,6 +116,10 @@ class SparkContext(
     throw new SparkException("An application must be set in your configuration")
   }
 
+  if (conf.get("spark.logConf", "false").toBoolean) {
+    logInfo("Spark configuration:\n" + conf.toDebugString)
+  }
+
   // Set Spark driver host and port system properties
   conf.setIfMissing("spark.driver.host", Utils.localHostName())
   conf.setIfMissing("spark.driver.port", "0")
diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 634a94f0a7..2e36ccb9a0 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark
 
-import collection.mutable
-import serializer.Serializer
+import scala.collection.mutable
+import scala.concurrent.Await
 
 import akka.actor._
-import akka.remote.RemoteActorRefProvider
 
 import org.apache.spark.broadcast.BroadcastManager
 import org.apache.spark.metrics.MetricsSystem
@@ -157,17 +156,18 @@ object SparkEnv extends Logging {
       conf.get("spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer"),
       conf)
 
-    def registerOrLookup(name: String, newActor: => Actor): Either[ActorRef, ActorSelection] = {
+    def registerOrLookup(name: String, newActor: => Actor): ActorRef = {
       if (isDriver) {
         logInfo("Registering " + name)
-        Left(actorSystem.actorOf(Props(newActor), name = name))
+        actorSystem.actorOf(Props(newActor), name = name)
       } else {
         val driverHost: String = conf.get("spark.driver.host", "localhost")
         val driverPort: Int = conf.get("spark.driver.port", "7077").toInt
         Utils.checkHost(driverHost, "Expected hostname")
-        val url = "akka.tcp://spark@%s:%s/user/%s".format(driverHost, driverPort, name)
-        logInfo("Connecting to " + name + ": " + url)
-        Right(actorSystem.actorSelection(url))
+        val url = s"akka.tcp://spark@$driverHost:$driverPort/user/$name"
+        val timeout = AkkaUtils.lookupTimeout(conf)
+        logInfo(s"Connecting to $name: $url")
+        Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout)
       }
     }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala
index 19d393a0db..e38459b883 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy
 
 private[spark] class ApplicationDescription(
     val name: String,
-    val maxCores: Int, /* Integer.MAX_VALUE denotes an unlimited number of cores */
+    val maxCores: Option[Int],
     val memoryPerSlave: Int,
     val command: Command,
     val sparkHome: String,
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
index 62567a25f6..ffa909c26b 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
@@ -48,7 +48,7 @@ private[spark] object TestClient {
     val (actorSystem, port) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0,
       conf = new SparkConf)
     val desc = new ApplicationDescription(
-      "TestClient", 1, 512, Command("spark.deploy.client.TestExecutor", Seq(), Map()),
+      "TestClient", Some(1), 512, Command("spark.deploy.client.TestExecutor", Seq(), Map()),
       "dummy-spark-home", "ignored")
     val listener = new TestListener
     val client = new AppClient(actorSystem, Array(url), desc, listener, new SparkConf)
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
index 5150b7c7de..3e26379166 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
@@ -28,7 +28,8 @@ private[spark] class ApplicationInfo(
     val desc: ApplicationDescription,
     val submitDate: Date,
     val driver: ActorRef,
-    val appUiUrl: String)
+    val appUiUrl: String,
+    defaultCores: Int)
   extends Serializable {
 
   @transient var state: ApplicationState.Value = _
@@ -81,7 +82,9 @@ private[spark] class ApplicationInfo(
     }
   }
 
-  def coresLeft: Int = desc.maxCores - coresGranted
+  private val myMaxCores = desc.maxCores.getOrElse(defaultCores)
+
+  def coresLeft: Int = myMaxCores - coresGranted
 
   private var _retryCount = 0
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 93fa700f29..f62601fa6c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -95,7 +95,13 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
   // As a temporary workaround before better ways of configuring memory, we allow users to set
   // a flag that will perform round-robin scheduling across the nodes (spreading out each app
   // among all the nodes) instead of trying to consolidate each app onto a small # of nodes.
-  val spreadOutApps = conf.get("spark.deploy.spreadOut", "true").toBoolean
+  val spreadOutApps = conf.getBoolean("spark.deploy.spreadOut", true)
+
+  // Default maxCores for applications that don't specify it (i.e. pass Int.MaxValue)
+  val defaultCores = conf.getInt("spark.deploy.defaultCores", Int.MaxValue)
+  if (defaultCores < 1) {
+    throw new SparkException("spark.deploy.defaultCores must be positive")
+  }
 
   override def preStart() {
     logInfo("Starting Spark master at " + masterUrl)
@@ -549,7 +555,8 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
   def createApplication(desc: ApplicationDescription, driver: ActorRef): ApplicationInfo = {
     val now = System.currentTimeMillis()
     val date = new Date(now)
-    new ApplicationInfo(now, newApplicationId(date), desc, date, driver, desc.appUiUrl)
+    new ApplicationInfo(
+      now, newApplicationId(date), desc, date, driver, desc.appUiUrl, defaultCores)
   }
 
   def registerApplication(app: ApplicationInfo): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index c8c77e99bf..faa6e1ebe8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -38,7 +38,7 @@ private[spark] class SparkDeploySchedulerBackend(
   var stopping = false
   var shutdownCallback : (SparkDeploySchedulerBackend) => Unit = _
 
-  val maxCores = conf.get("spark.cores.max",  Int.MaxValue.toString).toInt
+  val maxCores = conf.getOption("spark.cores.max").map(_.toInt)
 
   override def start() {
     super.start()
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
index b5afe8cd23..51a29ed8ef 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
@@ -28,8 +28,7 @@ import org.apache.spark.storage.BlockManagerMessages._
 import org.apache.spark.util.AkkaUtils
 
 private[spark]
-class BlockManagerMaster(var driverActor : Either[ActorRef, ActorSelection],
-    conf: SparkConf) extends Logging {
+class BlockManagerMaster(var driverActor : ActorRef, conf: SparkConf) extends Logging {
 
   val AKKA_RETRY_ATTEMPTS: Int = conf.get("spark.akka.num.retries", "3").toInt
   val AKKA_RETRY_INTERVAL_MS: Int = conf.get("spark.akka.retry.wait", "3000").toInt
@@ -159,10 +158,7 @@ class BlockManagerMaster(var driverActor : Either[ActorRef, ActorSelection],
     while (attempts < AKKA_RETRY_ATTEMPTS) {
       attempts += 1
       try {
-        val future = driverActor match {
-          case Left(a: ActorRef) => a.ask(message)(timeout)
-          case Right(b: ActorSelection) => b.ask(message)(timeout)
-        }
+        val future = driverActor.ask(message)(timeout)
         val result = Await.result(future, timeout)
         if (result == null) {
           throw new SparkException("BlockManagerMaster returned null")
diff --git a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
index dca98c6c05..729ba2c550 100644
--- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
@@ -95,7 +95,7 @@ private[spark] object ThreadingTest {
     val conf = new SparkConf()
     val serializer = new KryoSerializer(conf)
     val blockManagerMaster = new BlockManagerMaster(
-      Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf)))), conf)
+      actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf))), conf)
     val blockManager = new BlockManager(
       "<driver>", actorSystem, blockManagerMaster, serializer, 1024 * 1024, conf)
     val producers = (1 to numProducers).map(i => new ProducerThread(blockManager, i))
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index 7df7e3d8e5..3f009a8998 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.util
 
+import scala.collection.JavaConversions.mapAsJavaMap
 import scala.concurrent.duration.{Duration, FiniteDuration}
 
 import akka.actor.{ActorSystem, ExtendedActorSystem, IndestructibleActorSystem}
 import com.typesafe.config.ConfigFactory
-import org.apache.log4j.{Level, Logger}
 
+import org.apache.log4j.{Level, Logger}
 import org.apache.spark.SparkConf
 
 /**
@@ -64,7 +65,8 @@ private[spark] object AkkaUtils {
       conf.get("spark.akka.failure-detector.threshold", "300.0").toDouble
     val akkaHeartBeatInterval = conf.get("spark.akka.heartbeat.interval", "1000").toInt
 
-    val akkaConf = ConfigFactory.parseString(
+    val akkaConf = ConfigFactory.parseMap(conf.getAkkaConf.toMap[String, String]).withFallback(
+      ConfigFactory.parseString(
       s"""
       |akka.daemonic = on
       |akka.loggers = [""akka.event.slf4j.Slf4jLogger""]
@@ -86,7 +88,7 @@ private[spark] object AkkaUtils {
       |akka.remote.log-remote-lifecycle-events = $lifecycleEvents
       |akka.log-dead-letters = $lifecycleEvents
       |akka.log-dead-letters-during-shutdown = $lifecycleEvents
-      """.stripMargin)
+      """.stripMargin))
 
     val actorSystem = if (indestructible) {
       IndestructibleActorSystem(name, akkaConf)
@@ -103,4 +105,9 @@ private[spark] object AkkaUtils {
   def askTimeout(conf: SparkConf): FiniteDuration = {
     Duration.create(conf.get("spark.akka.askTimeout", "30").toLong, "seconds")
   }
+
+  /** Returns the default Spark timeout to use for Akka remote actor lookup. */
+  def lookupTimeout(conf: SparkConf): FiniteDuration = {
+    Duration.create(conf.get("spark.akka.lookupTimeout", "30").toLong, "seconds")
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
index 10b8b441fd..afc1beff98 100644
--- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
@@ -23,6 +23,7 @@ import akka.actor._
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.storage.BlockManagerId
 import org.apache.spark.util.AkkaUtils
+import scala.concurrent.Await
 
 class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
   private val conf = new SparkConf
@@ -49,14 +50,14 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
   test("master start and stop") {
     val actorSystem = ActorSystem("test")
     val tracker = new MapOutputTrackerMaster(conf)
-    tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker))))
+    tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))
     tracker.stop()
   }
 
   test("master register and fetch") {
     val actorSystem = ActorSystem("test")
     val tracker = new MapOutputTrackerMaster(conf)
-    tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker))))
+    tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))
     tracker.registerShuffle(10, 2)
     val compressedSize1000 = MapOutputTracker.compressSize(1000L)
     val compressedSize10000 = MapOutputTracker.compressSize(10000L)
@@ -75,7 +76,7 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
   test("master register and unregister and fetch") {
     val actorSystem = ActorSystem("test")
     val tracker = new MapOutputTrackerMaster(conf)
-    tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker))))
+    tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))
     tracker.registerShuffle(10, 2)
     val compressedSize1000 = MapOutputTracker.compressSize(1000L)
     val compressedSize10000 = MapOutputTracker.compressSize(10000L)
@@ -101,13 +102,15 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
     System.setProperty("spark.hostPort", hostname + ":" + boundPort)
 
     val masterTracker = new MapOutputTrackerMaster(conf)
-    masterTracker.trackerActor = Left(actorSystem.actorOf(
-        Props(new MapOutputTrackerMasterActor(masterTracker)), "MapOutputTracker"))
+    masterTracker.trackerActor = actorSystem.actorOf(
+        Props(new MapOutputTrackerMasterActor(masterTracker)), "MapOutputTracker")
 
     val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = conf)
     val slaveTracker = new MapOutputTracker(conf)
-    slaveTracker.trackerActor = Right(slaveSystem.actorSelection(
-        "akka.tcp://spark@localhost:" + boundPort + "/user/MapOutputTracker"))
+    val selection = slaveSystem.actorSelection(
+      s"akka.tcp://spark@localhost:$boundPort/user/MapOutputTracker")
+    val timeout = AkkaUtils.lookupTimeout(conf)
+    slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout)
 
     masterTracker.registerShuffle(10, 1)
     masterTracker.incrementEpoch()
diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
index 028196fe86..d05bbd6ff7 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -74,10 +74,12 @@ class JsonProtocolSuite extends FunSuite {
 
   def createAppDesc(): ApplicationDescription = {
     val cmd = new Command("mainClass", List("arg1", "arg2"), Map())
-    new ApplicationDescription("name", 4, 1234, cmd, "sparkHome", "appUiUrl")
+    new ApplicationDescription("name", Some(4), 1234, cmd, "sparkHome", "appUiUrl")
   }
-  def createAppInfo(): ApplicationInfo = {
-    new ApplicationInfo(3, "id", createAppDesc(), new Date(123456789), null, "appUriStr")
+
+  def createAppInfo() : ApplicationInfo = {
+    new ApplicationInfo(
+      3, "id", createAppDesc(), new Date(123456789), null, "appUriStr", Int.MaxValue)
   }
 
   def createDriverCommand() = new Command(
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
index bdb2c86d89..a79ee690d3 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
@@ -27,7 +27,7 @@ class ExecutorRunnerTest extends FunSuite {
   test("command includes appId") {
     def f(s:String) = new File(s)
     val sparkHome = sys.env.get("SPARK_HOME").orElse(sys.props.get("spark.home")).get
-    val appDesc = new ApplicationDescription("app name", 8, 500, Command("foo", Seq(),Map()),
+    val appDesc = new ApplicationDescription("app name", Some(8), 500, Command("foo", Seq(),Map()),
       sparkHome, "appUiUrl")
     val appId = "12345-worker321-9876"
     val er = new ExecutorRunner(appId, 1, appDesc, 8, 500, null, "blah", "worker321", f(sparkHome),
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index a0fc3445be..f60ce270c7 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -40,8 +40,6 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT
   var actorSystem: ActorSystem = null
   var master: BlockManagerMaster = null
   var oldArch: String = null
-  var oldOops: String = null
-  var oldHeartBeat: String = null
 
   // Reuse a serializer across tests to avoid creating a new thread-local buffer on each test
   conf.set("spark.kryoserializer.buffer.mb", "1")
@@ -58,10 +56,10 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT
     conf.set("spark.hostPort", "localhost:" + boundPort)
 
     master = new BlockManagerMaster(
-      Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf)))), conf)
+      actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf))), conf)
 
     // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case
-    System.setProperty("os.arch", "amd64")
+    oldArch = System.setProperty("os.arch", "amd64")
     conf.set("os.arch", "amd64")
     conf.set("spark.test.useCompressedOops", "true")
     conf.set("spark.storage.disableBlockManagerHeartBeat", "true")
@@ -94,11 +92,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT
       System.clearProperty("os.arch")
     }
 
-    if (oldOops != null) {
-      conf.set("spark.test.useCompressedOops", oldOops)
-    } else {
-      System.clearProperty("spark.test.useCompressedOops")
-    }
+    System.clearProperty("spark.test.useCompressedOops")
   }
 
   test("StorageLevel object caching") {
diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
index af4b31d53c..829f389460 100644
--- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
@@ -23,9 +23,7 @@ import scala.collection.mutable
 
 import com.google.common.io.Files
 import org.apache.spark.SparkConf
-import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite}
-import scala.util.Try
-import akka.actor.{Props, ActorSelection, ActorSystem}
+import org.scalatest.{BeforeAndAfterEach, FunSuite}
 
 class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach {
   private val testConf = new SparkConf(false)
diff --git a/docs/README.md b/docs/README.md
index e3d6c9a5bc..dfcf753553 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -27,10 +27,10 @@ To mark a block of code in your markdown to be syntax highlighted by jekyll duri
 
 ## API Docs (Scaladoc and Epydoc)
 
-You can build just the Spark scaladoc by running `sbt doc` from the SPARK_PROJECT_ROOT directory.
+You can build just the Spark scaladoc by running `sbt/sbt doc` from the SPARK_PROJECT_ROOT directory.
 
 Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the SPARK_PROJECT_ROOT/pyspark directory.
 
-When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
+When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
 
 NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 jekyll`.
diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb
index ef9912c808..431de909cb 100644
--- a/docs/_plugins/copy_api_dirs.rb
+++ b/docs/_plugins/copy_api_dirs.rb
@@ -26,8 +26,8 @@ if not (ENV['SKIP_API'] == '1' or ENV['SKIP_SCALADOC'] == '1')
   curr_dir = pwd
   cd("..")
 
-  puts "Running sbt doc from " + pwd + "; this may take a few minutes..."
-  puts `sbt doc`
+  puts "Running sbt/sbt doc from " + pwd + "; this may take a few minutes..."
+  puts `sbt/sbt doc`
 
   puts "Moving back into docs dir."
   cd("docs")
diff --git a/docs/api.md b/docs/api.md
index 11e2c15324..e86d07770a 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -3,7 +3,7 @@ layout: global
 title: Spark API documentation (Scaladoc)
 ---
 
-Here you can find links to the Scaladoc generated for the Spark sbt subprojects.  If the following links don't work, try running `sbt doc` from the Spark project home directory.
+Here you can find links to the Scaladoc generated for the Spark sbt subprojects.  If the following links don't work, try running `sbt/sbt doc` from the Spark project home directory.
 
 - [Spark](api/core/index.html)
 - [Spark Examples](api/examples/index.html)
diff --git a/docs/configuration.md b/docs/configuration.md
index 09342fedfc..1d6c3d1633 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -77,13 +77,14 @@ there are at least five properties that you will commonly want to control:
 </tr>
 <tr>
   <td>spark.cores.max</td>
-  <td>(infinite)</td>
+  <td>(not set)</td>
   <td>
     When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a
     <a href="running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained"
     sharing mode</a>, the maximum amount of CPU cores to request for the application from
-    across the cluster (not from each machine). The default will use all available cores
-    offered by the cluster manager.
+    across the cluster (not from each machine). If not set, the default will be
+    <code>spark.deploy.defaultCores</code> on Spark's standalone cluster manager, or
+    infinite (all available cores) on Mesos.
   </td>
 </tr>
 </table>
@@ -361,6 +362,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
+  <td>akka.x.y....</td>
+  <td>value</td>
+  <td>
+    An arbitrary akka configuration can be set directly on spark conf and it is applied for all the ActorSystems created spark wide for that SparkContext and its assigned executors as well.
+  </td>
+</tr>
+
+<tr>
   <td>spark.shuffle.consolidateFiles</td>
   <td>false</td>
   <td>
@@ -395,6 +404,37 @@ Apart from these, the following properties are also available, and may be useful
     How many times slower a task is than the median to be considered for speculation.
   </td>
 </tr>
+<tr>
+  <td>spark.logConf</td>
+  <td>false</td>
+  <td>
+    Log the supplied SparkConf as INFO at start of spark context.
+  </td>
+</tr>
+<tr>
+  <td>spark.deploy.spreadOut</td>
+  <td>true</td>
+  <td>
+    Whether the standalone cluster manager should spread applications out across nodes or try
+    to consolidate them onto as few nodes as possible. Spreading out is usually better for
+    data locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/>
+    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual
+    applications; you can set it through <code>SPARK_JAVA_OPTS</code> in <code>spark-env.sh</code>.
+  </td>
+</tr>
+<tr>
+  <td>spark.deploy.defaultCores</td>
+  <td>(infinite)</td>
+  <td>
+    Default number of cores to give to applications in Spark's standalone mode if they don't
+    set <code>spark.cores.max</code>. If not set, applications always get all available
+    cores unless they configure <code>spark.cores.max</code> themselves.
+    Set this lower on a shared cluster to prevent users from grabbing
+    the whole cluster by default. <br/>
+    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual
+    applications; you can set it through <code>SPARK_JAVA_OPTS</code> in <code>spark-env.sh</code>.
+  </td>
+</tr>
 </table>
 
 ## Viewing Spark Properties
diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css
index 119bb5dba7..30ea440c00 100644
--- a/docs/css/bootstrap.min.css
+++ b/docs/css/bootstrap.min.css
@@ -6,4 +6,4 @@
  * http://www.apache.org/licenses/LICENSE-2.0
  *
  * Designed and built with all the love in the world @twitter by @mdo and @fat.
- */article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}a:hover,a:active{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{height:auto;max-width:100%;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}#map_canvas img{max-width:none}button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}button,input{*overflow:visible;line-height:normal}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}button,input[type="button"],input[type="reset"],input[type="submit"]{cursor:pointer;-webkit-appearance:button}input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}textarea{overflow:auto;vertical-align:top}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 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diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index 141d475ba6..de6a2b0a43 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -12,7 +12,7 @@ with these distributions:
 When compiling Spark, you'll need to 
 [set the SPARK_HADOOP_VERSION flag](index.html#a-note-about-hadoop-versions):
 
-    SPARK_HADOOP_VERSION=1.0.4 sbt assembly
+    SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
 
 The table below lists the corresponding `SPARK_HADOOP_VERSION` code for each CDH/HDP release. Note that
 some Hadoop releases are binary compatible across client versions. This means the pre-built Spark
diff --git a/docs/index.md b/docs/index.md
index bf8d1c3375..86d574daaa 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -17,7 +17,7 @@ Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you n
 
 Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with it. To compile the code, go into the top-level Spark directory and run
 
-    sbt assembly
+    sbt/sbt assembly
 
 For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you will need to use this same version of Scala in your own program -- newer major versions may not work. You can get the right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
 
@@ -56,12 +56,12 @@ Hadoop, you must build Spark against the same version that your cluster uses.
 By default, Spark links to Hadoop 1.0.4. You can change this by setting the
 `SPARK_HADOOP_VERSION` variable when compiling:
 
-    SPARK_HADOOP_VERSION=2.2.0 sbt assembly
+    SPARK_HADOOP_VERSION=2.2.0 sbt/sbt assembly
 
 In addition, if you wish to run Spark on [YARN](running-on-yarn.html), set
 `SPARK_YARN` to `true`:
 
-    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt assembly
+    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
 Note that on Windows, you need to set the environment variables on separate lines, e.g., `set SPARK_HADOOP_VERSION=1.2.1`.
 
diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md
index 5951155fe3..df2faa5e41 100644
--- a/docs/job-scheduling.md
+++ b/docs/job-scheduling.md
@@ -32,9 +32,8 @@ Resource allocation can be configured as follows, based on the cluster type:
 
 * **Standalone mode:** By default, applications submitted to the standalone mode cluster will run in
   FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit
-  the number of nodes an application uses by setting the `spark.cores.max` configuration property in it. This
-  will allow multiple users/applications to run concurrently. For example, you might launch a long-running
-  server that uses 10 cores, and allow users to launch shells that use 20 cores each.
+  the number of nodes an application uses by setting the `spark.cores.max` configuration property in it,
+  or change the default for applications that don't set this setting through `spark.deploy.defaultCores`. 
   Finally, in addition to controlling cores, each application's `spark.executor.memory` setting controls
   its memory use.
 * **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`,
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 95537ef185..45ee166688 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -3,6 +3,9 @@ layout: global
 title: Machine Learning Library (MLlib)
 ---
 
+* Table of contents
+{:toc}
+
 MLlib is a Spark implementation of some common machine learning (ML)
 functionality, as well associated tests and data generators.  MLlib
 currently supports four common types of machine learning problem settings,
@@ -39,57 +42,6 @@ underlying gradient descent primitive (described
 parameter (*regParam*) along with various parameters associated with gradient
 descent (*stepSize*, *numIterations*, *miniBatchFraction*). 
 
-The following code snippet illustrates how to load a sample dataset, execute a
-training algorithm on this training data using a static method in the algorithm
-object, and make predictions with the resulting model to compute the training
-error.
-
-{% highlight scala %}
-import org.apache.spark.SparkContext
-import org.apache.spark.mllib.classification.SVMWithSGD
-import org.apache.spark.mllib.regression.LabeledPoint
-
-// Load and parse the data file
-val data = sc.textFile("mllib/data/sample_svm_data.txt")
-val parsedData = data.map { line =>
-  val parts = line.split(' ')
-  LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray)
-}
-
-// Run training algorithm
-val numIterations = 20
-val model = SVMWithSGD.train(parsedData, numIterations)
- 
-// Evaluate model on training examples and compute training error
-val labelAndPreds = parsedData.map { point =>
-  val prediction = model.predict(point.features)
-  (point.label, prediction)
-}
-val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count
-println("trainError = " + trainErr)
-{% endhighlight %}
-
-The `SVMWithSGD.train()` method by default performs L2 regularization with the
-regularization parameter set to 1.0. If we want to configure this algorithm, we
-can customize `SVMWithSGD` further by creating a new object directly and
-calling setter methods. All other MLlib algorithms support customization in
-this way as well. For example, the following code produces an L1 regularized
-variant of SVMs with regularization parameter set to 0.1, and runs the training
-algorithm for 200 iterations. 
-
-{% highlight scala %}
-import org.apache.spark.mllib.optimization.L1Updater
-
-val svmAlg = new SVMWithSGD()
-svmAlg.optimizer.setNumIterations(200)
-  .setRegParam(0.1)
-  .setUpdater(new L1Updater)
-val modelL1 = svmAlg.run(parsedData)
-{% endhighlight %}
-
-Both of the code snippets above can be executed in `bin/spark-shell` to generate a
-classifier for the provided dataset.
-
 Available algorithms for binary classification:
 
 * [SVMWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD)
@@ -121,14 +73,14 @@ of entities with one another based on some notion of similarity.  Clustering is
 often used for exploratory analysis and/or as a component of a hierarchical
 supervised learning pipeline (in which distinct classifiers or regression
 models are trained for each cluster). MLlib supports
-[k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, arguably
-the most commonly used clustering approach that clusters the data points into
-*k* clusters. The MLlib implementation includes a parallelized 
+[k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, one of
+the most commonly used clustering algorithms that clusters the data points into
+predfined number of clusters. The MLlib implementation includes a parallelized
 variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method
 called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf).
 The implementation in MLlib has the following parameters:  
 
-* *k* is the number of clusters.
+* *k* is the number of desired clusters.
 * *maxIterations* is the maximum number of iterations to run.
 * *initializationMode* specifies either random initialization or
 initialization via k-means\|\|.
@@ -169,7 +121,7 @@ the entries in the user-item matrix as *explicit* preferences given by the user
 It is common in many real-world use cases to only have access to *implicit feedback* 
 (e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with 
 such data is taken from 
-[Collaborative Filtering for Implicit Feedback Datasets](http://research.yahoo.com/pub/2433).
+[Collaborative Filtering for Implicit Feedback Datasets](http://www2.research.att.com/~yifanhu/PUB/cf.pdf).
 Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as 
 a combination of binary preferences and *confidence values*. The ratings are then related 
 to the level of confidence in observed user preferences, rather than explicit ratings given to items. 
@@ -210,3 +162,269 @@ at each iteration.
 Available algorithms for gradient descent:
 
 * [GradientDescent](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+
+# Using MLLib in Scala
+
+Following code snippets can be executed in `spark-shell`.
+
+## Binary Classification
+
+The following code snippet illustrates how to load a sample dataset, execute a
+training algorithm on this training data using a static method in the algorithm
+object, and make predictions with the resulting model to compute the training
+error.
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.classification.SVMWithSGD
+import org.apache.spark.mllib.regression.LabeledPoint
+
+// Load and parse the data file
+val data = sc.textFile("mllib/data/sample_svm_data.txt")
+val parsedData = data.map { line =>
+  val parts = line.split(' ')
+  LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray)
+}
+
+// Run training algorithm to build the model
+val numIterations = 20
+val model = SVMWithSGD.train(parsedData, numIterations)
+
+// Evaluate model on training examples and compute training error
+val labelAndPreds = parsedData.map { point =>
+  val prediction = model.predict(point.features)
+  (point.label, prediction)
+}
+val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count
+println("Training Error = " + trainErr)
+{% endhighlight %}
+
+
+The `SVMWithSGD.train()` method by default performs L2 regularization with the
+regularization parameter set to 1.0. If we want to configure this algorithm, we
+can customize `SVMWithSGD` further by creating a new object directly and
+calling setter methods. All other MLlib algorithms support customization in
+this way as well. For example, the following code produces an L1 regularized
+variant of SVMs with regularization parameter set to 0.1, and runs the training
+algorithm for 200 iterations.
+
+{% highlight scala %}
+import org.apache.spark.mllib.optimization.L1Updater
+
+val svmAlg = new SVMWithSGD()
+svmAlg.optimizer.setNumIterations(200)
+  .setRegParam(0.1)
+  .setUpdater(new L1Updater)
+val modelL1 = svmAlg.run(parsedData)
+{% endhighlight %}
+
+## Linear Regression
+The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The
+example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We
+compute the Mean Squared Error at the end to evaluate
+[goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit)
+
+{% highlight scala %}
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD
+import org.apache.spark.mllib.regression.LabeledPoint
+
+// Load and parse the data
+val data = sc.textFile("mllib/data/ridge-data/lpsa.data")
+val parsedData = data.map { line =>
+  val parts = line.split(',')
+  LabeledPoint(parts(0).toDouble, parts(1).split(' ').map(x => x.toDouble).toArray)
+}
+
+// Building the model
+val numIterations = 20
+val model = LinearRegressionWithSGD.train(parsedData, numIterations)
+
+// Evaluate model on training examples and compute training error
+val valuesAndPreds = parsedData.map { point =>
+  val prediction = model.predict(point.features)
+  (point.label, prediction)
+}
+val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.reduce(_ + _)/valuesAndPreds.count
+println("training Mean Squared Error = " + MSE)
+{% endhighlight %}
+
+
+Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training
+[Mean Squared Errors](http://en.wikipedia.org/wiki/Mean_squared_error).
+
+## Clustering
+In the following example after loading and parsing data, we use the KMeans object to cluster the data
+into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
+Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
+optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
+
+{% highlight scala %}
+import org.apache.spark.mllib.clustering.KMeans
+
+// Load and parse the data
+val data = sc.textFile("kmeans_data.txt")
+val parsedData = data.map( _.split(' ').map(_.toDouble))
+
+// Cluster the data into two classes using KMeans
+val numIterations = 20
+val numClusters = 2
+val clusters = KMeans.train(parsedData, numClusters, numIterations)
+
+// Evaluate clustering by computing Within Set Sum of Squared Errors
+val WSSSE = clusters.computeCost(parsedData)
+println("Within Set Sum of Squared Errors = " + WSSSE)
+{% endhighlight %}
+
+
+## Collaborative Filtering
+In the following example we load rating data. Each row consists of a user, a product and a rating.
+We use the default ALS.train() method which assumes ratings are explicit. We evaluate the recommendation
+model by measuring the Mean Squared Error of rating prediction.
+
+{% highlight scala %}
+import org.apache.spark.mllib.recommendation.ALS
+import org.apache.spark.mllib.recommendation.Rating
+
+// Load and parse the data
+val data = sc.textFile("mllib/data/als/test.data")
+val ratings = data.map(_.split(',') match {
+    case Array(user, item, rate) =>  Rating(user.toInt, item.toInt, rate.toDouble)
+})
+
+// Build the recommendation model using ALS
+val numIterations = 20
+val model = ALS.train(ratings, 1, 20, 0.01)
+
+// Evaluate the model on rating data
+val usersProducts = ratings.map{ case Rating(user, product, rate)  => (user, product)}
+val predictions = model.predict(usersProducts).map{
+    case Rating(user, product, rate) => ((user, product), rate)
+}
+val ratesAndPreds = ratings.map{
+    case Rating(user, product, rate) => ((user, product), rate)
+}.join(predictions)
+val MSE = ratesAndPreds.map{
+    case ((user, product), (r1, r2)) =>  math.pow((r1- r2), 2)
+}.reduce(_ + _)/ratesAndPreds.count
+println("Mean Squared Error = " + MSE)
+{% endhighlight %}
+
+If the rating matrix is derived from other source of information (i.e., it is inferred from
+other signals), you can use the trainImplicit method to get better results.
+
+{% highlight scala %}
+val model = ALS.trainImplicit(ratings, 1, 20, 0.01)
+{% endhighlight %}
+
+# Using MLLib in Python
+Following examples can be tested in the PySpark shell.
+
+## Binary Classification
+The following example shows how to load a sample dataset, build Logistic Regression model,
+and make predictions with the resulting model to compute the training error.
+
+{% highlight python %}
+from pyspark.mllib.classification import LogisticRegressionWithSGD
+from numpy import array
+
+# Load and parse the data
+data = sc.textFile("mllib/data/sample_svm_data.txt")
+parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
+model = LogisticRegressionWithSGD.train(sc, parsedData)
+
+# Build the model
+labelsAndPreds = parsedData.map(lambda point: (int(point.item(0)),
+        model.predict(point.take(range(1, point.size)))))
+
+# Evaluating the model on training data
+trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
+print("Training Error = " + str(trainErr))
+{% endhighlight %}
+
+## Linear Regression
+The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The
+example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We
+compute the Mean Squared Error at the end to evaluate
+[goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit)
+
+{% highlight python %}
+from pyspark.mllib.regression import LinearRegressionWithSGD
+from numpy import array
+
+# Load and parse the data
+data = sc.textFile("mllib/data/ridge-data/lpsa.data")
+parsedData = data.map(lambda line: array([float(x) for x in line.replace(',', ' ').split(' ')]))
+
+# Build the model
+model = LinearRegressionWithSGD.train(sc, parsedData)
+
+# Evaluate the model on training data
+valuesAndPreds = parsedData.map(lambda point: (point.item(0),
+        model.predict(point.take(range(1, point.size)))))
+MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y)/valuesAndPreds.count()
+print("Mean Squared Error = " + str(MSE))
+{% endhighlight %}
+
+
+## Clustering
+In the following example after loading and parsing data, we use the KMeans object to cluster the data
+into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
+Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
+optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
+
+{% highlight python %}
+from pyspark.mllib.clustering import KMeans
+from numpy import array
+from math import sqrt
+
+# Load and parse the data
+data = sc.textFile("kmeans_data.txt")
+parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
+
+# Build the model (cluster the data)
+clusters = KMeans.train(sc, parsedData, 2, maxIterations=10,
+        runs=30, initialization_mode="random")
+
+# Evaluate clustering by computing Within Set Sum of Squared Errors
+def error(point):
+    center = clusters.centers[clusters.predict(point)]
+    return sqrt(sum([x**2 for x in (point - center)]))
+
+WSSSE = parsedData.map(lambda point: error(point)).reduce(lambda x, y: x + y)
+print("Within Set Sum of Squared Error = " + str(WSSSE))
+{% endhighlight %}
+
+Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training Mean Squared
+Errors.
+
+## Collaborative Filtering
+In the following example we load rating data. Each row consists of a user, a product and a rating.
+We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
+recommendation by measuring the Mean Squared Error of rating prediction.
+
+{% highlight python %}
+from pyspark.mllib.recommendation import ALS
+from numpy import array
+
+# Load and parse the data
+data = sc.textFile("mllib/data/als/test.data")
+ratings = data.map(lambda line: array([float(x) for x in line.split(',')]))
+
+# Build the recommendation model using Alternating Least Squares
+model = ALS.train(sc, ratings, 1, 20)
+
+# Evaluate the model on training data
+testdata = ratings.map(lambda p: (int(p[0]), int(p[1])))
+predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
+ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
+MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y)/ratesAndPreds.count()
+print("Mean Squared Error = " + str(MSE))
+{% endhighlight %}
+
+If the rating matrix is derived from other source of information (i.e., it is inferred from other
+signals), you can use the trainImplicit method to get better results.
+
+{% highlight python %}
+# Build the recommendation model using Alternating Least Squares based on implicit ratings
+model = ALS.trainImplicit(sc, ratings, 1, 20)
+{% endhighlight %}
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 5d48cb676a..dc187b3efe 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -69,7 +69,7 @@ The script automatically adds the `bin/pyspark` package to the `PYTHONPATH`.
 The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options:
 
 {% highlight bash %}
-$ sbt assembly
+$ sbt/sbt assembly
 $ ./bin/pyspark
 {% endhighlight %}
 
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 9b9261cfff..153081bdaa 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -12,7 +12,7 @@ See the [programming guide](scala-programming-guide.html) for a more complete re
 To follow along with this guide, you only need to have successfully built Spark on one machine. Simply go into your Spark directory and run:
 
 {% highlight bash %}
-$ sbt assembly
+$ sbt/sbt assembly
 {% endhighlight %}
 
 # Interactive Analysis with the Spark Shell
@@ -146,7 +146,7 @@ If you also wish to read data from Hadoop's HDFS, you will also need to add a de
 libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "<your-hdfs-version>"
 {% endhighlight %}
 
-Finally, for sbt to work correctly, we'll need to layout `SimpleApp.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the application's code, then use `sbt run` to execute our program.
+Finally, for sbt to work correctly, we'll need to layout `SimpleApp.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the application's code, then use `sbt/sbt run` to execute our program.
 
 {% highlight bash %}
 $ find .
@@ -157,8 +157,8 @@ $ find .
 ./src/main/scala
 ./src/main/scala/SimpleApp.scala
 
-$ sbt package
-$ sbt run
+$ sbt/sbt package
+$ sbt/sbt run
 ...
 Lines with a: 46, Lines with b: 23
 {% endhighlight %}
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index a35e003cdc..717071d72c 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -12,7 +12,7 @@ was added to Spark in version 0.6.0, and improved in 0.7.0 and 0.8.0.
 We need a consolidated Spark JAR (which bundles all the required dependencies) to run Spark jobs on a YARN cluster.
 This can be built by setting the Hadoop version and `SPARK_YARN` environment variable, as follows:
 
-    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt assembly
+    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
 The assembled JAR will be something like this:
 `./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.
@@ -25,7 +25,7 @@ The build process now also supports new YARN versions (2.2.x). See below.
 - The assembled jar can be installed into HDFS or used locally.
 - Your application code must be packaged into a separate JAR file.
 
-If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
+If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
 
 # Configuration
 
@@ -72,7 +72,7 @@ The command to launch the YARN Client is as follows:
 For example:
 
     # Build the Spark assembly JAR and the Spark examples JAR
-    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt assembly
+    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
     # Configure logging
     $ cp conf/log4j.properties.template conf/log4j.properties
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index 3d0e8923d5..c1ef46a1cd 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -31,7 +31,7 @@ In addition, if you wish to access an HDFS cluster, you need to add a dependency
     artifactId = hadoop-client
     version = <your-hdfs-version>
 
-For other build systems, you can run `sbt assembly` to pack Spark and its dependencies into one JAR (`assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop*.jar`), then add this to your CLASSPATH. Set the HDFS version as described [here](index.html#a-note-about-hadoop-versions).
+For other build systems, you can run `sbt/sbt assembly` to pack Spark and its dependencies into one JAR (`assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop*.jar`), then add this to your CLASSPATH. Set the HDFS version as described [here](index.html#a-note-about-hadoop-versions).
 
 Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
 
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index ecd642cc60..2a186261b7 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -195,6 +195,16 @@ val conf = new SparkConf()
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
+In addition, you can configure `spark.deploy.defaultCores` on the cluster master process to change the
+default for applications that don't set `spark.cores.max` to something less than infinite.
+Do this by adding the following to `conf/spark-env.sh`:
+
+{% highlight bash %}
+export SPARK_JAVA_OPTS="-Dspark.deploy.defaultCores=<value>"
+{% endhighlight %}
+
+This is useful on shared clusters where users might not have configured a maximum number of cores
+individually.
 
 # Monitoring and Logging
 
diff --git a/examples/pom.xml b/examples/pom.xml
index 7a7032c319..cb4f7ee33b 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -72,6 +72,31 @@
       <scope>provided</scope>
     </dependency>
     <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-twitter_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-kafka_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-flume_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-zeromq_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-mqtt_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
       <groupId>org.apache.hbase</groupId>
       <artifactId>hbase</artifactId>
       <version>0.94.6</version>
@@ -87,21 +112,6 @@
       </exclusions>
     </dependency>
     <dependency>
-      <groupId>com.sksamuel.kafka</groupId>
-      <artifactId>kafka_${scala.binary.version}</artifactId>
-      <version>0.8.0-beta1</version>
-      <exclusions>
-        <exclusion>
-          <groupId>com.sun.jmx</groupId>
-          <artifactId>jmxri</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>com.sun.jdmk</groupId>
-          <artifactId>jmxtools</artifactId>
-        </exclusion>
-      </exclusions>
-    </dependency>
-    <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-server</artifactId>
     </dependency>
@@ -174,7 +184,7 @@
         <artifactId>maven-shade-plugin</artifactId>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
-          <outputFile>${project.build.directory}/scala-${scala.version}/${project.artifactId}-assembly-${project.version}.jar</outputFile>
+          <outputFile>${project.build.directory}/scala-${scala.binary.version}/${project.artifactId}-assembly-${project.version}.jar</outputFile>
           <artifactSet>
             <includes>
               <include>*:*</include>
@@ -203,6 +213,9 @@
                 <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                   <resource>reference.conf</resource>
                 </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
+                  <resource>log4j.properties</resource>
+                </transformer>
               </transformers>
             </configuration>
           </execution>
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java b/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
index 12f3355bc4..d552c47b22 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
@@ -24,19 +24,19 @@ import org.apache.spark.api.java.function.Function2;
 
 import java.io.Serializable;
 import java.util.Arrays;
-import java.util.StringTokenizer;
 import java.util.Random;
+import java.util.regex.Pattern;
 
 /**
  * Logistic regression based classification.
  */
-public class JavaHdfsLR {
+public final class JavaHdfsLR {
 
-  static int D = 10;   // Number of dimensions
-  static Random rand = new Random(42);
+  private static final int D = 10;   // Number of dimensions
+  private static final Random rand = new Random(42);
 
   static class DataPoint implements Serializable {
-    public DataPoint(double[] x, double y) {
+    DataPoint(double[] x, double y) {
       this.x = x;
       this.y = y;
     }
@@ -46,20 +46,22 @@ public class JavaHdfsLR {
   }
 
   static class ParsePoint extends Function<String, DataPoint> {
+    private static final Pattern SPACE = Pattern.compile(" ");
+
+    @Override
     public DataPoint call(String line) {
-      StringTokenizer tok = new StringTokenizer(line, " ");
-      double y = Double.parseDouble(tok.nextToken());
+      String[] tok = SPACE.split(line);
+      double y = Double.parseDouble(tok[0]);
       double[] x = new double[D];
-      int i = 0;
-      while (i < D) {
-        x[i] = Double.parseDouble(tok.nextToken());
-        i += 1;
+      for (int i = 0; i < D; i++) {
+        x[i] = Double.parseDouble(tok[i + 1]);
       }
       return new DataPoint(x, y);
     }
   }
 
   static class VectorSum extends Function2<double[], double[], double[]> {
+    @Override
     public double[] call(double[] a, double[] b) {
       double[] result = new double[D];
       for (int j = 0; j < D; j++) {
@@ -70,12 +72,13 @@ public class JavaHdfsLR {
   }
 
   static class ComputeGradient extends Function<DataPoint, double[]> {
-    double[] weights;
+    private final double[] weights;
 
-    public ComputeGradient(double[] weights) {
+    ComputeGradient(double[] weights) {
       this.weights = weights;
     }
 
+    @Override
     public double[] call(DataPoint p) {
       double[] gradient = new double[D];
       for (int i = 0; i < D; i++) {
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaKMeans.java b/examples/src/main/java/org/apache/spark/examples/JavaKMeans.java
index 63465a3bbf..0dc879275a 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaKMeans.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaKMeans.java
@@ -27,19 +27,24 @@ import org.apache.spark.util.Vector;
 
 import java.util.List;
 import java.util.Map;
+import java.util.regex.Pattern;
 
 /**
  * K-means clustering using Java API.
  */
-public class JavaKMeans {
+public final class JavaKMeans {
+
+  private static final Pattern SPACE = Pattern.compile(" ");
 
   /** Parses numbers split by whitespace to a vector */
   static Vector parseVector(String line) {
-    String[] splits = line.split(" ");
+    String[] splits = SPACE.split(line);
     double[] data = new double[splits.length];
     int i = 0;
-    for (String s : splits)
-      data[i] = Double.parseDouble(splits[i++]);
+    for (String s : splits) {
+      data[i] = Double.parseDouble(s);
+      i++;
+    }
     return new Vector(data);
   }
 
@@ -82,7 +87,7 @@ public class JavaKMeans {
     JavaRDD<Vector> data = sc.textFile(path).map(
       new Function<String, Vector>() {
         @Override
-        public Vector call(String line) throws Exception {
+        public Vector call(String line) {
           return parseVector(line);
         }
       }
@@ -96,7 +101,7 @@ public class JavaKMeans {
       JavaPairRDD<Integer, Vector> closest = data.map(
         new PairFunction<Vector, Integer, Vector>() {
           @Override
-          public Tuple2<Integer, Vector> call(Vector vector) throws Exception {
+          public Tuple2<Integer, Vector> call(Vector vector) {
             return new Tuple2<Integer, Vector>(
               closestPoint(vector, centroids), vector);
           }
@@ -107,7 +112,8 @@ public class JavaKMeans {
       JavaPairRDD<Integer, List<Vector>> pointsGroup = closest.groupByKey();
       Map<Integer, Vector> newCentroids = pointsGroup.mapValues(
         new Function<List<Vector>, Vector>() {
-          public Vector call(List<Vector> ps) throws Exception {
+          @Override
+          public Vector call(List<Vector> ps) {
             return average(ps);
           }
         }).collectAsMap();
@@ -122,8 +128,9 @@ public class JavaKMeans {
     } while (tempDist > convergeDist);
 
     System.out.println("Final centers:");
-    for (Vector c : centroids)
+    for (Vector c : centroids) {
       System.out.println(c);
+    }
 
     System.exit(0);
 
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
index 74e4d9291a..9eb1cadd71 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
@@ -35,9 +35,9 @@ import java.util.regex.Pattern;
 /**
  * Executes a roll up-style query against Apache logs.
  */
-public class JavaLogQuery {
+public final class JavaLogQuery {
 
-  public static List<String> exampleApacheLogs = Lists.newArrayList(
+  public static final List<String> exampleApacheLogs = Lists.newArrayList(
     "10.10.10.10 - \"FRED\" [18/Jan/2013:17:56:07 +1100] \"GET http://images.com/2013/Generic.jpg " +
       "HTTP/1.1\" 304 315 \"http://referall.com/\" \"Mozilla/4.0 (compatible; MSIE 7.0; " +
       "Windows NT 5.1; GTB7.4; .NET CLR 2.0.50727; .NET CLR 3.0.04506.30; .NET CLR 3.0.04506.648; " +
@@ -51,14 +51,14 @@ public class JavaLogQuery {
       "3.5.30729; Release=ARP)\" \"UD-1\" - \"image/jpeg\" \"whatever\" 0.352 \"-\" - \"\" 256 977 988 \"\" " +
       "0 73.23.2.15 images.com 1358492557 - Whatup");
 
-  public static Pattern apacheLogRegex = Pattern.compile(
+  public static final Pattern apacheLogRegex = Pattern.compile(
     "^([\\d.]+) (\\S+) (\\S+) \\[([\\w\\d:/]+\\s[+\\-]\\d{4})\\] \"(.+?)\" (\\d{3}) ([\\d\\-]+) \"([^\"]+)\" \"([^\"]+)\".*");
 
   /** Tracks the total query count and number of aggregate bytes for a particular group. */
   public static class Stats implements Serializable {
 
-    private int count;
-    private int numBytes;
+    private final int count;
+    private final int numBytes;
 
     public Stats(int count, int numBytes) {
       this.count = count;
@@ -92,12 +92,12 @@ public class JavaLogQuery {
     if (m.find()) {
       int bytes = Integer.parseInt(m.group(7));
       return new Stats(1, bytes);
-    }
-    else
+    } else {
       return new Stats(1, 0);
+    }
   }
 
-  public static void main(String[] args) throws Exception {
+  public static void main(String[] args) {
     if (args.length == 0) {
       System.err.println("Usage: JavaLogQuery <master> [logFile]");
       System.exit(1);
@@ -110,14 +110,14 @@ public class JavaLogQuery {
 
     JavaPairRDD<Tuple3<String, String, String>, Stats> extracted = dataSet.map(new PairFunction<String, Tuple3<String, String, String>, Stats>() {
       @Override
-      public Tuple2<Tuple3<String, String, String>, Stats> call(String s) throws Exception {
+      public Tuple2<Tuple3<String, String, String>, Stats> call(String s) {
         return new Tuple2<Tuple3<String, String, String>, Stats>(extractKey(s), extractStats(s));
       }
     });
 
     JavaPairRDD<Tuple3<String, String, String>, Stats> counts = extracted.reduceByKey(new Function2<Stats, Stats, Stats>() {
       @Override
-      public Stats call(Stats stats, Stats stats2) throws Exception {
+      public Stats call(Stats stats, Stats stats2) {
         return stats.merge(stats2);
       }
     });
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
index f774f6a04e..a84245b0c7 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
@@ -17,7 +17,6 @@
 
 package org.apache.spark.examples;
 
-import org.apache.spark.SparkContext;
 import scala.Tuple2;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
@@ -29,6 +28,7 @@ import org.apache.spark.api.java.function.PairFunction;
 
 import java.util.List;
 import java.util.ArrayList;
+import java.util.regex.Pattern;
 
 /**
  * Computes the PageRank of URLs from an input file. Input file should
@@ -39,7 +39,9 @@ import java.util.ArrayList;
  * ...
  * where URL and their neighbors are separated by space(s).
  */
-public class JavaPageRank {
+public final class JavaPageRank {
+  private static final Pattern SPACES = Pattern.compile("\\s+");
+
   private static class Sum extends Function2<Double, Double, Double> {
     @Override
     public Double call(Double a, Double b) {
@@ -67,7 +69,7 @@ public class JavaPageRank {
     JavaPairRDD<String, List<String>> links = lines.map(new PairFunction<String, String, String>() {
       @Override
       public Tuple2<String, String> call(String s) {
-        String[] parts = s.split("\\s+");
+        String[] parts = SPACES.split(s);
         return new Tuple2<String, String>(parts[0], parts[1]);
       }
     }).distinct().groupByKey().cache();
@@ -75,7 +77,7 @@ public class JavaPageRank {
     // Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one.
     JavaPairRDD<String, Double> ranks = links.mapValues(new Function<List<String>, Double>() {
       @Override
-      public Double call(List<String> rs) throws Exception {
+      public Double call(List<String> rs) {
         return 1.0;
       }
     });
@@ -98,7 +100,7 @@ public class JavaPageRank {
       // Re-calculates URL ranks based on neighbor contributions.
       ranks = contribs.reduceByKey(new Sum()).mapValues(new Function<Double, Double>() {
         @Override
-        public Double call(Double sum) throws Exception {
+        public Double call(Double sum) {
           return 0.15 + sum * 0.85;
         }
       });
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java b/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
index 5558ab7c03..3ec4a58d48 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
@@ -26,8 +26,7 @@ import java.util.ArrayList;
 import java.util.List;
 
 /** Computes an approximation to pi */
-public class JavaSparkPi {
-
+public final class JavaSparkPi {
 
   public static void main(String[] args) throws Exception {
     if (args.length == 0) {
@@ -41,21 +40,22 @@ public class JavaSparkPi {
     int slices = (args.length == 2) ? Integer.parseInt(args[1]) : 2;
     int n = 100000 * slices;
     List<Integer> l = new ArrayList<Integer>(n);
-    for (int i = 0; i < n; i++)
+    for (int i = 0; i < n; i++) {
       l.add(i);
+    }
 
     JavaRDD<Integer> dataSet = jsc.parallelize(l, slices);
 
     int count = dataSet.map(new Function<Integer, Integer>() {
       @Override
-      public Integer call(Integer integer) throws Exception {
+      public Integer call(Integer integer) {
         double x = Math.random() * 2 - 1;
         double y = Math.random() * 2 - 1;
         return (x * x + y * y < 1) ? 1 : 0;
       }
     }).reduce(new Function2<Integer, Integer, Integer>() {
       @Override
-      public Integer call(Integer integer, Integer integer2) throws Exception {
+      public Integer call(Integer integer, Integer integer2) {
         return integer + integer2;
       }
     });
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaTC.java b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
index 99e6ba347c..2ceb0fd94b 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaTC.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
@@ -31,11 +31,11 @@ import java.util.Set;
 /**
  * Transitive closure on a graph, implemented in Java.
  */
-public class JavaTC {
+public final class JavaTC {
 
-  static int numEdges = 200;
-  static int numVertices = 100;
-  static Random rand = new Random(42);
+  private static final int numEdges = 200;
+  private static final int numVertices = 100;
+  private static final Random rand = new Random(42);
 
   static List<Tuple2<Integer, Integer>> generateGraph() {
     Set<Tuple2<Integer, Integer>> edges = new HashSet<Tuple2<Integer, Integer>>(numEdges);
@@ -43,15 +43,18 @@ public class JavaTC {
       int from = rand.nextInt(numVertices);
       int to = rand.nextInt(numVertices);
       Tuple2<Integer, Integer> e = new Tuple2<Integer, Integer>(from, to);
-      if (from != to) edges.add(e);
+      if (from != to) {
+        edges.add(e);
+      }
     }
     return new ArrayList<Tuple2<Integer, Integer>>(edges);
   }
 
   static class ProjectFn extends PairFunction<Tuple2<Integer, Tuple2<Integer, Integer>>,
       Integer, Integer> {
-    static ProjectFn INSTANCE = new ProjectFn();
+    static final ProjectFn INSTANCE = new ProjectFn();
 
+    @Override
     public Tuple2<Integer, Integer> call(Tuple2<Integer, Tuple2<Integer, Integer>> triple) {
       return new Tuple2<Integer, Integer>(triple._2()._2(), triple._2()._1());
     }
@@ -76,6 +79,7 @@ public class JavaTC {
     // Because join() joins on keys, the edges are stored in reversed order.
     JavaPairRDD<Integer, Integer> edges = tc.map(
       new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() {
+        @Override
         public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> e) {
           return new Tuple2<Integer, Integer>(e._2(), e._1());
         }
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
index 8a071caf13..6651f98d56 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
@@ -27,8 +27,11 @@ import org.apache.spark.api.java.function.PairFunction;
 
 import java.util.Arrays;
 import java.util.List;
+import java.util.regex.Pattern;
+
+public final class JavaWordCount {
+  private static final Pattern SPACE = Pattern.compile(" ");
 
-public class JavaWordCount {
   public static void main(String[] args) throws Exception {
     if (args.length < 2) {
       System.err.println("Usage: JavaWordCount <master> <file>");
@@ -40,18 +43,21 @@ public class JavaWordCount {
     JavaRDD<String> lines = ctx.textFile(args[1], 1);
 
     JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
+      @Override
       public Iterable<String> call(String s) {
-        return Arrays.asList(s.split(" "));
+        return Arrays.asList(SPACE.split(s));
       }
     });
     
     JavaPairRDD<String, Integer> ones = words.map(new PairFunction<String, String, Integer>() {
+      @Override
       public Tuple2<String, Integer> call(String s) {
         return new Tuple2<String, Integer>(s, 1);
       }
     });
     
     JavaPairRDD<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {
+      @Override
       public Integer call(Integer i1, Integer i2) {
         return i1 + i2;
       }
diff --git a/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java b/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java
index 5e1a77baaa..435a86e62a 100644
--- a/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java
+++ b/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java
@@ -26,28 +26,32 @@ import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
 import org.apache.spark.mllib.recommendation.Rating;
 
 import java.util.Arrays;
-import java.util.StringTokenizer;
+import java.util.regex.Pattern;
 
 import scala.Tuple2;
 
 /**
  * Example using MLLib ALS from Java.
  */
-public class  JavaALS {
+public final class JavaALS {
 
   static class ParseRating extends Function<String, Rating> {
+    private static final Pattern COMMA = Pattern.compile(",");
+
+    @Override
     public Rating call(String line) {
-      StringTokenizer tok = new StringTokenizer(line, ",");
-      int x = Integer.parseInt(tok.nextToken());
-      int y = Integer.parseInt(tok.nextToken());
-      double rating = Double.parseDouble(tok.nextToken());
+      String[] tok = COMMA.split(line);
+      int x = Integer.parseInt(tok[0]);
+      int y = Integer.parseInt(tok[1]);
+      double rating = Double.parseDouble(tok[2]);
       return new Rating(x, y, rating);
     }
   }
 
   static class FeaturesToString extends Function<Tuple2<Object, double[]>, String> {
+    @Override
     public String call(Tuple2<Object, double[]> element) {
-      return element._1().toString() + "," + Arrays.toString(element._2());
+      return element._1() + "," + Arrays.toString(element._2());
     }
   }
 
diff --git a/examples/src/main/java/org/apache/spark/mllib/examples/JavaKMeans.java b/examples/src/main/java/org/apache/spark/mllib/examples/JavaKMeans.java
index 1f12f518a0..4b2658f257 100644
--- a/examples/src/main/java/org/apache/spark/mllib/examples/JavaKMeans.java
+++ b/examples/src/main/java/org/apache/spark/mllib/examples/JavaKMeans.java
@@ -25,20 +25,22 @@ import org.apache.spark.mllib.clustering.KMeans;
 import org.apache.spark.mllib.clustering.KMeansModel;
 
 import java.util.Arrays;
-import java.util.StringTokenizer;
+import java.util.regex.Pattern;
 
 /**
  * Example using MLLib KMeans from Java.
  */
-public class JavaKMeans {
+public final class JavaKMeans {
 
   static class ParsePoint extends Function<String, double[]> {
+    private static final Pattern SPACE = Pattern.compile(" ");
+
+    @Override
     public double[] call(String line) {
-      StringTokenizer tok = new StringTokenizer(line, " ");
-      int numTokens = tok.countTokens();
-      double[] point = new double[numTokens];
-      for (int i = 0; i < numTokens; ++i) {
-        point[i] = Double.parseDouble(tok.nextToken());
+      String[] tok = SPACE.split(line);
+      double[] point = new double[tok.length];
+      for (int i = 0; i < tok.length; ++i) {
+        point[i] = Double.parseDouble(tok[i]);
       }
       return point;
     }
diff --git a/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java b/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java
index 593e4df111..21586ce817 100644
--- a/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java
+++ b/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java
@@ -27,22 +27,25 @@ import org.apache.spark.mllib.classification.LogisticRegressionModel;
 import org.apache.spark.mllib.regression.LabeledPoint;
 
 import java.util.Arrays;
-import java.util.StringTokenizer;
+import java.util.regex.Pattern;
 
 /**
  * Logistic regression based classification using ML Lib.
  */
-public class JavaLR {
+public final class JavaLR {
 
   static class ParsePoint extends Function<String, LabeledPoint> {
+    private static final Pattern COMMA = Pattern.compile(",");
+    private static final Pattern SPACE = Pattern.compile(" ");
+
+    @Override
     public LabeledPoint call(String line) {
-      String[] parts = line.split(",");
+      String[] parts = COMMA.split(line);
       double y = Double.parseDouble(parts[0]);
-      StringTokenizer tok = new StringTokenizer(parts[1], " ");
-      int numTokens = tok.countTokens();
-      double[] x = new double[numTokens];
-      for (int i = 0; i < numTokens; ++i) {
-        x[i] = Double.parseDouble(tok.nextToken());
+      String[] tok = SPACE.split(parts[1]);
+      double[] x = new double[tok.length];
+      for (int i = 0; i < tok.length; ++i) {
+        x[i] = Double.parseDouble(tok[i]);
       }
       return new LabeledPoint(y, x);
     }
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
index 64ac72474b..b11cfa667e 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
@@ -20,7 +20,8 @@ package org.apache.spark.streaming.examples;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.streaming.*;
 import org.apache.spark.streaming.api.java.*;
-import org.apache.spark.streaming.dstream.SparkFlumeEvent;
+import org.apache.spark.streaming.flume.FlumeUtils;
+import org.apache.spark.streaming.flume.SparkFlumeEvent;
 
 /**
  *  Produces a count of events received from Flume.
@@ -36,7 +37,10 @@ import org.apache.spark.streaming.dstream.SparkFlumeEvent;
  *           creates a server and listens for flume events.
  *    <port> is the port the Flume receiver will listen on.
  */
-public class JavaFlumeEventCount {
+public final class JavaFlumeEventCount {
+  private JavaFlumeEventCount() {
+  }
+
   public static void main(String[] args) {
     if (args.length != 3) {
       System.err.println("Usage: JavaFlumeEventCount <master> <host> <port>");
@@ -49,11 +53,10 @@ public class JavaFlumeEventCount {
 
     Duration batchInterval = new Duration(2000);
 
-    JavaStreamingContext sc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval,
+    JavaStreamingContext ssc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval,
             System.getenv("SPARK_HOME"),
             JavaStreamingContext.jarOfClass(JavaFlumeEventCount.class));
-
-    JavaDStream<SparkFlumeEvent> flumeStream = sc.flumeStream("localhost", port);
+    JavaDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, "localhost", port);
 
     flumeStream.count();
 
@@ -64,6 +67,6 @@ public class JavaFlumeEventCount {
       }
     }).print();
 
-    sc.start();
+    ssc.start();
   }
 }
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java
index 0a56e7abdf..16b8a948e6 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java
@@ -19,6 +19,7 @@ package org.apache.spark.streaming.examples;
 
 import java.util.Map;
 import java.util.HashMap;
+import java.util.regex.Pattern;
 
 import com.google.common.collect.Lists;
 import org.apache.spark.api.java.function.FlatMapFunction;
@@ -29,6 +30,7 @@ import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
+import org.apache.spark.streaming.kafka.KafkaUtils;
 import scala.Tuple2;
 
 /**
@@ -45,7 +47,12 @@ import scala.Tuple2;
  *    zoo03 my-consumer-group topic1,topic2 1`
  */
 
-public class JavaKafkaWordCount {
+public final class JavaKafkaWordCount {
+  private static final Pattern SPACE = Pattern.compile(" ");
+
+  private JavaKafkaWordCount() {
+  }
+
   public static void main(String[] args) {
     if (args.length < 5) {
       System.err.println("Usage: KafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>");
@@ -53,7 +60,7 @@ public class JavaKafkaWordCount {
     }
 
     // Create the context with a 1 second batch size
-    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "KafkaWordCount",
+    JavaStreamingContext jssc = new JavaStreamingContext(args[0], "KafkaWordCount",
             new Duration(2000), System.getenv("SPARK_HOME"),
             JavaStreamingContext.jarOfClass(JavaKafkaWordCount.class));
 
@@ -64,11 +71,11 @@ public class JavaKafkaWordCount {
       topicMap.put(topic, numThreads);
     }
 
-    JavaPairDStream<String, String> messages = ssc.kafkaStream(args[1], args[2], topicMap);
+    JavaPairDStream<String, String> messages = KafkaUtils.createStream(jssc, args[1], args[2], topicMap);
 
     JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
       @Override
-      public String call(Tuple2<String, String> tuple2) throws Exception {
+      public String call(Tuple2<String, String> tuple2) {
         return tuple2._2();
       }
     });
@@ -76,24 +83,24 @@ public class JavaKafkaWordCount {
     JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
       public Iterable<String> call(String x) {
-        return Lists.newArrayList(x.split(" "));
+        return Lists.newArrayList(SPACE.split(x));
       }
     });
 
     JavaPairDStream<String, Integer> wordCounts = words.map(
       new PairFunction<String, String, Integer>() {
         @Override
-        public Tuple2<String, Integer> call(String s) throws Exception {
+        public Tuple2<String, Integer> call(String s) {
           return new Tuple2<String, Integer>(s, 1);
         }
       }).reduceByKey(new Function2<Integer, Integer, Integer>() {
         @Override
-        public Integer call(Integer i1, Integer i2) throws Exception {
+        public Integer call(Integer i1, Integer i2) {
           return i1 + i2;
         }
       });
 
     wordCounts.print();
-    ssc.start();
+    jssc.start();
   }
 }
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
index ec6f6a8c56..1e2efd359c 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
@@ -27,6 +27,8 @@ import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 
+import java.util.regex.Pattern;
+
 /**
  * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
  * Usage: NetworkWordCount <master> <hostname> <port>
@@ -38,7 +40,12 @@ import org.apache.spark.streaming.api.java.JavaStreamingContext;
  * and then run the example
  *    `$ ./run spark.streaming.examples.JavaNetworkWordCount local[2] localhost 9999`
  */
-public class JavaNetworkWordCount {
+public final class JavaNetworkWordCount {
+  private static final Pattern SPACE = Pattern.compile(" ");
+
+  private JavaNetworkWordCount() {
+  }
+
   public static void main(String[] args) {
     if (args.length < 3) {
       System.err.println("Usage: NetworkWordCount <master> <hostname> <port>\n" +
@@ -57,18 +64,18 @@ public class JavaNetworkWordCount {
     JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
       public Iterable<String> call(String x) {
-        return Lists.newArrayList(x.split(" "));
+        return Lists.newArrayList(SPACE.split(x));
       }
     });
     JavaPairDStream<String, Integer> wordCounts = words.map(
       new PairFunction<String, String, Integer>() {
         @Override
-        public Tuple2<String, Integer> call(String s) throws Exception {
+        public Tuple2<String, Integer> call(String s) {
           return new Tuple2<String, Integer>(s, 1);
         }
       }).reduceByKey(new Function2<Integer, Integer, Integer>() {
         @Override
-        public Integer call(Integer i1, Integer i2) throws Exception {
+        public Integer call(Integer i1, Integer i2) {
           return i1 + i2;
         }
       });
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaQueueStream.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaQueueStream.java
index 4b9fd52713..e05551ab83 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaQueueStream.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaQueueStream.java
@@ -31,8 +31,11 @@ import java.util.LinkedList;
 import java.util.List;
 import java.util.Queue;
 
-public class JavaQueueStream {
-  public static void main(String[] args) throws InterruptedException {
+public final class JavaQueueStream {
+  private JavaQueueStream() {
+  }
+
+  public static void main(String[] args) throws Exception {
     if (args.length < 1) {
       System.err.println("Usage: JavaQueueStream <master>");
       System.exit(1);
@@ -62,14 +65,14 @@ public class JavaQueueStream {
     JavaPairDStream<Integer, Integer> mappedStream = inputStream.map(
         new PairFunction<Integer, Integer, Integer>() {
           @Override
-          public Tuple2<Integer, Integer> call(Integer i) throws Exception {
+          public Tuple2<Integer, Integer> call(Integer i) {
             return new Tuple2<Integer, Integer>(i % 10, 1);
           }
         });
     JavaPairDStream<Integer, Integer> reducedStream = mappedStream.reduceByKey(
       new Function2<Integer, Integer, Integer>() {
         @Override
-        public Integer call(Integer i1, Integer i2) throws Exception {
+        public Integer call(Integer i1, Integer i2) {
           return i1 + i2;
         }
     });
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala
index 546495357f..4e0058cd70 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala
@@ -134,9 +134,9 @@ object FeederActor {
  *   <hostname> and <port> describe the AkkaSystem that Spark Sample feeder is running on.
  *
  * To run this example locally, you may run Feeder Actor as
- *    `$ ./bin/run-example spark.streaming.examples.FeederActor 127.0.1.1 9999`
+ *    `$ ./bin/run-example org.apache.spark.streaming.examples.FeederActor 127.0.1.1 9999`
  * and then run the example
- *    `$ ./bin/run-example spark.streaming.examples.ActorWordCount local[2] 127.0.1.1 9999`
+ *    `$ ./bin/run-example org.apache.spark.streaming.examples.ActorWordCount local[2] 127.0.1.1 9999`
  */
 object ActorWordCount {
   def main(args: Array[String]) {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala
index 5ef1928294..ae3709b3d9 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming.examples
 import org.apache.spark.util.IntParam
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
+import org.apache.spark.streaming.flume._
 
 /**
  *  Produces a count of events received from Flume.
@@ -51,7 +52,7 @@ object FlumeEventCount {
       System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass))
 
     // Create a flume stream
-    val stream = ssc.flumeStream(host,port,StorageLevel.MEMORY_ONLY)
+    val stream = FlumeUtils.createStream(ssc, host,port,StorageLevel.MEMORY_ONLY_SER_2)
 
     // Print out the count of events received from this server in each batch
     stream.count().map(cnt => "Received " + cnt + " flume events." ).print()
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/HdfsWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/HdfsWordCount.scala
index 1486d77d8a..ea6ea67419 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/HdfsWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/HdfsWordCount.scala
@@ -28,7 +28,7 @@ import org.apache.spark.streaming.StreamingContext._
  *   <directory> is the directory that Spark Streaming will use to find and read new text files.
  *
  * To run this on your local machine on directory `localdir`, run this example
- *    `$ ./bin/run-example spark.streaming.examples.HdfsWordCount local[2] localdir`
+ *    `$ ./bin/run-example org.apache.spark.streaming.examples.HdfsWordCount local[2] localdir`
  * Then create a text file in `localdir` and the words in the file will get counted.
  */
 object HdfsWordCount {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala
index 172091be2e..31a94bd224 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala
@@ -24,6 +24,7 @@ import kafka.producer._
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.util.RawTextHelper._
+import org.apache.spark.streaming.kafka._
 
 /**
  * Consumes messages from one or more topics in Kafka and does wordcount.
@@ -35,7 +36,7 @@ import org.apache.spark.streaming.util.RawTextHelper._
  *   <numThreads> is the number of threads the kafka consumer should use
  *
  * Example:
- *    `./bin/run-example spark.streaming.examples.KafkaWordCount local[2] zoo01,zoo02,zoo03 my-consumer-group topic1,topic2 1`
+ *    `./bin/run-example org.apache.spark.streaming.examples.KafkaWordCount local[2] zoo01,zoo02,zoo03 my-consumer-group topic1,topic2 1`
  */
 object KafkaWordCount {
   def main(args: Array[String]) {
@@ -52,7 +53,7 @@ object KafkaWordCount {
     ssc.checkpoint("checkpoint")
 
     val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap
-    val lines = ssc.kafkaStream(zkQuorum, group, topicpMap).map(_._2)
+    val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicpMap).map(_._2)
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1l))
       .reduceByKeyAndWindow(add _, subtract _, Minutes(10), Seconds(2), 2)
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala
index 2d02ef77c0..325290b66f 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala
@@ -17,11 +17,6 @@
 
 package org.apache.spark.streaming.examples
 
-import org.apache.spark.streaming.{ Seconds, StreamingContext }
-import org.apache.spark.streaming.StreamingContext._
-import org.apache.spark.streaming.dstream.MQTTReceiver
-import org.apache.spark.storage.StorageLevel
-
 import org.eclipse.paho.client.mqttv3.MqttClient
 import org.eclipse.paho.client.mqttv3.MqttClientPersistence
 import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence
@@ -29,6 +24,11 @@ import org.eclipse.paho.client.mqttv3.MqttException
 import org.eclipse.paho.client.mqttv3.MqttMessage
 import org.eclipse.paho.client.mqttv3.MqttTopic
 
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+import org.apache.spark.streaming.StreamingContext._
+import org.apache.spark.streaming.mqtt._
+
 /**
  * A simple Mqtt publisher for demonstration purposes, repeatedly publishes 
  * Space separated String Message "hello mqtt demo for spark streaming"
@@ -97,7 +97,7 @@ object MQTTWordCount {
 
     val ssc = new StreamingContext(master, "MqttWordCount", Seconds(2), System.getenv("SPARK_HOME"), 
     StreamingContext.jarOfClass(this.getClass))
-    val lines = ssc.mqttStream(brokerUrl, topic, StorageLevel.MEMORY_ONLY)
+    val lines = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_ONLY_SER_2)
 
     val words = lines.flatMap(x => x.toString.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala
index 74d76ec26c..6a32c75373 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala
@@ -29,7 +29,7 @@ import org.apache.spark.streaming.StreamingContext._
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/run-example spark.streaming.examples.NetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999`
  */
 object NetworkWordCount {
   def main(args: Array[String]) {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala
index f43c8ab61d..002db57d59 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala
@@ -29,7 +29,7 @@ import org.apache.spark.streaming.StreamingContext._
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/run-example spark.streaming.examples.StatefulNetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/run-example org.apache.spark.streaming.examples.StatefulNetworkWordCount local[2] localhost 9999`
  */
 object StatefulNetworkWordCount {
   def main(args: Array[String]) {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala
index 9d21d3178f..3ccdc908e2 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala
@@ -23,6 +23,8 @@ import com.twitter.algebird._
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.SparkContext._
 
+import org.apache.spark.streaming.twitter._
+
 /**
  * Illustrates the use of the Count-Min Sketch, from Twitter's Algebird library, to compute
  * windowed and global Top-K estimates of user IDs occurring in a Twitter stream.
@@ -33,7 +35,7 @@ import org.apache.spark.SparkContext._
  * <p>
  * <p>
  *   <a href="http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/">
- *   This blog post</a> has a good overview of the Count-Min Sketch (CMS). The CMS is a datastructure
+ *   This blog post</a> has a good overview of the Count-Min Sketch (CMS). The CMS is a data structure
  *   for approximate frequency estimation in data streams (e.g. Top-K elements, frequency of any given element, etc),
  *   that uses space sub-linear in the number of elements in the stream. Once elements are added to the CMS, the
  *   estimated count of an element can be computed, as well as "heavy-hitters" that occur more than a threshold
@@ -61,7 +63,7 @@ object TwitterAlgebirdCMS {
 
     val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10),
       System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass))
-    val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER)
+    val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER_2)
 
     val users = stream.map(status => status.getUser.getId)
 
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala
index 5111e6f62a..c7e83e76b0 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala
@@ -21,7 +21,7 @@ import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.storage.StorageLevel
 import com.twitter.algebird.HyperLogLog._
 import com.twitter.algebird.HyperLogLogMonoid
-import org.apache.spark.streaming.dstream.TwitterInputDStream
+import org.apache.spark.streaming.twitter._
 
 /**
  * Illustrates the use of the HyperLogLog algorithm, from Twitter's Algebird library, to compute
@@ -50,7 +50,7 @@ object TwitterAlgebirdHLL {
 
     val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5),
       System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass))
-    val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER)
+    val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER)
 
     val users = stream.map(status => status.getUser.getId)
 
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala
index 7a3df687b7..e2b0418d55 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming.examples
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import StreamingContext._
 import org.apache.spark.SparkContext._
+import org.apache.spark.streaming.twitter._
 
 /**
  * Calculates popular hashtags (topics) over sliding 10 and 60 second windows from a Twitter
@@ -39,7 +40,7 @@ object TwitterPopularTags {
 
     val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2),
       System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass))
-    val stream = ssc.twitterStream(None, filters)
+    val stream = TwitterUtils.createStream(ssc, None, filters)
 
     val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
 
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala
index 89d3042123..03902ec353 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala
@@ -20,11 +20,13 @@ package org.apache.spark.streaming.examples
 import akka.actor.ActorSystem
 import akka.actor.actorRef2Scala
 import akka.zeromq._
-import org.apache.spark.streaming.{ Seconds, StreamingContext }
-import org.apache.spark.streaming.StreamingContext._
 import akka.zeromq.Subscribe
 import akka.util.ByteString
 
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+import org.apache.spark.streaming.StreamingContext._
+import org.apache.spark.streaming.zeromq._
+
 /**
  * A simple publisher for demonstration purposes, repeatedly publishes random Messages
  * every one second.
@@ -62,9 +64,9 @@ object SimpleZeroMQPublisher {
  *   <zeroMQurl> and <topic> describe where zeroMq publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/run-example spark.streaming.examples.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
+ *    `$ ./bin/run-example org.apache.spark.streaming.examples.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
  * and run the example as
- *    `$ ./bin/run-example spark.streaming.examples.ZeroMQWordCount local[2] tcp://127.0.1.1:1234 foo`
+ *    `$ ./bin/run-example org.apache.spark.streaming.examples.ZeroMQWordCount local[2] tcp://127.0.1.1:1234 foo`
  */
 object ZeroMQWordCount {
   def main(args: Array[String]) {
@@ -83,11 +85,10 @@ object ZeroMQWordCount {
     def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator
 
     //For this stream, a zeroMQ publisher should be running.
-    val lines = ssc.zeroMQStream(url, Subscribe(topic), bytesToStringIterator)
+    val lines = ZeroMQUtils.createStream(ssc, url, Subscribe(topic), bytesToStringIterator _)
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
     wordCounts.print()
     ssc.start()
   }
-
 }
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala
index 1a40fdb9a3..4fe57de4a4 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala
@@ -39,8 +39,8 @@ object PageView extends Serializable {
 /** Generates streaming events to simulate page views on a website.
   *
   * This should be used in tandem with PageViewStream.scala. Example:
-  * $ ./bin/run-example spark.streaming.examples.clickstream.PageViewGenerator 44444 10
-  * $ ./bin/run-example spark.streaming.examples.clickstream.PageViewStream errorRatePerZipCode localhost 44444
+  * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewGenerator 44444 10
+  * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewStream errorRatePerZipCode localhost 44444
   *
   * When running this, you may want to set the root logging level to ERROR in
   * conf/log4j.properties to reduce the verbosity of the output.
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
index 0569846f18..807af199f4 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
@@ -25,8 +25,8 @@ import org.apache.spark.SparkContext._
   * operators available in Spark streaming.
   *
   * This should be used in tandem with PageViewStream.scala. Example:
-  * $ ./bin/run-example spark.streaming.examples.clickstream.PageViewGenerator 44444 10
-  * $ ./bin/run-example spark.streaming.examples.clickstream.PageViewStream errorRatePerZipCode localhost 44444
+  * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewGenerator 44444 10
+  * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewStream errorRatePerZipCode localhost 44444
   */
 object PageViewStream {
   def main(args: Array[String]) {
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
new file mode 100644
index 0000000000..443910a03a
--- /dev/null
+++ b/external/flume/pom.xml
@@ -0,0 +1,93 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>0.9.0-incubating-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-flume_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project External Flume</name>
+  <url>http://spark.incubator.apache.org/</url>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.flume</groupId>
+      <artifactId>flume-ng-sdk</artifactId>
+      <version>1.2.0</version>
+      <exclusions>
+        <exclusion>
+          <groupId>org.jboss.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.xerial.snappy</groupId>
+          <artifactId>*</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 60d79175f1..ce3ef47cfe 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.dstream
+package org.apache.spark.streaming.flume
 
 import java.net.InetSocketAddress
 import java.io.{ObjectInput, ObjectOutput, Externalizable}
@@ -30,9 +30,10 @@ import org.apache.flume.source.avro.Status
 import org.apache.avro.ipc.specific.SpecificResponder
 import org.apache.avro.ipc.NettyServer
 
-import org.apache.spark.streaming.StreamingContext
 import org.apache.spark.util.Utils
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.StreamingContext
+import org.apache.spark.streaming.dstream._
 
 private[streaming]
 class FlumeInputDStream[T: ClassTag](
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
new file mode 100644
index 0000000000..834b775d4f
--- /dev/null
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
@@ -0,0 +1,70 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.flume
+
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{StreamingContext, DStream}
+import org.apache.spark.streaming.api.java.{JavaStreamingContext, JavaDStream}
+
+object FlumeUtils {
+  /**
+   * Create a input stream from a Flume source.
+   * @param ssc      StreamingContext object
+   * @param hostname Hostname of the slave machine to which the flume data will be sent
+   * @param port     Port of the slave machine to which the flume data will be sent
+   * @param storageLevel  Storage level to use for storing the received objects
+   */
+  def createStream (
+      ssc: StreamingContext,
+      hostname: String,
+      port: Int,
+      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
+    ): DStream[SparkFlumeEvent] = {
+    val inputStream = new FlumeInputDStream[SparkFlumeEvent](ssc, hostname, port, storageLevel)
+    ssc.registerInputStream(inputStream)
+    inputStream
+  }
+
+  /**
+   * Creates a input stream from a Flume source.
+   * @param hostname Hostname of the slave machine to which the flume data will be sent
+   * @param port     Port of the slave machine to which the flume data will be sent
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      hostname: String,
+      port: Int
+    ): JavaDStream[SparkFlumeEvent] = {
+    createStream(jssc.ssc, hostname, port)
+  }
+
+  /**
+   * Creates a input stream from a Flume source.
+   * @param hostname Hostname of the slave machine to which the flume data will be sent
+   * @param port     Port of the slave machine to which the flume data will be sent
+   * @param storageLevel  Storage level to use for storing the received objects
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      hostname: String,
+      port: Int,
+      storageLevel: StorageLevel
+    ): JavaDStream[SparkFlumeEvent] = {
+    createStream(jssc.ssc, hostname, port, storageLevel)
+  }
+}
diff --git a/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java b/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java
new file mode 100644
index 0000000000..733389b98d
--- /dev/null
+++ b/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.flume;
+
+import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.LocalJavaStreamingContext;
+import org.apache.spark.streaming.api.java.JavaDStream;
+
+import org.junit.Test;
+
+public class JavaFlumeStreamSuite extends LocalJavaStreamingContext {
+  @Test
+  public void testFlumeStream() {
+    // tests the API, does not actually test data receiving
+    JavaDStream<SparkFlumeEvent> test1 = FlumeUtils.createStream(ssc, "localhost", 12345);
+    JavaDStream<SparkFlumeEvent> test2 = FlumeUtils.createStream(ssc, "localhost", 12345,
+      StorageLevel.MEMORY_AND_DISK_SER_2());
+  }
+}
diff --git a/external/flume/src/test/resources/log4j.properties b/external/flume/src/test/resources/log4j.properties
new file mode 100644
index 0000000000..063529a9cb
--- /dev/null
+++ b/external/flume/src/test/resources/log4j.properties
@@ -0,0 +1,29 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Set everything to be logged to the file streaming/target/unit-tests.log
+log4j.rootCategory=INFO, file
+# log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file.append=false
+log4j.appender.file.file=streaming/target/unit-tests.log
+log4j.appender.file.layout=org.apache.log4j.PatternLayout
+log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
+
+# Ignore messages below warning level from Jetty, because it's a bit verbose
+log4j.logger.org.eclipse.jetty=WARN
+
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
new file mode 100644
index 0000000000..2e8e9fac45
--- /dev/null
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
@@ -0,0 +1,86 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.flume
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
+
+import java.net.InetSocketAddress
+import java.nio.ByteBuffer
+import java.nio.charset.Charset
+
+import org.apache.avro.ipc.NettyTransceiver
+import org.apache.avro.ipc.specific.SpecificRequestor
+import org.apache.flume.source.avro.{AvroFlumeEvent, AvroSourceProtocol}
+
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{TestOutputStream, StreamingContext, TestSuiteBase}
+import org.apache.spark.streaming.util.ManualClock
+
+class FlumeStreamSuite extends TestSuiteBase {
+
+  val testPort = 9999
+
+  test("flume input stream") {
+    // Set up the streaming context and input streams
+    val ssc = new StreamingContext(conf, batchDuration)
+    val flumeStream = FlumeUtils.createStream(ssc, "localhost", testPort, StorageLevel.MEMORY_AND_DISK)
+    val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]]
+      with SynchronizedBuffer[Seq[SparkFlumeEvent]]
+    val outputStream = new TestOutputStream(flumeStream, outputBuffer)
+    ssc.registerOutputStream(outputStream)
+    ssc.start()
+
+    val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
+    val input = Seq(1, 2, 3, 4, 5)
+    Thread.sleep(1000)
+    val transceiver = new NettyTransceiver(new InetSocketAddress("localhost", testPort))
+    val client = SpecificRequestor.getClient(
+      classOf[AvroSourceProtocol], transceiver)
+
+    for (i <- 0 until input.size) {
+      val event = new AvroFlumeEvent
+      event.setBody(ByteBuffer.wrap(input(i).toString.getBytes()))
+      event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header"))
+      client.append(event)
+      Thread.sleep(500)
+      clock.addToTime(batchDuration.milliseconds)
+    }
+
+    val startTime = System.currentTimeMillis()
+    while (outputBuffer.size < input.size && System.currentTimeMillis() - startTime < maxWaitTimeMillis) {
+      logInfo("output.size = " + outputBuffer.size + ", input.size = " + input.size)
+      Thread.sleep(100)
+    }
+    Thread.sleep(1000)
+    val timeTaken = System.currentTimeMillis() - startTime
+    assert(timeTaken < maxWaitTimeMillis, "Operation timed out after " + timeTaken + " ms")
+    logInfo("Stopping context")
+    ssc.stop()
+
+    val decoder = Charset.forName("UTF-8").newDecoder()
+
+    assert(outputBuffer.size === input.length)
+    for (i <- 0 until outputBuffer.size) {
+      assert(outputBuffer(i).size === 1)
+      val str = decoder.decode(outputBuffer(i).head.event.getBody)
+      assert(str.toString === input(i).toString)
+      assert(outputBuffer(i).head.event.getHeaders.get("test") === "header")
+    }
+  }
+}
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
new file mode 100644
index 0000000000..f782e0e126
--- /dev/null
+++ b/external/kafka/pom.xml
@@ -0,0 +1,97 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>0.9.0-incubating-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-kafka_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project External Kafka</name>
+  <url>http://spark.incubator.apache.org/</url>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sksamuel.kafka</groupId>
+      <artifactId>kafka_${scala.binary.version}</artifactId>
+      <version>0.8.0-beta1</version>
+      <exclusions>
+        <exclusion>
+          <groupId>com.sun.jmx</groupId>
+          <artifactId>jmxri</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>com.sun.jdmk</groupId>
+          <artifactId>jmxtools</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>net.sf.jopt-simple</groupId>
+          <artifactId>jopt-simple</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/KafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
index 526f5564c7..a2cd49c573 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/KafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
@@ -15,11 +15,10 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.dstream
+package org.apache.spark.streaming.kafka
 
-import org.apache.spark.Logging
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.StreamingContext
+import scala.collection.Map
+import scala.reflect.ClassTag
 
 import java.util.Properties
 import java.util.concurrent.Executors
@@ -30,8 +29,10 @@ import kafka.utils.VerifiableProperties
 import kafka.utils.ZKStringSerializer
 import org.I0Itec.zkclient._
 
-import scala.collection.Map
-import scala.reflect.ClassTag
+import org.apache.spark.Logging
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.StreamingContext
+import org.apache.spark.streaming.dstream._
 
 /**
  * Input stream that pulls messages from a Kafka Broker.
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
new file mode 100644
index 0000000000..c2d851f943
--- /dev/null
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
@@ -0,0 +1,153 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kafka
+
+import scala.reflect.ClassTag
+import scala.collection.JavaConversions._
+
+import java.lang.{Integer => JInt}
+import java.util.{Map => JMap}
+
+import kafka.serializer.{Decoder, StringDecoder}
+
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{StreamingContext, DStream}
+import org.apache.spark.streaming.api.java.{JavaStreamingContext, JavaPairDStream}
+
+
+object KafkaUtils {
+  /**
+   * Create an input stream that pulls messages from a Kafka Broker.
+   * @param ssc       StreamingContext object
+   * @param zkQuorum  Zookeeper quorum (hostname:port,hostname:port,..)
+   * @param groupId   The group id for this consumer
+   * @param topics    Map of (topic_name -> numPartitions) to consume. Each partition is consumed
+   *                  in its own thread
+   * @param storageLevel  Storage level to use for storing the received objects
+   *                      (default: StorageLevel.MEMORY_AND_DISK_SER_2)
+   */
+  def createStream(
+      ssc: StreamingContext,
+      zkQuorum: String,
+      groupId: String,
+      topics: Map[String, Int],
+      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
+    ): DStream[(String, String)] = {
+    val kafkaParams = Map[String, String](
+      "zookeeper.connect" -> zkQuorum, "group.id" -> groupId,
+      "zookeeper.connection.timeout.ms" -> "10000")
+    createStream[String, String, StringDecoder, StringDecoder](
+      ssc, kafkaParams, topics, storageLevel)
+  }
+
+  /**
+   * Create an input stream that pulls messages from a Kafka Broker.
+   * @param ssc         StreamingContext object
+   * @param kafkaParams Map of kafka configuration parameters,
+   *                    see http://kafka.apache.org/08/configuration.html
+   * @param topics      Map of (topic_name -> numPartitions) to consume. Each partition is consumed
+   *                    in its own thread.
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  def createStream[K: ClassTag, V: ClassTag, U <: Decoder[_]: Manifest, T <: Decoder[_]: Manifest](
+      ssc: StreamingContext,
+      kafkaParams: Map[String, String],
+      topics: Map[String, Int],
+      storageLevel: StorageLevel
+    ): DStream[(K, V)] = {
+    val inputStream = new KafkaInputDStream[K, V, U, T](ssc, kafkaParams, topics, storageLevel)
+    ssc.registerInputStream(inputStream)
+    inputStream
+  }
+
+  /**
+   * Create an input stream that pulls messages form a Kafka Broker.
+   * @param jssc      JavaStreamingContext object
+   * @param zkQuorum  Zookeeper quorum (hostname:port,hostname:port,..)
+   * @param groupId   The group id for this consumer
+   * @param topics    Map of (topic_name -> numPartitions) to consume. Each partition is consumed
+   *                  in its own thread
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      zkQuorum: String,
+      groupId: String,
+      topics: JMap[String, JInt]
+    ): JavaPairDStream[String, String] = {
+    implicit val cmt: ClassTag[String] =
+      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]]
+    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*))
+  }
+
+  /**
+   * Create an input stream that pulls messages form a Kafka Broker.
+   * @param jssc      JavaStreamingContext object
+   * @param zkQuorum  Zookeeper quorum (hostname:port,hostname:port,..).
+   * @param groupId   The group id for this consumer.
+   * @param topics    Map of (topic_name -> numPartitions) to consume. Each partition is consumed
+   *                  in its own thread.
+   * @param storageLevel RDD storage level.
+   *
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      zkQuorum: String,
+      groupId: String,
+      topics: JMap[String, JInt],
+      storageLevel: StorageLevel
+    ): JavaPairDStream[String, String] = {
+    implicit val cmt: ClassTag[String] =
+      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]]
+    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel)
+  }
+
+  /**
+   * Create an input stream that pulls messages form a Kafka Broker.
+   * @param jssc      JavaStreamingContext object
+   * @param keyTypeClass Key type of RDD
+   * @param valueTypeClass value type of RDD
+   * @param keyDecoderClass Type of kafka key decoder
+   * @param valueDecoderClass Type of kafka value decoder
+   * @param kafkaParams Map of kafka configuration parameters,
+   *                    see http://kafka.apache.org/08/configuration.html
+   * @param topics  Map of (topic_name -> numPartitions) to consume. Each partition is consumed
+   *                in its own thread
+   * @param storageLevel RDD storage level. Defaults to MEMORY_AND_DISK_2.
+   */
+  def createStream[K, V, U <: Decoder[_], T <: Decoder[_]](
+      jssc: JavaStreamingContext,
+      keyTypeClass: Class[K],
+      valueTypeClass: Class[V],
+      keyDecoderClass: Class[U],
+      valueDecoderClass: Class[T],
+      kafkaParams: JMap[String, String],
+      topics: JMap[String, JInt],
+      storageLevel: StorageLevel
+    ): JavaPairDStream[K, V] = {
+    implicit val keyCmt: ClassTag[K] =
+      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]]
+    implicit val valueCmt: ClassTag[V] =
+      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
+
+    implicit val keyCmd: Manifest[U] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[U]]
+    implicit val valueCmd: Manifest[T] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[T]]
+
+    createStream[K, V, U, T](
+      jssc.ssc, kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel)
+  }
+}
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java
new file mode 100644
index 0000000000..7b4999447e
--- /dev/null
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java
@@ -0,0 +1,45 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kafka;
+
+import java.util.HashMap;
+import org.junit.Test;
+import com.google.common.collect.Maps;
+import kafka.serializer.StringDecoder;
+import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.LocalJavaStreamingContext;
+import org.apache.spark.streaming.api.java.JavaPairDStream;
+
+public class JavaKafkaStreamSuite extends LocalJavaStreamingContext {
+  @Test
+  public void testKafkaStream() {
+    HashMap<String, Integer> topics = Maps.newHashMap();
+
+    // tests the API, does not actually test data receiving
+    JavaPairDStream<String, String> test1 = KafkaUtils.createStream(ssc, "localhost:12345", "group", topics);
+    JavaPairDStream<String, String> test2 = KafkaUtils.createStream(ssc, "localhost:12345", "group", topics,
+      StorageLevel.MEMORY_AND_DISK_SER_2());
+
+    HashMap<String, String> kafkaParams = Maps.newHashMap();
+    kafkaParams.put("zookeeper.connect", "localhost:12345");
+    kafkaParams.put("group.id","consumer-group");
+    JavaPairDStream<String, String> test3 = KafkaUtils.createStream(ssc,
+      String.class, String.class, StringDecoder.class, StringDecoder.class,
+      kafkaParams, topics, StorageLevel.MEMORY_AND_DISK_SER_2());
+  }
+}
diff --git a/external/kafka/src/test/resources/log4j.properties b/external/kafka/src/test/resources/log4j.properties
new file mode 100644
index 0000000000..063529a9cb
--- /dev/null
+++ b/external/kafka/src/test/resources/log4j.properties
@@ -0,0 +1,29 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Set everything to be logged to the file streaming/target/unit-tests.log
+log4j.rootCategory=INFO, file
+# log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file.append=false
+log4j.appender.file.file=streaming/target/unit-tests.log
+log4j.appender.file.layout=org.apache.log4j.PatternLayout
+log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
+
+# Ignore messages below warning level from Jetty, because it's a bit verbose
+log4j.logger.org.eclipse.jetty=WARN
+
diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala
new file mode 100644
index 0000000000..9c81f23c19
--- /dev/null
+++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala
@@ -0,0 +1,39 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kafka
+
+import kafka.serializer.StringDecoder
+import org.apache.spark.streaming.{StreamingContext, TestSuiteBase}
+import org.apache.spark.storage.StorageLevel
+
+class KafkaStreamSuite extends TestSuiteBase {
+
+  test("kafka input stream") {
+    val ssc = new StreamingContext(master, framework, batchDuration)
+    val topics = Map("my-topic" -> 1)
+
+    // tests the API, does not actually test data receiving
+    val test1 = KafkaUtils.createStream(ssc, "localhost:1234", "group", topics)
+    val test2 = KafkaUtils.createStream(ssc, "localhost:12345", "group", topics, StorageLevel.MEMORY_AND_DISK_SER_2)
+    val kafkaParams = Map("zookeeper.connect"->"localhost:12345","group.id"->"consumer-group")
+    val test3 = KafkaUtils.createStream[String, String, StringDecoder, StringDecoder](
+      ssc, kafkaParams, topics, StorageLevel.MEMORY_AND_DISK_SER_2)
+
+    // TODO: Actually test receiving data
+  }
+}
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
new file mode 100644
index 0000000000..31b4fa87de
--- /dev/null
+++ b/external/mqtt/pom.xml
@@ -0,0 +1,108 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>0.9.0-incubating-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-mqtt_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project External MQTT</name>
+  <url>http://spark.incubator.apache.org/</url>
+
+  <repositories>
+    <repository>
+      <id>mqtt-repo</id>
+      <name>MQTT Repository</name>
+      <url>https://repo.eclipse.org/content/repositories/paho-releases</url>
+      <releases>
+        <enabled>true</enabled>
+      </releases>
+      <snapshots>
+        <enabled>false</enabled>
+      </snapshots>
+    </repository>
+  </repositories>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.eclipse.paho</groupId>
+      <artifactId>mqtt-client</artifactId>
+       <version>0.4.0</version>
+    </dependency>
+    <dependency>
+      <groupId>${akka.group}</groupId>
+      <artifactId>akka-zeromq_${scala.binary.version}</artifactId>
+      <version>${akka.version}</version>
+      <exclusions>
+        <exclusion>
+          <groupId>org.jboss.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MQTTInputDStream.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
index ef4a737568..c8987a3ee0 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MQTTInputDStream.scala
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
@@ -15,11 +15,12 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.dstream
+package org.apache.spark.streaming.mqtt
 
-import org.apache.spark.Logging
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.{ Time, DStreamCheckpointData, StreamingContext }
+import scala.collection.Map
+import scala.collection.mutable.HashMap
+import scala.collection.JavaConversions._
+import scala.reflect.ClassTag
 
 import java.util.Properties
 import java.util.concurrent.Executors
@@ -34,10 +35,10 @@ import org.eclipse.paho.client.mqttv3.MqttException
 import org.eclipse.paho.client.mqttv3.MqttMessage
 import org.eclipse.paho.client.mqttv3.MqttTopic
 
-import scala.collection.Map
-import scala.collection.mutable.HashMap
-import scala.collection.JavaConversions._
-import scala.reflect.ClassTag
+import org.apache.spark.Logging
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.StreamingContext
+import org.apache.spark.streaming.dstream._
 
 /**
  * Input stream that subscribe messages from a Mqtt Broker.
@@ -49,15 +50,14 @@ import scala.reflect.ClassTag
 
 private[streaming] 
 class MQTTInputDStream[T: ClassTag](
-  @transient ssc_ : StreamingContext,
-  brokerUrl: String,
-  topic: String,
-  storageLevel: StorageLevel
+    @transient ssc_ : StreamingContext,
+    brokerUrl: String,
+    topic: String,
+    storageLevel: StorageLevel
   ) extends NetworkInputDStream[T](ssc_) with Logging {
   
   def getReceiver(): NetworkReceiver[T] = {
-    new MQTTReceiver(brokerUrl, topic, storageLevel)
-      .asInstanceOf[NetworkReceiver[T]]
+    new MQTTReceiver(brokerUrl, topic, storageLevel).asInstanceOf[NetworkReceiver[T]]
   }
 }
 
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
new file mode 100644
index 0000000000..0e6c25dbee
--- /dev/null
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
@@ -0,0 +1,75 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.mqtt
+
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{StreamingContext, DStream}
+import org.apache.spark.streaming.api.java.{JavaStreamingContext, JavaDStream}
+import scala.reflect.ClassTag
+
+object MQTTUtils {
+  /**
+   * Create an input stream that receives messages pushed by a MQTT publisher.
+   * @param ssc           StreamingContext object
+   * @param brokerUrl     Url of remote MQTT publisher
+   * @param topic         Topic name to subscribe to
+   * @param storageLevel  RDD storage level. Defaults to StorageLevel.MEMORY_AND_DISK_SER_2.
+   */
+  def createStream(
+      ssc: StreamingContext,
+      brokerUrl: String,
+      topic: String,
+      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
+    ): DStream[String] = {
+    val inputStream = new MQTTInputDStream[String](ssc, brokerUrl, topic, storageLevel)
+    ssc.registerInputStream(inputStream)
+    inputStream
+  }
+
+  /**
+   * Create an input stream that receives messages pushed by a MQTT publisher.
+   * @param jssc      JavaStreamingContext object
+   * @param brokerUrl Url of remote MQTT publisher
+   * @param topic     Topic name to subscribe to
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      brokerUrl: String,
+      topic: String
+    ): JavaDStream[String] = {
+    implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]]
+    createStream(jssc.ssc, brokerUrl, topic)
+  }
+
+  /**
+   * Create an input stream that receives messages pushed by a MQTT publisher.
+   * @param jssc      JavaStreamingContext object
+   * @param brokerUrl     Url of remote MQTT publisher
+   * @param topic         Topic name to subscribe to
+   * @param storageLevel  RDD storage level.
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      brokerUrl: String,
+      topic: String,
+      storageLevel: StorageLevel
+    ): JavaDStream[String] = {
+    implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]]
+    createStream(jssc.ssc, brokerUrl, topic, storageLevel)
+  }
+}
diff --git a/external/mqtt/src/test/java/org/apache/spark/streaming/mqtt/JavaMQTTStreamSuite.java b/external/mqtt/src/test/java/org/apache/spark/streaming/mqtt/JavaMQTTStreamSuite.java
new file mode 100644
index 0000000000..44743aaecf
--- /dev/null
+++ b/external/mqtt/src/test/java/org/apache/spark/streaming/mqtt/JavaMQTTStreamSuite.java
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.mqtt;
+
+import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.api.java.JavaDStream;
+import org.junit.Test;
+
+import org.apache.spark.streaming.LocalJavaStreamingContext;
+
+public class JavaMQTTStreamSuite extends LocalJavaStreamingContext {
+  @Test
+  public void testMQTTStream() {
+    String brokerUrl = "abc";
+    String topic = "def";
+
+    // tests the API, does not actually test data receiving
+    JavaDStream<String> test1 = MQTTUtils.createStream(ssc, brokerUrl, topic);
+    JavaDStream<String> test2 = MQTTUtils.createStream(ssc, brokerUrl, topic,
+      StorageLevel.MEMORY_AND_DISK_SER_2());
+  }
+}
diff --git a/external/mqtt/src/test/resources/log4j.properties b/external/mqtt/src/test/resources/log4j.properties
new file mode 100644
index 0000000000..063529a9cb
--- /dev/null
+++ b/external/mqtt/src/test/resources/log4j.properties
@@ -0,0 +1,29 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Set everything to be logged to the file streaming/target/unit-tests.log
+log4j.rootCategory=INFO, file
+# log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file.append=false
+log4j.appender.file.file=streaming/target/unit-tests.log
+log4j.appender.file.layout=org.apache.log4j.PatternLayout
+log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
+
+# Ignore messages below warning level from Jetty, because it's a bit verbose
+log4j.logger.org.eclipse.jetty=WARN
+
diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala
new file mode 100644
index 0000000000..fcc159e85a
--- /dev/null
+++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.mqtt
+
+import org.apache.spark.streaming.{StreamingContext, TestSuiteBase}
+import org.apache.spark.storage.StorageLevel
+
+class MQTTStreamSuite extends TestSuiteBase {
+
+  test("MQTT input stream") {
+    val ssc = new StreamingContext(master, framework, batchDuration)
+    val brokerUrl = "abc"
+    val topic = "def"
+
+    // tests the API, does not actually test data receiving
+    val test1 = MQTTUtils.createStream(ssc, brokerUrl, topic)
+    val test2 = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_AND_DISK_SER_2)
+
+    // TODO: Actually test receiving data
+  }
+}
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
new file mode 100644
index 0000000000..216e6c1d8f
--- /dev/null
+++ b/external/twitter/pom.xml
@@ -0,0 +1,89 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>0.9.0-incubating-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-twitter_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project External Twitter</name>
+  <url>http://spark.incubator.apache.org/</url>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.twitter4j</groupId>
+      <artifactId>twitter4j-stream</artifactId>
+      <version>3.0.3</version>
+      <exclusions>
+        <exclusion>
+          <groupId>org.jboss.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
index 387e15b0e6..5cc721d7f9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TwitterInputDStream.scala
+++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
@@ -15,18 +15,19 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.dstream
+package org.apache.spark.streaming.twitter
 
-import org.apache.spark._
-import org.apache.spark.streaming._
-import storage.StorageLevel
+import java.util.prefs.Preferences
 import twitter4j._
 import twitter4j.auth.Authorization
-import java.util.prefs.Preferences
 import twitter4j.conf.ConfigurationBuilder
 import twitter4j.conf.PropertyConfiguration
 import twitter4j.auth.OAuthAuthorization
 import twitter4j.auth.AccessToken
+import org.apache.spark._
+import org.apache.spark.streaming._
+import org.apache.spark.streaming.dstream._
+import org.apache.spark.storage.StorageLevel
 
 /* A stream of Twitter statuses, potentially filtered by one or more keywords.
 *
diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterUtils.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterUtils.scala
new file mode 100644
index 0000000000..5e506ffabc
--- /dev/null
+++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterUtils.scala
@@ -0,0 +1,126 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.twitter
+
+import twitter4j.Status
+import twitter4j.auth.Authorization
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{StreamingContext, DStream}
+import org.apache.spark.streaming.api.java.{JavaDStream, JavaStreamingContext}
+
+object TwitterUtils {
+  /**
+   * Create a input stream that returns tweets received from Twitter.
+   * @param ssc         StreamingContext object
+   * @param twitterAuth Twitter4J authentication, or None to use Twitter4J's default OAuth
+   *        authorization; this uses the system properties twitter4j.oauth.consumerKey,
+   *        twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and
+   *        twitter4j.oauth.accessTokenSecret
+   * @param filters Set of filter strings to get only those tweets that match them
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  def createStream(
+      ssc: StreamingContext,
+      twitterAuth: Option[Authorization],
+      filters: Seq[String] = Nil,
+      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
+    ): DStream[Status] = {
+    val inputStream = new TwitterInputDStream(ssc, twitterAuth, filters, storageLevel)
+    ssc.registerInputStream(inputStream)
+    inputStream
+  }
+
+  /**
+   * Create a input stream that returns tweets received from Twitter using Twitter4J's default
+   * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey,
+   * twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and
+   * twitter4j.oauth.accessTokenSecret.
+   * @param jssc   JavaStreamingContext object
+   */
+  def createStream(jssc: JavaStreamingContext): JavaDStream[Status] = {
+    createStream(jssc.ssc, None)
+  }
+
+  /**
+   * Create a input stream that returns tweets received from Twitter using Twitter4J's default
+   * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey,
+   * twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and
+   * twitter4j.oauth.accessTokenSecret.
+   * @param jssc    JavaStreamingContext object
+   * @param filters Set of filter strings to get only those tweets that match them
+   */
+  def createStream(jssc: JavaStreamingContext, filters: Array[String]): JavaDStream[Status] = {
+    createStream(jssc.ssc, None, filters)
+  }
+
+  /**
+   * Create a input stream that returns tweets received from Twitter using Twitter4J's default
+   * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey,
+   * twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and
+   * twitter4j.oauth.accessTokenSecret.
+   * @param jssc         JavaStreamingContext object
+   * @param filters      Set of filter strings to get only those tweets that match them
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      filters: Array[String],
+      storageLevel: StorageLevel
+    ): JavaDStream[Status] = {
+    createStream(jssc.ssc, None, filters, storageLevel)
+  }
+
+  /**
+   * Create a input stream that returns tweets received from Twitter.
+   * @param jssc        JavaStreamingContext object
+   * @param twitterAuth Twitter4J Authorization
+   */
+  def createStream(jssc: JavaStreamingContext, twitterAuth: Authorization): JavaDStream[Status] = {
+    createStream(jssc.ssc, Some(twitterAuth))
+  }
+
+  /**
+   * Create a input stream that returns tweets received from Twitter.
+   * @param jssc        JavaStreamingContext object
+   * @param twitterAuth Twitter4J Authorization
+   * @param filters     Set of filter strings to get only those tweets that match them
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      twitterAuth: Authorization,
+      filters: Array[String]
+    ): JavaDStream[Status] = {
+    createStream(jssc.ssc, Some(twitterAuth), filters)
+  }
+
+  /**
+   * Create a input stream that returns tweets received from Twitter.
+   * @param jssc         JavaStreamingContext object
+   * @param twitterAuth  Twitter4J Authorization object
+   * @param filters      Set of filter strings to get only those tweets that match them
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      twitterAuth: Authorization,
+      filters: Array[String],
+      storageLevel: StorageLevel
+    ): JavaDStream[Status] = {
+    createStream(jssc.ssc, Some(twitterAuth), filters, storageLevel)
+  }
+}
diff --git a/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java b/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java
new file mode 100644
index 0000000000..e46b4e5c75
--- /dev/null
+++ b/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.twitter;
+
+import java.util.Arrays;
+
+import org.junit.Test;
+import twitter4j.Status;
+import twitter4j.auth.Authorization;
+import twitter4j.auth.NullAuthorization;
+import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.LocalJavaStreamingContext;
+import org.apache.spark.streaming.api.java.JavaDStream;
+
+public class JavaTwitterStreamSuite extends LocalJavaStreamingContext {
+  @Test
+  public void testTwitterStream() {
+    String[] filters = (String[])Arrays.<String>asList("filter1", "filter2").toArray();
+    Authorization auth = NullAuthorization.getInstance();
+
+    // tests the API, does not actually test data receiving
+    JavaDStream<Status> test1 = TwitterUtils.createStream(ssc);
+    JavaDStream<Status> test2 = TwitterUtils.createStream(ssc, filters);
+    JavaDStream<Status> test3 = TwitterUtils.createStream(
+      ssc, filters, StorageLevel.MEMORY_AND_DISK_SER_2());
+    JavaDStream<Status> test4 = TwitterUtils.createStream(ssc, auth);
+    JavaDStream<Status> test5 = TwitterUtils.createStream(ssc, auth, filters);
+    JavaDStream<Status> test6 = TwitterUtils.createStream(ssc,
+      auth, filters, StorageLevel.MEMORY_AND_DISK_SER_2());
+  }
+}
diff --git a/external/twitter/src/test/resources/log4j.properties b/external/twitter/src/test/resources/log4j.properties
new file mode 100644
index 0000000000..063529a9cb
--- /dev/null
+++ b/external/twitter/src/test/resources/log4j.properties
@@ -0,0 +1,29 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Set everything to be logged to the file streaming/target/unit-tests.log
+log4j.rootCategory=INFO, file
+# log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file.append=false
+log4j.appender.file.file=streaming/target/unit-tests.log
+log4j.appender.file.layout=org.apache.log4j.PatternLayout
+log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
+
+# Ignore messages below warning level from Jetty, because it's a bit verbose
+log4j.logger.org.eclipse.jetty=WARN
+
diff --git a/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala b/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala
new file mode 100644
index 0000000000..a0a8fe617b
--- /dev/null
+++ b/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.twitter
+
+import org.apache.spark.streaming.{StreamingContext, TestSuiteBase}
+import org.apache.spark.storage.StorageLevel
+import twitter4j.auth.{NullAuthorization, Authorization}
+
+class TwitterStreamSuite extends TestSuiteBase {
+
+  test("kafka input stream") {
+    val ssc = new StreamingContext(master, framework, batchDuration)
+    val filters = Seq("filter1", "filter2")
+    val authorization: Authorization = NullAuthorization.getInstance()
+
+    // tests the API, does not actually test data receiving
+    val test1 = TwitterUtils.createStream(ssc, None)
+    val test2 = TwitterUtils.createStream(ssc, None, filters)
+    val test3 = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_AND_DISK_SER_2)
+    val test4 = TwitterUtils.createStream(ssc, Some(authorization))
+    val test5 = TwitterUtils.createStream(ssc, Some(authorization), filters)
+    val test6 = TwitterUtils.createStream(ssc, Some(authorization), filters,
+      StorageLevel.MEMORY_AND_DISK_SER_2)
+
+    // Note that actually testing the data receiving is hard as authentication keys are
+    // necessary for accessing Twitter live stream
+  }
+}
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
new file mode 100644
index 0000000000..c240d59574
--- /dev/null
+++ b/external/zeromq/pom.xml
@@ -0,0 +1,89 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>0.9.0-incubating-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-zeromq_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project External ZeroMQ</name>
+  <url>http://spark.incubator.apache.org/</url>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>${akka.group}</groupId>
+      <artifactId>akka-zeromq_${scala.binary.version}</artifactId>
+      <version>${akka.version}</version>
+      <exclusions>
+        <exclusion>
+          <groupId>org.jboss.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ZeroMQReceiver.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala
index f164d516b0..769761e3b8 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ZeroMQReceiver.scala
+++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.receivers
+package org.apache.spark.streaming.zeromq
 
 import scala.reflect.ClassTag
 
@@ -24,6 +24,7 @@ import akka.util.ByteString
 import akka.zeromq._
 
 import org.apache.spark.Logging
+import org.apache.spark.streaming.receivers._
 
 /**
  * A receiver to subscribe to ZeroMQ stream.
diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala
new file mode 100644
index 0000000000..546d9df3b5
--- /dev/null
+++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala
@@ -0,0 +1,126 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.zeromq
+
+import scala.reflect.ClassTag
+import scala.collection.JavaConversions._
+import akka.actor.{Props, SupervisorStrategy}
+import akka.util.ByteString
+import akka.zeromq.Subscribe
+import org.apache.spark.api.java.function.{Function => JFunction}
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.receivers.ReceiverSupervisorStrategy
+import org.apache.spark.streaming.{StreamingContext, DStream}
+import org.apache.spark.streaming.api.java.{JavaStreamingContext, JavaDStream}
+
+object ZeroMQUtils {
+  /**
+   * Create an input stream that receives messages pushed by a zeromq publisher.
+   * @param ssc            StreamingContext object
+   * @param publisherUrl   Url of remote zeromq publisher
+   * @param subscribe      Topic to subscribe to
+   * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic
+   *                       and each frame has sequence of byte thus it needs the converter
+   *                       (which might be deserializer of bytes) to translate from sequence
+   *                       of sequence of bytes, where sequence refer to a frame
+   *                       and sub sequence refer to its payload.
+   * @param storageLevel   RDD storage level. Defaults to StorageLevel.MEMORY_AND_DISK_SER_2.
+   */
+  def createStream[T: ClassTag](
+      ssc: StreamingContext,
+      publisherUrl: String,
+      subscribe: Subscribe,
+      bytesToObjects: Seq[ByteString] ⇒ Iterator[T],
+      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2,
+      supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy
+    ): DStream[T] = {
+    ssc.actorStream(Props(new ZeroMQReceiver(publisherUrl, subscribe, bytesToObjects)),
+      "ZeroMQReceiver", storageLevel, supervisorStrategy)
+  }
+
+  /**
+   * Create an input stream that receives messages pushed by a zeromq publisher.
+   * @param jssc           JavaStreamingContext object
+   * @param publisherUrl   Url of remote ZeroMQ publisher
+   * @param subscribe      Topic to subscribe to
+   * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence
+   *                       of byte thus it needs the converter(which might be deserializer of bytes)
+   *                       to translate from sequence of sequence of bytes, where sequence refer to a frame
+   *                       and sub sequence refer to its payload.
+   * @param storageLevel  Storage level to use for storing the received objects
+   */
+  def createStream[T](
+      jssc: JavaStreamingContext,
+      publisherUrl: String,
+      subscribe: Subscribe,
+      bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]],
+      storageLevel: StorageLevel,
+      supervisorStrategy: SupervisorStrategy
+    ): JavaDStream[T] = {
+    implicit val cm: ClassTag[T] =
+      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
+    val fn = (x: Seq[ByteString]) => bytesToObjects.apply(x.map(_.toArray).toArray).toIterator
+    createStream[T](jssc.ssc, publisherUrl, subscribe, fn, storageLevel, supervisorStrategy)
+  }
+
+  /**
+   * Create an input stream that receives messages pushed by a zeromq publisher.
+   * @param jssc           JavaStreamingContext object
+   * @param publisherUrl   Url of remote zeromq publisher
+   * @param subscribe      Topic to subscribe to
+   * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence
+   *                       of byte thus it needs the converter(which might be deserializer of bytes)
+   *                       to translate from sequence of sequence of bytes, where sequence refer to a frame
+   *                       and sub sequence refer to its payload.
+   * @param storageLevel   RDD storage level.
+   */
+  def createStream[T](
+      jssc: JavaStreamingContext,
+      publisherUrl: String,
+      subscribe: Subscribe,
+      bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]],
+      storageLevel: StorageLevel
+    ): JavaDStream[T] = {
+    implicit val cm: ClassTag[T] =
+      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
+    val fn = (x: Seq[ByteString]) => bytesToObjects.apply(x.map(_.toArray).toArray).toIterator
+    createStream[T](jssc.ssc, publisherUrl, subscribe, fn, storageLevel)
+  }
+
+  /**
+   * Create an input stream that receives messages pushed by a zeromq publisher.
+   * @param jssc           JavaStreamingContext object
+   * @param publisherUrl   Url of remote zeromq publisher
+   * @param subscribe      Topic to subscribe to
+   * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence
+   *                       of byte thus it needs the converter(which might be deserializer of bytes)
+   *                       to translate from sequence of sequence of bytes, where sequence refer to a frame
+   *                       and sub sequence refer to its payload.
+   */
+  def createStream[T](
+      jssc: JavaStreamingContext,
+      publisherUrl: String,
+      subscribe: Subscribe,
+      bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]]
+    ): JavaDStream[T] = {
+    implicit val cm: ClassTag[T] =
+      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
+    val fn = (x: Seq[ByteString]) => bytesToObjects.apply(x.map(_.toArray).toArray).toIterator
+    createStream[T](jssc.ssc, publisherUrl, subscribe, fn)
+  }
+}
diff --git a/external/zeromq/src/test/java/org/apache/spark/streaming/zeromq/JavaZeroMQStreamSuite.java b/external/zeromq/src/test/java/org/apache/spark/streaming/zeromq/JavaZeroMQStreamSuite.java
new file mode 100644
index 0000000000..d2361e14b8
--- /dev/null
+++ b/external/zeromq/src/test/java/org/apache/spark/streaming/zeromq/JavaZeroMQStreamSuite.java
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.zeromq;
+
+import org.junit.Test;
+import akka.actor.SupervisorStrategy;
+import akka.util.ByteString;
+import akka.zeromq.Subscribe;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.LocalJavaStreamingContext;
+import org.apache.spark.streaming.api.java.JavaDStream;
+
+public class JavaZeroMQStreamSuite extends LocalJavaStreamingContext {
+
+  @Test // tests the API, does not actually test data receiving
+  public void testZeroMQStream() {
+    String publishUrl = "abc";
+    Subscribe subscribe = new Subscribe((ByteString)null);
+    Function<byte[][], Iterable<String>> bytesToObjects = new Function<byte[][], Iterable<String>>() {
+      @Override
+      public Iterable<String> call(byte[][] bytes) throws Exception {
+        return null;
+      }
+    };
+
+    JavaDStream<String> test1 = ZeroMQUtils.<String>createStream(
+      ssc, publishUrl, subscribe, bytesToObjects);
+    JavaDStream<String> test2 = ZeroMQUtils.<String>createStream(
+      ssc, publishUrl, subscribe, bytesToObjects, StorageLevel.MEMORY_AND_DISK_SER_2());
+    JavaDStream<String> test3 = ZeroMQUtils.<String>createStream(
+      ssc,publishUrl, subscribe, bytesToObjects, StorageLevel.MEMORY_AND_DISK_SER_2(),
+      SupervisorStrategy.defaultStrategy());
+  }
+}
diff --git a/external/zeromq/src/test/resources/log4j.properties b/external/zeromq/src/test/resources/log4j.properties
new file mode 100644
index 0000000000..063529a9cb
--- /dev/null
+++ b/external/zeromq/src/test/resources/log4j.properties
@@ -0,0 +1,29 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Set everything to be logged to the file streaming/target/unit-tests.log
+log4j.rootCategory=INFO, file
+# log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file.append=false
+log4j.appender.file.file=streaming/target/unit-tests.log
+log4j.appender.file.layout=org.apache.log4j.PatternLayout
+log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
+
+# Ignore messages below warning level from Jetty, because it's a bit verbose
+log4j.logger.org.eclipse.jetty=WARN
+
diff --git a/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala b/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala
new file mode 100644
index 0000000000..4193b8a02f
--- /dev/null
+++ b/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.zeromq
+
+import akka.actor.SupervisorStrategy
+import akka.util.ByteString
+import akka.zeromq.Subscribe
+
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{StreamingContext, TestSuiteBase}
+
+class ZeroMQStreamSuite extends TestSuiteBase {
+
+  test("zeromq input stream") {
+    val ssc = new StreamingContext(master, framework, batchDuration)
+    val publishUrl = "abc"
+    val subscribe = new Subscribe(null.asInstanceOf[ByteString])
+    val bytesToObjects = (bytes: Seq[ByteString]) => null.asInstanceOf[Iterator[String]]
+
+    // tests the API, does not actually test data receiving
+    val test1 = ZeroMQUtils.createStream(ssc, publishUrl, subscribe, bytesToObjects)
+    val test2 = ZeroMQUtils.createStream(
+      ssc, publishUrl, subscribe, bytesToObjects, StorageLevel.MEMORY_AND_DISK_SER_2)
+    val test3 = ZeroMQUtils.createStream(ssc, publishUrl, subscribe, bytesToObjects,
+      StorageLevel.MEMORY_AND_DISK_SER_2, SupervisorStrategy.defaultStrategy)
+
+    // TODO: Actually test data receiving
+  }
+}
diff --git a/make-distribution.sh b/make-distribution.sh
index 6c466c8a06..1a3a5d0209 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -44,13 +44,16 @@ DISTDIR="$FWDIR/dist"
 # Get version from SBT
 export TERM=dumb   # Prevents color codes in SBT output
 
-if ! test `which sbt` ;then
+VERSIONSTRING=$FWDIR/sbt/sbt "show version"
+
+if [ $? == -1 ] ;then
     echo -e "You need sbt installed and available on your path."
     echo -e "Download sbt from http://www.scala-sbt.org/"
     exit -1;
 fi
 
-VERSION=$(sbt "show version" | tail -1 | cut -f 2 | sed 's/^\([a-zA-Z0-9.-]*\).*/\1/')
+VERSION=$(echo "${VERSIONSTRING}" | tail -1 | cut -f 2 | sed 's/^\([a-zA-Z0-9.-]*\).*/\1/')
+echo "Version is ${VERSION}"
 
 # Initialize defaults
 SPARK_HADOOP_VERSION=1.0.4
@@ -92,7 +95,7 @@ export SPARK_HADOOP_VERSION
 export SPARK_YARN
 cd $FWDIR
 
-"sbt" "assembly/assembly"
+"sbt/sbt" "assembly/assembly"
 
 # Make directories
 rm -rf "$DISTDIR"
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 8247c1ebc5..2d8623392e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -197,6 +197,7 @@ class PythonMLLibAPI extends Serializable {
     return ret
   }
 
+  /** Unpack a Rating object from an array of bytes */
   private def unpackRating(ratingBytes: Array[Byte]): Rating = {
     val bb = ByteBuffer.wrap(ratingBytes)
     bb.order(ByteOrder.nativeOrder())
@@ -206,6 +207,35 @@ class PythonMLLibAPI extends Serializable {
     return new Rating(user, product, rating)
   }
 
+  /** Unpack a tuple of Ints from an array of bytes */
+  private[spark] def unpackTuple(tupleBytes: Array[Byte]): (Int, Int) = {
+    val bb = ByteBuffer.wrap(tupleBytes)
+    bb.order(ByteOrder.nativeOrder())
+    val v1 = bb.getInt()
+    val v2 = bb.getInt()
+    (v1, v2)
+  }
+
+  /**
+    * Serialize a Rating object into an array of bytes.
+    * It can be deserialized using RatingDeserializer().
+    *
+    * @param rate
+    * @return
+    */
+  private[spark] def serializeRating(rate: Rating): Array[Byte] = {
+    val len = 3
+    val bytes = new Array[Byte](4 + 8 * len)
+    val bb = ByteBuffer.wrap(bytes)
+    bb.order(ByteOrder.nativeOrder())
+    bb.putInt(len)
+    val db = bb.asDoubleBuffer()
+    db.put(rate.user.toDouble)
+    db.put(rate.product.toDouble)
+    db.put(rate.rating)
+    bytes
+  }
+
   /**
    * Java stub for Python mllib ALS.train().  This stub returns a handle
    * to the Java object instead of the content of the Java object.  Extra care
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
index 749e7364f4..c590492e7a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
@@ -50,8 +50,8 @@ class LogisticGradient extends Gradient {
 
     val gradient = data.mul(gradientMultiplier)
     val loss =
-      if (margin > 0) {
-        math.log(1 + math.exp(0 - margin))
+      if (label > 0) {
+        math.log(1 + math.exp(margin))
       } else {
         math.log(1 + math.exp(margin)) - margin
       }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index af43d89c70..443fc5de5b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -19,8 +19,11 @@ package org.apache.spark.mllib.recommendation
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.api.python.PythonMLLibAPI
 
 import org.jblas._
+import org.apache.spark.api.java.JavaRDD
+
 
 /**
  * Model representing the result of matrix factorization.
@@ -44,6 +47,39 @@ class MatrixFactorizationModel(
     userVector.dot(productVector)
   }
 
-  // TODO: Figure out what good bulk prediction methods would look like.
+  /**
+    * Predict the rating of many users for many products.
+    * The output RDD has an element per each element in the input RDD (including all duplicates)
+    * unless a user or product is missing in the training set.
+    *
+    * @param usersProducts  RDD of (user, product) pairs.
+    * @return RDD of Ratings.
+    */
+  def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
+    val users = userFeatures.join(usersProducts).map{
+      case (user, (uFeatures, product)) => (product, (user, uFeatures))
+    }
+    users.join(productFeatures).map {
+      case (product, ((user, uFeatures), pFeatures)) =>
+        val userVector = new DoubleMatrix(uFeatures)
+        val productVector = new DoubleMatrix(pFeatures)
+        Rating(user, product, userVector.dot(productVector))
+    }
+  }
+
+  /**
+   * Predict the rating of many users for many products.
+   * This is a Java stub for python predictAll()
+   *
+   * @param usersProductsJRDD A JavaRDD with serialized tuples (user, product)
+   * @return JavaRDD of serialized Rating objects.
+   */
+  def predict(usersProductsJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]] = {
+    val pythonAPI = new PythonMLLibAPI()
+    val usersProducts = usersProductsJRDD.rdd.map(xBytes => pythonAPI.unpackTuple(xBytes))
+    predict(usersProducts).map(rate => pythonAPI.serializeRating(rate))
+  }
+
+  // TODO: Figure out what other good bulk prediction methods would look like.
   // Probably want a way to get the top users for a product or vice-versa.
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
new file mode 100644
index 0000000000..a6028a1e98
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
@@ -0,0 +1,116 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.optimization
+
+import scala.util.Random
+import scala.collection.JavaConversions._
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+import org.scalatest.matchers.ShouldMatchers
+
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.regression._
+
+object GradientDescentSuite {
+
+  def generateLogisticInputAsList(
+      offset: Double,
+      scale: Double,
+      nPoints: Int,
+      seed: Int): java.util.List[LabeledPoint] = {
+    seqAsJavaList(generateGDInput(offset, scale, nPoints, seed))
+  }
+
+  // Generate input of the form Y = logistic(offset + scale * X)
+  def generateGDInput(
+      offset: Double,
+      scale: Double,
+      nPoints: Int,
+      seed: Int): Seq[LabeledPoint]  = {
+    val rnd = new Random(seed)
+    val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
+
+    val unifRand = new scala.util.Random(45)
+    val rLogis = (0 until nPoints).map { i =>
+      val u = unifRand.nextDouble()
+      math.log(u) - math.log(1.0-u)
+    }
+
+    val y: Seq[Int] = (0 until nPoints).map { i =>
+      val yVal = offset + scale * x1(i) + rLogis(i)
+      if (yVal > 0) 1 else 0
+    }
+
+    val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Array(x1(i))))
+    testData
+  }
+}
+
+class GradientDescentSuite extends FunSuite with BeforeAndAfterAll with ShouldMatchers {
+  @transient private var sc: SparkContext = _
+
+  override def beforeAll() {
+    sc = new SparkContext("local", "test")
+  }
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  test("Assert the loss is decreasing.") {
+    val nPoints = 10000
+    val A = 2.0
+    val B = -1.5
+
+    val initialB = -1.0
+    val initialWeights = Array(initialB)
+
+    val gradient = new LogisticGradient()
+    val updater = new SimpleUpdater()
+    val stepSize = 1.0
+    val numIterations = 10
+    val regParam = 0
+    val miniBatchFrac = 1.0
+
+    // Add a extra variable consisting of all 1.0's for the intercept.
+    val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42)
+    val data = testData.map { case LabeledPoint(label, features) =>
+      label -> Array(1.0, features: _*)
+    }
+
+    val dataRDD = sc.parallelize(data, 2).cache()
+    val initialWeightsWithIntercept = Array(1.0, initialWeights: _*)
+
+    val (_, loss) = GradientDescent.runMiniBatchSGD(
+      dataRDD,
+      gradient,
+      updater,
+      stepSize,
+      numIterations,
+      regParam,
+      miniBatchFrac,
+      initialWeightsWithIntercept)
+
+    assert(loss.last - loss.head < 0, "loss isn't decreasing.")
+
+    val lossDiff = loss.init.zip(loss.tail).map { case (lhs, rhs) => lhs - rhs }
+    assert(lossDiff.count(_ > 0).toDouble / lossDiff.size > 0.8)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
index fafc5ec5f2..e683a90f57 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
@@ -90,18 +90,34 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll {
     testALS(50, 100, 1, 15, 0.7, 0.3)
   }
 
+  test("rank-1 matrices bulk") {
+    testALS(50, 100, 1, 15, 0.7, 0.3, false, true)
+  }
+
   test("rank-2 matrices") {
     testALS(100, 200, 2, 15, 0.7, 0.3)
   }
 
+  test("rank-2 matrices bulk") {
+    testALS(100, 200, 2, 15, 0.7, 0.3, false, true)
+  }
+
   test("rank-1 matrices implicit") {
     testALS(80, 160, 1, 15, 0.7, 0.4, true)
   }
 
+  test("rank-1 matrices implicit bulk") {
+    testALS(80, 160, 1, 15, 0.7, 0.4, true, true)
+  }
+
   test("rank-2 matrices implicit") {
     testALS(100, 200, 2, 15, 0.7, 0.4, true)
   }
 
+  test("rank-2 matrices implicit bulk") {
+    testALS(100, 200, 2, 15, 0.7, 0.4, true, true)
+  }
+
   /**
    * Test if we can correctly factorize R = U * P where U and P are of known rank.
    *
@@ -111,9 +127,12 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll {
    * @param iterations     number of iterations to run
    * @param samplingRate   what fraction of the user-product pairs are known
    * @param matchThreshold max difference allowed to consider a predicted rating correct
+   * @param implicitPrefs  flag to test implicit feedback
+   * @param bulkPredict    flag to test bulk prediciton
    */
   def testALS(users: Int, products: Int, features: Int, iterations: Int,
-    samplingRate: Double, matchThreshold: Double, implicitPrefs: Boolean = false)
+    samplingRate: Double, matchThreshold: Double, implicitPrefs: Boolean = false,
+    bulkPredict: Boolean = false)
   {
     val (sampledRatings, trueRatings, truePrefs) = ALSSuite.generateRatings(users, products,
       features, samplingRate, implicitPrefs)
@@ -130,7 +149,17 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll {
     for ((p, vec) <- model.productFeatures.collect(); i <- 0 until features) {
       predictedP.put(p, i, vec(i))
     }
-    val predictedRatings = predictedU.mmul(predictedP.transpose)
+    val predictedRatings = bulkPredict match {
+      case false => predictedU.mmul(predictedP.transpose)
+      case true =>
+        val allRatings = new DoubleMatrix(users, products)
+        val usersProducts = for (u <- 0 until users; p <- 0 until products) yield (u, p)
+        val userProductsRDD = sc.parallelize(usersProducts)
+        model.predict(userProductsRDD).collect().foreach { elem =>
+          allRatings.put(elem.user, elem.product, elem.rating)
+        }
+        allRatings
+    }
 
     if (!implicitPrefs) {
       for (u <- 0 until users; p <- 0 until products) {
diff --git a/pom.xml b/pom.xml
index 7b734c5371..9f08e8ad4e 100644
--- a/pom.xml
+++ b/pom.xml
@@ -87,12 +87,17 @@
   <modules>
     <module>core</module>
     <module>bagel</module>
-    <module>examples</module>
     <module>mllib</module>
     <module>tools</module>
     <module>streaming</module>
     <module>repl</module>
     <module>assembly</module>
+    <module>external/twitter</module>
+    <module>external/kafka</module>
+    <module>external/flume</module>
+    <module>external/zeromq</module>
+    <module>external/mqtt</module>
+    <module>examples</module>
   </modules>
 
   <properties>
@@ -140,17 +145,6 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
-    <repository>
-      <id>mqtt-repo</id>
-      <name>MQTT Repository</name>
-      <url>https://repo.eclipse.org/content/repositories/paho-releases</url>
-      <releases>
-        <enabled>true</enabled>
-      </releases>
-      <snapshots>
-        <enabled>false</enabled>
-      </snapshots>
-    </repository>
   </repositories>
 
   <dependencyManagement>
@@ -260,20 +254,8 @@
       </dependency>
       <dependency>
         <groupId>${akka.group}</groupId>
-        <artifactId>akka-zeromq_${scala.binary.version}</artifactId>
-        <version>${akka.version}</version>
-        <exclusions>
-          <exclusion>
-            <groupId>org.jboss.netty</groupId>
-            <artifactId>netty</artifactId>
-          </exclusion>
-        </exclusions>
-      </dependency>
-      <dependency>
-        <groupId>${akka.group}</groupId>
         <artifactId>akka-testkit_${scala.binary.version}</artifactId>
         <version>${akka.version}</version>
-        <scope>test</scope>
         <exclusions>
           <exclusion>
             <groupId>org.jboss.netty</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index bd5f3f79c9..f794918d22 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -50,9 +50,6 @@ object SparkBuild extends Build {
   lazy val repl = Project("repl", file("repl"), settings = replSettings)
     .dependsOn(core, bagel, mllib)
 
-  lazy val examples = Project("examples", file("examples"), settings = examplesSettings)
-    .dependsOn(core, mllib, bagel, streaming)
-
   lazy val tools = Project("tools", file("tools"), settings = toolsSettings) dependsOn(core) dependsOn(streaming)
 
   lazy val bagel = Project("bagel", file("bagel"), settings = bagelSettings) dependsOn(core)
@@ -93,10 +90,31 @@ object SparkBuild extends Build {
   lazy val maybeYarn = if (isYarnEnabled) Seq[ClasspathDependency](if (isNewHadoop) yarn else yarnAlpha) else Seq[ClasspathDependency]()
   lazy val maybeYarnRef = if (isYarnEnabled) Seq[ProjectReference](if (isNewHadoop) yarn else yarnAlpha) else Seq[ProjectReference]()
 
+  lazy val externalTwitter = Project("external-twitter", file("external/twitter"), settings = twitterSettings) 
+    .dependsOn(streaming % "compile->compile;test->test")
+
+  lazy val externalKafka = Project("external-kafka", file("external/kafka"), settings = kafkaSettings)
+    .dependsOn(streaming % "compile->compile;test->test")
+
+  lazy val externalFlume = Project("external-flume", file("external/flume"), settings = flumeSettings)
+    .dependsOn(streaming % "compile->compile;test->test")
+  
+  lazy val externalZeromq = Project("external-zeromq", file("external/zeromq"), settings = zeromqSettings)
+    .dependsOn(streaming % "compile->compile;test->test")
+  
+  lazy val externalMqtt = Project("external-mqtt", file("external/mqtt"), settings = mqttSettings)
+    .dependsOn(streaming % "compile->compile;test->test")
+
+  lazy val allExternal = Seq[ClasspathDependency](externalTwitter, externalKafka, externalFlume, externalZeromq, externalMqtt)
+  lazy val allExternalRefs = Seq[ProjectReference](externalTwitter, externalKafka, externalFlume, externalZeromq, externalMqtt)
+  
+  lazy val examples = Project("examples", file("examples"), settings = examplesSettings)
+    .dependsOn(core, mllib, bagel, streaming, externalTwitter) dependsOn(allExternal: _*)
+
   // Everything except assembly, tools and examples belong to packageProjects
   lazy val packageProjects = Seq[ProjectReference](core, repl, bagel, streaming, mllib) ++ maybeYarnRef
 
-  lazy val allProjects = packageProjects ++ Seq[ProjectReference](examples, tools, assemblyProj)
+  lazy val allProjects = packageProjects ++ allExternalRefs ++ Seq[ProjectReference](examples, tools, assemblyProj) 
 
   def sharedSettings = Defaults.defaultSettings ++ Seq(
     organization       := "org.apache.spark",
@@ -125,7 +143,7 @@ object SparkBuild extends Build {
     // also check the local Maven repository ~/.m2
     resolvers ++= Seq(Resolver.file("Local Maven Repo", file(Path.userHome + "/.m2/repository"))),
 
-   // For Sonatype publishing
+    // For Sonatype publishing
     resolvers ++= Seq("sonatype-snapshots" at "https://oss.sonatype.org/content/repositories/snapshots",
       "sonatype-staging" at "https://oss.sonatype.org/service/local/staging/deploy/maven2/"),
 
@@ -167,7 +185,7 @@ object SparkBuild extends Build {
       </issueManagement>
     ),
 
-/*
+    /*
     publishTo <<= version { (v: String) =>
       val nexus = "https://oss.sonatype.org/"
       if (v.trim.endsWith("SNAPSHOT"))
@@ -176,8 +194,7 @@ object SparkBuild extends Build {
         Some("sonatype-staging"  at nexus + "service/local/staging/deploy/maven2")
     },
 
-*/
-
+    */
 
     libraryDependencies ++= Seq(
         "io.netty"          % "netty-all"       % "4.0.13.Final",
@@ -267,7 +284,6 @@ object SparkBuild extends Build {
    libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "scala-reflect"  % v )
   )
 
-
   def examplesSettings = sharedSettings ++ Seq(
     name := "spark-examples",
     libraryDependencies ++= Seq(
@@ -304,22 +320,8 @@ object SparkBuild extends Build {
 
   def streamingSettings = sharedSettings ++ Seq(
     name := "spark-streaming",
-    resolvers ++= Seq(
-      "Eclipse Repository" at "https://repo.eclipse.org/content/repositories/paho-releases/",
-      "Apache repo" at "https://repository.apache.org/content/repositories/releases"
-    ),
-
     libraryDependencies ++= Seq(
-      "org.apache.flume"        % "flume-ng-sdk"     % "1.2.0" % "compile"     excludeAll(excludeNetty, excludeSnappy),
-      "com.sksamuel.kafka"     %% "kafka"            % "0.8.0-beta1"
-        exclude("com.sun.jdmk", "jmxtools")
-        exclude("com.sun.jmx", "jmxri")
-        exclude("net.sf.jopt-simple", "jopt-simple")
-        excludeAll(excludeNetty),
-      "org.eclipse.paho"        % "mqtt-client"      % "0.4.0",
-      "com.github.sgroschupf"   % "zkclient"         % "0.1"                   excludeAll(excludeNetty),
-      "org.twitter4j"           % "twitter4j-stream" % "3.0.3"                 excludeAll(excludeNetty),
-      "org.spark-project.akka" %% "akka-zeromq"      % "2.2.3-shaded-protobuf" excludeAll(excludeNetty)
+      "commons-io" % "commons-io" % "2.4" 
     )
   )
 
@@ -353,8 +355,8 @@ object SparkBuild extends Build {
   def yarnEnabledSettings = Seq(
     libraryDependencies ++= Seq(
       // Exclude rule required for all ?
-      "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib),
-      "org.apache.hadoop" % "hadoop-yarn-api" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib),
+      "org.apache.hadoop" % "hadoop-client"      % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib),
+      "org.apache.hadoop" % "hadoop-yarn-api"    % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib),
       "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib),
       "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib)
     )
@@ -379,4 +381,43 @@ object SparkBuild extends Build {
       case _ => MergeStrategy.first
     }
   )
+
+  def twitterSettings() = sharedSettings ++ Seq(
+    name := "spark-streaming-twitter",
+    libraryDependencies ++= Seq(
+      "org.twitter4j" % "twitter4j-stream" % "3.0.3" excludeAll(excludeNetty)
+    )
+  )
+  
+  def kafkaSettings() = sharedSettings ++ Seq(
+    name := "spark-streaming-kafka",
+    libraryDependencies ++= Seq(
+      "com.github.sgroschupf"    % "zkclient"   % "0.1"          excludeAll(excludeNetty),
+      "com.sksamuel.kafka"      %% "kafka"      % "0.8.0-beta1"
+        exclude("com.sun.jdmk", "jmxtools")
+        exclude("com.sun.jmx", "jmxri")
+        exclude("net.sf.jopt-simple", "jopt-simple")
+        excludeAll(excludeNetty)
+    )
+  )
+  
+  def flumeSettings() = sharedSettings ++ Seq(
+    name := "spark-streaming-flume",
+    libraryDependencies ++= Seq(
+      "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty, excludeSnappy)
+    )
+  )
+
+  def zeromqSettings() = sharedSettings ++ Seq(
+    name := "spark-streaming-zeromq",
+    libraryDependencies ++= Seq(
+      "org.spark-project.akka" %% "akka-zeromq" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty)
+    )
+  )
+
+  def mqttSettings() = streamingSettings ++ Seq(
+    name := "spark-streaming-mqtt",
+    resolvers ++= Seq("Eclipse Repo" at "https://repo.eclipse.org/content/repositories/paho-releases/"),
+    libraryDependencies ++= Seq("org.eclipse.paho" % "mqtt-client" % "0.4.0")
+  )
 }
diff --git a/project/build.properties b/project/build.properties
index 9647277162..839f5fbb0c 100644
--- a/project/build.properties
+++ b/project/build.properties
@@ -14,5 +14,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-
 sbt.version=0.12.4
diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index e74ba0fabc..769d88dfb9 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -18,6 +18,9 @@
 from numpy import ndarray, copyto, float64, int64, int32, ones, array_equal, array, dot, shape
 from pyspark import SparkContext
 
+from pyspark.serializers import Serializer
+import struct
+
 # Double vector format:
 #
 # [8-byte 1] [8-byte length] [length*8 bytes of data]
@@ -213,6 +216,28 @@ def _serialize_rating(r):
     intpart[0], intpart[1], doublepart[0] = r
     return ba
 
+class RatingDeserializer(Serializer):
+    def loads(self, stream):
+        length = struct.unpack("!i", stream.read(4))[0]
+        ba = stream.read(length)
+        res = ndarray(shape=(3, ), buffer=ba, dtype="float64", offset=4)
+        return int(res[0]), int(res[1]), res[2]
+
+    def load_stream(self, stream):
+        while True:
+            try:
+                yield self.loads(stream)
+            except struct.error:
+                return
+            except EOFError:
+                return
+
+def _serialize_tuple(t):
+    ba = bytearray(8)
+    intpart = ndarray(shape=[2], buffer=ba, dtype=int32)
+    intpart[0], intpart[1] = t
+    return ba
+
 def _test():
     import doctest
     globs = globals().copy()
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 14d06cba21..0eeb5bb66b 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -20,7 +20,9 @@ from pyspark.mllib._common import \
     _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
     _serialize_double_matrix, _deserialize_double_matrix, \
     _serialize_double_vector, _deserialize_double_vector, \
-    _get_initial_weights, _serialize_rating, _regression_train_wrapper
+    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
+    _serialize_tuple, RatingDeserializer
+from pyspark.rdd import RDD
 
 class MatrixFactorizationModel(object):
     """A matrix factorisation model trained by regularized alternating
@@ -33,6 +35,9 @@ class MatrixFactorizationModel(object):
     >>> model = ALS.trainImplicit(sc, ratings, 1)
     >>> model.predict(2,2) is not None
     True
+    >>> testset = sc.parallelize([(1, 2), (1, 1)])
+    >>> model.predictAll(testset).count == 2
+    True
     """
 
     def __init__(self, sc, java_model):
@@ -45,6 +50,11 @@ class MatrixFactorizationModel(object):
     def predict(self, user, product):
         return self._java_model.predict(user, product)
 
+    def predictAll(self, usersProducts):
+        usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple)
+        return RDD(self._java_model.predict(usersProductsJRDD._jrdd),
+                   self._context, RatingDeserializer())
+
 class ALS(object):
     @classmethod
     def train(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala b/repl/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala
new file mode 100644
index 0000000000..05816941b5
--- /dev/null
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.repl
+
+import scala.tools.nsc.{Settings, CompilerCommand}
+import scala.Predef._
+
+/**
+ * Command class enabling Spark-specific command line options (provided by
+ * <i>org.apache.spark.repl.SparkRunnerSettings</i>).
+ */
+class SparkCommandLine(args: List[String], override val settings: Settings)
+    extends CompilerCommand(args, settings) {
+
+  def this(args: List[String], error: String => Unit) {
+    this(args, new SparkRunnerSettings(error))
+  }
+
+  def this(args: List[String]) {
+    this(args, str => Console.println("Error: " + str))
+  }
+}
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 7e54421b52..87d94d51be 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -835,7 +835,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
 
   // runs :load `file` on any files passed via -i
   def loadFiles(settings: Settings) = settings match {
-    case settings: GenericRunnerSettings =>
+    case settings: SparkRunnerSettings =>
       for (filename <- settings.loadfiles.value) {
         val cmd = ":load " + filename
         command(cmd)
@@ -902,7 +902,6 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
     addThunk(printWelcome())
     addThunk(initializeSpark())
 
-    loadFiles(settings)
     // it is broken on startup; go ahead and exit
     if (intp.reporter.hasErrors)
       return false
@@ -922,6 +921,8 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
     }
     // printWelcome()
 
+    loadFiles(settings)
+
     try loop()
     catch AbstractOrMissingHandler()
     finally closeInterpreter()
@@ -957,7 +958,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
 
   /** process command-line arguments and do as they request */
   def process(args: Array[String]): Boolean = {
-    val command = new CommandLine(args.toList, echo)
+    val command = new SparkCommandLine(args.toList, msg => echo(msg))
     def neededHelp(): String =
       (if (command.settings.help.value) command.usageMsg + "\n" else "") +
       (if (command.settings.Xhelp.value) command.xusageMsg + "\n" else "")
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkRunnerSettings.scala b/repl/src/main/scala/org/apache/spark/repl/SparkRunnerSettings.scala
new file mode 100644
index 0000000000..7fd5fbb424
--- /dev/null
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkRunnerSettings.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.repl
+
+import scala.tools.nsc.Settings
+
+/**
+ * <i>scala.tools.nsc.Settings</i> implementation adding Spark-specific REPL
+ * command line options.
+ */
+class SparkRunnerSettings(error: String => Unit) extends Settings(error){
+
+  val loadfiles = MultiStringSetting(
+      "-i",
+      "file",
+      "load a file (assumes the code is given interactively)")
+}
diff --git a/sbin/stop-slaves.sh b/sbin/stop-slaves.sh
index c6b0b6ab66..eb803b4900 100755
--- a/sbin/stop-slaves.sh
+++ b/sbin/stop-slaves.sh
@@ -17,8 +17,8 @@
 # limitations under the License.
 #
 
-bin=`dirname "$0"`
-bin=`cd "$sbin"; pwd`
+sbin=`dirname "$0"`
+sbin=`cd "$sbin"; pwd`
 
 . "$sbin/spark-config.sh"
 
diff --git a/sbt/sbt b/sbt/sbt
new file mode 100755
index 0000000000..7f47d90cf1
--- /dev/null
+++ b/sbt/sbt
@@ -0,0 +1,61 @@
+#!/bin/bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This script launches sbt for this project. If present it uses the system 
+# version of sbt. If there is no system version of sbt it attempts to download
+# sbt locally.
+SBT_VERSION=`awk -F "=" '/sbt\\.version/ {print $2}' ./project/build.properties`
+URL1=http://typesafe.artifactoryonline.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar
+URL2=http://repo.typesafe.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar
+JAR=sbt/sbt-launch-${SBT_VERSION}.jar
+
+printf "Checking for system sbt ["
+if hash sbt 2>/dev/null; then 
+  printf "FOUND]\n"
+  # Use System SBT
+  sbt "$@"
+else
+  printf "NOT FOUND]\n"
+  # Download sbt or use already downloaded
+  if [ ! -d .sbtlib ]; then
+    mkdir .sbtlib
+  fi
+  if [ ! -f ${JAR} ]; then
+    # Download
+    printf "Attempting to fetch sbt\n"
+    if hash curl 2>/dev/null; then
+      curl --progress-bar ${URL1} > ${JAR} || curl --progress-bar ${URL2} > ${JAR}
+    elif hash wget 2>/dev/null; then
+      wget --progress=bar ${URL1} -O ${JAR} || wget --progress=bar ${URL2} -O ${JAR}
+    else
+      printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n"
+      exit -1
+    fi
+  fi
+  if [ ! -f ${JAR} ]; then
+    # We failed to download
+    printf "Our attempt to download sbt locally to ${JAR} failed. Please install sbt manually from http://www.scala-sbt.org/\n"
+    exit -1
+  fi
+  printf "Launching sbt from ${JAR}\n"
+  java \
+    -Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=256m \
+    -jar ${JAR} \
+    "$@"
+fi
diff --git a/streaming/pom.xml b/streaming/pom.xml
index e3b6fee9b2..459756912d 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -61,59 +61,10 @@
       <version>1.9.11</version>
     </dependency>
     <dependency>
-      <groupId>com.sksamuel.kafka</groupId>
-      <artifactId>kafka_${scala.binary.version}</artifactId>
-      <version>0.8.0-beta1</version>
-      <exclusions>
-        <exclusion>
-          <groupId>com.sun.jmx</groupId>
-          <artifactId>jmxri</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>com.sun.jdmk</groupId>
-          <artifactId>jmxtools</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>net.sf.jopt-simple</groupId>
-          <artifactId>jopt-simple</artifactId>
-        </exclusion>
-      </exclusions>
-    </dependency>
-    <dependency>
-      <groupId>org.apache.flume</groupId>
-      <artifactId>flume-ng-sdk</artifactId>
-      <version>1.2.0</version>
-      <exclusions>
-        <exclusion>
-          <groupId>org.jboss.netty</groupId>
-          <artifactId>netty</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.xerial.snappy</groupId>
-          <artifactId>*</artifactId>
-        </exclusion>
-      </exclusions>
-    </dependency>
-    <dependency>
-      <groupId>org.twitter4j</groupId>
-      <artifactId>twitter4j-stream</artifactId>
-      <version>3.0.3</version>
-      <exclusions>
-        <exclusion>
-          <groupId>org.jboss.netty</groupId>
-          <artifactId>netty</artifactId>
-        </exclusion>
-      </exclusions>
-    </dependency>
-    <dependency>
       <groupId>org.scala-lang</groupId>
       <artifactId>scala-library</artifactId>
     </dependency>
     <dependency>
-      <groupId>${akka.group}</groupId>
-      <artifactId>akka-zeromq_${scala.binary.version}</artifactId>
-    </dependency>
-    <dependency>
       <groupId>org.scalatest</groupId>
       <artifactId>scalatest_${scala.binary.version}</artifactId>
       <scope>test</scope>
@@ -137,11 +88,6 @@
       <groupId>commons-io</groupId>
       <artifactId>commons-io</artifactId>
     </dependency>
-    <dependency>
-      <groupId>org.eclipse.paho</groupId>
-      <artifactId>mqtt-client</artifactId>
-       <version>0.4.0</version>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
@@ -151,6 +97,35 @@
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
       </plugin>
+      
+      <!-- 
+           This plugin forces the generation of jar containing streaming test classes, 
+           so that the tests classes of external modules can use them. The two execution profiles
+           are necessary - first one for 'mvn package', second one for 'mvn compile'. Ideally, 
+           'mvn compile' should not compile test classes and therefore should not need this. 
+           However, an open Maven bug (http://jira.codehaus.org/browse/MNG-3559)
+           causes the compilation to fail if streaming test-jar is not generated. Hence, the 
+           second execution profile for 'mvn compile'.
+      -->
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-jar-plugin</artifactId>
+        <version>2.2</version>
+        <executions>
+          <execution>
+            <goals>
+              <goal>test-jar</goal>
+            </goals>
+          </execution>
+          <execution>
+            <id>test-jar-on-compile</id>
+            <phase>compile</phase>
+            <goals>
+              <goal>test-jar</goal>
+            </goals>
+          </execution>
+        </executions>
+      </plugin>
     </plugins>
   </build>
 </project>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index b3a7cf08b9..693cb7fc30 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -17,23 +17,6 @@
 
 package org.apache.spark.streaming
 
-import akka.actor.Props
-import akka.actor.SupervisorStrategy
-import akka.zeromq.Subscribe
-import akka.util.ByteString
-
-import org.apache.spark.streaming.dstream._
-import org.apache.spark.streaming.scheduler.StreamingListener
-
-import org.apache.spark._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.streaming.receivers.ReceiverSupervisorStrategy
-import org.apache.spark.streaming.receivers.ZeroMQReceiver
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.MetadataCleaner
-import org.apache.spark.streaming.receivers.ActorReceiver
-import org.apache.spark.streaming.scheduler.{JobScheduler, NetworkInputTracker}
-
 import scala.collection.mutable.Queue
 import scala.collection.Map
 import scala.reflect.ClassTag
@@ -41,14 +24,21 @@ import scala.reflect.ClassTag
 import java.io.InputStream
 import java.util.concurrent.atomic.AtomicInteger
 
+import akka.actor.Props
+import akka.actor.SupervisorStrategy
 import org.apache.hadoop.io.LongWritable
 import org.apache.hadoop.io.Text
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
 import org.apache.hadoop.fs.Path
 
-import twitter4j.Status
-import twitter4j.auth.Authorization
+import org.apache.spark._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.MetadataCleaner
+import org.apache.spark.streaming.dstream._
+import org.apache.spark.streaming.receivers._
+import org.apache.spark.streaming.scheduler._
 
 /**
  * A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic
@@ -238,74 +228,6 @@ class StreamingContext private (
   }
 
   /**
-   * Create an input stream that receives messages pushed by a zeromq publisher.
-   * @param publisherUrl Url of remote zeromq publisher
-   * @param subscribe topic to subscribe to
-   * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic
-   *                       and each frame has sequence of byte thus it needs the converter
-   *                       (which might be deserializer of bytes) to translate from sequence
-   *                       of sequence of bytes, where sequence refer to a frame
-   *                       and sub sequence refer to its payload.
-   * @param storageLevel RDD storage level. Defaults to memory-only.
-   */
-  def zeroMQStream[T: ClassTag](
-      publisherUrl:String,
-      subscribe: Subscribe,
-      bytesToObjects: Seq[ByteString] ⇒ Iterator[T],
-      storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2,
-      supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy
-    ): DStream[T] = {
-    actorStream(Props(new ZeroMQReceiver(publisherUrl, subscribe, bytesToObjects)),
-        "ZeroMQReceiver", storageLevel, supervisorStrategy)
-  }
-
-  /**
-   * Create an input stream that pulls messages from a Kafka Broker.
-   * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..).
-   * @param groupId The group id for this consumer.
-   * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
-   *               in its own thread.
-   * @param storageLevel  Storage level to use for storing the received objects
-   *                      (default: StorageLevel.MEMORY_AND_DISK_SER_2)
-   */
-  def kafkaStream(
-      zkQuorum: String,
-      groupId: String,
-      topics: Map[String, Int],
-      storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2
-    ): DStream[(String, String)] = {
-    val kafkaParams = Map[String, String](
-      "zookeeper.connect" -> zkQuorum, "group.id" -> groupId,
-      "zookeeper.connection.timeout.ms" -> "10000")
-    kafkaStream[String, String, kafka.serializer.StringDecoder, kafka.serializer.StringDecoder](
-      kafkaParams,
-      topics,
-      storageLevel)
-  }
-
-  /**
-   * Create an input stream that pulls messages from a Kafka Broker.
-   * @param kafkaParams Map of kafka configuration paramaters.
-   *                    See: http://kafka.apache.org/configuration.html
-   * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
-   *               in its own thread.
-   * @param storageLevel  Storage level to use for storing the received objects
-   */
-  def kafkaStream[
-    K: ClassTag,
-    V: ClassTag,
-    U <: kafka.serializer.Decoder[_]: Manifest,
-    T <: kafka.serializer.Decoder[_]: Manifest](
-      kafkaParams: Map[String, String],
-      topics: Map[String, Int],
-      storageLevel: StorageLevel
-    ): DStream[(K, V)] = {
-    val inputStream = new KafkaInputDStream[K, V, U, T](this, kafkaParams, topics, storageLevel)
-    registerInputStream(inputStream)
-    inputStream
-  }
-
-  /**
    * Create a input stream from TCP source hostname:port. Data is received using
    * a TCP socket and the receive bytes is interpreted as UTF8 encoded `\n` delimited
    * lines.
@@ -344,22 +266,6 @@ class StreamingContext private (
   }
 
   /**
-   * Create a input stream from a Flume source.
-   * @param hostname Hostname of the slave machine to which the flume data will be sent
-   * @param port     Port of the slave machine to which the flume data will be sent
-   * @param storageLevel  Storage level to use for storing the received objects
-   */
-  def flumeStream (
-      hostname: String,
-      port: Int,
-      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
-    ): DStream[SparkFlumeEvent] = {
-    val inputStream = new FlumeInputDStream[SparkFlumeEvent](this, hostname, port, storageLevel)
-    registerInputStream(inputStream)
-    inputStream
-  }
-
-  /**
    * Create a input stream from network source hostname:port, where data is received
    * as serialized blocks (serialized using the Spark's serializer) that can be directly
    * pushed into the block manager without deserializing them. This is the most efficient
@@ -434,24 +340,6 @@ class StreamingContext private (
   }
 
   /**
-   * Create a input stream that returns tweets received from Twitter.
-   * @param twitterAuth Twitter4J authentication, or None to use Twitter4J's default OAuth
-   *        authorization; this uses the system properties twitter4j.oauth.consumerKey,
-   *        .consumerSecret, .accessToken and .accessTokenSecret.
-   * @param filters Set of filter strings to get only those tweets that match them
-   * @param storageLevel Storage level to use for storing the received objects
-   */
-  def twitterStream(
-      twitterAuth: Option[Authorization] = None,
-      filters: Seq[String] = Nil,
-      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
-    ): DStream[Status] = {
-    val inputStream = new TwitterInputDStream(this, twitterAuth, filters, storageLevel)
-    registerInputStream(inputStream)
-    inputStream
-  }
-
-  /**
    * Create an input stream from a queue of RDDs. In each batch,
    * it will process either one or all of the RDDs returned by the queue.
    * @param queue      Queue of RDDs
@@ -484,21 +372,6 @@ class StreamingContext private (
     inputStream
   }
 
-/**
-   * Create an input stream that receives messages pushed by a mqtt publisher.
-   * @param brokerUrl Url of remote mqtt publisher
-   * @param topic topic name to subscribe to
-   * @param storageLevel RDD storage level. Defaults to memory-only.
-   */
-
-  def mqttStream(
-    brokerUrl: String,
-    topic: String,
-    storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2): DStream[String] = {
-    val inputStream = new MQTTInputDStream[String](this, brokerUrl, topic, storageLevel)
-    registerInputStream(inputStream)
-    inputStream
-  }
   /**
    * Create a unified DStream from multiple DStreams of the same type and same slide duration.
    */
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 7dec4b3ad7..7068f32517 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -17,19 +17,16 @@
 
 package org.apache.spark.streaming.api.java
 
-import java.io.InputStream
-import java.lang.{Integer => JInt}
-import java.util.{List => JList, Map => JMap}
 
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
 
+import java.io.InputStream
+import java.lang.{Integer => JInt}
+import java.util.{List => JList, Map => JMap}
+
 import akka.actor.{Props, SupervisorStrategy}
-import akka.util.ByteString
-import akka.zeromq.Subscribe
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
-import twitter4j.Status
-import twitter4j.auth.Authorization
 
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext}
@@ -37,7 +34,6 @@ import org.apache.spark.api.java.function.{Function => JFunction, Function2 => J
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.scheduler.StreamingListener
 
 /**
@@ -141,81 +137,6 @@ class JavaStreamingContext(val ssc: StreamingContext) {
   val sc: JavaSparkContext = new JavaSparkContext(ssc.sc)
 
   /**
-   * Create an input stream that pulls messages form a Kafka Broker.
-   * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..).
-   * @param groupId The group id for this consumer.
-   * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
-   * in its own thread.
-   */
-  def kafkaStream(
-    zkQuorum: String,
-    groupId: String,
-    topics: JMap[String, JInt])
-  : JavaPairDStream[String, String] = {
-    implicit val cmt: ClassTag[String] =
-      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]]
-    ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*),
-      StorageLevel.MEMORY_ONLY_SER_2)
-
-  }
-
-  /**
-   * Create an input stream that pulls messages form a Kafka Broker.
-   * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..).
-   * @param groupId The group id for this consumer.
-   * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
-   *               in its own thread.
-   * @param storageLevel RDD storage level. Defaults to memory-only
-   *
-   */
-  def kafkaStream(
-    zkQuorum: String,
-    groupId: String,
-    topics: JMap[String, JInt],
-    storageLevel: StorageLevel)
-  : JavaPairDStream[String, String] = {
-    implicit val cmt: ClassTag[String] =
-      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]]
-    ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*),
-      storageLevel)
-  }
-
-  /**
-   * Create an input stream that pulls messages form a Kafka Broker.
-   * @param keyTypeClass Key type of RDD
-   * @param valueTypeClass value type of RDD
-   * @param keyDecoderClass Type of kafka key decoder
-   * @param valueDecoderClass Type of kafka value decoder
-   * @param kafkaParams Map of kafka configuration paramaters.
-   *                    See: http://kafka.apache.org/configuration.html
-   * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
-   * in its own thread.
-   * @param storageLevel RDD storage level. Defaults to memory-only
-   */
-  def kafkaStream[K, V, U <: kafka.serializer.Decoder[_], T <: kafka.serializer.Decoder[_]](
-    keyTypeClass: Class[K],
-    valueTypeClass: Class[V],
-    keyDecoderClass: Class[U],
-    valueDecoderClass: Class[T],
-    kafkaParams: JMap[String, String],
-    topics: JMap[String, JInt],
-    storageLevel: StorageLevel)
-  : JavaPairDStream[K, V] = {
-    implicit val keyCmt: ClassTag[K] =
-      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]]
-    implicit val valueCmt: ClassTag[V] =
-      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
-
-    implicit val keyCmd: Manifest[U] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[U]]
-    implicit val valueCmd: Manifest[T] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[T]]
-
-    ssc.kafkaStream[K, V, U, T](
-      kafkaParams.toMap,
-      Map(topics.mapValues(_.intValue()).toSeq: _*),
-      storageLevel)
-  }
-
-  /**
    * Create a input stream from network source hostname:port. Data is received using
    * a TCP socket and the receive bytes is interpreted as UTF8 encoded \n delimited
    * lines.
@@ -329,98 +250,6 @@ class JavaStreamingContext(val ssc: StreamingContext) {
   }
 
   /**
-   * Creates a input stream from a Flume source.
-   * @param hostname Hostname of the slave machine to which the flume data will be sent
-   * @param port     Port of the slave machine to which the flume data will be sent
-   * @param storageLevel  Storage level to use for storing the received objects
-   */
-  def flumeStream(hostname: String, port: Int, storageLevel: StorageLevel):
-    JavaDStream[SparkFlumeEvent] = {
-    ssc.flumeStream(hostname, port, storageLevel)
-  }
-
-
-  /**
-   * Create a input stream from a Flume source.
-   * @param hostname Hostname of the slave machine to which the flume data will be sent
-   * @param port     Port of the slave machine to which the flume data will be sent
-   */
-  def flumeStream(hostname: String, port: Int): JavaDStream[SparkFlumeEvent] = {
-    ssc.flumeStream(hostname, port)
-  }
-
-  /**
-   * Create a input stream that returns tweets received from Twitter.
-   * @param twitterAuth Twitter4J Authorization object
-   * @param filters Set of filter strings to get only those tweets that match them
-   * @param storageLevel Storage level to use for storing the received objects
-   */
-  def twitterStream(
-      twitterAuth: Authorization,
-      filters: Array[String],
-      storageLevel: StorageLevel
-    ): JavaDStream[Status] = {
-    ssc.twitterStream(Some(twitterAuth), filters, storageLevel)
-  }
-
-  /**
-   * Create a input stream that returns tweets received from Twitter using Twitter4J's default
-   * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey,
-   * .consumerSecret, .accessToken and .accessTokenSecret to be set.
-   * @param filters Set of filter strings to get only those tweets that match them
-   * @param storageLevel Storage level to use for storing the received objects
-   */
-  def twitterStream(
-      filters: Array[String],
-      storageLevel: StorageLevel
-    ): JavaDStream[Status] = {
-    ssc.twitterStream(None, filters, storageLevel)
-  }
-
-  /**
-   * Create a input stream that returns tweets received from Twitter.
-   * @param twitterAuth Twitter4J Authorization
-   * @param filters Set of filter strings to get only those tweets that match them
-   */
-  def twitterStream(
-      twitterAuth: Authorization,
-      filters: Array[String]
-    ): JavaDStream[Status] = {
-    ssc.twitterStream(Some(twitterAuth), filters)
-  }
-
-  /**
-   * Create a input stream that returns tweets received from Twitter using Twitter4J's default
-   * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey,
-   * .consumerSecret, .accessToken and .accessTokenSecret to be set.
-   * @param filters Set of filter strings to get only those tweets that match them
-   */
-  def twitterStream(
-      filters: Array[String]
-    ): JavaDStream[Status] = {
-    ssc.twitterStream(None, filters)
-  }
-
-  /**
-   * Create a input stream that returns tweets received from Twitter.
-   * @param twitterAuth Twitter4J Authorization
-   */
-  def twitterStream(
-      twitterAuth: Authorization
-    ): JavaDStream[Status] = {
-    ssc.twitterStream(Some(twitterAuth))
-  }
-
-  /**
-   * Create a input stream that returns tweets received from Twitter using Twitter4J's default
-   * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey,
-   * .consumerSecret, .accessToken and .accessTokenSecret to be set.
-   */
-  def twitterStream(): JavaDStream[Status] = {
-    ssc.twitterStream()
-  }
-
-  /**
    * Create an input stream with any arbitrary user implemented actor receiver.
    * @param props Props object defining creation of the actor
    * @param name Name of the actor
@@ -483,70 +312,6 @@ class JavaStreamingContext(val ssc: StreamingContext) {
   }
 
   /**
-   * Create an input stream that receives messages pushed by a zeromq publisher.
-   * @param publisherUrl Url of remote zeromq publisher
-   * @param subscribe topic to subscribe to
-   * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence
-   *                       of byte thus it needs the converter(which might be deserializer of bytes)
-   *                       to translate from sequence of sequence of bytes, where sequence refer to a frame
-   *                       and sub sequence refer to its payload.
-   * @param storageLevel  Storage level to use for storing the received objects
-   */
-  def zeroMQStream[T](
-      publisherUrl:String,
-      subscribe: Subscribe,
-      bytesToObjects: Seq[ByteString] ⇒ Iterator[T],
-      storageLevel: StorageLevel,
-      supervisorStrategy: SupervisorStrategy
-    ): JavaDStream[T] = {
-    implicit val cm: ClassTag[T] =
-      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    ssc.zeroMQStream[T](publisherUrl, subscribe, bytesToObjects, storageLevel, supervisorStrategy)
-  }
-
-  /**
-   * Create an input stream that receives messages pushed by a zeromq publisher.
-   * @param publisherUrl Url of remote zeromq publisher
-   * @param subscribe topic to subscribe to
-   * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence
-   *                       of byte thus it needs the converter(which might be deserializer of bytes)
-   *                       to translate from sequence of sequence of bytes, where sequence refer to a frame
-   *                       and sub sequence refer to its payload.
-   * @param storageLevel RDD storage level. Defaults to memory-only.
-   */
-  def zeroMQStream[T](
-      publisherUrl:String,
-      subscribe: Subscribe,
-      bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]],
-      storageLevel: StorageLevel
-    ): JavaDStream[T] = {
-    implicit val cm: ClassTag[T] =
-      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    def fn(x: Seq[ByteString]) = bytesToObjects.apply(x.map(_.toArray).toArray).toIterator
-    ssc.zeroMQStream[T](publisherUrl, subscribe, fn, storageLevel)
-  }
-
-  /**
-   * Create an input stream that receives messages pushed by a zeromq publisher.
-   * @param publisherUrl Url of remote zeromq publisher
-   * @param subscribe topic to subscribe to
-   * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence
-   *                       of byte thus it needs the converter(which might be deserializer of bytes)
-   *                       to translate from sequence of sequence of bytes, where sequence refer to a frame
-   *                       and sub sequence refer to its payload.
-   */
-  def zeroMQStream[T](
-      publisherUrl:String,
-      subscribe: Subscribe,
-      bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]]
-    ): JavaDStream[T] = {
-    implicit val cm: ClassTag[T] =
-      implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    def fn(x: Seq[ByteString]) = bytesToObjects.apply(x.map(_.toArray).toArray).toIterator
-    ssc.zeroMQStream[T](publisherUrl, subscribe, fn)
-  }
-
-  /**
    * Registers an output stream that will be computed every interval
    */
   def registerOutputStream(outputStream: JavaDStreamLike[_, _, _]) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala
index abff55d77c..75f7244643 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala
@@ -160,7 +160,9 @@ class NetworkInputTracker(
       }
       // Run the dummy Spark job to ensure that all slaves have registered.
       // This avoids all the receivers to be scheduled on the same node.
-      ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
+      if (!ssc.sparkContext.isLocal) {
+        ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
+      }
 
       // Distribute the receivers and start them
       ssc.sparkContext.runJob(tempRDD, startReceiver)
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
index d53d433693..0d2145da9a 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
@@ -17,24 +17,19 @@
 
 package org.apache.spark.streaming;
 
-import com.google.common.base.Optional;
-import com.google.common.collect.Lists;
-import com.google.common.collect.Maps;
-import com.google.common.io.Files;
-
-import kafka.serializer.StringDecoder;
+import scala.Tuple2;
 
-import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
-import org.apache.spark.SparkConf;
-import org.apache.spark.streaming.api.java.JavaDStreamLike;
 import org.junit.After;
 import org.junit.Assert;
-import org.junit.Before;
 import org.junit.Test;
+import java.io.*;
+import java.util.*;
 
-import scala.Tuple2;
-import twitter4j.Status;
+import com.google.common.base.Optional;
+import com.google.common.collect.Lists;
+import com.google.common.io.Files;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.HashPartitioner;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
@@ -44,42 +39,11 @@ import org.apache.spark.storage.StorageLevel;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
-import org.apache.spark.streaming.dstream.SparkFlumeEvent;
-import org.apache.spark.streaming.JavaTestUtils;
-import org.apache.spark.streaming.JavaCheckpointTestUtils;
-
-import java.io.*;
-import java.util.*;
-
-import akka.actor.Props;
-import akka.zeromq.Subscribe;
-
 
 // The test suite itself is Serializable so that anonymous Function implementations can be
 // serialized, as an alternative to converting these anonymous classes to static inner classes;
 // see http://stackoverflow.com/questions/758570/.
-public class JavaAPISuite implements Serializable {
-  private transient JavaStreamingContext ssc;
-
-  @Before
-  public void setUp() {
-    SparkConf conf = new SparkConf()
-        .setMaster("local[2]")
-        .setAppName("test")
-        .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock");
-    ssc = new JavaStreamingContext(conf, new Duration(1000));
-    ssc.checkpoint("checkpoint");
-  }
-
-  @After
-  public void tearDown() {
-    ssc.stop();
-    ssc = null;
-
-    // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
-    System.clearProperty("spark.driver.port");
-  }
-
+public class JavaAPISuite extends LocalJavaStreamingContext implements Serializable {
   @Test
   public void testCount() {
     List<List<Integer>> inputData = Arrays.asList(
@@ -1601,26 +1565,6 @@ public class JavaAPISuite implements Serializable {
   // Java arguments and assign it to a JavaDStream without producing type errors. Testing of the
   // InputStream functionality is deferred to the existing Scala tests.
   @Test
-  public void testKafkaStream() {
-    HashMap<String, Integer> topics = Maps.newHashMap();
-    JavaPairDStream<String, String> test1 = ssc.kafkaStream("localhost:12345", "group", topics);
-    JavaPairDStream<String, String> test2 = ssc.kafkaStream("localhost:12345", "group", topics,
-      StorageLevel.MEMORY_AND_DISK());
-
-    HashMap<String, String> kafkaParams = Maps.newHashMap();
-    kafkaParams.put("zookeeper.connect","localhost:12345");
-    kafkaParams.put("group.id","consumer-group");
-    JavaPairDStream<String, String> test3 = ssc.kafkaStream(
-      String.class,
-      String.class,
-      StringDecoder.class,
-      StringDecoder.class,
-      kafkaParams,
-      topics,
-      StorageLevel.MEMORY_AND_DISK());
-  }
-
-  @Test
   public void testSocketTextStream() {
     JavaDStream<String> test = ssc.socketTextStream("localhost", 12345);
   }
@@ -1658,36 +1602,4 @@ public class JavaAPISuite implements Serializable {
   public void testRawSocketStream() {
     JavaDStream<String> test = ssc.rawSocketStream("localhost", 12345);
   }
-
-  @Test
-  public void testFlumeStream() {
-    JavaDStream<SparkFlumeEvent> test = ssc.flumeStream("localhost", 12345, StorageLevel.MEMORY_ONLY());
-  }
-
-  @Test
-  public void testFileStream() {
-    JavaPairDStream<String, String> foo =
-      ssc.<String, String, SequenceFileInputFormat<String,String>>fileStream("/tmp/foo");
-  }
-
-  @Test
-  public void testTwitterStream() {
-    String[] filters = new String[] { "good", "bad", "ugly" };
-    JavaDStream<Status> test = ssc.twitterStream(filters, StorageLevel.MEMORY_ONLY());
-  }
-
-  @Test
-  public void testActorStream() {
-    JavaDStream<String> test = ssc.actorStream((Props)null, "TestActor", StorageLevel.MEMORY_ONLY());
-  }
-
-  @Test
-  public void testZeroMQStream() {
-    JavaDStream<String> test = ssc.zeroMQStream("url", (Subscribe) null, new Function<byte[][], Iterable<String>>() {
-      @Override
-      public Iterable<String> call(byte[][] b) throws Exception {
-        return null;
-      }
-    });
-  }
 }
diff --git a/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java b/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java
new file mode 100644
index 0000000000..34bee56885
--- /dev/null
+++ b/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming;
+
+import org.apache.spark.streaming.api.java.JavaStreamingContext;
+import org.junit.After;
+import org.junit.Before;
+
+public abstract class LocalJavaStreamingContext {
+
+    protected transient JavaStreamingContext ssc;
+
+    @Before
+    public void setUp() {
+        System.clearProperty("spark.driver.port");
+        System.clearProperty("spark.hostPort");
+        System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock");
+        ssc = new JavaStreamingContext("local[2]", "test", new Duration(1000));
+        ssc.checkpoint("checkpoint");
+    }
+
+    @After
+    public void tearDown() {
+        ssc.stop();
+        ssc = null;
+
+        // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
+        System.clearProperty("spark.driver.port");
+        System.clearProperty("spark.hostPort");
+    }
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index 5185954521..a8e053278a 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -23,7 +23,7 @@ import akka.actor.IOManager
 import akka.actor.Props
 import akka.util.ByteString
 
-import org.apache.spark.streaming.dstream.{FileInputDStream, NetworkReceiver, SparkFlumeEvent}
+import org.apache.spark.streaming.dstream.{NetworkReceiver}
 import java.net.{InetSocketAddress, SocketException, Socket, ServerSocket}
 import java.io.{File, BufferedWriter, OutputStreamWriter}
 import java.util.concurrent.{Executors, TimeUnit, ArrayBlockingQueue}
@@ -31,18 +31,11 @@ import collection.mutable.{SynchronizedBuffer, ArrayBuffer}
 import util.ManualClock
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.receivers.Receiver
-import org.apache.spark.{SparkContext, Logging}
+import org.apache.spark.Logging
 import scala.util.Random
 import org.apache.commons.io.FileUtils
 import org.scalatest.BeforeAndAfter
-import org.apache.flume.source.avro.AvroSourceProtocol
-import org.apache.flume.source.avro.AvroFlumeEvent
-import org.apache.flume.source.avro.Status
-import org.apache.avro.ipc.{specific, NettyTransceiver}
-import org.apache.avro.ipc.specific.SpecificRequestor
-import java.nio.ByteBuffer
 import collection.JavaConversions._
-import java.nio.charset.Charset
 import com.google.common.io.Files
 import java.util.concurrent.atomic.AtomicInteger
 
@@ -56,7 +49,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
     testServer.start()
 
     // Set up the streaming context and input streams
-    val ssc = new StreamingContext(new SparkContext(conf), batchDuration)
+    val ssc = new StreamingContext(conf, batchDuration)
     val networkStream = ssc.socketTextStream("localhost", testServer.port, StorageLevel.MEMORY_AND_DISK)
     val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
     val outputStream = new TestOutputStream(networkStream, outputBuffer)
@@ -99,62 +92,13 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
   }
 
 
-  test("flume input stream") {
-    // Set up the streaming context and input streams
-    val ssc = new StreamingContext(new SparkContext(conf), batchDuration)
-    val flumeStream = ssc.flumeStream("localhost", testPort, StorageLevel.MEMORY_AND_DISK)
-    val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]]
-      with SynchronizedBuffer[Seq[SparkFlumeEvent]]
-    val outputStream = new TestOutputStream(flumeStream, outputBuffer)
-    ssc.registerOutputStream(outputStream)
-    ssc.start()
-
-    val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
-    val input = Seq(1, 2, 3, 4, 5)
-    Thread.sleep(1000)
-    val transceiver = new NettyTransceiver(new InetSocketAddress("localhost", testPort))
-    val client = SpecificRequestor.getClient(
-      classOf[AvroSourceProtocol], transceiver)
-
-    for (i <- 0 until input.size) {
-      val event = new AvroFlumeEvent
-      event.setBody(ByteBuffer.wrap(input(i).toString.getBytes()))
-      event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header"))
-      client.append(event)
-      Thread.sleep(500)
-      clock.addToTime(batchDuration.milliseconds)
-    }
-
-    val startTime = System.currentTimeMillis()
-    while (outputBuffer.size < input.size && System.currentTimeMillis() - startTime < maxWaitTimeMillis) {
-      logInfo("output.size = " + outputBuffer.size + ", input.size = " + input.size)
-      Thread.sleep(100)
-    }
-    Thread.sleep(1000)
-    val timeTaken = System.currentTimeMillis() - startTime
-    assert(timeTaken < maxWaitTimeMillis, "Operation timed out after " + timeTaken + " ms")
-    logInfo("Stopping context")
-    ssc.stop()
-
-    val decoder = Charset.forName("UTF-8").newDecoder()
-
-    assert(outputBuffer.size === input.length)
-    for (i <- 0 until outputBuffer.size) {
-      assert(outputBuffer(i).size === 1)
-      val str = decoder.decode(outputBuffer(i).head.event.getBody)
-      assert(str.toString === input(i).toString)
-      assert(outputBuffer(i).head.event.getHeaders.get("test") === "header")
-    }
-  }
-
-
   test("file input stream") {
     // Disable manual clock as FileInputDStream does not work with manual clock
     conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock")
 
     // Set up the streaming context and input streams
     val testDir = Files.createTempDir()
-    val ssc = new StreamingContext(new SparkContext(conf), batchDuration)
+    val ssc = new StreamingContext(conf, batchDuration)
     val fileStream = ssc.textFileStream(testDir.toString)
     val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
     def output = outputBuffer.flatMap(x => x)
@@ -206,7 +150,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
     testServer.start()
 
     // Set up the streaming context and input streams
-    val ssc = new StreamingContext(new SparkContext(conf), batchDuration)
+    val ssc = new StreamingContext(conf, batchDuration)
     val networkStream = ssc.actorStream[String](Props(new TestActor(port)), "TestActor",
       StorageLevel.MEMORY_AND_DISK) //Had to pass the local value of port to prevent from closing over entire scope
     val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
@@ -249,20 +193,6 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
     }
   }
 
-  test("kafka input stream") {
-    val ssc = new StreamingContext(new SparkContext(conf), batchDuration)
-    val topics = Map("my-topic" -> 1)
-    val test1 = ssc.kafkaStream("localhost:12345", "group", topics)
-    val test2 = ssc.kafkaStream("localhost:12345", "group", topics, StorageLevel.MEMORY_AND_DISK)
-
-    // Test specifying decoder
-    val kafkaParams = Map("zookeeper.connect"->"localhost:12345","group.id"->"consumer-group")
-    val test3 = ssc.kafkaStream[
-      String,
-      String,
-      kafka.serializer.StringDecoder,
-      kafka.serializer.StringDecoder](kafkaParams, topics, StorageLevel.MEMORY_AND_DISK)
-  }
 
   test("multi-thread receiver") {
     // set up the test receiver
@@ -273,7 +203,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
     MultiThreadTestReceiver.haveAllThreadsFinished = false
 
     // set up the network stream using the test receiver
-    val ssc = new StreamingContext(new SparkContext(conf), batchDuration)
+    val ssc = new StreamingContext(conf, batchDuration)
     val networkStream = ssc.networkStream[Int](testReceiver)
     val countStream = networkStream.count
     val outputBuffer = new ArrayBuffer[Seq[Long]] with SynchronizedBuffer[Seq[Long]]
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
index 33464bc3a1..b20d02f996 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
@@ -142,16 +142,12 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
   // Default before function for any streaming test suite. Override this
   // if you want to add your stuff to "before" (i.e., don't call before { } )
   def beforeFunction() {
-    //if (useManualClock) {
-    //  System.setProperty(
-    //    "spark.streaming.clock",
-    //    "org.apache.spark.streaming.util.ManualClock"
-    //  )
-    //} else {
-    //  System.clearProperty("spark.streaming.clock")
-    //}
     if (useManualClock) {
+      logInfo("Using manual clock")
       conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock")
+    } else {
+      logInfo("Using real clock")
+      conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock")
     }
   }
 
@@ -175,9 +171,8 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
       operation: DStream[U] => DStream[V],
       numPartitions: Int = numInputPartitions
     ): StreamingContext = {
-    val sc = new SparkContext(conf)
     // Create StreamingContext
-    val ssc = new StreamingContext(sc, batchDuration)
+    val ssc = new StreamingContext(conf, batchDuration)
     if (checkpointDir != null) {
       ssc.checkpoint(checkpointDir)
     }
@@ -201,9 +196,8 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
       input2: Seq[Seq[V]],
       operation: (DStream[U], DStream[V]) => DStream[W]
     ): StreamingContext = {
-    val sc = new SparkContext(conf)
     // Create StreamingContext
-    val ssc = new StreamingContext(sc, batchDuration)
+    val ssc = new StreamingContext(conf, batchDuration)
     if (checkpointDir != null) {
       ssc.checkpoint(checkpointDir)
     }
@@ -279,7 +273,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
       val startTime = System.currentTimeMillis()
       while (output.size < numExpectedOutput && System.currentTimeMillis() - startTime < maxWaitTimeMillis) {
         logInfo("output.size = " + output.size + ", numExpectedOutput = " + numExpectedOutput)
-        Thread.sleep(100)
+        Thread.sleep(10)
       }
       val timeTaken = System.currentTimeMillis() - startTime