diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd
index 6e7efbd3349870bfc47daf99b578d2261b1f3a36..44826f339c1da69893ab8820a869c456ec973721 100644
--- a/bin/compute-classpath.cmd
+++ b/bin/compute-classpath.cmd
@@ -15,6 +15,7 @@ set CORE_DIR=%FWDIR%core
 set REPL_DIR=%FWDIR%repl
 set EXAMPLES_DIR=%FWDIR%examples
 set BAGEL_DIR=%FWDIR%bagel
+set MLLIB_DIR=%FWDIR%mllib
 set STREAMING_DIR=%FWDIR%streaming
 set PYSPARK_DIR=%FWDIR%python
 
@@ -29,6 +30,7 @@ set CLASSPATH=%CLASSPATH%;%FWDIR%lib_managed\bundles\*
 set CLASSPATH=%CLASSPATH%;%FWDIR%repl\lib\*
 set CLASSPATH=%CLASSPATH%;%FWDIR%python\lib\*
 set CLASSPATH=%CLASSPATH%;%BAGEL_DIR%\target\scala-%SCALA_VERSION%\classes
+set CLASSPATH=%CLASSPATH%;%MLLIB_DIR%\target\scala-%SCALA_VERSION%\classes
 
 rem Add hadoop conf dir - else FileSystem.*, etc fail
 rem Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts
diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index a0f35cb5eb18a33636bee87d83cff271c725444b..2caefe98c9018f20d44089f47787d7f95395d86a 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -18,6 +18,7 @@ REPL_DIR="$FWDIR/repl"
 REPL_BIN_DIR="$FWDIR/repl-bin"
 EXAMPLES_DIR="$FWDIR/examples"
 BAGEL_DIR="$FWDIR/bagel"
+MLLIB_DIR="$FWDIR/mllib"
 STREAMING_DIR="$FWDIR/streaming"
 PYSPARK_DIR="$FWDIR/python"
 
@@ -49,6 +50,7 @@ if [ -e $REPL_BIN_DIR/target ]; then
   CLASSPATH+=":$EXAMPLES_JAR"
 fi
 CLASSPATH="$CLASSPATH:$BAGEL_DIR/target/scala-$SCALA_VERSION/classes"
+CLASSPATH="$CLASSPATH:$MLLIB_DIR/target/scala-$SCALA_VERSION/classes"
 for jar in `find $PYSPARK_DIR/lib -name '*jar'`; do
   CLASSPATH="$CLASSPATH:$jar"
 done
diff --git a/conf/fairscheduler.xml.template b/conf/fairscheduler.xml.template
new file mode 100644
index 0000000000000000000000000000000000000000..04a6b418dc172c237b9db030b4bafe868ded64db
--- /dev/null
+++ b/conf/fairscheduler.xml.template
@@ -0,0 +1,15 @@
+<?xml version="1.0"?>
+<allocations>
+<pool name="production">
+    <minShare>2</minShare>
+    <weight>1</weight>
+    <schedulingMode>FAIR</schedulingMode>
+</pool>
+<pool name="test">
+    <minShare>3</minShare>
+    <weight>2</weight>
+    <schedulingMode>FIFO</schedulingMode>
+</pool>
+<pool name="data">
+</pool>
+</allocations>
diff --git a/core/pom.xml b/core/pom.xml
index 385663a638a2c8b2639369af02941bddd7aec967..a55f58a9dbbd1bc32702c531c08e6307fc6b33ff 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -56,32 +56,24 @@
       <artifactId>akka-slf4j_${scala.version}</artifactId>
     </dependency>
     <dependency>
-      <groupId>it.unimi.dsi</groupId>
-      <artifactId>fastutil</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>colt</groupId>
-      <artifactId>colt</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>io.spray</groupId>
-      <artifactId>spray-can</artifactId>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scalap</artifactId>
     </dependency>
     <dependency>
-      <groupId>io.spray</groupId>
-      <artifactId>spray-routing</artifactId>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
     </dependency>
     <dependency>
-      <groupId>io.spray</groupId>
-      <artifactId>spray-io</artifactId>
+      <groupId>net.liftweb</groupId>
+      <artifactId>lift-json_2.10</artifactId>
     </dependency>
     <dependency>
-      <groupId>io.spray</groupId>
-      <artifactId>spray-json_${scala.version}</artifactId>
+      <groupId>it.unimi.dsi</groupId>
+      <artifactId>fastutil</artifactId>
     </dependency>
     <dependency>
-      <groupId>org.tomdz.twirl</groupId>
-      <artifactId>twirl-api</artifactId>
+      <groupId>colt</groupId>
+      <artifactId>colt</artifactId>
     </dependency>
     <dependency>
       <groupId>com.github.scala-incubator.io</groupId>
@@ -175,10 +167,6 @@
           </environmentVariables>
         </configuration>
       </plugin>
-      <plugin>
-        <groupId>org.tomdz.twirl</groupId>
-        <artifactId>twirl-maven-plugin</artifactId>
-      </plugin>
     </plugins>
   </build>
 
diff --git a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala
index a0fb4fe25d188b02f1ac62245eaaa0de86b5b46c..f1c86de4cc96154b4254d2cfee6e07ad46602c2f 100644
--- a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala
@@ -1,5 +1,6 @@
 package spark.deploy
 import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.mapred.JobConf
 
 
 /**
@@ -20,4 +21,7 @@ object SparkHadoopUtil {
 
   // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems
   def newConfiguration(): Configuration = new Configuration()
+
+  // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster
+  def addCredentials(conf: JobConf) {}
 }
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala
index ab1ab9d8a7a8e8530938f6a1ddc4c860cffb1af0..301a57fffa2ca31df37eb7ca5da49a8555e30b6c 100644
--- a/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala
@@ -1,6 +1,7 @@
 package spark.deploy
 
 import collection.mutable.HashMap
+import org.apache.hadoop.mapred.JobConf
 import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.conf.Configuration
@@ -27,18 +28,7 @@ object SparkHadoopUtil {
   }
 
   def runAsUser(func: (Product) => Unit, args: Product, user: String) {
-
-    // println("running as user " + jobUserName)
-
-    UserGroupInformation.setConfiguration(yarnConf)
-    val appMasterUgi: UserGroupInformation = UserGroupInformation.createRemoteUser(user)
-    appMasterUgi.doAs(new PrivilegedExceptionAction[AnyRef] {
-      def run: AnyRef = {
-        func(args)
-        // no return value ...
-        null
-      }
-    })
+    func(args)
   }
 
   // Note that all params which start with SPARK are propagated all the way through, so if in yarn mode, this MUST be set to true.
@@ -60,4 +50,10 @@ object SparkHadoopUtil {
   // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems
   // Always create a new config, dont reuse yarnConf.
   def newConfiguration(): Configuration = new YarnConfiguration(new Configuration())
+
+  // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster
+  def addCredentials(conf: JobConf) {
+    val jobCreds = conf.getCredentials();
+    jobCreds.mergeAll(UserGroupInformation.getCurrentUser().getCredentials())
+  }
 }
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala
index aa72c1e5fef1c71484ccd82331206e7748d5ddb8..f19648ec6865ff78c8c80d527f19616f6c956fef 100644
--- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala
+++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala
@@ -30,23 +30,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e
 
   def run() {
     
-    // Initialization
-    val jobUserName = Utils.getUserNameFromEnvironment()
-    logInfo("running as user " + jobUserName)
-
-    // run as user ...
-    UserGroupInformation.setConfiguration(yarnConf)
-    val appMasterUgi: UserGroupInformation = UserGroupInformation.createRemoteUser(jobUserName)
-    appMasterUgi.doAs(new PrivilegedExceptionAction[AnyRef] {
-      def run: AnyRef = {
-        runImpl()
-        return null
-      }
-    })
-  }
-
-  private def runImpl() {
-
     appAttemptId = getApplicationAttemptId()
     resourceManager = registerWithResourceManager()
     val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster()
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala
index 7a881e26dfffc423f7ab60d719926a9581db4dd9..514c17f241eba48297ab69a615941fd33a88744f 100644
--- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala
+++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala
@@ -1,9 +1,13 @@
 package spark.deploy.yarn
 
 import java.net.{InetSocketAddress, URI}
+import java.nio.ByteBuffer
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, FileSystem, Path}
+import org.apache.hadoop.mapred.Master
 import org.apache.hadoop.net.NetUtils
+import org.apache.hadoop.io.DataOutputBuffer
+import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hadoop.yarn.api._
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.api.protocolrecords._
@@ -23,6 +27,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
   
   var rpc: YarnRPC = YarnRPC.create(conf)
   val yarnConf: YarnConfiguration = new YarnConfiguration(conf)
+  val credentials = UserGroupInformation.getCurrentUser().getCredentials();
   
   def run() {
     init(yarnConf)
@@ -40,8 +45,8 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
 
     appContext.setQueue(args.amQueue)
     appContext.setAMContainerSpec(amContainer)
-    appContext.setUser(args.amUser)
-    
+    appContext.setUser(UserGroupInformation.getCurrentUser().getShortUserName())
+
     submitApp(appContext)
     
     monitorApplication(appId)
@@ -62,14 +67,21 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
   
   def verifyClusterResources(app: GetNewApplicationResponse) = { 
     val maxMem = app.getMaximumResourceCapability().getMemory()
-    logInfo("Max mem capabililty of resources in this cluster " + maxMem)
+    logInfo("Max mem capabililty of a single resource in this cluster " + maxMem)
     
-    // If the cluster does not have enough memory resources, exit.
-    val requestedMem = (args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + args.numWorkers * args.workerMemory
-    if (requestedMem > maxMem) {
-      logError("Cluster cannot satisfy memory resource request of " + requestedMem)
+    // if we have requested more then the clusters max for a single resource then exit.
+    if (args.workerMemory > maxMem) {
+      logError("the worker size is to large to run on this cluster " + args.workerMemory);
+      System.exit(1)
+    }
+    val amMem = args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD
+    if (amMem > maxMem) {
+      logError("AM size is to large to run on this cluster "  + amMem)
       System.exit(1)
     }
+
+    // We could add checks to make sure the entire cluster has enough resources but that involves getting
+    // all the node reports and computing ourselves 
   }
   
   def createApplicationSubmissionContext(appId: ApplicationId): ApplicationSubmissionContext = {
@@ -86,6 +98,15 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
     // Upload Spark and the application JAR to the remote file system
     // Add them as local resources to the AM
     val fs = FileSystem.get(conf)
+
+    val delegTokenRenewer = Master.getMasterPrincipal(conf);
+    if (UserGroupInformation.isSecurityEnabled()) {
+      if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) {
+        logError("Can't get Master Kerberos principal for use as renewer")
+        System.exit(1)
+      }
+    }
+
     Map("spark.jar" -> System.getenv("SPARK_JAR"), "app.jar" -> args.userJar, "log4j.properties" -> System.getenv("SPARK_LOG4J_CONF"))
     .foreach { case(destName, _localPath) =>
       val localPath: String = if (_localPath != null) _localPath.trim() else ""
@@ -97,6 +118,11 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
         fs.copyFromLocalFile(false, true, src, dst)
         val destStatus = fs.getFileStatus(dst)
 
+        // get tokens for anything we upload to hdfs
+        if (UserGroupInformation.isSecurityEnabled()) {
+          fs.addDelegationTokens(delegTokenRenewer, credentials);
+        }
+
         val amJarRsrc = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource]
         amJarRsrc.setType(LocalResourceType.FILE)
         amJarRsrc.setVisibility(LocalResourceVisibility.APPLICATION)
@@ -106,6 +132,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
         locaResources(destName) = amJarRsrc
       }
     }
+    UserGroupInformation.getCurrentUser().addCredentials(credentials);
     return locaResources
   }
   
@@ -114,7 +141,6 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
     val log4jConfLocalRes = localResources.getOrElse("log4j.properties", null)
 
     val env = new HashMap[String, String]()
-    Apps.addToEnvironment(env, Environment.USER.name, args.amUser)
 
     // If log4j present, ensure ours overrides all others
     if (log4jConfLocalRes != null) Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./")
@@ -142,6 +168,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
       env("SPARK_YARN_LOG4J_SIZE") =  log4jConfLocalRes.getSize().toString()
     }
 
+
     // Add each SPARK-* key to the environment
     System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k,v) => env(k) = v }
     return env
@@ -195,7 +222,13 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
     }
 
     // Command for the ApplicationMaster
-    val commands = List[String]("java " +
+    var javaCommand = "java";
+    val javaHome = System.getenv("JAVA_HOME")
+    if (javaHome != null && !javaHome.isEmpty()) {
+      javaCommand = Environment.JAVA_HOME.$() + "/bin/java"
+    }
+
+    val commands = List[String](javaCommand + 
       " -server " +
       JAVA_OPTS +
       " spark.deploy.yarn.ApplicationMaster" +
@@ -214,7 +247,12 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
     // Memory for the ApplicationMaster
     capability.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
     amContainer.setResource(capability)
-    
+
+    // Setup security tokens
+    val dob = new DataOutputBuffer()
+    credentials.writeTokenStorageToStream(dob)
+    amContainer.setContainerTokens(ByteBuffer.wrap(dob.getData()))
+
     return amContainer
   }
   
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala
index 24110558e7da8333efae3fb25a0f3d375e610ba4..07e7edea36313b4690a932cca56388723332f794 100644
--- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala
+++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala
@@ -13,7 +13,6 @@ class ClientArguments(val args: Array[String]) {
   var workerMemory = 1024
   var workerCores = 1
   var numWorkers = 2
-  var amUser = System.getProperty("user.name")
   var amQueue = System.getProperty("QUEUE", "default")
   var amMemory: Int = 512
   // TODO
@@ -58,10 +57,6 @@ class ClientArguments(val args: Array[String]) {
           workerCores = value
           args = tail
 
-        case ("--user") :: value :: tail =>
-          amUser = value
-          args = tail
-
         case ("--queue") :: value :: tail =>
           amQueue = value
           args = tail
@@ -96,8 +91,7 @@ class ClientArguments(val args: Array[String]) {
       "  --worker-cores NUM   Number of cores for the workers (Default: 1). This is unsused right now.\n" +
       "  --master-memory MEM  Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)\n" +
       "  --worker-memory MEM  Memory per Worker (e.g. 1000M, 2G) (Default: 1G)\n" +
-      "  --queue QUEUE        The hadoop queue to use for allocation requests (Default: 'default')\n" +
-      "  --user USERNAME      Run the ApplicationMaster (and slaves) as a different user\n"
+      "  --queue QUEUE        The hadoop queue to use for allocation requests (Default: 'default')"
       )
     System.exit(exitCode)
   }
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala
index a2bf0af762417e2d9d3085abef1983a6caa39448..cc6f3344a1836970cfd55516c96860f93ee95463 100644
--- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala
+++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala
@@ -1,9 +1,12 @@
 package spark.deploy.yarn
 
 import java.net.URI
+import java.nio.ByteBuffer
+import java.security.PrivilegedExceptionAction
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, FileSystem, Path}
+import org.apache.hadoop.io.DataOutputBuffer
 import org.apache.hadoop.net.NetUtils
 import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hadoop.yarn.api._
@@ -11,7 +14,7 @@ import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.api.protocolrecords._
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.ipc.YarnRPC
-import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records}
+import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records, ProtoUtils}
 import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
 
 import scala.collection.JavaConversions._
@@ -76,7 +79,19 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S
 */
 
     ctx.setUser(UserGroupInformation.getCurrentUser().getShortUserName())
-    val commands = List[String]("java " +
+
+    val credentials = UserGroupInformation.getCurrentUser().getCredentials()
+    val dob = new DataOutputBuffer()
+    credentials.writeTokenStorageToStream(dob)
+    ctx.setContainerTokens(ByteBuffer.wrap(dob.getData()))
+
+    var javaCommand = "java";
+    val javaHome = System.getenv("JAVA_HOME")
+    if (javaHome != null && !javaHome.isEmpty()) {
+      javaCommand = Environment.JAVA_HOME.$() + "/bin/java"
+    }
+
+    val commands = List[String](javaCommand +
       " -server " +
       // Kill if OOM is raised - leverage yarn's failure handling to cause rescheduling.
       // Not killing the task leaves various aspects of the worker and (to some extent) the jvm in an inconsistent state.
@@ -143,8 +158,6 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S
   
   def prepareEnvironment: HashMap[String, String] = {
     val env = new HashMap[String, String]()
-    // should we add this ?
-    Apps.addToEnvironment(env, Environment.USER.name, Utils.getUserNameFromEnvironment())
 
     // If log4j present, ensure ours overrides all others
     if (System.getenv("SPARK_YARN_LOG4J_PATH") != null) {
@@ -165,7 +178,23 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S
     val cmHostPortStr = container.getNodeId().getHost() + ":" + container.getNodeId().getPort()
     val cmAddress = NetUtils.createSocketAddr(cmHostPortStr)
     logInfo("Connecting to ContainerManager at " + cmHostPortStr)
-    return rpc.getProxy(classOf[ContainerManager], cmAddress, conf).asInstanceOf[ContainerManager]
+
+    // use doAs and remoteUser here so we can add the container token and not 
+    // pollute the current users credentials with all of the individual container tokens
+    val user = UserGroupInformation.createRemoteUser(container.getId().toString());
+    val containerToken = container.getContainerToken();
+    if (containerToken != null) {
+      user.addToken(ProtoUtils.convertFromProtoFormat(containerToken, cmAddress))
+    }
+
+    val proxy = user
+        .doAs(new PrivilegedExceptionAction[ContainerManager] {
+          def run: ContainerManager = {
+            return rpc.getProxy(classOf[ContainerManager],
+                cmAddress, conf).asInstanceOf[ContainerManager]
+          }
+        });
+    return proxy;
   }
   
 }
diff --git a/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala
index a0fb4fe25d188b02f1ac62245eaaa0de86b5b46c..f1c86de4cc96154b4254d2cfee6e07ad46602c2f 100644
--- a/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala
@@ -1,5 +1,6 @@
 package spark.deploy
 import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.mapred.JobConf
 
 
 /**
@@ -20,4 +21,7 @@ object SparkHadoopUtil {
 
   // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems
   def newConfiguration(): Configuration = new Configuration()
+
+  // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster
+  def addCredentials(conf: JobConf) {}
 }
diff --git a/core/src/main/resources/spark/deploy/static/bootstrap-responsive.min.css b/core/src/main/resources/spark/deploy/static/bootstrap-responsive.min.css
deleted file mode 100644
index 1f55036a0746edc369bd0354ed8756a95f30981c..0000000000000000000000000000000000000000
--- a/core/src/main/resources/spark/deploy/static/bootstrap-responsive.min.css
+++ /dev/null
@@ -1,9 +0,0 @@
-/*!
- * Bootstrap Responsive v2.0.4
- *
- * Copyright 2012 Twitter, Inc
- * Licensed under the Apache License v2.0
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Designed and built with all the love in the world @twitter by @mdo and @fat.
- */.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:28px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;-ms-box-sizing:border-box;box-sizing:border-box}.hidden{display:none;visibility:hidden}.visible-phone{display:none!important}.visible-tablet{display:none!important}.hidden-desktop{display:none!important}@media(max-width:767px){.visible-phone{display:inherit!important}.hidden-phone{display:none!important}.hidden-desktop{display:inherit!important}.visible-desktop{display:none!important}}@media(min-width:768px) and (max-width:979px){.visible-tablet{display:inherit!important}.hidden-tablet{display:none!important}.hidden-desktop{display:inherit!important}.visible-desktop{display:none!important}}@media(max-width:480px){.nav-collapse{-webkit-transform:translate3d(0,0,0)}.page-header h1 small{display:block;line-height:18px}input[type="checkbox"],input[type="radio"]{border:1px solid #ccc}.form-horizontal .control-group>label{float:none;width:auto;padding-top:0;text-align:left}.form-horizontal .controls{margin-left:0}.form-horizontal .control-list{padding-top:0}.form-horizontal .form-actions{padding-right:10px;padding-left:10px}.modal{position:absolute;top:10px;right:10px;left:10px;width:auto;margin:0}.modal.fade.in{top:auto}.modal-header .close{padding:10px;margin:-10px}.carousel-caption{position:static}}@media(max-width:767px){body{padding-right:20px;padding-left:20px}.navbar-fixed-top,.navbar-fixed-bottom{margin-right:-20px;margin-left:-20px}.container-fluid{padding:0}.dl-horizontal 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diff --git a/core/src/main/resources/spark/deploy/static/bootstrap.min.css b/core/src/main/resources/spark/deploy/static/bootstrap.min.css
deleted file mode 100644
index b74b4546a62b43fd0c3936a0a59b1270dfb23adb..0000000000000000000000000000000000000000
--- a/core/src/main/resources/spark/deploy/static/bootstrap.min.css
+++ /dev/null
@@ -1,9 +0,0 @@
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diff --git a/core/src/main/resources/spark/ui/static/bootstrap-responsive.min.css b/core/src/main/resources/spark/ui/static/bootstrap-responsive.min.css
new file mode 100644
index 0000000000000000000000000000000000000000..f4ede63f32e2ec803876a38efac05dd49acc4684
--- /dev/null
+++ b/core/src/main/resources/spark/ui/static/bootstrap-responsive.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Bootstrap Responsive v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
+ */.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}.visible-phone{display:none!important}.visible-tablet{display:none!important}.hidden-desktop{display:none!important}.visible-desktop{display:inherit!important}@media(min-width:768px) and 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diff --git a/core/src/main/resources/spark/ui/static/bootstrap.min.css b/core/src/main/resources/spark/ui/static/bootstrap.min.css
new file mode 100644
index 0000000000000000000000000000000000000000..b6428e695860151f82076e87810fe2d133339c2a
--- /dev/null
+++ b/core/src/main/resources/spark/ui/static/bootstrap.min.css
@@ -0,0 +1,9 @@
+/*!
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+ * Copyright 2012 Twitter, Inc
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+ *
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diff --git a/core/src/main/resources/spark/deploy/static/sorttable.js b/core/src/main/resources/spark/ui/static/sorttable.js
similarity index 100%
rename from core/src/main/resources/spark/deploy/static/sorttable.js
rename to core/src/main/resources/spark/ui/static/sorttable.js
diff --git a/core/src/main/resources/spark/ui/static/spark-logo-77x50px-hd.png b/core/src/main/resources/spark/ui/static/spark-logo-77x50px-hd.png
new file mode 100644
index 0000000000000000000000000000000000000000..6c5f0993c43f0d9e1e1a04391d8a9157f0fdfba8
Binary files /dev/null and b/core/src/main/resources/spark/ui/static/spark-logo-77x50px-hd.png differ
diff --git a/core/src/main/resources/spark/deploy/static/spark_logo.png b/core/src/main/resources/spark/ui/static/spark_logo.png
similarity index 100%
rename from core/src/main/resources/spark/deploy/static/spark_logo.png
rename to core/src/main/resources/spark/ui/static/spark_logo.png
diff --git a/core/src/main/resources/spark/ui/static/webui.css b/core/src/main/resources/spark/ui/static/webui.css
new file mode 100644
index 0000000000000000000000000000000000000000..f7537bb7661919e20f66298273471d1629feb0a3
--- /dev/null
+++ b/core/src/main/resources/spark/ui/static/webui.css
@@ -0,0 +1,49 @@
+.navbar .brand {
+  height: 50px;
+  width: 110px;
+  margin-left: 1px;
+  padding: 0;
+}
+
+.version {
+  line-height: 30px;
+  vertical-align: bottom;
+  font-size: 12px;
+  padding: 0;
+  margin: 0;
+  font-weight: bold;
+  color: #777;
+}
+
+.navbar-inner {
+  padding-top: 2px;
+  height: 50px;
+}
+
+.navbar-inner .nav {
+  margin-top: 5px;
+  font-size: 15px;
+}
+
+
+#infolist {
+  margin-left: 400px;
+  margin-top: 14px;
+}
+
+#infolist li {
+  display: inline;
+  list-style-type: none;
+  list-style-position: outside;
+  padding-right: 20px;
+  padding-top: 10px;
+  padding-bottom: 10px;
+}
+
+.progress-cell {
+  width: 134px;
+  border-right: 0;
+  padding: 0;
+  padding-top: 7px;
+  padding-left: 4px;
+}
diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala
index fe812fe530ce41173f8dfe567d7736f77dd43b03..0095b868a833f452b9f1e58e47dda629b1b3ee35 100644
--- a/core/src/main/scala/spark/PairRDDFunctions.scala
+++ b/core/src/main/scala/spark/PairRDDFunctions.scala
@@ -22,6 +22,7 @@ import org.apache.hadoop.mapred.OutputFormat
 
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat}
 import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat, RecordWriter => NewRecordWriter, Job => NewAPIHadoopJob, HadoopMapReduceUtil}
+import org.apache.hadoop.security.UserGroupInformation
 
 import spark.partial.BoundedDouble
 import spark.partial.PartialResult
diff --git a/core/src/main/scala/spark/RDD.scala b/core/src/main/scala/spark/RDD.scala
index e88290fdb2de2bbf093baab8a45eed62e840c071..8dde5391d39f168e40d6beb91cf8d3f3903eabe1 100644
--- a/core/src/main/scala/spark/RDD.scala
+++ b/core/src/main/scala/spark/RDD.scala
@@ -108,6 +108,9 @@ abstract class RDD[T: ClassTag](
   // Methods and fields available on all RDDs
   // =======================================================================
 
+  /** The SparkContext that created this RDD. */
+  def sparkContext: SparkContext = sc
+
   /** A unique ID for this RDD (within its SparkContext). */
   val id: Int = sc.newRddId()
 
@@ -284,31 +287,35 @@ abstract class RDD[T: ClassTag](
   def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[T] = {
     var fraction = 0.0
     var total = 0
-    val multiplier = 3.0
-    val initialCount = count()
+    var multiplier = 3.0
+    var initialCount = this.count()
     var maxSelected = 0
 
+    if (num < 0) {
+      throw new IllegalArgumentException("Negative number of elements requested")
+    }
+
     if (initialCount > Integer.MAX_VALUE - 1) {
       maxSelected = Integer.MAX_VALUE - 1
     } else {
       maxSelected = initialCount.toInt
     }
 
-    if (num > initialCount) {
+    if (num > initialCount && !withReplacement) {
       total = maxSelected
-      fraction = math.min(multiplier * (maxSelected + 1) / initialCount, 1.0)
-    } else if (num < 0) {
-      throw(new IllegalArgumentException("Negative number of elements requested"))
+      fraction = multiplier * (maxSelected + 1) / initialCount
     } else {
-      fraction = math.min(multiplier * (num + 1) / initialCount, 1.0)
+      fraction = multiplier * (num + 1) / initialCount
       total = num
     }
 
     val rand = new Random(seed)
-    var samples = this.sample(withReplacement, fraction, rand.nextInt).collect()
+    var samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
 
+    // If the first sample didn't turn out large enough, keep trying to take samples;
+    // this shouldn't happen often because we use a big multiplier for thei initial size
     while (samples.length < total) {
-      samples = this.sample(withReplacement, fraction, rand.nextInt).collect()
+      samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
     }
 
     Utils.randomizeInPlace(samples, rand).take(total)
diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala
index ef6de87193b61cbb5e6d42355e57fdb39265e490..01d9a70a78728d870e35d655f879fc45c21d2206 100644
--- a/core/src/main/scala/spark/SparkContext.scala
+++ b/core/src/main/scala/spark/SparkContext.scala
@@ -39,6 +39,7 @@ import org.apache.hadoop.mapred.TextInputFormat
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 import org.apache.hadoop.mapreduce.{Job => NewHadoopJob}
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
+import org.apache.hadoop.security.UserGroupInformation
 
 import org.apache.mesos.MesosNativeLibrary
 
@@ -49,8 +50,9 @@ import spark.scheduler.{DAGScheduler, ResultTask, ShuffleMapTask, SparkListener,
 import spark.scheduler.cluster.{StandaloneSchedulerBackend, SparkDeploySchedulerBackend, ClusterScheduler}
 import spark.scheduler.local.LocalScheduler
 import spark.scheduler.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend}
-import spark.storage.{BlockManagerUI, StorageStatus, StorageUtils, RDDInfo}
+import spark.storage.{StorageStatus, StorageUtils, RDDInfo}
 import spark.util.{MetadataCleaner, TimeStampedHashMap}
+import ui.{SparkUI}
 
 /**
  * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark
@@ -96,11 +98,6 @@ class SparkContext(
     isLocal)
   SparkEnv.set(env)
 
-  // Start the BlockManager UI
-  private[spark] val ui = new BlockManagerUI(
-    env.actorSystem, env.blockManager.master.driverActor, this)
-  ui.start()
-
   // Used to store a URL for each static file/jar together with the file's local timestamp
   private[spark] val addedFiles = HashMap[String, Long]()
   private[spark] val addedJars = HashMap[String, Long]()
@@ -109,6 +106,9 @@ class SparkContext(
   private[spark] val persistentRdds = new TimeStampedHashMap[Int, RDD[_]]
   private[spark] val metadataCleaner = new MetadataCleaner("SparkContext", this.cleanup)
 
+  // Initalize the Spark UI
+  private[spark] val ui = new SparkUI(this)
+  ui.bind()
 
   // Add each JAR given through the constructor
   if (jars != null) {
@@ -217,6 +217,8 @@ class SparkContext(
   @volatile private var dagScheduler = new DAGScheduler(taskScheduler)
   dagScheduler.start()
 
+  ui.start()
+
   /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
   val hadoopConfiguration = {
     val conf = SparkHadoopUtil.newConfiguration()
@@ -579,6 +581,7 @@ class SparkContext(
 
   /** Shut down the SparkContext. */
   def stop() {
+    ui.stop()
     // Do this only if not stopped already - best case effort.
     // prevent NPE if stopped more than once.
     val dagSchedulerCopy = dagScheduler
diff --git a/core/src/main/scala/spark/TaskEndReason.scala b/core/src/main/scala/spark/TaskEndReason.scala
index 8140cba0841e519b6a0269638cb2207e8adf6b15..bb75ec208cbc95ef479642daad84e957a602ae30 100644
--- a/core/src/main/scala/spark/TaskEndReason.scala
+++ b/core/src/main/scala/spark/TaskEndReason.scala
@@ -1,5 +1,6 @@
 package spark
 
+import spark.executor.TaskMetrics
 import spark.storage.BlockManagerId
 
 /**
@@ -24,7 +25,8 @@ private[spark] case class FetchFailed(
 private[spark] case class ExceptionFailure(
     className: String,
     description: String,
-    stackTrace: Array[StackTraceElement])
+    stackTrace: Array[StackTraceElement],
+    metrics: Option[TaskMetrics])
   extends TaskEndReason
 
 private[spark] case class OtherFailure(message: String) extends TaskEndReason
diff --git a/core/src/main/scala/spark/Utils.scala b/core/src/main/scala/spark/Utils.scala
index e02507f83ec54c7e97ab26de69c9cab6d10fb124..f90b2ccaa1cc80d0b49efdea8f665f4134520a79 100644
--- a/core/src/main/scala/spark/Utils.scala
+++ b/core/src/main/scala/spark/Utils.scala
@@ -489,6 +489,26 @@ private object Utils extends Logging {
     "%.1f %s".formatLocal(Locale.US, value, unit)
   }
 
+  /**
+   * Returns a human-readable string representing a duration such as "35ms"
+   */
+  def msDurationToString(ms: Long): String = {
+    val second = 1000
+    val minute = 60 * second
+    val hour = 60 * minute
+
+    ms match {
+      case t if t < second =>
+        "%d ms".format(t)
+      case t if t < minute =>
+        "%.1f s".format(t.toFloat / second)
+      case t if t < hour =>
+        "%.1f m".format(t.toFloat / minute)
+      case t =>
+        "%.2f h".format(t.toFloat / hour)
+    }
+  }
+
   /**
    * Convert a memory quantity in megabytes to a human-readable string such as "4.0 MB".
    */
@@ -604,18 +624,19 @@ private object Utils extends Logging {
     "%s at %s:%s".format(callSiteInfo.lastSparkMethod, callSiteInfo.firstUserFile,
                          callSiteInfo.firstUserLine)
   }
-  /**
-   * Try to find a free port to bind to on the local host. This should ideally never be needed,
-   * except that, unfortunately, some of the networking libraries we currently rely on (e.g. Spray)
-   * don't let users bind to port 0 and then figure out which free port they actually bound to.
-   * We work around this by binding a ServerSocket and immediately unbinding it. This is *not*
-   * necessarily guaranteed to work, but it's the best we can do.
-   */
-  def findFreePort(): Int = {
-    val socket = new ServerSocket(0)
-    val portBound = socket.getLocalPort
-    socket.close()
-    portBound
+
+  /** Return a string containing the last `n` bytes of a file. */
+  def lastNBytes(path: String, n: Int): String = {
+    val file = new File(path)
+    val length = file.length()
+    val buff = new Array[Byte](math.min(n, length.toInt))
+    val skip = math.max(0, length - n)
+    val stream = new FileInputStream(file)
+
+    stream.skip(skip)
+    stream.read(buff)
+    stream.close()
+    Source.fromBytes(buff).mkString
   }
 
   /**
diff --git a/core/src/main/scala/spark/deploy/JsonProtocol.scala b/core/src/main/scala/spark/deploy/JsonProtocol.scala
index 88b03a007c16f743375112ee6301a260df437161..607d34d111f69632724409474334eb22ef05e717 100644
--- a/core/src/main/scala/spark/deploy/JsonProtocol.scala
+++ b/core/src/main/scala/spark/deploy/JsonProtocol.scala
@@ -1,79 +1,65 @@
 package spark.deploy
 
 import master.{ApplicationInfo, WorkerInfo}
+import net.liftweb.json.JsonDSL._
 import worker.ExecutorRunner
-import spray.json._
 
-/**
- * spray-json helper class containing implicit conversion to json for marshalling responses
- */
-private[spark] object JsonProtocol extends DefaultJsonProtocol {
-  implicit object WorkerInfoJsonFormat extends RootJsonWriter[WorkerInfo] {
-    def write(obj: WorkerInfo) = JsObject(
-      "id" -> JsString(obj.id),
-      "host" -> JsString(obj.host),
-      "port" -> JsNumber(obj.port),
-      "webuiaddress" -> JsString(obj.webUiAddress),
-      "cores" -> JsNumber(obj.cores),
-      "coresused" -> JsNumber(obj.coresUsed),
-      "memory" -> JsNumber(obj.memory),
-      "memoryused" -> JsNumber(obj.memoryUsed)
-    )
-  }
+private[spark] object JsonProtocol {
+ def writeWorkerInfo(obj: WorkerInfo) = {
+   ("id" -> obj.id) ~
+   ("host" -> obj.host) ~
+   ("port" -> obj.port) ~
+   ("webuiaddress" -> obj.webUiAddress) ~
+   ("cores" -> obj.cores) ~
+   ("coresused" -> obj.coresUsed) ~
+   ("memory" -> obj.memory) ~
+   ("memoryused" -> obj.memoryUsed)
+ }
 
-  implicit object AppInfoJsonFormat extends RootJsonWriter[ApplicationInfo] {
-    def write(obj: ApplicationInfo) = JsObject(
-      "starttime" -> JsNumber(obj.startTime),
-      "id" -> JsString(obj.id),
-      "name" -> JsString(obj.desc.name),
-      "cores" -> JsNumber(obj.desc.maxCores),
-      "user" -> JsString(obj.desc.user),
-      "memoryperslave" -> JsNumber(obj.desc.memoryPerSlave),
-      "submitdate" -> JsString(obj.submitDate.toString))
+  def writeApplicationInfo(obj: ApplicationInfo) = {
+    ("starttime" -> obj.startTime) ~
+    ("id" -> obj.id) ~
+    ("name" -> obj.desc.name) ~
+    ("cores" -> obj.desc.maxCores) ~
+    ("user" ->  obj.desc.user) ~
+    ("memoryperslave" -> obj.desc.memoryPerSlave) ~
+    ("submitdate" -> obj.submitDate.toString)
   }
 
-  implicit object AppDescriptionJsonFormat extends RootJsonWriter[ApplicationDescription] {
-    def write(obj: ApplicationDescription) = JsObject(
-      "name" -> JsString(obj.name),
-      "cores" -> JsNumber(obj.maxCores),
-      "memoryperslave" -> JsNumber(obj.memoryPerSlave),
-      "user" -> JsString(obj.user)
-    )
+  def writeApplicationDescription(obj: ApplicationDescription) = {
+    ("name" -> obj.name) ~
+    ("cores" -> obj.maxCores) ~
+    ("memoryperslave" -> obj.memoryPerSlave) ~
+    ("user" -> obj.user)
   }
 
-  implicit object ExecutorRunnerJsonFormat extends RootJsonWriter[ExecutorRunner] {
-    def write(obj: ExecutorRunner) = JsObject(
-      "id" -> JsNumber(obj.execId),
-      "memory" -> JsNumber(obj.memory),
-      "appid" -> JsString(obj.appId),
-      "appdesc" -> obj.appDesc.toJson.asJsObject
-    )
+  def writeExecutorRunner(obj: ExecutorRunner) = {
+    ("id" -> obj.execId) ~
+    ("memory" -> obj.memory) ~
+    ("appid" -> obj.appId) ~
+    ("appdesc" -> writeApplicationDescription(obj.appDesc))
   }
 
-  implicit object MasterStateJsonFormat extends RootJsonWriter[MasterState] {
-    def write(obj: MasterState) = JsObject(
-      "url" -> JsString("spark://" + obj.uri),
-      "workers" -> JsArray(obj.workers.toList.map(_.toJson)),
-      "cores" -> JsNumber(obj.workers.map(_.cores).sum),
-      "coresused" -> JsNumber(obj.workers.map(_.coresUsed).sum),
-      "memory" -> JsNumber(obj.workers.map(_.memory).sum),
-      "memoryused" -> JsNumber(obj.workers.map(_.memoryUsed).sum),
-      "activeapps" -> JsArray(obj.activeApps.toList.map(_.toJson)),
-      "completedapps" -> JsArray(obj.completedApps.toList.map(_.toJson))
-    )
+  def writeMasterState(obj: MasterState) = {
+    ("url" -> ("spark://" + obj.uri)) ~
+    ("workers" -> obj.workers.toList.map(writeWorkerInfo)) ~
+    ("cores" -> obj.workers.map(_.cores).sum) ~
+    ("coresused" -> obj.workers.map(_.coresUsed).sum) ~
+    ("memory" -> obj.workers.map(_.memory).sum) ~
+    ("memoryused" -> obj.workers.map(_.memoryUsed).sum) ~
+    ("activeapps" -> obj.activeApps.toList.map(writeApplicationInfo)) ~
+    ("completedapps" -> obj.completedApps.toList.map(writeApplicationInfo))
   }
 
-  implicit object WorkerStateJsonFormat extends RootJsonWriter[WorkerState] {
-    def write(obj: WorkerState) = JsObject(
-      "id" -> JsString(obj.workerId),
-      "masterurl" -> JsString(obj.masterUrl),
-      "masterwebuiurl" -> JsString(obj.masterWebUiUrl),
-      "cores" -> JsNumber(obj.cores),
-      "coresused" -> JsNumber(obj.coresUsed),
-      "memory" -> JsNumber(obj.memory),
-      "memoryused" -> JsNumber(obj.memoryUsed),
-      "executors" -> JsArray(obj.executors.toList.map(_.toJson)),
-      "finishedexecutors" -> JsArray(obj.finishedExecutors.toList.map(_.toJson))
-    )
+  def writeWorkerState(obj: WorkerState) = {
+    ("id" -> obj.workerId) ~
+    ("masterurl" -> obj.masterUrl) ~
+    ("masterwebuiurl" -> obj.masterWebUiUrl) ~
+    ("cores" -> obj.cores) ~
+    ("coresused" -> obj.coresUsed) ~
+    ("memory" -> obj.memory) ~
+    ("memoryused" -> obj.memoryUsed) ~
+    ("executors" -> obj.executors.toList.map(writeExecutorRunner)) ~
+    ("finishedexecutors" -> obj.finishedExecutors.toList.map(writeExecutorRunner))
   }
 }
diff --git a/core/src/main/scala/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/spark/deploy/LocalSparkCluster.scala
index 2b0b3b10e72b720acca3ca272a62fd8c5b930dea..494b1cc6f0558bf8eeddd93e7b0cc782472f5f28 100644
--- a/core/src/main/scala/spark/deploy/LocalSparkCluster.scala
+++ b/core/src/main/scala/spark/deploy/LocalSparkCluster.scala
@@ -17,11 +17,11 @@ import scala.collection.mutable.ArrayBuffer
  */
 private[spark]
 class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: Int) extends Logging {
-  
+
   private val localHostname = Utils.localHostName()
   private val masterActorSystems = ArrayBuffer[ActorSystem]()
   private val workerActorSystems = ArrayBuffer[ActorSystem]()
-  
+
   def start(): String = {
     logInfo("Starting a local Spark cluster with " + numWorkers + " workers.")
 
diff --git a/core/src/main/scala/spark/deploy/WebUI.scala b/core/src/main/scala/spark/deploy/WebUI.scala
index ad1a1092b2ebd137cef52ba2355dea8c1e72eef5..844c4142c7a9dd064489a7c191aadc6b8401b218 100644
--- a/core/src/main/scala/spark/deploy/WebUI.scala
+++ b/core/src/main/scala/spark/deploy/WebUI.scala
@@ -6,7 +6,7 @@ import java.util.Date
 /**
  * Utilities used throughout the web UI.
  */
-private[spark] object WebUI {
+private[spark] object DeployWebUI {
   val DATE_FORMAT = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss")
 
   def formatDate(date: Date): String = DATE_FORMAT.format(date)
diff --git a/core/src/main/scala/spark/deploy/master/Master.scala b/core/src/main/scala/spark/deploy/master/Master.scala
index 770cfe9d0528de90e6249c0b35ada4dcbc7935cf..d31e6735b7b19884d882b82178ee29ed4aaa2f8a 100644
--- a/core/src/main/scala/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/spark/deploy/master/Master.scala
@@ -13,6 +13,7 @@ import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 import spark.deploy._
 import spark.{Logging, SparkException, Utils}
 import spark.util.AkkaUtils
+import ui.MasterWebUI
 
 
 private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Actor with Logging {
@@ -35,6 +36,8 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
 
   var firstApp: Option[ApplicationInfo] = None
 
+  val webUi = new MasterWebUI(self)
+
   Utils.checkHost(host, "Expected hostname")
 
   val masterPublicAddress = {
@@ -51,20 +54,13 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
     logInfo("Starting Spark master at spark://" + host + ":" + port)
     // Listen for remote client disconnection events, since they don't go through Akka's watch()
     context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent])
-    startWebUi()
+    webUi.start()
     import context.dispatcher
     context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis)(timeOutDeadWorkers())
   }
 
-  def startWebUi() {
-    val webUi = new MasterWebUI(self)
-    try {
-      AkkaUtils.startSprayServer(context.system, "0.0.0.0", webUiPort, webUi.handler)
-    } catch {
-      case e: Exception =>
-        logError("Failed to create web UI", e)
-        System.exit(1)
-    }
+  override def postStop() {
+    webUi.stop()
   }
 
   override def receive = {
@@ -76,7 +72,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
       } else {
         addWorker(id, host, workerPort, cores, memory, worker_webUiPort, publicAddress)
         context.watch(sender)  // This doesn't work with remote actors but helps for testing
-        sender ! RegisteredWorker("http://" + masterPublicAddress + ":" + webUiPort)
+        sender ! RegisteredWorker("http://" + masterPublicAddress + ":" + webUi.boundPort)
         schedule()
       }
     }
@@ -279,7 +275,9 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
         exec.state = ExecutorState.KILLED
       }
       app.markFinished(state)
-      app.driver ! ApplicationRemoved(state.toString)
+      if (state != ApplicationState.FINISHED) {
+        app.driver ! ApplicationRemoved(state.toString)
+      }
       schedule()
     }
   }
diff --git a/core/src/main/scala/spark/deploy/master/MasterWebUI.scala b/core/src/main/scala/spark/deploy/master/MasterWebUI.scala
deleted file mode 100644
index 34cee878531158c8ea5627b29ac531be7ff7929c..0000000000000000000000000000000000000000
--- a/core/src/main/scala/spark/deploy/master/MasterWebUI.scala
+++ /dev/null
@@ -1,78 +0,0 @@
-package spark.deploy.master
-
-import akka.actor.{ActorRef, ActorContext, ActorRefFactory}
-import scala.concurrent.Await
-import akka.pattern.ask
-
-import akka.util.Timeout
-import scala.concurrent.duration._
-import spray.routing.Directives
-import spray.routing.directives._
-import spray.httpx.TwirlSupport._
-import spray.httpx.SprayJsonSupport._
-import spray.http.MediaTypes._
-
-import spark.deploy._
-import spark.deploy.JsonProtocol._
-
-/**
- * Web UI server for the standalone master.
- */
-private[spark]
-class MasterWebUI(master: ActorRef)(implicit val context: ActorContext) extends Directives {
-  import context.dispatcher
-
-  val actorSystem         = context.system
-  val RESOURCE_DIR = "spark/deploy/master/webui"
-  val STATIC_RESOURCE_DIR = "spark/deploy/static"
-
-  implicit val timeout = Timeout(Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds"))
-
-  val handler = {
-    get {
-      (path("") & parameters('format ?)) {
-        case Some(js) if js.equalsIgnoreCase("json") =>
-          val future = (master ? RequestMasterState).mapTo[MasterState]
-          respondWithMediaType(`application/json`) { ctx =>
-            ctx.complete(future.mapTo[MasterState])
-          }
-        case _ =>
-          complete {
-            val future = (master ? RequestMasterState).mapTo[MasterState]
-            future.map {
-              masterState => spark.deploy.master.html.index.render(masterState)
-            }
-          }
-      } ~
-      path("app") {
-        parameters("appId", 'format ?) {
-          case (appId, Some(js)) if (js.equalsIgnoreCase("json")) =>
-            val future = master ? RequestMasterState
-            val appInfo = for (masterState <- future.mapTo[MasterState]) yield {
-              masterState.activeApps.find(_.id == appId).getOrElse({
-                masterState.completedApps.find(_.id == appId).getOrElse(null)
-              })
-            }
-            respondWithMediaType(`application/json`) { ctx =>
-              ctx.complete(appInfo.mapTo[ApplicationInfo])
-            }
-          case (appId, _) =>
-            complete {
-              val future = master ? RequestMasterState
-              future.map { state =>
-                val masterState = state.asInstanceOf[MasterState]
-                val app = masterState.activeApps.find(_.id == appId).getOrElse({
-                  masterState.completedApps.find(_.id == appId).getOrElse(null)
-                })
-                spark.deploy.master.html.app_details.render(app)
-              }
-            }
-        }
-      } ~
-      pathPrefix("static") {
-        getFromResourceDirectory(STATIC_RESOURCE_DIR)
-      } ~
-      getFromResourceDirectory(RESOURCE_DIR)
-    }
-  }
-}
diff --git a/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala
new file mode 100644
index 0000000000000000000000000000000000000000..939f8c9587d3c38784e032aa2d97c4356b37d90e
--- /dev/null
+++ b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala
@@ -0,0 +1,101 @@
+package spark.deploy.master.ui
+
+import akka.pattern.ask
+
+import scala.concurrent.Await
+import scala.concurrent.duration._
+
+import javax.servlet.http.HttpServletRequest
+
+import net.liftweb.json.JsonAST.JValue
+
+import scala.xml.Node
+
+import spark.deploy.{RequestMasterState, JsonProtocol, MasterState}
+import spark.deploy.master.ExecutorInfo
+import spark.ui.UIUtils
+
+private[spark] class ApplicationPage(parent: MasterWebUI) {
+  val master = parent.master
+  implicit val timeout = parent.timeout
+
+  /** Executor details for a particular application */
+  def renderJson(request: HttpServletRequest): JValue = {
+    val appId = request.getParameter("appId")
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState]
+    val state = Await.result(stateFuture, 30 seconds)
+    val app = state.activeApps.find(_.id == appId).getOrElse({
+      state.completedApps.find(_.id == appId).getOrElse(null)
+    })
+    JsonProtocol.writeApplicationInfo(app)
+  }
+
+  /** Executor details for a particular application */
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val appId = request.getParameter("appId")
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState]
+    val state = Await.result(stateFuture, 30 seconds)
+    val app = state.activeApps.find(_.id == appId).getOrElse({
+      state.completedApps.find(_.id == appId).getOrElse(null)
+    })
+
+    val executorHeaders = Seq("ExecutorID", "Worker", "Cores", "Memory", "State", "Logs")
+    val executors = app.executors.values.toSeq
+    val executorTable = UIUtils.listingTable(executorHeaders, executorRow, executors)
+
+    val content =
+        <hr />
+        <div class="row">
+          <div class="span12">
+            <ul class="unstyled">
+              <li><strong>ID:</strong> {app.id}</li>
+              <li><strong>Description:</strong> {app.desc.name}</li>
+              <li><strong>User:</strong> {app.desc.user}</li>
+              <li><strong>Cores:</strong>
+                {
+                if (app.desc.maxCores == Integer.MAX_VALUE) {
+                  "Unlimited %s granted".format(app.coresGranted)
+                } else {
+                  "%s (%s granted, %s left)".format(
+                    app.desc.maxCores, app.coresGranted, app.coresLeft)
+                }
+                }
+              </li>
+              <li><strong>Memory per Slave:</strong> {app.desc.memoryPerSlave}</li>
+              <li><strong>Submit Date:</strong> {app.submitDate}</li>
+              <li><strong>State:</strong> {app.state}</li>
+              <li><strong><a href={app.appUiUrl}>Application Detail UI</a></strong></li>
+            </ul>
+          </div>
+        </div>
+
+          <hr/>
+
+        <div class="row"> <!-- Executors -->
+          <div class="span12">
+            <h3> Executor Summary </h3>
+            <br/>
+            {executorTable}
+          </div>
+        </div>;
+    UIUtils.basicSparkPage(content, "Application Info: " + app.desc.name)
+  }
+
+  def executorRow(executor: ExecutorInfo): Seq[Node] = {
+    <tr>
+      <td>{executor.id}</td>
+      <td>
+        <a href={executor.worker.webUiAddress}>{executor.worker.id}</a>
+      </td>
+      <td>{executor.cores}</td>
+      <td>{executor.memory}</td>
+      <td>{executor.state}</td>
+      <td>
+        <a href={"%s/log?appId=%s&executorId=%s&logType=stdout"
+          .format(executor.worker.webUiAddress, executor.application.id, executor.id)}>stdout</a>
+        <a href={"%s/log?appId=%s&executorId=%s&logType=stderr"
+          .format(executor.worker.webUiAddress, executor.application.id, executor.id)}>stderr</a>
+      </td>
+    </tr>
+  }
+}
diff --git a/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala
new file mode 100644
index 0000000000000000000000000000000000000000..73e378f98833add83842cffb3f49008816a8d2a1
--- /dev/null
+++ b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala
@@ -0,0 +1,119 @@
+package spark.deploy.master.ui
+
+import scala.concurrent.Await
+import akka.pattern.ask
+import scala.concurrent.duration._
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import spark.deploy.{RequestMasterState, DeployWebUI, MasterState}
+import spark.Utils
+import spark.ui.UIUtils
+import spark.deploy.master.{ApplicationInfo, WorkerInfo}
+
+private[spark] class IndexPage(parent: MasterWebUI) {
+  val master = parent.master
+  implicit val timeout = parent.timeout
+
+  /** Index view listing applications and executors */
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState]
+    val state = Await.result(stateFuture, 30 seconds)
+
+    val workerHeaders = Seq("Id", "Address", "State", "Cores", "Memory")
+    val workers = state.workers.sortBy(_.id)
+    val workerTable = UIUtils.listingTable(workerHeaders, workerRow, workers)
+
+    val appHeaders = Seq("ID", "Description", "Cores", "Memory per Node", "Submit Time", "User",
+      "State", "Duration")
+    val activeApps = state.activeApps.sortBy(_.startTime).reverse
+    val activeAppsTable = UIUtils.listingTable(appHeaders, appRow, activeApps)
+    val completedApps = state.completedApps.sortBy(_.endTime).reverse
+    val completedAppsTable = UIUtils.listingTable(appHeaders, appRow, completedApps)
+
+    val content =
+        <hr />
+        <div class="row">
+          <div class="span12">
+            <ul class="unstyled">
+              <li><strong>URL:</strong>{state.uri}</li>
+              <li><strong>Workers:</strong>{state.workers.size}</li>
+              <li><strong>Cores:</strong> {state.workers.map(_.cores).sum} Total,
+                {state.workers.map(_.coresUsed).sum} Used</li>
+              <li><strong>Memory:</strong>
+                {Utils.memoryMegabytesToString(state.workers.map(_.memory).sum)} Total,
+                {Utils.memoryMegabytesToString(state.workers.map(_.memoryUsed).sum)} Used</li>
+              <li><strong>Applications:</strong>
+                {state.activeApps.size} Running,
+                {state.completedApps.size} Completed </li>
+            </ul>
+          </div>
+        </div>
+
+        <div class="row">
+          <div class="span12">
+            <h3> Workers </h3>
+            <br/>
+            {workerTable}
+          </div>
+        </div>
+
+          <hr/>
+
+        <div class="row">
+          <div class="span12">
+            <h3> Running Applications </h3>
+            <br/>
+            {activeAppsTable}
+          </div>
+        </div>
+
+          <hr/>
+
+        <div class="row">
+          <div class="span12">
+            <h3> Completed Applications </h3>
+            <br/>
+            {completedAppsTable}
+          </div>
+        </div>;
+    UIUtils.basicSparkPage(content, "Spark Master: " + state.uri)
+  }
+
+  def workerRow(worker: WorkerInfo): Seq[Node] = {
+    <tr>
+      <td>
+        <a href={worker.webUiAddress}>{worker.id}</a>
+      </td>
+      <td>{worker.host}:{worker.port}</td>
+      <td>{worker.state}</td>
+      <td>{worker.cores} ({worker.coresUsed} Used)</td>
+      <td sorttable_customkey={"%s.%s".format(worker.memory, worker.memoryUsed)}>
+        {Utils.memoryMegabytesToString(worker.memory)}
+        ({Utils.memoryMegabytesToString(worker.memoryUsed)} Used)
+      </td>
+    </tr>
+  }
+
+
+  def appRow(app: ApplicationInfo): Seq[Node] = {
+    <tr>
+      <td>
+        <a href={"app?appId=" + app.id}>{app.id}</a>
+      </td>
+      <td>{app.desc.name}</td>
+      <td>
+        {app.coresGranted}
+      </td>
+      <td sorttable_customkey={app.desc.memoryPerSlave.toString}>
+        {Utils.memoryMegabytesToString(app.desc.memoryPerSlave)}
+      </td>
+      <td>{DeployWebUI.formatDate(app.submitDate)}</td>
+      <td>{app.desc.user}</td>
+      <td>{app.state.toString}</td>
+      <td>{DeployWebUI.formatDuration(app.duration)}</td>
+    </tr>
+  }
+}
diff --git a/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala
new file mode 100644
index 0000000000000000000000000000000000000000..bcc4d49234b57d19785716cb2cb001f217bbc9d9
--- /dev/null
+++ b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala
@@ -0,0 +1,59 @@
+package spark.deploy.master.ui
+
+import akka.actor.ActorRef
+import scala.concurrent.duration._
+
+import javax.servlet.http.HttpServletRequest
+
+import org.eclipse.jetty.server.{Handler, Server}
+
+import spark.{Logging, Utils}
+import spark.ui.JettyUtils
+import spark.ui.JettyUtils._
+
+/**
+ * Web UI server for the standalone master.
+ */
+private[spark]
+class MasterWebUI(val master: ActorRef, requestedPort: Option[Int] = None) extends Logging {
+  implicit val timeout = Duration.create(
+    System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")
+  val host = Utils.localHostName()
+  val port = requestedPort.getOrElse(
+    System.getProperty("master.ui.port", MasterWebUI.DEFAULT_PORT).toInt)
+
+  var server: Option[Server] = None
+  var boundPort: Option[Int] = None
+
+  val applicationPage = new ApplicationPage(this)
+  val indexPage = new IndexPage(this)
+
+  def start() {
+    try {
+      val (srv, bPort) = JettyUtils.startJettyServer("0.0.0.0", port, handlers)
+      server = Some(srv)
+      boundPort = Some(bPort)
+      logInfo("Started Master web UI at http://%s:%d".format(host, boundPort.get))
+    } catch {
+      case e: Exception =>
+        logError("Failed to create Master JettyUtils", e)
+        System.exit(1)
+    }
+  }
+
+  val handlers = Array[(String, Handler)](
+    ("/static", createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR)),
+    ("/app/json", (request: HttpServletRequest) => applicationPage.renderJson(request)),
+    ("/app", (request: HttpServletRequest) => applicationPage.render(request)),
+    ("*", (request: HttpServletRequest) => indexPage.render(request))
+  )
+
+  def stop() {
+    server.foreach(_.stop())
+  }
+}
+
+private[spark] object MasterWebUI {
+  val STATIC_RESOURCE_DIR = "spark/ui/static"
+  val DEFAULT_PORT = "8080"
+}
diff --git a/core/src/main/scala/spark/deploy/worker/Worker.scala b/core/src/main/scala/spark/deploy/worker/Worker.scala
index b5dfd16e67073bef5aa70cb0a1b215ade5c740eb..7c1871e047ea46adfcadd3fe63c2c435588f0daf 100644
--- a/core/src/main/scala/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/spark/deploy/worker/Worker.scala
@@ -14,7 +14,7 @@ import spark.deploy.LaunchExecutor
 import spark.deploy.RegisterWorkerFailed
 import spark.deploy.master.Master
 import java.io.File
-
+import ui.WorkerWebUI
 
 private[spark] class Worker(
     host: String,
@@ -45,6 +45,7 @@ private[spark] class Worker(
     val envVar = System.getenv("SPARK_PUBLIC_DNS")
     if (envVar != null) envVar else host
   }
+  var webUi: WorkerWebUI = null
 
   var coresUsed = 0
   var memoryUsed = 0
@@ -76,35 +77,26 @@ private[spark] class Worker(
     sparkHome = new File(Option(System.getenv("SPARK_HOME")).getOrElse("."))
     logInfo("Spark home: " + sparkHome)
     createWorkDir()
+    webUi = new WorkerWebUI(self, workDir, Some(webUiPort))
+    webUi.start()
     connectToMaster()
-    startWebUi()
   }
 
   def connectToMaster() {
     logInfo("Connecting to master " + masterUrl)
     master = context.actorFor(Master.toAkkaUrl(masterUrl))
-    master ! RegisterWorker(workerId, host, port, cores, memory, webUiPort, publicAddress)
+    master ! RegisterWorker(workerId, host, port, cores, memory, webUi.boundPort.get, publicAddress)
     context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent])
     context.watch(master) // Doesn't work with remote actors, but useful for testing
   }
 
-  def startWebUi() {
-    val webUi = new WorkerWebUI(self, workDir)
-    try {
-      AkkaUtils.startSprayServer(context.system, "0.0.0.0", webUiPort, webUi.handler)
-    } catch {
-      case e: Exception =>
-        logError("Failed to create web UI", e)
-        System.exit(1)
-    }
-  }
+  import context.dispatcher
 
   override def receive = {
     case RegisteredWorker(url) =>
       masterWebUiUrl = url
       logInfo("Successfully registered with master")
-      import context.dispatcher
-      context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis) {
+        context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis) {
         master ! Heartbeat(workerId)
       }
 
@@ -170,6 +162,7 @@ private[spark] class Worker(
 
   override def postStop() {
     executors.values.foreach(_.kill())
+    webUi.stop()
   }
 }
 
diff --git a/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala
deleted file mode 100644
index cc2ab6187aaf933a388ae011588a6f7995485f1a..0000000000000000000000000000000000000000
--- a/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala
+++ /dev/null
@@ -1,61 +0,0 @@
-package spark.deploy.worker
-
-import akka.actor.{ActorRef, ActorContext}
-import scala.concurrent.Await
-import akka.pattern.ask
-
-import akka.util.Timeout
-import scala.concurrent.duration._
-import spray.routing.Directives
-import spray.httpx.TwirlSupport._
-import spray.httpx.SprayJsonSupport._
-import spray.http.MediaTypes._
-
-import spark.deploy.{WorkerState, RequestWorkerState}
-import spark.deploy.JsonProtocol._
-import java.io.File
-
-/**
- * Web UI server for the standalone worker.
- */
-private[spark]
-class WorkerWebUI(worker: ActorRef, workDir: File)(implicit val context: ActorContext) extends Directives {
-  import context.dispatcher
-
-  val actorSystem         = context.system
-  val RESOURCE_DIR        = "spark/deploy/worker/webui"
-  val STATIC_RESOURCE_DIR = "spark/deploy/static"
-
-  implicit val timeout = Timeout(Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds"))
-
-  val handler = {
-    get {
-      (path("")  & parameters('format ?)) {
-        case Some(js) if js.equalsIgnoreCase("json") => {
-          val future = (worker ? RequestWorkerState).mapTo[WorkerState]
-          respondWithMediaType(`application/json`) { ctx =>
-            ctx.complete(future)
-          }
-        }
-        case _ =>
-          complete {
-            val future = (worker ? RequestWorkerState).mapTo[WorkerState]
-            future.map { workerState =>
-              spark.deploy.worker.html.index(workerState)
-            }
-          }
-      } ~
-      path("log") {
-        parameters("appId", "executorId", "logType") { (appId, executorId, logType) =>
-          respondWithMediaType(`text/plain`) {
-            getFromFile(workDir.getPath() + "/" + appId + "/" + executorId + "/" + logType)
-          }
-        }
-      } ~
-      pathPrefix("static") {
-        getFromResourceDirectory(STATIC_RESOURCE_DIR)
-      } ~
-      getFromResourceDirectory(RESOURCE_DIR)
-    }
-  }
-}
diff --git a/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala
new file mode 100644
index 0000000000000000000000000000000000000000..8cb74632aacee84d53401d4e80ec9c7fc2e90edd
--- /dev/null
+++ b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala
@@ -0,0 +1,100 @@
+package spark.deploy.worker.ui
+
+import scala.concurrent.duration._
+import scala.concurrent.Await
+
+import akka.pattern.ask
+
+import javax.servlet.http.HttpServletRequest
+
+import net.liftweb.json.JsonAST.JValue
+
+import scala.xml.Node
+
+import spark.deploy.{RequestWorkerState, JsonProtocol, WorkerState}
+import spark.deploy.worker.ExecutorRunner
+import spark.Utils
+import spark.ui.UIUtils
+
+private[spark] class IndexPage(parent: WorkerWebUI) {
+  val worker = parent.worker
+  val timeout = parent.timeout
+
+  def renderJson(request: HttpServletRequest): JValue = {
+    val stateFuture = (worker ? RequestWorkerState)(timeout).mapTo[WorkerState]
+    val workerState = Await.result(stateFuture, 30 seconds)
+    JsonProtocol.writeWorkerState(workerState)
+  }
+
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val stateFuture = (worker ? RequestWorkerState)(timeout).mapTo[WorkerState]
+    val workerState = Await.result(stateFuture, 30 seconds)
+
+    val executorHeaders = Seq("ExecutorID", "Cores", "Memory", "Job Details", "Logs")
+    val runningExecutorTable =
+      UIUtils.listingTable(executorHeaders, executorRow, workerState.executors)
+    val finishedExecutorTable =
+      UIUtils.listingTable(executorHeaders, executorRow, workerState.finishedExecutors)
+
+    val content =
+        <hr />
+        <div class="row"> <!-- Worker Details -->
+          <div class="span12">
+            <ul class="unstyled">
+              <li><strong>ID:</strong> {workerState.workerId}</li>
+              <li><strong>
+                Master URL:</strong> {workerState.masterUrl}
+              </li>
+              <li><strong>Cores:</strong> {workerState.cores} ({workerState.coresUsed} Used)</li>
+              <li><strong>Memory:</strong> {Utils.memoryMegabytesToString(workerState.memory)}
+                ({Utils.memoryMegabytesToString(workerState.memoryUsed)} Used)</li>
+            </ul>
+            <p><a href={workerState.masterWebUiUrl}>Back to Master</a></p>
+          </div>
+        </div>
+          <hr/>
+
+        <div class="row"> <!-- Running Executors -->
+          <div class="span12">
+            <h3> Running Executors {workerState.executors.size} </h3>
+            <br/>
+            {runningExecutorTable}
+          </div>
+        </div>
+          <hr/>
+
+        <div class="row"> <!-- Finished Executors  -->
+          <div class="span12">
+            <h3> Finished Executors </h3>
+            <br/>
+            {finishedExecutorTable}
+          </div>
+        </div>;
+
+    UIUtils.basicSparkPage(content, "Spark Worker on %s:%s".format(workerState.host, workerState.port))
+  }
+
+  def executorRow(executor: ExecutorRunner): Seq[Node] = {
+    <tr>
+      <td>{executor.execId}</td>
+      <td>{executor.cores}</td>
+      <td sorttable_customkey={executor.memory.toString}>
+        {Utils.memoryMegabytesToString(executor.memory)}
+      </td>
+      <td>
+        <ul class="unstyled">
+          <li><strong>ID:</strong> {executor.appId}</li>
+          <li><strong>Name:</strong> {executor.appDesc.name}</li>
+          <li><strong>User:</strong> {executor.appDesc.user}</li>
+        </ul>
+      </td>
+      <td>
+        <a href={"log?appId=%s&executorId=%s&logType=stdout"
+          .format(executor.appId, executor.execId)}>stdout</a>
+        <a href={"log?appId=%s&executorId=%s&logType=stderr"
+          .format(executor.appId, executor.execId)}>stderr</a>
+      </td>
+    </tr>
+  }
+
+}
diff --git a/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala
new file mode 100644
index 0000000000000000000000000000000000000000..b1336dd1af9e9faf24013b5b26fe7cce8d2dfb37
--- /dev/null
+++ b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala
@@ -0,0 +1,78 @@
+package spark.deploy.worker.ui
+
+import akka.actor.ActorRef
+import akka.util.Timeout
+
+import scala.concurrent.duration._
+
+import java.io.{FileInputStream, File}
+
+import javax.servlet.http.HttpServletRequest
+
+import org.eclipse.jetty.server.{Handler, Server}
+
+import spark.{Utils, Logging}
+import spark.ui.JettyUtils
+import spark.ui.JettyUtils._
+
+/**
+ * Web UI server for the standalone worker.
+ */
+private[spark]
+class WorkerWebUI(val worker: ActorRef, val workDir: File, requestedPort: Option[Int] = None)
+    extends Logging {
+  implicit val timeout = Timeout(
+    Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds"))
+  val host = Utils.localHostName()
+  val port = requestedPort.getOrElse(
+    System.getProperty("worker.ui.port", WorkerWebUI.DEFAULT_PORT).toInt)
+
+  var server: Option[Server] = None
+  var boundPort: Option[Int] = None
+
+  val indexPage = new IndexPage(this)
+
+  val handlers = Array[(String, Handler)](
+    ("/static", createStaticHandler(WorkerWebUI.STATIC_RESOURCE_DIR)),
+    ("/log", (request: HttpServletRequest) => log(request)),
+    ("/json", (request: HttpServletRequest) => indexPage.renderJson(request)),
+    ("*", (request: HttpServletRequest) => indexPage.render(request))
+  )
+
+  def start() {
+    try {
+      val (srv, bPort) = JettyUtils.startJettyServer("0.0.0.0", port, handlers)
+      server = Some(srv)
+      boundPort = Some(bPort)
+      logInfo("Started Worker web UI at http://%s:%d".format(host, bPort))
+    } catch {
+      case e: Exception =>
+        logError("Failed to create Worker JettyUtils", e)
+        System.exit(1)
+    }
+  }
+
+  def log(request: HttpServletRequest): String = {
+    val appId = request.getParameter("appId")
+    val executorId = request.getParameter("executorId")
+    val logType = request.getParameter("logType")
+
+    val maxBytes = 1024 * 1024 // Guard against OOM
+    val defaultBytes = 100 * 1024
+    val numBytes = Option(request.getParameter("numBytes"))
+      .flatMap(s => Some(s.toInt)).getOrElse(defaultBytes)
+
+    val path = "%s/%s/%s/%s".format(workDir.getPath, appId, executorId, logType)
+    val pre = "==== Last %s bytes of %s/%s/%s ====\n".format(numBytes, appId, executorId, logType)
+    pre + Utils.lastNBytes(path, math.min(numBytes, maxBytes))
+  }
+
+  def stop() {
+    server.foreach(_.stop())
+  }
+}
+
+private[spark] object WorkerWebUI {
+  val STATIC_RESOURCE_DIR = "spark/ui/static"
+  val DEFAULT_PORT="8081"
+}
diff --git a/core/src/main/scala/spark/executor/Executor.scala b/core/src/main/scala/spark/executor/Executor.scala
index 2bf55ea9a90851506b822adfa0c997e88d7d5e8a..8360547a7452fc65a92d514ef709a17e3c40111d 100644
--- a/core/src/main/scala/spark/executor/Executor.scala
+++ b/core/src/main/scala/spark/executor/Executor.scala
@@ -93,15 +93,18 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert
       val ser = SparkEnv.get.closureSerializer.newInstance()
       logInfo("Running task ID " + taskId)
       context.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
+      var attemptedTask: Option[Task[Any]] = None
+      var taskStart: Long = 0
       try {
         SparkEnv.set(env)
         Accumulators.clear()
         val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask)
         updateDependencies(taskFiles, taskJars)
         val task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader)
+        attemptedTask = Some(task)
         logInfo("Its generation is " + task.generation)
         env.mapOutputTracker.updateGeneration(task.generation)
-        val taskStart = System.currentTimeMillis()
+        taskStart = System.currentTimeMillis()
         val value = task.run(taskId.toInt)
         val taskFinish = System.currentTimeMillis()
         task.metrics.foreach{ m =>
@@ -129,7 +132,10 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert
         }
 
         case t: Throwable => {
-          val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace)
+          val serviceTime = (System.currentTimeMillis() - taskStart).toInt
+          val metrics = attemptedTask.flatMap(t => t.metrics)
+          metrics.foreach{m => m.executorRunTime = serviceTime}
+          val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics)
           context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))
 
           // TODO: Should we exit the whole executor here? On the one hand, the failed task may
diff --git a/core/src/main/scala/spark/rdd/HadoopRDD.scala b/core/src/main/scala/spark/rdd/HadoopRDD.scala
index cbf5512e247a46773294a428b8f2d6f9d5034876..07c103503c87bb1d82ba4df3c65b03dcd21649d4 100644
--- a/core/src/main/scala/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/spark/rdd/HadoopRDD.scala
@@ -15,6 +15,7 @@ import org.apache.hadoop.mapred.RecordReader
 import org.apache.hadoop.mapred.Reporter
 import org.apache.hadoop.util.ReflectionUtils
 
+import spark.deploy.SparkHadoopUtil
 import spark.{Dependency, Logging, Partition, RDD, SerializableWritable, SparkContext, TaskContext}
 import spark.util.NextIterator
 import org.apache.hadoop.conf.Configurable
@@ -50,6 +51,7 @@ class HadoopRDD[K, V](
   private val confBroadcast = sc.broadcast(new SerializableWritable(conf))
 
   override def getPartitions: Array[Partition] = {
+    SparkHadoopUtil.addCredentials(conf);
     val inputFormat = createInputFormat(conf)
     if (inputFormat.isInstanceOf[Configurable]) {
       inputFormat.asInstanceOf[Configurable].setConf(conf)
diff --git a/core/src/main/scala/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/spark/scheduler/DAGScheduler.scala
index 1164c40c43ce9268c0da2613046f4f8dfeab38f0..d4bda150cb53ff9ef7f27fc10729fa58284f0e7e 100644
--- a/core/src/main/scala/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/spark/scheduler/DAGScheduler.scala
@@ -257,7 +257,7 @@ class DAGScheduler(
     eventQueue.put(toSubmit)
     waiter.awaitResult() match {
       case JobSucceeded => {}
-      case JobFailed(exception: Exception) =>
+      case JobFailed(exception: Exception, _) =>
         logInfo("Failed to run " + callSite)
         throw exception
     }
@@ -313,7 +313,7 @@ class DAGScheduler(
         handleExecutorLost(execId)
 
       case completion: CompletionEvent =>
-        sparkListeners.foreach(_.onTaskEnd(SparkListenerTaskEnd(completion.task, 
+        sparkListeners.foreach(_.onTaskEnd(SparkListenerTaskEnd(completion.task,
                                completion.reason, completion.taskInfo, completion.taskMetrics)))
         handleTaskCompletion(completion)
 
@@ -325,7 +325,7 @@ class DAGScheduler(
         for (job <- activeJobs) {
           val error = new SparkException("Job cancelled because SparkContext was shut down")
           job.listener.jobFailed(error)
-          sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error))))
+          sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error, None))))
         }
         return true
     }
@@ -504,6 +504,7 @@ class DAGScheduler(
         case _ => "Unkown"
       }
       logInfo("%s (%s) finished in %s s".format(stage, stage.origin, serviceTime))
+      stage.completionTime = Some(System.currentTimeMillis)
       val stageComp = StageCompleted(stageToInfos(stage))
       sparkListeners.foreach{_.onStageCompleted(stageComp)}
       running -= stage
@@ -525,6 +526,7 @@ class DAGScheduler(
                   job.numFinished += 1
                   // If the whole job has finished, remove it
                   if (job.numFinished == job.numPartitions) {
+                    idToActiveJob -= stage.priority
                     activeJobs -= job
                     resultStageToJob -= stage
                     markStageAsFinished(stage)
@@ -618,8 +620,11 @@ class DAGScheduler(
           handleExecutorLost(bmAddress.executorId, Some(task.generation))
         }
 
+      case ExceptionFailure(className, description, stackTrace, metrics) =>
+        // Do nothing here, left up to the TaskScheduler to decide how to handle user failures
+
       case other =>
-        // Non-fetch failure -- probably a bug in user code; abort all jobs depending on this stage
+        // Unrecognized failure - abort all jobs depending on this stage
         abortStage(idToStage(task.stageId), task + " failed: " + other)
     }
   }
@@ -652,7 +657,7 @@ class DAGScheduler(
                "(generation " + currentGeneration + ")")
     }
   }
-  
+
   private def handleExecutorGained(execId: String, hostPort: String) {
     // remove from failedGeneration(execId) ?
     if (failedGeneration.contains(execId)) {
@@ -667,11 +672,13 @@ class DAGScheduler(
    */
   private def abortStage(failedStage: Stage, reason: String) {
     val dependentStages = resultStageToJob.keys.filter(x => stageDependsOn(x, failedStage)).toSeq
+    failedStage.completionTime = Some(System.currentTimeMillis())
     for (resultStage <- dependentStages) {
       val job = resultStageToJob(resultStage)
       val error = new SparkException("Job failed: " + reason)
       job.listener.jobFailed(error)
-      sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error))))
+      sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage)))))
+      idToActiveJob -= resultStage.priority
       activeJobs -= job
       resultStageToJob -= resultStage
     }
@@ -748,6 +755,10 @@ class DAGScheduler(
     sizeBefore = pendingTasks.size
     pendingTasks.clearOldValues(cleanupTime)
     logInfo("pendingTasks " + sizeBefore + " --> " + pendingTasks.size)
+
+    sizeBefore = stageToInfos.size
+    stageToInfos.clearOldValues(cleanupTime)
+    logInfo("stageToInfos " + sizeBefore + " --> " + stageToInfos.size)
   }
 
   def stop() {
diff --git a/core/src/main/scala/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/spark/scheduler/InputFormatInfo.scala
index 287f731787f1b184d4e2781dbd28e1f4463f5866..17d0ea4f80f6401299cf153d35f81a95485f0b35 100644
--- a/core/src/main/scala/spark/scheduler/InputFormatInfo.scala
+++ b/core/src/main/scala/spark/scheduler/InputFormatInfo.scala
@@ -3,11 +3,13 @@ package spark.scheduler
 import spark.Logging
 import scala.collection.immutable.Set
 import org.apache.hadoop.mapred.{FileInputFormat, JobConf}
+import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hadoop.util.ReflectionUtils
 import org.apache.hadoop.mapreduce.Job
 import org.apache.hadoop.conf.Configuration
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 import scala.collection.JavaConversions._
+import spark.deploy.SparkHadoopUtil
 
 
 /**
@@ -70,6 +72,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl
   // This method does not expect failures, since validate has already passed ...
   private def prefLocsFromMapreduceInputFormat(): Set[SplitInfo] = {
     val conf = new JobConf(configuration)
+    SparkHadoopUtil.addCredentials(conf);
     FileInputFormat.setInputPaths(conf, path)
 
     val instance: org.apache.hadoop.mapreduce.InputFormat[_, _] =
@@ -89,6 +92,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl
   // This method does not expect failures, since validate has already passed ...
   private def prefLocsFromMapredInputFormat(): Set[SplitInfo] = {
     val jobConf = new JobConf(configuration)
+    SparkHadoopUtil.addCredentials(jobConf);
     FileInputFormat.setInputPaths(jobConf, path)
 
     val instance: org.apache.hadoop.mapred.InputFormat[_, _] =
diff --git a/core/src/main/scala/spark/scheduler/JobLogger.scala b/core/src/main/scala/spark/scheduler/JobLogger.scala
index 178bfaba3d923de7f116656e65902f7d5fa3ee9e..6a9d52f35615a0a5a7f2c274232d583cc1eb5eb4 100644
--- a/core/src/main/scala/spark/scheduler/JobLogger.scala
+++ b/core/src/main/scala/spark/scheduler/JobLogger.scala
@@ -275,7 +275,7 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging {
     var info = "JOB_ID=" + job.runId
     reason match {
       case JobSucceeded => info += " STATUS=SUCCESS"
-      case JobFailed(exception) =>
+      case JobFailed(exception, _) =>
         info += " STATUS=FAILED REASON="
         exception.getMessage.split("\\s+").foreach(info += _ + "_")
       case _ =>
diff --git a/core/src/main/scala/spark/scheduler/JobResult.scala b/core/src/main/scala/spark/scheduler/JobResult.scala
index 654131ee8417b209f735074856da271b04414233..a0fdf391e6814dd3ab8ea4b286230571e202d5e1 100644
--- a/core/src/main/scala/spark/scheduler/JobResult.scala
+++ b/core/src/main/scala/spark/scheduler/JobResult.scala
@@ -6,4 +6,4 @@ package spark.scheduler
 private[spark] sealed trait JobResult
 
 private[spark] case object JobSucceeded extends JobResult
-private[spark] case class JobFailed(exception: Exception) extends JobResult
+private[spark] case class JobFailed(exception: Exception, failedStage: Option[Stage]) extends JobResult
diff --git a/core/src/main/scala/spark/scheduler/JobWaiter.scala b/core/src/main/scala/spark/scheduler/JobWaiter.scala
index 3cc6a86345fd195828b999f7b33b7c9c31e7d111..6ff2e2943457008b6d0fb98f192576501e9853a7 100644
--- a/core/src/main/scala/spark/scheduler/JobWaiter.scala
+++ b/core/src/main/scala/spark/scheduler/JobWaiter.scala
@@ -35,7 +35,7 @@ private[spark] class JobWaiter[T](totalTasks: Int, resultHandler: (Int, T) => Un
         throw new UnsupportedOperationException("jobFailed() called on a finished JobWaiter")
       }
       jobFinished = true
-      jobResult = JobFailed(exception)
+      jobResult = JobFailed(exception, None)
       this.notifyAll()
     }
   }
diff --git a/core/src/main/scala/spark/scheduler/SparkListener.scala b/core/src/main/scala/spark/scheduler/SparkListener.scala
index bac984b5c942cc9bc13c2c8db18ae07fede6ac55..8de3aa91a4d9938dc909a967b7f37639c0b5c4cc 100644
--- a/core/src/main/scala/spark/scheduler/SparkListener.scala
+++ b/core/src/main/scala/spark/scheduler/SparkListener.scala
@@ -68,6 +68,7 @@ class StatsReportListener extends SparkListener with Logging {
     showBytesDistribution("task result size:", (_, metric) => Some(metric.resultSize))
 
     //runtime breakdown
+
     val runtimePcts = stageCompleted.stageInfo.taskInfos.map{
       case (info, metrics) => RuntimePercentage(info.duration, metrics)
     }
diff --git a/core/src/main/scala/spark/scheduler/Stage.scala b/core/src/main/scala/spark/scheduler/Stage.scala
index 7fc9e13fd9dc42e1c459b19167a7bf0cd06f3278..539cf8233bfe12aed9c2781b41d0df2684c7fa13 100644
--- a/core/src/main/scala/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/spark/scheduler/Stage.scala
@@ -34,6 +34,7 @@ private[spark] class Stage(
 
   /** When first task was submitted to scheduler. */
   var submissionTime: Option[Long] = None
+  var completionTime: Option[Long] = None
 
   private var nextAttemptId = 0
 
diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala
index d72b0bfc9f53bb8ba2898174f3b04d616bfa2663..fe6420a522cecd89083f8c0eed57831c217964bc 100644
--- a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala
@@ -571,6 +571,7 @@ private[spark] class ClusterTaskSetManager(
             return
 
           case ef: ExceptionFailure =>
+            sched.listener.taskEnded(tasks(index), ef, null, null, info, ef.metrics.getOrElse(null))
             val key = ef.description
             val now = System.currentTimeMillis
             val (printFull, dupCount) = {
diff --git a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala
index 93d4318b2990bb1fff9e6b021ce64d76ee6bdae0..b000e328e678472dc5b05497c651b1c897b52260 100644
--- a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala
+++ b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala
@@ -145,6 +145,9 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc:
     // Set the Spark execution environment for the worker thread
     SparkEnv.set(env)
     val ser = SparkEnv.get.closureSerializer.newInstance()
+    var attemptedTask: Option[Task[_]] = None
+    val start = System.currentTimeMillis()
+    var taskStart: Long = 0
     try {
       Accumulators.clear()
       Thread.currentThread().setContextClassLoader(classLoader)
@@ -153,10 +156,11 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc:
       // this adds a bit of unnecessary overhead but matches how the Mesos Executor works.
       val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(bytes)
       updateDependencies(taskFiles, taskJars)   // Download any files added with addFile
-      val deserStart = System.currentTimeMillis()
       val deserializedTask = ser.deserialize[Task[_]](
         taskBytes, Thread.currentThread.getContextClassLoader)
-      val deserTime = System.currentTimeMillis() - deserStart
+      attemptedTask = Some(deserializedTask)
+      val deserTime = System.currentTimeMillis() - start
+      taskStart = System.currentTimeMillis()
 
       // Run it
       val result: Any = deserializedTask.run(taskId)
@@ -170,16 +174,19 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc:
       val resultToReturn = ser.deserialize[Any](serResult)
       val accumUpdates = ser.deserialize[collection.mutable.Map[Long, Any]](
         ser.serialize(Accumulators.values))
+      val serviceTime = System.currentTimeMillis() - taskStart
       logInfo("Finished " + taskId)
-      deserializedTask.metrics.get.executorRunTime = deserTime.toInt//info.duration.toInt  //close enough
+      deserializedTask.metrics.get.executorRunTime = serviceTime.toInt
       deserializedTask.metrics.get.executorDeserializeTime = deserTime.toInt
-
       val taskResult = new TaskResult(result, accumUpdates, deserializedTask.metrics.getOrElse(null))
       val serializedResult = ser.serialize(taskResult)
       localActor ! LocalStatusUpdate(taskId, TaskState.FINISHED, serializedResult)
     } catch {
       case t: Throwable => {
-        val failure = new ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace)
+        val serviceTime = System.currentTimeMillis() - taskStart
+        val metrics = attemptedTask.flatMap(t => t.metrics)
+        metrics.foreach{m => m.executorRunTime = serviceTime.toInt}
+        val failure = new ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics)
         localActor ! LocalStatusUpdate(taskId, TaskState.FAILED, ser.serialize(failure))
       }
     }
diff --git a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala
index 70b69bb26feabd53eb4ed7f0340a3e0b75158b09..f12fec41d506bf478cc19552d4232717a96afaa6 100644
--- a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala
+++ b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala
@@ -152,6 +152,7 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas
     info.markFailed()
     decreaseRunningTasks(1)
     val reason: ExceptionFailure = ser.deserialize[ExceptionFailure](serializedData, getClass.getClassLoader)
+    sched.listener.taskEnded(task, reason, null, null, info, reason.metrics.getOrElse(null))
     if (!finished(index)) {
       copiesRunning(index) -= 1
       numFailures(index) += 1
diff --git a/core/src/main/scala/spark/storage/BlockManagerUI.scala b/core/src/main/scala/spark/storage/BlockManagerUI.scala
deleted file mode 100644
index 631455abcd008e62cf6587aa66a915efd06ba9c5..0000000000000000000000000000000000000000
--- a/core/src/main/scala/spark/storage/BlockManagerUI.scala
+++ /dev/null
@@ -1,82 +0,0 @@
-package spark.storage
-
-import akka.actor.{ActorRef, ActorSystem}
-
-import akka.util.Timeout
-import scala.concurrent.duration._
-import spray.httpx.TwirlSupport._
-import spray.routing.Directives
-
-import spark.{Logging, SparkContext}
-import spark.util.AkkaUtils
-import spark.Utils
-
-
-/**
- * Web UI server for the BlockManager inside each SparkContext.
- */
-private[spark]
-class BlockManagerUI(val actorSystem: ActorSystem, blockManagerMaster: ActorRef, sc: SparkContext)
-  extends Directives with Logging {
-
-  implicit val implicitActorSystem = actorSystem
-  val STATIC_RESOURCE_DIR          = "spark/deploy/static"
-
-  implicit val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")
-  val host = Utils.localHostName()
-  val port = if (System.getProperty("spark.ui.port") != null) {
-    System.getProperty("spark.ui.port").toInt
-  } else {
-    // TODO: Unfortunately, it's not possible to pass port 0 to spray and figure out which
-    // random port it bound to, so we have to try to find a local one by creating a socket.
-    Utils.findFreePort()
-  }
-
-  /** Start a HTTP server to run the Web interface */
-  def start() {
-    try {
-      AkkaUtils.startSprayServer(actorSystem, "0.0.0.0", port, handler, "BlockManagerHTTPServer")
-      logInfo("Started BlockManager web UI at http://%s:%d".format(host, port))
-    } catch {
-      case e: Exception =>
-        logError("Failed to create BlockManager WebUI", e)
-        System.exit(1)
-    }
-  }
-
-  val handler = {
-    get {
-      path("") {
-        complete {
-          // Request the current storage status from the Master
-          val storageStatusList = sc.getExecutorStorageStatus
-          // Calculate macro-level statistics
-          val maxMem = storageStatusList.map(_.maxMem).reduce(_+_)
-          val remainingMem = storageStatusList.map(_.memRemaining).reduce(_+_)
-          val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize))
-            .reduceOption(_+_).getOrElse(0L)
-          val rdds = StorageUtils.rddInfoFromStorageStatus(storageStatusList, sc)
-          spark.storage.html.index.
-            render(maxMem, remainingMem, diskSpaceUsed, rdds, storageStatusList)
-        }
-      } ~
-      path("rdd") {
-        parameter("id") { id =>
-          complete {
-            val prefix = "rdd_" + id.toString
-            val storageStatusList = sc.getExecutorStorageStatus
-            val filteredStorageStatusList = StorageUtils.
-              filterStorageStatusByPrefix(storageStatusList, prefix)
-            val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head
-            spark.storage.html.rdd.render(rddInfo, filteredStorageStatusList)
-          }
-        }
-      } ~
-      pathPrefix("static") {
-        getFromResourceDirectory(STATIC_RESOURCE_DIR)
-      }
-    }
-  }
-
-  private[spark] def appUIAddress = "http://" + host + ":" + port
-}
diff --git a/core/src/main/scala/spark/ui/JettyUtils.scala b/core/src/main/scala/spark/ui/JettyUtils.scala
new file mode 100644
index 0000000000000000000000000000000000000000..bc6f9c10d5a71a1c93361c98e3ef2695c8f0512d
--- /dev/null
+++ b/core/src/main/scala/spark/ui/JettyUtils.scala
@@ -0,0 +1,115 @@
+package spark.ui
+
+import annotation.tailrec
+
+import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
+
+import net.liftweb.json.{JValue, pretty, render}
+
+import org.eclipse.jetty.server.{Server, Request, Handler}
+import org.eclipse.jetty.server.handler.{ResourceHandler, HandlerList, ContextHandler, AbstractHandler}
+import org.eclipse.jetty.util.thread.QueuedThreadPool
+
+import scala.util.{Try, Success, Failure}
+import scala.xml.Node
+
+import spark.Logging
+
+/** Utilities for launching a web server using Jetty's HTTP Server class */
+private[spark] object JettyUtils extends Logging {
+  // Base type for a function that returns something based on an HTTP request. Allows for
+  // implicit conversion from many types of functions to jetty Handlers.
+  type Responder[T] = HttpServletRequest => T
+
+  // Conversions from various types of Responder's to jetty Handlers
+  implicit def jsonResponderToHandler(responder: Responder[JValue]): Handler =
+    createHandler(responder, "text/json", (in: JValue) => pretty(render(in)))
+
+  implicit def htmlResponderToHandler(responder: Responder[Seq[Node]]): Handler =
+    createHandler(responder, "text/html", (in: Seq[Node]) => "<!DOCTYPE html>" + in.toString)
+
+  implicit def textResponderToHandler(responder: Responder[String]): Handler =
+    createHandler(responder, "text/plain")
+
+  private def createHandler[T <% AnyRef](responder: Responder[T], contentType: String,
+                                 extractFn: T => String = (in: Any) => in.toString): Handler = {
+    new AbstractHandler {
+      def handle(target: String,
+                 baseRequest: Request,
+                 request: HttpServletRequest,
+                 response: HttpServletResponse) {
+        response.setContentType("%s;charset=utf-8".format(contentType))
+        response.setStatus(HttpServletResponse.SC_OK)
+        baseRequest.setHandled(true)
+        val result = responder(request)
+        response.setHeader("Cache-Control", "no-cache, no-store, must-revalidate")
+        response.getWriter().println(extractFn(result))
+      }
+    }
+  }
+
+  /** Creates a handler that always redirects the user to a given path */
+  def createRedirectHandler(newPath: String): Handler = {
+    new AbstractHandler {
+      def handle(target: String,
+                 baseRequest: Request,
+                 request: HttpServletRequest,
+                 response: HttpServletResponse) {
+        response.setStatus(302)
+        response.setHeader("Location", baseRequest.getRootURL + newPath)
+        baseRequest.setHandled(true)
+      }
+    }
+  }
+
+  /** Creates a handler for serving files from a static directory */
+  def createStaticHandler(resourceBase: String): ResourceHandler = {
+    val staticHandler = new ResourceHandler
+    Option(getClass.getClassLoader.getResource(resourceBase)) match {
+      case Some(res) =>
+        staticHandler.setResourceBase(res.toString)
+      case None =>
+        throw new Exception("Could not find resource path for Web UI: " + resourceBase)
+    }
+    staticHandler
+  }
+
+  /**
+   * Attempts to start a Jetty server at the supplied ip:port which uses the supplied handlers.
+   *
+   * If the desired port number is contented, continues incrementing ports until a free port is
+   * found. Returns the chosen port and the jetty Server object.
+   */
+  def startJettyServer(ip: String, port: Int, handlers: Seq[(String, Handler)]): (Server, Int) = {
+    val handlersToRegister = handlers.map { case(path, handler) =>
+      val contextHandler = new ContextHandler(path)
+      contextHandler.setHandler(handler)
+      contextHandler.asInstanceOf[org.eclipse.jetty.server.Handler]
+    }
+
+    val handlerList = new HandlerList
+    handlerList.setHandlers(handlersToRegister.toArray)
+
+    @tailrec
+    def connect(currentPort: Int): (Server, Int) = {
+      val server = new Server(currentPort)
+      val pool = new QueuedThreadPool
+      pool.setDaemon(true)
+      server.setThreadPool(pool)
+      server.setHandler(handlerList)
+
+      Try { server.start() } match {
+        case s: Success[_] =>
+          sys.addShutdownHook(server.stop()) // Be kind, un-bind
+          (server, server.getConnectors.head.getLocalPort)
+        case f: Failure[_] =>
+          server.stop()
+          logInfo("Failed to create UI at port, %s. Trying again.".format(currentPort))
+          logInfo("Error was: " + f.toString)
+          connect((currentPort + 1) % 65536)
+      }
+    }
+
+    connect(port)
+  }
+}
diff --git a/core/src/main/scala/spark/ui/Page.scala b/core/src/main/scala/spark/ui/Page.scala
new file mode 100644
index 0000000000000000000000000000000000000000..c853b44b76554235fb60b610e998fa0bd97af944
--- /dev/null
+++ b/core/src/main/scala/spark/ui/Page.scala
@@ -0,0 +1,3 @@
+package spark.ui
+
+private[spark] object Page extends Enumeration { val Storage, Jobs = Value }
\ No newline at end of file
diff --git a/core/src/main/scala/spark/ui/SparkUI.scala b/core/src/main/scala/spark/ui/SparkUI.scala
new file mode 100644
index 0000000000000000000000000000000000000000..b3bdc2c4902bad676b06ad24e54c53dc843bc13f
--- /dev/null
+++ b/core/src/main/scala/spark/ui/SparkUI.scala
@@ -0,0 +1,63 @@
+package spark.ui
+
+import javax.servlet.http.HttpServletRequest
+
+import org.eclipse.jetty.server.{Handler, Server}
+
+import spark.{Logging, SparkContext, Utils}
+import spark.ui.storage.BlockManagerUI
+import spark.ui.jobs.JobProgressUI
+import spark.ui.UIUtils._
+import spark.ui.JettyUtils._
+
+/** Top level user interface for Spark */
+private[spark] class SparkUI(sc: SparkContext) extends Logging {
+  // TODO(pwendell): It would be nice to add a view that prints out environment information
+
+  val host = Utils.localHostName()
+  val port = Option(System.getProperty("spark.ui.port")).getOrElse(SparkUI.DEFAULT_PORT).toInt
+  var boundPort: Option[Int] = None
+  var server: Option[Server] = None
+
+  val handlers = Seq[(String, Handler)](
+    ("/static", createStaticHandler(SparkUI.STATIC_RESOURCE_DIR)),
+    ("/", createRedirectHandler("/stages"))
+  )
+  val storage = new BlockManagerUI(sc)
+  val jobs = new JobProgressUI(sc)
+  val allHandlers = storage.getHandlers ++ jobs.getHandlers ++ handlers
+
+  /** Bind the HTTP server which backs this web interface */
+  def bind() {
+    try {
+      val (srv, usedPort) = JettyUtils.startJettyServer("0.0.0.0", port, allHandlers)
+      logInfo("Started Spark Web UI at http://%s:%d".format(host, usedPort))
+      server = Some(srv)
+      boundPort = Some(usedPort)
+    } catch {
+    case e: Exception =>
+      logError("Failed to create Spark JettyUtils", e)
+      System.exit(1)
+    }
+  }
+
+  /** Initialize all components of the server */
+  def start() {
+    // NOTE: This is decoupled from bind() because of the following dependency cycle:
+    //  DAGScheduler() requires that the port of this server is known
+    //  This server must register all handlers, including JobProgressUI, before binding
+    //  JobProgressUI registers a listener with SparkContext, which requires sc to initialize
+    jobs.start()
+  }
+
+  def stop() {
+    server.foreach(_.stop())
+  }
+
+  private[spark] def appUIAddress = "http://" + host + ":" + boundPort.getOrElse("-1")
+}
+
+private[spark] object SparkUI {
+  val DEFAULT_PORT = "33000"
+  val STATIC_RESOURCE_DIR = "spark/ui/static"
+}
diff --git a/core/src/main/scala/spark/ui/UIUtils.scala b/core/src/main/scala/spark/ui/UIUtils.scala
new file mode 100644
index 0000000000000000000000000000000000000000..7b79290d1bd15eb572cdf3022fe61e19dedf5ac9
--- /dev/null
+++ b/core/src/main/scala/spark/ui/UIUtils.scala
@@ -0,0 +1,113 @@
+package spark.ui
+
+import scala.xml.Node
+
+import spark.SparkContext
+
+/** Utility functions for generating XML pages with spark content. */
+private[spark] object UIUtils {
+  import Page._
+
+  /** Returns a spark page with correctly formatted headers */
+  def headerSparkPage(content: => Seq[Node], sc: SparkContext, title: String, page: Page.Value)
+  : Seq[Node] = {
+    val storage = page match {
+      case Storage => <li class="active"><a href="/storage">Storage</a></li>
+      case _ => <li><a href="/storage">Storage</a></li>
+    }
+    val jobs = page match {
+      case Jobs => <li class="active"><a href="/stages">Jobs</a></li>
+      case _ => <li><a href="/stages">Jobs</a></li>
+    }
+
+    <html>
+      <head>
+        <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
+        <link rel="stylesheet" href="/static/bootstrap.min.css" type="text/css" />
+        <link rel="stylesheet" href="/static/webui.css" type="text/css" />
+        <link rel="stylesheet" href="/static/bootstrap-responsive.min.css" type="text/css" />
+        <script src="/static/sorttable.js"></script>
+        <title>{title}</title>
+        <style type="text/css">
+          table.sortable thead {{ cursor: pointer; }}
+        </style>
+      </head>
+      <body>
+        <div class="container">
+
+          <div class="row">
+            <div class="span12">
+              <div class="navbar">
+                <div class="navbar-inner">
+                  <div class="container">
+                    <div class="brand"><img src="/static/spark-logo-77x50px-hd.png" /></div>
+                    <ul class="nav">
+                      {storage}
+                      {jobs}
+                    </ul>
+                    <ul id="infolist">
+                      <li>Application: <strong>{sc.appName}</strong></li>
+                      <li>Master: <strong>{sc.master}</strong></li>
+                      <li>Executors: <strong>{sc.getExecutorStorageStatus.size}</strong></li>
+                    </ul>
+                  </div>
+                </div>
+              </div>
+            </div>
+          </div>
+
+          <div class="row" style="padding-top: 5px;">
+            <div class="span12">
+              <h1 style="vertical-align: bottom; display: inline-block;">
+                {title}
+              </h1>
+            </div>
+          </div>
+          <hr/>
+          {content}
+        </div>
+      </body>
+    </html>
+  }
+
+  /** Returns a page with the spark css/js and a simple format. Used for scheduler UI. */
+  def basicSparkPage(content: => Seq[Node], title: String): Seq[Node] = {
+    <html>
+      <head>
+        <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
+        <link rel="stylesheet" href="/static/bootstrap.min.css" type="text/css" />
+        <link rel="stylesheet" href="/static/bootstrap-responsive.min.css" type="text/css" />
+        <script src="/static/sorttable.js"></script>
+        <title>{title}</title>
+        <style type="text/css">
+          table.sortable thead {{ cursor: pointer; }}
+        </style>
+      </head>
+      <body>
+        <div class="container">
+          <div class="row">
+            <div class="span2">
+              <img src="/static/spark_logo.png" />
+            </div>
+            <div class="span10">
+              <h1 style="vertical-align: bottom; margin-top: 40px; display: inline-block;">
+                {title}
+              </h1>
+            </div>
+          </div>
+          {content}
+        </div>
+      </body>
+    </html>
+  }
+
+  /** Returns an HTML table constructed by generating a row for each object in a sequence. */
+  def listingTable[T](headers: Seq[String], makeRow: T => Seq[Node], rows: Seq[T]): Seq[Node] = {
+    <table class="table table-bordered table-striped table-condensed sortable">
+      <thead>{headers.map(h => <th>{h}</th>)}</thead>
+      <tbody>
+        {rows.map(r => makeRow(r))}
+      </tbody>
+    </table>
+  }
+}
\ No newline at end of file
diff --git a/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala
new file mode 100644
index 0000000000000000000000000000000000000000..8bbc6ce88ea8c29ab9c3ea9794772230e3e2123d
--- /dev/null
+++ b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala
@@ -0,0 +1,71 @@
+package spark.ui
+
+import scala.util.Random
+
+import spark.SparkContext
+import spark.SparkContext._
+
+/**
+ * Continuously generates jobs that expose various features of the WebUI (internal testing tool).
+ *
+ * Usage: ./run spark.ui.UIWorkloadGenerator [master]
+ */
+private[spark] object UIWorkloadGenerator {
+  val NUM_PARTITIONS = 100
+  val INTER_JOB_WAIT_MS = 500
+
+  def main(args: Array[String]) {
+    val master = args(0)
+    val appName = "Spark UI Tester"
+    val sc = new SparkContext(master, appName)
+
+    // NOTE: Right now there is no easy way for us to show spark.job.annotation for a given phase,
+    //       but we pass it here anyways since it will be useful once we do.
+    def setName(s: String) = {
+      sc.addLocalProperties("spark.job.annotation", s)
+    }
+    val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS)
+    def nextFloat() = (new Random()).nextFloat()
+
+    val jobs = Seq[(String, () => Long)](
+      ("Count", baseData.count),
+      ("Cache and Count", baseData.map(x => x).cache.count),
+      ("Single Shuffle", baseData.map(x => (x % 10, x)).reduceByKey(_ + _).count),
+      ("Entirely failed phase", baseData.map(x => throw new Exception).count),
+      ("Partially failed phase", {
+        baseData.map{x =>
+          val probFailure = (4.0 / NUM_PARTITIONS)
+          if (nextFloat() < probFailure) {
+            throw new Exception("This is a task failure")
+          }
+          1
+        }.count
+      }),
+      ("Partially failed phase (longer tasks)", {
+        baseData.map{x =>
+          val probFailure = (4.0 / NUM_PARTITIONS)
+          if (nextFloat() < probFailure) {
+            Thread.sleep(100)
+            throw new Exception("This is a task failure")
+          }
+          1
+        }.count
+      }),
+      ("Job with delays", baseData.map(x => Thread.sleep(100)).count)
+    )
+
+    while (true) {
+      for ((desc, job) <- jobs) {
+        try {
+          setName(desc)
+          job()
+          println("Job funished: " + desc)
+        } catch {
+          case e: Exception =>
+            println("Job Failed: " + desc)
+        }
+        Thread.sleep(INTER_JOB_WAIT_MS)
+      }
+    }
+  }
+}
diff --git a/core/src/main/scala/spark/ui/jobs/IndexPage.scala b/core/src/main/scala/spark/ui/jobs/IndexPage.scala
new file mode 100644
index 0000000000000000000000000000000000000000..1e675ab2cb2cd4cb91784ee8fdfb30adff14b235
--- /dev/null
+++ b/core/src/main/scala/spark/ui/jobs/IndexPage.scala
@@ -0,0 +1,112 @@
+package spark.ui.jobs
+
+import java.util.Date
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.Some
+import scala.xml.{NodeSeq, Node}
+
+import spark.scheduler.Stage
+import spark.ui.UIUtils._
+import spark.ui.Page._
+import spark.storage.StorageLevel
+
+/** Page showing list of all ongoing and recently finished stages */
+private[spark] class IndexPage(parent: JobProgressUI) {
+  def listener = parent.listener
+  val dateFmt = parent.dateFmt
+
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val activeStages = listener.activeStages.toSeq
+    val completedStages = listener.completedStages.reverse.toSeq
+    val failedStages = listener.failedStages.reverse.toSeq
+
+    /** Special table which merges two header cells. */
+    def stageTable[T](makeRow: T => Seq[Node], rows: Seq[T]): Seq[Node] = {
+      <table class="table table-bordered table-striped table-condensed sortable">
+        <thead>
+          <th>Stage Id</th>
+          <th>Origin</th>
+          <th>Submitted</th>
+          <td>Duration</td>
+          <td colspan="2">Tasks: Complete/Total</td>
+          <td>Shuffle Activity</td>
+          <td>Stored RDD</td>
+        </thead>
+        <tbody>
+          {rows.map(r => makeRow(r))}
+        </tbody>
+      </table>
+    }
+
+    val activeStageTable: NodeSeq = stageTable(stageRow, activeStages)
+    val completedStageTable = stageTable(stageRow, completedStages)
+    val failedStageTable: NodeSeq = stageTable(stageRow, failedStages)
+
+    val content = <h2>Active Stages</h2> ++ activeStageTable ++
+                  <h2>Completed Stages</h2>  ++ completedStageTable ++
+                  <h2>Failed Stages</h2>  ++ failedStageTable
+
+    headerSparkPage(content, parent.sc, "Spark Stages", Jobs)
+  }
+
+  def getElapsedTime(submitted: Option[Long], completed: Long): String = {
+    submitted match {
+      case Some(t) => parent.formatDuration(completed - t)
+      case _ => "Unknown"
+    }
+  }
+
+  def makeProgressBar(completed: Int, total: Int): Seq[Node] = {
+    val width=130
+    val height=15
+    val completeWidth = (completed.toDouble / total) * width
+
+    <svg width={width.toString} height={height.toString}>
+      <rect width={width.toString} height={height.toString}
+            fill="white" stroke="rgb(51,51,51)" stroke-width="1" />
+      <rect width={completeWidth.toString} height={height.toString}
+            fill="rgb(0,136,204)" stroke="black" stroke-width="1" />
+    </svg>
+  }
+
+
+  def stageRow(s: Stage): Seq[Node] = {
+    val submissionTime = s.submissionTime match {
+      case Some(t) => dateFmt.format(new Date(t))
+      case None => "Unknown"
+    }
+    val (read, write) = (listener.hasShuffleRead(s.id), listener.hasShuffleWrite(s.id))
+    val shuffleInfo = (read, write) match {
+      case (true, true) => "Read/Write"
+      case (true, false) => "Read"
+      case (false, true) => "Write"
+      case _ => ""
+    }
+    val completedTasks = listener.stageToTasksComplete.getOrElse(s.id, 0)
+    val totalTasks = s.numPartitions
+
+    <tr>
+      <td>{s.id}</td>
+      <td><a href={"/stages/stage?id=%s".format(s.id)}>{s.origin}</a></td>
+      <td>{submissionTime}</td>
+      <td>{getElapsedTime(s.submissionTime,
+             s.completionTime.getOrElse(System.currentTimeMillis()))}</td>
+      <td class="progress-cell">{makeProgressBar(completedTasks, totalTasks)}</td>
+      <td style="border-left: 0; text-align: center;">{completedTasks} / {totalTasks}
+        {listener.stageToTasksFailed.getOrElse(s.id, 0) match {
+        case f if f > 0 => "(%s failed)".format(f)
+        case _ =>
+      }}
+      </td>
+      <td>{shuffleInfo}</td>
+      <td>{if (s.rdd.getStorageLevel != StorageLevel.NONE) {
+             <a href={"/storage/rdd?id=%s".format(s.rdd.id)}>
+               {Option(s.rdd.name).getOrElse(s.rdd.id)}
+             </a>
+          }}
+      </td>
+    </tr>
+  }
+}
diff --git a/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala
new file mode 100644
index 0000000000000000000000000000000000000000..c5d2aa5d60c82ae2f5868adcbaca7670b0061868
--- /dev/null
+++ b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala
@@ -0,0 +1,127 @@
+package spark.ui.jobs
+
+import scala.concurrent.duration._
+
+import java.text.SimpleDateFormat
+
+import javax.servlet.http.HttpServletRequest
+
+import org.eclipse.jetty.server.Handler
+
+import scala.Seq
+import scala.collection.mutable.{HashSet, ListBuffer, HashMap, ArrayBuffer}
+
+import spark.ui.JettyUtils._
+import spark.{ExceptionFailure, SparkContext, Success, Utils}
+import spark.scheduler._
+import spark.scheduler.cluster.TaskInfo
+import spark.executor.TaskMetrics
+import collection.mutable
+
+/** Web UI showing progress status of all jobs in the given SparkContext. */
+private[spark] class JobProgressUI(val sc: SparkContext) {
+  private var _listener: Option[JobProgressListener] = None
+  def listener = _listener.get
+  val dateFmt = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss")
+
+  private val indexPage = new IndexPage(this)
+  private val stagePage = new StagePage(this)
+
+  def start() {
+    _listener = Some(new JobProgressListener)
+    sc.addSparkListener(listener)
+  }
+
+  def formatDuration(ms: Long) = Utils.msDurationToString(ms)
+
+  def getHandlers = Seq[(String, Handler)](
+    ("/stages/stage", (request: HttpServletRequest) => stagePage.render(request)),
+    ("/stages", (request: HttpServletRequest) => indexPage.render(request))
+  )
+}
+
+private[spark] class JobProgressListener extends SparkListener {
+  // How many stages to remember
+  val RETAINED_STAGES = System.getProperty("spark.ui.retained_stages", "1000").toInt
+
+  val activeStages = HashSet[Stage]()
+  val completedStages = ListBuffer[Stage]()
+  val failedStages = ListBuffer[Stage]()
+
+  val stageToTasksComplete = HashMap[Int, Int]()
+  val stageToTasksFailed = HashMap[Int, Int]()
+  val stageToTaskInfos =
+    HashMap[Int, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]]()
+
+  override def onJobStart(jobStart: SparkListenerJobStart) {}
+
+  override def onStageCompleted(stageCompleted: StageCompleted) = {
+    val stage = stageCompleted.stageInfo.stage
+    activeStages -= stage
+    completedStages += stage
+    trimIfNecessary(completedStages)
+  }
+
+  /** If stages is too large, remove and garbage collect old stages */
+  def trimIfNecessary(stages: ListBuffer[Stage]) {
+    if (stages.size > RETAINED_STAGES) {
+      val toRemove = RETAINED_STAGES / 10
+      stages.takeRight(toRemove).foreach( s => {
+        stageToTaskInfos.remove(s.id)
+      })
+      stages.trimEnd(toRemove)
+    }
+  }
+
+  override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) =
+    activeStages += stageSubmitted.stage
+
+  override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
+    val sid = taskEnd.task.stageId
+    val (failureInfo, metrics): (Option[ExceptionFailure], Option[TaskMetrics]) =
+      taskEnd.reason match {
+        case e: ExceptionFailure =>
+          stageToTasksFailed(sid) = stageToTasksFailed.getOrElse(sid, 0) + 1
+          (Some(e), e.metrics)
+        case _ =>
+          stageToTasksComplete(sid) = stageToTasksComplete.getOrElse(sid, 0) + 1
+          (None, Some(taskEnd.taskMetrics))
+      }
+    val taskList = stageToTaskInfos.getOrElse(
+      sid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]())
+    taskList += ((taskEnd.taskInfo, metrics, failureInfo))
+    stageToTaskInfos(sid) = taskList
+  }
+
+  override def onJobEnd(jobEnd: SparkListenerJobEnd) {
+    jobEnd match {
+      case end: SparkListenerJobEnd =>
+        end.jobResult match {
+          case JobFailed(ex, Some(stage)) =>
+            activeStages -= stage
+            failedStages += stage
+            trimIfNecessary(failedStages)
+          case _ =>
+        }
+      case _ =>
+    }
+  }
+
+  /** Is this stage's input from a shuffle read. */
+  def hasShuffleRead(stageID: Int): Boolean = {
+    // This is written in a slightly complicated way to avoid having to scan all tasks
+    for (s <- stageToTaskInfos.get(stageID).getOrElse(Seq())) {
+      if (s._2 != null) return s._2.flatMap(m => m.shuffleReadMetrics).isDefined
+    }
+    return false // No tasks have finished for this stage
+  }
+
+  /** Is this stage's output to a shuffle write. */
+  def hasShuffleWrite(stageID: Int): Boolean = {
+    // This is written in a slightly complicated way to avoid having to scan all tasks
+    for (s <- stageToTaskInfos.get(stageID).getOrElse(Seq())) {
+      if (s._2 != null) return s._2.flatMap(m => m.shuffleWriteMetrics).isDefined
+    }
+    return false // No tasks have finished for this stage
+  }
+}
diff --git a/core/src/main/scala/spark/ui/jobs/StagePage.scala b/core/src/main/scala/spark/ui/jobs/StagePage.scala
new file mode 100644
index 0000000000000000000000000000000000000000..51b82b6a8c2b54bf58edfaeb6592e7e5815382f1
--- /dev/null
+++ b/core/src/main/scala/spark/ui/jobs/StagePage.scala
@@ -0,0 +1,114 @@
+package spark.ui.jobs
+
+import java.util.Date
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import spark.ui.UIUtils._
+import spark.ui.Page._
+import spark.util.Distribution
+import spark.{ExceptionFailure, Utils}
+import spark.scheduler.cluster.TaskInfo
+import spark.executor.TaskMetrics
+
+/** Page showing statistics and task list for a given stage */
+private[spark] class StagePage(parent: JobProgressUI) {
+  def listener = parent.listener
+  val dateFmt = parent.dateFmt
+
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val stageId = request.getParameter("id").toInt
+
+    if (!listener.stageToTaskInfos.contains(stageId)) {
+      val content =
+        <div>
+          <h2>Summary Metrics</h2> No tasks have finished yet
+          <h2>Tasks</h2> No tasks have finished yet
+        </div>
+      return headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs)
+    }
+
+    val tasks = listener.stageToTaskInfos(stageId)
+
+    val shuffleRead = listener.hasShuffleRead(stageId)
+    val shuffleWrite = listener.hasShuffleWrite(stageId)
+
+    val taskHeaders: Seq[String] =
+      Seq("Task ID", "Duration", "Locality Level", "Worker", "Launch Time") ++
+        {if (shuffleRead) Seq("Shuffle Read")  else Nil} ++
+        {if (shuffleWrite) Seq("Shuffle Write") else Nil} ++
+      Seq("Details")
+
+    val taskTable = listingTable(taskHeaders, taskRow, tasks)
+
+    // Excludes tasks which failed and have incomplete metrics
+    val validTasks = tasks.filter(t => Option(t._2).isDefined)
+
+    val summaryTable: Option[Seq[Node]] =
+      if (validTasks.size == 0) {
+        None
+      }
+      else {
+        val serviceTimes = validTasks.map{case (info, metrics, exception) =>
+          metrics.get.executorRunTime.toDouble}
+        val serviceQuantiles = "Duration" +: Distribution(serviceTimes).get.getQuantiles().map(
+          ms => parent.formatDuration(ms.toLong))
+
+        def getQuantileCols(data: Seq[Double]) =
+          Distribution(data).get.getQuantiles().map(d => Utils.memoryBytesToString(d.toLong))
+
+        val shuffleReadSizes = validTasks.map {
+          case(info, metrics, exception) =>
+            metrics.get.shuffleReadMetrics.map(_.remoteBytesRead).getOrElse(0L).toDouble
+        }
+        val shuffleReadQuantiles = "Shuffle Read (Remote)" +: getQuantileCols(shuffleReadSizes)
+
+        val shuffleWriteSizes = validTasks.map {
+          case(info, metrics, exception) =>
+            metrics.get.shuffleWriteMetrics.map(_.shuffleBytesWritten).getOrElse(0L).toDouble
+        }
+        val shuffleWriteQuantiles = "Shuffle Write" +: getQuantileCols(shuffleWriteSizes)
+
+        val listings: Seq[Seq[String]] = Seq(serviceQuantiles,
+          if (shuffleRead) shuffleReadQuantiles else Nil,
+          if (shuffleWrite) shuffleWriteQuantiles else Nil)
+
+        val quantileHeaders = Seq("Metric", "Min", "25%", "50%", "75%", "Max")
+        def quantileRow(data: Seq[String]): Seq[Node] = <tr> {data.map(d => <td>{d}</td>)} </tr>
+        Some(listingTable(quantileHeaders, quantileRow, listings))
+      }
+
+    val content =
+      <h2>Summary Metrics</h2> ++ summaryTable.getOrElse(Nil) ++ <h2>Tasks</h2> ++ taskTable;
+
+    headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs)
+  }
+
+
+  def taskRow(taskData: (TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])): Seq[Node] = {
+    def fmtStackTrace(trace: Seq[StackTraceElement]): Seq[Node] =
+      trace.map(e => <span style="display:block;">{e.toString}</span>)
+    val (info, metrics, exception) = taskData
+    <tr>
+      <td>{info.taskId}</td>
+      <td sorttable_customkey={metrics.map{m => m.executorRunTime.toString}.getOrElse("1")}>
+        {metrics.map{m => parent.formatDuration(m.executorRunTime)}.getOrElse("")}
+      </td>
+      <td>{info.taskLocality}</td>
+      <td>{info.hostPort}</td>
+      <td>{dateFmt.format(new Date(info.launchTime))}</td>
+      {metrics.flatMap{m => m.shuffleReadMetrics}.map{s =>
+        <td>{Utils.memoryBytesToString(s.remoteBytesRead)}</td>}.getOrElse("")}
+      {metrics.flatMap{m => m.shuffleWriteMetrics}.map{s =>
+        <td>{Utils.memoryBytesToString(s.shuffleBytesWritten)}</td>}.getOrElse("")}
+      <td>{exception.map(e =>
+        <span>
+          {e.className} ({e.description})<br/>
+          {fmtStackTrace(e.stackTrace)}
+        </span>).getOrElse("")}
+      </td>
+    </tr>
+  }
+}
diff --git a/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala b/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala
new file mode 100644
index 0000000000000000000000000000000000000000..252600aad4e3ef32e54699a84311c708442a2978
--- /dev/null
+++ b/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala
@@ -0,0 +1,24 @@
+package spark.ui.storage
+
+import scala.concurrent.duration._
+
+import javax.servlet.http.HttpServletRequest
+
+import org.eclipse.jetty.server.Handler
+
+import spark.{Logging, SparkContext}
+import spark.ui.JettyUtils._
+
+/** Web UI showing storage status of all RDD's in the given SparkContext. */
+private[spark] class BlockManagerUI(val sc: SparkContext) extends Logging {
+  implicit val timeout = Duration.create(
+    System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")
+
+  val indexPage = new IndexPage(this)
+  val rddPage = new RDDPage(this)
+
+  def getHandlers = Seq[(String, Handler)](
+    ("/storage/rdd", (request: HttpServletRequest) => rddPage.render(request)),
+    ("/storage", (request: HttpServletRequest) => indexPage.render(request))
+  )
+}
diff --git a/core/src/main/scala/spark/ui/storage/IndexPage.scala b/core/src/main/scala/spark/ui/storage/IndexPage.scala
new file mode 100644
index 0000000000000000000000000000000000000000..d284134391f87140628c83c7094e1fdc60265b87
--- /dev/null
+++ b/core/src/main/scala/spark/ui/storage/IndexPage.scala
@@ -0,0 +1,64 @@
+package spark.ui.storage
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import spark.storage.{RDDInfo, StorageUtils}
+import spark.Utils
+import spark.ui.UIUtils._
+import spark.ui.Page._
+
+/** Page showing list of RDD's currently stored in the cluster */
+private[spark] class IndexPage(parent: BlockManagerUI) {
+  val sc = parent.sc
+
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val storageStatusList = sc.getExecutorStorageStatus
+    // Calculate macro-level statistics
+    val maxMem = storageStatusList.map(_.maxMem).reduce(_+_)
+    val remainingMem = storageStatusList.map(_.memRemaining).reduce(_+_)
+    val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize))
+      .reduceOption(_+_).getOrElse(0L)
+
+    val rddHeaders = Seq(
+      "RDD Name",
+      "Storage Level",
+      "Cached Partitions",
+      "Fraction Partitions Cached",
+      "Size in Memory",
+      "Size on Disk")
+    val rdds = StorageUtils.rddInfoFromStorageStatus(storageStatusList, sc)
+    val rddTable = listingTable(rddHeaders, rddRow, rdds)
+
+    val content =
+      <div class="row">
+        <div class="span12">
+          <ul class="unstyled">
+            <li><strong>Memory:</strong>
+              {Utils.memoryBytesToString(maxMem - remainingMem)} Used
+              ({Utils.memoryBytesToString(remainingMem)} Available) </li>
+            <li><strong>Disk:</strong> {Utils.memoryBytesToString(diskSpaceUsed)} Used </li>
+          </ul>
+        </div>
+      </div> ++ {rddTable};
+
+    headerSparkPage(content, parent.sc, "Spark Storage ", Storage)
+  }
+
+  def rddRow(rdd: RDDInfo): Seq[Node] = {
+    <tr>
+      <td>
+        <a href={"/storage/rdd?id=%s".format(rdd.id)}>
+          {rdd.name}
+        </a>
+      </td>
+      <td>{rdd.storageLevel.description}
+      </td>
+      <td>{rdd.numCachedPartitions}</td>
+      <td>{rdd.numCachedPartitions / rdd.numPartitions.toDouble}</td>
+      <td>{Utils.memoryBytesToString(rdd.memSize)}</td>
+      <td>{Utils.memoryBytesToString(rdd.diskSize)}</td>
+    </tr>
+  }
+}
diff --git a/core/src/main/scala/spark/ui/storage/RDDPage.scala b/core/src/main/scala/spark/ui/storage/RDDPage.scala
new file mode 100644
index 0000000000000000000000000000000000000000..0cb1e47ea551eafb0604c092dbaf5ce5439a0b03
--- /dev/null
+++ b/core/src/main/scala/spark/ui/storage/RDDPage.scala
@@ -0,0 +1,104 @@
+package spark.ui.storage
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import spark.storage.{StorageStatus, StorageUtils}
+import spark.ui.UIUtils._
+import spark.Utils
+import spark.storage.BlockManagerMasterActor.BlockStatus
+import spark.ui.Page._
+
+/** Page showing storage details for a given RDD */
+private[spark] class RDDPage(parent: BlockManagerUI) {
+  val sc = parent.sc
+
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val id = request.getParameter("id")
+    val prefix = "rdd_" + id.toString
+    val storageStatusList = sc.getExecutorStorageStatus
+    val filteredStorageStatusList = StorageUtils.
+      filterStorageStatusByPrefix(storageStatusList, prefix)
+    val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head
+
+    val workerHeaders = Seq("Host", "Memory Usage", "Disk Usage")
+    val workers = filteredStorageStatusList.map((prefix, _))
+    val workerTable = listingTable(workerHeaders, workerRow, workers)
+
+    val blockHeaders = Seq("Block Name", "Storage Level", "Size in Memory", "Size on Disk")
+    val blocks = filteredStorageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1)
+    val blockTable = listingTable(blockHeaders, blockRow, blocks)
+
+    val content =
+      <div class="row">
+        <div class="span12">
+          <ul class="unstyled">
+            <li>
+              <strong>Storage Level:</strong>
+              {rddInfo.storageLevel.description}
+            </li>
+            <li>
+              <strong>Cached Partitions:</strong>
+              {rddInfo.numCachedPartitions}
+            </li>
+            <li>
+              <strong>Total Partitions:</strong>
+              {rddInfo.numPartitions}
+            </li>
+            <li>
+              <strong>Memory Size:</strong>
+              {Utils.memoryBytesToString(rddInfo.memSize)}
+            </li>
+            <li>
+              <strong>Disk Size:</strong>
+              {Utils.memoryBytesToString(rddInfo.diskSize)}
+            </li>
+          </ul>
+        </div>
+      </div>
+      <hr/>
+      <div class="row">
+        <div class="span12">
+          {workerTable}
+        </div>
+      </div>
+      <hr/>
+      <div class="row">
+        <div class="span12">
+          <h3> RDD Summary </h3>
+          {blockTable}
+        </div>
+      </div>;
+
+    headerSparkPage(content, parent.sc, "RDD Info: " + rddInfo.name, Jobs)
+  }
+
+  def blockRow(blk: (String, BlockStatus)): Seq[Node] = {
+    val (id, block) = blk
+    <tr>
+      <td>{id}</td>
+      <td>
+        {block.storageLevel.description}
+      </td>
+      <td sorttable_customkey={block.memSize.toString}>
+        {Utils.memoryBytesToString(block.memSize)}
+      </td>
+      <td sorttable_customkey={block.diskSize.toString}>
+        {Utils.memoryBytesToString(block.diskSize)}
+      </td>
+    </tr>
+  }
+
+  def workerRow(worker: (String, StorageStatus)): Seq[Node] = {
+    val (prefix, status) = worker
+    <tr>
+      <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td>
+      <td>
+        {Utils.memoryBytesToString(status.memUsed(prefix))}
+        ({Utils.memoryBytesToString(status.memRemaining)} Total Available)
+      </td>
+      <td>{Utils.memoryBytesToString(status.diskUsed(prefix))}</td>
+    </tr>
+  }
+}
diff --git a/core/src/main/scala/spark/util/AkkaUtils.scala b/core/src/main/scala/spark/util/AkkaUtils.scala
index ea39888c21d9923297e69ad3a278cc3161b16b48..c381a0510b59bcae0cf880f2bb67d6fc3fc21497 100644
--- a/core/src/main/scala/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/spark/util/AkkaUtils.scala
@@ -1,20 +1,10 @@
 package spark.util
 
-import akka.actor.{Props, ActorSystem, ExtendedActorSystem}
+import akka.actor.{ActorSystem, ExtendedActorSystem}
 import com.typesafe.config.ConfigFactory
 import scala.concurrent.duration._
-import akka.pattern.ask
-import akka.remote.RemoteActorRefProvider
-
-import spray.routing.Route
-import spray.io.IOExtension
-import spray.routing.HttpServiceActor
-import spray.can.server.{HttpServer, ServerSettings}
-import spray.io.SingletonHandler
 import scala.concurrent.Await
-import spark.{Utils, SparkException}
-
-import java.util.concurrent.TimeoutException
+import akka.remote.RemoteActorRefProvider
 
 /**
  * Various utility classes for working with Akka.
@@ -65,29 +55,4 @@ private[spark] object AkkaUtils {
     return (actorSystem, boundPort)
   }
 
-  /**
-   * Creates a Spray HTTP server bound to a given IP and port with a given Spray Route object to
-   * handle requests. Returns the bound port or throws a SparkException on failure.
-   * TODO: Not changing ip to host here - is it required ?
-   */
-  def startSprayServer(actorSystem: ActorSystem, ip: String, port: Int, route: Route, name: String = "HttpServer") = {
-    val ioWorker    = IOExtension(actorSystem).ioBridge()
-    val httpService = actorSystem.actorOf(Props(HttpServiceActor(route)))
-    val server      = actorSystem.actorOf(
-      Props(new HttpServer(ioWorker, SingletonHandler(httpService), ServerSettings())), name = name)
-    actorSystem.registerOnTermination { actorSystem.stop(ioWorker) }
-    val timeout = 3.seconds
-    val future  = server.ask(HttpServer.Bind(ip, port))(timeout)
-    try {
-      Await.result(future, timeout) match {
-        case bound: HttpServer.Bound =>
-          server
-        case other: Any =>
-          throw new SparkException("Failed to bind web UI to port " + port + ": " + other)
-      }
-    } catch {
-      case e: TimeoutException =>
-        throw new SparkException("Failed to bind web UI to port " + port)
-    }
-  }
 }
diff --git a/core/src/main/twirl/spark/common/layout.scala.html b/core/src/main/twirl/spark/common/layout.scala.html
deleted file mode 100644
index b9192060aa5aefee60a78a31a84a70920e57a6c4..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/common/layout.scala.html
+++ /dev/null
@@ -1,35 +0,0 @@
-@(title: String)(content: Html)
-
-<!DOCTYPE html>
-<html>
-
-  <head>
-    <meta http-equiv="Content-type" content="text/html; charset=utf-8">
-    <link rel="stylesheet" href="static/bootstrap.min.css" type="text/css">
-    <link rel="stylesheet" href="static/bootstrap-responsive.min.css" type="text/css">
-    <script src="static/sorttable.js"></script>
-    <title>@title</title>
-    <style type="text/css">
-      table.sortable thead { cursor: pointer; }
-    </style>
-  </head>
-
-  <body>
-    <div class="container">
-    
-      <!-- HEADER -->
-      <div class="row">
-        <div class="span12">
-          <img src="static/spark_logo.png">
-          <h1 style="vertical-align: bottom; margin-bottom: 10px; margin-left: 30px; display: inline-block;"> @title </h1>
-        </div>
-      </div>
-    
-      <hr/>
-      
-      @content
-    
-    </div>
-  </body>
-
-</html>
\ No newline at end of file
diff --git a/core/src/main/twirl/spark/deploy/master/app_details.scala.html b/core/src/main/twirl/spark/deploy/master/app_details.scala.html
deleted file mode 100644
index 5e5e5de5513fbd0cfd90e8c84dc3b76f30805ea6..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/master/app_details.scala.html
+++ /dev/null
@@ -1,38 +0,0 @@
-@(app: spark.deploy.master.ApplicationInfo)
-
-@spark.common.html.layout(title = "Application Details") {
-      
-  <!-- Application Details -->
-  <div class="row">
-    <div class="span12">
-      <ul class="unstyled">
-        <li><strong>ID:</strong> @app.id</li>
-        <li><strong>Description:</strong> @app.desc.name</li>
-        <li><strong>User:</strong> @app.desc.user</li>
-        <li><strong>Cores:</strong>
-          @if(app.desc.maxCores == Integer.MAX_VALUE) {
-            Unlimited (@app.coresGranted granted)
-          } else {
-            @app.desc.maxCores (@app.coresGranted granted, @app.coresLeft left)
-          }
-        </li>
-        <li><strong>Memory per Slave:</strong> @app.desc.memoryPerSlave</li>
-        <li><strong>Submit Date:</strong> @app.submitDate</li>
-        <li><strong>State:</strong> @app.state</li>
-        <li><strong><a href="@app.appUiUrl">Application Detail UI</a></strong></li>
-      </ul>
-    </div>
-  </div>
-  
-  <hr/>
-  
-  <!-- Executors -->
-  <div class="row">
-    <div class="span12">
-      <h3> Executor Summary </h3>
-      <br/>
-      @executors_table(app.executors.values.toList)
-    </div>
-  </div>
-      
-}
diff --git a/core/src/main/twirl/spark/deploy/master/app_row.scala.html b/core/src/main/twirl/spark/deploy/master/app_row.scala.html
deleted file mode 100644
index feb306f35ccb58d6b0097dd72584589034364e90..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/master/app_row.scala.html
+++ /dev/null
@@ -1,20 +0,0 @@
-@(app: spark.deploy.master.ApplicationInfo)
-
-@import spark.Utils
-@import spark.deploy.WebUI.formatDate
-@import spark.deploy.WebUI.formatDuration
-
-<tr>
-  <td>
-    <a href="app?appId=@(app.id)">@app.id</a>
-  </td>
-  <td>@app.desc.name</td>
-  <td>
-    @app.coresGranted
-  </td>
-  <td>@Utils.memoryMegabytesToString(app.desc.memoryPerSlave)</td>
-  <td>@formatDate(app.submitDate)</td>
-  <td>@app.desc.user</td>
-  <td>@app.state.toString()</td>
-  <td>@formatDuration(app.duration)</td>
-</tr>
diff --git a/core/src/main/twirl/spark/deploy/master/app_table.scala.html b/core/src/main/twirl/spark/deploy/master/app_table.scala.html
deleted file mode 100644
index f789cee0f16ea834cecea5a76634d310a5929d66..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/master/app_table.scala.html
+++ /dev/null
@@ -1,21 +0,0 @@
-@(apps: Array[spark.deploy.master.ApplicationInfo])
-
-<table class="table table-bordered table-striped table-condensed sortable">
-  <thead>
-    <tr>
-      <th>ID</th>
-      <th>Description</th>
-      <th>Cores</th>
-      <th>Memory per Node</th>
-      <th>Submit Time</th>
-      <th>User</th>
-      <th>State</th>
-      <th>Duration</th>
-    </tr>
-  </thead>
-  <tbody>
-    @for(j <- apps) {
-      @app_row(j)
-    }
-  </tbody>
-</table>
diff --git a/core/src/main/twirl/spark/deploy/master/executor_row.scala.html b/core/src/main/twirl/spark/deploy/master/executor_row.scala.html
deleted file mode 100644
index 21e72c7aabf7ceb17103ff9ab913bd5fba357670..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/master/executor_row.scala.html
+++ /dev/null
@@ -1,15 +0,0 @@
-@(executor: spark.deploy.master.ExecutorInfo)
-
-<tr>
-  <td>@executor.id</td>
-  <td>
-    <a href="@executor.worker.webUiAddress">@executor.worker.id</a>
-  </td>
-  <td>@executor.cores</td>
-  <td>@executor.memory</td>
-  <td>@executor.state</td>
-  <td>
-    <a href="@(executor.worker.webUiAddress)/log?appId=@(executor.application.id)&executorId=@(executor.id)&logType=stdout">stdout</a>
-    <a href="@(executor.worker.webUiAddress)/log?appId=@(executor.application.id)&executorId=@(executor.id)&logType=stderr">stderr</a>
-  </td>
-</tr>
diff --git a/core/src/main/twirl/spark/deploy/master/executors_table.scala.html b/core/src/main/twirl/spark/deploy/master/executors_table.scala.html
deleted file mode 100644
index cafc42c80e822f69cd20fe1d5480c0a95957ee5f..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/master/executors_table.scala.html
+++ /dev/null
@@ -1,19 +0,0 @@
-@(executors: List[spark.deploy.master.ExecutorInfo])
-
-<table class="table table-bordered table-striped table-condensed">
-  <thead>
-    <tr>
-      <th>ExecutorID</th>
-      <th>Worker</th>
-      <th>Cores</th>
-      <th>Memory</th>
-      <th>State</th>
-      <th>Logs</th>
-    </tr>
-  </thead>
-  <tbody>
-    @for(e <- executors) {
-      @executor_row(e)
-    }
-  </tbody>
-</table>
\ No newline at end of file
diff --git a/core/src/main/twirl/spark/deploy/master/index.scala.html b/core/src/main/twirl/spark/deploy/master/index.scala.html
deleted file mode 100644
index b9b9f08810930a0e452e523e680bb7c44897be65..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/master/index.scala.html
+++ /dev/null
@@ -1,53 +0,0 @@
-@(state: spark.deploy.MasterState)
-@import spark.deploy.master._
-@import spark.Utils
-
-@spark.common.html.layout(title = "Spark Master on " + state.host + ":" + state.port) {
-
-  <!-- Cluster Details -->
-  <div class="row">
-    <div class="span12">
-      <ul class="unstyled">
-        <li><strong>URL:</strong> @(state.uri)</li>
-        <li><strong>Workers:</strong> @state.workers.size </li>
-        <li><strong>Cores:</strong> @{state.workers.map(_.cores).sum} Total,
-                                    @{state.workers.map(_.coresUsed).sum} Used</li>
-        <li><strong>Memory:</strong> @{Utils.memoryMegabytesToString(state.workers.map(_.memory).sum)} Total, 
-                                     @{Utils.memoryMegabytesToString(state.workers.map(_.memoryUsed).sum)} Used</li>
-        <li><strong>Applications:</strong> @state.activeApps.size Running, @state.completedApps.size Completed </li>
-      </ul>
-    </div>
-  </div>
-
-  <!-- Worker Summary -->
-  <div class="row">
-    <div class="span12">
-      <h3> Workers </h3>
-      <br/>
-      @worker_table(state.workers.sortBy(_.id))
-    </div>
-  </div>
-
-  <hr/>
-
-  <!-- App Summary (Running) -->
-  <div class="row">
-    <div class="span12">
-      <h3> Running Applications </h3>
-      <br/>
-      @app_table(state.activeApps.sortBy(_.startTime).reverse)
-    </div>
-  </div>
-
-  <hr/>
-
-  <!-- App Summary (Completed) -->
-  <div class="row">
-    <div class="span12">
-      <h3> Completed Applications </h3>
-      <br/>
-      @app_table(state.completedApps.sortBy(_.endTime).reverse)
-    </div>
-  </div>
-
-}
diff --git a/core/src/main/twirl/spark/deploy/master/worker_row.scala.html b/core/src/main/twirl/spark/deploy/master/worker_row.scala.html
deleted file mode 100644
index 46277ca421bd22df2cc68c637ec3dd3a7a3ae620..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/master/worker_row.scala.html
+++ /dev/null
@@ -1,14 +0,0 @@
-@(worker: spark.deploy.master.WorkerInfo)
-
-@import spark.Utils
-
-<tr>
-  <td>
-    <a href="@worker.webUiAddress">@worker.id</a>
-  </td>
-  <td>@{worker.host}:@{worker.port}</td>
-  <td>@worker.state</td>
-  <td>@worker.cores (@worker.coresUsed Used)</td>
-  <td>@{Utils.memoryMegabytesToString(worker.memory)}
-     (@{Utils.memoryMegabytesToString(worker.memoryUsed)} Used)</td>
-</tr>
diff --git a/core/src/main/twirl/spark/deploy/master/worker_table.scala.html b/core/src/main/twirl/spark/deploy/master/worker_table.scala.html
deleted file mode 100644
index b249411a62551a76ae5b159c88081e963cca66b6..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/master/worker_table.scala.html
+++ /dev/null
@@ -1,18 +0,0 @@
-@(workers: Array[spark.deploy.master.WorkerInfo])
-
-<table class="table table-bordered table-striped table-condensed sortable">
-  <thead>
-    <tr>
-      <th>ID</th>
-      <th>Address</th>
-      <th>State</th>
-      <th>Cores</th>
-      <th>Memory</th>
-    </tr>
-  </thead>
-  <tbody>
-    @for(w <- workers) {
-      @worker_row(w)
-    }
-  </tbody>
-</table>
diff --git a/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html b/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html
deleted file mode 100644
index dad0a89080f2528848190b6803e7727d5bf932b8..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html
+++ /dev/null
@@ -1,20 +0,0 @@
-@(executor: spark.deploy.worker.ExecutorRunner)
-
-@import spark.Utils
-
-<tr>
-  <td>@executor.execId</td>
-  <td>@executor.cores</td>
-  <td>@Utils.memoryMegabytesToString(executor.memory)</td>
-  <td>
-    <ul class="unstyled">
-      <li><strong>ID:</strong> @executor.appId</li>
-      <li><strong>Name:</strong> @executor.appDesc.name</li>
-      <li><strong>User:</strong> @executor.appDesc.user</li>
-    </ul>
-  </td>
-  <td>
-    <a href="log?appId=@(executor.appId)&executorId=@(executor.execId)&logType=stdout">stdout</a>
-    <a href="log?appId=@(executor.appId)&executorId=@(executor.execId)&logType=stderr">stderr</a>
-  </td>
-</tr>
diff --git a/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html b/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html
deleted file mode 100644
index 327a2399c739a68e8b44f8f1dae22971d58c9841..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html
+++ /dev/null
@@ -1,18 +0,0 @@
-@(executors: List[spark.deploy.worker.ExecutorRunner])
-
-<table class="table table-bordered table-striped table-condensed sortable">
-  <thead>
-    <tr>
-      <th>ExecutorID</th>
-      <th>Cores</th>
-      <th>Memory</th>
-      <th>Job Details</th>
-      <th>Logs</th>
-    </tr>
-  </thead>
-  <tbody>
-    @for(e <- executors) {
-      @executor_row(e)
-    }
-  </tbody>
-</table>
\ No newline at end of file
diff --git a/core/src/main/twirl/spark/deploy/worker/index.scala.html b/core/src/main/twirl/spark/deploy/worker/index.scala.html
deleted file mode 100644
index 0e66af9284762abebd4acc8dbb0b8edd3aa58e9a..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/deploy/worker/index.scala.html
+++ /dev/null
@@ -1,44 +0,0 @@
-@(worker: spark.deploy.WorkerState)
-@import spark.Utils
-
-@spark.common.html.layout(title = "Spark Worker on " + worker.host + ":" + worker.port) {
-
-  <!-- Worker Details -->
-  <div class="row">
-    <div class="span12">
-      <ul class="unstyled">
-        <li><strong>ID:</strong> @worker.workerId</li>
-        <li><strong>
-          Master URL:</strong> @worker.masterUrl 
-        </li>
-        <li><strong>Cores:</strong> @worker.cores (@worker.coresUsed Used)</li>
-        <li><strong>Memory:</strong> @{Utils.memoryMegabytesToString(worker.memory)}
-          (@{Utils.memoryMegabytesToString(worker.memoryUsed)} Used)</li>
-      </ul>
-      <p><a href="@worker.masterWebUiUrl">Back to Master</a></p>
-    </div>
-  </div>
-
-  <hr/>
-
-  <!-- Running Executors -->
-  <div class="row">
-    <div class="span12">
-      <h3> Running Executors </h3>
-      <br/>
-      @executors_table(worker.executors)
-    </div>
-  </div>
-
-  <hr/>
-
-  <!-- Finished Executors  -->
-  <div class="row">
-    <div class="span12">
-      <h3> Finished Executors </h3>
-      <br/>
-      @executors_table(worker.finishedExecutors)
-    </div>
-  </div>
-      
-}
diff --git a/core/src/main/twirl/spark/storage/index.scala.html b/core/src/main/twirl/spark/storage/index.scala.html
deleted file mode 100644
index 2b337f61339b213bea1ac62941363708373e4c58..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/storage/index.scala.html
+++ /dev/null
@@ -1,40 +0,0 @@
-@(maxMem: Long, remainingMem: Long, diskSpaceUsed: Long, rdds: Array[spark.storage.RDDInfo], storageStatusList: Array[spark.storage.StorageStatus])
-@import spark.Utils
-
-@spark.common.html.layout(title = "Storage Dashboard") {
-  
-  <!-- High-Level Information -->
-  <div class="row">
-    <div class="span12">
-      <ul class="unstyled">
-        <li><strong>Memory:</strong> 
-          @{Utils.memoryBytesToString(maxMem - remainingMem)} Used 
-          (@{Utils.memoryBytesToString(remainingMem)} Available) </li>
-        <li><strong>Disk:</strong> @{Utils.memoryBytesToString(diskSpaceUsed)} Used </li>
-      </ul>
-    </div>
-  </div>
-
-  <hr/>
-
-  <!-- RDD Summary -->
-  <div class="row">
-    <div class="span12">
-      <h3> RDD Summary </h3>
-      <br/>
-       @rdd_table(rdds)
-    </div>
-  </div>
-
-  <hr/>
-
-  <!-- Worker Summary -->
-  <div class="row">
-    <div class="span12">
-      <h3> Worker Summary </h3>
-      <br/>
-       @worker_table(storageStatusList)
-    </div>
-  </div>
-
-}
\ No newline at end of file
diff --git a/core/src/main/twirl/spark/storage/rdd.scala.html b/core/src/main/twirl/spark/storage/rdd.scala.html
deleted file mode 100644
index d85addeb1755e77fa0c7ee947b8e6892aa3e29d4..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/storage/rdd.scala.html
+++ /dev/null
@@ -1,81 +0,0 @@
-@(rddInfo: spark.storage.RDDInfo, storageStatusList: Array[spark.storage.StorageStatus])
-@import spark.Utils
-
-@spark.common.html.layout(title = "RDD Info ") {
-  
-  <!-- High-Level Information -->
-  <div class="row">
-    <div class="span12">
-      <ul class="unstyled">
-        <li>
-          <strong>Storage Level:</strong> 
-          @(rddInfo.storageLevel.description)
-        <li>
-          <strong>Cached Partitions:</strong>
-          @(rddInfo.numCachedPartitions)
-        </li>
-        <li>
-          <strong>Total Partitions:</strong>
-          @(rddInfo.numPartitions)
-        </li>
-        <li>
-          <strong>Memory Size:</strong>
-          @{Utils.memoryBytesToString(rddInfo.memSize)}
-        </li>
-        <li>
-          <strong>Disk Size:</strong>
-          @{Utils.memoryBytesToString(rddInfo.diskSize)}
-        </li>
-      </ul>
-    </div>
-  </div>
-
-  <hr/>
-
-  <!-- RDD Summary -->
-  <div class="row">
-    <div class="span12">
-      <h3> RDD Summary </h3>
-      <br/>
-      
-
-      <!-- Block Table Summary -->
-      <table class="table table-bordered table-striped table-condensed sortable">
-        <thead>
-          <tr>
-            <th>Block Name</th>
-            <th>Storage Level</th>
-            <th>Size in Memory</th>
-            <th>Size on Disk</th>
-          </tr>
-        </thead>
-        <tbody>
-          @storageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1).map { case (k,v) => 
-            <tr>
-              <td>@k</td>
-              <td>
-                 @(v.storageLevel.description)
-              </td>
-              <td>@{Utils.memoryBytesToString(v.memSize)}</td>
-              <td>@{Utils.memoryBytesToString(v.diskSize)}</td>
-            </tr>
-          }
-        </tbody>
-      </table>
-
-
-    </div>
-  </div>
-
-  <hr/>
-
-  <!-- Worker Table -->
-  <div class="row">
-    <div class="span12">
-      <h3> Worker Summary </h3>
-      <br/>
-       @worker_table(storageStatusList, "rdd_" + rddInfo.id )
-    </div>
-  </div>
-
-}
\ No newline at end of file
diff --git a/core/src/main/twirl/spark/storage/rdd_table.scala.html b/core/src/main/twirl/spark/storage/rdd_table.scala.html
deleted file mode 100644
index a51e64aed007292782fa3df720a7ebf2130c76f8..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/storage/rdd_table.scala.html
+++ /dev/null
@@ -1,32 +0,0 @@
-@(rdds: Array[spark.storage.RDDInfo])
-@import spark.Utils
-
-<table class="table table-bordered table-striped table-condensed sortable">
-  <thead>
-    <tr>
-      <th>RDD Name</th>
-      <th>Storage Level</th>
-      <th>Cached Partitions</th>
-      <th>Fraction Partitions Cached</th>
-      <th>Size in Memory</th>
-      <th>Size on Disk</th>
-    </tr>
-  </thead>
-  <tbody>
-    @for(rdd <- rdds) {
-      <tr>
-        <td>
-          <a href="rdd?id=@(rdd.id)">
-            @rdd.name
-          </a>
-        </td>
-        <td>@(rdd.storageLevel.description)
-        </td>
-        <td>@rdd.numCachedPartitions</td>
-        <td>@(rdd.numCachedPartitions / rdd.numPartitions.toDouble)</td>
-        <td>@{Utils.memoryBytesToString(rdd.memSize)}</td>
-        <td>@{Utils.memoryBytesToString(rdd.diskSize)}</td>
-      </tr>
-    }
-  </tbody>
-</table>
\ No newline at end of file
diff --git a/core/src/main/twirl/spark/storage/worker_table.scala.html b/core/src/main/twirl/spark/storage/worker_table.scala.html
deleted file mode 100644
index cd72a688c1ffbd71e93f66e18b143d248b814ca3..0000000000000000000000000000000000000000
--- a/core/src/main/twirl/spark/storage/worker_table.scala.html
+++ /dev/null
@@ -1,24 +0,0 @@
-@(workersStatusList: Array[spark.storage.StorageStatus], prefix: String = "")
-@import spark.Utils
-
-<table class="table table-bordered table-striped table-condensed sortable">
-  <thead>
-    <tr>
-      <th>Host</th>
-      <th>Memory Usage</th>
-      <th>Disk Usage</th>
-    </tr>
-  </thead>
-  <tbody>
-    @for(status <- workersStatusList) {
-      <tr>
-        <td>@(status.blockManagerId.host + ":" + status.blockManagerId.port)</td>
-        <td>
-          @(Utils.memoryBytesToString(status.memUsed(prefix)))
-          (@(Utils.memoryBytesToString(status.memRemaining)) Total Available)
-        </td>
-        <td>@(Utils.memoryBytesToString(status.diskUsed(prefix)))</td>
-    </tr>
-    }
-  </tbody>
-</table>
\ No newline at end of file
diff --git a/core/src/test/resources/fairscheduler.xml b/core/src/test/resources/fairscheduler.xml
index 5a688b0ebb973105fa603f924fe819eecda8ba46..6e573b1883678f22cae10b06750435aab1b51b81 100644
--- a/core/src/test/resources/fairscheduler.xml
+++ b/core/src/test/resources/fairscheduler.xml
@@ -1,3 +1,4 @@
+<?xml version="1.0"?>
 <allocations>
 <pool name="1">
     <minShare>2</minShare>
diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties
index 6ec89c01840b6935c061d30820e72cf36aa3314d..d05cf3dec1ebb1e02b030533d45456f1356be237 100644
--- a/core/src/test/resources/log4j.properties
+++ b/core/src/test/resources/log4j.properties
@@ -8,3 +8,4 @@ log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}:
 
 # Ignore messages below warning level from Jetty, because it's a bit verbose
 log4j.logger.org.eclipse.jetty=WARN
+org.eclipse.jetty.LEVEL=WARN
diff --git a/core/src/test/scala/spark/DistributedSuite.scala b/core/src/test/scala/spark/DistributedSuite.scala
index 0866fb47b39500ba84257565ce39eededc977001..0024ede828a56cac949ddc3675a9d371bf285199 100644
--- a/core/src/test/scala/spark/DistributedSuite.scala
+++ b/core/src/test/scala/spark/DistributedSuite.scala
@@ -10,6 +10,7 @@ import org.scalatest.time.{Span, Millis}
 import org.scalacheck.Arbitrary._
 import org.scalacheck.Gen
 import org.scalacheck.Prop._
+import org.eclipse.jetty.server.{Server, Request, Handler}
 
 import com.google.common.io.Files
 
@@ -17,6 +18,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import SparkContext._
 import storage.{GetBlock, BlockManagerWorker, StorageLevel}
+import ui.JettyUtils
 
 
 class NotSerializableClass
diff --git a/core/src/test/scala/spark/DriverSuite.scala b/core/src/test/scala/spark/DriverSuite.scala
index 5e84b3a66a1a9a885f7bcea95b4d60eedab4c9e1..31c3dd75fb2556c91cda4481fed4c60bd5c8e137 100644
--- a/core/src/test/scala/spark/DriverSuite.scala
+++ b/core/src/test/scala/spark/DriverSuite.scala
@@ -2,6 +2,9 @@ package spark
 
 import java.io.File
 
+import org.apache.log4j.Logger
+import org.apache.log4j.Level
+
 import org.scalatest.FunSuite
 import org.scalatest.concurrent.Timeouts
 import org.scalatest.prop.TableDrivenPropertyChecks._
@@ -27,6 +30,7 @@ class DriverSuite extends FunSuite with Timeouts {
  */
 object DriverWithoutCleanup {
   def main(args: Array[String]) {
+    Logger.getRootLogger().setLevel(Level.WARN)
     val sc = new SparkContext(args(0), "DriverWithoutCleanup")
     sc.parallelize(1 to 100, 4).count()
   }
diff --git a/core/src/test/scala/spark/FileServerSuite.scala b/core/src/test/scala/spark/FileServerSuite.scala
index f1a35bced3ad7df19f848709811feeed85feca7f..9c24ca430daf4c3301a3f869ff6f9f22acc7c4a2 100644
--- a/core/src/test/scala/spark/FileServerSuite.scala
+++ b/core/src/test/scala/spark/FileServerSuite.scala
@@ -85,7 +85,6 @@ class FileServerSuite extends FunSuite with LocalSparkContext {
       in.close()
       _ * fileVal + _ * fileVal
     }.collect
-    println(result)
     assert(result.toSet === Set((1,200), (2,300), (3,500)))
   }
 
diff --git a/core/src/test/scala/spark/LocalSparkContext.scala b/core/src/test/scala/spark/LocalSparkContext.scala
index 76d5258b02b9712fae6d5a5935f6036a1a23fb44..bd184222ed30176d8e920dcfe65e36a0bf97dfc7 100644
--- a/core/src/test/scala/spark/LocalSparkContext.scala
+++ b/core/src/test/scala/spark/LocalSparkContext.scala
@@ -2,12 +2,21 @@ package spark
 
 import org.scalatest.Suite
 import org.scalatest.BeforeAndAfterEach
+import org.scalatest.BeforeAndAfterAll
+
+import org.jboss.netty.logging.InternalLoggerFactory
+import org.jboss.netty.logging.Slf4JLoggerFactory
 
 /** Manages a local `sc` {@link SparkContext} variable, correctly stopping it after each test. */
-trait LocalSparkContext extends BeforeAndAfterEach { self: Suite =>
+trait LocalSparkContext extends BeforeAndAfterEach with BeforeAndAfterAll { self: Suite =>
 
   @transient var sc: SparkContext = _
 
+  override def beforeAll() {
+    InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory());
+    super.beforeAll()
+  }
+
   override def afterEach() {
     resetSparkContext()
     super.afterEach()
diff --git a/core/src/test/scala/spark/PipedRDDSuite.scala b/core/src/test/scala/spark/PipedRDDSuite.scala
index 1c9ca5081120ae9fd9e14e2b1b1da1ce9d8c5c19..d263bb00e9c6bb4d96a32725f226945af483dc52 100644
--- a/core/src/test/scala/spark/PipedRDDSuite.scala
+++ b/core/src/test/scala/spark/PipedRDDSuite.scala
@@ -67,7 +67,7 @@ class PipedRDDSuite extends FunSuite with SharedSparkContext {
 
   test("pipe with non-zero exit status") {
     val nums = sc.makeRDD(Array(1, 2, 3, 4), 2)
-    val piped = nums.pipe("cat nonexistent_file")
+    val piped = nums.pipe(Seq("cat nonexistent_file", "2>", "/dev/null"))
     intercept[SparkException] {
       piped.collect()
     }
diff --git a/core/src/test/scala/spark/RDDSuite.scala b/core/src/test/scala/spark/RDDSuite.scala
index d8db69b1c918c0988b1910ef812abf01a3c1ace5..e41ae385c0d833323ca9bbcda89c5d64520a8fe2 100644
--- a/core/src/test/scala/spark/RDDSuite.scala
+++ b/core/src/test/scala/spark/RDDSuite.scala
@@ -251,4 +251,37 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(topK.size === 2)
     assert(topK.sorted === Array("b", "a"))
   }
+
+  test("takeSample") {
+    val data = sc.parallelize(1 to 100, 2)
+    for (seed <- 1 to 5) {
+      val sample = data.takeSample(withReplacement=false, 20, seed)
+      assert(sample.size === 20)        // Got exactly 20 elements
+      assert(sample.toSet.size === 20)  // Elements are distinct
+      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+    }
+    for (seed <- 1 to 5) {
+      val sample = data.takeSample(withReplacement=false, 200, seed)
+      assert(sample.size === 100)        // Got only 100 elements
+      assert(sample.toSet.size === 100)  // Elements are distinct
+      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+    }
+    for (seed <- 1 to 5) {
+      val sample = data.takeSample(withReplacement=true, 20, seed)
+      assert(sample.size === 20)        // Got exactly 20 elements
+      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+    }
+    for (seed <- 1 to 5) {
+      val sample = data.takeSample(withReplacement=true, 100, seed)
+      assert(sample.size === 100)        // Got exactly 100 elements
+      // Chance of getting all distinct elements is astronomically low, so test we got < 100
+      assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements")
+    }
+    for (seed <- 1 to 5) {
+      val sample = data.takeSample(withReplacement=true, 200, seed)
+      assert(sample.size === 200)        // Got exactly 200 elements
+      // Chance of getting all distinct elements is still quite low, so test we got < 100
+      assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements")
+    }
+  }
 }
diff --git a/core/src/test/scala/spark/ui/UISuite.scala b/core/src/test/scala/spark/ui/UISuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..b40c29bede3f30e97b9914e0cc0fb9f1a9091ce7
--- /dev/null
+++ b/core/src/test/scala/spark/ui/UISuite.scala
@@ -0,0 +1,75 @@
+package spark.ui
+
+import org.scalatest.FunSuite
+import org.eclipse.jetty.server.Server
+import java.net.ServerSocket
+import scala.util.{Failure, Success, Try}
+import spark.Utils
+import com.google.common.io.Files
+import java.io.{FileOutputStream, File}
+import com.google.common.base.Charsets
+
+class UISuite extends FunSuite {
+  test("jetty port increases under contention") {
+    val startPort = 33333
+    val server = new Server(startPort)
+    server.start()
+    val (jettyServer1, boundPort1) = JettyUtils.startJettyServer("localhost", startPort, Seq())
+    val (jettyServer2, boundPort2) = JettyUtils.startJettyServer("localhost", startPort, Seq())
+
+    assert(boundPort1 === startPort + 1)
+    assert(boundPort2 === startPort + 2)
+  }
+
+  test("jetty binds to port 0 correctly") {
+    val (jettyServer, boundPort) = JettyUtils.startJettyServer("localhost", 0, Seq())
+    assert(jettyServer.getState === "STARTED")
+    assert(boundPort != 0)
+    Try {new ServerSocket(boundPort)} match {
+      case Success(s) => fail("Port %s doesn't seem used by jetty server".format(boundPort))
+      case Failure  (e) =>
+    }
+  }
+
+  test("string formatting of time durations") {
+    val second = 1000
+    val minute = second * 60
+    val hour = minute * 60
+    def str = Utils.msDurationToString(_)
+
+    assert(str(123) === "123 ms")
+    assert(str(second) === "1.0 s")
+    assert(str(second + 462) === "1.5 s")
+    assert(str(hour) === "1.00 h")
+    assert(str(minute) === "1.0 m")
+    assert(str(minute + 4 * second + 34) === "1.1 m")
+    assert(str(10 * hour + minute + 4 * second) === "10.02 h")
+    assert(str(10 * hour + 59 * minute + 59 * second + 999) === "11.00 h")
+  }
+
+  test("reading last n bytes of a file") {
+    val tmpDir = Files.createTempDir()
+
+    // File smaller than limit
+    val f1Path = tmpDir + "/f1"
+    val f1 = new FileOutputStream(f1Path)
+    f1.write("a\nb\nc\nd".getBytes(Charsets.UTF_8))
+    f1.close()
+    assert(Utils.lastNBytes(f1Path, 1024) === "a\nb\nc\nd")
+
+    // File larger than limit
+    val f2Path = tmpDir + "/f2"
+    val f2 = new FileOutputStream(f2Path)
+    f2.write("1\n2\n3\n4\n5\n6\n7\n8\n".getBytes(Charsets.UTF_8))
+    f2.close()
+    assert(Utils.lastNBytes(f2Path, 8) === "5\n6\n7\n8\n")
+
+    // Request limit too
+    val f3Path = tmpDir + "/f2"
+    val f3 = new FileOutputStream(f3Path)
+    f3.write("1\n2\n3\n4\n5\n6\n7\n8\n".getBytes(Charsets.UTF_8))
+    f3.close()
+    assert(Utils.lastNBytes(f3Path, 8) === "5\n6\n7\n8\n")
+
+  }
+}
diff --git a/docs/configuration.md b/docs/configuration.md
index 3266db7af149e6366416b113fe97e31779c22728..5a805109593372df236a28e0a35595738f219f37 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -146,9 +146,16 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.ui.port</td>
-  <td>(random)</td>
+  <td>33000</td>
+  <td>
+    Port for your application's dashboard, which shows memory and workload data
+  </td>
+</tr>
+<tr>
+  <td>spark.ui.retained_stages</td>
+  <td>1000</td>
   <td>
-    Port for your application's dashboard, which shows memory usage of each RDD.
+    How many stages the Spark UI remembers before garbage collecting.
   </td>
 </tr>
 <tr>
diff --git a/examples/pom.xml b/examples/pom.xml
index 064f1179e5fc02f944f1c34a6b22250a5b0f28e9..64e68711bd4649b3647fb50b67e67b73c5261de7 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -15,6 +15,10 @@
   <url>http://spark-project.org/</url>
 
   <dependencies>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-server</artifactId>
diff --git a/mllib/data/als/test.data b/mllib/data/als/test.data
new file mode 100644
index 0000000000000000000000000000000000000000..e476cc23e047d78aa4e2bda8fa2f5cea631ef708
--- /dev/null
+++ b/mllib/data/als/test.data
@@ -0,0 +1,16 @@
+1,1,5.0
+1,2,1.0
+1,3,5.0
+1,4,1.0
+2,1,5.0
+2,2,1.0
+2,3,5.0
+2,4,1.0
+3,1,1.0
+3,2,5.0
+3,3,1.0
+3,4,5.0
+4,1,1.0
+4,2,5.0
+4,3,1.0
+4,4,5.0
diff --git a/mllib/data/lr-data/random.data b/mllib/data/lr-data/random.data
new file mode 100755
index 0000000000000000000000000000000000000000..29bcb8acbaacb8050f92c3d186b7ccc90b38d877
--- /dev/null
+++ b/mllib/data/lr-data/random.data
@@ -0,0 +1,1000 @@
+0.0,-0.19138793197590276 0.7834675900121327
+1.0,3.712420417753061 3.55967640829891
+0.0,-0.3173743619974614 0.9034702789806682
+1.0,4.759494447180777 3.407011867344781
+0.0,-0.7078607074437426 -0.7866705652344417
+1.0,2.6708084832010215 2.5322909406378016
+0.0,-0.07553885038446313 -0.1297104483563081
+1.0,2.759487072285262 2.474689814713741
+0.0,-2.2199161547238107 0.7543109438660762
+1.0,1.922617509832946 1.9412373902594937
+0.0,0.8140942462004225 1.883920822277784
+1.0,1.7649295902120172 3.8195077526061363
+0.0,-1.1173052428096684 -1.468964723960145
+1.0,1.8733449544967458 2.913026590975709
+0.0,-0.11212965215910947 1.068087981775071
+1.0,2.3368459971730227 5.453870208593922
+0.0,-1.2802488543364463 -0.47218504171867676
+1.0,4.1917343620336895 3.5602286778418355
+0.0,0.5995976502137177 -0.797374550890321
+1.0,3.721592294428238 4.824418090974808
+0.0,-0.0721649164244053 -1.3952880192542576
+1.0,3.609764030146346 3.4730043476891277
+0.0,-1.5078269860498976 -2.6460421495665987
+1.0,1.8510254911824193 1.6748364225650059
+0.0,1.021485727769095 -0.14476425336866738
+1.0,4.10105000223134 2.3772502437548493
+0.0,2.6132710211418675 -1.061646527586342
+1.0,2.6444875273854653 4.043302750329545
+0.0,1.115723715938777 0.38401588153403887
+1.0,2.045759949164019 3.156447533448806
+0.0,-1.0543022640565405 -0.6820337845705753
+1.0,3.535337069948117 3.8121122972294965
+0.0,0.9427529503486505 -0.25123516319259886
+1.0,3.9611643301316795 3.3144121016644443
+0.0,-0.15013188927817916 0.8178862482229886
+1.0,3.200504584029051 2.3088398886136057
+0.0,0.819731993393585 -0.47386644109886344
+1.0,3.283317566020217 3.4828146842654513
+0.0,-2.3283941193793303 -0.6148925379529
+1.0,3.901670215294089 3.6356776610143324
+0.0,-0.28635769830042973 0.049586437072917544
+1.0,3.1114746381043927 3.6314805300338775
+0.0,-1.3085536069757229 0.11172767926766304
+1.0,3.3676979357140744 4.689661419564771
+0.0,-1.5820787210442733 1.3226576351191428
+1.0,2.5957586701668207 3.0648240201825923
+0.0,-2.116823743560968 0.272822309954307
+1.0,3.31672509500716 3.870172182480263
+0.0,0.09751166932653511 0.6469052579904877
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diff --git a/mllib/data/ridge-data/lpsa.data b/mllib/data/ridge-data/lpsa.data
new file mode 100644
index 0000000000000000000000000000000000000000..fdd16e36b49997c7b6959cfdc66ec1faa76927ba
--- /dev/null
+++ b/mllib/data/ridge-data/lpsa.data
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diff --git a/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala
new file mode 100644
index 0000000000000000000000000000000000000000..b0e141ff3299fdb2f9d540b940e31bfb1a28b6a1
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala
@@ -0,0 +1,317 @@
+package spark.mllib.clustering
+
+import scala.collection.mutable.ArrayBuffer
+import scala.util.Random
+
+import spark.{SparkContext, RDD}
+import spark.SparkContext._
+import spark.Logging
+import spark.mllib.util.MLUtils
+
+import org.jblas.DoubleMatrix
+
+
+/**
+ * K-means clustering with support for multiple parallel runs and a k-means++ like initialization
+ * mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
+ * they are executed together with joint passes over the data for efficiency.
+ *
+ * This is an iterative algorithm that will make multiple passes over the data, so any RDDs given
+ * to it should be cached by the user.
+ */
+class KMeans private (
+    var k: Int,
+    var maxIterations: Int,
+    var runs: Int,
+    var initializationMode: String,
+    var initializationSteps: Int,
+    var epsilon: Double)
+  extends Serializable with Logging
+{
+  private type ClusterCenters = Array[Array[Double]]
+
+  def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4)
+
+  /** Set the number of clusters to create (k). Default: 2. */
+  def setK(k: Int): KMeans = {
+    this.k = k
+    this
+  }
+
+  /** Set maximum number of iterations to run. Default: 20. */
+  def setMaxIterations(maxIterations: Int): KMeans = {
+    this.maxIterations = maxIterations
+    this
+  }
+
+  /**
+   * Set the initialization algorithm. This can be either "random" to choose random points as
+   * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
+   * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
+   */
+  def setInitializationMode(initializationMode: String): KMeans = {
+    if (initializationMode != KMeans.RANDOM && initializationMode != KMeans.K_MEANS_PARALLEL) {
+      throw new IllegalArgumentException("Invalid initialization mode: " + initializationMode)
+    }
+    this.initializationMode = initializationMode
+    this
+  }
+
+  /**
+   * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm
+   * this many times with random starting conditions (configured by the initialization mode), then
+   * return the best clustering found over any run. Default: 1.
+   */
+  def setRuns(runs: Int): KMeans = {
+    if (runs <= 0) {
+      throw new IllegalArgumentException("Number of runs must be positive")
+    }
+    this.runs = runs
+    this
+  }
+
+  /**
+   * Set the number of steps for the k-means|| initialization mode. This is an advanced
+   * setting -- the default of 5 is almost always enough. Default: 5.
+   */
+  def setInitializationSteps(initializationSteps: Int): KMeans = {
+    if (initializationSteps <= 0) {
+      throw new IllegalArgumentException("Number of initialization steps must be positive")
+    }
+    this.initializationSteps = initializationSteps
+    this
+  }
+
+  /**
+   * Set the distance threshold within which we've consider centers to have converged.
+   * If all centers move less than this Euclidean distance, we stop iterating one run.
+   */
+  def setEpsilon(epsilon: Double): KMeans = {
+    this.epsilon = epsilon
+    this
+  }
+
+  /**
+   * Train a K-means model on the given set of points; `data` should be cached for high
+   * performance, because this is an iterative algorithm.
+   */
+  def train(data: RDD[Array[Double]]): KMeansModel = {
+    // TODO: check whether data is persistent; this needs RDD.storageLevel to be publicly readable
+
+    val sc = data.sparkContext
+
+    val centers = if (initializationMode == KMeans.RANDOM) {
+      initRandom(data)
+    } else {
+      initKMeansParallel(data)
+    }
+
+    val active = Array.fill(runs)(true)
+    val costs = Array.fill(runs)(0.0)
+
+    var activeRuns = new ArrayBuffer[Int] ++ (0 until runs)
+    var iteration = 0
+
+    // Execute iterations of Lloyd's algorithm until all runs have converged
+    while (iteration < maxIterations && !activeRuns.isEmpty) {
+      type WeightedPoint = (DoubleMatrix, Long)
+      def mergeContribs(p1: WeightedPoint, p2: WeightedPoint): WeightedPoint = {
+        (p1._1.addi(p2._1), p1._2 + p2._2)
+      }
+
+      val activeCenters = activeRuns.map(r => centers(r)).toArray
+      val costAccums = activeRuns.map(_ => sc.accumulator(0.0))
+
+      // Find the sum and count of points mapping to each center
+      val totalContribs = data.mapPartitions { points =>
+        val runs = activeCenters.length
+        val k = activeCenters(0).length
+        val dims = activeCenters(0)(0).length
+
+        val sums = Array.fill(runs, k)(new DoubleMatrix(dims))
+        val counts = Array.fill(runs, k)(0L)
+
+        for (point <- points; (centers, runIndex) <- activeCenters.zipWithIndex) {
+          val (bestCenter, cost) = KMeans.findClosest(centers, point)
+          costAccums(runIndex) += cost
+          sums(runIndex)(bestCenter).addi(new DoubleMatrix(point))
+          counts(runIndex)(bestCenter) += 1
+        }
+
+        val contribs = for (i <- 0 until runs; j <- 0 until k) yield {
+          ((i, j), (sums(i)(j), counts(i)(j)))
+        }
+        contribs.iterator
+      }.reduceByKey(mergeContribs).collectAsMap()
+
+      // Update the cluster centers and costs for each active run
+      for ((run, i) <- activeRuns.zipWithIndex) {
+        var changed = false
+        for (j <- 0 until k) {
+          val (sum, count) = totalContribs((i, j))
+          if (count != 0) {
+            val newCenter = sum.divi(count).data
+            if (MLUtils.squaredDistance(newCenter, centers(run)(j)) > epsilon * epsilon) {
+              changed = true
+            }
+            centers(run)(j) = newCenter
+          }
+        }
+        if (!changed) {
+          active(run) = false
+          logInfo("Run " + run + " finished in " + (iteration + 1) + " iterations")
+        }
+        costs(run) = costAccums(i).value
+      }
+
+      activeRuns = activeRuns.filter(active(_))
+      iteration += 1
+    }
+
+    val bestRun = costs.zipWithIndex.min._2
+    new KMeansModel(centers(bestRun))
+  }
+
+  /**
+   * Initialize `runs` sets of cluster centers at random.
+   */
+  private def initRandom(data: RDD[Array[Double]]): Array[ClusterCenters] = {
+    // Sample all the cluster centers in one pass to avoid repeated scans
+    val sample = data.takeSample(true, runs * k, new Random().nextInt())
+    Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k))
+  }
+
+  /**
+   * Initialize `runs` sets of cluster centers using the k-means|| algorithm by Bahmani et al.
+   * (Bahmani et al., Scalable K-Means++, VLDB 2012). This is a variant of k-means++ that tries
+   * to find with dissimilar cluster centers by starting with a random center and then doing
+   * passes where more centers are chosen with probability proportional to their squared distance
+   * to the current cluster set. It results in a provable approximation to an optimal clustering.
+   *
+   * The original paper can be found at http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf.
+   */
+  private def initKMeansParallel(data: RDD[Array[Double]]): Array[ClusterCenters] = {
+    // Initialize each run's center to a random point
+    val seed = new Random().nextInt()
+    val sample = data.takeSample(true, runs, seed)
+    val centers = Array.tabulate(runs)(r => ArrayBuffer(sample(r)))
+
+    // On each step, sample 2 * k points on average for each run with probability proportional
+    // to their squared distance from that run's current centers
+    for (step <- 0 until initializationSteps) {
+      val centerArrays = centers.map(_.toArray)
+      val sumCosts = data.flatMap { point =>
+        for (r <- 0 until runs) yield (r, KMeans.pointCost(centerArrays(r), point))
+      }.reduceByKey(_ + _).collectAsMap()
+      val chosen = data.mapPartitionsWithIndex { (index, points) =>
+        val rand = new Random(seed ^ (step << 16) ^ index)
+        for {
+          p <- points
+          r <- 0 until runs
+          if rand.nextDouble() < KMeans.pointCost(centerArrays(r), p) * 2 * k / sumCosts(r)
+        } yield (r, p)
+      }.collect()
+      for ((r, p) <- chosen) {
+        centers(r) += p
+      }
+    }
+
+    // Finally, we might have a set of more than k candidate centers for each run; weigh each
+    // candidate by the number of points in the dataset mapping to it and run a local k-means++
+    // on the weighted centers to pick just k of them
+    val centerArrays = centers.map(_.toArray)
+    val weightMap = data.flatMap { p =>
+      for (r <- 0 until runs) yield ((r, KMeans.findClosest(centerArrays(r), p)._1), 1.0)
+    }.reduceByKey(_ + _).collectAsMap()
+    val finalCenters = (0 until runs).map { r =>
+      val myCenters = centers(r).toArray
+      val myWeights = (0 until myCenters.length).map(i => weightMap.getOrElse((r, i), 0.0)).toArray
+      LocalKMeans.kMeansPlusPlus(r, myCenters, myWeights, k, 30)
+    }
+
+    finalCenters.toArray
+  }
+}
+
+
+/**
+ * Top-level methods for calling K-means clustering.
+ */
+object KMeans {
+  // Initialization mode names
+  val RANDOM = "random"
+  val K_MEANS_PARALLEL = "k-means||"
+
+  def train(
+      data: RDD[Array[Double]],
+      k: Int,
+      maxIterations: Int,
+      runs: Int,
+      initializationMode: String)
+    : KMeansModel =
+  {
+    new KMeans().setK(k)
+                .setMaxIterations(maxIterations)
+                .setRuns(runs)
+                .setInitializationMode(initializationMode)
+                .train(data)
+  }
+
+  def train(data: RDD[Array[Double]], k: Int, maxIterations: Int, runs: Int): KMeansModel = {
+    train(data, k, maxIterations, runs, K_MEANS_PARALLEL)
+  }
+
+  def train(data: RDD[Array[Double]], k: Int, maxIterations: Int): KMeansModel = {
+    train(data, k, maxIterations, 1, K_MEANS_PARALLEL)
+  }
+
+  /**
+   * Return the index of the closest point in `centers` to `point`, as well as its distance.
+   */
+  private[mllib] def findClosest(centers: Array[Array[Double]], point: Array[Double])
+    : (Int, Double) =
+  {
+    var bestDistance = Double.PositiveInfinity
+    var bestIndex = 0
+    for (i <- 0 until centers.length) {
+      val distance = MLUtils.squaredDistance(point, centers(i))
+      if (distance < bestDistance) {
+        bestDistance = distance
+        bestIndex = i
+      }
+    }
+    (bestIndex, bestDistance)
+  }
+
+  /**
+   * Return the K-means cost of a given point against the given cluster centers.
+   */
+  private[mllib] def pointCost(centers: Array[Array[Double]], point: Array[Double]): Double = {
+    var bestDistance = Double.PositiveInfinity
+    for (i <- 0 until centers.length) {
+      val distance = MLUtils.squaredDistance(point, centers(i))
+      if (distance < bestDistance) {
+        bestDistance = distance
+      }
+    }
+    bestDistance
+  }
+
+  def main(args: Array[String]) {
+    if (args.length != 4) {
+      println("Usage: KMeans <master> <input_file> <k> <max_iterations>")
+      System.exit(1)
+    }
+    val (master, inputFile, k, iters) = (args(0), args(1), args(2).toInt, args(3).toInt)
+    val sc = new SparkContext(master, "KMeans")
+    val data = sc.textFile(inputFile).map(line => line.split(' ').map(_.toDouble))
+    val model = KMeans.train(data, k, iters)
+    val cost = model.computeCost(data)
+    println("Cluster centers:")
+    for (c <- model.clusterCenters) {
+      println("  " + c.mkString(" "))
+    }
+    println("Cost: " + cost)
+    System.exit(0)
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala
new file mode 100644
index 0000000000000000000000000000000000000000..4fd0646160e08e05f7a0bdcaef31ebde1833c8ae
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala
@@ -0,0 +1,27 @@
+package spark.mllib.clustering
+
+import spark.RDD
+import spark.SparkContext._
+import spark.mllib.util.MLUtils
+
+
+/**
+ * A clustering model for K-means. Each point belongs to the cluster with the closest center.
+ */
+class KMeansModel(val clusterCenters: Array[Array[Double]]) extends Serializable {
+  /** Total number of clusters. */
+  def k: Int = clusterCenters.length
+
+  /** Return the cluster index that a given point belongs to. */
+  def predict(point: Array[Double]): Int = {
+    KMeans.findClosest(clusterCenters, point)._1
+  }
+
+  /**
+   * Return the K-means cost (sum of squared distances of points to their nearest center) for this
+   * model on the given data.
+   */
+  def computeCost(data: RDD[Array[Double]]): Double = {
+    data.map(p => KMeans.pointCost(clusterCenters, p)).sum
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala b/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala
new file mode 100644
index 0000000000000000000000000000000000000000..e12b3be251246d643a9bbefa4c96e09a55ac4e0d
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala
@@ -0,0 +1,88 @@
+package spark.mllib.clustering
+
+import scala.util.Random
+
+import org.jblas.{DoubleMatrix, SimpleBlas}
+
+/**
+ * An utility object to run K-means locally. This is private to the ML package because it's used
+ * in the initialization of KMeans but not meant to be publicly exposed.
+ */
+private[mllib] object LocalKMeans {
+  /**
+   * Run K-means++ on the weighted point set `points`. This first does the K-means++
+   * initialization procedure and then roudns of Lloyd's algorithm.
+   */
+  def kMeansPlusPlus(
+      seed: Int,
+      points: Array[Array[Double]],
+      weights: Array[Double],
+      k: Int,
+      maxIterations: Int)
+    : Array[Array[Double]] =
+  {
+    val rand = new Random(seed)
+    val dimensions = points(0).length
+    val centers = new Array[Array[Double]](k)
+
+    // Initialize centers by sampling using the k-means++ procedure
+    centers(0) = pickWeighted(rand, points, weights)
+    for (i <- 1 until k) {
+      // Pick the next center with a probability proportional to cost under current centers
+      val curCenters = centers.slice(0, i)
+      val sum = points.zip(weights).map { case (p, w) =>
+        w * KMeans.pointCost(curCenters, p)
+      }.sum
+      val r = rand.nextDouble() * sum
+      var cumulativeScore = 0.0
+      var j = 0
+      while (j < points.length && cumulativeScore < r) {
+        cumulativeScore += weights(j) * KMeans.pointCost(curCenters, points(j))
+        j += 1
+      }
+      centers(i) = points(j-1)
+    }
+
+    // Run up to maxIterations iterations of Lloyd's algorithm
+    val oldClosest = Array.fill(points.length)(-1)
+    var iteration = 0
+    var moved = true
+    while (moved && iteration < maxIterations) {
+      moved = false
+      val sums = Array.fill(k)(new DoubleMatrix(dimensions))
+      val counts = Array.fill(k)(0.0)
+      for ((p, i) <- points.zipWithIndex) {
+        val index = KMeans.findClosest(centers, p)._1
+        SimpleBlas.axpy(weights(i), new DoubleMatrix(p), sums(index))
+        counts(index) += weights(i)
+        if (index != oldClosest(i)) {
+          moved = true
+          oldClosest(i) = index
+        }
+      }
+      // Update centers
+      for (i <- 0 until k) {
+        if (counts(i) == 0.0) {
+          // Assign center to a random point
+          centers(i) = points(rand.nextInt(points.length))
+        } else {
+          centers(i) = sums(i).divi(counts(i)).data
+        }
+      }
+      iteration += 1
+    }
+
+    centers
+  }
+
+  private def pickWeighted[T](rand: Random, data: Array[T], weights: Array[Double]): T = {
+    val r = rand.nextDouble() * weights.sum
+    var i = 0
+    var curWeight = 0.0
+    while (i < data.length && curWeight < r) {
+      curWeight += weights(i)
+      i += 1
+    }
+    data(i - 1)
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala
new file mode 100644
index 0000000000000000000000000000000000000000..90b0999a5ec40c7af2ddba908de89f1ec7ee4b89
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala
@@ -0,0 +1,33 @@
+package spark.mllib.optimization
+
+import org.jblas.DoubleMatrix
+
+abstract class Gradient extends Serializable {
+  /**
+   * Compute the gradient for a given row of data.
+   *
+   * @param data - One row of data. Row matrix of size 1xn where n is the number of features.
+   * @param label - Label for this data item.
+   * @param weights - Column matrix containing weights for every feature.
+   */
+  def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): 
+      (DoubleMatrix, Double)
+}
+
+class LogisticGradient extends Gradient {
+  override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): 
+      (DoubleMatrix, Double) = {
+    val margin: Double = -1.0 * data.dot(weights)
+    val gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label
+
+    val gradient = data.mul(gradientMultiplier)
+    val loss =
+      if (margin > 0) {
+        math.log(1 + math.exp(0 - margin))
+      } else {
+        math.log(1 + math.exp(margin)) - margin
+      }
+
+    (gradient, loss)
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala
new file mode 100644
index 0000000000000000000000000000000000000000..eff853f379b8aac6f8dab410f5091123e5388ad4
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala
@@ -0,0 +1,62 @@
+package spark.mllib.optimization
+
+import spark.{Logging, RDD, SparkContext}
+import spark.SparkContext._
+
+import org.jblas.DoubleMatrix
+
+import scala.collection.mutable.ArrayBuffer
+
+
+object GradientDescent {
+
+  /**
+   * Run gradient descent in parallel using mini batches.
+   * Based on Matlab code written by John Duchi.
+   *
+   * @param data - Input data for SGD. RDD of form (label, [feature values]).
+   * @param gradient - Gradient object that will be used to compute the gradient.
+   * @param updater - Updater object that will be used to update the model.
+   * @param stepSize - stepSize to be used during update.
+   * @param numIters - number of iterations that SGD should be run.
+   * @param miniBatchFraction - fraction of the input data set that should be used for
+   *                            one iteration of SGD. Default value 1.0.
+   *
+   * @return weights - Column matrix containing weights for every feature.
+   * @return lossHistory - Array containing the loss computed for every iteration.
+   */
+  def runMiniBatchSGD(
+    data: RDD[(Double, Array[Double])],
+    gradient: Gradient,
+    updater: Updater,
+    stepSize: Double,
+    numIters: Int,
+    miniBatchFraction: Double=1.0) : (DoubleMatrix, Array[Double]) = {
+
+    val lossHistory = new ArrayBuffer[Double](numIters)
+
+    val nfeatures: Int = data.take(1)(0)._2.length
+    val nexamples: Long = data.count()
+    val miniBatchSize = nexamples * miniBatchFraction
+
+    // Initialize weights as a column matrix
+    var weights = DoubleMatrix.ones(nfeatures)
+    var reg_val = 0.0
+
+    for (i <- 1 to numIters) {
+      val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42+i).map {
+        case (y, features) =>
+          val featuresRow = new DoubleMatrix(features.length, 1, features:_*)
+          val (grad, loss) = gradient.compute(featuresRow, y, weights)
+          (grad, loss)
+      }.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2))
+
+      lossHistory.append(lossSum / miniBatchSize + reg_val)
+      val update = updater.compute(weights, gradientSum.div(miniBatchSize), stepSize, i)
+      weights = update._1
+      reg_val = update._2
+    }
+
+    (weights, lossHistory.toArray)
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala
new file mode 100644
index 0000000000000000000000000000000000000000..ea80bfcbfd40500b9e795db30cc351f21c533f8c
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala
@@ -0,0 +1,27 @@
+package spark.mllib.optimization
+
+import org.jblas.DoubleMatrix
+
+abstract class Updater extends Serializable {
+  /**
+   * Compute an updated value for weights given the gradient, stepSize and iteration number.
+   *
+   * @param weightsOld - Column matrix of size nx1 where n is the number of features.
+   * @param gradient - Column matrix of size nx1 where n is the number of features.
+   * @param stepSize - step size across iterations
+   * @param iter - Iteration number
+   *
+   * @return weightsNew - Column matrix containing updated weights
+   * @return reg_val - regularization value
+   */
+  def compute(weightsOlds: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int):
+      (DoubleMatrix, Double)
+}
+
+class SimpleUpdater extends Updater {
+  override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix,
+      stepSize: Double, iter: Int): (DoubleMatrix, Double) = {
+    val normGradient = gradient.mul(stepSize / math.sqrt(iter))
+    (weightsOld.sub(normGradient), 0)
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
new file mode 100644
index 0000000000000000000000000000000000000000..6c9fb2359cb91bfbd8e5c0f708cad234bbb2cc05
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
@@ -0,0 +1,389 @@
+package spark.mllib.recommendation
+
+import scala.collection.mutable.{ArrayBuffer, BitSet}
+import scala.util.Random
+
+import spark.{HashPartitioner, Partitioner, SparkContext, RDD}
+import spark.storage.StorageLevel
+import spark.SparkContext._
+
+import org.jblas.{DoubleMatrix, SimpleBlas, Solve}
+
+
+/**
+ * Out-link information for a user or product block. This includes the original user/product IDs
+ * of the elements within this block, and the list of destination blocks that each user or
+ * product will need to send its feature vector to.
+ */
+private[recommendation] case class OutLinkBlock(
+  elementIds: Array[Int], shouldSend: Array[BitSet])
+
+
+/**
+ * In-link information for a user (or product) block. This includes the original user/product IDs
+ * of the elements within this block, as well as an array of indices and ratings that specify
+ * which user in the block will be rated by which products from each product block (or vice-versa).
+ * Specifically, if this InLinkBlock is for users, ratingsForBlock(b)(i) will contain two arrays,
+ * indices and ratings, for the i'th product that will be sent to us by product block b (call this
+ * P). These arrays represent the users that product P had ratings for (by their index in this
+ * block), as well as the corresponding rating for each one. We can thus use this information when
+ * we get product block b's message to update the corresponding users.
+ */
+private[recommendation] case class InLinkBlock(
+  elementIds: Array[Int], ratingsForBlock: Array[Array[(Array[Int], Array[Double])]])
+
+
+/**
+ * Alternating Least Squares matrix factorization.
+ *
+ * This is a blocked implementation of the ALS factorization algorithm that groups the two sets
+ * of factors (referred to as "users" and "products") into blocks and reduces communication by only
+ * sending one copy of each user vector to each product block on each iteration, and only for the
+ * product blocks that need that user's feature vector. This is achieved by precomputing some
+ * information about the ratings matrix to determine the "out-links" of each user (which blocks of
+ * products it will contribute to) and "in-link" information for each product (which of the feature
+ * vectors it receives from each user block it will depend on). This allows us to send only an
+ * array of feature vectors between each user block and product block, and have the product block
+ * find the users' ratings and update the products based on these messages.
+ */
+class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var lambda: Double)
+  extends Serializable
+{
+  def this() = this(-1, 10, 10, 0.01)
+
+  /**
+   * Set the number of blocks to parallelize the computation into; pass -1 for an auto-configured
+   * number of blocks. Default: -1.
+   */
+  def setBlocks(numBlocks: Int): ALS = {
+    this.numBlocks = numBlocks
+    this
+  }
+
+  /** Set the rank of the feature matrices computed (number of features). Default: 10. */
+  def setRank(rank: Int): ALS = {
+    this.rank = rank
+    this
+  }
+
+  /** Set the number of iterations to run. Default: 10. */
+  def setIterations(iterations: Int): ALS = {
+    this.iterations = iterations
+    this
+  }
+
+  /** Set the regularization parameter, lambda. Default: 0.01. */
+  def setLambda(lambda: Double): ALS = {
+    this.lambda = lambda
+    this
+  }
+
+  /**
+   * Run ALS with the configured parmeters on an input RDD of (user, product, rating) triples.
+   * Returns a MatrixFactorizationModel with feature vectors for each user and product.
+   */
+  def train(ratings: RDD[(Int, Int, Double)]): MatrixFactorizationModel = {
+    val numBlocks = if (this.numBlocks == -1) {
+      math.max(ratings.context.defaultParallelism, ratings.partitions.size)
+    } else {
+      this.numBlocks
+    }
+
+    val partitioner = new HashPartitioner(numBlocks)
+
+    val ratingsByUserBlock = ratings.map{ case (u, p, r) => (u % numBlocks, (u, p, r)) }
+    val ratingsByProductBlock = ratings.map{ case (u, p, r) => (p % numBlocks, (p, u, r)) }
+
+    val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock)
+    val (productInLinks, productOutLinks) = makeLinkRDDs(numBlocks, ratingsByProductBlock)
+
+    // Initialize user and product factors randomly
+    val seed = new Random().nextInt()
+    var users = userOutLinks.mapValues(_.elementIds.map(u => randomFactor(rank, seed ^ u)))
+    var products = productOutLinks.mapValues(_.elementIds.map(p => randomFactor(rank, seed ^ ~p)))
+
+    for (iter <- 0 until iterations) {
+      // perform ALS update
+      products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda)
+      users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda)
+    }
+
+    // Flatten and cache the two final RDDs to un-block them
+    val usersOut = users.join(userOutLinks).flatMap { case (b, (factors, outLinkBlock)) =>
+      for (i <- 0 until factors.length) yield (outLinkBlock.elementIds(i), factors(i))
+    }
+    val productsOut = products.join(productOutLinks).flatMap { case (b, (factors, outLinkBlock)) =>
+      for (i <- 0 until factors.length) yield (outLinkBlock.elementIds(i), factors(i))
+    }
+
+    usersOut.persist()
+    productsOut.persist()
+
+    new MatrixFactorizationModel(rank, usersOut, productsOut)
+  }
+
+  /**
+   * Make the out-links table for a block of the users (or products) dataset given the list of
+   * (user, product, rating) values for the users in that block (or the opposite for products).
+   */
+  private def makeOutLinkBlock(numBlocks: Int, ratings: Array[(Int, Int, Double)]): OutLinkBlock = {
+    val userIds = ratings.map(_._1).distinct.sorted
+    val numUsers = userIds.length
+    val userIdToPos = userIds.zipWithIndex.toMap
+    val shouldSend = Array.fill(numUsers)(new BitSet(numBlocks))
+    for ((u, p, r) <- ratings) {
+      shouldSend(userIdToPos(u))(p % numBlocks) = true
+    }
+    OutLinkBlock(userIds, shouldSend)
+  }
+
+  /**
+   * Make the in-links table for a block of the users (or products) dataset given a list of
+   * (user, product, rating) values for the users in that block (or the opposite for products).
+   */
+  private def makeInLinkBlock(numBlocks: Int, ratings: Array[(Int, Int, Double)]): InLinkBlock = {
+    val userIds = ratings.map(_._1).distinct.sorted
+    val numUsers = userIds.length
+    val userIdToPos = userIds.zipWithIndex.toMap
+    val ratingsForBlock = new Array[Array[(Array[Int], Array[Double])]](numBlocks)
+    for (productBlock <- 0 until numBlocks) {
+      val ratingsInBlock = ratings.filter(t => t._2 % numBlocks == productBlock)
+      val ratingsByProduct = ratingsInBlock.groupBy(_._2)  // (p, Seq[(u, p, r)])
+                               .toArray
+                               .sortBy(_._1)
+                               .map{case (p, rs) => (rs.map(t => userIdToPos(t._1)), rs.map(_._3))}
+      ratingsForBlock(productBlock) = ratingsByProduct
+    }
+    InLinkBlock(userIds, ratingsForBlock)
+  }
+
+  /**
+   * Make RDDs of InLinkBlocks and OutLinkBlocks given an RDD of (blockId, (u, p, r)) values for
+   * the users (or (blockId, (p, u, r)) for the products). We create these simultaneously to avoid
+   * having to shuffle the (blockId, (u, p, r)) RDD twice, or to cache it.
+   */
+  private def makeLinkRDDs(numBlocks: Int, ratings: RDD[(Int, (Int, Int, Double))])
+    : (RDD[(Int, InLinkBlock)], RDD[(Int, OutLinkBlock)]) =
+  {
+    val grouped = ratings.partitionBy(new HashPartitioner(numBlocks))
+    val links = grouped.mapPartitionsWithIndex((blockId, elements) => {
+      val ratings = elements.map(_._2).toArray
+      val inLinkBlock = makeInLinkBlock(numBlocks, ratings)
+      val outLinkBlock = makeOutLinkBlock(numBlocks, ratings)
+      Iterator.single((blockId, (inLinkBlock, outLinkBlock)))
+    }, true)
+    links.persist(StorageLevel.MEMORY_AND_DISK)
+    (links.mapValues(_._1), links.mapValues(_._2))
+  }
+
+  /**
+   * Make a random factor vector with the given seed.
+   * TODO: Initialize things using mapPartitionsWithIndex to make it faster?
+   */
+  private def randomFactor(rank: Int, seed: Int): Array[Double] = {
+    val rand = new Random(seed)
+    Array.fill(rank)(rand.nextDouble)
+  }
+
+  /**
+   * Compute the user feature vectors given the current products (or vice-versa). This first joins
+   * the products with their out-links to generate a set of messages to each destination block
+   * (specifically, the features for the products that user block cares about), then groups these
+   * by destination and joins them with the in-link info to figure out how to update each user.
+   * It returns an RDD of new feature vectors for each user block.
+   */
+  private def updateFeatures(
+      products: RDD[(Int, Array[Array[Double]])],
+      productOutLinks: RDD[(Int, OutLinkBlock)],
+      userInLinks: RDD[(Int, InLinkBlock)],
+      partitioner: Partitioner,
+      rank: Int,
+      lambda: Double)
+    : RDD[(Int, Array[Array[Double]])] =
+  {
+    val numBlocks = products.partitions.size
+    productOutLinks.join(products).flatMap { case (bid, (outLinkBlock, factors)) =>
+        val toSend = Array.fill(numBlocks)(new ArrayBuffer[Array[Double]])
+        for (p <- 0 until outLinkBlock.elementIds.length; userBlock <- 0 until numBlocks) {
+          if (outLinkBlock.shouldSend(p)(userBlock)) {
+            toSend(userBlock) += factors(p)
+          }
+        }
+        toSend.zipWithIndex.map{ case (buf, idx) => (idx, (bid, buf.toArray)) }
+    }.groupByKey(partitioner)
+     .join(userInLinks)
+     .mapValues{ case (messages, inLinkBlock) => updateBlock(messages, inLinkBlock, rank, lambda) }
+  }
+
+  /**
+   * Compute the new feature vectors for a block of the users matrix given the list of factors
+   * it received from each product and its InLinkBlock.
+   */
+  def updateBlock(messages: Seq[(Int, Array[Array[Double]])], inLinkBlock: InLinkBlock,
+      rank: Int, lambda: Double)
+    : Array[Array[Double]] =
+  {
+    // Sort the incoming block factor messages by block ID and make them an array
+    val blockFactors = messages.sortBy(_._1).map(_._2).toArray // Array[Array[Double]]
+    val numBlocks = blockFactors.length
+    val numUsers = inLinkBlock.elementIds.length
+
+    // We'll sum up the XtXes using vectors that represent only the lower-triangular part, since
+    // the matrices are symmetric
+    val triangleSize = rank * (rank + 1) / 2
+    val userXtX = Array.fill(numUsers)(DoubleMatrix.zeros(triangleSize))
+    val userXy = Array.fill(numUsers)(DoubleMatrix.zeros(rank))
+
+    // Some temp variables to avoid memory allocation
+    val tempXtX = DoubleMatrix.zeros(triangleSize)
+    val fullXtX = DoubleMatrix.zeros(rank, rank)
+
+    // Compute the XtX and Xy values for each user by adding products it rated in each product block
+    for (productBlock <- 0 until numBlocks) {
+      for (p <- 0 until blockFactors(productBlock).length) {
+        val x = new DoubleMatrix(blockFactors(productBlock)(p))
+        fillXtX(x, tempXtX)
+        val (us, rs) = inLinkBlock.ratingsForBlock(productBlock)(p)
+        for (i <- 0 until us.length) {
+          userXtX(us(i)).addi(tempXtX)
+          SimpleBlas.axpy(rs(i), x, userXy(us(i)))
+        }
+      }
+    }
+
+    // Solve the least-squares problem for each user and return the new feature vectors
+    userXtX.zipWithIndex.map{ case (triangularXtX, index) =>
+      // Compute the full XtX matrix from the lower-triangular part we got above
+      fillFullMatrix(triangularXtX, fullXtX)
+      // Add regularization
+      (0 until rank).foreach(i => fullXtX.data(i*rank + i) += lambda)
+      // Solve the resulting matrix, which is symmetric and positive-definite
+      Solve.solvePositive(fullXtX, userXy(index)).data
+    }
+  }
+
+  /**
+   * Set xtxDest to the lower-triangular part of x transpose * x. For efficiency in summing
+   * these matrices, we store xtxDest as only rank * (rank+1) / 2 values, namely the values
+   * at (0,0), (1,0), (1,1), (2,0), (2,1), (2,2), etc in that order.
+   */
+  private def fillXtX(x: DoubleMatrix, xtxDest: DoubleMatrix) {
+    var i = 0
+    var pos = 0
+    while (i < x.length) {
+      var j = 0
+      while (j <= i) {
+        xtxDest.data(pos) = x.data(i) * x.data(j)
+        pos += 1
+        j += 1
+      }
+      i += 1
+    }
+  }
+
+  /**
+   * Given a triangular matrix in the order of fillXtX above, compute the full symmetric square
+   * matrix that it represents, storing it into destMatrix.
+   */
+  private def fillFullMatrix(triangularMatrix: DoubleMatrix, destMatrix: DoubleMatrix) {
+    val rank = destMatrix.rows
+    var i = 0
+    var pos = 0
+    while (i < rank) {
+      var j = 0
+      while (j <= i) {
+        destMatrix.data(i*rank + j) = triangularMatrix.data(pos)
+        destMatrix.data(j*rank + i) = triangularMatrix.data(pos)
+        pos += 1
+        j += 1
+      }
+      i += 1
+    }
+  }
+}
+
+
+/**
+ * Top-level methods for calling Alternating Least Squares (ALS) matrix factorizaton.
+ */
+object ALS {
+  /**
+   * Train a matrix factorization model given an RDD of ratings given by users to some products,
+   * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the
+   * product of two lower-rank matrices of a given rank (number of features). To solve for these
+   * features, we run a given number of iterations of ALS. This is done using a level of
+   * parallelism given by `blocks`.
+   *
+   * @param ratings    RDD of (userID, productID, rating) pairs
+   * @param rank       number of features to use
+   * @param iterations number of iterations of ALS (recommended: 10-20)
+   * @param lambda     regularization factor (recommended: 0.01)
+   * @param blocks     level of parallelism to split computation into
+   */
+  def train(
+      ratings: RDD[(Int, Int, Double)],
+      rank: Int,
+      iterations: Int,
+      lambda: Double,
+      blocks: Int)
+    : MatrixFactorizationModel =
+  {
+    new ALS(blocks, rank, iterations, lambda).train(ratings)
+  }
+
+  /**
+   * Train a matrix factorization model given an RDD of ratings given by users to some products,
+   * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the
+   * product of two lower-rank matrices of a given rank (number of features). To solve for these
+   * features, we run a given number of iterations of ALS. The level of parallelism is determined
+   * automatically based on the number of partitions in `ratings`.
+   *
+   * @param ratings    RDD of (userID, productID, rating) pairs
+   * @param rank       number of features to use
+   * @param iterations number of iterations of ALS (recommended: 10-20)
+   * @param lambda     regularization factor (recommended: 0.01)
+   */
+  def train(ratings: RDD[(Int, Int, Double)], rank: Int, iterations: Int, lambda: Double)
+    : MatrixFactorizationModel =
+  {
+    train(ratings, rank, iterations, lambda, -1)
+  }
+
+  /**
+   * Train a matrix factorization model given an RDD of ratings given by users to some products,
+   * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the
+   * product of two lower-rank matrices of a given rank (number of features). To solve for these
+   * features, we run a given number of iterations of ALS. The level of parallelism is determined
+   * automatically based on the number of partitions in `ratings`.
+   *
+   * @param ratings    RDD of (userID, productID, rating) pairs
+   * @param rank       number of features to use
+   * @param iterations number of iterations of ALS (recommended: 10-20)
+   */
+  def train(ratings: RDD[(Int, Int, Double)], rank: Int, iterations: Int)
+    : MatrixFactorizationModel =
+  {
+    train(ratings, rank, iterations, 0.01, -1)
+  }
+
+  def main(args: Array[String]) {
+    if (args.length != 5) {
+      println("Usage: ALS <master> <ratings_file> <rank> <iterations> <output_dir>")
+      System.exit(1)
+    }
+    val (master, ratingsFile, rank, iters, outputDir) =
+      (args(0), args(1), args(2).toInt, args(3).toInt, args(4))
+    val sc = new SparkContext(master, "ALS")
+    val ratings = sc.textFile(ratingsFile).map { line =>
+      val fields = line.split(',')
+      (fields(0).toInt, fields(1).toInt, fields(2).toDouble)
+    }
+    val model = ALS.train(ratings, rank, iters)
+    model.userFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") }
+                      .saveAsTextFile(outputDir + "/userFeatures")
+    model.productFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") }
+                         .saveAsTextFile(outputDir + "/productFeatures")
+    println("Final user/product features written to " + outputDir)
+    System.exit(0)
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala
new file mode 100644
index 0000000000000000000000000000000000000000..fb812a6dbeb5322f8b8c31565c8205e0b3f10628
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -0,0 +1,23 @@
+package spark.mllib.recommendation
+
+import spark.RDD
+import spark.SparkContext._
+
+import org.jblas._
+
+class MatrixFactorizationModel(
+    val rank: Int,
+    val userFeatures: RDD[(Int, Array[Double])],
+    val productFeatures: RDD[(Int, Array[Double])])
+  extends Serializable
+{
+  /** Predict the rating of one user for one product. */
+  def predict(user: Int, product: Int): Double = {
+    val userVector = new DoubleMatrix(userFeatures.lookup(user).head)
+    val productVector = new DoubleMatrix(productFeatures.lookup(product).head)
+    userVector.dot(productVector)
+  }
+
+  // TODO: Figure out what 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/main/scala/spark/mllib/regression/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala
new file mode 100644
index 0000000000000000000000000000000000000000..e4db7bb9b736fad3287436cecce1403299659e3e
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala
@@ -0,0 +1,158 @@
+package spark.mllib.regression
+
+import spark.{Logging, RDD, SparkContext}
+import spark.mllib.optimization._
+import spark.mllib.util.MLUtils
+
+import org.jblas.DoubleMatrix
+
+/**
+ * Logistic Regression using Stochastic Gradient Descent.
+ * Based on Matlab code written by John Duchi.
+ */
+class LogisticRegressionModel(
+  val weights: DoubleMatrix,
+  val intercept: Double,
+  val losses: Array[Double]) extends RegressionModel {
+
+  override def predict(testData: spark.RDD[Array[Double]]) = {
+    testData.map { x =>
+      val margin = new DoubleMatrix(1, x.length, x:_*).mmul(this.weights).get(0) + this.intercept
+      1.0/ (1.0 + math.exp(margin * -1))
+    }
+  }
+
+  override def predict(testData: Array[Double]): Double = {
+    val dataMat = new DoubleMatrix(1, testData.length, testData:_*)
+    val margin = dataMat.mmul(this.weights).get(0) + this.intercept
+    1.0/ (1.0 + math.exp(margin * -1))
+  }
+}
+
+class LogisticRegression private (var stepSize: Double, var miniBatchFraction: Double,
+    var numIters: Int)
+  extends Logging {
+
+  /**
+   * Construct a LogisticRegression object with default parameters
+   */
+  def this() = this(1.0, 1.0, 100)
+
+  /**
+   * Set the step size per-iteration of SGD. Default 1.0.
+   */
+  def setStepSize(step: Double) = {
+    this.stepSize = step
+    this
+  }
+
+  /**
+   * Set fraction of data to be used for each SGD iteration. Default 1.0.
+   */
+  def setMiniBatchFraction(fraction: Double) = {
+    this.miniBatchFraction = fraction
+    this
+  }
+
+  /**
+   * Set the number of iterations for SGD. Default 100.
+   */
+  def setNumIterations(iters: Int) = {
+    this.numIters = iters
+    this
+  }
+
+  def train(input: RDD[(Double, Array[Double])]): LogisticRegressionModel = {
+    // Add a extra variable consisting of all 1.0's for the intercept.
+    val data = input.map { case (y, features) =>
+      (y, Array(1.0, features:_*))
+    }
+
+    val (weights, losses) = GradientDescent.runMiniBatchSGD(
+      data, new LogisticGradient(), new SimpleUpdater(), stepSize, numIters, miniBatchFraction)
+
+    val weightsScaled = weights.getRange(1, weights.length)
+    val intercept = weights.get(0)
+
+    val model = new LogisticRegressionModel(weightsScaled, intercept, losses)
+
+    logInfo("Final model weights " + model.weights)
+    logInfo("Final model intercept " + model.intercept)
+    logInfo("Last 10 losses " + model.losses.takeRight(10).mkString(", "))
+    model
+  }
+}
+
+/**
+ * Top-level methods for calling Logistic Regression.
+ */
+object LogisticRegression {
+
+  /**
+   * Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed number
+   * of iterations of gradient descent using the specified step size. Each iteration uses
+   * `miniBatchFraction` fraction of the data to calculate the gradient.
+   *
+   * @param input RDD of (label, array of features) pairs.
+   * @param numIterations Number of iterations of gradient descent to run.
+   * @param stepSize Step size to be used for each iteration of gradient descent.
+   * @param miniBatchFraction Fraction of data to be used per iteration.
+   */
+  def train(
+      input: RDD[(Double, Array[Double])],
+      numIterations: Int,
+      stepSize: Double,
+      miniBatchFraction: Double)
+    : LogisticRegressionModel =
+  {
+    new LogisticRegression(stepSize, miniBatchFraction, numIterations).train(input)
+  }
+
+  /**
+   * Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed number
+   * of iterations of gradient descent using the specified step size. We use the entire data set to update
+   * the gradient in each iteration.
+   *
+   * @param input RDD of (label, array of features) pairs.
+   * @param stepSize Step size to be used for each iteration of Gradient Descent.
+   * @param numIterations Number of iterations of gradient descent to run.
+   * @return a LogisticRegressionModel which has the weights and offset from training.
+   */
+  def train(
+      input: RDD[(Double, Array[Double])],
+      numIterations: Int,
+      stepSize: Double)
+    : LogisticRegressionModel =
+  {
+    train(input, numIterations, stepSize, 1.0)
+  }
+
+  /**
+   * Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed number
+   * of iterations of gradient descent using a step size of 1.0. We use the entire data set to update
+   * the gradient in each iteration.
+   *
+   * @param input RDD of (label, array of features) pairs.
+   * @param numIterations Number of iterations of gradient descent to run.
+   * @return a LogisticRegressionModel which has the weights and offset from training.
+   */
+  def train(
+      input: RDD[(Double, Array[Double])],
+      numIterations: Int)
+    : LogisticRegressionModel =
+  {
+    train(input, numIterations, 1.0, 1.0)
+  }
+
+  def main(args: Array[String]) {
+    if (args.length != 4) {
+      println("Usage: LogisticRegression <master> <input_dir> <step_size> <niters>")
+      System.exit(1)
+    }
+    val sc = new SparkContext(args(0), "LogisticRegression")
+    val data = MLUtils.loadLabeledData(sc, args(1))
+    val model = LogisticRegression.train(data, args(3).toInt, args(2).toDouble)
+
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/regression/LogisticRegressionGenerator.scala b/mllib/src/main/scala/spark/mllib/regression/LogisticRegressionGenerator.scala
new file mode 100644
index 0000000000000000000000000000000000000000..6e7c023bac79bcaeddef7b7c06afe72901605065
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/regression/LogisticRegressionGenerator.scala
@@ -0,0 +1,41 @@
+package spark.mllib.regression
+
+import scala.util.Random
+
+import org.jblas.DoubleMatrix
+
+import spark.{RDD, SparkContext}
+import spark.mllib.util.MLUtils
+
+object LogisticRegressionGenerator {
+
+  def main(args: Array[String]) {
+    if (args.length != 5) {
+      println("Usage: LogisticRegressionGenerator " +
+        "<master> <output_dir> <num_examples> <num_features> <num_partitions>")
+      System.exit(1)
+    }
+
+    val sparkMaster: String = args(0)
+    val outputPath: String = args(1)
+    val nexamples: Int = if (args.length > 2) args(2).toInt else 1000
+    val nfeatures: Int = if (args.length > 3) args(3).toInt else 2
+    val parts: Int = if (args.length > 4) args(4).toInt else 2
+    val eps = 3
+
+    val sc = new SparkContext(sparkMaster, "LogisticRegressionGenerator")
+
+    val data: RDD[(Double, Array[Double])] = sc.parallelize(0 until nexamples, parts).map { idx =>
+      val rnd = new Random(42 + idx)
+
+      val y = if (idx % 2 == 0) 0 else 1
+      val x = Array.fill[Double](nfeatures) {
+        rnd.nextGaussian() + (y * eps)
+      }
+      (y, x)
+    }
+
+    MLUtils.saveLabeledData(data, outputPath)
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/regression/Regression.scala b/mllib/src/main/scala/spark/mllib/regression/Regression.scala
new file mode 100644
index 0000000000000000000000000000000000000000..f79974c1915f9d593df0f868becdc3b9c2a8dae8
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/regression/Regression.scala
@@ -0,0 +1,21 @@
+package spark.mllib.regression
+
+import spark.RDD
+
+trait RegressionModel {
+  /**
+   * Predict values for the given data set using the model trained.
+   *
+   * @param testData RDD representing data points to be predicted
+   * @return RDD[Double] where each entry contains the corresponding prediction
+   */
+  def predict(testData: RDD[Array[Double]]): RDD[Double]
+
+  /**
+   * Predict values for a single data point using the model trained.
+   *
+   * @param testData array representing a single data point
+   * @return Double prediction from the trained model
+   */
+  def predict(testData: Array[Double]): Double
+}
diff --git a/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala
new file mode 100644
index 0000000000000000000000000000000000000000..5f813df402afa4f4a8fe9ec265d52382c0136f7c
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala
@@ -0,0 +1,194 @@
+package spark.mllib.regression
+
+import spark.{Logging, RDD, SparkContext}
+import spark.mllib.util.MLUtils
+
+import org.jblas.DoubleMatrix
+import org.jblas.Solve
+
+import scala.annotation.tailrec
+import scala.collection.mutable
+
+/**
+ * Ridge Regression from Joseph Gonzalez's implementation in MLBase
+ */
+class RidgeRegressionModel(
+    val weights: DoubleMatrix,
+    val intercept: Double,
+    val lambdaOpt: Double,
+    val lambdas: Seq[(Double, Double, DoubleMatrix)])
+  extends RegressionModel {
+
+  override def predict(testData: RDD[Array[Double]]): RDD[Double] = {
+    testData.map { x =>
+      (new DoubleMatrix(1, x.length, x:_*).mmul(this.weights)).get(0) + this.intercept
+    }
+  }
+
+  override def predict(testData: Array[Double]): Double = {
+    (new DoubleMatrix(1, testData.length, testData:_*).mmul(this.weights)).get(0) + this.intercept
+  }
+}
+
+class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double)
+  extends Logging {
+
+  def this() = this(0.0, 100.0)
+
+  /**
+   * Set the lower bound on binary search for lambda's. Default is 0.
+   */
+  def setLowLambda(low: Double) = {
+    this.lambdaLow = low
+    this
+  }
+
+  /**
+   * Set the upper bound on binary search for lambda's. Default is 100.0.
+   */
+  def setHighLambda(hi: Double) = {
+    this.lambdaHigh = hi
+    this
+  }
+
+  def train(input: RDD[(Double, Array[Double])]): RidgeRegressionModel = {
+    val nfeatures: Int = input.take(1)(0)._2.length
+    val nexamples: Long = input.count()
+
+    val (yMean, xColMean, xColSd) = MLUtils.computeStats(input, nfeatures, nexamples)
+
+    val data = input.map { case(y, features) =>
+      val yNormalized = y - yMean
+      val featuresMat = new DoubleMatrix(nfeatures, 1, features:_*)
+      val featuresNormalized = featuresMat.sub(xColMean).divi(xColSd)
+      (yNormalized, featuresNormalized.toArray)
+    }
+
+    // Compute XtX - Size of XtX is nfeatures by nfeatures
+    val XtX: DoubleMatrix = data.map { case (y, features) =>
+      val x = new DoubleMatrix(1, features.length, features:_*)
+      x.transpose().mmul(x)
+    }.reduce(_.addi(_))
+
+    // Compute Xt*y - Size of Xty is nfeatures by 1
+    val Xty: DoubleMatrix = data.map { case (y, features) =>
+      new DoubleMatrix(features.length, 1, features:_*).mul(y)
+    }.reduce(_.addi(_))
+
+    // Define a function to compute the leave one out cross validation error
+    // for a single example
+    def crossValidate(lambda: Double): (Double, Double, DoubleMatrix) = {
+      // Compute the MLE ridge regression parameter value
+
+      // Ridge Regression parameter = inv(XtX + \lambda*I) * Xty
+      val XtXlambda = DoubleMatrix.eye(nfeatures).muli(lambda).addi(XtX)
+      val w = Solve.solveSymmetric(XtXlambda, Xty)
+
+      val invXtX = Solve.solveSymmetric(XtXlambda, DoubleMatrix.eye(nfeatures))
+
+      // compute the generalized cross validation score
+      val cverror = data.map {
+        case (y, features) =>
+          val x = new DoubleMatrix(features.length, 1, features:_*)
+          val yhat = w.transpose().mmul(x).get(0)
+          val H_ii = x.transpose().mmul(invXtX).mmul(x).get(0)
+          val residual = (y - yhat) / (1.0 - H_ii)
+          residual * residual
+      }.reduce(_ + _) / nexamples
+
+      (lambda, cverror, w)
+    }
+
+    // Binary search for the best assignment to lambda.
+    def binSearch(low: Double, high: Double): Seq[(Double, Double, DoubleMatrix)] = {
+      val buffer = mutable.ListBuffer.empty[(Double, Double, DoubleMatrix)]
+
+      @tailrec
+      def loop(low: Double, high: Double): Seq[(Double, Double, DoubleMatrix)] = {
+        val mid = (high - low) / 2 + low
+        val lowValue = crossValidate((mid - low) / 2 + low)
+        val highValue = crossValidate((high - mid) / 2 + mid)
+        val (newLow, newHigh) = if (lowValue._2 < highValue._2) {
+          (low, mid + (high-low)/4)
+        } else {
+          (mid - (high-low)/4, high)
+        }
+        if (newHigh - newLow > 1.0E-7) {
+          buffer += lowValue += highValue
+          loop(newLow, newHigh)
+        } else {
+          buffer += lowValue += highValue
+          buffer.result()
+        }
+      }
+
+      loop(low, high)
+    }
+
+    // Actually compute the best lambda
+    val lambdas = binSearch(lambdaLow, lambdaHigh).sortBy(_._1)
+
+    // Find the best parameter set by taking the lowest cverror.
+    val (lambdaOpt, cverror, weights) = lambdas.reduce((a, b) => if (a._2 < b._2) a else b)
+
+    // Return the model which contains the solution
+    val weightsScaled = weights.div(xColSd)
+    val intercept = yMean - (weights.transpose().mmul(xColMean.div(xColSd)).get(0))
+    val model = new RidgeRegressionModel(weightsScaled, intercept, lambdaOpt, lambdas)
+
+    logInfo("RidgeRegression: optimal lambda " + model.lambdaOpt)
+    logInfo("RidgeRegression: optimal weights " + model.weights)
+    logInfo("RidgeRegression: optimal intercept " + model.intercept)
+    logInfo("RidgeRegression: cross-validation error " + cverror)
+
+    model
+  }
+}
+
+/**
+ * Top-level methods for calling Ridge Regression.
+ */
+object RidgeRegression {
+
+  /**
+   * Train a ridge regression model given an RDD of (response, features) pairs.
+   * We use the closed form solution to compute the cross-validation score for
+   * a given lambda. The optimal lambda is computed by performing binary search
+   * between the provided bounds of lambda.
+   *
+   * @param input RDD of (response, array of features) pairs.
+   * @param lambdaLow lower bound used in binary search for lambda
+   * @param lambdaHigh upper bound used in binary search for lambda
+   */
+  def train(
+      input: RDD[(Double, Array[Double])],
+      lambdaLow: Double,
+      lambdaHigh: Double)
+    : RidgeRegressionModel =
+  {
+    new RidgeRegression(lambdaLow, lambdaHigh).train(input)
+  }
+
+  /**
+   * Train a ridge regression model given an RDD of (response, features) pairs.
+   * We use the closed form solution to compute the cross-validation score for
+   * a given lambda. The optimal lambda is computed by performing binary search
+   * between lambda values of 0 and 100.
+   *
+   * @param input RDD of (response, array of features) pairs.
+   */
+  def train(input: RDD[(Double, Array[Double])]) : RidgeRegressionModel = {
+    train(input, 0.0, 100.0)
+  }
+
+  def main(args: Array[String]) {
+    if (args.length != 2) {
+      println("Usage: RidgeRegression <master> <input_dir>")
+      System.exit(1)
+    }
+    val sc = new SparkContext(args(0), "RidgeRegression")
+    val data = MLUtils.loadLabeledData(sc, args(1))
+    val model = RidgeRegression.train(data, 0, 1000)
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala b/mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala
new file mode 100644
index 0000000000000000000000000000000000000000..b83f505d8e41a5896b64566f1a7f50c2f448f574
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala
@@ -0,0 +1,55 @@
+package spark.mllib.regression
+
+import scala.util.Random
+
+import org.jblas.DoubleMatrix
+
+import spark.{RDD, SparkContext}
+import spark.mllib.util.MLUtils
+
+
+object RidgeRegressionGenerator {
+
+  def main(args: Array[String]) {
+    if (args.length != 5) {
+      println("Usage: RidgeRegressionGenerator " +
+        "<master> <output_dir> <num_examples> <num_features> <num_partitions>")
+      System.exit(1)
+    }
+
+    val sparkMaster: String = args(0)
+    val outputPath: String = args(1)
+    val nexamples: Int = if (args.length > 2) args(2).toInt else 1000
+    val nfeatures: Int = if (args.length > 3) args(3).toInt else 100
+    val parts: Int = if (args.length > 4) args(4).toInt else 2
+    val eps = 10
+
+    org.jblas.util.Random.seed(42)
+    val sc = new SparkContext(sparkMaster, "RidgeRegressionGenerator")
+
+    // Random values distributed uniformly in [-0.5, 0.5]
+    val w = DoubleMatrix.rand(nfeatures, 1).subi(0.5)
+    w.put(0, 0, 10)
+    w.put(1, 0, 10)
+
+    val data: RDD[(Double, Array[Double])] = sc.parallelize(0 until parts, parts).flatMap { p =>
+      org.jblas.util.Random.seed(42 + p)
+      val examplesInPartition = nexamples / parts
+
+      val X = DoubleMatrix.rand(examplesInPartition, nfeatures)
+      val y = X.mmul(w)
+
+      val rnd = new Random(42 + p)
+
+      val normalValues = Array.fill[Double](examplesInPartition)(rnd.nextGaussian() * eps)
+      val yObs = new DoubleMatrix(normalValues).addi(y)
+
+      Iterator.tabulate(examplesInPartition) { i =>
+        (yObs.get(i, 0), X.getRow(i).toArray)
+      }
+    }
+
+    MLUtils.saveLabeledData(data, outputPath)
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala
new file mode 100644
index 0000000000000000000000000000000000000000..08a031dded1783ff260f0c78aeafc7da96df8072
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala
@@ -0,0 +1,95 @@
+package spark.mllib.util
+
+import spark.{RDD, SparkContext}
+import spark.SparkContext._
+
+import org.jblas.DoubleMatrix
+
+/**
+ * Helper methods to load and save data
+ * Data format:
+ * <l>, <f1> <f2> ...
+ * where <f1>, <f2> are feature values in Double and <l> is the corresponding label as Double.
+ */
+object MLUtils {
+
+  /**
+   * @param sc SparkContext
+   * @param dir Directory to the input data files.
+   * @return An RDD of tuples. For each tuple, the first element is the label, and the second
+   *         element represents the feature values (an array of Double).
+   */
+  def loadLabeledData(sc: SparkContext, dir: String): RDD[(Double, Array[Double])] = {
+    sc.textFile(dir).map { line =>
+      val parts = line.split(",")
+      val label = parts(0).toDouble
+      val features = parts(1).trim().split(" ").map(_.toDouble)
+      (label, features)
+    }
+  }
+
+  def saveLabeledData(data: RDD[(Double, Array[Double])], dir: String) {
+    val dataStr = data.map(x => x._1 + "," + x._2.mkString(" "))
+    dataStr.saveAsTextFile(dir)
+  }
+
+  /**
+   * Utility function to compute mean and standard deviation on a given dataset.
+   *
+   * @param data - input data set whose statistics are computed
+   * @param nfeatures - number of features
+   * @param nexamples - number of examples in input dataset
+   *
+   * @return (yMean, xColMean, xColSd) - Tuple consisting of
+   *     yMean - mean of the labels
+   *     xColMean - Row vector with mean for every column (or feature) of the input data
+   *     xColSd - Row vector standard deviation for every column (or feature) of the input data.
+   */
+  def computeStats(data: RDD[(Double, Array[Double])], nfeatures: Int, nexamples: Long):
+      (Double, DoubleMatrix, DoubleMatrix) = {
+    val yMean: Double = data.map { case (y, features) => y }.reduce(_ + _) / nexamples
+
+    // NOTE: We shuffle X by column here to compute column sum and sum of squares.
+    val xColSumSq: RDD[(Int, (Double, Double))] = data.flatMap { case(y, features) =>
+      val nCols = features.length
+      // Traverse over every column and emit (col, value, value^2)
+      Iterator.tabulate(nCols) { i =>
+        (i, (features(i), features(i)*features(i)))
+      }
+    }.reduceByKey { case(x1, x2) =>
+      (x1._1 + x2._1, x1._2 + x2._2)
+    }
+    val xColSumsMap = xColSumSq.collectAsMap()
+
+    val xColMean = DoubleMatrix.zeros(nfeatures, 1)
+    val xColSd = DoubleMatrix.zeros(nfeatures, 1)
+
+    // Compute mean and unbiased variance using column sums
+    var col = 0
+    while (col < nfeatures) {
+      xColMean.put(col, xColSumsMap(col)._1 / nexamples)
+      val variance =
+        (xColSumsMap(col)._2 - (math.pow(xColSumsMap(col)._1, 2) / nexamples)) / (nexamples)
+      xColSd.put(col, math.sqrt(variance))
+      col += 1
+    }
+
+    (yMean, xColMean, xColSd)
+  }
+
+  /**
+   * Return the squared Euclidean distance between two vectors.
+   */
+  def squaredDistance(v1: Array[Double], v2: Array[Double]): Double = {
+    if (v1.length != v2.length) {
+      throw new IllegalArgumentException("Vector sizes don't match")
+    }
+    var i = 0
+    var sum = 0.0
+    while (i < v1.length) {
+      sum += (v1(i) - v2(i)) * (v1(i) - v2(i))
+      i += 1
+    }
+    sum
+  }
+}
diff --git a/mllib/src/test/resources/log4j.properties b/mllib/src/test/resources/log4j.properties
new file mode 100644
index 0000000000000000000000000000000000000000..390c92763cb2e6afed7e12a7e5148d3f7b4ee89f
--- /dev/null
+++ b/mllib/src/test/resources/log4j.properties
@@ -0,0 +1,11 @@
+# Set everything to be logged to the file core/target/unit-tests.log 
+log4j.rootCategory=INFO, file
+log4j.appender.file=org.apache.log4j.FileAppender
+log4j.appender.file.append=false
+log4j.appender.file.file=ml/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/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..cb096f39a9733f9e1e93386ee1c21f2e3f11d52b
--- /dev/null
+++ b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala
@@ -0,0 +1,153 @@
+package spark.mllib.clustering
+
+import scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+
+import spark.SparkContext
+import spark.SparkContext._
+
+import org.jblas._
+
+
+class KMeansSuite extends FunSuite with BeforeAndAfterAll {
+  val sc = new SparkContext("local", "test")
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  val EPSILON = 1e-4
+
+  import KMeans.{RANDOM, K_MEANS_PARALLEL}
+
+  def prettyPrint(point: Array[Double]): String = point.mkString("(", ", ", ")")
+
+  def prettyPrint(points: Array[Array[Double]]): String = {
+    points.map(prettyPrint).mkString("(", "; ", ")")
+  }
+
+  // L1 distance between two points
+  def distance1(v1: Array[Double], v2: Array[Double]): Double = {
+    v1.zip(v2).map{ case (a, b) => math.abs(a-b) }.max
+  }
+
+  // Assert that two vectors are equal within tolerance EPSILON
+  def assertEqual(v1: Array[Double], v2: Array[Double]) {
+    def errorMessage = prettyPrint(v1) + " did not equal " + prettyPrint(v2)
+    assert(v1.length == v2.length, errorMessage)
+    assert(distance1(v1, v2) <= EPSILON, errorMessage)
+  }
+
+  // Assert that two sets of points are equal, within EPSILON tolerance
+  def assertSetsEqual(set1: Array[Array[Double]], set2: Array[Array[Double]]) {
+    def errorMessage = prettyPrint(set1) + " did not equal " + prettyPrint(set2)
+    assert(set1.length == set2.length, errorMessage)
+    for (v <- set1) {
+      val closestDistance = set2.map(w => distance1(v, w)).min
+      if (closestDistance > EPSILON) {
+        fail(errorMessage)
+      }
+    }
+    for (v <- set2) {
+      val closestDistance = set1.map(w => distance1(v, w)).min
+      if (closestDistance > EPSILON) {
+        fail(errorMessage)
+      }
+    }
+  }
+
+  test("single cluster") {
+    val data = sc.parallelize(Array(
+      Array(1.0, 2.0, 6.0),
+      Array(1.0, 3.0, 0.0),
+      Array(1.0, 4.0, 6.0)
+    ))
+
+    // No matter how many runs or iterations we use, we should get one cluster,
+    // centered at the mean of the points
+
+    var model = KMeans.train(data, k=1, maxIterations=1)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=2)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=5)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(
+      data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+  }
+
+  test("single cluster with big dataset") {
+    val smallData = Array(
+      Array(1.0, 2.0, 6.0),
+      Array(1.0, 3.0, 0.0),
+      Array(1.0, 4.0, 6.0)
+    )
+    val data = sc.parallelize((1 to 100).flatMap(_ => smallData), 4)
+
+    // No matter how many runs or iterations we use, we should get one cluster,
+    // centered at the mean of the points
+
+    var model = KMeans.train(data, k=1, maxIterations=1)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=2)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=5)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+
+    model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL)
+    assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
+  }
+
+  test("k-means|| initialization") {
+    val points = Array(
+      Array(1.0, 2.0, 6.0),
+      Array(1.0, 3.0, 0.0),
+      Array(1.0, 4.0, 6.0),
+      Array(1.0, 0.0, 1.0),
+      Array(1.0, 1.0, 1.0)
+    )
+    val rdd = sc.parallelize(points)
+
+    // K-means|| initialization should place all clusters into distinct centers because
+    // it will make at least five passes, and it will give non-zero probability to each
+    // unselected point as long as it hasn't yet selected all of them
+
+    var model = KMeans.train(rdd, k=5, maxIterations=1)
+    assertSetsEqual(model.clusterCenters, points)
+
+    // Iterations of Lloyd's should not change the answer either
+    model = KMeans.train(rdd, k=5, maxIterations=10)
+    assertSetsEqual(model.clusterCenters, points)
+
+    // Neither should more runs
+    model = KMeans.train(rdd, k=5, maxIterations=10, runs=5)
+    assertSetsEqual(model.clusterCenters, points)
+  }
+}
diff --git a/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..2ada9ae76b52639fbd1cb62fd9bbab781a17dc64
--- /dev/null
+++ b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala
@@ -0,0 +1,80 @@
+package spark.mllib.recommendation
+
+import scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+
+import spark.SparkContext
+import spark.SparkContext._
+
+import org.jblas._
+
+
+class ALSSuite extends FunSuite with BeforeAndAfterAll {
+  val sc = new SparkContext("local", "test")
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  test("rank-1 matrices") {
+    testALS(10, 20, 1, 15, 0.7, 0.3)
+  }
+
+  test("rank-2 matrices") {
+    testALS(20, 30, 2, 15, 0.7, 0.3)
+  }
+
+  /**
+   * Test if we can correctly factorize R = U * P where U and P are of known rank.
+   *
+   * @param users          number of users
+   * @param products       number of products
+   * @param features       number of features (rank of problem)
+   * @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
+   */
+  def testALS(users: Int, products: Int, features: Int, iterations: Int,
+    samplingRate: Double, matchThreshold: Double)
+  {
+    val rand = new Random(42)
+
+    // Create a random matrix with uniform values from -1 to 1
+    def randomMatrix(m: Int, n: Int) =
+      new DoubleMatrix(m, n, Array.fill(m * n)(rand.nextDouble() * 2 - 1): _*)
+
+    val userMatrix = randomMatrix(users, features)
+    val productMatrix = randomMatrix(features, products)
+    val trueRatings = userMatrix.mmul(productMatrix)
+
+    val sampledRatings = {
+      for (u <- 0 until users; p <- 0 until products if rand.nextDouble() < samplingRate)
+        yield (u, p, trueRatings.get(u, p))
+    }
+
+    val model = ALS.train(sc.parallelize(sampledRatings), features, iterations)
+
+    val predictedU = new DoubleMatrix(users, features)
+    for ((u, vec) <- model.userFeatures.collect(); i <- 0 until features) {
+      predictedU.put(u, i, vec(i))
+    }
+    val predictedP = new DoubleMatrix(products, features)
+    for ((p, vec) <- model.productFeatures.collect(); i <- 0 until features) {
+      predictedP.put(p, i, vec(i))
+    }
+    val predictedRatings = predictedU.mmul(predictedP.transpose)
+
+    for (u <- 0 until users; p <- 0 until products) {
+      val prediction = predictedRatings.get(u, p)
+      val correct = trueRatings.get(u, p)
+      if (math.abs(prediction - correct) > matchThreshold) {
+        fail("Model failed to predict (%d, %d): %f vs %f\ncorr: %s\npred: %s\nU: %s\n P: %s".format(
+          u, p, correct, prediction, trueRatings, predictedRatings, predictedU, predictedP))
+      }
+    }
+  }
+}
+
diff --git a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..04d3400cb4260f90094c3de6fd20f19d8c8d3af2
--- /dev/null
+++ b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
@@ -0,0 +1,57 @@
+package spark.mllib.regression
+
+import scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+
+import spark.SparkContext
+import spark.SparkContext._
+
+
+class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
+  val sc = new SparkContext("local", "test")
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  // Test if we can correctly learn A, B where Y = logistic(A + B*X)
+  test("logistic regression") {
+    val nPoints = 10000
+    val rnd = new Random(42)
+
+    val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
+
+    val A = 2.0
+    val B = -1.5
+
+    // NOTE: if U is uniform[0, 1] then ln(u) - ln(1-u) is Logistic(0,1)
+    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)
+    }
+
+    // y <- A + B*x + rlogis(100)
+    // y <- as.numeric(y > 0)
+    val y = (0 until nPoints).map { i =>
+      val yVal = A + B * x1(i) + rLogis(i)
+      if (yVal > 0) 1.0 else 0.0
+    }
+
+    val testData = (0 until nPoints).map(i => (y(i).toDouble, Array(x1(i)))).toArray
+
+    val testRDD = sc.parallelize(testData, 2)
+    testRDD.cache()
+    val lr = new LogisticRegression().setStepSize(10.0)
+                                     .setNumIterations(20)
+
+    val model = lr.train(testRDD)
+
+    val weight0 = model.weights.get(0)
+    assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
+    assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
+  }
+}
diff --git a/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..df41dbbdff88fc137d173207f64646677d447006
--- /dev/null
+++ b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala
@@ -0,0 +1,47 @@
+package spark.mllib.regression
+
+import scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+
+import spark.SparkContext
+import spark.SparkContext._
+
+
+class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll {
+  val sc = new SparkContext("local", "test")
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  // Test if we can correctly learn Y = 3 + X1 + X2 when
+  // X1 and X2 are collinear.
+  test("multi-collinear variables") {
+    val rnd = new Random(43)
+    val x1 = Array.fill[Double](20)(rnd.nextGaussian())
+
+    // Pick a mean close to mean of x1
+    val rnd1 = new Random(42) //new NormalDistribution(0.1, 0.01)
+    val x2 = Array.fill[Double](20)(0.1 + rnd1.nextGaussian() * 0.01)
+
+    val xMat = (0 until 20).map(i => Array(x1(i), x2(i))).toArray
+
+    val y = xMat.map(i => 3 + i(0) + i(1))
+    val testData = (0 until 20).map(i => (y(i), xMat(i))).toArray
+
+    val testRDD = sc.parallelize(testData, 2)
+    testRDD.cache()
+    val ridgeReg = new RidgeRegression().setLowLambda(0)
+                                        .setHighLambda(10)
+
+    val model = ridgeReg.train(testRDD)
+
+    assert(model.intercept >= 2.9 && model.intercept <= 3.1)
+    assert(model.weights.length === 2)
+    assert(model.weights.get(0) >= 0.9 && model.weights.get(0) <= 1.1)
+    assert(model.weights.get(1) >= 0.9 && model.weights.get(1) <= 1.1)
+  }
+}
diff --git a/pom.xml b/pom.xml
index 35eb9bf7a1dddb2f173f2702845f50e52421703d..66dfe4c42e90ce8d217b2e53d6f435da853d04e3 100644
--- a/pom.xml
+++ b/pom.xml
@@ -98,17 +98,6 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
-    <repository>
-      <id>spray-repo</id>
-      <name>Spray Repository</name>
-      <url>http://repo.spray.cc/</url>
-      <releases>
-        <enabled>true</enabled>
-      </releases>
-      <snapshots>
-        <enabled>false</enabled>
-      </snapshots>
-    </repository>
   </repositories>
   <pluginRepositories>
     <pluginRepository>
@@ -218,31 +207,6 @@
         <artifactId>colt</artifactId>
         <version>1.2.0</version>
       </dependency>
-      <dependency>
-        <groupId>io.spray</groupId>
-        <artifactId>spray-can</artifactId>
-        <version>${spray.version}</version>
-      </dependency>
-      <dependency>
-        <groupId>io.spray</groupId>
-        <artifactId>spray-routing</artifactId>
-        <version>${spray.version}</version>
-      </dependency>
-      <dependency>
-        <groupId>io.spray</groupId>
-        <artifactId>spray-io</artifactId>
-        <version>${spray.version}</version>
-      </dependency>
-      <dependency>
-        <groupId>io.spray</groupId>
-        <artifactId>spray-json_${scala.version}</artifactId>
-        <version>${spray.json.version}</version>
-      </dependency>
-      <dependency>
-        <groupId>org.tomdz.twirl</groupId>
-        <artifactId>twirl-api</artifactId>
-        <version>1.0.2</version>
-      </dependency>
       <dependency>
         <groupId>com.github.scala-incubator.io</groupId>
         <artifactId>scala-io-file_2.10</artifactId>
@@ -264,7 +228,11 @@
         <version>10.4.2.0</version>
         <scope>test</scope>
       </dependency>
-
+      <dependency>
+        <groupId>net.liftweb</groupId>
+        <artifactId>lift-json_2.10</artifactId>
+        <version>2.5.1</version>
+      </dependency>
       <dependency>
         <groupId>org.scala-lang</groupId>
         <artifactId>scala-compiler</artifactId>
@@ -275,6 +243,16 @@
         <artifactId>jline</artifactId>
         <version>2.10.0</version>
       </dependency>
+      <dependency>
+        <groupId>org.scala-lang</groupId>
+        <artifactId>scala-library</artifactId>
+        <version>${scala.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.scala-lang</groupId>
+        <artifactId>scalap</artifactId>
+        <version>${scala.version}</version>
+      </dependency>
 
       <dependency>
         <groupId>log4j</groupId>
@@ -340,18 +318,6 @@
           <artifactId>build-helper-maven-plugin</artifactId>
           <version>1.7</version>
         </plugin>
-        <plugin>
-          <groupId>org.tomdz.twirl</groupId>
-          <artifactId>twirl-maven-plugin</artifactId>
-          <version>1.0.1</version>
-          <executions>
-            <execution>
-              <goals>
-                <goal>generate</goal>
-              </goals>
-            </execution>
-          </executions>
-        </plugin>
         <plugin>
           <groupId>net.alchim31.maven</groupId>
           <artifactId>scala-maven-plugin</artifactId>
@@ -558,12 +524,12 @@
           <dependency>
             <groupId>org.apache.avro</groupId>
             <artifactId>avro</artifactId>
-            <version>1.7.1.cloudera.2</version>
+            <version>1.7.4</version>
           </dependency>
           <dependency>
             <groupId>org.apache.avro</groupId>
             <artifactId>avro-ipc</artifactId>
-            <version>1.7.1.cloudera.2</version>
+            <version>1.7.4</version>
             <exclusions>
               <exclusion>
                 <groupId>org.jboss.netty</groupId>
@@ -625,12 +591,12 @@
           <dependency>
             <groupId>org.apache.avro</groupId>
             <artifactId>avro</artifactId>
-            <version>1.7.1.cloudera.2</version>
+            <version>1.7.4</version>
           </dependency>
           <dependency>
             <groupId>org.apache.avro</groupId>
             <artifactId>avro-ipc</artifactId>
-            <version>1.7.1.cloudera.2</version>
+            <version>1.7.4</version>
           </dependency>
         </dependencies>
       </dependencyManagement>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index d35b39fcf36ee13a6935e6773b9f2b3382294338..bb2a97837c8c1c73e8cf9b8e9b6c4b75b1bfa8b9 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -4,7 +4,6 @@ import sbt.Classpaths.publishTask
 import Keys._
 import sbtassembly.Plugin._
 import AssemblyKeys._
-import twirl.sbt.TwirlPlugin._
 // For Sonatype publishing
 //import com.jsuereth.pgp.sbtplugin.PgpKeys._
 
@@ -25,7 +24,7 @@ object SparkBuild extends Build {
   //val HADOOP_MAJOR_VERSION = "2"
   //val HADOOP_YARN = true
 
-  lazy val root = Project("root", file("."), settings = rootSettings) aggregate(core, repl, examples, bagel, streaming)
+  lazy val root = Project("root", file("."), settings = rootSettings) aggregate(core, repl, examples, bagel, streaming, mllib)
 
   lazy val core = Project("core", file("core"), settings = coreSettings)
 
@@ -37,6 +36,8 @@ object SparkBuild extends Build {
 
   lazy val streaming = Project("streaming", file("streaming"), settings = streamingSettings) dependsOn (core)
 
+  lazy val mllib = Project("mllib", file("mllib"), settings = mllibSettings) dependsOn (core)
+
   // A configuration to set an alternative publishLocalConfiguration
   lazy val MavenCompile = config("m2r") extend(Compile)
   lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy")
@@ -141,28 +142,26 @@ object SparkBuild extends Build {
     ),
 
     libraryDependencies ++= Seq(
-        "com.google.guava"    % "guava"            % "11.0.1",
-        "log4j"               % "log4j"            % "1.2.16",
-        "org.slf4j"           % "slf4j-api"        % slf4jVersion,
-        "org.slf4j"           % "slf4j-log4j12"    % slf4jVersion,
-        "com.ning"            % "compress-lzf"     % "0.8.4",
-        "commons-daemon"      % "commons-daemon"   % "1.0.10",
-        "org.apache.hadoop"   % "hadoop-core"      % HADOOP_VERSION excludeAll(excludeNetty, excludeJackson),
-        "org.ow2.asm"         % "asm"              % "4.0",
-        "com.google.protobuf" % "protobuf-java"    % "2.4.1",
-        "de.javakaffee"       % "kryo-serializers" % "0.22",
-        "com.typesafe.akka"  %% "akka-remote"      % "2.1.4"        excludeAll(excludeNetty),
-        "com.typesafe.akka"  %% "akka-slf4j"       % "2.1.4"        excludeAll(excludeNetty),
-        "it.unimi.dsi"        % "fastutil"         % "6.4.4",
-        "io.spray"            % "spray-can"        % "1.1-M7"       excludeAll(excludeNetty),
-        "io.spray"            % "spray-io"         % "1.1-M7"       excludeAll(excludeNetty),
-        "io.spray"            % "spray-routing"    % "1.1-M7"       excludeAll(excludeNetty),
-        "io.spray"           %% "spray-json"       % "1.2.3"        excludeAll(excludeNetty),
-        "colt"                % "colt"             % "1.2.0",
-        "org.apache.mesos"    % "mesos"            % "0.9.0-incubating",
-        "org.apache.derby"    % "derby"            % "10.4.2.0"                     % "test",
-        "org.scala-lang"      % "jline"            % "2.10.1",
-        "org.scala-lang"      % "scala-reflect"    % "2.10.1"
+        "com.google.guava"         % "guava"            % "14.0.1",
+        "com.google.code.findbugs" % "jsr305"           % "1.3.+",
+        "log4j"                    % "log4j"            % "1.2.16",
+        "org.slf4j"                % "slf4j-api"        % slf4jVersion,
+        "org.slf4j"                % "slf4j-log4j12"    % slf4jVersion,
+        "com.ning"                 % "compress-lzf"     % "0.8.4",
+        "commons-daemon"           % "commons-daemon"   % "1.0.10",
+        "org.apache.hadoop"        % "hadoop-core"      % HADOOP_VERSION excludeAll(excludeNetty, excludeJackson),
+        "org.ow2.asm"              % "asm"              % "4.0",
+        "com.google.protobuf"      % "protobuf-java"    % "2.4.1",
+        "de.javakaffee"            % "kryo-serializers" % "0.22",
+        "com.typesafe.akka"       %% "akka-remote"      % "2.1.4"        excludeAll(excludeNetty),
+        "com.typesafe.akka"       %% "akka-slf4j"       % "2.1.4"        excludeAll(excludeNetty),
+        "net.liftweb"             %% "lift-json"        % "2.5.1",
+        "it.unimi.dsi"             % "fastutil"         % "6.4.4",
+        "colt"                     % "colt"             % "1.2.0",
+        "org.apache.mesos"         % "mesos"            % "0.9.0-incubating",
+        "org.apache.derby"         % "derby"            % "10.4.2.0"                     % "test",
+        "org.scala-lang"           % "jline"            % "2.10.1",
+        "org.scala-lang"           % "scala-reflect"    % "2.10.1"
       ) ++ (
       if (HADOOP_MAJOR_VERSION == "2") {
         if (HADOOP_YARN) {
@@ -189,7 +188,7 @@ object SparkBuild extends Build {
         "src/hadoop" + HADOOP_MAJOR_VERSION + "/scala"
       } )
     }
-  ) ++ assemblySettings ++ extraAssemblySettings ++ Twirl.settings
+  ) ++ assemblySettings ++ extraAssemblySettings
 
   def rootSettings = sharedSettings ++ Seq(
     publish := {}
@@ -219,6 +218,13 @@ object SparkBuild extends Build {
 
   def bagelSettings = sharedSettings ++ Seq(name := "spark-bagel")
 
+  def mllibSettings = sharedSettings ++ Seq(
+    name := "spark-mllib",
+    libraryDependencies ++= Seq(
+      "org.jblas" % "jblas" % "1.2.3"
+    )
+  )
+
   def streamingSettings = sharedSettings ++ Seq(
     name := "spark-streaming",
     libraryDependencies ++= Seq(
diff --git a/project/plugins.sbt b/project/plugins.sbt
index f806e6648134e828238d1aef62ce55d91da3684f..1b0f879b94800d142c91948156ae563bacb24da1 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -10,8 +10,6 @@ addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.1.1")
 
 addSbtPlugin("com.github.mpeltonen" % "sbt-idea" % "1.2.0")
 
-addSbtPlugin("io.spray" %% "sbt-twirl" % "0.6.1")
-
 // For Sonatype publishing
 //resolvers += Resolver.url("sbt-plugin-releases", new URL("http://scalasbt.artifactoryonline.com/scalasbt/sbt-plugin-releases/"))(Resolver.ivyStylePatterns)
 
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 1589e3202f8c19866e29ff697fa5701f8a45dec9..7b621c123904d87600856db95c8a55c09494c333 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -58,6 +58,10 @@
       <artifactId>twitter4j-stream</artifactId>
       <version>3.0.3</version>
     </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+    </dependency>
     <dependency>
       <groupId>com.typesafe.akka</groupId>
       <artifactId>akka-zeromq_${scala.version}</artifactId>
diff --git a/streaming/src/main/scala/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/spark/streaming/Checkpoint.scala
index 66e67cbfa1d786931580a7379e6aa0b10cb80d5b..450e48d66e22b0ff792efab740978a3d9df60838 100644
--- a/streaming/src/main/scala/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/spark/streaming/Checkpoint.scala
@@ -8,7 +8,7 @@ import org.apache.hadoop.conf.Configuration
 import java.io._
 import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream}
 import java.util.concurrent.Executors
-
+import java.util.concurrent.RejectedExecutionException
 
 private[streaming]
 class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
@@ -91,7 +91,12 @@ class CheckpointWriter(checkpointDir: String) extends Logging {
     oos.writeObject(checkpoint)
     oos.close()
     bos.close()
-    executor.execute(new CheckpointWriteHandler(checkpoint.checkpointTime, bos.toByteArray))
+    try {
+      executor.execute(new CheckpointWriteHandler(checkpoint.checkpointTime, bos.toByteArray))
+    } catch {
+      case rej: RejectedExecutionException =>
+        logError("Could not submit checkpoint task to the thread pool executor", rej)
+    }
   }
 
   def stop() {
diff --git a/streaming/src/main/scala/spark/streaming/Duration.scala b/streaming/src/main/scala/spark/streaming/Duration.scala
index ee26206e249a8abf6bf9f502b7dac6eaef65e0e6..c2135195d89c439f49f30cc30c9823b2a59e244e 100644
--- a/streaming/src/main/scala/spark/streaming/Duration.scala
+++ b/streaming/src/main/scala/spark/streaming/Duration.scala
@@ -1,5 +1,7 @@
 package spark.streaming
 
+import spark.Utils
+
 case class Duration (private val millis: Long) {
 
   def < (that: Duration): Boolean = (this.millis < that.millis)
@@ -32,8 +34,10 @@ case class Duration (private val millis: Long) {
   def toFormattedString: String = millis.toString
 
   def milliseconds: Long = millis
-}
 
+  def prettyPrint = Utils.msDurationToString(millis)
+
+}
 
 /**
  * Helper object that creates instance of [[spark.streaming.Duration]] representing
diff --git a/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala b/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala
index aa1a8b6ba23b4c03e99e13c4cc1870393f19b375..b0ebdea01cd3f3e01a187e2b094535b7316dbad6 100644
--- a/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala
+++ b/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala
@@ -15,6 +15,7 @@ import scala.reflect.{ClassTag, classTag}
 import org.apache.hadoop.mapred.{JobConf, OutputFormat}
 import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat}
 import org.apache.hadoop.mapred.OutputFormat
+import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hadoop.conf.Configuration
 
 class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
@@ -471,7 +472,7 @@ extends Serializable {
       valueClass: Class[_],
       outputFormatClass: Class[_ <: OutputFormat[_, _]],
       conf: JobConf = new JobConf
-    ) {
+    ) {  
     val saveFunc = (rdd: RDD[(K, V)], time: Time) => {
       val file = rddToFileName(prefix, suffix, time)
       rdd.saveAsHadoopFile(file, keyClass, valueClass, outputFormatClass, conf)
diff --git a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala
index 3b9cd0e89baf98aa3e5b487ff5e096f88fadf99e..71d2b9715cd1c738bc02e86c2914ad9ef04e7f96 100644
--- a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala
+++ b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala
@@ -143,12 +143,10 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging
 
 
   /**
-   * Pushes a block (as iterator of values) into the block manager.
+   * Pushes a block (as an ArrayBuffer filled with data) into the block manager.
    */
-  def pushBlock(blockId: String, iterator: Iterator[T], metadata: Any, level: StorageLevel) {
-    val buffer = new ArrayBuffer[T] ++ iterator
-    env.blockManager.put(blockId, buffer.asInstanceOf[ArrayBuffer[Any]], level)
-
+  def pushBlock(blockId: String, arrayBuffer: ArrayBuffer[T], metadata: Any, level: StorageLevel) {
+    env.blockManager.put(blockId, arrayBuffer.asInstanceOf[ArrayBuffer[Any]], level)
     actor ! ReportBlock(blockId, metadata)
   }
 
@@ -198,7 +196,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging
   class BlockGenerator(storageLevel: StorageLevel)
     extends Serializable with Logging {
 
-    case class Block(id: String, iterator: Iterator[T], metadata: Any = null)
+    case class Block(id: String, buffer: ArrayBuffer[T], metadata: Any = null)
 
     val clock = new SystemClock()
     val blockInterval = System.getProperty("spark.streaming.blockInterval", "200").toLong
@@ -225,17 +223,13 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging
       currentBuffer += obj
     }
 
-    private def createBlock(blockId: String, iterator: Iterator[T]) : Block = {
-      new Block(blockId, iterator)
-    }
-
     private def updateCurrentBuffer(time: Long) {
       try {
         val newBlockBuffer = currentBuffer
         currentBuffer = new ArrayBuffer[T]
         if (newBlockBuffer.size > 0) {
           val blockId = "input-" + NetworkReceiver.this.streamId + "-" + (time - blockInterval)
-          val newBlock = createBlock(blockId, newBlockBuffer.toIterator)
+          val newBlock = new Block(blockId, newBlockBuffer)
           blocksForPushing.add(newBlock)
         }
       } catch {
@@ -251,7 +245,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging
       try {
         while(true) {
           val block = blocksForPushing.take()
-          NetworkReceiver.this.pushBlock(block.id, block.iterator, block.metadata, storageLevel)
+          NetworkReceiver.this.pushBlock(block.id, block.buffer, block.metadata, storageLevel)
         }
       } catch {
         case ie: InterruptedException =>
diff --git a/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala b/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala
index 42e885af5c32ef81bb2ac00e704af003767c32d7..279e39d6d647858913a36b5a13f735bdf0548c75 100644
--- a/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala
+++ b/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala
@@ -13,6 +13,8 @@ import spark.streaming.dstream.NetworkReceiver
 
 import java.util.concurrent.atomic.AtomicInteger
 
+import scala.collection.mutable.ArrayBuffer
+
 /** A helper with set of defaults for supervisor strategy */
 object ReceiverSupervisorStrategy {
 
@@ -137,8 +139,9 @@ private[streaming] class ActorReceiver[T: ClassTag](
   }
 
   protected def pushBlock(iter: Iterator[T]) {
-    pushBlock("block-" + streamId + "-" + System.nanoTime(),
-      iter, null, storageLevel)
+    val buffer = new ArrayBuffer[T]
+    buffer ++= iter
+    pushBlock("block-" + streamId + "-" + System.nanoTime(), buffer, null, storageLevel)
   }
 
   protected def onStart() = {