diff --git a/assembly/pom.xml b/assembly/pom.xml
index 1382539f2459c6c9224770f5e989e5d2af8ae765..ca20ccadba8de54bdfe299cb714348afdd7753fa 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -37,68 +37,31 @@
     </plugins>
   </build>
 
-  <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <properties>
-        <classifier.name>hadoop1</classifier.name>
-      </properties>
-    </profile>
-    <profile>
-      <id>hadoop2</id>
-      <properties>
-        <classifier.name>hadoop2</classifier.name>
-      </properties>
-    </profile>
-    <profile>
-      <id>hadoop2-yarn</id>
-      <properties>
-        <classifier.name>hadoop2-yarn</classifier.name>
-      </properties>
-    </profile>
-  </profiles>
   <dependencies>
     <dependency>
       <groupId>org.spark-project</groupId>
       <artifactId>spark-core</artifactId>
-      <classifier>${classifier.name}</classifier>
-      <version>0.8.0-SNAPSHOT</version>
+      <version>${project.version}</version>
     </dependency>
     <dependency>
       <groupId>org.spark-project</groupId>
       <artifactId>spark-bagel</artifactId>
-      <classifier>${classifier.name}</classifier>
-      <version>0.8.0-SNAPSHOT</version>
+      <version>${project.version}</version>
     </dependency>
     <dependency>
       <groupId>org.spark-project</groupId>
-      <artifactId>spark-examples</artifactId>
-      <classifier>${classifier.name}</classifier>
-      <version>0.8.0-SNAPSHOT</version>
-    </dependency>
-    <dependency>
-      <groupId>org.spark-project</groupId>
-      <artifactId>spark-examples</artifactId>
-      <classifier>javadoc</classifier>
-      <version>0.8.0-SNAPSHOT</version>
-    </dependency>
-    <dependency>
-      <groupId>org.spark-project</groupId>
-      <artifactId>spark-examples</artifactId>
-      <classifier>sources</classifier>
-      <version>0.8.0-SNAPSHOT</version>
+      <artifactId>spark-mllib</artifactId>
+      <version>${project.version}</version>
     </dependency>
     <dependency>
       <groupId>org.spark-project</groupId>
       <artifactId>spark-repl</artifactId>
-      <classifier>${classifier.name}</classifier>
-      <version>0.8.0-SNAPSHOT</version>
+      <version>${project.version}</version>
     </dependency>
     <dependency>
       <groupId>org.spark-project</groupId>
       <artifactId>spark-streaming</artifactId>
-      <classifier>${classifier.name}</classifier>
-      <version>0.8.0-SNAPSHOT</version>
+      <version>${project.version}</version>
     </dependency>
   </dependencies>
-</project>
\ No newline at end of file
+</project>
diff --git a/assembly/src/main/assembly/assembly.xml b/assembly/src/main/assembly/assembly.xml
index dd05f35f1fb10e4e000dc434002834c3e016d113..14485b7181e90ad901548c0e4ff96c41b6c2a6ba 100644
--- a/assembly/src/main/assembly/assembly.xml
+++ b/assembly/src/main/assembly/assembly.xml
@@ -49,7 +49,7 @@
         <include>org.spark-project:*:jar</include>
       </includes>
       <excludes>
-        <exclude>org.spark-project:spark-dist:jar</exclude>
+        <exclude>org.spark-project:spark-assembly:jar</exclude>
       </excludes>
     </dependencySet>
     <dependencySet>
diff --git a/bagel/pom.xml b/bagel/pom.xml
index 60bbc49e6c5c1bf079a10eac61aa07e1050fe586..cbcf8d123930a0e1205a2ad90fa363db492d943b 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -32,11 +32,15 @@
   <url>http://spark-project.org/</url>
 
   <dependencies>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-core</artifactId>
+      <version>${project.version}</version>
+    </dependency>
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-server</artifactId>
     </dependency>
-
     <dependency>
       <groupId>org.scalatest</groupId>
       <artifactId>scalatest_${scala.version}</artifactId>
@@ -58,103 +62,4 @@
       </plugin>
     </plugins>
   </build>
-
-  <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop1</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2-yarn</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-api</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-common</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2-yarn</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-  </profiles>
 </project>
diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd
index eb836b0ffd1fdc3f3974261276c53a1eea179d4d..9178b852e6bfaea25ee794b16dde7b1b82d8eff4 100644
--- a/bin/compute-classpath.cmd
+++ b/bin/compute-classpath.cmd
@@ -34,6 +34,7 @@ set EXAMPLES_DIR=%FWDIR%examples
 set BAGEL_DIR=%FWDIR%bagel
 set MLLIB_DIR=%FWDIR%mllib
 set TOOLS_DIR=%FWDIR%tools
+set YARN_DIR=%FWDIR%yarn
 set STREAMING_DIR=%FWDIR%streaming
 set PYSPARK_DIR=%FWDIR%python
 
@@ -50,6 +51,7 @@ 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
 set CLASSPATH=%CLASSPATH%;%TOOLS_DIR%\target\scala-%SCALA_VERSION%\classes
+set CLASSPATH=%CLASSPATH%;%YARN_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 e4ce1ca84819250d41c9f2c82db3b72b6a2ad453..7a21b3c4a140e4f7f006cba08097523be4923e57 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -37,6 +37,7 @@ EXAMPLES_DIR="$FWDIR/examples"
 BAGEL_DIR="$FWDIR/bagel"
 MLLIB_DIR="$FWDIR/mllib"
 TOOLS_DIR="$FWDIR/tools"
+YARN_DIR="$FWDIR/yarn"
 STREAMING_DIR="$FWDIR/streaming"
 PYSPARK_DIR="$FWDIR/python"
 
@@ -62,16 +63,18 @@ function dev_classpath {
   CLASSPATH="$CLASSPATH:$REPL_DIR/lib/*"
   # Add the shaded JAR for Maven builds
   if [ -e $REPL_BIN_DIR/target ]; then
-    for jar in `find "$REPL_BIN_DIR/target" -name 'spark-repl-*-shaded-hadoop*.jar'`; do
+    for jar in `find "$REPL_BIN_DIR/target" -name 'spark-repl-*-shaded.jar'`; do
       CLASSPATH="$CLASSPATH:$jar"
     done
     # The shaded JAR doesn't contain examples, so include those separately
-    EXAMPLES_JAR=`ls "$EXAMPLES_DIR/target/spark-examples"*[0-9T].jar`
-    CLASSPATH+=":$EXAMPLES_JAR"
+    for jar in `find "$EXAMPLES_DIR/target" -name 'spark-examples*[0-9T].jar'`; do
+      CLASSPATH="$CLASSPATH:$jar"
+    done
   fi
   CLASSPATH="$CLASSPATH:$BAGEL_DIR/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$MLLIB_DIR/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$TOOLS_DIR/target/scala-$SCALA_VERSION/classes"
+  CLASSPATH="$CLASSPATH:$YARN_DIR/target/scala-$SCALA_VERSION/classes"
   for jar in `find $PYSPARK_DIR/lib -name '*jar'`; do
     CLASSPATH="$CLASSPATH:$jar"
   done
diff --git a/bin/spark-daemon.sh b/bin/spark-daemon.sh
index a5b88ca785a8413a6f1a4f00bf3668b3c47af21d..96c71e66caebdb7307be4eb29c36560841e99d08 100755
--- a/bin/spark-daemon.sh
+++ b/bin/spark-daemon.sh
@@ -75,6 +75,8 @@ if [ "$SPARK_IDENT_STRING" = "" ]; then
   export SPARK_IDENT_STRING="$USER"
 fi
 
+export SPARK_PRINT_LAUNCH_COMMAND="1"
+
 # get log directory
 if [ "$SPARK_LOG_DIR" = "" ]; then
   export SPARK_LOG_DIR="$SPARK_HOME/logs"
@@ -124,8 +126,9 @@ case $startStop in
 
     spark_rotate_log $log
     echo starting $command, logging to $log
+    echo "Spark Daemon: $command" > $log
     cd "$SPARK_PREFIX"
-    nohup nice -n $SPARK_NICENESS "$SPARK_PREFIX"/run $command "$@" > "$log" 2>&1 < /dev/null &
+    nohup nice -n $SPARK_NICENESS "$SPARK_PREFIX"/run $command "$@" >> "$log" 2>&1 < /dev/null &
     echo $! > $pid
     sleep 1; head "$log"
     ;;
diff --git a/conf/metrics.properties.template b/conf/metrics.properties.template
index 0486ca4c79213edb35f8f56a3123deecd289e029..6c36f3cca45e0aaa849784c1ed184b32c673a2ae 100644
--- a/conf/metrics.properties.template
+++ b/conf/metrics.properties.template
@@ -1,48 +1,51 @@
-# syntax: [instance].[sink|source].[name].[options]
-
-#  "instance" specify "who" (the role) use metrics system. In spark there are
-#  several roles like master, worker, executor, driver, these roles will
-#  create metrics system for monitoring. So instance represents these roles.
-#  Currently in Spark, several instances have already implemented: master,
-#  worker, executor, driver.
-#
-#  [instance] field can be "master", "worker", "executor", "driver", which means
-#  only the specified instance has this property.
-#  a wild card "*" can be used to represent instance name, which means all the
-#  instances will have this property.
+#  syntax: [instance].sink|source.[name].[options]=[value]
+
+#  This file configures Spark's internal metrics system. The metrics system is
+#  divided into instances which correspond to internal components.
+#  Each instance can be configured to report its metrics to one or more sinks.
+#  Accepted values for [instance] are "master", "worker", "executor", "driver",
+#  and "applications". A wild card "*" can be used as an instance name, in
+#  which case all instances will inherit the supplied property.
 #
-#  "source" specify "where" (source) to collect metrics data. In metrics system,
-#  there exists two kinds of source:
-#    1. Spark internal source, like MasterSource, WorkerSource, etc, which will
-#    collect Spark component's internal state, these sources are related to
-#    instance and will be added after specific metrics system is created.
-#    2. Common source, like JvmSource, which will collect low level state, is
-#    configured by configuration and loaded through reflection.
+#  Within an instance, a "source" specifies a particular set of grouped metrics.
+#  there are two kinds of sources:
+#    1. Spark internal sources, like MasterSource, WorkerSource, etc, which will
+#    collect a Spark component's internal state. Each instance is paired with a
+#    Spark source that is added automatically.
+#    2. Common sources, like JvmSource, which will collect low level state.
+#    These can be added through configuration options and are then loaded
+#    using reflection.
 #
-#  "sink" specify "where" (destination) to output metrics data to. Several sinks
-#  can be coexisted and flush metrics to all these sinks.
+#  A "sink" specifies where metrics are delivered to. Each instance can be
+#  assigned one or more sinks.
 #
-#  [sink|source] field specify this property is source related or sink, this
-#  field can only be source or sink.
+#  The sink|source field specifies whether the property relates to a sink or
+#  source.
 #
-#  [name] field specify the name of source or sink, this is custom defined.
+#  The [name] field specifies the name of source or sink.
 #
-#  [options] field is the specific property of this source or sink, this source
-#  or sink is responsible for parsing this property.
+#  The [options] field is the specific property of this source or sink. The
+#  source or sink is responsible for parsing this property.
 #
 #  Notes:
-#    1. Sinks should be added through configuration, like console sink, class
-#    full name should be specified by class property.
-#    2. Some sinks can specify polling period, like console sink, which is 10 seconds,
-#    it should be attention minimal polling period is 1 seconds, any period
-#    below than 1s is illegal.
-#    3. Wild card property can be overlapped by specific instance property, for
-#    example, *.sink.console.period can be overlapped by master.sink.console.period.
+#    1. To add a new sink, set the "class" option to a fully qualified class
+#    name (see examples below).
+#    2. Some sinks involve a polling period. The minimum allowed polling period
+#    is  1 second.
+#    3. Wild card properties can be overridden by more specific properties.
+#    For example, master.sink.console.period takes precedence over
+#    *.sink.console.period.
 #    4. A metrics specific configuration
 #    "spark.metrics.conf=${SPARK_HOME}/conf/metrics.properties" should be
-#    added to Java property using -Dspark.metrics.conf=xxx if you want to
-#    customize metrics system, or you can put it in ${SPARK_HOME}/conf,
-#    metrics system will search and load it automatically.
+#    added to Java properties using -Dspark.metrics.conf=xxx if you want to
+#    customize metrics system. You can also put the file in ${SPARK_HOME}/conf
+#    and it will be loaded automatically.
+#    5. MetricsServlet is added by default as a sink in master, worker and client
+#    driver, you can send http request "/metrics/json" to get a snapshot of all the
+#    registered metrics in json format. For master, requests "/metrics/master/json" and
+#    "/metrics/applications/json" can be sent seperately to get metrics snapshot of
+#    instance master and applications. MetricsServlet may not be configured by self.
+#
 
 # Enable JmxSink for all instances by class name
 #*.sink.jmx.class=spark.metrics.sink.JmxSink
diff --git a/conf/slaves b/conf/slaves
index 6e315a854025200141eeaf1689ea279ea1606da1..da0a01343d20aff07b9143a3c2698867ffcf6770 100644
--- a/conf/slaves
+++ b/conf/slaves
@@ -1,2 +1,2 @@
-# A Spark Worker will be started on each of the machines listes below.
+# A Spark Worker will be started on each of the machines listed below.
 localhost
\ No newline at end of file
diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index b8936314ecce282f835201c56dac6db6e99bbe1f..c978db00d9574553ea37c10dae516ce9c7a30f73 100755
--- a/conf/spark-env.sh.template
+++ b/conf/spark-env.sh.template
@@ -16,4 +16,9 @@
 # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT
 # - SPARK_WORKER_INSTANCES, to set the number of worker instances/processes
 #   to be spawned on every slave machine
+# - SPARK_JAVA_OPTS, to set the jvm options for executor backend. Note: This is
+#   only for node-specific options, whereas app-specific options should be set
+#   in the application.
+#   Examples of node-speicic options : -Dspark.local.dir, GC related options.
+#   Examples of app-specific options : -Dspark.serializer
 
diff --git a/core/pom.xml b/core/pom.xml
index f0c936c86accdabb1b6323574d0879855cc7b985..6627a87de1d34b95940b7107af4daf163d3e2f85 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -32,6 +32,18 @@
   <url>http://spark-project.org/</url>
 
   <dependencies>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-ipc</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-server</artifactId>
@@ -48,6 +60,10 @@
       <groupId>com.ning</groupId>
       <artifactId>compress-lzf</artifactId>
     </dependency>
+    <dependency>
+      <groupId>org.xerial.snappy</groupId>
+      <artifactId>snappy-java</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.ow2.asm</groupId>
       <artifactId>asm</artifactId>
@@ -59,12 +75,12 @@
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>chill_2.9.3</artifactId>
-      <version>0.3.0</version>
+      <version>0.3.1</version>
     </dependency>
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>chill-java</artifactId>
-      <version>0.3.0</version>
+      <version>0.3.1</version>
     </dependency>
     <dependency>
       <groupId>com.typesafe.akka</groupId>
@@ -86,10 +102,6 @@
       <groupId>org.scala-lang</groupId>
       <artifactId>scala-library</artifactId>
     </dependency>
-    <dependency>
-      <groupId>net.liftweb</groupId>
-      <artifactId>lift-json_2.9.2</artifactId>
-    </dependency>
     <dependency>
       <groupId>it.unimi.dsi</groupId>
       <artifactId>fastutil</artifactId>
@@ -122,7 +134,10 @@
       <groupId>com.codahale.metrics</groupId>
       <artifactId>metrics-jvm</artifactId>
     </dependency>
-
+    <dependency>
+      <groupId>com.codahale.metrics</groupId>
+      <artifactId>metrics-json</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.derby</groupId>
       <artifactId>derby</artifactId>
@@ -200,183 +215,4 @@
       </plugin>
     </plugins>
   </build>
-
-  <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.codehaus.mojo</groupId>
-            <artifactId>build-helper-maven-plugin</artifactId>
-            <executions>
-              <execution>
-                <id>add-source</id>
-                <phase>generate-sources</phase>
-                <goals>
-                  <goal>add-source</goal>
-                </goals>
-                <configuration>
-                  <sources>
-                    <source>src/main/scala</source>
-                    <source>src/hadoop1/scala</source>
-                  </sources>
-                </configuration>
-              </execution>
-              <execution>
-                <id>add-scala-test-sources</id>
-                <phase>generate-test-sources</phase>
-                <goals>
-                  <goal>add-test-source</goal>
-                </goals>
-                <configuration>
-                  <sources>
-                    <source>src/test/scala</source>
-                  </sources>
-                </configuration>
-              </execution>
-            </executions>
-          </plugin>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop1</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.codehaus.mojo</groupId>
-            <artifactId>build-helper-maven-plugin</artifactId>
-            <executions>
-              <execution>
-                <id>add-source</id>
-                <phase>generate-sources</phase>
-                <goals>
-                  <goal>add-source</goal>
-                </goals>
-                <configuration>
-                  <sources>
-                    <source>src/main/scala</source>
-                    <source>src/hadoop2/scala</source>
-                  </sources>
-                </configuration>
-              </execution>
-              <execution>
-                <id>add-scala-test-sources</id>
-                <phase>generate-test-sources</phase>
-                <goals>
-                  <goal>add-test-source</goal>
-                </goals>
-                <configuration>
-                  <sources>
-                    <source>src/test/scala</source>
-                  </sources>
-                </configuration>
-              </execution>
-            </executions>
-          </plugin>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2-yarn</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-api</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-common</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.codehaus.mojo</groupId>
-            <artifactId>build-helper-maven-plugin</artifactId>
-            <executions>
-              <execution>
-                <id>add-source</id>
-                <phase>generate-sources</phase>
-                <goals>
-                  <goal>add-source</goal>
-                </goals>
-                <configuration>
-                  <sources>
-                    <source>src/main/scala</source>
-                    <source>src/hadoop2-yarn/scala</source>
-                  </sources>
-                </configuration>
-              </execution>
-              <execution>
-                <id>add-scala-test-sources</id>
-                <phase>generate-test-sources</phase>
-                <goals>
-                  <goal>add-test-source</goal>
-                </goals>
-                <configuration>
-                  <sources>
-                    <source>src/test/scala</source>
-                  </sources>
-                </configuration>
-              </execution>
-            </executions>
-          </plugin>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2-yarn</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-  </profiles>
 </project>
diff --git a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala
deleted file mode 100644
index 617954cb9872aa1b487486afe37dc36233bf7692..0000000000000000000000000000000000000000
--- a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package spark.deploy
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.mapred.JobConf
-
-
-/**
- * Contains util methods to interact with Hadoop from spark.
- */
-object SparkHadoopUtil {
-
-  def getUserNameFromEnvironment(): String = {
-    // defaulting to -D ...
-    System.getProperty("user.name")
-  }
-
-  def runAsUser(func: (Product) => Unit, args: Product) {
-
-    // Add support, if exists - for now, simply run func !
-    func(args)
-  }
-
-  // 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) {}
-
-  def isYarnMode(): Boolean = { false }
-
-}
diff --git a/core/src/hadoop2/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala b/core/src/hadoop2/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala
deleted file mode 100644
index aa3b1ed3a58a136f94995657d867d97c7b0d32a8..0000000000000000000000000000000000000000
--- a/core/src/hadoop2/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.hadoop.mapreduce
-
-import org.apache.hadoop.conf.Configuration
-import task.{TaskAttemptContextImpl, JobContextImpl}
-
-trait HadoopMapReduceUtil {
-  def newJobContext(conf: Configuration, jobId: JobID): JobContext = new JobContextImpl(conf, jobId)
-
-  def newTaskAttemptContext(conf: Configuration, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContextImpl(conf, attemptId)
-
-  def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = new TaskAttemptID(jtIdentifier,
-    jobId, isMap, taskId, attemptId)
-}
diff --git a/core/src/main/resources/spark/ui/static/bootstrap.min.css b/core/src/main/resources/spark/ui/static/bootstrap.min.css
index b6428e695860151f82076e87810fe2d133339c2a..c177b6172aefb675c022d4e99e84483ae58919f2 100644
--- a/core/src/main/resources/spark/ui/static/bootstrap.min.css
+++ b/core/src/main/resources/spark/ui/static/bootstrap.min.css
@@ -1,9 +1,9 @@
-/*!
+@import url('//fonts.googleapis.com/css?family=Open+Sans:400italic,700italic,400,700');/*!
  * Bootstrap v2.3.2
  *
- * Copyright 2012 Twitter, Inc
+ * Copyright 2013 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}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}a:hover,a:active{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{width:auto\9;height:auto;max-width:100%;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}#map_canvas img,.google-maps img{max-width:none}button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}button,input{*overflow:visible;line-height:normal}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}button,html input[type="button"],input[type="reset"],input[type="submit"]{cursor:pointer;-webkit-appearance:button}label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer}input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}textarea{overflow:auto;vertical-align:top}@media print{*{color:#000!important;text-shadow:none!important;background:transparent!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100%!important}@page{margin:.5cm}p,h2,h3{orphans:3;widows:3}h2,h3{page-break-after:avoid}}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:20px;color:#333;background-color:#fff}a{color:#08c;text-decoration:none}a:hover,a:focus{color:#005580;text-decoration:underline}.img-rounded{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px rgba(0,0,0,0.1)}.img-circle{-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px}.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;line-height:0;content:""}.row:after{clear:both}[class*="span"]{float:left;min-height:1px;margin-left:20px}.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}.span12{width:940px}.span11{width:860px}.span10{width:780px}.span9{width:700px}.span8{width:620px}.span7{width:540px}.span6{width:460px}.span5{width:380px}.span4{width:300px}.span3{width:220px}.span2{width:140px}.span1{width:60px}.offset12{margin-left:980px}.offset11{margin-left:900px}.offset10{margin-left:820px}.offset9{margin-left:740px}.offset8{margin-left:660px}.offset7{margin-left:580px}.offset6{margin-left:500px}.offset5{margin-left:420px}.offset4{margin-left:340px}.offset3{margin-left:260px}.offset2{margin-left:180px}.offset1{margin-left:100px}.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;line-height:0;content:""}.row-fluid:after{clear:both}.row-fluid [class*="span"]{display:block;float:left;width:100%;min-height:30px;margin-left:2.127659574468085%;*margin-left:2.074468085106383%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.row-fluid [class*="span"]:first-child{margin-left:0}.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%}.row-fluid .span12{width:100%;*width:99.94680851063829%}.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%}.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%}.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%}.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%}.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%}.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%}.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%}.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%}.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%}.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%}.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%}.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%}.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%}.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%}.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%}.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%}.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%}.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%}.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%}.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%}.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%}.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%}.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%}.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%}.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%}.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%}.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%}.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%}.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%}.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%}.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%}.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%}.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%}.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%}.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%}[class*="span"].hide,.row-fluid [class*="span"].hide{display:none}[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right}.container{margin-right:auto;margin-left:auto;*zoom:1}.container:before,.container:after{display:table;line-height:0;content:""}.container:after{clear:both}.container-fluid{padding-right:20px;padding-left:20px;*zoom:1}.container-fluid:before,.container-fluid:after{display:table;line-height:0;content:""}.container-fluid:after{clear:both}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:21px;font-weight:200;line-height:30px}small{font-size:85%}strong{font-weight:bold}em{font-style:italic}cite{font-style:normal}.muted{color:#999}a.muted:hover,a.muted:focus{color:#808080}.text-warning{color:#c09853}a.text-warning:hover,a.text-warning:focus{color:#a47e3c}.text-error{color:#b94a48}a.text-error:hover,a.text-error:focus{color:#953b39}.text-info{color:#3a87ad}a.text-info:hover,a.text-info:focus{color:#2d6987}.text-success{color:#468847}a.text-success:hover,a.text-success:focus{color:#356635}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}h1,h2,h3,h4,h5,h6{margin:10px 0;font-family:inherit;font-weight:bold;line-height:20px;color:inherit;text-rendering:optimizelegibility}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#999}h1,h2,h3{line-height:40px}h1{font-size:38.5px}h2{font-size:31.5px}h3{font-size:24.5px}h4{font-size:17.5px}h5{font-size:14px}h6{font-size:11.9px}h1 small{font-size:24.5px}h2 small{font-size:17.5px}h3 small{font-size:14px}h4 small{font-size:14px}.page-header{padding-bottom:9px;margin:20px 0 30px;border-bottom:1px solid #eee}ul,ol{padding:0;margin:0 0 10px 25px}ul ul,ul ol,ol ol,ol ul{margin-bottom:0}li{line-height:20px}ul.unstyled,ol.unstyled{margin-left:0;list-style:none}ul.inline,ol.inline{margin-left:0;list-style:none}ul.inline>li,ol.inline>li{display:inline-block;*display:inline;padding-right:5px;padding-left:5px;*zoom:1}dl{margin-bottom:20px}dt,dd{line-height:20px}dt{font-weight:bold}dd{margin-left:10px}.dl-horizontal{*zoom:1}.dl-horizontal:before,.dl-horizontal:after{display:table;line-height:0;content:""}.dl-horizontal:after{clear:both}.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}hr{margin:20px 0;border:0;border-top:1px solid #eee;border-bottom:1px solid #fff}abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #999}abbr.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:0 0 0 15px;margin:0 0 20px;border-left:5px solid #eee}blockquote p{margin-bottom:0;font-size:17.5px;font-weight:300;line-height:1.25}blockquote small{display:block;line-height:20px;color:#999}blockquote small:before{content:'\2014 \00A0'}blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eee;border-left:0}blockquote.pull-right p,blockquote.pull-right small{text-align:right}blockquote.pull-right small:before{content:''}blockquote.pull-right small:after{content:'\00A0 \2014'}q:before,q:after,blockquote:before,blockquote:after{content:""}address{display:block;margin-bottom:20px;font-style:normal;line-height:20px}code,pre{padding:0 3px 2px;font-family:Monaco,Menlo,Consolas,"Courier New",monospace;font-size:12px;color:#333;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}code{padding:2px 4px;color:#d14;white-space:nowrap;background-color:#f7f7f9;border:1px solid #e1e1e8}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:20px;word-break:break-all;word-wrap:break-word;white-space:pre;white-space:pre-wrap;background-color:#f5f5f5;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.15);-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}pre.prettyprint{margin-bottom:20px}pre code{padding:0;color:inherit;white-space:pre;white-space:pre-wrap;background-color:transparent;border:0}.pre-scrollable{max-height:340px;overflow-y:scroll}form{margin:0 0 20px}fieldset{padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:40px;color:#333;border:0;border-bottom:1px solid #e5e5e5}legend small{font-size:15px;color:#999}label,input,button,select,textarea{font-size:14px;font-weight:normal;line-height:20px}input,button,select,textarea{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif}label{display:block;margin-bottom:5px}select,textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{display:inline-block;height:20px;padding:4px 6px;margin-bottom:10px;font-size:14px;line-height:20px;color:#555;vertical-align:middle;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}input,textarea,.uneditable-input{width:206px}textarea{height:auto}textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{background-color:#fff;border:1px solid #ccc;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-webkit-transition:border linear .2s,box-shadow linear .2s;-moz-transition:border linear .2s,box-shadow linear .2s;-o-transition:border linear .2s,box-shadow linear .2s;transition:border linear .2s,box-shadow linear .2s}textarea:focus,input[type="text"]:focus,input[type="password"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="date"]:focus,input[type="month"]:focus,input[type="time"]:focus,input[type="week"]:focus,input[type="number"]:focus,input[type="email"]:focus,input[type="url"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="color"]:focus,.uneditable-input:focus{border-color:rgba(82,168,236,0.8);outline:0;outline:thin dotted \9;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6)}input[type="radio"],input[type="checkbox"]{margin:4px 0 0;margin-top:1px \9;*margin-top:0;line-height:normal}input[type="file"],input[type="image"],input[type="submit"],input[type="reset"],input[type="button"],input[type="radio"],input[type="checkbox"]{width:auto}select,input[type="file"]{height:30px;*margin-top:4px;line-height:30px}select{width:220px;background-color:#fff;border:1px solid #ccc}select[multiple],select[size]{height:auto}select:focus,input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.uneditable-input,.uneditable-textarea{color:#999;cursor:not-allowed;background-color:#fcfcfc;border-color:#ccc;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);box-shadow:inset 0 1px 2px rgba(0,0,0,0.025)}.uneditable-input{overflow:hidden;white-space:nowrap}.uneditable-textarea{width:auto;height:auto}input:-moz-placeholder,textarea:-moz-placeholder{color:#999}input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999}input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999}.radio,.checkbox{min-height:20px;padding-left:20px}.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px}.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px}.radio.inline,.checkbox.inline{display:inline-block;padding-top:5px;margin-bottom:0;vertical-align:middle}.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px}.input-mini{width:60px}.input-small{width:90px}.input-medium{width:150px}.input-large{width:210px}.input-xlarge{width:270px}.input-xxlarge{width:530px}input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"]{float:none;margin-left:0}.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"]{display:inline-block}input,textarea,.uneditable-input{margin-left:0}.controls-row [class*="span"]+[class*="span"]{margin-left:20px}input.span12,textarea.span12,.uneditable-input.span12{width:926px}input.span11,textarea.span11,.uneditable-input.span11{width:846px}input.span10,textarea.span10,.uneditable-input.span10{width:766px}input.span9,textarea.span9,.uneditable-input.span9{width:686px}input.span8,textarea.span8,.uneditable-input.span8{width:606px}input.span7,textarea.span7,.uneditable-input.span7{width:526px}input.span6,textarea.span6,.uneditable-input.span6{width:446px}input.span5,textarea.span5,.uneditable-input.span5{width:366px}input.span4,textarea.span4,.uneditable-input.span4{width:286px}input.span3,textarea.span3,.uneditable-input.span3{width:206px}input.span2,textarea.span2,.uneditable-input.span2{width:126px}input.span1,textarea.span1,.uneditable-input.span1{width:46px}.controls-row{*zoom:1}.controls-row:before,.controls-row:after{display:table;line-height:0;content:""}.controls-row:after{clear:both}.controls-row [class*="span"],.row-fluid .controls-row [class*="span"]{float:left}.controls-row .checkbox[class*="span"],.controls-row .radio[class*="span"]{padding-top:5px}input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly]{cursor:not-allowed;background-color:#eee}input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly]{background-color:transparent}.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#c09853}.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#c09853}.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#c09853;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#a47e3c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e}.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#c09853;background-color:#fcf8e3;border-color:#c09853}.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#b94a48}.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#b94a48}.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#b94a48;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#953b39;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392}.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#b94a48;background-color:#f2dede;border-color:#b94a48}.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#468847}.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#468847}.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#468847;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#356635;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b}.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847}.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad}.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad}.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3}.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3a87ad;background-color:#d9edf7;border-color:#3a87ad}input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;line-height:0;content:""}.form-actions:after{clear:both}.help-block,.help-inline{color:#595959}.help-block{display:block;margin-bottom:10px}.help-inline{display:inline-block;*display:inline;padding-left:5px;vertical-align:middle;*zoom:1}.input-append,.input-prepend{display:inline-block;margin-bottom:10px;font-size:0;white-space:nowrap;vertical-align:middle}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input,.input-append .dropdown-menu,.input-prepend .dropdown-menu,.input-append .popover,.input-prepend .popover{font-size:14px}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input{position:relative;margin-bottom:0;*margin-left:0;vertical-align:top;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append input:focus,.input-prepend input:focus,.input-append select:focus,.input-prepend select:focus,.input-append .uneditable-input:focus,.input-prepend .uneditable-input:focus{z-index:2}.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:14px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546}.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}.input-prepend .add-on:first-child,.input-prepend .btn:first-child{-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input,.input-append select,.input-append .uneditable-input{-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append .btn-group:first-child{margin-left:0}input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.form-search .input-append .search-query,.form-search .input-prepend .search-query{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.form-search .input-append .search-query{-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}.form-search .input-append .btn{-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}.form-search .input-prepend .search-query{-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}.form-search .input-prepend .btn{-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;margin-bottom:0;vertical-align:middle;*zoom:1}.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}.control-group{margin-bottom:10px}legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;line-height:0;content:""}.form-horizontal .control-group:after{clear:both}.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}.form-horizontal .help-block{margin-bottom:0}.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px}.form-horizontal .form-actions{padding-left:180px}table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}.table{width:100%;margin-bottom:20px}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #ddd}.table th{font-weight:bold}.table thead th{vertical-align:bottom}.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}.table tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed th,.table-condensed td{padding:4px 5px}.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topleft:4px}.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-moz-border-radius-topright:4px}.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px}.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px}.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;border-bottom-left-radius:0;-moz-border-radius-bottomleft:0}.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;border-bottom-right-radius:0;-moz-border-radius-bottomright:0}.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topleft:4px}.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-moz-border-radius-topright:4px}.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}.table tbody tr.success>td{background-color:#dff0d8}.table tbody tr.error>td{background-color:#f2dede}.table tbody tr.warning>td{background-color:#fcf8e3}.table tbody tr.info>td{background-color:#d9edf7}.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}.table-hover tbody tr.error:hover>td{background-color:#ebcccc}.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;margin-top:1px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat}.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}.icon-glass{background-position:0 0}.icon-music{background-position:-24px 0}.icon-search{background-position:-48px 0}.icon-envelope{background-position:-72px 0}.icon-heart{background-position:-96px 0}.icon-star{background-position:-120px 0}.icon-star-empty{background-position:-144px 0}.icon-user{background-position:-168px 0}.icon-film{background-position:-192px 0}.icon-th-large{background-position:-216px 0}.icon-th{background-position:-240px 0}.icon-th-list{background-position:-264px 0}.icon-ok{background-position:-288px 0}.icon-remove{background-position:-312px 0}.icon-zoom-in{background-position:-336px 0}.icon-zoom-out{background-position:-360px 0}.icon-off{background-position:-384px 0}.icon-signal{background-position:-408px 0}.icon-cog{background-position:-432px 0}.icon-trash{background-position:-456px 0}.icon-home{background-position:0 -24px}.icon-file{background-position:-24px -24px}.icon-time{background-position:-48px -24px}.icon-road{background-position:-72px -24px}.icon-download-alt{background-position:-96px -24px}.icon-download{background-position:-120px -24px}.icon-upload{background-position:-144px -24px}.icon-inbox{background-position:-168px -24px}.icon-play-circle{background-position:-192px -24px}.icon-repeat{background-position:-216px -24px}.icon-refresh{background-position:-240px -24px}.icon-list-alt{background-position:-264px -24px}.icon-lock{background-position:-287px -24px}.icon-flag{background-position:-312px -24px}.icon-headphones{background-position:-336px -24px}.icon-volume-off{background-position:-360px -24px}.icon-volume-down{background-position:-384px -24px}.icon-volume-up{background-position:-408px -24px}.icon-qrcode{background-position:-432px -24px}.icon-barcode{background-position:-456px -24px}.icon-tag{background-position:0 -48px}.icon-tags{background-position:-25px -48px}.icon-book{background-position:-48px -48px}.icon-bookmark{background-position:-72px -48px}.icon-print{background-position:-96px -48px}.icon-camera{background-position:-120px -48px}.icon-font{background-position:-144px -48px}.icon-bold{background-position:-167px -48px}.icon-italic{background-position:-192px -48px}.icon-text-height{background-position:-216px -48px}.icon-text-width{background-position:-240px -48px}.icon-align-left{background-position:-264px -48px}.icon-align-center{background-position:-288px -48px}.icon-align-right{background-position:-312px -48px}.icon-align-justify{background-position:-336px -48px}.icon-list{background-position:-360px -48px}.icon-indent-left{background-position:-384px -48px}.icon-indent-right{background-position:-408px -48px}.icon-facetime-video{background-position:-432px -48px}.icon-picture{background-position:-456px -48px}.icon-pencil{background-position:0 -72px}.icon-map-marker{background-position:-24px -72px}.icon-adjust{background-position:-48px -72px}.icon-tint{background-position:-72px -72px}.icon-edit{background-position:-96px -72px}.icon-share{background-position:-120px -72px}.icon-check{background-position:-144px -72px}.icon-move{background-position:-168px -72px}.icon-step-backward{background-position:-192px -72px}.icon-fast-backward{background-position:-216px -72px}.icon-backward{background-position:-240px -72px}.icon-play{background-position:-264px -72px}.icon-pause{background-position:-288px -72px}.icon-stop{background-position:-312px -72px}.icon-forward{background-position:-336px -72px}.icon-fast-forward{background-position:-360px -72px}.icon-step-forward{background-position:-384px -72px}.icon-eject{background-position:-408px -72px}.icon-chevron-left{background-position:-432px -72px}.icon-chevron-right{background-position:-456px -72px}.icon-plus-sign{background-position:0 -96px}.icon-minus-sign{background-position:-24px -96px}.icon-remove-sign{background-position:-48px -96px}.icon-ok-sign{background-position:-72px -96px}.icon-question-sign{background-position:-96px -96px}.icon-info-sign{background-position:-120px -96px}.icon-screenshot{background-position:-144px -96px}.icon-remove-circle{background-position:-168px -96px}.icon-ok-circle{background-position:-192px -96px}.icon-ban-circle{background-position:-216px -96px}.icon-arrow-left{background-position:-240px -96px}.icon-arrow-right{background-position:-264px -96px}.icon-arrow-up{background-position:-289px -96px}.icon-arrow-down{background-position:-312px -96px}.icon-share-alt{background-position:-336px -96px}.icon-resize-full{background-position:-360px -96px}.icon-resize-small{background-position:-384px -96px}.icon-plus{background-position:-408px -96px}.icon-minus{background-position:-433px -96px}.icon-asterisk{background-position:-456px -96px}.icon-exclamation-sign{background-position:0 -120px}.icon-gift{background-position:-24px -120px}.icon-leaf{background-position:-48px -120px}.icon-fire{background-position:-72px -120px}.icon-eye-open{background-position:-96px -120px}.icon-eye-close{background-position:-120px -120px}.icon-warning-sign{background-position:-144px -120px}.icon-plane{background-position:-168px -120px}.icon-calendar{background-position:-192px -120px}.icon-random{width:16px;background-position:-216px -120px}.icon-comment{background-position:-240px -120px}.icon-magnet{background-position:-264px -120px}.icon-chevron-up{background-position:-288px -120px}.icon-chevron-down{background-position:-313px -119px}.icon-retweet{background-position:-336px -120px}.icon-shopping-cart{background-position:-360px -120px}.icon-folder-close{width:16px;background-position:-384px -120px}.icon-folder-open{width:16px;background-position:-408px -120px}.icon-resize-vertical{background-position:-432px -119px}.icon-resize-horizontal{background-position:-456px -118px}.icon-hdd{background-position:0 -144px}.icon-bullhorn{background-position:-24px -144px}.icon-bell{background-position:-48px -144px}.icon-certificate{background-position:-72px -144px}.icon-thumbs-up{background-position:-96px -144px}.icon-thumbs-down{background-position:-120px -144px}.icon-hand-right{background-position:-144px -144px}.icon-hand-left{background-position:-168px -144px}.icon-hand-up{background-position:-192px -144px}.icon-hand-down{background-position:-216px -144px}.icon-circle-arrow-right{background-position:-240px -144px}.icon-circle-arrow-left{background-position:-264px -144px}.icon-circle-arrow-up{background-position:-288px -144px}.icon-circle-arrow-down{background-position:-312px -144px}.icon-globe{background-position:-336px -144px}.icon-wrench{background-position:-360px -144px}.icon-tasks{background-position:-384px -144px}.icon-filter{background-position:-408px -144px}.icon-briefcase{background-position:-432px -144px}.icon-fullscreen{background-position:-456px -144px}.dropup,.dropdown{position:relative}.dropdown-toggle{*margin-bottom:-3px}.dropdown-toggle:active,.open .dropdown-toggle{outline:0}.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}.dropdown .caret{margin-top:8px;margin-left:2px}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{color:#fff;text-decoration:none;background-color:#0081c2;background-image:-moz-linear-gradient(top,#08c,#0077b3);background-image:-webkit-gradient(linear,0 0,0 100%,from(#08c),to(#0077b3));background-image:-webkit-linear-gradient(top,#08c,#0077b3);background-image:-o-linear-gradient(top,#08c,#0077b3);background-image:linear-gradient(to bottom,#08c,#0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc',endColorstr='#ff0077b3',GradientType=0)}.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;background-color:#0081c2;background-image:-moz-linear-gradient(top,#08c,#0077b3);background-image:-webkit-gradient(linear,0 0,0 100%,from(#08c),to(#0077b3));background-image:-webkit-linear-gradient(top,#08c,#0077b3);background-image:-o-linear-gradient(top,#08c,#0077b3);background-image:linear-gradient(to bottom,#08c,#0077b3);background-repeat:repeat-x;outline:0;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc',endColorstr='#ff0077b3',GradientType=0)}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;cursor:default;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open{*z-index:1000}.open>.dropdown-menu{display:block}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}.dropdown-submenu{position:relative}.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}.dropdown-submenu:hover>.dropdown-menu{display:block}.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}.dropdown-submenu>a:after{display:block;float:right;width:0;height:0;margin-top:5px;margin-right:-10px;border-color:transparent;border-left-color:#ccc;border-style:solid;border-width:5px 0 5px 5px;content:" "}.dropdown-submenu:hover>a:after{border-left-color:#fff}.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}.dropdown .dropdown-menu .nav-header{padding-right:20px;padding-left:20px}.typeahead{z-index:1051;margin-top:2px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}.well-large{padding:24px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.well-small{padding:9px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}.btn{display:inline-block;*display:inline;padding:4px 12px;margin-bottom:0;*margin-left:.3em;font-size:14px;line-height:20px;color:#333;text-align:center;text-shadow:0 1px 1px rgba(255,255,255,0.75);vertical-align:middle;cursor:pointer;background-color:#f5f5f5;*background-color:#e6e6e6;background-image:-moz-linear-gradient(top,#fff,#e6e6e6);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fff),to(#e6e6e6));background-image:-webkit-linear-gradient(top,#fff,#e6e6e6);background-image:-o-linear-gradient(top,#fff,#e6e6e6);background-image:linear-gradient(to bottom,#fff,#e6e6e6);background-repeat:repeat-x;border:1px solid #ccc;*border:0;border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff',endColorstr='#ffe6e6e6',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);*zoom:1;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}.btn:active,.btn.active{background-color:#ccc \9}.btn:first-child{*margin-left:0}.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05)}.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}.btn-large{padding:11px 19px;font-size:17.5px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}.btn-small{padding:2px 10px;font-size:11.9px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}.btn-mini{padding:0 6px;font-size:10.5px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.btn-block{display:block;width:100%;padding-right:0;padding-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.btn-block+.btn-block{margin-top:5px}input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;*background-color:#04c;background-image:-moz-linear-gradient(top,#08c,#04c);background-image:-webkit-gradient(linear,0 0,0 100%,from(#08c),to(#04c));background-image:-webkit-linear-gradient(top,#08c,#04c);background-image:-o-linear-gradient(top,#08c,#04c);background-image:linear-gradient(to bottom,#08c,#04c);background-repeat:repeat-x;border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc',endColorstr='#ff0044cc',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}.btn-primary:active,.btn-primary.active{background-color:#039 \9}.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;*background-color:#f89406;background-image:-moz-linear-gradient(top,#fbb450,#f89406);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fbb450),to(#f89406));background-image:-webkit-linear-gradient(top,#fbb450,#f89406);background-image:-o-linear-gradient(top,#fbb450,#f89406);background-image:linear-gradient(to bottom,#fbb450,#f89406);background-repeat:repeat-x;border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450',endColorstr='#fff89406',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;*background-color:#bd362f;background-image:-moz-linear-gradient(top,#ee5f5b,#bd362f);background-image:-webkit-gradient(linear,0 0,0 100%,from(#ee5f5b),to(#bd362f));background-image:-webkit-linear-gradient(top,#ee5f5b,#bd362f);background-image:-o-linear-gradient(top,#ee5f5b,#bd362f);background-image:linear-gradient(to bottom,#ee5f5b,#bd362f);background-repeat:repeat-x;border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b',endColorstr='#ffbd362f',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;*background-color:#51a351;background-image:-moz-linear-gradient(top,#62c462,#51a351);background-image:-webkit-gradient(linear,0 0,0 100%,from(#62c462),to(#51a351));background-image:-webkit-linear-gradient(top,#62c462,#51a351);background-image:-o-linear-gradient(top,#62c462,#51a351);background-image:linear-gradient(to bottom,#62c462,#51a351);background-repeat:repeat-x;border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462',endColorstr='#ff51a351',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}.btn-success:active,.btn-success.active{background-color:#408140 \9}.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;*background-color:#2f96b4;background-image:-moz-linear-gradient(top,#5bc0de,#2f96b4);background-image:-webkit-gradient(linear,0 0,0 100%,from(#5bc0de),to(#2f96b4));background-image:-webkit-linear-gradient(top,#5bc0de,#2f96b4);background-image:-o-linear-gradient(top,#5bc0de,#2f96b4);background-image:linear-gradient(to bottom,#5bc0de,#2f96b4);background-repeat:repeat-x;border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de',endColorstr='#ff2f96b4',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}.btn-info:active,.btn-info.active{background-color:#24748c \9}.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;*background-color:#222;background-image:-moz-linear-gradient(top,#444,#222);background-image:-webkit-gradient(linear,0 0,0 100%,from(#444),to(#222));background-image:-webkit-linear-gradient(top,#444,#222);background-image:-o-linear-gradient(top,#444,#222);background-image:linear-gradient(to bottom,#444,#222);background-repeat:repeat-x;border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444',endColorstr='#ff222222',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}.btn-link{color:#08c;cursor:pointer;border-color:transparent;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}.btn-group{position:relative;display:inline-block;*display:inline;*margin-left:.3em;font-size:0;white-space:nowrap;vertical-align:middle;*zoom:1}.btn-group:first-child{*margin-left:0}.btn-group+.btn-group{margin-left:5px}.btn-toolbar{margin-top:10px;margin-bottom:10px;font-size:0}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}.btn-group>.btn{position:relative;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-group>.btn+.btn{margin-left:-1px}.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:14px}.btn-group>.btn-mini{font-size:10.5px}.btn-group>.btn-small{font-size:11.9px}.btn-group>.btn-large{font-size:17.5px}.btn-group>.btn:first-child{margin-left:0;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-bottomleft:4px;-moz-border-radius-topleft:4px}.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-bottomright:4px}.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-bottom-left-radius:6px;border-bottom-left-radius:6px;-webkit-border-top-left-radius:6px;border-top-left-radius:6px;-moz-border-radius-bottomleft:6px;-moz-border-radius-topleft:6px}.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;border-bottom-right-radius:6px;-moz-border-radius-topright:6px;-moz-border-radius-bottomright:6px}.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{*padding-top:5px;padding-right:8px;*padding-bottom:5px;padding-left:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05)}.btn-group>.btn-mini+.dropdown-toggle{*padding-top:2px;padding-right:5px;*padding-bottom:2px;padding-left:5px}.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}.btn-group>.btn-large+.dropdown-toggle{*padding-top:7px;padding-right:12px;*padding-bottom:7px;padding-left:12px}.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05)}.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}.btn .caret{margin-top:8px;margin-left:0}.btn-large .caret{margin-top:6px}.btn-large .caret{border-top-width:5px;border-right-width:5px;border-left-width:5px}.btn-mini .caret,.btn-small .caret{margin-top:8px}.dropup .btn-large .caret{border-bottom-width:5px}.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}.btn-group-vertical>.btn{display:block;float:none;max-width:100%;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-group-vertical>.btn+.btn{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:first-child{-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.btn-group-vertical>.btn:last-child{-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.btn-group-vertical>.btn-large:first-child{-webkit-border-radius:6px 6px 0 0;-moz-border-radius:6px 6px 0 0;border-radius:6px 6px 0 0}.btn-group-vertical>.btn-large:last-child{-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.alert,.alert h4{color:#c09853}.alert h4{margin:0}.alert .close{position:relative;top:-2px;right:-21px;line-height:20px}.alert-success{color:#468847;background-color:#dff0d8;border-color:#d6e9c6}.alert-success h4{color:#468847}.alert-danger,.alert-error{color:#b94a48;background-color:#f2dede;border-color:#eed3d7}.alert-danger h4,.alert-error h4{color:#b94a48}.alert-info{color:#3a87ad;background-color:#d9edf7;border-color:#bce8f1}.alert-info h4{color:#3a87ad}.alert-block{padding-top:14px;padding-bottom:14px}.alert-block>p,.alert-block>ul{margin-bottom:0}.alert-block p+p{margin-top:5px}.nav{margin-bottom:20px;margin-left:0;list-style:none}.nav>li>a{display:block}.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eee}.nav>li>a>img{max-width:none}.nav>.pull-right{float:right}.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:20px;color:#999;text-shadow:0 1px 0 rgba(255,255,255,0.5);text-transform:uppercase}.nav li+.nav-header{margin-top:9px}.nav-list{padding-right:15px;padding-left:15px;margin-bottom:0}.nav-list>li>a,.nav-list .nav-header{margin-right:-15px;margin-left:-15px;text-shadow:0 1px 0 rgba(255,255,255,0.5)}.nav-list>li>a{padding:3px 15px}.nav-list>.active>a,.nav-list>.active>a:hover,.nav-list>.active>a:focus{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.2);background-color:#08c}.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{margin-right:2px}.nav-list .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}.nav-tabs,.nav-pills{*zoom:1}.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;line-height:0;content:""}.nav-tabs:after,.nav-pills:after{clear:both}.nav-tabs>li,.nav-pills>li{float:left}.nav-tabs>li>a,.nav-pills>li>a{padding-right:12px;padding-left:12px;margin-right:2px;line-height:14px}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{margin-bottom:-1px}.nav-tabs>li>a{padding-top:8px;padding-bottom:8px;line-height:20px;border:1px solid transparent;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover,.nav-tabs>li>a:focus{border-color:#eee #eee #ddd}.nav-tabs>.active>a,.nav-tabs>.active>a:hover,.nav-tabs>.active>a:focus{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-pills>li>a{padding-top:8px;padding-bottom:8px;margin-top:2px;margin-bottom:2px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}.nav-pills>.active>a,.nav-pills>.active>a:hover,.nav-pills>.active>a:focus{color:#fff;background-color:#08c}.nav-stacked>li{float:none}.nav-stacked>li>a{margin-right:0}.nav-tabs.nav-stacked{border-bottom:0}.nav-tabs.nav-stacked>li>a{border:1px solid #ddd;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.nav-tabs.nav-stacked>li:first-child>a{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-topleft:4px}.nav-tabs.nav-stacked>li:last-child>a{-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-moz-border-radius-bottomright:4px;-moz-border-radius-bottomleft:4px}.nav-tabs.nav-stacked>li>a:hover,.nav-tabs.nav-stacked>li>a:focus{z-index:2;border-color:#ddd}.nav-pills.nav-stacked>li>a{margin-bottom:3px}.nav-pills.nav-stacked>li:last-child>a{margin-bottom:1px}.nav-tabs .dropdown-menu{-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}.nav-pills .dropdown-menu{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.nav .dropdown-toggle .caret{margin-top:6px;border-top-color:#08c;border-bottom-color:#08c}.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#005580;border-bottom-color:#005580}.nav-tabs .dropdown-toggle .caret{margin-top:8px}.nav .active .dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}.nav-tabs .active .dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}.nav>.dropdown.active>a:hover,.nav>.dropdown.active>a:focus{cursor:pointer}.nav-tabs .open .dropdown-toggle,.nav-pills .open .dropdown-toggle,.nav>li.dropdown.open.active>a:hover,.nav>li.dropdown.open.active>a:focus{color:#fff;background-color:#999;border-color:#999}.nav li.dropdown.open .caret,.nav li.dropdown.open.active .caret,.nav li.dropdown.open a:hover .caret,.nav li.dropdown.open a:focus .caret{border-top-color:#fff;border-bottom-color:#fff;opacity:1;filter:alpha(opacity=100)}.tabs-stacked .open>a:hover,.tabs-stacked .open>a:focus{border-color:#999}.tabbable{*zoom:1}.tabbable:before,.tabbable:after{display:table;line-height:0;content:""}.tabbable:after{clear:both}.tab-content{overflow:auto}.tabs-below>.nav-tabs,.tabs-right>.nav-tabs,.tabs-left>.nav-tabs{border-bottom:0}.tab-content>.tab-pane,.pill-content>.pill-pane{display:none}.tab-content>.active,.pill-content>.active{display:block}.tabs-below>.nav-tabs{border-top:1px solid #ddd}.tabs-below>.nav-tabs>li{margin-top:-1px;margin-bottom:0}.tabs-below>.nav-tabs>li>a{-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-top-color:#ddd;border-bottom-color:transparent}.tabs-below>.nav-tabs>.active>a,.tabs-below>.nav-tabs>.active>a:hover,.tabs-below>.nav-tabs>.active>a:focus{border-color:transparent #ddd #ddd #ddd}.tabs-left>.nav-tabs>li,.tabs-right>.nav-tabs>li{float:none}.tabs-left>.nav-tabs>li>a,.tabs-right>.nav-tabs>li>a{min-width:74px;margin-right:0;margin-bottom:3px}.tabs-left>.nav-tabs{float:left;margin-right:19px;border-right:1px solid #ddd}.tabs-left>.nav-tabs>li>a{margin-right:-1px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.tabs-left>.nav-tabs>li>a:hover,.tabs-left>.nav-tabs>li>a:focus{border-color:#eee #ddd #eee #eee}.tabs-left>.nav-tabs .active>a,.tabs-left>.nav-tabs .active>a:hover,.tabs-left>.nav-tabs .active>a:focus{border-color:#ddd transparent #ddd #ddd;*border-right-color:#fff}.tabs-right>.nav-tabs{float:right;margin-left:19px;border-left:1px solid #ddd}.tabs-right>.nav-tabs>li>a{margin-left:-1px;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.tabs-right>.nav-tabs>li>a:hover,.tabs-right>.nav-tabs>li>a:focus{border-color:#eee #eee #eee #ddd}.tabs-right>.nav-tabs .active>a,.tabs-right>.nav-tabs .active>a:hover,.tabs-right>.nav-tabs .active>a:focus{border-color:#ddd #ddd #ddd transparent;*border-left-color:#fff}.nav>.disabled>a{color:#999}.nav>.disabled>a:hover,.nav>.disabled>a:focus{text-decoration:none;cursor:default;background-color:transparent}.navbar{*position:relative;*z-index:2;margin-bottom:20px;overflow:visible}.navbar-inner{min-height:40px;padding-right:20px;padding-left:20px;background-color:#fafafa;background-image:-moz-linear-gradient(top,#fff,#f2f2f2);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fff),to(#f2f2f2));background-image:-webkit-linear-gradient(top,#fff,#f2f2f2);background-image:-o-linear-gradient(top,#fff,#f2f2f2);background-image:linear-gradient(to bottom,#fff,#f2f2f2);background-repeat:repeat-x;border:1px solid #d4d4d4;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff',endColorstr='#fff2f2f2',GradientType=0);*zoom:1;-webkit-box-shadow:0 1px 4px rgba(0,0,0,0.065);-moz-box-shadow:0 1px 4px rgba(0,0,0,0.065);box-shadow:0 1px 4px rgba(0,0,0,0.065)}.navbar-inner:before,.navbar-inner:after{display:table;line-height:0;content:""}.navbar-inner:after{clear:both}.navbar .container{width:auto}.nav-collapse.collapse{height:auto;overflow:visible}.navbar .brand{display:block;float:left;padding:10px 20px 10px;margin-left:-20px;font-size:20px;font-weight:200;color:#777;text-shadow:0 1px 0 #fff}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none}.navbar-text{margin-bottom:0;line-height:40px;color:#777}.navbar-link{color:#777}.navbar-link:hover,.navbar-link:focus{color:#333}.navbar .divider-vertical{height:40px;margin:0 9px;border-right:1px solid #fff;border-left:1px solid #f2f2f2}.navbar .btn,.navbar .btn-group{margin-top:5px}.navbar .btn-group .btn,.navbar .input-prepend .btn,.navbar .input-append .btn,.navbar .input-prepend .btn-group,.navbar .input-append .btn-group{margin-top:0}.navbar-form{margin-bottom:0;*zoom:1}.navbar-form:before,.navbar-form:after{display:table;line-height:0;content:""}.navbar-form:after{clear:both}.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:5px}.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0}.navbar-search{position:relative;float:left;margin-top:5px;margin-bottom:0}.navbar-search .search-query{padding:4px 14px;margin-bottom:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.navbar-static-top{position:static;margin-bottom:0}.navbar-static-top .navbar-inner{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030;margin-bottom:0}.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{border-width:0 0 1px}.navbar-fixed-bottom .navbar-inner{border-width:1px 0 0}.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding-right:0;padding-left:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}.navbar-fixed-top{top:0}.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{-webkit-box-shadow:0 1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 10px rgba(0,0,0,0.1);box-shadow:0 1px 10px rgba(0,0,0,0.1)}.navbar-fixed-bottom{bottom:0}.navbar-fixed-bottom .navbar-inner{-webkit-box-shadow:0 -1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,0.1);box-shadow:0 -1px 10px rgba(0,0,0,0.1)}.navbar .nav{position:relative;left:0;display:block;float:left;margin:0 10px 0 0}.navbar .nav.pull-right{float:right;margin-right:0}.navbar .nav>li{float:left}.navbar .nav>li>a{float:none;padding:10px 15px 10px;color:#777;text-decoration:none;text-shadow:0 1px 0 #fff}.navbar .nav .dropdown-toggle .caret{margin-top:8px}.navbar .nav>li>a:focus,.navbar .nav>li>a:hover{color:#333;text-decoration:none;background-color:transparent}.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#555;text-decoration:none;background-color:#e5e5e5;-webkit-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);-moz-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);box-shadow:inset 0 3px 8px rgba(0,0,0,0.125)}.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-right:5px;margin-left:5px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#ededed;*background-color:#e5e5e5;background-image:-moz-linear-gradient(top,#f2f2f2,#e5e5e5);background-image:-webkit-gradient(linear,0 0,0 100%,from(#f2f2f2),to(#e5e5e5));background-image:-webkit-linear-gradient(top,#f2f2f2,#e5e5e5);background-image:-o-linear-gradient(top,#f2f2f2,#e5e5e5);background-image:linear-gradient(to bottom,#f2f2f2,#e5e5e5);background-repeat:repeat-x;border-color:#e5e5e5 #e5e5e5 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2',endColorstr='#ffe5e5e5',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075);box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075)}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#fff;background-color:#e5e5e5;*background-color:#d9d9d9}.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#ccc \9}.navbar .btn-navbar .icon-bar{display:block;width:18px;height:2px;background-color:#f5f5f5;-webkit-border-radius:1px;-moz-border-radius:1px;border-radius:1px;-webkit-box-shadow:0 1px 0 rgba(0,0,0,0.25);-moz-box-shadow:0 1px 0 rgba(0,0,0,0.25);box-shadow:0 1px 0 rgba(0,0,0,0.25)}.btn-navbar .icon-bar+.icon-bar{margin-top:3px}.navbar .nav>li>.dropdown-menu:before{position:absolute;top:-7px;left:9px;display:inline-block;border-right:7px solid transparent;border-bottom:7px solid #ccc;border-left:7px solid transparent;border-bottom-color:rgba(0,0,0,0.2);content:''}.navbar .nav>li>.dropdown-menu:after{position:absolute;top:-6px;left:10px;display:inline-block;border-right:6px solid transparent;border-bottom:6px solid #fff;border-left:6px solid transparent;content:''}.navbar-fixed-bottom .nav>li>.dropdown-menu:before{top:auto;bottom:-7px;border-top:7px solid #ccc;border-bottom:0;border-top-color:rgba(0,0,0,0.2)}.navbar-fixed-bottom .nav>li>.dropdown-menu:after{top:auto;bottom:-6px;border-top:6px solid #fff;border-bottom:0}.navbar .nav li.dropdown>a:hover .caret,.navbar .nav li.dropdown>a:focus .caret{border-top-color:#333;border-bottom-color:#333}.navbar .nav li.dropdown.open>.dropdown-toggle,.navbar .nav li.dropdown.active>.dropdown-toggle,.navbar .nav li.dropdown.open.active>.dropdown-toggle{color:#555;background-color:#e5e5e5}.navbar .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#777;border-bottom-color:#777}.navbar .nav li.dropdown.open>.dropdown-toggle .caret,.navbar .nav li.dropdown.active>.dropdown-toggle .caret,.navbar .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}.navbar .pull-right>li>.dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right{right:0;left:auto}.navbar .pull-right>li>.dropdown-menu:before,.navbar .nav>li>.dropdown-menu.pull-right:before{right:12px;left:auto}.navbar .pull-right>li>.dropdown-menu:after,.navbar .nav>li>.dropdown-menu.pull-right:after{right:13px;left:auto}.navbar .pull-right>li>.dropdown-menu .dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right .dropdown-menu{right:100%;left:auto;margin-right:-1px;margin-left:0;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}.navbar-inverse .navbar-inner{background-color:#1b1b1b;background-image:-moz-linear-gradient(top,#222,#111);background-image:-webkit-gradient(linear,0 0,0 100%,from(#222),to(#111));background-image:-webkit-linear-gradient(top,#222,#111);background-image:-o-linear-gradient(top,#222,#111);background-image:linear-gradient(to bottom,#222,#111);background-repeat:repeat-x;border-color:#252525;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222',endColorstr='#ff111111',GradientType=0)}.navbar-inverse .brand,.navbar-inverse .nav>li>a{color:#999;text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.navbar-inverse .brand:hover,.navbar-inverse .nav>li>a:hover,.navbar-inverse .brand:focus,.navbar-inverse .nav>li>a:focus{color:#fff}.navbar-inverse .brand{color:#999}.navbar-inverse .navbar-text{color:#999}.navbar-inverse .nav>li>a:focus,.navbar-inverse .nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .nav .active>a,.navbar-inverse .nav .active>a:hover,.navbar-inverse .nav .active>a:focus{color:#fff;background-color:#111}.navbar-inverse .navbar-link{color:#999}.navbar-inverse .navbar-link:hover,.navbar-inverse .navbar-link:focus{color:#fff}.navbar-inverse .divider-vertical{border-right-color:#222;border-left-color:#111}.navbar-inverse .nav li.dropdown.open>.dropdown-toggle,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle{color:#fff;background-color:#111}.navbar-inverse .nav li.dropdown>a:hover .caret,.navbar-inverse .nav li.dropdown>a:focus .caret{border-top-color:#fff;border-bottom-color:#fff}.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#999;border-bottom-color:#999}.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}.navbar-inverse .navbar-search .search-query{color:#fff;background-color:#515151;border-color:#111;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);-webkit-transition:none;-moz-transition:none;-o-transition:none;transition:none}.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#ccc}.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#ccc}.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#ccc}.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#333;text-shadow:0 1px 0 #fff;background-color:#fff;border:0;outline:0;-webkit-box-shadow:0 0 3px rgba(0,0,0,0.15);-moz-box-shadow:0 0 3px rgba(0,0,0,0.15);box-shadow:0 0 3px rgba(0,0,0,0.15)}.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e0e0e;*background-color:#040404;background-image:-moz-linear-gradient(top,#151515,#040404);background-image:-webkit-gradient(linear,0 0,0 100%,from(#151515),to(#040404));background-image:-webkit-linear-gradient(top,#151515,#040404);background-image:-o-linear-gradient(top,#151515,#040404);background-image:linear-gradient(to bottom,#151515,#040404);background-repeat:repeat-x;border-color:#040404 #040404 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515',endColorstr='#ff040404',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#040404;*background-color:#000}.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;text-shadow:0 1px 0 #fff;*zoom:1}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}.breadcrumb>.active{color:#999}.pagination{margin:20px 0}.pagination ul{display:inline-block;*display:inline;margin-bottom:0;margin-left:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*zoom:1;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}.pagination ul>li{display:inline}.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;cursor:default;background-color:transparent}.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-bottomleft:4px;-moz-border-radius-topleft:4px}.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-bottomright:4px}.pagination-centered{text-align:center}.pagination-right{text-align:right}.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:17.5px}.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-bottom-left-radius:6px;border-bottom-left-radius:6px;-webkit-border-top-left-radius:6px;border-top-left-radius:6px;-moz-border-radius-bottomleft:6px;-moz-border-radius-topleft:6px}.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;border-bottom-right-radius:6px;-moz-border-radius-topright:6px;-moz-border-radius-bottomright:6px}.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-bottom-left-radius:3px;border-bottom-left-radius:3px;-webkit-border-top-left-radius:3px;border-top-left-radius:3px;-moz-border-radius-bottomleft:3px;-moz-border-radius-topleft:3px}.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;border-bottom-right-radius:3px;-moz-border-radius-topright:3px;-moz-border-radius-bottomright:3px}.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.9px}.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:10.5px}.pager{margin:20px 0;text-align:center;list-style:none;*zoom:1}.pager:before,.pager:after{display:table;line-height:0;content:""}.pager:after{clear:both}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;cursor:default;background-color:#fff}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;outline:0;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box}.modal.fade{top:-25%;-webkit-transition:opacity .3s linear,top .3s ease-out;-moz-transition:opacity .3s linear,top .3s ease-out;-o-transition:opacity .3s linear,top .3s ease-out;transition:opacity .3s linear,top .3s ease-out}.modal.fade.in{top:10%}.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}.modal-header h3{margin:0;line-height:30px}.modal-body{position:relative;max-height:400px;padding:15px;overflow-y:auto}.modal-form{margin-bottom:0}.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;*zoom:1;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff}.modal-footer:before,.modal-footer:after{display:table;line-height:0;content:""}.modal-footer:after{clear:both}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.tooltip{position:absolute;z-index:1030;display:block;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0);visibility:visible}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-top-color:#000;border-width:5px 5px 0}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-right-color:#000;border-width:5px 5px 5px 0}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-left-color:#000;border-width:5px 0 5px 5px}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-bottom-color:#000;border-width:0 5px 5px}.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;white-space:normal;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}.popover-content{padding:9px 14px}.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover .arrow{border-width:11px}.popover .arrow:after{border-width:10px;content:""}.popover.top .arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);border-bottom-width:0}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-top-color:#fff;border-bottom-width:0}.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,0.25);border-left-width:0}.popover.right .arrow:after{bottom:-10px;left:1px;border-right-color:#fff;border-left-width:0}.popover.bottom .arrow{top:-11px;left:50%;margin-left:-11px;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);border-top-width:0}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-bottom-color:#fff;border-top-width:0}.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-left-color:#999;border-left-color:rgba(0,0,0,0.25);border-right-width:0}.popover.left .arrow:after{right:1px;bottom:-10px;border-left-color:#fff;border-right-width:0}.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;line-height:0;content:""}.thumbnails:after{clear:both}.row-fluid .thumbnails{margin-left:0}.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}.thumbnail>img{display:block;max-width:100%;margin-right:auto;margin-left:auto}.thumbnail .caption{padding:9px;color:#555}.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}.media,.media .media{margin-top:15px}.media:first-child{margin-top:0}.media-object{display:block}.media-heading{margin:0 0 5px}.media>.pull-left{margin-right:10px}.media>.pull-right{margin-left:10px}.media-list{margin-left:0;list-style:none}.label,.badge{display:inline-block;padding:2px 4px;font-size:11.844px;font-weight:bold;line-height:14px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);white-space:nowrap;vertical-align:baseline;background-color:#999}.label{-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.badge{padding-right:9px;padding-left:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}.label:empty,.badge:empty{display:none}a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}.label-important,.badge-important{background-color:#b94a48}.label-important[href],.badge-important[href]{background-color:#953b39}.label-warning,.badge-warning{background-color:#f89406}.label-warning[href],.badge-warning[href]{background-color:#c67605}.label-success,.badge-success{background-color:#468847}.label-success[href],.badge-success[href]{background-color:#356635}.label-info,.badge-info{background-color:#3a87ad}.label-info[href],.badge-info[href]{background-color:#2d6987}.label-inverse,.badge-inverse{background-color:#333}.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}.btn .label,.btn .badge{position:relative;top:-1px}.btn-mini .label,.btn-mini .badge{top:0}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0}to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f7f7f7;background-image:-moz-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:-webkit-gradient(linear,0 0,0 100%,from(#f5f5f5),to(#f9f9f9));background-image:-webkit-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:-o-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:linear-gradient(to bottom,#f5f5f5,#f9f9f9);background-repeat:repeat-x;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5',endColorstr='#fff9f9f9',GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1)}.progress .bar{float:left;width:0;height:100%;font-size:12px;color:#fff;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top,#149bdf,#0480be);background-image:-webkit-gradient(linear,0 0,0 100%,from(#149bdf),to(#0480be));background-image:-webkit-linear-gradient(top,#149bdf,#0480be);background-image:-o-linear-gradient(top,#149bdf,#0480be);background-image:linear-gradient(to bottom,#149bdf,#0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf',endColorstr='#ff0480be',GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15)}.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top,#ee5f5b,#c43c35);background-image:-webkit-gradient(linear,0 0,0 100%,from(#ee5f5b),to(#c43c35));background-image:-webkit-linear-gradient(top,#ee5f5b,#c43c35);background-image:-o-linear-gradient(top,#ee5f5b,#c43c35);background-image:linear-gradient(to bottom,#ee5f5b,#c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b',endColorstr='#ffc43c35',GradientType=0)}.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top,#62c462,#57a957);background-image:-webkit-gradient(linear,0 0,0 100%,from(#62c462),to(#57a957));background-image:-webkit-linear-gradient(top,#62c462,#57a957);background-image:-o-linear-gradient(top,#62c462,#57a957);background-image:linear-gradient(to bottom,#62c462,#57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462',endColorstr='#ff57a957',GradientType=0)}.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top,#5bc0de,#339bb9);background-image:-webkit-gradient(linear,0 0,0 100%,from(#5bc0de),to(#339bb9));background-image:-webkit-linear-gradient(top,#5bc0de,#339bb9);background-image:-o-linear-gradient(top,#5bc0de,#339bb9);background-image:linear-gradient(to bottom,#5bc0de,#339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de',endColorstr='#ff339bb9',GradientType=0)}.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top,#fbb450,#f89406);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fbb450),to(#f89406));background-image:-webkit-linear-gradient(top,#fbb450,#f89406);background-image:-o-linear-gradient(top,#fbb450,#f89406);background-image:linear-gradient(to bottom,#fbb450,#f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450',endColorstr='#fff89406',GradientType=0)}.progress-warning.progress-striped .bar,.progress-striped .bar-warning{background-color:#fbb450;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.accordion{margin-bottom:20px}.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.accordion-heading{border-bottom:0}.accordion-heading .accordion-toggle{display:block;padding:8px 15px}.accordion-toggle{cursor:pointer}.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5}.carousel{position:relative;margin-bottom:20px;line-height:1}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-moz-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#fff;text-align:center;background:#222;border:3px solid #fff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:.5;filter:alpha(opacity=50)}.carousel-control.right{right:15px;left:auto}.carousel-control:hover,.carousel-control:focus{color:#fff;text-decoration:none;opacity:.9;filter:alpha(opacity=90)}.carousel-indicators{position:absolute;top:15px;right:15px;z-index:5;margin:0;list-style:none}.carousel-indicators li{display:block;float:left;width:10px;height:10px;margin-left:5px;text-indent:-999px;background-color:#ccc;background-color:rgba(255,255,255,0.25);border-radius:5px}.carousel-indicators .active{background-color:#fff}.carousel-caption{position:absolute;right:0;bottom:0;left:0;padding:15px;background:#333;background:rgba(0,0,0,0.75)}.carousel-caption h4,.carousel-caption p{line-height:20px;color:#fff}.carousel-caption h4{margin:0 0 5px}.carousel-caption p{margin-bottom:0}.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:30px;color:inherit;background-color:#eee;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;letter-spacing:-1px;color:inherit}.hero-unit li{line-height:30px}.pull-right{float:right}.pull-left{float:left}.hide{display:none}.show{display:block}.invisible{visibility:hidden}.affix{position:fixed}
+ * Designed and built with all the love in the world 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:31px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}a:hover,a:active{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{width:auto\9;height:auto;max-width:100%;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}#map_canvas img,.google-maps img{max-width:none}button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}button,input{*overflow:visible;line-height:normal}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}button,html input[type="button"],input[type="reset"],input[type="submit"]{cursor:pointer;-webkit-appearance:button}label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer}input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}textarea{overflow:auto;vertical-align:top}@media print{*{color:#000!important;text-shadow:none!important;background:transparent!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100%!important}@page{margin:.5cm}p,h2,h3{orphans:3;widows:3}h2,h3{page-break-after:avoid}}body{margin:0;font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12.75px;line-height:21px;color:#666;background-color:#fff}a{color:#09d;text-decoration:none}a:hover,a:focus{color:#09d;text-decoration:underline}.img-rounded{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px rgba(0,0,0,0.1)}.img-circle{-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px}.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;line-height:0;content:""}.row:after{clear:both}[class*="span"]{float:left;min-height:1px;margin-left:20px}.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}.span12{width:940px}.span11{width:860px}.span10{width:780px}.span9{width:700px}.span8{width:620px}.span7{width:540px}.span6{width:460px}.span5{width:380px}.span4{width:300px}.span3{width:220px}.span2{width:140px}.span1{width:60px}.offset12{margin-left:980px}.offset11{margin-left:900px}.offset10{margin-left:820px}.offset9{margin-left:740px}.offset8{margin-left:660px}.offset7{margin-left:580px}.offset6{margin-left:500px}.offset5{margin-left:420px}.offset4{margin-left:340px}.offset3{margin-left:260px}.offset2{margin-left:180px}.offset1{margin-left:100px}.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;line-height:0;content:""}.row-fluid:after{clear:both}.row-fluid [class*="span"]{display:block;float:left;width:100%;min-height:31px;margin-left:2.127659574468085%;*margin-left:2.074468085106383%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.row-fluid [class*="span"]:first-child{margin-left:0}.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%}.row-fluid .span12{width:100%;*width:99.94680851063829%}.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%}.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%}.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%}.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%}.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%}.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%}.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%}.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%}.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%}.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%}.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%}.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%}.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%}.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%}.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%}.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%}.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%}.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%}.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%}.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%}.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%}.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%}.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%}.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%}.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%}.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%}.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%}.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%}.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%}.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%}.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%}.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%}.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%}.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%}.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%}[class*="span"].hide,.row-fluid [class*="span"].hide{display:none}[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right}.container{margin-right:auto;margin-left:auto;*zoom:1}.container:before,.container:after{display:table;line-height:0;content:""}.container:after{clear:both}.container-fluid{padding-right:20px;padding-left:20px;*zoom:1}.container-fluid:before,.container-fluid:after{display:table;line-height:0;content:""}.container-fluid:after{clear:both}p{margin:0 0 10.5px}.lead{margin-bottom:21px;font-size:22.5px;font-weight:200;line-height:31.5px}small{font-size:85%}strong{font-weight:bold}em{font-style:italic}cite{font-style:normal}.muted{color:#ccc}a.muted:hover,a.muted:focus{color:#b3b3b3}.text-warning{color:#fff}a.text-warning:hover,a.text-warning:focus{color:#e6e6e6}.text-error{color:#fff}a.text-error:hover,a.text-error:focus{color:#e6e6e6}.text-info{color:#fff}a.text-info:hover,a.text-info:focus{color:#e6e6e6}.text-success{color:#fff}a.text-success:hover,a.text-success:focus{color:#e6e6e6}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}h1,h2,h3,h4,h5,h6{margin:10.5px 0;font-family:inherit;font-weight:normal;line-height:21px;color:#2d2d2d;text-rendering:optimizelegibility}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#ccc}h1,h2,h3{line-height:42px}h1{font-size:41.25px}h2{font-size:33.75px}h3{font-size:26.25px}h4{font-size:18.75px}h5{font-size:15px}h6{font-size:12.75px}h1 small{font-size:26.25px}h2 small{font-size:18.75px}h3 small{font-size:15px}h4 small{font-size:15px}.page-header{padding-bottom:9.5px;margin:21px 0 31.5px;border-bottom:1px solid #eee}ul,ol{padding:0;margin:0 0 10.5px 25px}ul ul,ul ol,ol ol,ol ul{margin-bottom:0}li{line-height:21px}ul.unstyled,ol.unstyled{margin-left:0;list-style:none}ul.inline,ol.inline{margin-left:0;list-style:none}ul.inline>li,ol.inline>li{display:inline-block;*display:inline;padding-right:5px;padding-left:5px;*zoom:1}dl{margin-bottom:21px}dt,dd{line-height:21px}dt{font-weight:bold}dd{margin-left:10.5px}.dl-horizontal{*zoom:1}.dl-horizontal:before,.dl-horizontal:after{display:table;line-height:0;content:""}.dl-horizontal:after{clear:both}.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}hr{border:0;border-top:1px solid #eee;border-bottom:1px solid #fff}abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #ccc}abbr.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:0 0 0 15px;margin:0 0 21px;border-left:5px solid #eee}blockquote p{margin-bottom:0;font-size:18.75px;font-weight:300;line-height:1.25}blockquote small{display:block;line-height:21px;color:#ccc}blockquote small:before{content:'\2014 \00A0'}blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eee;border-left:0}blockquote.pull-right p,blockquote.pull-right small{text-align:right}blockquote.pull-right small:before{content:''}blockquote.pull-right small:after{content:'\00A0 \2014'}q:before,q:after,blockquote:before,blockquote:after{content:""}address{display:block;margin-bottom:21px;font-style:normal;line-height:21px}code,pre{padding:0 3px 2px;font-family:Menlo,Monaco,Consolas,"Courier New",monospace;font-size:13px;color:#434848;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}code{padding:2px 4px;color:#d14;white-space:nowrap;background-color:#f7f7f9;border:1px solid #e1e1e8}pre{display:block;padding:10px;margin:0 0 10.5px;font-size:14px;line-height:21px;word-break:break-all;word-wrap:break-word;white-space:pre;white-space:pre-wrap;background-color:#f5f5f5;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.15);-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}pre.prettyprint{margin-bottom:21px}pre code{padding:0;color:inherit;white-space:pre;white-space:pre-wrap;background-color:transparent;border:0}.pre-scrollable{max-height:340px;overflow-y:scroll}form{margin:0 0 21px}fieldset{padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:21px;font-size:22.5px;line-height:42px;color:#434848;border:0;border-bottom:1px solid #e5e5e5}legend small{font-size:15.75px;color:#ccc}label,input,button,select,textarea{font-size:15px;font-weight:normal;line-height:21px}input,button,select,textarea{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif}label{display:block;margin-bottom:5px}select,textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{display:inline-block;height:21px;padding:4px 6px;margin-bottom:10.5px;font-size:15px;line-height:21px;color:#666;vertical-align:middle;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}input,textarea,.uneditable-input{width:206px}textarea{height:auto}textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{background-color:#fff;border:1px solid #ccc;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-webkit-transition:border linear .2s,box-shadow linear .2s;-moz-transition:border linear .2s,box-shadow linear .2s;-o-transition:border linear .2s,box-shadow linear .2s;transition:border linear .2s,box-shadow linear .2s}textarea:focus,input[type="text"]:focus,input[type="password"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="date"]:focus,input[type="month"]:focus,input[type="time"]:focus,input[type="week"]:focus,input[type="number"]:focus,input[type="email"]:focus,input[type="url"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="color"]:focus,.uneditable-input:focus{border-color:rgba(82,168,236,0.8);outline:0;outline:thin dotted \9;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 8px rgba(82,168,236,0.6)}input[type="radio"],input[type="checkbox"]{margin:4px 0 0;margin-top:1px \9;*margin-top:0;line-height:normal}input[type="file"],input[type="image"],input[type="submit"],input[type="reset"],input[type="button"],input[type="radio"],input[type="checkbox"]{width:auto}select,input[type="file"]{height:31px;*margin-top:4px;line-height:31px}select{width:220px;background-color:#fff;border:1px solid #ccc}select[multiple],select[size]{height:auto}select:focus,input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.uneditable-input,.uneditable-textarea{color:#ccc;cursor:not-allowed;background-color:#fcfcfc;border-color:#ccc;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);box-shadow:inset 0 1px 2px rgba(0,0,0,0.025)}.uneditable-input{overflow:hidden;white-space:nowrap}.uneditable-textarea{width:auto;height:auto}input:-moz-placeholder,textarea:-moz-placeholder{color:#ccc}input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#ccc}input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#ccc}.radio,.checkbox{min-height:21px;padding-left:20px}.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px}.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px}.radio.inline,.checkbox.inline{display:inline-block;padding-top:5px;margin-bottom:0;vertical-align:middle}.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px}.input-mini{width:60px}.input-small{width:90px}.input-medium{width:150px}.input-large{width:210px}.input-xlarge{width:270px}.input-xxlarge{width:530px}input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"]{float:none;margin-left:0}.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"]{display:inline-block}input,textarea,.uneditable-input{margin-left:0}.controls-row [class*="span"]+[class*="span"]{margin-left:20px}input.span12,textarea.span12,.uneditable-input.span12{width:926px}input.span11,textarea.span11,.uneditable-input.span11{width:846px}input.span10,textarea.span10,.uneditable-input.span10{width:766px}input.span9,textarea.span9,.uneditable-input.span9{width:686px}input.span8,textarea.span8,.uneditable-input.span8{width:606px}input.span7,textarea.span7,.uneditable-input.span7{width:526px}input.span6,textarea.span6,.uneditable-input.span6{width:446px}input.span5,textarea.span5,.uneditable-input.span5{width:366px}input.span4,textarea.span4,.uneditable-input.span4{width:286px}input.span3,textarea.span3,.uneditable-input.span3{width:206px}input.span2,textarea.span2,.uneditable-input.span2{width:126px}input.span1,textarea.span1,.uneditable-input.span1{width:46px}.controls-row{*zoom:1}.controls-row:before,.controls-row:after{display:table;line-height:0;content:""}.controls-row:after{clear:both}.controls-row [class*="span"],.row-fluid .controls-row [class*="span"]{float:left}.controls-row .checkbox[class*="span"],.controls-row .radio[class*="span"]{padding-top:5px}input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly]{cursor:not-allowed;background-color:#eee}input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly]{background-color:transparent}.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#fff}.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#fff}.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#fff;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#e6e6e6;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff}.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#fff;background-color:#d47500;border-color:#fff}.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#fff}.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#fff}.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#fff;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#e6e6e6;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff}.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#fff;background-color:#cd0200;border-color:#fff}.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#fff}.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#fff}.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#fff;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#e6e6e6;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff}.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#fff;background-color:#3cb521;border-color:#fff}.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#fff}.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#fff}.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#fff;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#e6e6e6;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #fff}.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#fff;background-color:#3399f3;border-color:#fff}input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}.form-actions{padding:20px 20px 21px;margin-top:21px;margin-bottom:21px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;line-height:0;content:""}.form-actions:after{clear:both}.help-block,.help-inline{color:#8c8c8c}.help-block{display:block;margin-bottom:10.5px}.help-inline{display:inline-block;*display:inline;padding-left:5px;vertical-align:middle;*zoom:1}.input-append,.input-prepend{display:inline-block;margin-bottom:10.5px;font-size:0;white-space:nowrap;vertical-align:middle}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input,.input-append .dropdown-menu,.input-prepend .dropdown-menu,.input-append .popover,.input-prepend .popover{font-size:15px}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input{position:relative;margin-bottom:0;*margin-left:0;vertical-align:top;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append input:focus,.input-prepend input:focus,.input-append select:focus,.input-prepend select:focus,.input-append .uneditable-input:focus,.input-prepend .uneditable-input:focus{z-index:2}.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:21px;min-width:16px;padding:4px 5px;font-size:15px;font-weight:normal;line-height:21px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-append .active,.input-prepend .active{background-color:#98e986;border-color:#3cb521}.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}.input-prepend .add-on:first-child,.input-prepend .btn:first-child{-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input,.input-append select,.input-append .uneditable-input{-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-prepend.input-append .btn-group:first-child{margin-left:0}input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.form-search .input-append .search-query,.form-search .input-prepend .search-query{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.form-search .input-append .search-query{-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}.form-search .input-append .btn{-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}.form-search .input-prepend .search-query{-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}.form-search .input-prepend .btn{-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;margin-bottom:0;vertical-align:middle;*zoom:1}.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}.control-group{margin-bottom:10.5px}legend+.control-group{margin-top:21px;-webkit-margin-top-collapse:separate}.form-horizontal .control-group{margin-bottom:21px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;line-height:0;content:""}.form-horizontal .control-group:after{clear:both}.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}.form-horizontal .help-block{margin-bottom:0}.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10.5px}.form-horizontal .form-actions{padding-left:180px}table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}.table{width:100%;margin-bottom:21px}.table th,.table td{padding:8px;line-height:21px;text-align:left;vertical-align:top;border-top:1px solid #ddd}.table th{font-weight:bold}.table thead th{vertical-align:bottom}.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}.table tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed th,.table-condensed td{padding:4px 5px}.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topleft:4px}.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-moz-border-radius-topright:4px}.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px}.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px}.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;border-bottom-left-radius:0;-moz-border-radius-bottomleft:0}.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;border-bottom-right-radius:0;-moz-border-radius-bottomright:0}.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topleft:4px}.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-moz-border-radius-topright:4px}.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}.table tbody tr.success>td{background-color:#3cb521}.table tbody tr.error>td{background-color:#cd0200}.table tbody tr.warning>td{background-color:#d47500}.table tbody tr.info>td{background-color:#3399f3}.table-hover tbody tr.success:hover>td{background-color:#359f1d}.table-hover tbody tr.error:hover>td{background-color:#b40200}.table-hover tbody tr.warning:hover>td{background-color:#bb6700}.table-hover tbody tr.info:hover>td{background-color:#1b8df2}[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;margin-top:1px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat}.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}.icon-glass{background-position:0 0}.icon-music{background-position:-24px 0}.icon-search{background-position:-48px 0}.icon-envelope{background-position:-72px 0}.icon-heart{background-position:-96px 0}.icon-star{background-position:-120px 0}.icon-star-empty{background-position:-144px 0}.icon-user{background-position:-168px 0}.icon-film{background-position:-192px 0}.icon-th-large{background-position:-216px 0}.icon-th{background-position:-240px 0}.icon-th-list{background-position:-264px 0}.icon-ok{background-position:-288px 0}.icon-remove{background-position:-312px 0}.icon-zoom-in{background-position:-336px 0}.icon-zoom-out{background-position:-360px 0}.icon-off{background-position:-384px 0}.icon-signal{background-position:-408px 0}.icon-cog{background-position:-432px 0}.icon-trash{background-position:-456px 0}.icon-home{background-position:0 -24px}.icon-file{background-position:-24px -24px}.icon-time{background-position:-48px -24px}.icon-road{background-position:-72px -24px}.icon-download-alt{background-position:-96px -24px}.icon-download{background-position:-120px -24px}.icon-upload{background-position:-144px -24px}.icon-inbox{background-position:-168px -24px}.icon-play-circle{background-position:-192px -24px}.icon-repeat{background-position:-216px -24px}.icon-refresh{background-position:-240px -24px}.icon-list-alt{background-position:-264px -24px}.icon-lock{background-position:-287px -24px}.icon-flag{background-position:-312px -24px}.icon-headphones{background-position:-336px -24px}.icon-volume-off{background-position:-360px -24px}.icon-volume-down{background-position:-384px -24px}.icon-volume-up{background-position:-408px -24px}.icon-qrcode{background-position:-432px -24px}.icon-barcode{background-position:-456px -24px}.icon-tag{background-position:0 -48px}.icon-tags{background-position:-25px -48px}.icon-book{background-position:-48px -48px}.icon-bookmark{background-position:-72px -48px}.icon-print{background-position:-96px -48px}.icon-camera{background-position:-120px -48px}.icon-font{background-position:-144px -48px}.icon-bold{background-position:-167px -48px}.icon-italic{background-position:-192px -48px}.icon-text-height{background-position:-216px -48px}.icon-text-width{background-position:-240px -48px}.icon-align-left{background-position:-264px -48px}.icon-align-center{background-position:-288px -48px}.icon-align-right{background-position:-312px -48px}.icon-align-justify{background-position:-336px -48px}.icon-list{background-position:-360px -48px}.icon-indent-left{background-position:-384px -48px}.icon-indent-right{background-position:-408px -48px}.icon-facetime-video{background-position:-432px -48px}.icon-picture{background-position:-456px -48px}.icon-pencil{background-position:0 -72px}.icon-map-marker{background-position:-24px -72px}.icon-adjust{background-position:-48px -72px}.icon-tint{background-position:-72px -72px}.icon-edit{background-position:-96px -72px}.icon-share{background-position:-120px -72px}.icon-check{background-position:-144px -72px}.icon-move{background-position:-168px -72px}.icon-step-backward{background-position:-192px -72px}.icon-fast-backward{background-position:-216px -72px}.icon-backward{background-position:-240px -72px}.icon-play{background-position:-264px -72px}.icon-pause{background-position:-288px -72px}.icon-stop{background-position:-312px -72px}.icon-forward{background-position:-336px -72px}.icon-fast-forward{background-position:-360px -72px}.icon-step-forward{background-position:-384px -72px}.icon-eject{background-position:-408px -72px}.icon-chevron-left{background-position:-432px -72px}.icon-chevron-right{background-position:-456px -72px}.icon-plus-sign{background-position:0 -96px}.icon-minus-sign{background-position:-24px -96px}.icon-remove-sign{background-position:-48px -96px}.icon-ok-sign{background-position:-72px -96px}.icon-question-sign{background-position:-96px -96px}.icon-info-sign{background-position:-120px -96px}.icon-screenshot{background-position:-144px -96px}.icon-remove-circle{background-position:-168px -96px}.icon-ok-circle{background-position:-192px -96px}.icon-ban-circle{background-position:-216px -96px}.icon-arrow-left{background-position:-240px -96px}.icon-arrow-right{background-position:-264px -96px}.icon-arrow-up{background-position:-289px -96px}.icon-arrow-down{background-position:-312px -96px}.icon-share-alt{background-position:-336px -96px}.icon-resize-full{background-position:-360px -96px}.icon-resize-small{background-position:-384px -96px}.icon-plus{background-position:-408px -96px}.icon-minus{background-position:-433px -96px}.icon-asterisk{background-position:-456px -96px}.icon-exclamation-sign{background-position:0 -120px}.icon-gift{background-position:-24px -120px}.icon-leaf{background-position:-48px -120px}.icon-fire{background-position:-72px -120px}.icon-eye-open{background-position:-96px -120px}.icon-eye-close{background-position:-120px -120px}.icon-warning-sign{background-position:-144px -120px}.icon-plane{background-position:-168px -120px}.icon-calendar{background-position:-192px -120px}.icon-random{width:16px;background-position:-216px -120px}.icon-comment{background-position:-240px -120px}.icon-magnet{background-position:-264px -120px}.icon-chevron-up{background-position:-288px -120px}.icon-chevron-down{background-position:-313px -119px}.icon-retweet{background-position:-336px -120px}.icon-shopping-cart{background-position:-360px -120px}.icon-folder-close{width:16px;background-position:-384px -120px}.icon-folder-open{width:16px;background-position:-408px -120px}.icon-resize-vertical{background-position:-432px -119px}.icon-resize-horizontal{background-position:-456px -118px}.icon-hdd{background-position:0 -144px}.icon-bullhorn{background-position:-24px -144px}.icon-bell{background-position:-48px -144px}.icon-certificate{background-position:-72px -144px}.icon-thumbs-up{background-position:-96px -144px}.icon-thumbs-down{background-position:-120px -144px}.icon-hand-right{background-position:-144px -144px}.icon-hand-left{background-position:-168px -144px}.icon-hand-up{background-position:-192px -144px}.icon-hand-down{background-position:-216px -144px}.icon-circle-arrow-right{background-position:-240px -144px}.icon-circle-arrow-left{background-position:-264px -144px}.icon-circle-arrow-up{background-position:-288px -144px}.icon-circle-arrow-down{background-position:-312px -144px}.icon-globe{background-position:-336px -144px}.icon-wrench{background-position:-360px -144px}.icon-tasks{background-position:-384px -144px}.icon-filter{background-position:-408px -144px}.icon-briefcase{background-position:-432px -144px}.icon-fullscreen{background-position:-456px -144px}.dropup,.dropdown{position:relative}.dropdown-toggle{*margin-bottom:-3px}.dropdown-toggle:active,.open .dropdown-toggle{outline:0}.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}.dropdown .caret{margin-top:8px;margin-left:2px}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{*width:100%;height:1px;margin:9.5px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:rgba(0,0,0,0.1);border-bottom:1px solid rgba(255,255,255,0.5)}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:21px;color:#666;white-space:nowrap}.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{color:#fff;text-decoration:none;background-color:#0092d3;background-image:-moz-linear-gradient(top,#09d,#0087c4);background-image:-webkit-gradient(linear,0 0,0 100%,from(#09d),to(#0087c4));background-image:-webkit-linear-gradient(top,#09d,#0087c4);background-image:-o-linear-gradient(top,#09d,#0087c4);background-image:linear-gradient(to bottom,#09d,#0087c4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0099dd',endColorstr='#ff0087c4',GradientType=0)}.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;background-color:#0092d3;background-image:-moz-linear-gradient(top,#09d,#0087c4);background-image:-webkit-gradient(linear,0 0,0 100%,from(#09d),to(#0087c4));background-image:-webkit-linear-gradient(top,#09d,#0087c4);background-image:-o-linear-gradient(top,#09d,#0087c4);background-image:linear-gradient(to bottom,#09d,#0087c4);background-repeat:repeat-x;outline:0;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0099dd',endColorstr='#ff0087c4',GradientType=0)}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#ccc}.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;cursor:default;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open{*z-index:1000}.open>.dropdown-menu{display:block}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}.dropdown-submenu{position:relative}.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}.dropdown-submenu:hover>.dropdown-menu{display:block}.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}.dropdown-submenu>a:after{display:block;float:right;width:0;height:0;margin-top:5px;margin-right:-10px;border-color:transparent;border-left-color:#ccc;border-style:solid;border-width:5px 0 5px 5px;content:" "}.dropdown-submenu:hover>a:after{border-left-color:#fff}.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}.dropdown .dropdown-menu .nav-header{padding-right:20px;padding-left:20px}.typeahead{z-index:1051;margin-top:2px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#eee;border:1px solid #dcdcdc;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}.well-large{padding:24px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.well-small{padding:9px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}.close{float:right;font-size:20px;font-weight:bold;line-height:21px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}.btn{display:inline-block;*display:inline;padding:4px 12px;margin-bottom:0;*margin-left:.3em;font-size:15px;line-height:21px;color:#434848;text-align:center;text-shadow:0 1px 1px rgba(255,255,255,0.75);vertical-align:middle;cursor:pointer;background-color:#f8f8f8;*background-color:#eee;background-image:-moz-linear-gradient(top,#fff,#eee);background-image:-webkit-gradient(linear,0 0,0 100%,from(#fff),to(#eee));background-image:-webkit-linear-gradient(top,#fff,#eee);background-image:-o-linear-gradient(top,#fff,#eee);background-image:linear-gradient(to bottom,#fff,#eee);background-repeat:repeat-x;border:1px solid #ccc;*border:0;border-color:#eee #eee #c8c8c8;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff',endColorstr='#ffeeeeee',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);*zoom:1;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#434848;background-color:#eee;*background-color:#e1e1e1}.btn:active,.btn.active{background-color:#d5d5d5 \9}.btn:first-child{*margin-left:0}.btn:hover,.btn:focus{color:#434848;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05)}.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}.btn-large{padding:11px 19px;font-size:18.75px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}.btn-small{padding:2px 10px;font-size:12.75px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}.btn-mini{padding:0 6px;font-size:11.25px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.btn-block{display:block;width:100%;padding-right:0;padding-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.btn-block+.btn-block{margin-top:5px}input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0aaaf1;*background-color:#09d;background-image:-moz-linear-gradient(top,#11b6ff,#09d);background-image:-webkit-gradient(linear,0 0,0 100%,from(#11b6ff),to(#09d));background-image:-webkit-linear-gradient(top,#11b6ff,#09d);background-image:-o-linear-gradient(top,#11b6ff,#09d);background-image:linear-gradient(to bottom,#11b6ff,#09d);background-repeat:repeat-x;border-color:#09d #09d #006491;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff11b6ff',endColorstr='#ff0099dd',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#09d;*background-color:#0087c4}.btn-primary:active,.btn-primary.active{background-color:#0076aa \9}.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#ee8505;*background-color:#d47500;background-image:-moz-linear-gradient(top,#ff9008,#d47500);background-image:-webkit-gradient(linear,0 0,0 100%,from(#ff9008),to(#d47500));background-image:-webkit-linear-gradient(top,#ff9008,#d47500);background-image:-o-linear-gradient(top,#ff9008,#d47500);background-image:linear-gradient(to bottom,#ff9008,#d47500);background-repeat:repeat-x;border-color:#d47500 #d47500 #884b00;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffff9008',endColorstr='#ffd47500',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#d47500;*background-color:#bb6700}.btn-warning:active,.btn-warning.active{background-color:#a15900 \9}.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#eb0301;*background-color:#cd0200;background-image:-moz-linear-gradient(top,#ff0301,#cd0200);background-image:-webkit-gradient(linear,0 0,0 100%,from(#ff0301),to(#cd0200));background-image:-webkit-linear-gradient(top,#ff0301,#cd0200);background-image:-o-linear-gradient(top,#ff0301,#cd0200);background-image:linear-gradient(to bottom,#ff0301,#cd0200);background-repeat:repeat-x;border-color:#cd0200 #cd0200 #810100;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffff0301',endColorstr='#ffcd0200',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#cd0200;*background-color:#b40200}.btn-danger:active,.btn-danger.active{background-color:#9a0200 \9}.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#47cb2a;*background-color:#3cb521;background-image:-moz-linear-gradient(top,#4fd930,#3cb521);background-image:-webkit-gradient(linear,0 0,0 100%,from(#4fd930),to(#3cb521));background-image:-webkit-linear-gradient(top,#4fd930,#3cb521);background-image:-o-linear-gradient(top,#4fd930,#3cb521);background-image:linear-gradient(to bottom,#4fd930,#3cb521);background-repeat:repeat-x;border-color:#3cb521 #3cb521 #277415;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff4fd930',endColorstr='#ff3cb521',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#3cb521;*background-color:#359f1d}.btn-success:active,.btn-success.active{background-color:#2e8a19 \9}.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#436c98;*background-color:#3a5d83;background-image:-moz-linear-gradient(top,#4a76a6,#3a5d83);background-image:-webkit-gradient(linear,0 0,0 100%,from(#4a76a6),to(#3a5d83));background-image:-webkit-linear-gradient(top,#4a76a6,#3a5d83);background-image:-o-linear-gradient(top,#4a76a6,#3a5d83);background-image:linear-gradient(to bottom,#4a76a6,#3a5d83);background-repeat:repeat-x;border-color:#3a5d83 #3a5d83 #23374e;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff4a76a6',endColorstr='#ff3a5d83',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#3a5d83;*background-color:#325071}.btn-info:active,.btn-info.active{background-color:#2a4460 \9}.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#848a94;*background-color:#6c737e;background-image:-moz-linear-gradient(top,#949aa3,#6c737e);background-image:-webkit-gradient(linear,0 0,0 100%,from(#949aa3),to(#6c737e));background-image:-webkit-linear-gradient(top,#949aa3,#6c737e);background-image:-o-linear-gradient(top,#949aa3,#6c737e);background-image:linear-gradient(to bottom,#949aa3,#6c737e);background-repeat:repeat-x;border-color:#6c737e #6c737e #494d55;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff949aa3',endColorstr='#ff6c737e',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#6c737e;*background-color:#606670}.btn-inverse:active,.btn-inverse.active{background-color:#545a63 \9}button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}.btn-link{color:#09d;cursor:pointer;border-color:transparent;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-link:hover,.btn-link:focus{color:#09d;text-decoration:underline;background-color:transparent}.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#434848;text-decoration:none}.btn-group{position:relative;display:inline-block;*display:inline;*margin-left:.3em;font-size:0;white-space:nowrap;vertical-align:middle;*zoom:1}.btn-group:first-child{*margin-left:0}.btn-group+.btn-group{margin-left:5px}.btn-toolbar{margin-top:10.5px;margin-bottom:10.5px;font-size:0}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}.btn-group>.btn{position:relative;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-group>.btn+.btn{margin-left:-1px}.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:15px}.btn-group>.btn-mini{font-size:11.25px}.btn-group>.btn-small{font-size:12.75px}.btn-group>.btn-large{font-size:18.75px}.btn-group>.btn:first-child{margin-left:0;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-bottomleft:4px;-moz-border-radius-topleft:4px}.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-bottomright:4px}.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-bottom-left-radius:6px;border-bottom-left-radius:6px;-webkit-border-top-left-radius:6px;border-top-left-radius:6px;-moz-border-radius-bottomleft:6px;-moz-border-radius-topleft:6px}.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;border-bottom-right-radius:6px;-moz-border-radius-topright:6px;-moz-border-radius-bottomright:6px}.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{*padding-top:5px;padding-right:8px;*padding-bottom:5px;padding-left:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 1px 0 0 rgba(255,255,255,0.125),inset 0 1px 0 rgba(255,255,255,0.2),0 1px 2px rgba(0,0,0,0.05)}.btn-group>.btn-mini+.dropdown-toggle{*padding-top:2px;padding-right:5px;*padding-bottom:2px;padding-left:5px}.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}.btn-group>.btn-large+.dropdown-toggle{*padding-top:7px;padding-right:12px;*padding-bottom:7px;padding-left:12px}.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05);box-shadow:inset 0 2px 4px rgba(0,0,0,0.15),0 1px 2px rgba(0,0,0,0.05)}.btn-group.open .btn.dropdown-toggle{background-color:#eee}.btn-group.open .btn-primary.dropdown-toggle{background-color:#09d}.btn-group.open .btn-warning.dropdown-toggle{background-color:#d47500}.btn-group.open .btn-danger.dropdown-toggle{background-color:#cd0200}.btn-group.open .btn-success.dropdown-toggle{background-color:#3cb521}.btn-group.open .btn-info.dropdown-toggle{background-color:#3a5d83}.btn-group.open .btn-inverse.dropdown-toggle{background-color:#6c737e}.btn .caret{margin-top:8px;margin-left:0}.btn-large .caret{margin-top:6px}.btn-large .caret{border-top-width:5px;border-right-width:5px;border-left-width:5px}.btn-mini .caret,.btn-small .caret{margin-top:8px}.dropup .btn-large .caret{border-bottom-width:5px}.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}.btn-group-vertical>.btn{display:block;float:none;max-width:100%;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.btn-group-vertical>.btn+.btn{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:first-child{-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.btn-group-vertical>.btn:last-child{-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.btn-group-vertical>.btn-large:first-child{-webkit-border-radius:6px 6px 0 0;-moz-border-radius:6px 6px 0 0;border-radius:6px 6px 0 0}.btn-group-vertical>.btn-large:last-child{-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}.alert{padding:8px 35px 8px 14px;margin-bottom:21px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#d47500;border:1px solid #c54c00;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.alert,.alert h4{color:#fff}.alert h4{margin:0}.alert .close{position:relative;top:-2px;right:-21px;line-height:21px}.alert-success{color:#fff;background-color:#3cb521;border-color:#4b9f1d}.alert-success h4{color:#fff}.alert-danger,.alert-error{color:#fff;background-color:#cd0200;border-color:#be001e}.alert-danger h4,.alert-error h4{color:#fff}.alert-info{color:#fff;background-color:#3399f3;border-color:#11adf1}.alert-info h4{color:#fff}.alert-block{padding-top:14px;padding-bottom:14px}.alert-block>p,.alert-block>ul{margin-bottom:0}.alert-block p+p{margin-top:5px}.nav{margin-bottom:21px;margin-left:0;list-style:none}.nav>li>a{display:block}.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eee}.nav>li>a>img{max-width:none}.nav>.pull-right{float:right}.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:21px;color:#ccc;text-shadow:0 1px 0 rgba(255,255,255,0.5);text-transform:uppercase}.nav li+.nav-header{margin-top:9px}.nav-list{padding-right:15px;padding-left:15px;margin-bottom:0}.nav-list>li>a,.nav-list .nav-header{margin-right:-15px;margin-left:-15px;text-shadow:0 1px 0 rgba(255,255,255,0.5)}.nav-list>li>a{padding:3px 15px}.nav-list>.active>a,.nav-list>.active>a:hover,.nav-list>.active>a:focus{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.2);background-color:#09d}.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{margin-right:2px}.nav-list .divider{*width:100%;height:1px;margin:9.5px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}.nav-tabs,.nav-pills{*zoom:1}.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;line-height:0;content:""}.nav-tabs:after,.nav-pills:after{clear:both}.nav-tabs>li,.nav-pills>li{float:left}.nav-tabs>li>a,.nav-pills>li>a{padding-right:12px;padding-left:12px;margin-right:2px;line-height:14px}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{margin-bottom:-1px}.nav-tabs>li>a{padding-top:8px;padding-bottom:8px;line-height:21px;border:1px solid transparent;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover,.nav-tabs>li>a:focus{border-color:#eee #eee #ddd}.nav-tabs>.active>a,.nav-tabs>.active>a:hover,.nav-tabs>.active>a:focus{color:#666;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-pills>li>a{padding-top:8px;padding-bottom:8px;margin-top:2px;margin-bottom:2px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}.nav-pills>.active>a,.nav-pills>.active>a:hover,.nav-pills>.active>a:focus{color:#fff;background-color:#09d}.nav-stacked>li{float:none}.nav-stacked>li>a{margin-right:0}.nav-tabs.nav-stacked{border-bottom:0}.nav-tabs.nav-stacked>li>a{border:1px solid #ddd;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.nav-tabs.nav-stacked>li:first-child>a{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-topleft:4px}.nav-tabs.nav-stacked>li:last-child>a{-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-moz-border-radius-bottomright:4px;-moz-border-radius-bottomleft:4px}.nav-tabs.nav-stacked>li>a:hover,.nav-tabs.nav-stacked>li>a:focus{z-index:2;border-color:#ddd}.nav-pills.nav-stacked>li>a{margin-bottom:3px}.nav-pills.nav-stacked>li:last-child>a{margin-bottom:1px}.nav-tabs .dropdown-menu{-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}.nav-pills .dropdown-menu{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.nav .dropdown-toggle .caret{margin-top:6px;border-top-color:#09d;border-bottom-color:#09d}.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#09d;border-bottom-color:#09d}.nav-tabs .dropdown-toggle .caret{margin-top:8px}.nav .active .dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}.nav-tabs .active .dropdown-toggle .caret{border-top-color:#666;border-bottom-color:#666}.nav>.dropdown.active>a:hover,.nav>.dropdown.active>a:focus{cursor:pointer}.nav-tabs .open .dropdown-toggle,.nav-pills .open .dropdown-toggle,.nav>li.dropdown.open.active>a:hover,.nav>li.dropdown.open.active>a:focus{color:#fff;background-color:#ccc;border-color:#ccc}.nav li.dropdown.open .caret,.nav li.dropdown.open.active .caret,.nav li.dropdown.open a:hover .caret,.nav li.dropdown.open a:focus .caret{border-top-color:#fff;border-bottom-color:#fff;opacity:1;filter:alpha(opacity=100)}.tabs-stacked .open>a:hover,.tabs-stacked .open>a:focus{border-color:#ccc}.tabbable{*zoom:1}.tabbable:before,.tabbable:after{display:table;line-height:0;content:""}.tabbable:after{clear:both}.tab-content{overflow:auto}.tabs-below>.nav-tabs,.tabs-right>.nav-tabs,.tabs-left>.nav-tabs{border-bottom:0}.tab-content>.tab-pane,.pill-content>.pill-pane{display:none}.tab-content>.active,.pill-content>.active{display:block}.tabs-below>.nav-tabs{border-top:1px solid #ddd}.tabs-below>.nav-tabs>li{margin-top:-1px;margin-bottom:0}.tabs-below>.nav-tabs>li>a{-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-top-color:#ddd;border-bottom-color:transparent}.tabs-below>.nav-tabs>.active>a,.tabs-below>.nav-tabs>.active>a:hover,.tabs-below>.nav-tabs>.active>a:focus{border-color:transparent #ddd #ddd #ddd}.tabs-left>.nav-tabs>li,.tabs-right>.nav-tabs>li{float:none}.tabs-left>.nav-tabs>li>a,.tabs-right>.nav-tabs>li>a{min-width:74px;margin-right:0;margin-bottom:3px}.tabs-left>.nav-tabs{float:left;margin-right:19px;border-right:1px solid #ddd}.tabs-left>.nav-tabs>li>a{margin-right:-1px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.tabs-left>.nav-tabs>li>a:hover,.tabs-left>.nav-tabs>li>a:focus{border-color:#eee #ddd #eee #eee}.tabs-left>.nav-tabs .active>a,.tabs-left>.nav-tabs .active>a:hover,.tabs-left>.nav-tabs .active>a:focus{border-color:#ddd transparent #ddd #ddd;*border-right-color:#fff}.tabs-right>.nav-tabs{float:right;margin-left:19px;border-left:1px solid #ddd}.tabs-right>.nav-tabs>li>a{margin-left:-1px;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.tabs-right>.nav-tabs>li>a:hover,.tabs-right>.nav-tabs>li>a:focus{border-color:#eee #eee #eee #ddd}.tabs-right>.nav-tabs .active>a,.tabs-right>.nav-tabs .active>a:hover,.tabs-right>.nav-tabs .active>a:focus{border-color:#ddd #ddd #ddd transparent;*border-left-color:#fff}.nav>.disabled>a{color:#ccc}.nav>.disabled>a:hover,.nav>.disabled>a:focus{text-decoration:none;cursor:default;background-color:transparent}.navbar{*position:relative;*z-index:2;margin-bottom:21px;overflow:visible}.navbar-inner{min-height:40px;padding-right:20px;padding-left:20px;background-color:#e9e9e9;background-image:-moz-linear-gradient(top,#eee,#e1e1e1);background-image:-webkit-gradient(linear,0 0,0 100%,from(#eee),to(#e1e1e1));background-image:-webkit-linear-gradient(top,#eee,#e1e1e1);background-image:-o-linear-gradient(top,#eee,#e1e1e1);background-image:linear-gradient(to bottom,#eee,#e1e1e1);background-repeat:repeat-x;border:1px solid #ccc;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffeeeeee',endColorstr='#ffe1e1e1',GradientType=0);*zoom:1;-webkit-box-shadow:0 1px 4px rgba(0,0,0,0.065);-moz-box-shadow:0 1px 4px rgba(0,0,0,0.065);box-shadow:0 1px 4px rgba(0,0,0,0.065)}.navbar-inner:before,.navbar-inner:after{display:table;line-height:0;content:""}.navbar-inner:after{clear:both}.navbar .container{width:auto}.nav-collapse.collapse{height:auto;overflow:visible}.navbar .brand{display:block;float:left;padding:9.5px 20px 9.5px;margin-left:-20px;font-size:20px;font-weight:200;color:#666;text-shadow:0 1px 0 #eee}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none}.navbar-text{margin-bottom:0;line-height:40px;color:#666}.navbar-link{color:#666}.navbar-link:hover,.navbar-link:focus{color:#09d}.navbar .divider-vertical{height:40px;margin:0 9px;border-right:1px solid #eee;border-left:1px solid #e1e1e1}.navbar .btn,.navbar .btn-group{margin-top:5px}.navbar .btn-group .btn,.navbar .input-prepend .btn,.navbar .input-append .btn,.navbar .input-prepend .btn-group,.navbar .input-append .btn-group{margin-top:0}.navbar-form{margin-bottom:0;*zoom:1}.navbar-form:before,.navbar-form:after{display:table;line-height:0;content:""}.navbar-form:after{clear:both}.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:5px}.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0}.navbar-search{position:relative;float:left;margin-top:5px;margin-bottom:0}.navbar-search .search-query{padding:4px 14px;margin-bottom:0;font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.navbar-static-top{position:static;margin-bottom:0}.navbar-static-top .navbar-inner{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030;margin-bottom:0}.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{border-width:0 0 1px}.navbar-fixed-bottom .navbar-inner{border-width:1px 0 0}.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding-right:0;padding-left:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}.navbar-fixed-top{top:0}.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{-webkit-box-shadow:0 1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 10px rgba(0,0,0,0.1);box-shadow:0 1px 10px rgba(0,0,0,0.1)}.navbar-fixed-bottom{bottom:0}.navbar-fixed-bottom .navbar-inner{-webkit-box-shadow:0 -1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,0.1);box-shadow:0 -1px 10px rgba(0,0,0,0.1)}.navbar .nav{position:relative;left:0;display:block;float:left;margin:0 10px 0 0}.navbar .nav.pull-right{float:right;margin-right:0}.navbar .nav>li{float:left}.navbar .nav>li>a{float:none;padding:9.5px 15px 9.5px;color:#666;text-decoration:none;text-shadow:0 1px 0 #eee}.navbar .nav .dropdown-toggle .caret{margin-top:8px}.navbar .nav>li>a:focus,.navbar .nav>li>a:hover{color:#09d;text-decoration:none;background-color:transparent}.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#09d;text-decoration:none;background-color:transparent;-webkit-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);-moz-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);box-shadow:inset 0 3px 8px rgba(0,0,0,0.125)}.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-right:5px;margin-left:5px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#dcdcdc;*background-color:#d4d4d4;background-image:-moz-linear-gradient(top,#e1e1e1,#d4d4d4);background-image:-webkit-gradient(linear,0 0,0 100%,from(#e1e1e1),to(#d4d4d4));background-image:-webkit-linear-gradient(top,#e1e1e1,#d4d4d4);background-image:-o-linear-gradient(top,#e1e1e1,#d4d4d4);background-image:linear-gradient(to bottom,#e1e1e1,#d4d4d4);background-repeat:repeat-x;border-color:#d4d4d4 #d4d4d4 #aeaeae;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe1e1e1',endColorstr='#ffd4d4d4',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075);box-shadow:inset 0 1px 0 rgba(255,255,255,0.1),0 1px 0 rgba(255,255,255,0.075)}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#fff;background-color:#d4d4d4;*background-color:#c8c8c8}.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#bbb \9}.navbar .btn-navbar .icon-bar{display:block;width:18px;height:2px;background-color:#f5f5f5;-webkit-border-radius:1px;-moz-border-radius:1px;border-radius:1px;-webkit-box-shadow:0 1px 0 rgba(0,0,0,0.25);-moz-box-shadow:0 1px 0 rgba(0,0,0,0.25);box-shadow:0 1px 0 rgba(0,0,0,0.25)}.btn-navbar .icon-bar+.icon-bar{margin-top:3px}.navbar .nav>li>.dropdown-menu:before{position:absolute;top:-7px;left:9px;display:inline-block;border-right:7px solid transparent;border-bottom:7px solid #ccc;border-left:7px solid transparent;border-bottom-color:rgba(0,0,0,0.2);content:''}.navbar .nav>li>.dropdown-menu:after{position:absolute;top:-6px;left:10px;display:inline-block;border-right:6px solid transparent;border-bottom:6px solid #fff;border-left:6px solid transparent;content:''}.navbar-fixed-bottom .nav>li>.dropdown-menu:before{top:auto;bottom:-7px;border-top:7px solid #ccc;border-bottom:0;border-top-color:rgba(0,0,0,0.2)}.navbar-fixed-bottom .nav>li>.dropdown-menu:after{top:auto;bottom:-6px;border-top:6px solid #fff;border-bottom:0}.navbar .nav li.dropdown>a:hover .caret,.navbar .nav li.dropdown>a:focus .caret{border-top-color:#09d;border-bottom-color:#09d}.navbar .nav li.dropdown.open>.dropdown-toggle,.navbar .nav li.dropdown.active>.dropdown-toggle,.navbar .nav li.dropdown.open.active>.dropdown-toggle{color:#09d;background-color:transparent}.navbar .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#666;border-bottom-color:#666}.navbar .nav li.dropdown.open>.dropdown-toggle .caret,.navbar .nav li.dropdown.active>.dropdown-toggle .caret,.navbar .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#09d;border-bottom-color:#09d}.navbar .pull-right>li>.dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right{right:0;left:auto}.navbar .pull-right>li>.dropdown-menu:before,.navbar .nav>li>.dropdown-menu.pull-right:before{right:12px;left:auto}.navbar .pull-right>li>.dropdown-menu:after,.navbar .nav>li>.dropdown-menu.pull-right:after{right:13px;left:auto}.navbar .pull-right>li>.dropdown-menu .dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right .dropdown-menu{right:100%;left:auto;margin-right:-1px;margin-left:0;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}.navbar-inverse .navbar-inner{background-color:#7c828d;background-image:-moz-linear-gradient(top,#868d97,#6c737e);background-image:-webkit-gradient(linear,0 0,0 100%,from(#868d97),to(#6c737e));background-image:-webkit-linear-gradient(top,#868d97,#6c737e);background-image:-o-linear-gradient(top,#868d97,#6c737e);background-image:linear-gradient(to bottom,#868d97,#6c737e);background-repeat:repeat-x;border-color:#656b76;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff868d97',endColorstr='#ff6c737e',GradientType=0)}.navbar-inverse .brand,.navbar-inverse .nav>li>a{color:#ccc;text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.navbar-inverse .brand:hover,.navbar-inverse .nav>li>a:hover,.navbar-inverse .brand:focus,.navbar-inverse .nav>li>a:focus{color:#fff}.navbar-inverse .brand{color:#ccc}.navbar-inverse .navbar-text{color:#ccc}.navbar-inverse .nav>li>a:focus,.navbar-inverse .nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .nav .active>a,.navbar-inverse .nav .active>a:hover,.navbar-inverse .nav .active>a:focus{color:#fff;background-color:#6c737e}.navbar-inverse .navbar-link{color:#ccc}.navbar-inverse .navbar-link:hover,.navbar-inverse .navbar-link:focus{color:#fff}.navbar-inverse .divider-vertical{border-right-color:#868d97;border-left-color:#6c737e}.navbar-inverse .nav li.dropdown.open>.dropdown-toggle,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle{color:#fff;background-color:#6c737e}.navbar-inverse .nav li.dropdown>a:hover .caret,.navbar-inverse .nav li.dropdown>a:focus .caret{border-top-color:#fff;border-bottom-color:#fff}.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#ccc;border-bottom-color:#ccc}.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}.navbar-inverse .navbar-search .search-query{color:#fff;background-color:#afb3ba;border-color:#6c737e;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1),0 1px 0 rgba(255,255,255,0.15);-webkit-transition:none;-moz-transition:none;-o-transition:none;transition:none}.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#eee}.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#eee}.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#eee}.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#434848;text-shadow:0 1px 0 #fff;background-color:#fff;border:0;outline:0;-webkit-box-shadow:0 0 3px rgba(0,0,0,0.15);-moz-box-shadow:0 0 3px rgba(0,0,0,0.15);box-shadow:0 0 3px rgba(0,0,0,0.15)}.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#6f7681;*background-color:#606670;background-image:-moz-linear-gradient(top,#78808b,#606670);background-image:-webkit-gradient(linear,0 0,0 100%,from(#78808b),to(#606670));background-image:-webkit-linear-gradient(top,#78808b,#606670);background-image:-o-linear-gradient(top,#78808b,#606670);background-image:linear-gradient(to bottom,#78808b,#606670);background-repeat:repeat-x;border-color:#606670 #606670 #3d4147;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff78808b',endColorstr='#ff606670',GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#606670;*background-color:#545a63}.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#494d55 \9}.breadcrumb{padding:8px 15px;margin:0 0 21px;list-style:none;background-color:#f5f5f5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;text-shadow:0 1px 0 #fff;*zoom:1}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}.breadcrumb>.active{color:#ccc}.pagination{margin:21px 0}.pagination ul{display:inline-block;*display:inline;margin-bottom:0;margin-left:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*zoom:1;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}.pagination ul>li{display:inline}.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:21px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}.pagination ul>.active>a,.pagination ul>.active>span{color:#ccc;cursor:default}.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#ccc;cursor:default;background-color:transparent}.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-bottom-left-radius:4px;border-bottom-left-radius:4px;-webkit-border-top-left-radius:4px;border-top-left-radius:4px;-moz-border-radius-bottomleft:4px;-moz-border-radius-topleft:4px}.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;border-bottom-right-radius:4px;-moz-border-radius-topright:4px;-moz-border-radius-bottomright:4px}.pagination-centered{text-align:center}.pagination-right{text-align:right}.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:18.75px}.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-bottom-left-radius:6px;border-bottom-left-radius:6px;-webkit-border-top-left-radius:6px;border-top-left-radius:6px;-moz-border-radius-bottomleft:6px;-moz-border-radius-topleft:6px}.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;border-bottom-right-radius:6px;-moz-border-radius-topright:6px;-moz-border-radius-bottomright:6px}.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-bottom-left-radius:3px;border-bottom-left-radius:3px;-webkit-border-top-left-radius:3px;border-top-left-radius:3px;-moz-border-radius-bottomleft:3px;-moz-border-radius-topleft:3px}.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;border-bottom-right-radius:3px;-moz-border-radius-topright:3px;-moz-border-radius-bottomright:3px}.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:12.75px}.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:11.25px}.pager{margin:21px 0;text-align:center;list-style:none;*zoom:1}.pager:before,.pager:after{display:table;line-height:0;content:""}.pager:after{clear:both}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#ccc;cursor:default;background-color:#fff}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;outline:0;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box}.modal.fade{top:-25%;-webkit-transition:opacity .3s linear,top .3s ease-out;-moz-transition:opacity .3s linear,top .3s ease-out;-o-transition:opacity .3s linear,top .3s ease-out;transition:opacity .3s linear,top .3s ease-out}.modal.fade.in{top:10%}.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}.modal-header h3{margin:0;line-height:30px}.modal-body{position:relative;max-height:400px;padding:15px;overflow-y:auto}.modal-form{margin-bottom:0}.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;*zoom:1;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff}.modal-footer:before,.modal-footer:after{display:table;line-height:0;content:""}.modal-footer:after{clear:both}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.tooltip{position:absolute;z-index:1030;display:block;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0);visibility:visible}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-top-color:#000;border-width:5px 5px 0}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-right-color:#000;border-width:5px 5px 5px 0}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-left-color:#000;border-width:5px 0 5px 5px}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-bottom-color:#000;border-width:0 5px 5px}.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;white-space:normal;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}.popover-content{padding:9px 14px}.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover .arrow{border-width:11px}.popover .arrow:after{border-width:10px;content:""}.popover.top .arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);border-bottom-width:0}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-top-color:#fff;border-bottom-width:0}.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,0.25);border-left-width:0}.popover.right .arrow:after{bottom:-10px;left:1px;border-right-color:#fff;border-left-width:0}.popover.bottom .arrow{top:-11px;left:50%;margin-left:-11px;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);border-top-width:0}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-bottom-color:#fff;border-top-width:0}.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-left-color:#999;border-left-color:rgba(0,0,0,0.25);border-right-width:0}.popover.left .arrow:after{right:1px;bottom:-10px;border-left-color:#fff;border-right-width:0}.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;line-height:0;content:""}.thumbnails:after{clear:both}.row-fluid .thumbnails{margin-left:0}.thumbnails>li{float:left;margin-bottom:21px;margin-left:20px}.thumbnail{display:block;padding:4px;line-height:21px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}a.thumbnail:hover,a.thumbnail:focus{border-color:#09d;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}.thumbnail>img{display:block;max-width:100%;margin-right:auto;margin-left:auto}.thumbnail .caption{padding:9px;color:#666}.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}.media,.media .media{margin-top:15px}.media:first-child{margin-top:0}.media-object{display:block}.media-heading{margin:0 0 5px}.media>.pull-left{margin-right:10px}.media>.pull-right{margin-left:10px}.media-list{margin-left:0;list-style:none}.label,.badge{display:inline-block;padding:2px 4px;font-size:12.69px;font-weight:bold;line-height:14px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);white-space:nowrap;vertical-align:baseline;background-color:#ccc}.label{-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.badge{padding-right:9px;padding-left:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}.label:empty,.badge:empty{display:none}a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}.label-important,.badge-important{background-color:#fff}.label-important[href],.badge-important[href]{background-color:#e6e6e6}.label-warning,.badge-warning{background-color:#d47500}.label-warning[href],.badge-warning[href]{background-color:#a15900}.label-success,.badge-success{background-color:#fff}.label-success[href],.badge-success[href]{background-color:#e6e6e6}.label-info,.badge-info{background-color:#fff}.label-info[href],.badge-info[href]{background-color:#e6e6e6}.label-inverse,.badge-inverse{background-color:#434848}.label-inverse[href],.badge-inverse[href]{background-color:#2a2e2e}.btn .label,.btn .badge{position:relative;top:-1px}.btn-mini .label,.btn-mini .badge{top:0}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0}to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:21px;margin-bottom:21px;overflow:hidden;background-color:#f7f7f7;background-image:-moz-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:-webkit-gradient(linear,0 0,0 100%,from(#f5f5f5),to(#f9f9f9));background-image:-webkit-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:-o-linear-gradient(top,#f5f5f5,#f9f9f9);background-image:linear-gradient(to bottom,#f5f5f5,#f9f9f9);background-repeat:repeat-x;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5',endColorstr='#fff9f9f9',GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1)}.progress .bar{float:left;width:0;height:100%;font-size:12px;color:#fff;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top,#149bdf,#0480be);background-image:-webkit-gradient(linear,0 0,0 100%,from(#149bdf),to(#0480be));background-image:-webkit-linear-gradient(top,#149bdf,#0480be);background-image:-o-linear-gradient(top,#149bdf,#0480be);background-image:linear-gradient(to bottom,#149bdf,#0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf',endColorstr='#ff0480be',GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 1px 0 0 rgba(0,0,0,0.15),inset 0 -1px 0 rgba(0,0,0,0.15)}.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top,#ee5f5b,#c43c35);background-image:-webkit-gradient(linear,0 0,0 100%,from(#ee5f5b),to(#c43c35));background-image:-webkit-linear-gradient(top,#ee5f5b,#c43c35);background-image:-o-linear-gradient(top,#ee5f5b,#c43c35);background-image:linear-gradient(to bottom,#ee5f5b,#c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b',endColorstr='#ffc43c35',GradientType=0)}.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top,#62c462,#57a957);background-image:-webkit-gradient(linear,0 0,0 100%,from(#62c462),to(#57a957));background-image:-webkit-linear-gradient(top,#62c462,#57a957);background-image:-o-linear-gradient(top,#62c462,#57a957);background-image:linear-gradient(to bottom,#62c462,#57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462',endColorstr='#ff57a957',GradientType=0)}.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top,#5bc0de,#339bb9);background-image:-webkit-gradient(linear,0 0,0 100%,from(#5bc0de),to(#339bb9));background-image:-webkit-linear-gradient(top,#5bc0de,#339bb9);background-image:-o-linear-gradient(top,#5bc0de,#339bb9);background-image:linear-gradient(to bottom,#5bc0de,#339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de',endColorstr='#ff339bb9',GradientType=0)}.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.progress-warning .bar,.progress .bar-warning{background-color:#ee8c14;background-image:-moz-linear-gradient(top,#ff9c21,#d47500);background-image:-webkit-gradient(linear,0 0,0 100%,from(#ff9c21),to(#d47500));background-image:-webkit-linear-gradient(top,#ff9c21,#d47500);background-image:-o-linear-gradient(top,#ff9c21,#d47500);background-image:linear-gradient(to bottom,#ff9c21,#d47500);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffff9c21',endColorstr='#ffd47500',GradientType=0)}.progress-warning.progress-striped .bar,.progress-striped .bar-warning{background-color:#ff9c21;background-image:-webkit-gradient(linear,0 100%,100% 0,color-stop(0.25,rgba(255,255,255,0.15)),color-stop(0.25,transparent),color-stop(0.5,transparent),color-stop(0.5,rgba(255,255,255,0.15)),color-stop(0.75,rgba(255,255,255,0.15)),color-stop(0.75,transparent),to(transparent));background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-moz-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,0.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,0.15) 50%,rgba(255,255,255,0.15) 75%,transparent 75%,transparent)}.accordion{margin-bottom:21px}.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.accordion-heading{border-bottom:0}.accordion-heading .accordion-toggle{display:block;padding:8px 15px}.accordion-toggle{cursor:pointer}.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5}.carousel{position:relative;margin-bottom:21px;line-height:1}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-moz-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#fff;text-align:center;background:#2d2d2d;border:3px solid #fff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:.5;filter:alpha(opacity=50)}.carousel-control.right{right:15px;left:auto}.carousel-control:hover,.carousel-control:focus{color:#fff;text-decoration:none;opacity:.9;filter:alpha(opacity=90)}.carousel-indicators{position:absolute;top:15px;right:15px;z-index:5;margin:0;list-style:none}.carousel-indicators li{display:block;float:left;width:10px;height:10px;margin-left:5px;text-indent:-999px;background-color:#ccc;background-color:rgba(255,255,255,0.25);border-radius:5px}.carousel-indicators .active{background-color:#fff}.carousel-caption{position:absolute;right:0;bottom:0;left:0;padding:15px;background:#434848;background:rgba(0,0,0,0.75)}.carousel-caption h4,.carousel-caption p{line-height:21px;color:#fff}.carousel-caption h4{margin:0 0 5px}.carousel-caption p{margin-bottom:0}.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:31.5px;color:inherit;background-color:#eee;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;letter-spacing:-1px;color:#2d2d2d}.hero-unit li{line-height:31.5px}.pull-right{float:right}.pull-left{float:left}.hide{display:none}.show{display:block}.invisible{visibility:hidden}.affix{position:fixed}.text-warning{color:#d47500}a.text-warning:hover,a.text-warning:focus{color:#a15900}.text-error{color:#cd0200}a.text-error:hover,a.text-error:focus{color:#9a0200}.text-info{color:#3399f3}a.text-info:hover,a.text-info:focus{color:#0e80e5}.text-success{color:#3cb521}a.text-success:hover,a.text-success:focus{color:#2e8a19}.navbar .brand{text-shadow:0 1px 0 rgba(255,255,255,0.3);-webkit-transition:color ease-in-out .2s;-moz-transition:color ease-in-out .2s;-o-transition:color ease-in-out .2s;transition:color ease-in-out .2s}.navbar .brand:hover{color:#09d;-webkit-transition:color ease-in-out .2s;-moz-transition:color ease-in-out .2s;-o-transition:color ease-in-out .2s;transition:color ease-in-out .2s}.navbar .nav>li>a{padding:11px 10px 8px;text-shadow:0 1px 0 rgba(255,255,255,0.3);-webkit-transition:color ease-in-out .2s;-moz-transition:color ease-in-out .2s;-o-transition:color ease-in-out .2s;transition:color ease-in-out .2s}.navbar .nav>li>a:hover{-webkit-transition:color ease-in-out .2s;-moz-transition:color ease-in-out .2s;-o-transition:color ease-in-out .2s;transition:color ease-in-out .2s}.navbar .navbar-text{padding:11px 10px 8px;line-height:inherit}.navbar .navbar-search .search-query,.navbar .navbar-search .search-query:hover{margin-bottom:0;line-height:normal;color:#ccc;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.5);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.5);box-shadow:inset 0 1px 2px rgba(0,0,0,0.5)}.navbar .navbar-search .search-query:-moz-placeholder,.navbar .navbar-search .search-query:hover:-moz-placeholder{color:#ccc}.navbar .navbar-search .search-query:-ms-input-placeholder,.navbar .navbar-search .search-query:hover:-ms-input-placeholder{color:#ccc}.navbar .navbar-search .search-query::-webkit-input-placeholder,.navbar .navbar-search .search-query:hover::-webkit-input-placeholder{color:#ccc}.navbar .navbar-search .search-query:focus,.navbar .navbar-search .search-query:hover:focus,.navbar .navbar-search .search-query.focused,.navbar .navbar-search .search-query:hover.focused{color:#666;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.5);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.5);box-shadow:inset 0 1px 2px rgba(0,0,0,0.5)}.navbar-inverse .brand{text-shadow:-1px -1px 0 rgba(0,0,0,0.3)}.navbar-inverse .brand:hover{color:#fff}.navbar-inverse .nav>li>a{text-shadow:-1px -1px 0 rgba(0,0,0,0.3)}.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown:hover>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open:hover>.dropdown-toggle .caret{border-top-color:#fff}.navbar-inverse .navbar-search .search-query,.navbar-inverse .navbar-search .search-query:hover{color:#fff}.navbar-inverse .navbar-search .search-query:-moz-placeholder,.navbar-inverse .navbar-search .search-query:hover:-moz-placeholder{color:#eee}.navbar-inverse .navbar-search .search-query:-ms-input-placeholder,.navbar-inverse .navbar-search .search-query:hover:-ms-input-placeholder{color:#eee}.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder,.navbar-inverse .navbar-search .search-query:hover::-webkit-input-placeholder{color:#eee}@media(max-width:979px){.navbar .nav-collapse .nav li>a:hover{color:#fff;text-shadow:none;background-color:#09d}.navbar .nav-collapse .navbar-search{border-top:0;border-bottom:0}.navbar-inverse .nav-collapse .nav li>a{color:#ccc!important}.navbar-inverse .nav-collapse .nav li>a:hover{background-color:#09d!important}.navbar-inverse .nav-collapse .nav-header{color:#eee}}div.subnav .nav>li>a{color:#666;-webkit-transition:color ease-in-out .2s;-moz-transition:color ease-in-out .2s;-o-transition:color ease-in-out .2s;transition:color ease-in-out .2s}div.subnav .nav>li>a:hover{color:#09d;border-left-color:#ccc;-webkit-transition:color ease-in-out .2s;-moz-transition:color ease-in-out .2s;-o-transition:color ease-in-out .2s;transition:color ease-in-out .2s}div.subnav .nav>li.active>a{color:#666}div.subnav .nav>li.active>a:hover{color:#666}div.subnav .nav>li.dropdown>.dropdown-toggle{background-color:transparent}div.subnav .nav>li.dropdown.open>.dropdown-toggle{color:#666;border-right:1px solid #e5e5e5;border-left:1px solid whiteSmoke}div.subnav .nav>li.dropdown.open>.dropdown-toggle:hover{color:#09d}div.subnav-fixed{top:41px}.nav>li>a:hover,.nav>li>a:focus{background-color:rgba(0,0,0,0.05)}.nav>li.dropdown>.dropdown-toggle .caret,.nav>li.dropdown.active>.dropdown-toggle .caret,.nav>li.dropdown.open>.dropdown-toggle .caret,.nav>li.dropdown.open.active>.dropdown-toggle .caret{border-top:4px solid #666;border-top-color:#666;opacity:1}.nav>li.dropdown>.dropdown-toggle:hover .caret,.nav>li.dropdown.active>.dropdown-toggle:hover .caret,.nav>li.dropdown.open>.dropdown-toggle:hover .caret,.nav>li.dropdown.open.active>.dropdown-toggle:hover .caret{border-top:4px solid #09d;border-top-color:#09d}.nav-list .divider{background-color:rgba(0,0,0,0.1);border-bottom-color:rgba(255,255,255,0.5)}.table tbody tr.success>td,.table tbody tr.error>td,.table tbody tr.info>td{color:#fff}.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#e29235}.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#e29235}.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#e29235;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#c7781d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #efc28e;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #efc28e;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #efc28e}.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#e29235;background-color:#d47500;border-color:#e29235}.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#c00}.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#c00}.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#c00;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#900;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #f33;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #f33;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #f33}.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#c00;background-color:#cd0200;border-color:#c00}.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#2ba949}.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#2ba949}.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#2ba949;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#218037;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #63d77e;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #63d77e;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #63d77e}.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#2ba949;background-color:#3cb521;border-color:#2ba949}.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3399f3}.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3399f3}.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3399f3;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#0e80e5;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #93c9f9;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #93c9f9;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #93c9f9}.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3399f3;background-color:#3399f3;border-color:#3399f3}.alert{text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.alert h1,.alert h2,.alert h3,.alert h4,.alert h5,.alert h6{font-weight:bold;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.alert a{color:#fff;text-decoration:underline}.label-important,.badge-important{background-color:#cd0200}.label-warning,.badge-warning{background-color:#d47500}.label-success,.badge-success{background-color:#3cb521}.label-info,.badge-info{background-color:#3399f3}.hero-unit{border:1px solid rgba(0,0,0,0.1)}.pull-right{float:right}.pull-left{float:left}.hide{display:none}.show{display:block}.invisible{visibility:hidden}.affix{position:fixed}
\ No newline at end of file
diff --git a/core/src/main/resources/spark/ui/static/webui.css b/core/src/main/resources/spark/ui/static/webui.css
index f7537bb7661919e20f66298273471d1629feb0a3..9914a8ad2a39c1ce3ce849bd13a7c94ad88c4209 100644
--- a/core/src/main/resources/spark/ui/static/webui.css
+++ b/core/src/main/resources/spark/ui/static/webui.css
@@ -46,4 +46,37 @@
   padding: 0;
   padding-top: 7px;
   padding-left: 4px;
+  line-height: 15px !important;
+}
+
+.table-fixed {
+  table-layout:fixed;
+}
+
+.table td {
+  vertical-align: middle !important;
+}
+
+.progress-completed .bar,
+.progress .bar-completed {
+  background-color: #b3def9;
+  background-image: -moz-linear-gradient(top, #addfff, #badcf2);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#addfff), to(#badcf2));
+  background-image: -webkit-linear-gradient(top, #addfff, #badcf2);
+  background-image: -o-linear-gradient(top, #addfff, #badcf2);
+  background-image: linear-gradient(to bottom, #addfff, #badcf2);
+  background-repeat: repeat-x;
+  filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffaddfff', endColorstr='#ffbadcf2', GradientType=0);
+}
+
+.progress-running .bar,
+.progress .bar-running {
+  background-color: #c2ebfa;
+  background-image: -moz-linear-gradient(top, #bdedff, #c7e8f5);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#bdedff), to(#c7e8f5));
+  background-image: -webkit-linear-gradient(top, #bdedff, #c7e8f5);
+  background-image: -o-linear-gradient(top, #bdedff, #c7e8f5);
+  background-image: linear-gradient(to bottom, #bdedff, #c7e8f5);
+  background-repeat: repeat-x;
+  filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffbdedff', endColorstr='#ffc7e8f5', GradientType=0);
 }
diff --git a/core/src/main/scala/org/apache/hadoop/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/hadoop/mapred/SparkHadoopMapRedUtil.scala
new file mode 100644
index 0000000000000000000000000000000000000000..f87460039b0217855b0b4e262976078e8cb8d71f
--- /dev/null
+++ b/core/src/main/scala/org/apache/hadoop/mapred/SparkHadoopMapRedUtil.scala
@@ -0,0 +1,45 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.hadoop.mapred
+
+trait SparkHadoopMapRedUtil {
+  def newJobContext(conf: JobConf, jobId: JobID): JobContext = {
+    val klass = firstAvailableClass("org.apache.hadoop.mapred.JobContextImpl", "org.apache.hadoop.mapred.JobContext");
+    val ctor = klass.getDeclaredConstructor(classOf[JobConf], classOf[org.apache.hadoop.mapreduce.JobID])
+    ctor.newInstance(conf, jobId).asInstanceOf[JobContext]
+  }
+
+  def newTaskAttemptContext(conf: JobConf, attemptId: TaskAttemptID): TaskAttemptContext = {
+    val klass = firstAvailableClass("org.apache.hadoop.mapred.TaskAttemptContextImpl", "org.apache.hadoop.mapred.TaskAttemptContext")
+    val ctor = klass.getDeclaredConstructor(classOf[JobConf], classOf[TaskAttemptID])
+    ctor.newInstance(conf, attemptId).asInstanceOf[TaskAttemptContext]
+  }
+
+  def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = {
+    new TaskAttemptID(jtIdentifier, jobId, isMap, taskId, attemptId)
+  }
+
+  private def firstAvailableClass(first: String, second: String): Class[_] = {
+    try {
+      Class.forName(first)
+    } catch {
+      case e: ClassNotFoundException =>
+        Class.forName(second)
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/hadoop/mapreduce/SparkHadoopMapReduceUtil.scala b/core/src/main/scala/org/apache/hadoop/mapreduce/SparkHadoopMapReduceUtil.scala
new file mode 100644
index 0000000000000000000000000000000000000000..93180307fa3a5c4293485cb93e1301fb7e1bdfa1
--- /dev/null
+++ b/core/src/main/scala/org/apache/hadoop/mapreduce/SparkHadoopMapReduceUtil.scala
@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.hadoop.mapreduce
+
+import org.apache.hadoop.conf.Configuration
+import java.lang.{Integer => JInteger, Boolean => JBoolean}
+
+trait SparkHadoopMapReduceUtil {
+  def newJobContext(conf: Configuration, jobId: JobID): JobContext = {
+    val klass = firstAvailableClass(
+        "org.apache.hadoop.mapreduce.task.JobContextImpl",  // hadoop2, hadoop2-yarn
+        "org.apache.hadoop.mapreduce.JobContext")           // hadoop1
+    val ctor = klass.getDeclaredConstructor(classOf[Configuration], classOf[JobID])
+    ctor.newInstance(conf, jobId).asInstanceOf[JobContext]
+  }
+
+  def newTaskAttemptContext(conf: Configuration, attemptId: TaskAttemptID): TaskAttemptContext = {
+    val klass = firstAvailableClass(
+        "org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl",  // hadoop2, hadoop2-yarn
+        "org.apache.hadoop.mapreduce.TaskAttemptContext")           // hadoop1
+    val ctor = klass.getDeclaredConstructor(classOf[Configuration], classOf[TaskAttemptID])
+    ctor.newInstance(conf, attemptId).asInstanceOf[TaskAttemptContext]
+  }
+
+  def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = {
+    val klass = Class.forName("org.apache.hadoop.mapreduce.TaskAttemptID");
+    try {
+      // first, attempt to use the old-style constructor that takes a boolean isMap (not available in YARN)
+      val ctor = klass.getDeclaredConstructor(classOf[String], classOf[Int], classOf[Boolean],
+          classOf[Int], classOf[Int])
+      ctor.newInstance(jtIdentifier, new JInteger(jobId), new JBoolean(isMap), new JInteger(taskId), new
+        JInteger(attemptId)).asInstanceOf[TaskAttemptID]
+    } catch {
+      case exc: NoSuchMethodException => {
+        // failed, look for the new ctor that takes a TaskType (not available in 1.x)
+        val taskTypeClass = Class.forName("org.apache.hadoop.mapreduce.TaskType").asInstanceOf[Class[Enum[_]]]
+        val taskType = taskTypeClass.getMethod("valueOf", classOf[String]).invoke(taskTypeClass, if(isMap) "MAP" else "REDUCE")
+        val ctor = klass.getDeclaredConstructor(classOf[String], classOf[Int], taskTypeClass,
+          classOf[Int], classOf[Int])
+        ctor.newInstance(jtIdentifier, new JInteger(jobId), taskType, new JInteger(taskId), new
+          JInteger(attemptId)).asInstanceOf[TaskAttemptID]
+      }
+    }
+  }
+
+  private def firstAvailableClass(first: String, second: String): Class[_] = {
+    try {
+      Class.forName(first)
+    } catch {
+      case e: ClassNotFoundException =>
+        Class.forName(second)
+    }
+  }
+}
diff --git a/core/src/main/scala/spark/Aggregator.scala b/core/src/main/scala/spark/Aggregator.scala
index 136b4da61e5cf6afe8b0ca8aea84ff4e36be19a8..9af401986d350ec4b47495638d64ccc33531034a 100644
--- a/core/src/main/scala/spark/Aggregator.scala
+++ b/core/src/main/scala/spark/Aggregator.scala
@@ -28,18 +28,18 @@ import scala.collection.JavaConversions._
   * @param mergeCombiners function to merge outputs from multiple mergeValue function.
   */
 case class Aggregator[K, V, C] (
-    val createCombiner: V => C,
-    val mergeValue: (C, V) => C,
-    val mergeCombiners: (C, C) => C) {
+    createCombiner: V => C,
+    mergeValue: (C, V) => C,
+    mergeCombiners: (C, C) => C) {
 
-  def combineValuesByKey(iter: Iterator[(K, V)]) : Iterator[(K, C)] = {
+  def combineValuesByKey(iter: Iterator[_ <: Product2[K, V]]) : Iterator[(K, C)] = {
     val combiners = new JHashMap[K, C]
-    for ((k, v) <- iter) {
-      val oldC = combiners.get(k)
+    for (kv <- iter) {
+      val oldC = combiners.get(kv._1)
       if (oldC == null) {
-        combiners.put(k, createCombiner(v))
+        combiners.put(kv._1, createCombiner(kv._2))
       } else {
-        combiners.put(k, mergeValue(oldC, v))
+        combiners.put(kv._1, mergeValue(oldC, kv._2))
       }
     }
     combiners.iterator
@@ -47,7 +47,7 @@ case class Aggregator[K, V, C] (
 
   def combineCombinersByKey(iter: Iterator[(K, C)]) : Iterator[(K, C)] = {
     val combiners = new JHashMap[K, C]
-    for ((k, c) <- iter) {
+    iter.foreach { case(k, c) =>
       val oldC = combiners.get(k)
       if (oldC == null) {
         combiners.put(k, c)
diff --git a/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala b/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala
index 8f6953b1f52c5ef495e0db044daedcbd217132ef..1ec95ed9b88e049b72d30d56f06f0c386fbc6ebc 100644
--- a/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala
+++ b/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala
@@ -28,8 +28,9 @@ import spark.util.CompletionIterator
 
 private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Logging {
 
-  override def fetch[K, V](
-    shuffleId: Int, reduceId: Int, metrics: TaskMetrics, serializer: Serializer) = {
+  override def fetch[T](shuffleId: Int, reduceId: Int, metrics: TaskMetrics, serializer: Serializer)
+    : Iterator[T] =
+  {
 
     logDebug("Fetching outputs for shuffle %d, reduce %d".format(shuffleId, reduceId))
     val blockManager = SparkEnv.get.blockManager
@@ -49,12 +50,12 @@ private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Loggin
         (address, splits.map(s => ("shuffle_%d_%d_%d".format(shuffleId, s._1, reduceId), s._2)))
     }
 
-    def unpackBlock(blockPair: (String, Option[Iterator[Any]])) : Iterator[(K, V)] = {
+    def unpackBlock(blockPair: (String, Option[Iterator[Any]])) : Iterator[T] = {
       val blockId = blockPair._1
       val blockOption = blockPair._2
       blockOption match {
         case Some(block) => {
-          block.asInstanceOf[Iterator[(K, V)]]
+          block.asInstanceOf[Iterator[T]]
         }
         case None => {
           val regex = "shuffle_([0-9]*)_([0-9]*)_([0-9]*)".r
@@ -73,7 +74,7 @@ private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Loggin
     val blockFetcherItr = blockManager.getMultiple(blocksByAddress, serializer)
     val itr = blockFetcherItr.flatMap(unpackBlock)
 
-    CompletionIterator[(K,V), Iterator[(K,V)]](itr, {
+    CompletionIterator[T, Iterator[T]](itr, {
       val shuffleMetrics = new ShuffleReadMetrics
       shuffleMetrics.shuffleFinishTime = System.currentTimeMillis
       shuffleMetrics.remoteFetchTime = blockFetcherItr.remoteFetchTime
diff --git a/core/src/main/scala/spark/Cache.scala b/core/src/main/scala/spark/Cache.scala
deleted file mode 100644
index b0c83ce59d13d2f2c9191aa472aae55b05208f64..0000000000000000000000000000000000000000
--- a/core/src/main/scala/spark/Cache.scala
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package spark
-
-import java.util.concurrent.atomic.AtomicInteger
-
-private[spark] sealed trait CachePutResponse
-private[spark] case class CachePutSuccess(size: Long) extends CachePutResponse
-private[spark] case class CachePutFailure() extends CachePutResponse
-
-/**
- * An interface for caches in Spark, to allow for multiple implementations. Caches are used to store
- * both partitions of cached RDDs and broadcast variables on Spark executors. Caches are also aware
- * of which entries are part of the same dataset (for example, partitions in the same RDD). The key
- * for each value in a cache is a (datasetID, partition) pair.
- *
- * A single Cache instance gets created on each machine and is shared by all caches (i.e. both the 
- * RDD split cache and the broadcast variable cache), to enable global replacement policies. 
- * However, because these several independent modules all perform caching, it is important to give
- * them separate key namespaces, so that an RDD and a broadcast variable (for example) do not use
- * the same key. For this purpose, Cache has the notion of KeySpaces. Each client module must first
- * ask for a KeySpace, and then call get() and put() on that space using its own keys.
- * 
- * This abstract class handles the creation of key spaces, so that subclasses need only deal with
- * keys that are unique across modules.
- */
-private[spark] abstract class Cache {
-  private val nextKeySpaceId = new AtomicInteger(0)
-  private def newKeySpaceId() = nextKeySpaceId.getAndIncrement()
-
-  def newKeySpace() = new KeySpace(this, newKeySpaceId())
-
-  /**
-   * Get the value for a given (datasetId, partition), or null if it is not
-   * found.
-   */
-  def get(datasetId: Any, partition: Int): Any
-
-  /**
-   * Attempt to put a value in the cache; returns CachePutFailure if this was
-   * not successful (e.g. because the cache replacement policy forbids it), and
-   * CachePutSuccess if successful. If size estimation is available, the cache
-   * implementation should set the size field in CachePutSuccess.
-   */
-  def put(datasetId: Any, partition: Int, value: Any): CachePutResponse
-
-  /**
-   * Report the capacity of the cache partition. By default this just reports
-   * zero. Specific implementations can choose to provide the capacity number.
-   */
-  def getCapacity: Long = 0L
-}
-
-/**
- * A key namespace in a Cache.
- */
-private[spark] class KeySpace(cache: Cache, val keySpaceId: Int) {
-  def get(datasetId: Any, partition: Int): Any =
-    cache.get((keySpaceId, datasetId), partition)
-
-  def put(datasetId: Any, partition: Int, value: Any): CachePutResponse =
-    cache.put((keySpaceId, datasetId), partition, value)
-
-  def getCapacity: Long = cache.getCapacity
-}
diff --git a/core/src/main/scala/spark/Dependency.scala b/core/src/main/scala/spark/Dependency.scala
index d17e70a4faf7aa7bf2b3e760b57a188e199569d0..d5a960657095634151ddb3e4513b934212a0e408 100644
--- a/core/src/main/scala/spark/Dependency.scala
+++ b/core/src/main/scala/spark/Dependency.scala
@@ -39,16 +39,15 @@ abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {
 
 /**
  * Represents a dependency on the output of a shuffle stage.
- * @param shuffleId the shuffle id
  * @param rdd the parent RDD
  * @param partitioner partitioner used to partition the shuffle output
  * @param serializerClass class name of the serializer to use
  */
 class ShuffleDependency[K, V](
-    @transient rdd: RDD[(K, V)],
+    @transient rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
     val serializerClass: String = null)
-  extends Dependency(rdd) {
+  extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) {
 
   val shuffleId: Int = rdd.context.newShuffleId()
 }
diff --git a/core/src/main/scala/spark/MapOutputTracker.scala b/core/src/main/scala/spark/MapOutputTracker.scala
index 2c417e31dba6328a60ab34fb7d9918626b632cb7..0cd0341a7242e18006e5dbfa0e0235472222a12a 100644
--- a/core/src/main/scala/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/spark/MapOutputTracker.scala
@@ -64,11 +64,11 @@ private[spark] class MapOutputTracker extends Logging {
 
   // Incremented every time a fetch fails so that client nodes know to clear
   // their cache of map output locations if this happens.
-  private var generation: Long = 0
-  private val generationLock = new java.lang.Object
+  private var epoch: Long = 0
+  private val epochLock = new java.lang.Object
 
   // Cache a serialized version of the output statuses for each shuffle to send them out faster
-  var cacheGeneration = generation
+  var cacheEpoch = epoch
   private val cachedSerializedStatuses = new TimeStampedHashMap[Int, Array[Byte]]
 
   val metadataCleaner = new MetadataCleaner("MapOutputTracker", this.cleanup)
@@ -108,10 +108,10 @@ private[spark] class MapOutputTracker extends Logging {
   def registerMapOutputs(
       shuffleId: Int,
       statuses: Array[MapStatus],
-      changeGeneration: Boolean = false) {
+      changeEpoch: Boolean = false) {
     mapStatuses.put(shuffleId, Array[MapStatus]() ++ statuses)
-    if (changeGeneration) {
-      incrementGeneration()
+    if (changeEpoch) {
+      incrementEpoch()
     }
   }
 
@@ -124,7 +124,7 @@ private[spark] class MapOutputTracker extends Logging {
           array(mapId) = null
         }
       }
-      incrementGeneration()
+      incrementEpoch()
     } else {
       throw new SparkException("unregisterMapOutput called for nonexistent shuffle ID")
     }
@@ -206,58 +206,58 @@ private[spark] class MapOutputTracker extends Logging {
     trackerActor = null
   }
 
-  // Called on master to increment the generation number
-  def incrementGeneration() {
-    generationLock.synchronized {
-      generation += 1
-      logDebug("Increasing generation to " + generation)
+  // Called on master to increment the epoch number
+  def incrementEpoch() {
+    epochLock.synchronized {
+      epoch += 1
+      logDebug("Increasing epoch to " + epoch)
     }
   }
 
-  // Called on master or workers to get current generation number
-  def getGeneration: Long = {
-    generationLock.synchronized {
-      return generation
+  // Called on master or workers to get current epoch number
+  def getEpoch: Long = {
+    epochLock.synchronized {
+      return epoch
     }
   }
 
-  // Called on workers to update the generation number, potentially clearing old outputs
-  // because of a fetch failure. (Each Mesos task calls this with the latest generation
+  // Called on workers to update the epoch number, potentially clearing old outputs
+  // because of a fetch failure. (Each worker task calls this with the latest epoch
   // number on the master at the time it was created.)
-  def updateGeneration(newGen: Long) {
-    generationLock.synchronized {
-      if (newGen > generation) {
-        logInfo("Updating generation to " + newGen + " and clearing cache")
+  def updateEpoch(newEpoch: Long) {
+    epochLock.synchronized {
+      if (newEpoch > epoch) {
+        logInfo("Updating epoch to " + newEpoch + " and clearing cache")
         // mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]]
         mapStatuses.clear()
-        generation = newGen
+        epoch = newEpoch
       }
     }
   }
 
   def getSerializedLocations(shuffleId: Int): Array[Byte] = {
     var statuses: Array[MapStatus] = null
-    var generationGotten: Long = -1
-    generationLock.synchronized {
-      if (generation > cacheGeneration) {
+    var epochGotten: Long = -1
+    epochLock.synchronized {
+      if (epoch > cacheEpoch) {
         cachedSerializedStatuses.clear()
-        cacheGeneration = generation
+        cacheEpoch = epoch
       }
       cachedSerializedStatuses.get(shuffleId) match {
         case Some(bytes) =>
           return bytes
         case None =>
           statuses = mapStatuses(shuffleId)
-          generationGotten = generation
+          epochGotten = epoch
       }
     }
     // If we got here, we failed to find the serialized locations in the cache, so we pulled
     // out a snapshot of the locations as "locs"; let's serialize and return that
     val bytes = serializeStatuses(statuses)
     logInfo("Size of output statuses for shuffle %d is %d bytes".format(shuffleId, bytes.length))
-    // Add them into the table only if the generation hasn't changed while we were working
-    generationLock.synchronized {
-      if (generation == generationGotten) {
+    // Add them into the table only if the epoch hasn't changed while we were working
+    epochLock.synchronized {
+      if (epoch == epochGotten) {
         cachedSerializedStatuses(shuffleId) = bytes
       }
     }
diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala
index 6b0cc2fbf15e6787d330cefa74bf72ed5fd9bd5d..cc1285dd95f4765d9cbcd994db021cb1830031d7 100644
--- a/core/src/main/scala/spark/PairRDDFunctions.scala
+++ b/core/src/main/scala/spark/PairRDDFunctions.scala
@@ -21,9 +21,8 @@ import java.nio.ByteBuffer
 import java.util.{Date, HashMap => JHashMap}
 import java.text.SimpleDateFormat
 
-import scala.collection.Map
+import scala.collection.{mutable, Map}
 import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.HashMap
 import scala.collection.JavaConversions._
 
 import org.apache.hadoop.conf.Configuration
@@ -32,12 +31,13 @@ import org.apache.hadoop.io.compress.CompressionCodec
 import org.apache.hadoop.io.SequenceFile.CompressionType
 import org.apache.hadoop.mapred.FileOutputCommitter
 import org.apache.hadoop.mapred.FileOutputFormat
-import org.apache.hadoop.mapred.HadoopWriter
+import org.apache.hadoop.mapred.SparkHadoopWriter
 import org.apache.hadoop.mapred.JobConf
 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.mapreduce.{OutputFormat => NewOutputFormat,
+    RecordWriter => NewRecordWriter, Job => NewAPIHadoopJob, SparkHadoopMapReduceUtil}
 import org.apache.hadoop.security.UserGroupInformation
 
 import spark.partial.BoundedDouble
@@ -50,10 +50,9 @@ import spark.Partitioner._
  * Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
  * Import `spark.SparkContext._` at the top of your program to use these functions.
  */
-class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
-    self: RDD[(K, V)])
+class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)])
   extends Logging
-  with HadoopMapReduceUtil
+  with SparkHadoopMapReduceUtil
   with Serializable {
 
   /**
@@ -85,17 +84,18 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
     }
     val aggregator = new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners)
     if (self.partitioner == Some(partitioner)) {
-      self.mapPartitions(aggregator.combineValuesByKey(_), true)
+      self.mapPartitions(aggregator.combineValuesByKey, preservesPartitioning = true)
     } else if (mapSideCombine) {
-      val mapSideCombined = self.mapPartitions(aggregator.combineValuesByKey(_), true)
-      val partitioned = new ShuffledRDD[K, C](mapSideCombined, partitioner, serializerClass)
-      partitioned.mapPartitions(aggregator.combineCombinersByKey(_), true)
+      val combined = self.mapPartitions(aggregator.combineValuesByKey, preservesPartitioning = true)
+      val partitioned = new ShuffledRDD[K, C, (K, C)](combined, partitioner)
+        .setSerializer(serializerClass)
+      partitioned.mapPartitions(aggregator.combineCombinersByKey, preservesPartitioning = true)
     } else {
       // Don't apply map-side combiner.
       // A sanity check to make sure mergeCombiners is not defined.
       assert(mergeCombiners == null)
-      val values = new ShuffledRDD[K, V](self, partitioner, serializerClass)
-      values.mapPartitions(aggregator.combineValuesByKey(_), true)
+      val values = new ShuffledRDD[K, V, (K, V)](self, partitioner).setSerializer(serializerClass)
+      values.mapPartitions(aggregator.combineValuesByKey, preservesPartitioning = true)
     }
   }
 
@@ -167,7 +167,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
 
     def reducePartition(iter: Iterator[(K, V)]): Iterator[JHashMap[K, V]] = {
       val map = new JHashMap[K, V]
-      for ((k, v) <- iter) {
+      iter.foreach { case (k, v) =>
         val old = map.get(k)
         map.put(k, if (old == null) v else func(old, v))
       }
@@ -175,11 +175,11 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
     }
 
     def mergeMaps(m1: JHashMap[K, V], m2: JHashMap[K, V]): JHashMap[K, V] = {
-      for ((k, v) <- m2) {
+      m2.foreach { case (k, v) =>
         val old = m1.get(k)
         m1.put(k, if (old == null) v else func(old, v))
       }
-      return m1
+      m1
     }
 
     self.mapPartitions(reducePartition).reduce(mergeMaps)
@@ -233,31 +233,13 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
   }
 
   /**
-   * Return a copy of the RDD partitioned using the specified partitioner. If `mapSideCombine`
-   * is true, Spark will group values of the same key together on the map side before the
-   * repartitioning, to only send each key over the network once. If a large number of
-   * duplicated keys are expected, and the size of the keys are large, `mapSideCombine` should
-   * be set to true.
+   * Return a copy of the RDD partitioned using the specified partitioner.
    */
-  def partitionBy(partitioner: Partitioner, mapSideCombine: Boolean = false): RDD[(K, V)] = {
-    if (getKeyClass().isArray) {
-      if (mapSideCombine) {
-        throw new SparkException("Cannot use map-side combining with array keys.")
-      }
-      if (partitioner.isInstanceOf[HashPartitioner]) {
-        throw new SparkException("Default partitioner cannot partition array keys.")
-      }
-    }
-    if (mapSideCombine) {
-      def createCombiner(v: V) = ArrayBuffer(v)
-      def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v
-      def mergeCombiners(b1: ArrayBuffer[V], b2: ArrayBuffer[V]) = b1 ++= b2
-      val bufs = combineByKey[ArrayBuffer[V]](
-        createCombiner _, mergeValue _, mergeCombiners _, partitioner)
-      bufs.flatMapValues(buf => buf)
-    } else {
-      new ShuffledRDD[K, V](self, partitioner)
+  def partitionBy(partitioner: Partitioner): RDD[(K, V)] = {
+    if (getKeyClass().isArray && partitioner.isInstanceOf[HashPartitioner]) {
+      throw new SparkException("Default partitioner cannot partition array keys.")
     }
+    new ShuffledRDD[K, V, (K, V)](self, partitioner)
   }
 
   /**
@@ -266,9 +248,8 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
    * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD.
    */
   def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = {
-    this.cogroup(other, partitioner).flatMapValues {
-      case (vs, ws) =>
-        for (v <- vs.iterator; w <- ws.iterator) yield (v, w)
+    this.cogroup(other, partitioner).flatMapValues { case (vs, ws) =>
+      for (v <- vs.iterator; w <- ws.iterator) yield (v, w)
     }
   }
 
@@ -279,13 +260,12 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
    * partition the output RDD.
    */
   def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = {
-    this.cogroup(other, partitioner).flatMapValues {
-      case (vs, ws) =>
-        if (ws.isEmpty) {
-          vs.iterator.map(v => (v, None))
-        } else {
-          for (v <- vs.iterator; w <- ws.iterator) yield (v, Some(w))
-        }
+    this.cogroup(other, partitioner).flatMapValues { case (vs, ws) =>
+      if (ws.isEmpty) {
+        vs.iterator.map(v => (v, None))
+      } else {
+        for (v <- vs.iterator; w <- ws.iterator) yield (v, Some(w))
+      }
     }
   }
 
@@ -297,13 +277,12 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
    */
   def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner)
       : RDD[(K, (Option[V], W))] = {
-    this.cogroup(other, partitioner).flatMapValues {
-      case (vs, ws) =>
-        if (vs.isEmpty) {
-          ws.iterator.map(w => (None, w))
-        } else {
-          for (v <- vs.iterator; w <- ws.iterator) yield (Some(v), w)
-        }
+    this.cogroup(other, partitioner).flatMapValues { case (vs, ws) =>
+      if (vs.isEmpty) {
+        ws.iterator.map(w => (None, w))
+      } else {
+        for (v <- vs.iterator; w <- ws.iterator) yield (Some(v), w)
+      }
     }
   }
 
@@ -395,7 +374,13 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
   /**
    * Return the key-value pairs in this RDD to the master as a Map.
    */
-  def collectAsMap(): Map[K, V] = HashMap(self.collect(): _*)
+  def collectAsMap(): Map[K, V] = {
+    val data = self.toArray()
+    val map = new mutable.HashMap[K, V]
+    map.sizeHint(data.length)
+    data.foreach { case (k, v) => map.put(k, v) }
+    map
+  }
 
   /**
    * Pass each value in the key-value pair RDD through a map function without changing the keys;
@@ -423,13 +408,10 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
     if (partitioner.isInstanceOf[HashPartitioner] && getKeyClass().isArray) {
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
-    val cg = new CoGroupedRDD[K](
-        Seq(self.asInstanceOf[RDD[(K, _)]], other.asInstanceOf[RDD[(K, _)]]),
-        partitioner)
+    val cg = new CoGroupedRDD[K](Seq(self, other), partitioner)
     val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classManifest[K], Manifests.seqSeqManifest)
-    prfs.mapValues {
-      case Seq(vs, ws) =>
-        (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]])
+    prfs.mapValues { case Seq(vs, ws) =>
+      (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]])
     }
   }
 
@@ -442,15 +424,10 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
     if (partitioner.isInstanceOf[HashPartitioner] && getKeyClass().isArray) {
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
-    val cg = new CoGroupedRDD[K](
-        Seq(self.asInstanceOf[RDD[(K, _)]],
-            other1.asInstanceOf[RDD[(K, _)]],
-            other2.asInstanceOf[RDD[(K, _)]]),
-        partitioner)
+    val cg = new CoGroupedRDD[K](Seq(self, other1, other2), partitioner)
     val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classManifest[K], Manifests.seqSeqManifest)
-    prfs.mapValues {
-      case Seq(vs, w1s, w2s) =>
-        (vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W1]], w2s.asInstanceOf[Seq[W2]])
+    prfs.mapValues { case Seq(vs, w1s, w2s) =>
+      (vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W1]], w2s.asInstanceOf[Seq[W2]])
     }
   }
 
@@ -652,7 +629,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
       conf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
     }
     conf.setOutputCommitter(classOf[FileOutputCommitter])
-    FileOutputFormat.setOutputPath(conf, HadoopWriter.createPathFromString(path, conf))
+    FileOutputFormat.setOutputPath(conf, SparkHadoopWriter.createPathFromString(path, conf))
     saveAsHadoopDataset(conf)
   }
 
@@ -678,10 +655,10 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
 
     logInfo("Saving as hadoop file of type (" + keyClass.getSimpleName+ ", " + valueClass.getSimpleName+ ")")
 
-    val writer = new HadoopWriter(conf)
+    val writer = new SparkHadoopWriter(conf)
     writer.preSetup()
 
-    def writeToFile(context: TaskContext, iter: Iterator[(K,V)]) {
+    def writeToFile(context: TaskContext, iter: Iterator[(K, V)]) {
       // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
       // around by taking a mod. We expect that no task will be attempted 2 billion times.
       val attemptNumber = (context.attemptId % Int.MaxValue).toInt
@@ -720,54 +697,6 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
   private[spark] def getValueClass() = implicitly[ClassManifest[V]].erasure
 }
 
-/**
- * Extra functions available on RDDs of (key, value) pairs where the key is sortable through
- * an implicit conversion. Import `spark.SparkContext._` at the top of your program to use these
- * functions. They will work with any key type that has a `scala.math.Ordered` implementation.
- */
-class OrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest](
-  self: RDD[(K, V)])
-  extends Logging
-  with Serializable {
-
-  /**
-   * Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
-   * `collect` or `save` on the resulting RDD will return or output an ordered list of records
-   * (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
-   * order of the keys).
-   */
-  def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size): RDD[(K,V)] = {
-    val shuffled =
-      new ShuffledRDD[K, V](self, new RangePartitioner(numPartitions, self, ascending))
-    shuffled.mapPartitions(iter => {
-      val buf = iter.toArray
-      if (ascending) {
-        buf.sortWith((x, y) => x._1 < y._1).iterator
-      } else {
-        buf.sortWith((x, y) => x._1 > y._1).iterator
-      }
-    }, true)
-  }
-}
-
-private[spark]
-class MappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => U) extends RDD[(K, U)](prev) {
-  override def getPartitions = firstParent[(K, V)].partitions
-  override val partitioner = firstParent[(K, V)].partitioner
-  override def compute(split: Partition, context: TaskContext) =
-    firstParent[(K, V)].iterator(split, context).map{ case (k, v) => (k, f(v)) }
-}
-
-private[spark]
-class FlatMappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => TraversableOnce[U])
-  extends RDD[(K, U)](prev) {
-
-  override def getPartitions = firstParent[(K, V)].partitions
-  override val partitioner = firstParent[(K, V)].partitioner
-  override def compute(split: Partition, context: TaskContext) = {
-    firstParent[(K, V)].iterator(split, context).flatMap { case (k, v) => f(v).map(x => (k, x)) }
-  }
-}
 
 private[spark] object Manifests {
   val seqSeqManifest = classManifest[Seq[Seq[_]]]
diff --git a/core/src/main/scala/spark/Partitioner.scala b/core/src/main/scala/spark/Partitioner.scala
index 660af70d524f37ab609b98ba0ad9bb4f1ed6f079..65da8235d75085bdca8ce20f6c40a537990fd203 100644
--- a/core/src/main/scala/spark/Partitioner.scala
+++ b/core/src/main/scala/spark/Partitioner.scala
@@ -65,17 +65,9 @@ object Partitioner {
 class HashPartitioner(partitions: Int) extends Partitioner {
   def numPartitions = partitions
 
-  def getPartition(key: Any): Int = {
-    if (key == null) {
-      return 0
-    } else {
-      val mod = key.hashCode % partitions
-      if (mod < 0) {
-        mod + partitions
-      } else {
-        mod // Guard against negative hash codes
-      }
-    }
+  def getPartition(key: Any): Int = key match {
+    case null => 0
+    case _ => Utils.nonNegativeMod(key.hashCode, numPartitions)
   }
   
   override def equals(other: Any): Boolean = other match {
@@ -92,7 +84,7 @@ class HashPartitioner(partitions: Int) extends Partitioner {
  */
 class RangePartitioner[K <% Ordered[K]: ClassManifest, V](
     partitions: Int,
-    @transient rdd: RDD[(K,V)],
+    @transient rdd: RDD[_ <: Product2[K,V]],
     private val ascending: Boolean = true) 
   extends Partitioner {
 
diff --git a/core/src/main/scala/spark/RDD.scala b/core/src/main/scala/spark/RDD.scala
index ca7cdd622aea944fac3cb92b5b486c30af8c0a61..25a6951732b055d87123be8b6044ca0eb548bd98 100644
--- a/core/src/main/scala/spark/RDD.scala
+++ b/core/src/main/scala/spark/RDD.scala
@@ -31,8 +31,8 @@ import org.apache.hadoop.mapred.TextOutputFormat
 
 import it.unimi.dsi.fastutil.objects.{Object2LongOpenHashMap => OLMap}
 
-import spark.broadcast.Broadcast
 import spark.Partitioner._
+import spark.api.java.JavaRDD
 import spark.partial.BoundedDouble
 import spark.partial.CountEvaluator
 import spark.partial.GroupedCountEvaluator
@@ -220,8 +220,8 @@ abstract class RDD[T: ClassManifest](
   }
 
   /**
-   * Get the preferred location of a split, taking into account whether the
-   * RDD is checkpointed or not.
+   * Get the preferred locations of a partition (as hostnames), taking into account whether the
+   * RDD is checkpointed.
    */
   final def preferredLocations(split: Partition): Seq[String] = {
     checkpointRDD.map(_.getPreferredLocations(split)).getOrElse {
@@ -286,7 +286,10 @@ abstract class RDD[T: ClassManifest](
   def coalesce(numPartitions: Int, shuffle: Boolean = false): RDD[T] = {
     if (shuffle) {
       // include a shuffle step so that our upstream tasks are still distributed
-      new CoalescedRDD(new ShuffledRDD(map(x => (x, null)), new HashPartitioner(numPartitions)), numPartitions).keys
+      new CoalescedRDD(
+        new ShuffledRDD[T, Null, (T, Null)](map(x => (x, null)),
+        new HashPartitioner(numPartitions)),
+        numPartitions).keys
     } else {
       new CoalescedRDD(this, numPartitions)
     }
@@ -301,8 +304,8 @@ abstract class RDD[T: ClassManifest](
   def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[T] = {
     var fraction = 0.0
     var total = 0
-    var multiplier = 3.0
-    var initialCount = this.count()
+    val multiplier = 3.0
+    val initialCount = this.count()
     var maxSelected = 0
 
     if (num < 0) {
@@ -514,22 +517,19 @@ abstract class RDD[T: ClassManifest](
    * *same number of partitions*, but does *not* require them to have the same number
    * of elements in each partition.
    */
-  def zipPartitions[B: ClassManifest, V: ClassManifest](
-      f: (Iterator[T], Iterator[B]) => Iterator[V],
-      rdd2: RDD[B]): RDD[V] =
+  def zipPartitions[B: ClassManifest, V: ClassManifest]
+      (rdd2: RDD[B])
+      (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] =
     new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2)
 
-  def zipPartitions[B: ClassManifest, C: ClassManifest, V: ClassManifest](
-      f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V],
-      rdd2: RDD[B],
-      rdd3: RDD[C]): RDD[V] =
+  def zipPartitions[B: ClassManifest, C: ClassManifest, V: ClassManifest]
+      (rdd2: RDD[B], rdd3: RDD[C])
+      (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] =
     new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3)
 
-  def zipPartitions[B: ClassManifest, C: ClassManifest, D: ClassManifest, V: ClassManifest](
-      f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V],
-      rdd2: RDD[B],
-      rdd3: RDD[C],
-      rdd4: RDD[D]): RDD[V] =
+  def zipPartitions[B: ClassManifest, C: ClassManifest, D: ClassManifest, V: ClassManifest]
+      (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])
+      (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] =
     new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4)
 
 
@@ -950,4 +950,8 @@ abstract class RDD[T: ClassManifest](
     id,
     origin)
 
+  def toJavaRDD() : JavaRDD[T] = {
+    new JavaRDD(this)(elementClassManifest)
+  }
+
 }
diff --git a/core/src/main/scala/spark/SerializableWritable.scala b/core/src/main/scala/spark/SerializableWritable.scala
index 0236611ef9ddac514e14887ef3e918aa0b76f9d2..936d8e62412e7853ed823e66f53fd95a13811c6a 100644
--- a/core/src/main/scala/spark/SerializableWritable.scala
+++ b/core/src/main/scala/spark/SerializableWritable.scala
@@ -21,7 +21,7 @@ import java.io._
 
 import org.apache.hadoop.io.ObjectWritable
 import org.apache.hadoop.io.Writable
-import org.apache.hadoop.mapred.JobConf
+import org.apache.hadoop.conf.Configuration
 
 class SerializableWritable[T <: Writable](@transient var t: T) extends Serializable {
   def value = t
@@ -35,7 +35,7 @@ class SerializableWritable[T <: Writable](@transient var t: T) extends Serializa
   private def readObject(in: ObjectInputStream) {
     in.defaultReadObject()
     val ow = new ObjectWritable()
-    ow.setConf(new JobConf())
+    ow.setConf(new Configuration())
     ow.readFields(in)
     t = ow.get().asInstanceOf[T]
   }
diff --git a/core/src/main/scala/spark/ShuffleFetcher.scala b/core/src/main/scala/spark/ShuffleFetcher.scala
index dcced035e74d4a8d407b4303a4f7e3f2f5231a62..a6839cf7a40dc94b6cd49010acee7f8d259c5f5c 100644
--- a/core/src/main/scala/spark/ShuffleFetcher.scala
+++ b/core/src/main/scala/spark/ShuffleFetcher.scala
@@ -22,12 +22,13 @@ import spark.serializer.Serializer
 
 
 private[spark] abstract class ShuffleFetcher {
+
   /**
    * Fetch the shuffle outputs for a given ShuffleDependency.
    * @return An iterator over the elements of the fetched shuffle outputs.
    */
-  def fetch[K, V](shuffleId: Int, reduceId: Int, metrics: TaskMetrics,
-    serializer: Serializer = SparkEnv.get.serializerManager.default): Iterator[(K,V)]
+  def fetch[T](shuffleId: Int, reduceId: Int, metrics: TaskMetrics,
+      serializer: Serializer = SparkEnv.get.serializerManager.default): Iterator[T]
 
   /** Stop the fetcher */
   def stop() {}
diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala
index 77cb0ee0cd6b06d1cc286023030e059381bb7594..fdd2dfa810a55e47a08d6170577168fe88d5fd82 100644
--- a/core/src/main/scala/spark/SparkContext.scala
+++ b/core/src/main/scala/spark/SparkContext.scala
@@ -20,18 +20,14 @@ package spark
 import java.io._
 import java.net.URI
 import java.util.Properties
-import java.util.concurrent.ConcurrentHashMap
 import java.util.concurrent.atomic.AtomicInteger
 
-import scala.collection.JavaConversions._
 import scala.collection.Map
 import scala.collection.generic.Growable
-import scala.collection.mutable.HashMap
 import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.mutable.HashMap
 import scala.util.DynamicVariable
-import scala.collection.mutable.{ConcurrentMap, HashMap}
-
-import akka.actor.Actor._
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
@@ -53,21 +49,22 @@ 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
 
-import spark.deploy.{LocalSparkCluster, SparkHadoopUtil}
+import spark.deploy.LocalSparkCluster
 import spark.partial.{ApproximateEvaluator, PartialResult}
-import spark.rdd.{CheckpointRDD, HadoopRDD, NewHadoopRDD, UnionRDD, ParallelCollectionRDD}
-import spark.scheduler.{DAGScheduler, DAGSchedulerSource, ResultTask, ShuffleMapTask, SparkListener, SplitInfo, Stage, StageInfo, TaskScheduler}
-import spark.scheduler.cluster.{StandaloneSchedulerBackend, SparkDeploySchedulerBackend, ClusterScheduler}
+import spark.rdd.{CheckpointRDD, HadoopRDD, NewHadoopRDD, UnionRDD, ParallelCollectionRDD,
+  OrderedRDDFunctions}
+import spark.scheduler.{DAGScheduler, DAGSchedulerSource, ResultTask, ShuffleMapTask, SparkListener,
+  SplitInfo, Stage, StageInfo, TaskScheduler}
+import spark.scheduler.cluster.{StandaloneSchedulerBackend, SparkDeploySchedulerBackend,
+  ClusterScheduler, Schedulable, SchedulingMode}
 import spark.scheduler.local.LocalScheduler
 import spark.scheduler.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend}
 import spark.storage.{StorageStatus, StorageUtils, RDDInfo, BlockManagerSource}
+import spark.ui.SparkUI
 import spark.util.{MetadataCleaner, TimeStampedHashMap}
-import ui.{SparkUI}
-import spark.metrics._
 
 /**
  * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark
@@ -125,6 +122,8 @@ class SparkContext(
   private[spark] val ui = new SparkUI(this)
   ui.bind()
 
+  val startTime = System.currentTimeMillis()
+
   // Add each JAR given through the constructor
   if (jars != null) {
     jars.foreach { addJar(_) }
@@ -236,7 +235,8 @@ class SparkContext(
 
   /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
   val hadoopConfiguration = {
-    val conf = SparkHadoopUtil.newConfiguration()
+    val env = SparkEnv.get
+    val conf = env.hadoop.newConfiguration()
     // Explicitly check for S3 environment variables
     if (System.getenv("AWS_ACCESS_KEY_ID") != null && System.getenv("AWS_SECRET_ACCESS_KEY") != null) {
       conf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
@@ -262,12 +262,22 @@ class SparkContext(
       localProperties.value = new Properties()
   }
 
-  def addLocalProperties(key: String, value: String) {
-    if(localProperties.value == null) {
+  def setLocalProperty(key: String, value: String) {
+    if (localProperties.value == null) {
       localProperties.value = new Properties()
     }
-    localProperties.value.setProperty(key,value)
+    if (value == null) {
+      localProperties.value.remove(key)
+    } else {
+      localProperties.value.setProperty(key, value)
+    }
+  }
+
+  /** Set a human readable description of the current job. */
+  def setJobDescription(value: String) {
+    setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, value)
   }
+
   // Post init
   taskScheduler.postStartHook()
 
@@ -536,7 +546,7 @@ class SparkContext(
   }
 
   def addSparkListener(listener: SparkListener) {
-    dagScheduler.sparkListeners += listener
+    dagScheduler.addSparkListener(listener)
   }
 
   /**
@@ -574,6 +584,28 @@ class SparkContext(
     env.blockManager.master.getStorageStatus
   }
 
+  /**
+   *  Return pools for fair scheduler
+   *  TODO(xiajunluan): We should take nested pools into account
+   */
+  def getAllPools: ArrayBuffer[Schedulable] = {
+    taskScheduler.rootPool.schedulableQueue
+  }
+
+  /**
+   * Return the pool associated with the given name, if one exists
+   */
+  def getPoolForName(pool: String): Option[Schedulable] = {
+    taskScheduler.rootPool.schedulableNameToSchedulable.get(pool)
+  }
+
+  /**
+   *  Return current scheduling mode
+   */
+  def getSchedulingMode: SchedulingMode.SchedulingMode = {
+    taskScheduler.schedulingMode
+  }
+
   /**
    * Clear the job's list of files added by `addFile` so that they do not get downloaded to
    * any new nodes.
@@ -592,10 +624,11 @@ class SparkContext(
       logWarning("null specified as parameter to addJar",
         new SparkException("null specified as parameter to addJar"))
     } else {
+      val env = SparkEnv.get
       val uri = new URI(path)
       val key = uri.getScheme match {
         case null | "file" =>
-          if (SparkHadoopUtil.isYarnMode()) {
+          if (env.hadoop.isYarnMode()) {
             logWarning("local jar specified as parameter to addJar under Yarn mode")
             return
           }
@@ -778,8 +811,9 @@ class SparkContext(
    * prevent accidental overriding of checkpoint files in the existing directory.
    */
   def setCheckpointDir(dir: String, useExisting: Boolean = false) {
+    val env = SparkEnv.get
     val path = new Path(dir)
-    val fs = path.getFileSystem(SparkHadoopUtil.newConfiguration())
+    val fs = path.getFileSystem(env.hadoop.newConfiguration())
     if (!useExisting) {
       if (fs.exists(path)) {
         throw new Exception("Checkpoint directory '" + path + "' already exists.")
@@ -796,11 +830,11 @@ class SparkContext(
   /** Default min number of partitions for Hadoop RDDs when not given by user */
   def defaultMinSplits: Int = math.min(defaultParallelism, 2)
 
-  private var nextShuffleId = new AtomicInteger(0)
+  private val nextShuffleId = new AtomicInteger(0)
 
   private[spark] def newShuffleId(): Int = nextShuffleId.getAndIncrement()
 
-  private var nextRddId = new AtomicInteger(0)
+  private val nextRddId = new AtomicInteger(0)
 
   /** Register a new RDD, returning its RDD ID */
   private[spark] def newRddId(): Int = nextRddId.getAndIncrement()
@@ -816,6 +850,7 @@ class SparkContext(
  * various Spark features.
  */
 object SparkContext {
+  val SPARK_JOB_DESCRIPTION = "spark.job.description"
 
   implicit object DoubleAccumulatorParam extends AccumulatorParam[Double] {
     def addInPlace(t1: Double, t2: Double): Double = t1 + t2
@@ -848,7 +883,7 @@ object SparkContext {
 
   implicit def rddToOrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest](
       rdd: RDD[(K, V)]) =
-    new OrderedRDDFunctions(rdd)
+    new OrderedRDDFunctions[K, V, (K, V)](rdd)
 
   implicit def doubleRDDToDoubleRDDFunctions(rdd: RDD[Double]) = new DoubleRDDFunctions(rdd)
 
@@ -933,7 +968,6 @@ object SparkContext {
   }
 }
 
-
 /**
  * A class encapsulating how to convert some type T to Writable. It stores both the Writable class
  * corresponding to T (e.g. IntWritable for Int) and a function for doing the conversion.
@@ -945,3 +979,4 @@ private[spark] class WritableConverter[T](
     val writableClass: ClassManifest[T] => Class[_ <: Writable],
     val convert: Writable => T)
   extends Serializable
+
diff --git a/core/src/main/scala/spark/SparkEnv.scala b/core/src/main/scala/spark/SparkEnv.scala
index 4a1d341f5d26b5f60f67c0b015d102a77923e908..1f66e9cc7f3b36a59d054f7ab0aa453e16886544 100644
--- a/core/src/main/scala/spark/SparkEnv.scala
+++ b/core/src/main/scala/spark/SparkEnv.scala
@@ -25,6 +25,7 @@ import akka.remote.RemoteActorRefProvider
 
 import spark.broadcast.BroadcastManager
 import spark.metrics.MetricsSystem
+import spark.deploy.SparkHadoopUtil
 import spark.storage.BlockManager
 import spark.storage.BlockManagerMaster
 import spark.network.ConnectionManager
@@ -54,14 +55,23 @@ class SparkEnv (
     val connectionManager: ConnectionManager,
     val httpFileServer: HttpFileServer,
     val sparkFilesDir: String,
-    val metricsSystem: MetricsSystem,
-    // To be set only as part of initialization of SparkContext.
-    // (executorId, defaultHostPort) => executorHostPort
-    // If executorId is NOT found, return defaultHostPort
-    var executorIdToHostPort: Option[(String, String) => String]) {
+    val metricsSystem: MetricsSystem) {
 
   private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]()
 
+  val hadoop = {
+    val yarnMode = java.lang.Boolean.valueOf(System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE")))
+    if(yarnMode) {
+      try {
+        Class.forName("spark.deploy.yarn.YarnSparkHadoopUtil").newInstance.asInstanceOf[SparkHadoopUtil]
+      } catch {
+        case th: Throwable => throw new SparkException("Unable to load YARN support", th)
+      }
+    } else {
+      new SparkHadoopUtil
+    }
+  }
+
   def stop() {
     pythonWorkers.foreach { case(key, worker) => worker.stop() }
     httpFileServer.stop()
@@ -83,27 +93,30 @@ class SparkEnv (
       pythonWorkers.getOrElseUpdate(key, new PythonWorkerFactory(pythonExec, envVars)).create()
     }
   }
-
-  def resolveExecutorIdToHostPort(executorId: String, defaultHostPort: String): String = {
-    val env = SparkEnv.get
-    if (env.executorIdToHostPort.isEmpty) {
-      // default to using host, not host port. Relevant to non cluster modes.
-      return defaultHostPort
-    }
-
-    env.executorIdToHostPort.get(executorId, defaultHostPort)
-  }
 }
 
 object SparkEnv extends Logging {
   private val env = new ThreadLocal[SparkEnv]
+  @volatile private var lastSetSparkEnv : SparkEnv = _
 
   def set(e: SparkEnv) {
+	  lastSetSparkEnv = e
     env.set(e)
   }
 
+  /**
+   * Returns the ThreadLocal SparkEnv, if non-null. Else returns the SparkEnv
+   * previously set in any thread.
+   */
   def get: SparkEnv = {
-    env.get()
+    Option(env.get()).getOrElse(lastSetSparkEnv)
+  }
+
+  /**
+   * Returns the ThreadLocal SparkEnv.
+   */
+  def getThreadLocal : SparkEnv = {
+	  env.get()
   }
 
   def createFromSystemProperties(
@@ -223,7 +236,6 @@ object SparkEnv extends Logging {
       connectionManager,
       httpFileServer,
       sparkFilesDir,
-      metricsSystem,
-      None)
+      metricsSystem)
   }
 }
diff --git a/core/src/main/scala/spark/HadoopWriter.scala b/core/src/main/scala/spark/SparkHadoopWriter.scala
similarity index 96%
rename from core/src/main/scala/spark/HadoopWriter.scala
rename to core/src/main/scala/spark/SparkHadoopWriter.scala
index b1fe0075a3597eb0e96312e760bd19f872e09d27..6b330ef5727be767df763dd61c42caa88012b353 100644
--- a/core/src/main/scala/spark/HadoopWriter.scala
+++ b/core/src/main/scala/spark/SparkHadoopWriter.scala
@@ -36,7 +36,7 @@ import spark.SerializableWritable
  * Saves the RDD using a JobConf, which should contain an output key class, an output value class,
  * a filename to write to, etc, exactly like in a Hadoop MapReduce job.
  */
-class HadoopWriter(@transient jobConf: JobConf) extends Logging with HadoopMapRedUtil with Serializable {
+class SparkHadoopWriter(@transient jobConf: JobConf) extends Logging with SparkHadoopMapRedUtil with Serializable {
 
   private val now = new Date()
   private val conf = new SerializableWritable(jobConf)
@@ -165,7 +165,7 @@ class HadoopWriter(@transient jobConf: JobConf) extends Logging with HadoopMapRe
     splitID = splitid
     attemptID = attemptid
 
-    jID = new SerializableWritable[JobID](HadoopWriter.createJobID(now, jobid))
+    jID = new SerializableWritable[JobID](SparkHadoopWriter.createJobID(now, jobid))
     taID = new SerializableWritable[TaskAttemptID](
         new TaskAttemptID(new TaskID(jID.value, true, splitID), attemptID))
   }
@@ -179,7 +179,7 @@ class HadoopWriter(@transient jobConf: JobConf) extends Logging with HadoopMapRe
   }
 }
 
-object HadoopWriter {
+object SparkHadoopWriter {
   def createJobID(time: Date, id: Int): JobID = {
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
     val jobtrackerID = formatter.format(new Date())
diff --git a/core/src/main/scala/spark/TaskState.scala b/core/src/main/scala/spark/TaskState.scala
index 9df7d8277b8b02299b1ec1b42fbf72b641aa44f7..bf757530565db259fe40a4064c66ae7b4ed9288d 100644
--- a/core/src/main/scala/spark/TaskState.scala
+++ b/core/src/main/scala/spark/TaskState.scala
@@ -24,9 +24,11 @@ private[spark] object TaskState
 
   val LAUNCHING, RUNNING, FINISHED, FAILED, KILLED, LOST = Value
 
+  val FINISHED_STATES = Set(FINISHED, FAILED, KILLED, LOST)
+
   type TaskState = Value
 
-  def isFinished(state: TaskState) = Seq(FINISHED, FAILED, LOST).contains(state)
+  def isFinished(state: TaskState) = FINISHED_STATES.contains(state)
 
   def toMesos(state: TaskState): MesosTaskState = state match {
     case LAUNCHING => MesosTaskState.TASK_STARTING
diff --git a/core/src/main/scala/spark/Utils.scala b/core/src/main/scala/spark/Utils.scala
index e6a96a5ec12c26f28e97a28be95db8326e3fb141..bb8aad3f4ce0a316085be423152f266375fdd4fb 100644
--- a/core/src/main/scala/spark/Utils.scala
+++ b/core/src/main/scala/spark/Utils.scala
@@ -33,8 +33,9 @@ import com.google.common.util.concurrent.ThreadFactoryBuilder
 
 import org.apache.hadoop.fs.{Path, FileSystem, FileUtil}
 
-import spark.serializer.SerializerInstance
+import spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance}
 import spark.deploy.SparkHadoopUtil
+import java.nio.ByteBuffer
 
 
 /**
@@ -68,6 +69,47 @@ private object Utils extends Logging {
     return ois.readObject.asInstanceOf[T]
   }
 
+  /** Serialize via nested stream using specific serializer */
+  def serializeViaNestedStream(os: OutputStream, ser: SerializerInstance)(f: SerializationStream => Unit) = {
+    val osWrapper = ser.serializeStream(new OutputStream {
+      def write(b: Int) = os.write(b)
+
+      override def write(b: Array[Byte], off: Int, len: Int) = os.write(b, off, len)
+    })
+    try {
+      f(osWrapper)
+    } finally {
+      osWrapper.close()
+    }
+  }
+
+  /** Deserialize via nested stream using specific serializer */
+  def deserializeViaNestedStream(is: InputStream, ser: SerializerInstance)(f: DeserializationStream => Unit) = {
+    val isWrapper = ser.deserializeStream(new InputStream {
+      def read(): Int = is.read()
+
+      override def read(b: Array[Byte], off: Int, len: Int): Int = is.read(b, off, len)
+    })
+    try {
+      f(isWrapper)
+    } finally {
+      isWrapper.close()
+    }
+  }
+
+  /**
+   * Primitive often used when writing {@link java.nio.ByteBuffer} to {@link java.io.DataOutput}.
+   */
+  def writeByteBuffer(bb: ByteBuffer, out: ObjectOutput) = {
+    if (bb.hasArray) {
+      out.write(bb.array(), bb.arrayOffset() + bb.position(), bb.remaining())
+    } else {
+      val bbval = new Array[Byte](bb.remaining())
+      bb.get(bbval)
+      out.write(bbval)
+    }
+  }
+
   def isAlpha(c: Char): Boolean = {
     (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z')
   }
@@ -224,8 +266,9 @@ private object Utils extends Logging {
         }
       case _ =>
         // Use the Hadoop filesystem library, which supports file://, hdfs://, s3://, and others
+        val env = SparkEnv.get
         val uri = new URI(url)
-        val conf = SparkHadoopUtil.newConfiguration()
+        val conf = env.hadoop.newConfiguration()
         val fs = FileSystem.get(uri, conf)
         val in = fs.open(new Path(uri))
         val out = new FileOutputStream(tempFile)
@@ -351,48 +394,17 @@ private object Utils extends Logging {
     retval
   }
 
-/*
-  // Used by DEBUG code : remove when all testing done
-  private val ipPattern = Pattern.compile("^[0-9]+(\\.[0-9]+)*$")
   def checkHost(host: String, message: String = "") {
-    // Currently catches only ipv4 pattern, this is just a debugging tool - not rigourous !
-    // if (host.matches("^[0-9]+(\\.[0-9]+)*$")) {
-    if (ipPattern.matcher(host).matches()) {
-      Utils.logErrorWithStack("Unexpected to have host " + host + " which matches IP pattern. Message " + message)
-    }
-    if (Utils.parseHostPort(host)._2 != 0){
-      Utils.logErrorWithStack("Unexpected to have host " + host + " which has port in it. Message " + message)
-    }
+    assert(host.indexOf(':') == -1, message)
   }
 
-  // Used by DEBUG code : remove when all testing done
   def checkHostPort(hostPort: String, message: String = "") {
-    val (host, port) = Utils.parseHostPort(hostPort)
-    checkHost(host)
-    if (port <= 0){
-      Utils.logErrorWithStack("Unexpected to have port " + port + " which is not valid in " + hostPort + ". Message " + message)
-    }
+    assert(hostPort.indexOf(':') != -1, message)
   }
 
   // Used by DEBUG code : remove when all testing done
   def logErrorWithStack(msg: String) {
     try { throw new Exception } catch { case ex: Exception => { logError(msg, ex) } }
-    // temp code for debug
-    System.exit(-1)
-  }
-*/
-
-  // Once testing is complete in various modes, replace with this ?
-  def checkHost(host: String, message: String = "") {}
-  def checkHostPort(hostPort: String, message: String = "") {}
-
-  // Used by DEBUG code : remove when all testing done
-  def logErrorWithStack(msg: String) {
-    try { throw new Exception } catch { case ex: Exception => { logError(msg, ex) } }
-  }
-
-  def getUserNameFromEnvironment(): String = {
-    SparkHadoopUtil.getUserNameFromEnvironment
   }
 
   // Typically, this will be of order of number of nodes in cluster
@@ -479,9 +491,9 @@ private object Utils extends Logging {
   }
 
   /**
-   * Convert a memory quantity in bytes to a human-readable string such as "4.0 MB".
+   * Convert a quantity in bytes to a human-readable string such as "4.0 MB".
    */
-  def memoryBytesToString(size: Long): String = {
+  def bytesToString(size: Long): String = {
     val TB = 1L << 40
     val GB = 1L << 30
     val MB = 1L << 20
@@ -524,10 +536,10 @@ private object Utils extends Logging {
   }
 
   /**
-   * Convert a memory quantity in megabytes to a human-readable string such as "4.0 MB".
+   * Convert a quantity in megabytes to a human-readable string such as "4.0 MB".
    */
-  def memoryMegabytesToString(megabytes: Long): String = {
-    memoryBytesToString(megabytes * 1024L * 1024L)
+  def megabytesToString(megabytes: Long): String = {
+    bytesToString(megabytes * 1024L * 1024L)
   }
 
   /**
@@ -596,7 +608,7 @@ private object Utils extends Logging {
     output.toString
   }
 
-  /** 
+  /**
    * A regular expression to match classes of the "core" Spark API that we want to skip when
    * finding the call site of a method.
    */
@@ -756,4 +768,13 @@ private object Utils extends Logging {
     }
     return buf
   }
+
+ /* Calculates 'x' modulo 'mod', takes to consideration sign of x,
+  * i.e. if 'x' is negative, than 'x' % 'mod' is negative too
+  * so function return (x % mod) + mod in that case.
+  */
+  def nonNegativeMod(x: Int, mod: Int): Int = {
+    val rawMod = x % mod
+    rawMod + (if (rawMod < 0) mod else 0)
+  }
 }
diff --git a/core/src/main/scala/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/spark/api/java/JavaPairRDD.scala
index ccc511dc5ff0cba3b93326a67e685f6c794c8ed6..effe6e5e0d6cd6d9626192f07e2d811e055412f9 100644
--- a/core/src/main/scala/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/spark/api/java/JavaPairRDD.scala
@@ -23,23 +23,25 @@ import java.util.Comparator
 import scala.Tuple2
 import scala.collection.JavaConversions._
 
+import com.google.common.base.Optional
 import org.apache.hadoop.io.compress.CompressionCodec
 import org.apache.hadoop.mapred.JobConf
 import org.apache.hadoop.mapred.OutputFormat
 import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat}
 import org.apache.hadoop.conf.Configuration
 
-import spark.api.java.function.{Function2 => JFunction2}
-import spark.api.java.function.{Function => JFunction}
-import spark.partial.BoundedDouble
-import spark.partial.PartialResult
-import spark.OrderedRDDFunctions
-import spark.storage.StorageLevel
 import spark.HashPartitioner
 import spark.Partitioner
 import spark.Partitioner._
 import spark.RDD
 import spark.SparkContext.rddToPairRDDFunctions
+import spark.api.java.function.{Function2 => JFunction2}
+import spark.api.java.function.{Function => JFunction}
+import spark.partial.BoundedDouble
+import spark.partial.PartialResult
+import spark.rdd.OrderedRDDFunctions
+import spark.storage.StorageLevel
+
 
 class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManifest[K],
   implicit val vManifest: ClassManifest[V]) extends JavaRDDLike[(K, V), JavaPairRDD[K, V]] {
@@ -252,11 +254,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
     fromRDD(rdd.subtract(other, p))
 
   /**
-   * Return a copy of the RDD partitioned using the specified partitioner. If `mapSideCombine`
-   * is true, Spark will group values of the same key together on the map side before the
-   * repartitioning, to only send each key over the network once. If a large number of
-   * duplicated keys are expected, and the size of the keys are large, `mapSideCombine` should
-   * be set to true.
+   * Return a copy of the RDD partitioned using the specified partitioner.
    */
   def partitionBy(partitioner: Partitioner): JavaPairRDD[K, V] =
     fromRDD(rdd.partitionBy(partitioner))
@@ -276,8 +274,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * partition the output RDD.
    */
   def leftOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
-  : JavaPairRDD[K, (V, Option[W])] =
-    fromRDD(rdd.leftOuterJoin(other, partitioner))
+  : JavaPairRDD[K, (V, Optional[W])] = {
+    val joinResult = rdd.leftOuterJoin(other, partitioner)
+    fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))})
+  }
 
   /**
    * Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
@@ -286,8 +286,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * partition the output RDD.
    */
   def rightOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
-  : JavaPairRDD[K, (Option[V], W)] =
-    fromRDD(rdd.rightOuterJoin(other, partitioner))
+  : JavaPairRDD[K, (Optional[V], W)] = {
+    val joinResult = rdd.rightOuterJoin(other, partitioner)
+    fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)})
+  }
 
   /** 
    * Simplified version of combineByKey that hash-partitions the resulting RDD using the existing
@@ -340,8 +342,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
    * using the existing partitioner/parallelism level.
    */
-  def leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Option[W])] =
-    fromRDD(rdd.leftOuterJoin(other))
+  def leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Optional[W])] = {
+    val joinResult = rdd.leftOuterJoin(other)
+    fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))})
+  }
 
   /**
    * Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
@@ -349,8 +353,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
    * into `numPartitions` partitions.
    */
-  def leftOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, Option[W])] =
-    fromRDD(rdd.leftOuterJoin(other, numPartitions))
+  def leftOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, Optional[W])] = {
+    val joinResult = rdd.leftOuterJoin(other, numPartitions)
+    fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))})
+  }
 
   /**
    * Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
@@ -358,8 +364,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
    * RDD using the existing partitioner/parallelism level.
    */
-  def rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Option[V], W)] =
-    fromRDD(rdd.rightOuterJoin(other))
+  def rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], W)] = {
+    val joinResult = rdd.rightOuterJoin(other)
+    fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)})
+  }
 
   /**
    * Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
@@ -367,8 +375,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
    * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
    * RDD into the given number of partitions.
    */
-  def rightOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Option[V], W)] =
-    fromRDD(rdd.rightOuterJoin(other, numPartitions))
+  def rightOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Optional[V], W)] = {
+    val joinResult = rdd.rightOuterJoin(other, numPartitions)
+    fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)})
+  }
 
   /**
    * Return the key-value pairs in this RDD to the master as a Map.
@@ -554,7 +564,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif
       override def compare(b: K) = comp.compare(a, b)
     }
     implicit def toOrdered(x: K): Ordered[K] = new KeyOrdering(x)
-    fromRDD(new OrderedRDDFunctions(rdd).sortByKey(ascending))
+    fromRDD(new OrderedRDDFunctions[K, V, (K, V)](rdd).sortByKey(ascending))
   }
 
   /**
diff --git a/core/src/main/scala/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/spark/api/java/JavaRDDLike.scala
index 21b5abf053fc65ba0b607f7f6020464b7062babf..2c2b138f16c2073dde18295cbe0d4473aaf0300d 100644
--- a/core/src/main/scala/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/spark/api/java/JavaRDDLike.scala
@@ -207,12 +207,12 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * of elements in each partition.
    */
   def zipPartitions[U, V](
-      f: FlatMapFunction2[java.util.Iterator[T], java.util.Iterator[U], V],
-      other: JavaRDDLike[U, _]): JavaRDD[V] = {
+      other: JavaRDDLike[U, _],
+      f: FlatMapFunction2[java.util.Iterator[T], java.util.Iterator[U], V]): JavaRDD[V] = {
     def fn = (x: Iterator[T], y: Iterator[U]) => asScalaIterator(
       f.apply(asJavaIterator(x), asJavaIterator(y)).iterator())
     JavaRDD.fromRDD(
-      rdd.zipPartitions(fn, other.rdd)(other.classManifest, f.elementType()))(f.elementType())
+      rdd.zipPartitions(other.rdd)(fn)(other.classManifest, f.elementType()))(f.elementType())
   }
 
   // Actions (launch a job to return a value to the user program)
@@ -366,10 +366,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Gets the name of the file to which this RDD was checkpointed
    */
   def getCheckpointFile(): Optional[String] = {
-    rdd.getCheckpointFile match {
-      case Some(file) => Optional.of(file)
-      case _ => Optional.absent()
-    }
+    JavaUtils.optionToOptional(rdd.getCheckpointFile)
   }
 
   /** A description of this RDD and its recursive dependencies for debugging. */
diff --git a/core/src/main/scala/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/spark/api/java/JavaSparkContext.scala
index fe182e7ab60eb7078e4b2ff53014f7090c99bbd2..29d57004b5643f17131a7003a97b8d8bc331acd8 100644
--- a/core/src/main/scala/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/spark/api/java/JavaSparkContext.scala
@@ -32,6 +32,8 @@ import spark.SparkContext.IntAccumulatorParam
 import spark.SparkContext.DoubleAccumulatorParam
 import spark.broadcast.Broadcast
 
+import com.google.common.base.Optional
+
 /**
  * A Java-friendly version of [[spark.SparkContext]] that returns [[spark.api.java.JavaRDD]]s and
  * works with Java collections instead of Scala ones.
@@ -337,7 +339,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    * or the spark.home Java property, or the SPARK_HOME environment variable
    * (in that order of preference). If neither of these is set, return None.
    */
-  def getSparkHome(): Option[String] = sc.getSparkHome()
+  def getSparkHome(): Optional[String] = JavaUtils.optionToOptional(sc.getSparkHome())
 
   /**
    * Add a file to be downloaded with this Spark job on every node.
diff --git a/core/src/main/scala/spark/api/java/JavaUtils.scala b/core/src/main/scala/spark/api/java/JavaUtils.scala
new file mode 100644
index 0000000000000000000000000000000000000000..ffc131ac83c1ef27ff12b19707ea4eeb8bd742e8
--- /dev/null
+++ b/core/src/main/scala/spark/api/java/JavaUtils.scala
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.api.java
+
+import com.google.common.base.Optional
+
+object JavaUtils {
+  def optionToOptional[T](option: Option[T]): Optional[T] =
+    option match {
+      case Some(value) => Optional.of(value)
+      case None => Optional.absent()
+    }
+}
diff --git a/core/src/main/scala/spark/api/python/PythonPartitioner.scala b/core/src/main/scala/spark/api/python/PythonPartitioner.scala
index 31a719fbff3fa981dd39377c37c0cdb83aa84b96..ac112b8c2c773078983d4acab3c894e53a5b9021 100644
--- a/core/src/main/scala/spark/api/python/PythonPartitioner.scala
+++ b/core/src/main/scala/spark/api/python/PythonPartitioner.scala
@@ -18,7 +18,7 @@
 package spark.api.python
 
 import spark.Partitioner
-
+import spark.Utils
 import java.util.Arrays
 
 /**
@@ -35,25 +35,10 @@ private[spark] class PythonPartitioner(
   val pyPartitionFunctionId: Long)
   extends Partitioner {
 
-  override def getPartition(key: Any): Int = {
-    if (key == null) {
-      return 0
-    }
-    else {
-      val hashCode = {
-        if (key.isInstanceOf[Array[Byte]]) {
-          Arrays.hashCode(key.asInstanceOf[Array[Byte]])
-        } else {
-          key.hashCode()
-        }
-      }
-      val mod = hashCode % numPartitions
-      if (mod < 0) {
-        mod + numPartitions
-      } else {
-        mod // Guard against negative hash codes
-      }
-    }
+  override def getPartition(key: Any): Int = key match {
+    case null => 0
+    case key: Array[Byte] => Utils.nonNegativeMod(Arrays.hashCode(key), numPartitions)
+    case _ => Utils.nonNegativeMod(key.hashCode(), numPartitions)
   }
 
   override def equals(other: Any): Boolean = other match {
diff --git a/core/src/main/scala/spark/api/python/PythonRDD.scala b/core/src/main/scala/spark/api/python/PythonRDD.scala
index af10822dbd2b1f189cb988b5f530f906ac21c06b..49671437d041b329c910de6ff1cc5360c45f66a3 100644
--- a/core/src/main/scala/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/spark/api/python/PythonRDD.scala
@@ -33,6 +33,7 @@ private[spark] class PythonRDD[T: ClassManifest](
     parent: RDD[T],
     command: Seq[String],
     envVars: JMap[String, String],
+    pythonIncludes: JList[String],
     preservePartitoning: Boolean,
     pythonExec: String,
     broadcastVars: JList[Broadcast[Array[Byte]]],
@@ -44,10 +45,11 @@ private[spark] class PythonRDD[T: ClassManifest](
   // Similar to Runtime.exec(), if we are given a single string, split it into words
   // using a standard StringTokenizer (i.e. by spaces)
   def this(parent: RDD[T], command: String, envVars: JMap[String, String],
+      pythonIncludes: JList[String],
       preservePartitoning: Boolean, pythonExec: String,
       broadcastVars: JList[Broadcast[Array[Byte]]],
       accumulator: Accumulator[JList[Array[Byte]]]) =
-    this(parent, PipedRDD.tokenize(command), envVars, preservePartitoning, pythonExec,
+    this(parent, PipedRDD.tokenize(command), envVars, pythonIncludes, preservePartitoning, pythonExec,
       broadcastVars, accumulator)
 
   override def getPartitions = parent.partitions
@@ -63,34 +65,47 @@ private[spark] class PythonRDD[T: ClassManifest](
     // Start a thread to feed the process input from our parent's iterator
     new Thread("stdin writer for " + pythonExec) {
       override def run() {
-        SparkEnv.set(env)
-        val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize)
-        val dataOut = new DataOutputStream(stream)
-        val printOut = new PrintWriter(stream)
-        // Partition index
-        dataOut.writeInt(split.index)
-        // sparkFilesDir
-        PythonRDD.writeAsPickle(SparkFiles.getRootDirectory, dataOut)
-        // Broadcast variables
-        dataOut.writeInt(broadcastVars.length)
-        for (broadcast <- broadcastVars) {
-          dataOut.writeLong(broadcast.id)
-          dataOut.writeInt(broadcast.value.length)
-          dataOut.write(broadcast.value)
-        }
-        dataOut.flush()
-        // Serialized user code
-        for (elem <- command) {
-          printOut.println(elem)
-        }
-        printOut.flush()
-        // Data values
-        for (elem <- parent.iterator(split, context)) {
-          PythonRDD.writeAsPickle(elem, dataOut)
+        try {
+          SparkEnv.set(env)
+          val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize)
+          val dataOut = new DataOutputStream(stream)
+          val printOut = new PrintWriter(stream)
+          // Partition index
+          dataOut.writeInt(split.index)
+          // sparkFilesDir
+          PythonRDD.writeAsPickle(SparkFiles.getRootDirectory, dataOut)
+          // Broadcast variables
+          dataOut.writeInt(broadcastVars.length)
+          for (broadcast <- broadcastVars) {
+            dataOut.writeLong(broadcast.id)
+            dataOut.writeInt(broadcast.value.length)
+            dataOut.write(broadcast.value)
+          }
+          // Python includes (*.zip and *.egg files)
+          dataOut.writeInt(pythonIncludes.length)
+          for (f <- pythonIncludes) {
+            PythonRDD.writeAsPickle(f, dataOut)
+          }
+          dataOut.flush()
+          // Serialized user code
+          for (elem <- command) {
+            printOut.println(elem)
+          }
+          printOut.flush()
+          // Data values
+          for (elem <- parent.iterator(split, context)) {
+            PythonRDD.writeAsPickle(elem, dataOut)
+          }
+          dataOut.flush()
+          printOut.flush()
+          worker.shutdownOutput()
+        } catch {
+          case e: IOException =>
+            // This can happen for legitimate reasons if the Python code stops returning data before we are done
+            // passing elements through, e.g., for take(). Just log a message to say it happened.
+            logInfo("stdin writer to Python finished early")
+            logDebug("stdin writer to Python finished early", e)
         }
-        dataOut.flush()
-        printOut.flush()
-        worker.shutdownOutput()
       }
     }.start()
 
@@ -297,7 +312,7 @@ class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int)
   Utils.checkHost(serverHost, "Expected hostname")
 
   val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt
-  
+
   override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new JArrayList
 
   override def addInPlace(val1: JList[Array[Byte]], val2: JList[Array[Byte]])
diff --git a/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala
index 078ad45ce8533e2b60798c853f2d62ab0f250ac0..14f8320678217e3e79ed1c42fc0109bd7ea4422f 100644
--- a/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala
@@ -17,7 +17,7 @@
 
 package spark.api.python
 
-import java.io.{DataInputStream, IOException}
+import java.io.{File, DataInputStream, IOException}
 import java.net.{Socket, SocketException, InetAddress}
 
 import scala.collection.JavaConversions._
@@ -67,6 +67,8 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
         val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/daemon.py"))
         val workerEnv = pb.environment()
         workerEnv.putAll(envVars)
+        val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
+        workerEnv.put("PYTHONPATH", pythonPath)
         daemon = pb.start()
 
         // Redirect the stderr to ours
diff --git a/core/src/main/scala/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/spark/broadcast/HttpBroadcast.scala
index c56587695041cf9c1a9ee9811a6b5418c6a625fb..138a8c21bcfeb09bed73fc9cd77a435218f527f6 100644
--- a/core/src/main/scala/spark/broadcast/HttpBroadcast.scala
+++ b/core/src/main/scala/spark/broadcast/HttpBroadcast.scala
@@ -17,21 +17,20 @@
 
 package spark.broadcast
 
-import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream}
-
-import java.io._
-import java.net._
-import java.util.UUID
+import java.io.{File, FileOutputStream, ObjectInputStream, OutputStream}
+import java.net.URL
 
 import it.unimi.dsi.fastutil.io.FastBufferedInputStream
 import it.unimi.dsi.fastutil.io.FastBufferedOutputStream
 
-import spark._
+import spark.{HttpServer, Logging, SparkEnv, Utils}
+import spark.io.CompressionCodec
 import spark.storage.StorageLevel
-import util.{MetadataCleaner, TimeStampedHashSet}
+import spark.util.{MetadataCleaner, TimeStampedHashSet}
+
 
 private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long)
-extends Broadcast[T](id) with Logging with Serializable {
+  extends Broadcast[T](id) with Logging with Serializable {
   
   def value = value_
 
@@ -85,6 +84,7 @@ private object HttpBroadcast extends Logging {
   private val files = new TimeStampedHashSet[String]
   private val cleaner = new MetadataCleaner("HttpBroadcast", cleanup)
 
+  private lazy val compressionCodec = CompressionCodec.createCodec()
 
   def initialize(isDriver: Boolean) {
     synchronized {
@@ -122,10 +122,12 @@ private object HttpBroadcast extends Logging {
 
   def write(id: Long, value: Any) {
     val file = new File(broadcastDir, "broadcast-" + id)
-    val out: OutputStream = if (compress) {
-      new LZFOutputStream(new FileOutputStream(file)) // Does its own buffering
-    } else {
-      new FastBufferedOutputStream(new FileOutputStream(file), bufferSize)
+    val out: OutputStream = {
+      if (compress) {
+        compressionCodec.compressedOutputStream(new FileOutputStream(file))
+      } else {
+        new FastBufferedOutputStream(new FileOutputStream(file), bufferSize)
+      }
     }
     val ser = SparkEnv.get.serializer.newInstance()
     val serOut = ser.serializeStream(out)
@@ -136,10 +138,12 @@ private object HttpBroadcast extends Logging {
 
   def read[T](id: Long): T = {
     val url = serverUri + "/broadcast-" + id
-    var in = if (compress) {
-      new LZFInputStream(new URL(url).openStream()) // Does its own buffering
-    } else {
-      new FastBufferedInputStream(new URL(url).openStream(), bufferSize)
+    val in = {
+      if (compress) {
+        compressionCodec.compressedInputStream(new URL(url).openStream())
+      } else {
+        new FastBufferedInputStream(new URL(url).openStream(), bufferSize)
+      }
     }
     val ser = SparkEnv.get.serializer.newInstance()
     val serIn = ser.deserializeStream(in)
diff --git a/core/src/main/scala/spark/deploy/DeployMessage.scala b/core/src/main/scala/spark/deploy/DeployMessage.scala
index e1f8aff6f516aa2b60225903d9532a6a6fa72303..0db13ffc98723ff59183e02212a1e13a5575a5b9 100644
--- a/core/src/main/scala/spark/deploy/DeployMessage.scala
+++ b/core/src/main/scala/spark/deploy/DeployMessage.scala
@@ -17,109 +17,114 @@
 
 package spark.deploy
 
+import scala.collection.immutable.List
+
+import spark.Utils
 import spark.deploy.ExecutorState.ExecutorState
 import spark.deploy.master.{WorkerInfo, ApplicationInfo}
 import spark.deploy.worker.ExecutorRunner
-import scala.collection.immutable.List
-import spark.Utils
 
 
-private[spark] sealed trait DeployMessage extends Serializable
+private[deploy] sealed trait DeployMessage extends Serializable
 
-// Worker to Master
+private[deploy] object DeployMessages {
 
-private[spark]
-case class RegisterWorker(
-    id: String,
-    host: String,
-    port: Int,
-    cores: Int,
-    memory: Int,
-    webUiPort: Int,
-    publicAddress: String)
-  extends DeployMessage {
-  Utils.checkHost(host, "Required hostname")
-  assert (port > 0)
-}
+  // Worker to Master
 
-private[spark] 
-case class ExecutorStateChanged(
-    appId: String,
-    execId: Int,
-    state: ExecutorState,
-    message: Option[String],
-    exitStatus: Option[Int])
-  extends DeployMessage
+  case class RegisterWorker(
+      id: String,
+      host: String,
+      port: Int,
+      cores: Int,
+      memory: Int,
+      webUiPort: Int,
+      publicAddress: String)
+    extends DeployMessage {
+    Utils.checkHost(host, "Required hostname")
+    assert (port > 0)
+  }
 
-private[spark] case class Heartbeat(workerId: String) extends DeployMessage
+  case class ExecutorStateChanged(
+      appId: String,
+      execId: Int,
+      state: ExecutorState,
+      message: Option[String],
+      exitStatus: Option[Int])
+    extends DeployMessage
 
-// Master to Worker
+  case class Heartbeat(workerId: String) extends DeployMessage
 
-private[spark] case class RegisteredWorker(masterWebUiUrl: String) extends DeployMessage
-private[spark] case class RegisterWorkerFailed(message: String) extends DeployMessage
-private[spark] case class KillExecutor(appId: String, execId: Int) extends DeployMessage
+  // Master to Worker
 
-private[spark] case class LaunchExecutor(
-    appId: String,
-    execId: Int,
-    appDesc: ApplicationDescription,
-    cores: Int,
-    memory: Int,
-    sparkHome: String)
-  extends DeployMessage
+  case class RegisteredWorker(masterWebUiUrl: String) extends DeployMessage
 
-// Client to Master
+  case class RegisterWorkerFailed(message: String) extends DeployMessage
 
-private[spark] case class RegisterApplication(appDescription: ApplicationDescription)
-  extends DeployMessage
+  case class KillExecutor(appId: String, execId: Int) extends DeployMessage
 
-// Master to Client
+  case class LaunchExecutor(
+      appId: String,
+      execId: Int,
+      appDesc: ApplicationDescription,
+      cores: Int,
+      memory: Int,
+      sparkHome: String)
+    extends DeployMessage
 
-private[spark] 
-case class RegisteredApplication(appId: String) extends DeployMessage
+  // Client to Master
 
-private[spark] 
-case class ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) {
-  Utils.checkHostPort(hostPort, "Required hostport")
-}
+  case class RegisterApplication(appDescription: ApplicationDescription)
+    extends DeployMessage
 
-private[spark]
-case class ExecutorUpdated(id: Int, state: ExecutorState, message: Option[String],
-                           exitStatus: Option[Int])
+  // Master to Client
 
-private[spark]
-case class ApplicationRemoved(message: String)
+  case class RegisteredApplication(appId: String) extends DeployMessage
 
-// Internal message in Client
+  // TODO(matei): replace hostPort with host
+  case class ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) {
+    Utils.checkHostPort(hostPort, "Required hostport")
+  }
 
-private[spark] case object StopClient
+  case class ExecutorUpdated(id: Int, state: ExecutorState, message: Option[String],
+    exitStatus: Option[Int])
 
-// MasterWebUI To Master
+  case class ApplicationRemoved(message: String)
 
-private[spark] case object RequestMasterState
+  // Internal message in Client
 
-// Master to MasterWebUI
+  case object StopClient
 
-private[spark] 
-case class MasterState(host: String, port: Int, workers: Array[WorkerInfo],
-  activeApps: Array[ApplicationInfo], completedApps: Array[ApplicationInfo]) {
+  // MasterWebUI To Master
 
-  Utils.checkHost(host, "Required hostname")
-  assert (port > 0)
+  case object RequestMasterState
 
-  def uri = "spark://" + host + ":" + port
-}
+  // Master to MasterWebUI
+
+  case class MasterStateResponse(host: String, port: Int, workers: Array[WorkerInfo],
+    activeApps: Array[ApplicationInfo], completedApps: Array[ApplicationInfo]) {
+
+    Utils.checkHost(host, "Required hostname")
+    assert (port > 0)
+
+    def uri = "spark://" + host + ":" + port
+  }
+
+  //  WorkerWebUI to Worker
+
+  case object RequestWorkerState
+
+  // Worker to WorkerWebUI
+
+  case class WorkerStateResponse(host: String, port: Int, workerId: String,
+    executors: List[ExecutorRunner], finishedExecutors: List[ExecutorRunner], masterUrl: String,
+    cores: Int, memory: Int, coresUsed: Int, memoryUsed: Int, masterWebUiUrl: String) {
 
-//  WorkerWebUI to Worker
-private[spark] case object RequestWorkerState
+    Utils.checkHost(host, "Required hostname")
+    assert (port > 0)
+  }
 
-// Worker to WorkerWebUI
+  // Actor System to Master
 
-private[spark]
-case class WorkerState(host: String, port: Int, workerId: String, executors: List[ExecutorRunner],
-  finishedExecutors: List[ExecutorRunner], masterUrl: String, cores: Int, memory: Int, 
-  coresUsed: Int, memoryUsed: Int, masterWebUiUrl: String) {
+  case object CheckForWorkerTimeOut
 
-  Utils.checkHost(host, "Required hostname")
-  assert (port > 0)
 }
diff --git a/core/src/main/scala/spark/deploy/JsonProtocol.scala b/core/src/main/scala/spark/deploy/JsonProtocol.scala
index 64f89623e14c775ad176888cf8bb34e739348956..6b71b953dd782947b2bbc61fadb85bd51fc7078a 100644
--- a/core/src/main/scala/spark/deploy/JsonProtocol.scala
+++ b/core/src/main/scala/spark/deploy/JsonProtocol.scala
@@ -17,66 +17,71 @@
 
 package spark.deploy
 
-import master.{ApplicationInfo, WorkerInfo}
-import net.liftweb.json.JsonDSL._
-import worker.ExecutorRunner
+import scala.util.parsing.json.{JSONArray, JSONObject, JSONType}
+
+import spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse}
+import spark.deploy.master.{ApplicationInfo, WorkerInfo}
+import spark.deploy.worker.ExecutorRunner
+
 
 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)
- }
 
-  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)
-  }
+  def writeWorkerInfo(obj: WorkerInfo): JSONType = JSONObject(Map(
+    "id" -> obj.id,
+    "host" -> obj.host,
+    "port" -> obj.port,
+    "webuiaddress" -> obj.webUiAddress,
+    "cores" -> obj.cores,
+    "coresused" -> obj.coresUsed,
+    "memory" -> obj.memory,
+    "memoryused" -> obj.memoryUsed,
+    "state" -> obj.state.toString
+  ))
+
+  def writeApplicationInfo(obj: ApplicationInfo): JSONType = JSONObject(Map(
+    "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
+  ))
 
-  def writeApplicationDescription(obj: ApplicationDescription) = {
-    ("name" -> obj.name) ~
-    ("cores" -> obj.maxCores) ~
-    ("memoryperslave" -> obj.memoryPerSlave) ~
-    ("user" -> obj.user)
-  }
+  def writeApplicationDescription(obj: ApplicationDescription): JSONType = JSONObject(Map(
+    "name" -> obj.name,
+    "cores" -> obj.maxCores,
+    "memoryperslave" -> obj.memoryPerSlave,
+    "user" -> obj.user
+  ))
 
-  def writeExecutorRunner(obj: ExecutorRunner) = {
-    ("id" -> obj.execId) ~
-    ("memory" -> obj.memory) ~
-    ("appid" -> obj.appId) ~
-    ("appdesc" -> writeApplicationDescription(obj.appDesc))
-  }
+  def writeExecutorRunner(obj: ExecutorRunner): JSONType = JSONObject(Map(
+    "id" -> obj.execId,
+    "memory" -> obj.memory,
+    "appid" -> obj.appId,
+    "appdesc" -> writeApplicationDescription(obj.appDesc)
+  ))
 
-  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))
-  }
+  def writeMasterState(obj: MasterStateResponse): JSONType = JSONObject(Map(
+    "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" -> JSONArray(obj.activeApps.toList.map(writeApplicationInfo)),
+    "completedapps" -> JSONArray(obj.completedApps.toList.map(writeApplicationInfo))
+  ))
 
-  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))
-  }
+  def writeWorkerState(obj: WorkerStateResponse): JSONType = JSONObject(Map(
+    "id" -> obj.workerId,
+    "masterurl" -> obj.masterUrl,
+    "masterwebuiurl" -> obj.masterWebUiUrl,
+    "cores" -> obj.cores,
+    "coresused" -> obj.coresUsed,
+    "memory" -> obj.memory,
+    "memoryused" -> obj.memoryUsed,
+    "executors" -> JSONArray(obj.executors.toList.map(writeExecutorRunner)),
+    "finishedexecutors" -> JSONArray(obj.finishedExecutors.toList.map(writeExecutorRunner))
+  ))
 }
diff --git a/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/spark/deploy/SparkHadoopUtil.scala
similarity index 82%
rename from core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala
rename to core/src/main/scala/spark/deploy/SparkHadoopUtil.scala
index 617954cb9872aa1b487486afe37dc36233bf7692..882161e66997005b61ea3120ffee0023f58bd010 100644
--- a/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/spark/deploy/SparkHadoopUtil.scala
@@ -23,18 +23,7 @@ import org.apache.hadoop.mapred.JobConf
 /**
  * Contains util methods to interact with Hadoop from spark.
  */
-object SparkHadoopUtil {
-
-  def getUserNameFromEnvironment(): String = {
-    // defaulting to -D ...
-    System.getProperty("user.name")
-  }
-
-  def runAsUser(func: (Product) => Unit, args: Product) {
-
-    // Add support, if exists - for now, simply run func !
-    func(args)
-  }
+class SparkHadoopUtil {
 
   // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems
   def newConfiguration(): Configuration = new Configuration()
diff --git a/core/src/main/scala/spark/deploy/client/Client.scala b/core/src/main/scala/spark/deploy/client/Client.scala
index edefa0292d0043a91a2642137f6b6c135f87873a..9d5ba8a796013a0ffb5ceeab0eb3fe1461811560 100644
--- a/core/src/main/scala/spark/deploy/client/Client.scala
+++ b/core/src/main/scala/spark/deploy/client/Client.scala
@@ -17,21 +17,23 @@
 
 package spark.deploy.client
 
-import spark.deploy._
+import java.util.concurrent.TimeoutException
+
 import akka.actor._
+import akka.actor.Terminated
 import akka.pattern.ask
 import akka.util.Duration
-import akka.util.duration._
-import akka.pattern.AskTimeoutException
-import spark.{SparkException, Logging}
+import akka.remote.RemoteClientDisconnected
 import akka.remote.RemoteClientLifeCycleEvent
 import akka.remote.RemoteClientShutdown
-import spark.deploy.RegisterApplication
-import spark.deploy.master.Master
-import akka.remote.RemoteClientDisconnected
-import akka.actor.Terminated
 import akka.dispatch.Await
 
+import spark.Logging
+import spark.deploy.{ApplicationDescription, ExecutorState}
+import spark.deploy.DeployMessages._
+import spark.deploy.master.Master
+
+
 /**
  * The main class used to talk to a Spark deploy cluster. Takes a master URL, an app description,
  * and a listener for cluster events, and calls back the listener when various events occur.
@@ -134,7 +136,8 @@ private[spark] class Client(
         val future = actor.ask(StopClient)(timeout)
         Await.result(future, timeout)
       } catch {
-        case e: AskTimeoutException =>  // Ignore it, maybe master went away
+        case e: TimeoutException =>
+          logInfo("Stop request to Master timed out; it may already be shut down.")
       }
       actor = null
     }
diff --git a/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala
index 15ff9197382c8c06f5ae2173dbab0f0c37c6665d..6dd2f06126527dd5c3dae462b07f9978fc23f502 100644
--- a/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala
+++ b/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala
@@ -34,6 +34,7 @@ private[spark] class ApplicationInfo(
   var executors = new mutable.HashMap[Int, ExecutorInfo]
   var coresGranted = 0
   var endTime = -1L
+  val appSource = new ApplicationSource(this)
 
   private var nextExecutorId = 0
 
@@ -51,8 +52,10 @@ private[spark] class ApplicationInfo(
   }
 
   def removeExecutor(exec: ExecutorInfo) {
-    executors -= exec.id
-    coresGranted -= exec.cores
+    if (executors.contains(exec.id)) {
+      executors -= exec.id
+      coresGranted -= exec.cores
+    }
   }
 
   def coresLeft: Int = desc.maxCores - coresGranted
diff --git a/core/src/main/scala/spark/deploy/master/ApplicationSource.scala b/core/src/main/scala/spark/deploy/master/ApplicationSource.scala
new file mode 100644
index 0000000000000000000000000000000000000000..4df2b6bfddd7503ef4b5b59b8e9b3c660ddfd3ec
--- /dev/null
+++ b/core/src/main/scala/spark/deploy/master/ApplicationSource.scala
@@ -0,0 +1,24 @@
+package spark.deploy.master
+
+import com.codahale.metrics.{Gauge, MetricRegistry}
+
+import spark.metrics.source.Source
+
+class ApplicationSource(val application: ApplicationInfo) extends Source {
+  val metricRegistry = new MetricRegistry()
+  val sourceName = "%s.%s.%s".format("application", application.desc.name,
+    System.currentTimeMillis())
+
+  metricRegistry.register(MetricRegistry.name("status"), new Gauge[String] {
+    override def getValue: String = application.state.toString
+  })
+
+  metricRegistry.register(MetricRegistry.name("runtime_ms"), new Gauge[Long] {
+    override def getValue: Long = application.duration
+  })
+
+  metricRegistry.register(MetricRegistry.name("cores", "number"), new Gauge[Int] {
+    override def getValue: Int = application.coresGranted
+  })
+
+}
diff --git a/core/src/main/scala/spark/deploy/master/Master.scala b/core/src/main/scala/spark/deploy/master/Master.scala
index 9692af52953f8f30517ef6b1abaa0ec42a75686b..04af5e149c62d3431c27095ac66ff3152c431e91 100644
--- a/core/src/main/scala/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/spark/deploy/master/Master.scala
@@ -17,27 +17,30 @@
 
 package spark.deploy.master
 
-import akka.actor._
-import akka.actor.Terminated
-import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientDisconnected, RemoteClientShutdown}
-import akka.util.duration._
-
 import java.text.SimpleDateFormat
 import java.util.Date
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
-import spark.deploy._
+import akka.actor._
+import akka.actor.Terminated
+import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientDisconnected, RemoteClientShutdown}
+import akka.util.duration._
+
 import spark.{Logging, SparkException, Utils}
+import spark.deploy.{ApplicationDescription, ExecutorState}
+import spark.deploy.DeployMessages._
+import spark.deploy.master.ui.MasterWebUI
 import spark.metrics.MetricsSystem
 import spark.util.AkkaUtils
-import ui.MasterWebUI
 
 
 private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Actor with Logging {
   val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss")  // For application IDs
   val WORKER_TIMEOUT = System.getProperty("spark.worker.timeout", "60").toLong * 1000
-
+  val RETAINED_APPLICATIONS = System.getProperty("spark.deploy.retainedApplications", "200").toInt
+  val REAPER_ITERATIONS = System.getProperty("spark.dead.worker.persistence", "15").toInt
+ 
   var nextAppNumber = 0
   val workers = new HashSet[WorkerInfo]
   val idToWorker = new HashMap[String, WorkerInfo]
@@ -54,13 +57,14 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
 
   var firstApp: Option[ApplicationInfo] = None
 
-  val webUi = new MasterWebUI(self, webUiPort)
-
   Utils.checkHost(host, "Expected hostname")
 
-  val metricsSystem = MetricsSystem.createMetricsSystem("master")
+  val masterMetricsSystem = MetricsSystem.createMetricsSystem("master")
+  val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications")
   val masterSource = new MasterSource(this)
 
+  val webUi = new MasterWebUI(this, webUiPort)
+
   val masterPublicAddress = {
     val envVar = System.getenv("SPARK_PUBLIC_DNS")
     if (envVar != null) envVar else host
@@ -76,21 +80,23 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
     // Listen for remote client disconnection events, since they don't go through Akka's watch()
     context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent])
     webUi.start()
-    context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis)(timeOutDeadWorkers())
+    context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis, self, CheckForWorkerTimeOut)
 
-    metricsSystem.registerSource(masterSource)
-    metricsSystem.start()
+    masterMetricsSystem.registerSource(masterSource)
+    masterMetricsSystem.start()
+    applicationMetricsSystem.start()
   }
 
   override def postStop() {
     webUi.stop()
-    metricsSystem.stop()
+    masterMetricsSystem.stop()
+    applicationMetricsSystem.stop()
   }
 
   override def receive = {
     case RegisterWorker(id, host, workerPort, cores, memory, worker_webUiPort, publicAddress) => {
       logInfo("Registering worker %s:%d with %d cores, %s RAM".format(
-        host, workerPort, cores, Utils.memoryMegabytesToString(memory)))
+        host, workerPort, cores, Utils.megabytesToString(memory)))
       if (idToWorker.contains(id)) {
         sender ! RegisterWorkerFailed("Duplicate worker ID")
       } else {
@@ -168,7 +174,11 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
     }
 
     case RequestMasterState => {
-      sender ! MasterState(host, port, workers.toArray, apps.toArray, completedApps.toArray)
+      sender ! MasterStateResponse(host, port, workers.toArray, apps.toArray, completedApps.toArray)
+    }
+
+    case CheckForWorkerTimeOut => {
+      timeOutDeadWorkers()
     }
   }
 
@@ -233,20 +243,27 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
   def launchExecutor(worker: WorkerInfo, exec: ExecutorInfo, sparkHome: String) {
     logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
     worker.addExecutor(exec)
-    worker.actor ! LaunchExecutor(exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory, sparkHome)
-    exec.application.driver ! ExecutorAdded(exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
+    worker.actor ! LaunchExecutor(
+      exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory, sparkHome)
+    exec.application.driver ! ExecutorAdded(
+      exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
   }
 
   def addWorker(id: String, host: String, port: Int, cores: Int, memory: Int, webUiPort: Int,
     publicAddress: String): WorkerInfo = {
-    // There may be one or more refs to dead workers on this same node (w/ different ID's), remove them.
-    workers.filter(w => (w.host == host && w.port == port) && (w.state == WorkerState.DEAD)).foreach(workers -= _)
+    // There may be one or more refs to dead workers on this same node (w/ different ID's),
+    // remove them.
+    workers.filter { w =>
+      (w.host == host && w.port == port) && (w.state == WorkerState.DEAD)
+    }.foreach { w =>
+      workers -= w
+    }
     val worker = new WorkerInfo(id, host, port, cores, memory, sender, webUiPort, publicAddress)
     workers += worker
     idToWorker(worker.id) = worker
     actorToWorker(sender) = worker
     addressToWorker(sender.path.address) = worker
-    return worker
+    worker
   }
 
   def removeWorker(worker: WorkerInfo) {
@@ -257,7 +274,8 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
     addressToWorker -= worker.actor.path.address
     for (exec <- worker.executors.values) {
       logInfo("Telling app of lost executor: " + exec.id)
-      exec.application.driver ! ExecutorUpdated(exec.id, ExecutorState.LOST, Some("worker lost"), None)
+      exec.application.driver ! ExecutorUpdated(
+        exec.id, ExecutorState.LOST, Some("worker lost"), None)
       exec.application.removeExecutor(exec)
     }
   }
@@ -266,6 +284,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
     val now = System.currentTimeMillis()
     val date = new Date(now)
     val app = new ApplicationInfo(now, newApplicationId(date), desc, date, driver, desc.appUiUrl)
+    applicationMetricsSystem.registerSource(app.appSource)
     apps += app
     idToApp(app.id) = app
     actorToApp(driver) = app
@@ -277,7 +296,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
     if (workersAlive.size > 0 && !workersAlive.exists(_.memoryFree >= desc.memoryPerSlave)) {
       logWarning("Could not find any workers with enough memory for " + firstApp.get.id)
     }
-    return app
+    app
   }
 
   def finishApplication(app: ApplicationInfo) {
@@ -291,7 +310,14 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
       idToApp -= app.id
       actorToApp -= app.driver
       addressToApp -= app.driver.path.address
-      completedApps += app   // Remember it in our history
+      if (completedApps.size >= RETAINED_APPLICATIONS) {
+        val toRemove = math.max(RETAINED_APPLICATIONS / 10, 1)
+        completedApps.take(toRemove).foreach( a => {
+          applicationMetricsSystem.removeSource(a.appSource)
+        })
+        completedApps.trimStart(toRemove)
+      }
+      completedApps += app // Remember it in our history
       waitingApps -= app
       for (exec <- app.executors.values) {
         exec.worker.removeExecutor(exec)
@@ -316,12 +342,17 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act
   /** Check for, and remove, any timed-out workers */
   def timeOutDeadWorkers() {
     // Copy the workers into an array so we don't modify the hashset while iterating through it
-    val expirationTime = System.currentTimeMillis() - WORKER_TIMEOUT
-    val toRemove = workers.filter(_.lastHeartbeat < expirationTime).toArray
+    val currentTime = System.currentTimeMillis()
+    val toRemove = workers.filter(_.lastHeartbeat < currentTime - WORKER_TIMEOUT).toArray
     for (worker <- toRemove) {
-      logWarning("Removing %s because we got no heartbeat in %d seconds".format(
-        worker.id, WORKER_TIMEOUT))
-      removeWorker(worker)
+      if (worker.state != WorkerState.DEAD) {
+        logWarning("Removing %s because we got no heartbeat in %d seconds".format(
+          worker.id, WORKER_TIMEOUT/1000))
+        removeWorker(worker)
+      } else {
+        if (worker.lastHeartbeat < currentTime - ((REAPER_ITERATIONS + 1) * WORKER_TIMEOUT))
+          workers -= worker // we've seen this DEAD worker in the UI, etc. for long enough; cull it 
+      }
     }
   }
 }
diff --git a/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala
index 32264af39341c0408913235faf7240b085ec5059..494a9b914d753ffe2d814f4fc2dea3aa673bcf1e 100644
--- a/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala
+++ b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala
@@ -17,28 +17,28 @@
 
 package spark.deploy.master.ui
 
+import scala.util.parsing.json.JSONType
+import scala.xml.Node
+
 import akka.dispatch.Await
 import akka.pattern.ask
 import akka.util.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.DeployMessages.{MasterStateResponse, RequestMasterState}
+import spark.deploy.JsonProtocol
 import spark.deploy.master.ExecutorInfo
 import spark.ui.UIUtils
 
 private[spark] class ApplicationPage(parent: MasterWebUI) {
-  val master = parent.master
+  val master = parent.masterActorRef
   implicit val timeout = parent.timeout
 
   /** Executor details for a particular application */
-  def renderJson(request: HttpServletRequest): JValue = {
+  def renderJson(request: HttpServletRequest): JSONType = {
     val appId = request.getParameter("appId")
-    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState]
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
     val state = Await.result(stateFuture, 30 seconds)
     val app = state.activeApps.find(_.id == appId).getOrElse({
       state.completedApps.find(_.id == appId).getOrElse(null)
@@ -49,7 +49,7 @@ private[spark] class ApplicationPage(parent: MasterWebUI) {
   /** 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 stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
     val state = Await.result(stateFuture, 30 seconds)
     val app = state.activeApps.find(_.id == appId).getOrElse({
       state.completedApps.find(_.id == appId).getOrElse(null)
@@ -89,8 +89,7 @@ private[spark] class ApplicationPage(parent: MasterWebUI) {
 
         <div class="row"> <!-- Executors -->
           <div class="span12">
-            <h3> Executor Summary </h3>
-            <br/>
+            <h4> Executor Summary </h4>
             {executorTable}
           </div>
         </div>;
diff --git a/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala
index b05197c1b97b58bf40fd6714c39901218bdc98ee..28e421e3bc1c60278bfb7cc9f56050a346257a39 100644
--- a/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala
+++ b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala
@@ -17,33 +17,42 @@
 
 package spark.deploy.master.ui
 
-import akka.dispatch.Await
-import akka.pattern.ask
-import akka.util.duration._
-
 import javax.servlet.http.HttpServletRequest
 
+import scala.util.parsing.json.JSONType
 import scala.xml.Node
 
-import spark.deploy.{RequestMasterState, DeployWebUI, MasterState}
+import akka.dispatch.Await
+import akka.pattern.ask
+import akka.util.duration._
+
 import spark.Utils
-import spark.ui.UIUtils
+import spark.deploy.DeployWebUI
+import spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState}
+import spark.deploy.JsonProtocol
 import spark.deploy.master.{ApplicationInfo, WorkerInfo}
+import spark.ui.UIUtils
 
 private[spark] class IndexPage(parent: MasterWebUI) {
-  val master = parent.master
+  val master = parent.masterActorRef
   implicit val timeout = parent.timeout
 
+  def renderJson(request: HttpServletRequest): JSONType = {
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
+    val state = Await.result(stateFuture, 30 seconds)
+    JsonProtocol.writeMasterState(state)
+  }
+
   /** Index view listing applications and executors */
   def render(request: HttpServletRequest): Seq[Node] = {
-    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState]
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
     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",
+    val appHeaders = Seq("ID", "Name", "Cores", "Memory per Node", "Submitted Time", "User",
       "State", "Duration")
     val activeApps = state.activeApps.sortBy(_.startTime).reverse
     val activeAppsTable = UIUtils.listingTable(appHeaders, appRow, activeApps)
@@ -60,8 +69,8 @@ private[spark] class IndexPage(parent: MasterWebUI) {
               <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>
+                {Utils.megabytesToString(state.workers.map(_.memory).sum)} Total,
+                {Utils.megabytesToString(state.workers.map(_.memoryUsed).sum)} Used</li>
               <li><strong>Applications:</strong>
                 {state.activeApps.size} Running,
                 {state.completedApps.size} Completed </li>
@@ -71,8 +80,7 @@ private[spark] class IndexPage(parent: MasterWebUI) {
 
         <div class="row">
           <div class="span12">
-            <h3> Workers </h3>
-            <br/>
+            <h4> Workers </h4>
             {workerTable}
           </div>
         </div>
@@ -81,8 +89,8 @@ private[spark] class IndexPage(parent: MasterWebUI) {
 
         <div class="row">
           <div class="span12">
-            <h3> Running Applications </h3>
-            <br/>
+            <h4> Running Applications </h4>
+
             {activeAppsTable}
           </div>
         </div>
@@ -91,8 +99,7 @@ private[spark] class IndexPage(parent: MasterWebUI) {
 
         <div class="row">
           <div class="span12">
-            <h3> Completed Applications </h3>
-            <br/>
+            <h4> Completed Applications </h4>
             {completedAppsTable}
           </div>
         </div>;
@@ -108,8 +115,8 @@ private[spark] class IndexPage(parent: MasterWebUI) {
       <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)
+        {Utils.megabytesToString(worker.memory)}
+        ({Utils.megabytesToString(worker.memoryUsed)} Used)
       </td>
     </tr>
   }
@@ -127,7 +134,7 @@ private[spark] class IndexPage(parent: MasterWebUI) {
         {app.coresGranted}
       </td>
       <td sorttable_customkey={app.desc.memoryPerSlave.toString}>
-        {Utils.memoryMegabytesToString(app.desc.memoryPerSlave)}
+        {Utils.megabytesToString(app.desc.memoryPerSlave)}
       </td>
       <td>{DeployWebUI.formatDate(app.submitDate)}</td>
       <td>{app.desc.user}</td>
diff --git a/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala
index dabc2d8dc765378331c386da649a8fcbfde0d4c5..c91e1db9f24e67ecc59dbea91a7458d8d3a8c4a0 100644
--- a/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala
+++ b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala
@@ -17,7 +17,6 @@
 
 package spark.deploy.master.ui
 
-import akka.actor.ActorRef
 import akka.util.Duration
 
 import javax.servlet.http.HttpServletRequest
@@ -25,6 +24,7 @@ import javax.servlet.http.HttpServletRequest
 import org.eclipse.jetty.server.{Handler, Server}
 
 import spark.{Logging, Utils}
+import spark.deploy.master.Master
 import spark.ui.JettyUtils
 import spark.ui.JettyUtils._
 
@@ -32,12 +32,14 @@ import spark.ui.JettyUtils._
  * Web UI server for the standalone master.
  */
 private[spark]
-class MasterWebUI(val master: ActorRef, requestedPort: Int) extends Logging {
+class MasterWebUI(val master: Master, requestedPort: Int) extends Logging {
   implicit val timeout = Duration.create(
     System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")
   val host = Utils.localHostName()
   val port = requestedPort
 
+  val masterActorRef = master.self
+
   var server: Option[Server] = None
   var boundPort: Option[Int] = None
 
@@ -57,10 +59,14 @@ class MasterWebUI(val master: ActorRef, requestedPort: Int) extends Logging {
     }
   }
 
-  val handlers = Array[(String, Handler)](
+  val metricsHandlers = master.masterMetricsSystem.getServletHandlers ++
+    master.applicationMetricsSystem.getServletHandlers
+
+  val handlers = metricsHandlers ++ Array[(String, Handler)](
     ("/static", createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR)),
     ("/app/json", (request: HttpServletRequest) => applicationPage.renderJson(request)),
     ("/app", (request: HttpServletRequest) => applicationPage.render(request)),
+    ("/json", (request: HttpServletRequest) => indexPage.renderJson(request)),
     ("*", (request: HttpServletRequest) => indexPage.render(request))
   )
 
diff --git a/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala
index 47d3390928bb148160537c85779e4938b9e60dc5..5e53d95ac2f364dd01e601200e4d361b4a0596f2 100644
--- a/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala
@@ -19,14 +19,15 @@ package spark.deploy.worker
 
 import java.io._
 import java.lang.System.getenv
-import spark.deploy.{ExecutorState, ExecutorStateChanged, ApplicationDescription}
+
 import akka.actor.ActorRef
+
+import com.google.common.base.Charsets
+import com.google.common.io.Files
+
 import spark.{Utils, Logging}
-import java.net.{URI, URL}
-import org.apache.hadoop.fs.{Path, FileSystem}
-import org.apache.hadoop.conf.Configuration
-import scala.Some
-import spark.deploy.ExecutorStateChanged
+import spark.deploy.{ExecutorState, ApplicationDescription}
+import spark.deploy.DeployMessages.ExecutorStateChanged
 
 /**
  * Manages the execution of one executor process.
@@ -39,13 +40,11 @@ private[spark] class ExecutorRunner(
     val memory: Int,
     val worker: ActorRef,
     val workerId: String,
-    val hostPort: String,
+    val host: String,
     val sparkHome: File,
     val workDir: File)
   extends Logging {
 
-  Utils.checkHostPort(hostPort, "Expected hostport")
-
   val fullId = appId + "/" + execId
   var workerThread: Thread = null
   var process: Process = null
@@ -91,7 +90,7 @@ private[spark] class ExecutorRunner(
   /** Replace variables such as {{EXECUTOR_ID}} and {{CORES}} in a command argument passed to us */
   def substituteVariables(argument: String): String = argument match {
     case "{{EXECUTOR_ID}}" => execId.toString
-    case "{{HOSTNAME}}" => Utils.parseHostPort(hostPort)._1
+    case "{{HOSTNAME}}" => host
     case "{{CORES}}" => cores.toString
     case other => other
   }
@@ -113,6 +112,7 @@ private[spark] class ExecutorRunner(
     val libraryOpts = getAppEnv("SPARK_LIBRARY_PATH")
       .map(p => List("-Djava.library.path=" + p))
       .getOrElse(Nil)
+    val workerLocalOpts = Option(getenv("SPARK_JAVA_OPTS")).map(Utils.splitCommandString).getOrElse(Nil)
     val userOpts = getAppEnv("SPARK_JAVA_OPTS").map(Utils.splitCommandString).getOrElse(Nil)
     val memoryOpts = Seq("-Xms" + memory + "M", "-Xmx" + memory + "M")
 
@@ -122,12 +122,12 @@ private[spark] class ExecutorRunner(
         Seq(sparkHome + "/bin/compute-classpath" + ext),
         extraEnvironment=appDesc.command.environment)
 
-    Seq("-cp", classPath) ++ libraryOpts ++ userOpts ++ memoryOpts
+    Seq("-cp", classPath) ++ libraryOpts ++ workerLocalOpts ++ userOpts ++ memoryOpts
   }
 
   /** Spawn a thread that will redirect a given stream to a file */
   def redirectStream(in: InputStream, file: File) {
-    val out = new FileOutputStream(file)
+    val out = new FileOutputStream(file, true)
     new Thread("redirect output to " + file) {
       override def run() {
         try {
@@ -163,9 +163,16 @@ private[spark] class ExecutorRunner(
       env.put("SPARK_LAUNCH_WITH_SCALA", "0")
       process = builder.start()
 
+      val header = "Spark Executor Command: %s\n%s\n\n".format(
+        command.mkString("\"", "\" \"", "\""), "=" * 40)
+
       // Redirect its stdout and stderr to files
-      redirectStream(process.getInputStream, new File(executorDir, "stdout"))
-      redirectStream(process.getErrorStream, new File(executorDir, "stderr"))
+      val stdout = new File(executorDir, "stdout")
+      redirectStream(process.getInputStream, stdout)
+
+      val stderr = new File(executorDir, "stderr")
+      Files.write(header, stderr, Charsets.UTF_8)
+      redirectStream(process.getErrorStream, stderr)
 
       // Wait for it to exit; this is actually a bad thing if it happens, because we expect to run
       // long-lived processes only. However, in the future, we might restart the executor a few
diff --git a/core/src/main/scala/spark/deploy/worker/Worker.scala b/core/src/main/scala/spark/deploy/worker/Worker.scala
index 8fa0d12b828aa35b48505ae7c6856e8116b1e978..053ac5522607b0d9bd57df2d4501163adae0806c 100644
--- a/core/src/main/scala/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/spark/deploy/worker/Worker.scala
@@ -17,22 +17,24 @@
 
 package spark.deploy.worker
 
-import scala.collection.mutable.{ArrayBuffer, HashMap}
+import java.text.SimpleDateFormat
+import java.util.Date
+import java.io.File
+
+import scala.collection.mutable.HashMap
+
 import akka.actor.{ActorRef, Props, Actor, ActorSystem, Terminated}
+import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected}
 import akka.util.duration._
+
 import spark.{Logging, Utils}
-import spark.util.AkkaUtils
-import spark.deploy._
-import spark.metrics.MetricsSystem
-import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected}
-import java.text.SimpleDateFormat
-import java.util.Date
-import spark.deploy.RegisterWorker
-import spark.deploy.LaunchExecutor
-import spark.deploy.RegisterWorkerFailed
+import spark.deploy.ExecutorState
+import spark.deploy.DeployMessages._
 import spark.deploy.master.Master
-import java.io.File
-import ui.WorkerWebUI
+import spark.deploy.worker.ui.WorkerWebUI
+import spark.metrics.MetricsSystem
+import spark.util.AkkaUtils
+
 
 private[spark] class Worker(
     host: String,
@@ -94,11 +96,12 @@ private[spark] class Worker(
 
   override def preStart() {
     logInfo("Starting Spark worker %s:%d with %d cores, %s RAM".format(
-      host, port, cores, Utils.memoryMegabytesToString(memory)))
+      host, port, cores, Utils.megabytesToString(memory)))
     sparkHome = new File(Option(System.getenv("SPARK_HOME")).getOrElse("."))
     logInfo("Spark home: " + sparkHome)
     createWorkDir()
     webUi = new WorkerWebUI(this, workDir, Some(webUiPort))
+
     webUi.start()
     connectToMaster()
 
@@ -129,7 +132,7 @@ private[spark] class Worker(
     case LaunchExecutor(appId, execId, appDesc, cores_, memory_, execSparkHome_) =>
       logInfo("Asked to launch executor %s/%d for %s".format(appId, execId, appDesc.name))
       val manager = new ExecutorRunner(
-        appId, execId, appDesc, cores_, memory_, self, workerId, host + ":" + port, new File(execSparkHome_), workDir)
+        appId, execId, appDesc, cores_, memory_, self, workerId, host, new File(execSparkHome_), workDir)
       executors(appId + "/" + execId) = manager
       manager.start()
       coresUsed += cores_
@@ -164,7 +167,7 @@ private[spark] class Worker(
       masterDisconnected()
 
     case RequestWorkerState => {
-      sender ! WorkerState(host, port, workerId, executors.values.toList,
+      sender ! WorkerStateResponse(host, port, workerId, executors.values.toList,
         finishedExecutors.values.toList, masterUrl, cores, memory,
         coresUsed, memoryUsed, masterWebUiUrl)
     }
diff --git a/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala
index 7548a26c2ef1aa3b5bb58d553ae66aef61dae309..02993d58a0ab1d4af240f79954de70acff0c41b3 100644
--- a/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala
+++ b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala
@@ -17,34 +17,35 @@
 
 package spark.deploy.worker.ui
 
-import akka.dispatch.Await
-import akka.pattern.ask
-import akka.util.duration._
-
 import javax.servlet.http.HttpServletRequest
 
-import net.liftweb.json.JsonAST.JValue
-
+import scala.util.parsing.json.JSONType
 import scala.xml.Node
 
-import spark.deploy.{RequestWorkerState, JsonProtocol, WorkerState}
-import spark.deploy.worker.ExecutorRunner
+import akka.dispatch.Await
+import akka.pattern.ask
+import akka.util.duration._
+
 import spark.Utils
+import spark.deploy.JsonProtocol
+import spark.deploy.DeployMessages.{RequestWorkerState, WorkerStateResponse}
+import spark.deploy.worker.ExecutorRunner
 import spark.ui.UIUtils
 
+
 private[spark] class IndexPage(parent: WorkerWebUI) {
   val workerActor = parent.worker.self
   val worker = parent.worker
   val timeout = parent.timeout
 
-  def renderJson(request: HttpServletRequest): JValue = {
-    val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerState]
+  def renderJson(request: HttpServletRequest): JSONType = {
+    val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse]
     val workerState = Await.result(stateFuture, 30 seconds)
     JsonProtocol.writeWorkerState(workerState)
   }
 
   def render(request: HttpServletRequest): Seq[Node] = {
-    val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerState]
+    val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse]
     val workerState = Await.result(stateFuture, 30 seconds)
 
     val executorHeaders = Seq("ExecutorID", "Cores", "Memory", "Job Details", "Logs")
@@ -63,18 +64,17 @@ private[spark] class IndexPage(parent: WorkerWebUI) {
                 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>
+              <li><strong>Memory:</strong> {Utils.megabytesToString(workerState.memory)}
+                ({Utils.megabytesToString(workerState.memoryUsed)} Used)</li>
             </ul>
             <p><a href={workerState.masterWebUiUrl}>Back to Master</a></p>
           </div>
         </div>
-          <hr/>
+        <hr/>
 
         <div class="row"> <!-- Running Executors -->
           <div class="span12">
-            <h3> Running Executors {workerState.executors.size} </h3>
-            <br/>
+            <h4> Running Executors {workerState.executors.size} </h4>
             {runningExecutorTable}
           </div>
         </div>
@@ -82,13 +82,13 @@ private[spark] class IndexPage(parent: WorkerWebUI) {
 
         <div class="row"> <!-- Finished Executors  -->
           <div class="span12">
-            <h3> Finished Executors </h3>
-            <br/>
+            <h4> Finished Executors </h4>
             {finishedExecutorTable}
           </div>
         </div>;
 
-    UIUtils.basicSparkPage(content, "Spark Worker on %s:%s".format(workerState.host, workerState.port))
+    UIUtils.basicSparkPage(content, "Spark Worker on %s:%s".format(
+      workerState.host, workerState.port))
   }
 
   def executorRow(executor: ExecutorRunner): Seq[Node] = {
@@ -96,7 +96,7 @@ private[spark] class IndexPage(parent: WorkerWebUI) {
       <td>{executor.execId}</td>
       <td>{executor.cores}</td>
       <td sorttable_customkey={executor.memory.toString}>
-        {Utils.memoryMegabytesToString(executor.memory)}
+        {Utils.megabytesToString(executor.memory)}
       </td>
       <td>
         <ul class="unstyled">
diff --git a/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala
index 61d4cd6d99fda7c1101004c34028b5d918658d03..717619f80d14dc029e0d3d61459d173b90d0f7e5 100644
--- a/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala
+++ b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala
@@ -17,7 +17,6 @@
 
 package spark.deploy.worker.ui
 
-import akka.actor.ActorRef
 import akka.util.{Duration, Timeout}
 
 import java.io.{FileInputStream, File}
@@ -37,7 +36,7 @@ import spark.ui.UIUtils
  */
 private[spark]
 class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[Int] = None)
-    extends Logging {
+  extends Logging {
   implicit val timeout = Timeout(
     Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds"))
   val host = Utils.localHostName()
@@ -49,7 +48,9 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I
 
   val indexPage = new IndexPage(this)
 
-  val handlers = Array[(String, Handler)](
+  val metricsHandlers = worker.metricsSystem.getServletHandlers
+
+  val handlers = metricsHandlers ++ Array[(String, Handler)](
     ("/static", createStaticHandler(WorkerWebUI.STATIC_RESOURCE_DIR)),
     ("/log", (request: HttpServletRequest) => log(request)),
     ("/logPage", (request: HttpServletRequest) => logPage(request)),
@@ -111,23 +112,31 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I
       if (startByte > 0) {
         <a href={"?appId=%s&executorId=%s&logType=%s&offset=%s&byteLength=%s"
           .format(appId, executorId, logType, math.max(startByte-byteLength, 0),
-            byteLength)}>
-          <button>Previous {Utils.memoryBytesToString(math.min(byteLength, startByte))}</button>
+          byteLength)}>
+          <button type="button" class="btn btn-default">
+            Previous {Utils.bytesToString(math.min(byteLength, startByte))}
+          </button>
         </a>
       }
       else {
-        <button disabled="disabled">Previous 0 B</button>
+        <button type="button" class="btn btn-default" disabled="disabled">
+          Previous 0 B
+        </button>
       }
 
     val nextButton =
       if (endByte < logLength) {
         <a href={"?appId=%s&executorId=%s&logType=%s&offset=%s&byteLength=%s".
           format(appId, executorId, logType, endByte, byteLength)}>
-          <button>Next {Utils.memoryBytesToString(math.min(byteLength, logLength-endByte))}</button>
+          <button type="button" class="btn btn-default">
+            Next {Utils.bytesToString(math.min(byteLength, logLength-endByte))}
+          </button>
         </a>
       }
       else {
-        <button disabled="disabled">Next 0 B</button>
+        <button type="button" class="btn btn-default" disabled="disabled">
+          Next 0 B
+        </button>
       }
 
     val content =
diff --git a/core/src/main/scala/spark/executor/Executor.scala b/core/src/main/scala/spark/executor/Executor.scala
index 8a74a8d853afd8abec3cc314c8415abdcc30a113..036c7191adcffd1383c05c184105ff50b15f662f 100644
--- a/core/src/main/scala/spark/executor/Executor.scala
+++ b/core/src/main/scala/spark/executor/Executor.scala
@@ -17,24 +17,27 @@
 
 package spark.executor
 
-import java.io.{File, FileOutputStream}
-import java.net.{URI, URL, URLClassLoader}
+import java.io.{File}
+import java.lang.management.ManagementFactory
+import java.nio.ByteBuffer
 import java.util.concurrent._
 
-import org.apache.hadoop.fs.FileUtil
-
-import scala.collection.mutable.{ArrayBuffer, Map, HashMap}
+import scala.collection.JavaConversions._
+import scala.collection.mutable.HashMap
 
-import spark.broadcast._
 import spark.scheduler._
 import spark._
-import java.nio.ByteBuffer
+
 
 /**
  * The Mesos executor for Spark.
  */
-private[spark] class Executor(executorId: String, slaveHostname: String, properties: Seq[(String, String)]) extends Logging {
-
+private[spark] class Executor(
+    executorId: String,
+    slaveHostname: String,
+    properties: Seq[(String, String)])
+  extends Logging
+{
   // Application dependencies (added through SparkContext) that we've fetched so far on this node.
   // Each map holds the master's timestamp for the version of that file or JAR we got.
   private val currentFiles: HashMap[String, Long] = new HashMap[String, Long]()
@@ -116,6 +119,9 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert
       context.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
       var attemptedTask: Option[Task[Any]] = None
       var taskStart: Long = 0
+      def getTotalGCTime = ManagementFactory.getGarbageCollectorMXBeans.map(g => g.getCollectionTime).sum
+      val startGCTime = getTotalGCTime
+
       try {
         SparkEnv.set(env)
         Accumulators.clear()
@@ -123,15 +129,16 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert
         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)
+        logInfo("Its epoch is " + task.epoch)
+        env.mapOutputTracker.updateEpoch(task.epoch)
         taskStart = System.currentTimeMillis()
         val value = task.run(taskId.toInt)
         val taskFinish = System.currentTimeMillis()
-        task.metrics.foreach{ m =>
+        for (m <- task.metrics) {
           m.hostname = Utils.localHostName
           m.executorDeserializeTime = (taskStart - startTime).toInt
           m.executorRunTime = (taskFinish - taskStart).toInt
+          m.jvmGCTime = getTotalGCTime - startGCTime
         }
         //TODO I'd also like to track the time it takes to serialize the task results, but that is huge headache, b/c
         // we need to serialize the task metrics first.  If TaskMetrics had a custom serialized format, we could
@@ -155,7 +162,10 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert
         case t: Throwable => {
           val serviceTime = (System.currentTimeMillis() - taskStart).toInt
           val metrics = attemptedTask.flatMap(t => t.metrics)
-          metrics.foreach{m => m.executorRunTime = serviceTime}
+          for (m <- metrics) {
+            m.executorRunTime = serviceTime
+            m.jvmGCTime = getTotalGCTime - startGCTime
+          }
           val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics)
           context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))
 
diff --git a/core/src/main/scala/spark/executor/ExecutorSource.scala b/core/src/main/scala/spark/executor/ExecutorSource.scala
index 94116edfcf8841e978ce00e8ac6c13a1e0562c81..d491a3c0c9599140f27f9fb1508e5453520ac77d 100644
--- a/core/src/main/scala/spark/executor/ExecutorSource.scala
+++ b/core/src/main/scala/spark/executor/ExecutorSource.scala
@@ -2,9 +2,25 @@ package spark.executor
 
 import com.codahale.metrics.{Gauge, MetricRegistry}
 
+import org.apache.hadoop.fs.FileSystem
+import org.apache.hadoop.hdfs.DistributedFileSystem
+import org.apache.hadoop.fs.LocalFileSystem
+
+import scala.collection.JavaConversions._
+
 import spark.metrics.source.Source
 
 class ExecutorSource(val executor: Executor) extends Source {
+  private def fileStats(scheme: String) : Option[FileSystem.Statistics] =
+    FileSystem.getAllStatistics().filter(s => s.getScheme.equals(scheme)).headOption
+
+  private def registerFileSystemStat[T](
+        scheme: String, name: String, f: FileSystem.Statistics => T, defaultValue: T) = {
+    metricRegistry.register(MetricRegistry.name("filesystem", scheme, name), new Gauge[T] {
+      override def getValue: T = fileStats(scheme).map(f).getOrElse(defaultValue)
+    })
+  }
+
   val metricRegistry = new MetricRegistry()
   val sourceName = "executor"
 
@@ -27,4 +43,13 @@ class ExecutorSource(val executor: Executor) extends Source {
   metricRegistry.register(MetricRegistry.name("threadpool", "maxPool", "size"), new Gauge[Int] {
     override def getValue: Int = executor.threadPool.getMaximumPoolSize()
   })
+
+  // Gauge for file system stats of this executor
+  for (scheme <- Array("hdfs", "file")) {
+    registerFileSystemStat(scheme, "bytesRead", _.getBytesRead(), 0L)
+    registerFileSystemStat(scheme, "bytesWritten", _.getBytesWritten(), 0L)
+    registerFileSystemStat(scheme, "readOps", _.getReadOps(), 0)
+    registerFileSystemStat(scheme, "largeReadOps", _.getLargeReadOps(), 0)
+    registerFileSystemStat(scheme, "writeOps", _.getWriteOps(), 0)
+  }
 }
diff --git a/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala b/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala
index f4003da73298d07123572b12c74bea8696c44d3b..b5fb6dbe29ec0da147e7354276722bc0ac287c0e 100644
--- a/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala
+++ b/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala
@@ -18,19 +18,15 @@
 package spark.executor
 
 import java.nio.ByteBuffer
-import spark.Logging
-import spark.TaskState.TaskState
-import spark.util.AkkaUtils
+
 import akka.actor.{ActorRef, Actor, Props, Terminated}
 import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected}
-import java.util.concurrent.{TimeUnit, ThreadPoolExecutor, SynchronousQueue}
-import spark.scheduler.cluster._
-import spark.scheduler.cluster.RegisteredExecutor
-import spark.scheduler.cluster.LaunchTask
-import spark.scheduler.cluster.RegisterExecutorFailed
-import spark.scheduler.cluster.RegisterExecutor
-import spark.Utils
-import spark.deploy.SparkHadoopUtil
+
+import spark.{Logging, Utils, SparkEnv}
+import spark.TaskState.TaskState
+import spark.scheduler.cluster.StandaloneClusterMessages._
+import spark.util.AkkaUtils
+
 
 private[spark] class StandaloneExecutorBackend(
     driverUrl: String,
@@ -85,19 +81,6 @@ private[spark] class StandaloneExecutorBackend(
 
 private[spark] object StandaloneExecutorBackend {
   def run(driverUrl: String, executorId: String, hostname: String, cores: Int) {
-    SparkHadoopUtil.runAsUser(run0, Tuple4[Any, Any, Any, Any] (driverUrl, executorId, hostname, cores))
-  }
-
-  // This will be run 'as' the user
-  def run0(args: Product) {
-    assert(4 == args.productArity)
-    runImpl(args.productElement(0).asInstanceOf[String], 
-      args.productElement(1).asInstanceOf[String],
-      args.productElement(2).asInstanceOf[String],
-      args.productElement(3).asInstanceOf[Int])
-  }
-  
-  private def runImpl(driverUrl: String, executorId: String, hostname: String, cores: Int) {
     // Debug code
     Utils.checkHost(hostname)
 
diff --git a/core/src/main/scala/spark/executor/TaskMetrics.scala b/core/src/main/scala/spark/executor/TaskMetrics.scala
index 315162783981f8e2d18e61f86f90c529c4923492..47b8890bee96dc1ef0a2d1fdb7c6e1418e20960f 100644
--- a/core/src/main/scala/spark/executor/TaskMetrics.scala
+++ b/core/src/main/scala/spark/executor/TaskMetrics.scala
@@ -31,13 +31,18 @@ class TaskMetrics extends Serializable {
   /**
    * Time the executor spends actually running the task (including fetching shuffle data)
    */
-  var executorRunTime:Int = _
+  var executorRunTime: Int = _
 
   /**
    * The number of bytes this task transmitted back to the driver as the TaskResult
    */
   var resultSize: Long = _
 
+  /**
+   * Amount of time the JVM spent in garbage collection while executing this task
+   */
+  var jvmGCTime: Long = _
+
   /**
    * If this task reads from shuffle output, metrics on getting shuffle data will be collected here
    */
diff --git a/core/src/main/scala/spark/io/CompressionCodec.scala b/core/src/main/scala/spark/io/CompressionCodec.scala
new file mode 100644
index 0000000000000000000000000000000000000000..0adebecadb3e5bde51799ea6816ae77e8e51b987
--- /dev/null
+++ b/core/src/main/scala/spark/io/CompressionCodec.scala
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.io
+
+import java.io.{InputStream, OutputStream}
+
+import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream}
+
+import org.xerial.snappy.{SnappyInputStream, SnappyOutputStream}
+
+
+/**
+ * CompressionCodec allows the customization of choosing different compression implementations
+ * to be used in block storage.
+ */
+trait CompressionCodec {
+
+  def compressedOutputStream(s: OutputStream): OutputStream
+
+  def compressedInputStream(s: InputStream): InputStream
+}
+
+
+private[spark] object CompressionCodec {
+
+  def createCodec(): CompressionCodec = {
+    // Set the default codec to Snappy since the LZF implementation initializes a pretty large
+    // buffer for every stream, which results in a lot of memory overhead when the number of
+    // shuffle reduce buckets are large.
+    createCodec(classOf[SnappyCompressionCodec].getName)
+  }
+
+  def createCodec(codecName: String): CompressionCodec = {
+    Class.forName(
+      System.getProperty("spark.io.compression.codec", codecName),
+      true,
+      Thread.currentThread.getContextClassLoader).newInstance().asInstanceOf[CompressionCodec]
+  }
+}
+
+
+/**
+ * LZF implementation of [[spark.io.CompressionCodec]].
+ */
+class LZFCompressionCodec extends CompressionCodec {
+
+  override def compressedOutputStream(s: OutputStream): OutputStream = {
+    new LZFOutputStream(s).setFinishBlockOnFlush(true)
+  }
+
+  override def compressedInputStream(s: InputStream): InputStream = new LZFInputStream(s)
+}
+
+
+/**
+ * Snappy implementation of [[spark.io.CompressionCodec]].
+ * Block size can be configured by spark.io.compression.snappy.block.size.
+ */
+class SnappyCompressionCodec extends CompressionCodec {
+
+  override def compressedOutputStream(s: OutputStream): OutputStream = {
+    val blockSize = System.getProperty("spark.io.compression.snappy.block.size", "32768").toInt
+    new SnappyOutputStream(s, blockSize)
+  }
+
+  override def compressedInputStream(s: InputStream): InputStream = new SnappyInputStream(s)
+}
diff --git a/core/src/main/scala/spark/metrics/MetricsConfig.scala b/core/src/main/scala/spark/metrics/MetricsConfig.scala
index ed505b0aa73de2e7a7574553360f58f8607e36a2..d7fb5378a495aa1488970a035f3a9653c75321f6 100644
--- a/core/src/main/scala/spark/metrics/MetricsConfig.scala
+++ b/core/src/main/scala/spark/metrics/MetricsConfig.scala
@@ -1,3 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package spark.metrics
 
 import java.util.Properties
@@ -19,7 +36,11 @@ private[spark] class MetricsConfig(val configFile: Option[String]) extends Loggi
   var propertyCategories: mutable.HashMap[String, Properties] = null
 
   private def setDefaultProperties(prop: Properties) {
-    // empty function, any default property can be set here
+    prop.setProperty("*.sink.servlet.class", "spark.metrics.sink.MetricsServlet")
+    prop.setProperty("*.sink.servlet.uri", "/metrics/json")
+    prop.setProperty("*.sink.servlet.sample", "false")
+    prop.setProperty("master.sink.servlet.uri", "/metrics/master/json")
+    prop.setProperty("applications.sink.servlet.uri", "/metrics/applications/json")
   }
 
   def initialize() {
diff --git a/core/src/main/scala/spark/metrics/MetricsSystem.scala b/core/src/main/scala/spark/metrics/MetricsSystem.scala
index 2f87577ff335f56f4c12f508cc4701bb1db60fa6..4e6c6b26c86be236819f3f73945dc0cbf97f5ac6 100644
--- a/core/src/main/scala/spark/metrics/MetricsSystem.scala
+++ b/core/src/main/scala/spark/metrics/MetricsSystem.scala
@@ -1,6 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package spark.metrics
 
-import com.codahale.metrics.{JmxReporter, MetricSet, MetricRegistry}
+import com.codahale.metrics.{Metric, MetricFilter, MetricRegistry}
 
 import java.util.Properties
 import java.util.concurrent.TimeUnit
@@ -8,7 +25,7 @@ import java.util.concurrent.TimeUnit
 import scala.collection.mutable
 
 import spark.Logging
-import spark.metrics.sink.Sink
+import spark.metrics.sink.{MetricsServlet, Sink}
 import spark.metrics.source.Source
 
 /**
@@ -18,7 +35,7 @@ import spark.metrics.source.Source
  * "instance" specify "who" (the role) use metrics system. In spark there are several roles
  * like master, worker, executor, client driver, these roles will create metrics system
  * for monitoring. So instance represents these roles. Currently in Spark, several instances
- * have already implemented: master, worker, executor, driver.
+ * have already implemented: master, worker, executor, driver, applications.
  *
  * "source" specify "where" (source) to collect metrics data. In metrics system, there exists
  * two kinds of source:
@@ -34,8 +51,8 @@ import spark.metrics.source.Source
  * Metrics configuration format is like below:
  * [instance].[sink|source].[name].[options] = xxxx
  *
- * [instance] can be "master", "worker", "executor", "driver", which means only the specified
- * instance has this property.
+ * [instance] can be "master", "worker", "executor", "driver", "applications" which means only
+ * the specified instance has this property.
  * wild card "*" can be used to replace instance name, which means all the instances will have
  * this property.
  *
@@ -55,6 +72,12 @@ private[spark] class MetricsSystem private (val instance: String) extends Loggin
   val sources = new mutable.ArrayBuffer[Source]
   val registry = new MetricRegistry()
 
+  // Treat MetricsServlet as a special sink as it should be exposed to add handlers to web ui
+  private var metricsServlet: Option[MetricsServlet] = None
+
+  /** Get any UI handlers used by this metrics system. */
+  def getServletHandlers = metricsServlet.map(_.getHandlers).getOrElse(Array())
+
   metricsConfig.initialize()
   registerSources()
   registerSinks()
@@ -76,6 +99,13 @@ private[spark] class MetricsSystem private (val instance: String) extends Loggin
     }
   }
 
+  def removeSource(source: Source) {
+    sources -= source
+    registry.removeMatching(new MetricFilter {
+      def matches(name: String, metric: Metric): Boolean = name.startsWith(source.sourceName)
+    })
+  }
+
   def registerSources() {
     val instConfig = metricsConfig.getInstance(instance)
     val sourceConfigs = metricsConfig.subProperties(instConfig, MetricsSystem.SOURCE_REGEX)
@@ -102,7 +132,11 @@ private[spark] class MetricsSystem private (val instance: String) extends Loggin
         val sink = Class.forName(classPath)
           .getConstructor(classOf[Properties], classOf[MetricRegistry])
           .newInstance(kv._2, registry)
-        sinks += sink.asInstanceOf[Sink]
+        if (kv._1 == "servlet") {
+           metricsServlet = Some(sink.asInstanceOf[MetricsServlet])
+        } else {
+          sinks += sink.asInstanceOf[Sink]
+        }
       } catch {
         case e: Exception => logError("Sink class " + classPath + " cannot be instantialized", e)
       }
diff --git a/core/src/main/scala/spark/metrics/sink/ConsoleSink.scala b/core/src/main/scala/spark/metrics/sink/ConsoleSink.scala
index eaaac5d153a2e305377481b3fb94352821968ac8..966ba37c20e8f618e77350cb2be8b024f0b12366 100644
--- a/core/src/main/scala/spark/metrics/sink/ConsoleSink.scala
+++ b/core/src/main/scala/spark/metrics/sink/ConsoleSink.scala
@@ -1,3 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package spark.metrics.sink
 
 import com.codahale.metrics.{ConsoleReporter, MetricRegistry}
diff --git a/core/src/main/scala/spark/metrics/sink/CsvSink.scala b/core/src/main/scala/spark/metrics/sink/CsvSink.scala
index aa5bff0d34d0da22dbaed3ad7832c3fe5e990adb..cb990afdefc4c4b3261bcd8386d19baa57221269 100644
--- a/core/src/main/scala/spark/metrics/sink/CsvSink.scala
+++ b/core/src/main/scala/spark/metrics/sink/CsvSink.scala
@@ -1,3 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package spark.metrics.sink
 
 import com.codahale.metrics.{CsvReporter, MetricRegistry}
diff --git a/core/src/main/scala/spark/metrics/sink/JmxSink.scala b/core/src/main/scala/spark/metrics/sink/JmxSink.scala
index 6a40885b7818cfd5335755f34fe714fcf286379c..ee04544c0e9ed8fcf9eade3347d30a937b0c93b4 100644
--- a/core/src/main/scala/spark/metrics/sink/JmxSink.scala
+++ b/core/src/main/scala/spark/metrics/sink/JmxSink.scala
@@ -1,3 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package spark.metrics.sink
 
 import com.codahale.metrics.{JmxReporter, MetricRegistry}
diff --git a/core/src/main/scala/spark/metrics/sink/MetricsServlet.scala b/core/src/main/scala/spark/metrics/sink/MetricsServlet.scala
new file mode 100644
index 0000000000000000000000000000000000000000..17432b1ed1a56e6c7d58351ad33e406357374067
--- /dev/null
+++ b/core/src/main/scala/spark/metrics/sink/MetricsServlet.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.metrics.sink
+
+import com.codahale.metrics.MetricRegistry
+import com.codahale.metrics.json.MetricsModule
+
+import com.fasterxml.jackson.databind.ObjectMapper
+
+import java.util.Properties
+import java.util.concurrent.TimeUnit
+import javax.servlet.http.HttpServletRequest
+
+import org.eclipse.jetty.server.Handler
+
+import spark.ui.JettyUtils
+
+class MetricsServlet(val property: Properties, val registry: MetricRegistry) extends Sink {
+  val SERVLET_KEY_URI = "uri"
+  val SERVLET_KEY_SAMPLE = "sample"
+
+  val servletURI = property.getProperty(SERVLET_KEY_URI)
+
+  val servletShowSample = property.getProperty(SERVLET_KEY_SAMPLE).toBoolean
+
+  val mapper = new ObjectMapper().registerModule(
+    new MetricsModule(TimeUnit.SECONDS, TimeUnit.MILLISECONDS, servletShowSample))
+
+  def getHandlers = Array[(String, Handler)](
+    (servletURI, JettyUtils.createHandler(request => getMetricsSnapshot(request), "text/json"))
+  )
+
+  def getMetricsSnapshot(request: HttpServletRequest): String = {
+    mapper.writeValueAsString(registry)
+  }
+
+  override def start() { }
+
+  override def stop() { }
+}
diff --git a/core/src/main/scala/spark/metrics/sink/Sink.scala b/core/src/main/scala/spark/metrics/sink/Sink.scala
index 3ffdcbdaba41459f7b46ac8c49e48646a6130cfd..dad1a7f0fe7022e1e021cecf020df1628c13649e 100644
--- a/core/src/main/scala/spark/metrics/sink/Sink.scala
+++ b/core/src/main/scala/spark/metrics/sink/Sink.scala
@@ -1,3 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package spark.metrics.sink
 
 trait Sink {
diff --git a/core/src/main/scala/spark/metrics/source/JvmSource.scala b/core/src/main/scala/spark/metrics/source/JvmSource.scala
index 79f505079c8bab7a7d8437ee2da47348530db2c9..e7710085577b5cd586de3306a979c1e5b65d3fff 100644
--- a/core/src/main/scala/spark/metrics/source/JvmSource.scala
+++ b/core/src/main/scala/spark/metrics/source/JvmSource.scala
@@ -1,3 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package spark.metrics.source
 
 import com.codahale.metrics.MetricRegistry
diff --git a/core/src/main/scala/spark/metrics/source/Source.scala b/core/src/main/scala/spark/metrics/source/Source.scala
index 5607e2c40a719f47d81098480308815be9da6245..76199a004b560e708aa5d4cb0dd5bda79b90b51e 100644
--- a/core/src/main/scala/spark/metrics/source/Source.scala
+++ b/core/src/main/scala/spark/metrics/source/Source.scala
@@ -1,3 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package spark.metrics.source
 
 import com.codahale.metrics.MetricRegistry
diff --git a/core/src/main/scala/spark/network/Connection.scala b/core/src/main/scala/spark/network/Connection.scala
index b66c00b58c7f15846ec20e98da3f0c1845476cba..1e571d39ae5763e756bc49509c1dfbaeb104030e 100644
--- a/core/src/main/scala/spark/network/Connection.scala
+++ b/core/src/main/scala/spark/network/Connection.scala
@@ -45,12 +45,15 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
   channel.socket.setKeepAlive(true)
   /*channel.socket.setReceiveBufferSize(32768) */
 
+  @volatile private var closed = false
   var onCloseCallback: Connection => Unit = null
   var onExceptionCallback: (Connection, Exception) => Unit = null
   var onKeyInterestChangeCallback: (Connection, Int) => Unit = null
 
   val remoteAddress = getRemoteAddress()
 
+  def resetForceReregister(): Boolean
+
   // Read channels typically do not register for write and write does not for read
   // Now, we do have write registering for read too (temporarily), but this is to detect
   // channel close NOT to actually read/consume data on it !
@@ -95,6 +98,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
   }
 
   def close() {
+    closed = true
     val k = key()
     if (k != null) {
       k.cancel()
@@ -103,6 +107,8 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
     callOnCloseCallback()
   }
 
+  protected def isClosed: Boolean = closed
+
   def onClose(callback: Connection => Unit) {
     onCloseCallback = callback
   }
@@ -168,7 +174,7 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
     remoteId_ : ConnectionManagerId)
   extends Connection(SocketChannel.open, selector_, remoteId_) {
 
-  class Outbox(fair: Int = 0) {
+  private class Outbox(fair: Int = 0) {
     val messages = new Queue[Message]()
     val defaultChunkSize = 65536  //32768 //16384
     var nextMessageToBeUsed = 0
@@ -245,7 +251,17 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
     }
   }
 
+  // outbox is used as a lock - ensure that it is always used as a leaf (since methods which 
+  // lock it are invoked in context of other locks)
   private val outbox = new Outbox(1)
+  /*
+    This is orthogonal to whether we have pending bytes to write or not - and satisfies a slightly 
+    different purpose. This flag is to see if we need to force reregister for write even when we 
+    do not have any pending bytes to write to socket.
+    This can happen due to a race between adding pending buffers, and checking for existing of 
+    data as detailed in https://github.com/mesos/spark/pull/791
+   */
+  private var needForceReregister = false
   val currentBuffers = new ArrayBuffer[ByteBuffer]()
 
   /*channel.socket.setSendBufferSize(256 * 1024)*/
@@ -267,9 +283,19 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
   def send(message: Message) {
     outbox.synchronized {
       outbox.addMessage(message)
-      if (channel.isConnected) {
-        registerInterest()
-      }
+      needForceReregister = true
+    }
+    if (channel.isConnected) {
+      registerInterest()
+    }
+  }
+
+  // return previous value after resetting it.
+  def resetForceReregister(): Boolean = {
+    outbox.synchronized {
+      val result = needForceReregister
+      needForceReregister = false
+      result
     }
   }
 
@@ -322,7 +348,11 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
           outbox.synchronized {
             outbox.getChunk() match {
               case Some(chunk) => {
-                currentBuffers ++= chunk.buffers
+                val buffers = chunk.buffers
+                // If we have 'seen' pending messages, then reset flag - since we handle that as normal 
+                // registering of event (below)
+                if (needForceReregister && buffers.exists(_.remaining() > 0)) resetForceReregister()
+                currentBuffers ++= buffers
               }
               case None => {
                 // changeConnectionKeyInterest(0)
@@ -384,7 +414,7 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
 
   override def changeInterestForRead(): Boolean = false
 
-  override def changeInterestForWrite(): Boolean = true
+  override def changeInterestForWrite(): Boolean = ! isClosed
 }
 
 
@@ -534,6 +564,7 @@ private[spark] class ReceivingConnection(channel_ : SocketChannel, selector_ : S
 
   def onReceive(callback: (Connection, Message) => Unit) {onReceiveCallback = callback}
 
+  // override def changeInterestForRead(): Boolean = ! isClosed
   override def changeInterestForRead(): Boolean = true
 
   override def changeInterestForWrite(): Boolean = {
@@ -549,4 +580,7 @@ private[spark] class ReceivingConnection(channel_ : SocketChannel, selector_ : S
   override def unregisterInterest() {
     changeConnectionKeyInterest(0)
   }
+
+  // For read conn, always false.
+  override def resetForceReregister(): Boolean = false
 }
diff --git a/core/src/main/scala/spark/network/ConnectionManager.scala b/core/src/main/scala/spark/network/ConnectionManager.scala
index 6c4e7dc03eb446b0c288e747e704677689b86137..8b9f3ae18c5c90a395f33e800d2861dc29ba31be 100644
--- a/core/src/main/scala/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/spark/network/ConnectionManager.scala
@@ -123,7 +123,8 @@ private[spark] class ConnectionManager(port: Int) extends Logging {
         } finally {
           writeRunnableStarted.synchronized {
             writeRunnableStarted -= key
-            if (register && conn.changeInterestForWrite()) {
+            val needReregister = register || conn.resetForceReregister()
+            if (needReregister && conn.changeInterestForWrite()) {
               conn.registerInterest()
             }
           }
diff --git a/core/src/main/scala/spark/rdd/BlockRDD.scala b/core/src/main/scala/spark/rdd/BlockRDD.scala
index 0ebb722d73618dd47ae1a80c3db67e1112a50094..03800584ae0a012edea90042904d86380fd6f020 100644
--- a/core/src/main/scala/spark/rdd/BlockRDD.scala
+++ b/core/src/main/scala/spark/rdd/BlockRDD.scala
@@ -28,13 +28,12 @@ private[spark]
 class BlockRDD[T: ClassManifest](sc: SparkContext, @transient blockIds: Array[String])
   extends RDD[T](sc, Nil) {
 
-  @transient lazy val locations_ = BlockManager.blockIdsToExecutorLocations(blockIds, SparkEnv.get)
+  @transient lazy val locations_ = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get)
 
   override def getPartitions: Array[Partition] = (0 until blockIds.size).map(i => {
     new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition]
   }).toArray
 
-
   override def compute(split: Partition, context: TaskContext): Iterator[T] = {
     val blockManager = SparkEnv.get.blockManager
     val blockId = split.asInstanceOf[BlockRDDPartition].blockId
@@ -45,8 +44,8 @@ class BlockRDD[T: ClassManifest](sc: SparkContext, @transient blockIds: Array[St
     }
   }
 
-  override def getPreferredLocations(split: Partition): Seq[String] =
+  override def getPreferredLocations(split: Partition): Seq[String] = {
     locations_(split.asInstanceOf[BlockRDDPartition].blockId)
-
+  }
 }
 
diff --git a/core/src/main/scala/spark/rdd/CartesianRDD.scala b/core/src/main/scala/spark/rdd/CartesianRDD.scala
index 150e5bca29d4ffc4ebcea95c5f98082808153837..91b3e69d6fb3e759916c917be1d4e8b004466b53 100644
--- a/core/src/main/scala/spark/rdd/CartesianRDD.scala
+++ b/core/src/main/scala/spark/rdd/CartesianRDD.scala
@@ -64,7 +64,7 @@ class CartesianRDD[T: ClassManifest, U:ClassManifest](
 
   override def getPreferredLocations(split: Partition): Seq[String] = {
     val currSplit = split.asInstanceOf[CartesianPartition]
-    rdd1.preferredLocations(currSplit.s1) ++ rdd2.preferredLocations(currSplit.s2)
+    (rdd1.preferredLocations(currSplit.s1) ++ rdd2.preferredLocations(currSplit.s2)).distinct
   }
 
   override def compute(split: Partition, context: TaskContext) = {
diff --git a/core/src/main/scala/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/spark/rdd/CheckpointRDD.scala
index 6794e0e2018f85b1c7c56b27e0eab5d8e48be211..1ad5fe6539017fb0e2a9a1dd7b598758da03faf3 100644
--- a/core/src/main/scala/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/spark/rdd/CheckpointRDD.scala
@@ -25,7 +25,6 @@ import org.apache.hadoop.util.ReflectionUtils
 import org.apache.hadoop.fs.Path
 import java.io.{File, IOException, EOFException}
 import java.text.NumberFormat
-import spark.deploy.SparkHadoopUtil
 
 private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {}
 
@@ -82,8 +81,9 @@ private[spark] object CheckpointRDD extends Logging {
   }
 
   def writeToFile[T](path: String, blockSize: Int = -1)(ctx: TaskContext, iterator: Iterator[T]) {
+    val env = SparkEnv.get
     val outputDir = new Path(path)
-    val fs = outputDir.getFileSystem(SparkHadoopUtil.newConfiguration())
+    val fs = outputDir.getFileSystem(env.hadoop.newConfiguration())
 
     val finalOutputName = splitIdToFile(ctx.splitId)
     val finalOutputPath = new Path(outputDir, finalOutputName)
@@ -101,7 +101,7 @@ private[spark] object CheckpointRDD extends Logging {
       // This is mainly for testing purpose
       fs.create(tempOutputPath, false, bufferSize, fs.getDefaultReplication, blockSize)
     }
-    val serializer = SparkEnv.get.serializer.newInstance()
+    val serializer = env.serializer.newInstance()
     val serializeStream = serializer.serializeStream(fileOutputStream)
     serializeStream.writeAll(iterator)
     serializeStream.close()
@@ -121,10 +121,11 @@ private[spark] object CheckpointRDD extends Logging {
   }
 
   def readFromFile[T](path: Path, context: TaskContext): Iterator[T] = {
-    val fs = path.getFileSystem(SparkHadoopUtil.newConfiguration())
+    val env = SparkEnv.get
+    val fs = path.getFileSystem(env.hadoop.newConfiguration())
     val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt
     val fileInputStream = fs.open(path, bufferSize)
-    val serializer = SparkEnv.get.serializer.newInstance()
+    val serializer = env.serializer.newInstance()
     val deserializeStream = serializer.deserializeStream(fileInputStream)
 
     // Register an on-task-completion callback to close the input stream.
@@ -140,10 +141,11 @@ private[spark] object CheckpointRDD extends Logging {
     import spark._
 
     val Array(cluster, hdfsPath) = args
+    val env = SparkEnv.get
     val sc = new SparkContext(cluster, "CheckpointRDD Test")
     val rdd = sc.makeRDD(1 to 10, 10).flatMap(x => 1 to 10000)
     val path = new Path(hdfsPath, "temp")
-    val fs = path.getFileSystem(SparkHadoopUtil.newConfiguration())
+    val fs = path.getFileSystem(env.hadoop.newConfiguration())
     sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, 1024) _)
     val cpRDD = new CheckpointRDD[Int](sc, path.toString)
     assert(cpRDD.partitions.length == rdd.partitions.length, "Number of partitions is not the same")
diff --git a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
index c540cd36eb994d46aae3a448601fecb5630e723a..01b6c23dcc202a5b701dbbf34d97aa529a0eaeec 100644
--- a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
@@ -23,7 +23,7 @@ import java.util.{HashMap => JHashMap}
 import scala.collection.JavaConversions
 import scala.collection.mutable.ArrayBuffer
 
-import spark.{Aggregator, Partition, Partitioner, RDD, SparkEnv, TaskContext}
+import spark.{Partition, Partitioner, RDD, SparkEnv, TaskContext}
 import spark.{Dependency, OneToOneDependency, ShuffleDependency}
 
 
@@ -52,13 +52,6 @@ class CoGroupPartition(idx: Int, val deps: Array[CoGroupSplitDep])
   override def hashCode(): Int = idx
 }
 
-private[spark] class CoGroupAggregator
-  extends Aggregator[Any, Any, ArrayBuffer[Any]](
-    { x => ArrayBuffer(x) },
-    { (b, x) => b += x },
-    { (b1, b2) => b1 ++ b2 })
-  with Serializable
-
 
 /**
  * A RDD that cogroups its parents. For each key k in parent RDDs, the resulting RDD contains a
@@ -66,34 +59,25 @@ private[spark] class CoGroupAggregator
  *
  * @param rdds parent RDDs.
  * @param part partitioner used to partition the shuffle output.
- * @param mapSideCombine flag indicating whether to merge values before shuffle step. If the flag
- *                       is on, Spark does an extra pass over the data on the map side to merge
- *                       all values belonging to the same key together. This can reduce the amount
- *                       of data shuffled if and only if the number of distinct keys is very small,
- *                       and the ratio of key size to value size is also very small.
  */
-class CoGroupedRDD[K](
-  @transient var rdds: Seq[RDD[(K, _)]],
-  part: Partitioner,
-  val mapSideCombine: Boolean = false,
-  val serializerClass: String = null)
+class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: Partitioner)
   extends RDD[(K, Seq[Seq[_]])](rdds.head.context, Nil) {
 
-  private val aggr = new CoGroupAggregator
+  private var serializerClass: String = null
+
+  def setSerializer(cls: String): CoGroupedRDD[K] = {
+    serializerClass = cls
+    this
+  }
 
   override def getDependencies: Seq[Dependency[_]] = {
-    rdds.map { rdd =>
+    rdds.map { rdd: RDD[_ <: Product2[K, _]] =>
       if (rdd.partitioner == Some(part)) {
-        logInfo("Adding one-to-one dependency with " + rdd)
+        logDebug("Adding one-to-one dependency with " + rdd)
         new OneToOneDependency(rdd)
       } else {
-        logInfo("Adding shuffle dependency with " + rdd)
-        if (mapSideCombine) {
-          val mapSideCombinedRDD = rdd.mapPartitions(aggr.combineValuesByKey(_), true)
-          new ShuffleDependency[Any, ArrayBuffer[Any]](mapSideCombinedRDD, part, serializerClass)
-        } else {
-          new ShuffleDependency[Any, Any](rdd.asInstanceOf[RDD[(Any, Any)]], part, serializerClass)
-        }
+        logDebug("Adding shuffle dependency with " + rdd)
+        new ShuffleDependency[Any, Any](rdd, part, serializerClass)
       }
     }
   }
@@ -138,23 +122,15 @@ class CoGroupedRDD[K](
     for ((dep, depNum) <- split.deps.zipWithIndex) dep match {
       case NarrowCoGroupSplitDep(rdd, _, itsSplit) => {
         // Read them from the parent
-        for ((k, v) <- rdd.iterator(itsSplit, context)) {
-          getSeq(k.asInstanceOf[K])(depNum) += v
+        rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, Any]]].foreach { kv =>
+          getSeq(kv._1)(depNum) += kv._2
         }
       }
       case ShuffleCoGroupSplitDep(shuffleId) => {
         // Read map outputs of shuffle
         val fetcher = SparkEnv.get.shuffleFetcher
-        if (mapSideCombine) {
-          // With map side combine on, for each key, the shuffle fetcher returns a list of values.
-          fetcher.fetch[K, Seq[Any]](shuffleId, split.index, context.taskMetrics, ser).foreach {
-            case (key, values) => getSeq(key)(depNum) ++= values
-          }
-        } else {
-          // With map side combine off, for each key the shuffle fetcher returns a single value.
-          fetcher.fetch[K, Any](shuffleId, split.index, context.taskMetrics, ser).foreach {
-            case (key, value) => getSeq(key)(depNum) += value
-          }
+        fetcher.fetch[Product2[K, Any]](shuffleId, split.index, context.taskMetrics, ser).foreach {
+          kv => getSeq(kv._1)(depNum) += kv._2
         }
       }
     }
diff --git a/core/src/main/scala/spark/rdd/FlatMappedValuesRDD.scala b/core/src/main/scala/spark/rdd/FlatMappedValuesRDD.scala
new file mode 100644
index 0000000000000000000000000000000000000000..a6bdce89d8a4ce317107e31da8d11ba8b14cfb2d
--- /dev/null
+++ b/core/src/main/scala/spark/rdd/FlatMappedValuesRDD.scala
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.rdd
+
+import spark.{TaskContext, Partition, RDD}
+
+
+private[spark]
+class FlatMappedValuesRDD[K, V, U](prev: RDD[_ <: Product2[K, V]], f: V => TraversableOnce[U])
+  extends RDD[(K, U)](prev) {
+
+  override def getPartitions = firstParent[Product2[K, V]].partitions
+
+  override val partitioner = firstParent[Product2[K, V]].partitioner
+
+  override def compute(split: Partition, context: TaskContext) = {
+    firstParent[Product2[K, V]].iterator(split, context).flatMap { case Product2(k, v) =>
+      f(v).map(x => (k, x))
+    }
+  }
+}
diff --git a/core/src/main/scala/spark/rdd/HadoopRDD.scala b/core/src/main/scala/spark/rdd/HadoopRDD.scala
index d0fdeb741e4e5041bd5eca49e60ff777609d327e..6c41b9778095ed934433b3bebdb120a90a32508f 100644
--- a/core/src/main/scala/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/spark/rdd/HadoopRDD.scala
@@ -32,8 +32,7 @@ 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.{Dependency, Logging, Partition, RDD, SerializableWritable, SparkContext, SparkEnv, TaskContext}
 import spark.util.NextIterator
 import org.apache.hadoop.conf.Configurable
 
@@ -68,7 +67,8 @@ class HadoopRDD[K, V](
   private val confBroadcast = sc.broadcast(new SerializableWritable(conf))
 
   override def getPartitions: Array[Partition] = {
-    SparkHadoopUtil.addCredentials(conf);
+    val env = SparkEnv.get
+    env.hadoop.addCredentials(conf)
     val inputFormat = createInputFormat(conf)
     if (inputFormat.isInstanceOf[Configurable]) {
       inputFormat.asInstanceOf[Configurable].setConf(conf)
@@ -88,6 +88,7 @@ class HadoopRDD[K, V](
 
   override def compute(theSplit: Partition, context: TaskContext) = new NextIterator[(K, V)] {
     val split = theSplit.asInstanceOf[HadoopPartition]
+    logInfo("Input split: " + split.inputSplit)
     var reader: RecordReader[K, V] = null
 
     val conf = confBroadcast.value.value
diff --git a/core/src/hadoop1/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala b/core/src/main/scala/spark/rdd/MappedValuesRDD.scala
similarity index 61%
rename from core/src/hadoop1/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala
rename to core/src/main/scala/spark/rdd/MappedValuesRDD.scala
index b1002e0cace8ce93f1d87a2a9dbad0457b373c4d..8334e3b557dd22f8deb597b8e11c775a0d4dee63 100644
--- a/core/src/hadoop1/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala
+++ b/core/src/main/scala/spark/rdd/MappedValuesRDD.scala
@@ -15,15 +15,20 @@
  * limitations under the License.
  */
 
-package org.apache.hadoop.mapreduce
+package spark.rdd
 
-import org.apache.hadoop.conf.Configuration
 
-trait HadoopMapReduceUtil {
-  def newJobContext(conf: Configuration, jobId: JobID): JobContext = new JobContext(conf, jobId)
+import spark.{TaskContext, Partition, RDD}
 
-  def newTaskAttemptContext(conf: Configuration, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContext(conf, attemptId)
+private[spark]
+class MappedValuesRDD[K, V, U](prev: RDD[_ <: Product2[K, V]], f: V => U)
+  extends RDD[(K, U)](prev) {
 
-  def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = new TaskAttemptID(jtIdentifier,
-    jobId, isMap, taskId, attemptId)
+  override def getPartitions = firstParent[Product2[K, U]].partitions
+
+  override val partitioner = firstParent[Product2[K, U]].partitioner
+
+  override def compute(split: Partition, context: TaskContext): Iterator[(K, U)] = {
+    firstParent[Product2[K, V]].iterator(split, context).map { case Product2(k ,v) => (k, f(v)) }
+  }
 }
diff --git a/core/src/main/scala/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/spark/rdd/NewHadoopRDD.scala
index 17fe805fd435857efdbe0c7a329ab104ab3d150c..184685528e34af5a81a6a391591271c3d895238b 100644
--- a/core/src/main/scala/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/spark/rdd/NewHadoopRDD.scala
@@ -43,7 +43,7 @@ class NewHadoopRDD[K, V](
     valueClass: Class[V],
     @transient conf: Configuration)
   extends RDD[(K, V)](sc, Nil)
-  with HadoopMapReduceUtil
+  with SparkHadoopMapReduceUtil
   with Logging {
 
   // A Hadoop Configuration can be about 10 KB, which is pretty big, so broadcast it
@@ -73,6 +73,7 @@ class NewHadoopRDD[K, V](
 
   override def compute(theSplit: Partition, context: TaskContext) = new Iterator[(K, V)] {
     val split = theSplit.asInstanceOf[NewHadoopPartition]
+    logInfo("Input split: " + split.serializableHadoopSplit)
     val conf = confBroadcast.value.value
     val attemptId = newTaskAttemptID(jobtrackerId, id, true, split.index, 0)
     val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId)
diff --git a/core/src/main/scala/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/spark/rdd/OrderedRDDFunctions.scala
new file mode 100644
index 0000000000000000000000000000000000000000..9154b760359aa1836d214219f216a136dfc25b09
--- /dev/null
+++ b/core/src/main/scala/spark/rdd/OrderedRDDFunctions.scala
@@ -0,0 +1,51 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.rdd
+
+import spark.{RangePartitioner, Logging, RDD}
+
+/**
+ * Extra functions available on RDDs of (key, value) pairs where the key is sortable through
+ * an implicit conversion. Import `spark.SparkContext._` at the top of your program to use these
+ * functions. They will work with any key type that has a `scala.math.Ordered` implementation.
+ */
+class OrderedRDDFunctions[K <% Ordered[K]: ClassManifest,
+                          V: ClassManifest,
+                          P <: Product2[K, V] : ClassManifest](
+    self: RDD[P])
+  extends Logging with Serializable {
+
+  /**
+   * Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
+   * `collect` or `save` on the resulting RDD will return or output an ordered list of records
+   * (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
+   * order of the keys).
+   */
+  def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size): RDD[P] = {
+    val part = new RangePartitioner(numPartitions, self, ascending)
+    val shuffled = new ShuffledRDD[K, V, P](self, part)
+    shuffled.mapPartitions(iter => {
+      val buf = iter.toArray
+      if (ascending) {
+        buf.sortWith((x, y) => x._1 < y._1).iterator
+      } else {
+        buf.sortWith((x, y) => x._1 > y._1).iterator
+      }
+    }, preservesPartitioning = true)
+  }
+}
diff --git a/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala
index 16ba0c26f8ee10414d5843b7ecbec520765ec0c0..33079cd53937d99062c2fd6ddc0e04a68476a007 100644
--- a/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala
+++ b/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala
@@ -20,13 +20,15 @@ package spark.rdd
 import scala.collection.immutable.NumericRange
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.Map
-import spark.{RDD, TaskContext, SparkContext, Partition}
+import spark._
+import java.io._
+import scala.Serializable
 
 private[spark] class ParallelCollectionPartition[T: ClassManifest](
-    val rddId: Long,
-    val slice: Int,
-    values: Seq[T])
-  extends Partition with Serializable {
+    var rddId: Long,
+    var slice: Int,
+    var values: Seq[T])
+    extends Partition with Serializable {
 
   def iterator: Iterator[T] = values.iterator
 
@@ -37,15 +39,49 @@ private[spark] class ParallelCollectionPartition[T: ClassManifest](
     case _ => false
   }
 
-  override val index: Int = slice
+  override def index: Int = slice
+
+  @throws(classOf[IOException])
+  private def writeObject(out: ObjectOutputStream): Unit = {
+
+    val sfactory = SparkEnv.get.serializer
+
+    // Treat java serializer with default action rather than going thru serialization, to avoid a
+    // separate serialization header.
+
+    sfactory match {
+      case js: JavaSerializer => out.defaultWriteObject()
+      case _ =>
+        out.writeLong(rddId)
+        out.writeInt(slice)
+
+        val ser = sfactory.newInstance()
+        Utils.serializeViaNestedStream(out, ser)(_.writeObject(values))
+    }
+  }
+
+  @throws(classOf[IOException])
+  private def readObject(in: ObjectInputStream): Unit = {
+
+    val sfactory = SparkEnv.get.serializer
+    sfactory match {
+      case js: JavaSerializer => in.defaultReadObject()
+      case _ =>
+        rddId = in.readLong()
+        slice = in.readInt()
+
+        val ser = sfactory.newInstance()
+        Utils.deserializeViaNestedStream(in, ser)(ds => values = ds.readObject())
+    }
+  }
 }
 
 private[spark] class ParallelCollectionRDD[T: ClassManifest](
     @transient sc: SparkContext,
     @transient data: Seq[T],
     numSlices: Int,
-    locationPrefs: Map[Int,Seq[String]])
-  extends RDD[T](sc, Nil) {
+    locationPrefs: Map[Int, Seq[String]])
+    extends RDD[T](sc, Nil) {
   // TODO: Right now, each split sends along its full data, even if later down the RDD chain it gets
   // cached. It might be worthwhile to write the data to a file in the DFS and read it in the split
   // instead.
@@ -82,16 +118,17 @@ private object ParallelCollectionRDD {
           1
         }
         slice(new Range(
-            r.start, r.end + sign, r.step).asInstanceOf[Seq[T]], numSlices)
+          r.start, r.end + sign, r.step).asInstanceOf[Seq[T]], numSlices)
       }
       case r: Range => {
         (0 until numSlices).map(i => {
           val start = ((i * r.length.toLong) / numSlices).toInt
-          val end = (((i+1) * r.length.toLong) / numSlices).toInt
+          val end = (((i + 1) * r.length.toLong) / numSlices).toInt
           new Range(r.start + start * r.step, r.start + end * r.step, r.step)
         }).asInstanceOf[Seq[Seq[T]]]
       }
-      case nr: NumericRange[_] => { // For ranges of Long, Double, BigInteger, etc
+      case nr: NumericRange[_] => {
+        // For ranges of Long, Double, BigInteger, etc
         val slices = new ArrayBuffer[Seq[T]](numSlices)
         val sliceSize = (nr.size + numSlices - 1) / numSlices // Round up to catch everything
         var r = nr
@@ -102,10 +139,10 @@ private object ParallelCollectionRDD {
         slices
       }
       case _ => {
-        val array = seq.toArray  // To prevent O(n^2) operations for List etc
+        val array = seq.toArray // To prevent O(n^2) operations for List etc
         (0 until numSlices).map(i => {
           val start = ((i * array.length.toLong) / numSlices).toInt
-          val end = (((i+1) * array.length.toLong) / numSlices).toInt
+          val end = (((i + 1) * array.length.toLong) / numSlices).toInt
           array.slice(start, end).toSeq
         })
       }
diff --git a/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala
index 191cfde5651b53457ac6be208fc7e7d7e4527b88..d8700becb0e870ff5242c77219b53c777771c210 100644
--- a/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala
+++ b/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala
@@ -33,8 +33,9 @@ class PruneDependency[T](rdd: RDD[T], @transient partitionFilterFunc: Int => Boo
   extends NarrowDependency[T](rdd) {
 
   @transient
-  val partitions: Array[Partition] = rdd.partitions.filter(s => partitionFilterFunc(s.index))
-    .zipWithIndex.map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition }
+  val partitions: Array[Partition] = rdd.partitions.zipWithIndex
+    .filter(s => partitionFilterFunc(s._2))
+    .map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition }
 
   override def getParents(partitionId: Int) = List(partitions(partitionId).index)
 }
diff --git a/core/src/main/scala/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/spark/rdd/ShuffledRDD.scala
index 0137f80953e02a14e537f34539b4f6710682c4f8..51c05af064ef7456c0020d0f78a68ebd31a9b069 100644
--- a/core/src/main/scala/spark/rdd/ShuffledRDD.scala
+++ b/core/src/main/scala/spark/rdd/ShuffledRDD.scala
@@ -17,8 +17,7 @@
 
 package spark.rdd
 
-import spark.{Partitioner, RDD, SparkEnv, ShuffleDependency, Partition, TaskContext}
-import spark.SparkContext._
+import spark.{Dependency, Partitioner, RDD, SparkEnv, ShuffleDependency, Partition, TaskContext}
 
 
 private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition {
@@ -30,15 +29,24 @@ private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition {
  * The resulting RDD from a shuffle (e.g. repartitioning of data).
  * @param prev the parent RDD.
  * @param part the partitioner used to partition the RDD
- * @param serializerClass class name of the serializer to use.
  * @tparam K the key class.
  * @tparam V the value class.
  */
-class ShuffledRDD[K, V](
-    @transient prev: RDD[(K, V)],
-    part: Partitioner,
-    serializerClass: String = null)
-  extends RDD[(K, V)](prev.context, List(new ShuffleDependency(prev, part, serializerClass))) {
+class ShuffledRDD[K, V, P <: Product2[K, V] : ClassManifest](
+    @transient var prev: RDD[P],
+    part: Partitioner)
+  extends RDD[P](prev.context, Nil) {
+
+  private var serializerClass: String = null
+
+  def setSerializer(cls: String): ShuffledRDD[K, V, P] = {
+    serializerClass = cls
+    this
+  }
+
+  override def getDependencies: Seq[Dependency[_]] = {
+    List(new ShuffleDependency(prev, part, serializerClass))
+  }
 
   override val partitioner = Some(part)
 
@@ -46,9 +54,14 @@ class ShuffledRDD[K, V](
     Array.tabulate[Partition](part.numPartitions)(i => new ShuffledRDDPartition(i))
   }
 
-  override def compute(split: Partition, context: TaskContext): Iterator[(K, V)] = {
+  override def compute(split: Partition, context: TaskContext): Iterator[P] = {
     val shuffledId = dependencies.head.asInstanceOf[ShuffleDependency[K, V]].shuffleId
-    SparkEnv.get.shuffleFetcher.fetch[K, V](shuffledId, split.index, context.taskMetrics,
+    SparkEnv.get.shuffleFetcher.fetch[P](shuffledId, split.index, context.taskMetrics,
       SparkEnv.get.serializerManager.get(serializerClass))
   }
+
+  override def clearDependencies() {
+    super.clearDependencies()
+    prev = null
+  }
 }
diff --git a/core/src/main/scala/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/spark/rdd/SubtractedRDD.scala
index 0402b9f2501d62e2fa9e49aa096ee193926519be..dadef5e17de594431ca70002438b025448248cfc 100644
--- a/core/src/main/scala/spark/rdd/SubtractedRDD.scala
+++ b/core/src/main/scala/spark/rdd/SubtractedRDD.scala
@@ -47,20 +47,26 @@ import spark.OneToOneDependency
  * out of memory because of the size of `rdd2`.
  */
 private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassManifest](
-    @transient var rdd1: RDD[(K, V)],
-    @transient var rdd2: RDD[(K, W)],
-    part: Partitioner,
-    val serializerClass: String = null)
+    @transient var rdd1: RDD[_ <: Product2[K, V]],
+    @transient var rdd2: RDD[_ <: Product2[K, W]],
+    part: Partitioner)
   extends RDD[(K, V)](rdd1.context, Nil) {
 
+  private var serializerClass: String = null
+
+  def setSerializer(cls: String): SubtractedRDD[K, V, W] = {
+    serializerClass = cls
+    this
+  }
+
   override def getDependencies: Seq[Dependency[_]] = {
     Seq(rdd1, rdd2).map { rdd =>
       if (rdd.partitioner == Some(part)) {
-        logInfo("Adding one-to-one dependency with " + rdd)
+        logDebug("Adding one-to-one dependency with " + rdd)
         new OneToOneDependency(rdd)
       } else {
-        logInfo("Adding shuffle dependency with " + rdd)
-        new ShuffleDependency(rdd.asInstanceOf[RDD[(K, Any)]], part, serializerClass)
+        logDebug("Adding shuffle dependency with " + rdd)
+        new ShuffleDependency(rdd, part, serializerClass)
       }
     }
   }
@@ -97,16 +103,14 @@ private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassM
         seq
       }
     }
-    def integrate(dep: CoGroupSplitDep, op: ((K, V)) => Unit) = dep match {
+    def integrate(dep: CoGroupSplitDep, op: Product2[K, V] => Unit) = dep match {
       case NarrowCoGroupSplitDep(rdd, _, itsSplit) => {
-        for (t <- rdd.iterator(itsSplit, context))
-          op(t.asInstanceOf[(K, V)])
+        rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, V]]].foreach(op)
       }
       case ShuffleCoGroupSplitDep(shuffleId) => {
-        val iter = SparkEnv.get.shuffleFetcher.fetch(shuffleId, partition.index,
+        val iter = SparkEnv.get.shuffleFetcher.fetch[Product2[K, V]](shuffleId, partition.index,
           context.taskMetrics, serializer)
-        for (t <- iter)
-          op(t.asInstanceOf[(K, V)])
+        iter.foreach(op)
       }
     }
     // the first dep is rdd1; add all values to the map
diff --git a/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala
index 6a4fa13ad61b8db3a30d07ba8d3ce274ac83b711..9a0831bd899e2ff48e7db6601e8fdc72ac279208 100644
--- a/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala
+++ b/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala
@@ -55,27 +55,15 @@ abstract class ZippedPartitionsBaseRDD[V: ClassManifest](
   }
 
   override def getPreferredLocations(s: Partition): Seq[String] = {
-    // Note that as number of rdd's increase and/or number of slaves in cluster increase, the computed preferredLocations below
-    // become diminishingly small : so we might need to look at alternate strategies to alleviate this.
-    // If there are no (or very small number of preferred locations), we will end up transferred the blocks to 'any' node in the
-    // cluster - paying with n/w and cache cost.
-    // Maybe pick a node which figures max amount of time ?
-    // Choose node which is hosting 'larger' of some subset of blocks ?
-    // Look at rack locality to ensure chosen host is atleast rack local to both hosting node ?, etc (would be good to defer this if possible)
-    val splits = s.asInstanceOf[ZippedPartitionsPartition].partitions
-    val rddSplitZip = rdds.zip(splits)
-
-    // exact match.
-    val exactMatchPreferredLocations = rddSplitZip.map(x => x._1.preferredLocations(x._2))
-    val exactMatchLocations = exactMatchPreferredLocations.reduce((x, y) => x.intersect(y))
-
-    // Remove exact match and then do host local match.
-    val exactMatchHosts = exactMatchLocations.map(Utils.parseHostPort(_)._1)
-    val matchPreferredHosts = exactMatchPreferredLocations.map(locs => locs.map(Utils.parseHostPort(_)._1))
-      .reduce((x, y) => x.intersect(y))
-    val otherNodeLocalLocations = matchPreferredHosts.filter { s => !exactMatchHosts.contains(s) }
-
-    otherNodeLocalLocations ++ exactMatchLocations
+    val parts = s.asInstanceOf[ZippedPartitionsPartition].partitions
+    val prefs = rdds.zip(parts).map { case (rdd, p) => rdd.preferredLocations(p) }
+    // Check whether there are any hosts that match all RDDs; otherwise return the union
+    val exactMatchLocations = prefs.reduce((x, y) => x.intersect(y))
+    if (!exactMatchLocations.isEmpty) {
+      exactMatchLocations
+    } else {
+      prefs.flatten.distinct
+    }
   }
 
   override def clearDependencies() {
diff --git a/core/src/main/scala/spark/rdd/ZippedRDD.scala b/core/src/main/scala/spark/rdd/ZippedRDD.scala
index b1c43b3195ab63b0be8a5ae9ed423d984e198319..4074e50e44040b96b756efeb69c238b7165ff142 100644
--- a/core/src/main/scala/spark/rdd/ZippedRDD.scala
+++ b/core/src/main/scala/spark/rdd/ZippedRDD.scala
@@ -65,27 +65,16 @@ class ZippedRDD[T: ClassManifest, U: ClassManifest](
   }
 
   override def getPreferredLocations(s: Partition): Seq[String] = {
-    // Note that as number of slaves in cluster increase, the computed preferredLocations can become small : so we might need
-    // to look at alternate strategies to alleviate this. (If there are no (or very small number of preferred locations), we
-    // will end up transferred the blocks to 'any' node in the cluster - paying with n/w and cache cost.
-    // Maybe pick one or the other ? (so that atleast one block is local ?).
-    // Choose node which is hosting 'larger' of the blocks ?
-    // Look at rack locality to ensure chosen host is atleast rack local to both hosting node ?, etc (would be good to defer this if possible)
     val (partition1, partition2) = s.asInstanceOf[ZippedPartition[T, U]].partitions
     val pref1 = rdd1.preferredLocations(partition1)
     val pref2 = rdd2.preferredLocations(partition2)
-
-    // exact match - instance local and host local.
+    // Check whether there are any hosts that match both RDDs; otherwise return the union
     val exactMatchLocations = pref1.intersect(pref2)
-
-    // remove locations which are already handled via exactMatchLocations, and intersect where both partitions are node local.
-    val otherNodeLocalPref1 = pref1.filter(loc => ! exactMatchLocations.contains(loc)).map(loc => Utils.parseHostPort(loc)._1)
-    val otherNodeLocalPref2 = pref2.filter(loc => ! exactMatchLocations.contains(loc)).map(loc => Utils.parseHostPort(loc)._1)
-    val otherNodeLocalLocations = otherNodeLocalPref1.intersect(otherNodeLocalPref2)
-
-
-    // Can have mix of instance local (hostPort) and node local (host) locations as preference !
-    exactMatchLocations ++ otherNodeLocalLocations
+    if (!exactMatchLocations.isEmpty) {
+      exactMatchLocations
+    } else {
+      (pref1 ++ pref2).distinct
+    }
   }
 
   override def clearDependencies() {
diff --git a/core/src/main/scala/spark/scheduler/ActiveJob.scala b/core/src/main/scala/spark/scheduler/ActiveJob.scala
index 71cc94edb68d50971b4b451bdc262ac7dfc6afad..fecc3e9648a4ab749857c9dd371df4d3e98a8b64 100644
--- a/core/src/main/scala/spark/scheduler/ActiveJob.scala
+++ b/core/src/main/scala/spark/scheduler/ActiveJob.scala
@@ -25,7 +25,7 @@ import java.util.Properties
  * Tracks information about an active job in the DAGScheduler.
  */
 private[spark] class ActiveJob(
-    val runId: Int,
+    val jobId: Int,
     val finalStage: Stage,
     val func: (TaskContext, Iterator[_]) => _,
     val partitions: Array[Int],
diff --git a/core/src/main/scala/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/spark/scheduler/DAGScheduler.scala
index 9b45fc29388eaba971624bd031b891abbeac2de5..9402f18a0f672e6fac72392c3f57fecb9f2a3f80 100644
--- a/core/src/main/scala/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/spark/scheduler/DAGScheduler.scala
@@ -32,10 +32,22 @@ import spark.storage.{BlockManager, BlockManagerMaster}
 import spark.util.{MetadataCleaner, TimeStampedHashMap}
 
 /**
- * A Scheduler subclass that implements stage-oriented scheduling. It computes a DAG of stages for
- * each job, keeps track of which RDDs and stage outputs are materialized, and computes a minimal
- * schedule to run the job. Subclasses only need to implement the code to send a task to the cluster
- * and to report fetch failures (the submitTasks method, and code to add CompletionEvents).
+ * The high-level scheduling layer that implements stage-oriented scheduling. It computes a DAG of
+ * stages for each job, keeps track of which RDDs and stage outputs are materialized, and finds a
+ * minimal schedule to run the job. It then submits stages as TaskSets to an underlying
+ * TaskScheduler implementation that runs them on the cluster.
+ *
+ * In addition to coming up with a DAG of stages, this class also determines the preferred
+ * locations to run each task on, based on the current cache status, and passes these to the
+ * low-level TaskScheduler. Furthermore, it handles failures due to shuffle output files being
+ * lost, in which case old stages may need to be resubmitted. Failures *within* a stage that are
+ * not caused by shuffie file loss are handled by the TaskScheduler, which will retry each task
+ * a small number of times before cancelling the whole stage.
+ *
+ * THREADING: This class runs all its logic in a single thread executing the run() method, to which
+ * events are submitted using a synchonized queue (eventQueue). The public API methods, such as
+ * runJob, taskEnded and executorLost, post events asynchronously to this queue. All other methods
+ * should be private.
  */
 private[spark]
 class DAGScheduler(
@@ -72,8 +84,8 @@ class DAGScheduler(
   }
 
   // Called by TaskScheduler when a host is added
-  override def executorGained(execId: String, hostPort: String) {
-    eventQueue.put(ExecutorGained(execId, hostPort))
+  override def executorGained(execId: String, host: String) {
+    eventQueue.put(ExecutorGained(execId, host))
   }
 
   // Called by TaskScheduler to cancel an entire TaskSet due to repeated failures.
@@ -92,27 +104,28 @@ class DAGScheduler(
 
   private val eventQueue = new LinkedBlockingQueue[DAGSchedulerEvent]
 
-  val nextRunId = new AtomicInteger(0)
+  val nextJobId = new AtomicInteger(0)
 
   val nextStageId = new AtomicInteger(0)
 
-  val idToStage = new TimeStampedHashMap[Int, Stage]
+  val stageIdToStage = new TimeStampedHashMap[Int, Stage]
 
   val shuffleToMapStage = new TimeStampedHashMap[Int, Stage]
 
   private[spark] val stageToInfos = new TimeStampedHashMap[Stage, StageInfo]
 
-  private[spark] val sparkListeners = ArrayBuffer[SparkListener]()
+  private val listenerBus = new SparkListenerBus()
 
-  var cacheLocs = new HashMap[Int, Array[List[String]]]
+  // Contains the locations that each RDD's partitions are cached on
+  private val cacheLocs = new HashMap[Int, Array[Seq[TaskLocation]]]
 
-  // For tracking failed nodes, we use the MapOutputTracker's generation number, which is
-  // sent with every task. When we detect a node failing, we note the current generation number
-  // and failed executor, increment it for new tasks, and use this to ignore stray ShuffleMapTask
-  // results.
-  // TODO: Garbage collect information about failure generations when we know there are no more
+  // For tracking failed nodes, we use the MapOutputTracker's epoch number, which is sent with
+  // every task. When we detect a node failing, we note the current epoch number and failed
+  // executor, increment it for new tasks, and use this to ignore stray ShuffleMapTask results.
+  //
+  // TODO: Garbage collect information about failure epochs when we know there are no more
   //       stray messages to detect.
-  val failedGeneration = new HashMap[String, Long]
+  val failedEpoch = new HashMap[String, Long]
 
   val idToActiveJob = new HashMap[Int, ActiveJob]
 
@@ -137,11 +150,17 @@ class DAGScheduler(
     }.start()
   }
 
-  private def getCacheLocs(rdd: RDD[_]): Array[List[String]] = {
+  def addSparkListener(listener: SparkListener) {
+    listenerBus.addListener(listener)
+  }
+
+  private def getCacheLocs(rdd: RDD[_]): Array[Seq[TaskLocation]] = {
     if (!cacheLocs.contains(rdd.id)) {
       val blockIds = rdd.partitions.indices.map(index=> "rdd_%d_%d".format(rdd.id, index)).toArray
-      val locs = BlockManager.blockIdsToExecutorLocations(blockIds, env, blockManagerMaster)
-      cacheLocs(rdd.id) = blockIds.map(locs.getOrElse(_, Nil))
+      val locs = BlockManager.blockIdsToBlockManagers(blockIds, env, blockManagerMaster)
+      cacheLocs(rdd.id) = blockIds.map { id =>
+        locs.getOrElse(id, Nil).map(bm => TaskLocation(bm.host, bm.executorId))
+      }
     }
     cacheLocs(rdd.id)
   }
@@ -152,14 +171,14 @@ class DAGScheduler(
 
   /**
    * Get or create a shuffle map stage for the given shuffle dependency's map side.
-   * The priority value passed in will be used if the stage doesn't already exist with
-   * a lower priority (we assume that priorities always increase across jobs for now).
+   * The jobId value passed in will be used if the stage doesn't already exist with
+   * a lower jobId (jobId always increases across jobs.)
    */
-  private def getShuffleMapStage(shuffleDep: ShuffleDependency[_,_], priority: Int): Stage = {
+  private def getShuffleMapStage(shuffleDep: ShuffleDependency[_,_], jobId: Int): Stage = {
     shuffleToMapStage.get(shuffleDep.shuffleId) match {
       case Some(stage) => stage
       case None =>
-        val stage = newStage(shuffleDep.rdd, Some(shuffleDep), priority)
+        val stage = newStage(shuffleDep.rdd, Some(shuffleDep), jobId)
         shuffleToMapStage(shuffleDep.shuffleId) = stage
         stage
     }
@@ -167,13 +186,13 @@ class DAGScheduler(
 
   /**
    * Create a Stage for the given RDD, either as a shuffle map stage (for a ShuffleDependency) or
-   * as a result stage for the final RDD used directly in an action. The stage will also be given
-   * the provided priority.
+   * as a result stage for the final RDD used directly in an action. The stage will also be
+   * associated with the provided jobId.
    */
   private def newStage(
       rdd: RDD[_],
       shuffleDep: Option[ShuffleDependency[_,_]],
-      priority: Int,
+      jobId: Int,
       callSite: Option[String] = None)
     : Stage =
   {
@@ -184,17 +203,17 @@ class DAGScheduler(
       mapOutputTracker.registerShuffle(shuffleDep.get.shuffleId, rdd.partitions.size)
     }
     val id = nextStageId.getAndIncrement()
-    val stage = new Stage(id, rdd, shuffleDep, getParentStages(rdd, priority), priority, callSite)
-    idToStage(id) = stage
+    val stage = new Stage(id, rdd, shuffleDep, getParentStages(rdd, jobId), jobId, callSite)
+    stageIdToStage(id) = stage
     stageToInfos(stage) = StageInfo(stage)
     stage
   }
 
   /**
    * Get or create the list of parent stages for a given RDD. The stages will be assigned the
-   * provided priority if they haven't already been created with a lower priority.
+   * provided jobId if they haven't already been created with a lower jobId.
    */
-  private def getParentStages(rdd: RDD[_], priority: Int): List[Stage] = {
+  private def getParentStages(rdd: RDD[_], jobId: Int): List[Stage] = {
     val parents = new HashSet[Stage]
     val visited = new HashSet[RDD[_]]
     def visit(r: RDD[_]) {
@@ -205,7 +224,7 @@ class DAGScheduler(
         for (dep <- r.dependencies) {
           dep match {
             case shufDep: ShuffleDependency[_,_] =>
-              parents += getShuffleMapStage(shufDep, priority)
+              parents += getShuffleMapStage(shufDep, jobId)
             case _ =>
               visit(dep.rdd)
           }
@@ -226,7 +245,7 @@ class DAGScheduler(
           for (dep <- rdd.dependencies) {
             dep match {
               case shufDep: ShuffleDependency[_,_] =>
-                val mapStage = getShuffleMapStage(shufDep, stage.priority)
+                val mapStage = getShuffleMapStage(shufDep, stage.jobId)
                 if (!mapStage.isAvailable) {
                   missing += mapStage
                 }
@@ -263,7 +282,7 @@ class DAGScheduler(
     val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
     val toSubmit = JobSubmitted(finalRdd, func2, partitions.toArray, allowLocal, callSite, waiter,
       properties)
-    return (toSubmit, waiter)
+    (toSubmit, waiter)
   }
 
   def runJob[T, U: ClassManifest](
@@ -310,8 +329,8 @@ class DAGScheduler(
     val listener = new ApproximateActionListener(rdd, func, evaluator, timeout)
     val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
     val partitions = (0 until rdd.partitions.size).toArray
-    eventQueue.put(JobSubmitted(rdd, func2, partitions, false, callSite, listener, properties))
-    return listener.awaitResult()    // Will throw an exception if the job fails
+    eventQueue.put(JobSubmitted(rdd, func2, partitions, allowLocal = false, callSite, listener, properties))
+    listener.awaitResult()    // Will throw an exception if the job fails
   }
 
   /**
@@ -321,11 +340,11 @@ class DAGScheduler(
   private[scheduler] def processEvent(event: DAGSchedulerEvent): Boolean = {
     event match {
       case JobSubmitted(finalRDD, func, partitions, allowLocal, callSite, listener, properties) =>
-        val runId = nextRunId.getAndIncrement()
-        val finalStage = newStage(finalRDD, None, runId, Some(callSite))
-        val job = new ActiveJob(runId, finalStage, func, partitions, callSite, listener, properties)
+        val jobId = nextJobId.getAndIncrement()
+        val finalStage = newStage(finalRDD, None, jobId, Some(callSite))
+        val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
         clearCacheLocs()
-        logInfo("Got job " + job.runId + " (" + callSite + ") with " + partitions.length +
+        logInfo("Got job " + job.jobId + " (" + callSite + ") with " + partitions.length +
                 " output partitions (allowLocal=" + allowLocal + ")")
         logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
         logInfo("Parents of final stage: " + finalStage.parents)
@@ -334,40 +353,40 @@ class DAGScheduler(
           // Compute very short actions like first() or take() with no parent stages locally.
           runLocally(job)
         } else {
-          sparkListeners.foreach(_.onJobStart(SparkListenerJobStart(job, properties)))
-          idToActiveJob(runId) = job
+          listenerBus.post(SparkListenerJobStart(job, properties))
+          idToActiveJob(jobId) = job
           activeJobs += job
           resultStageToJob(finalStage) = job
           submitStage(finalStage)
         }
 
-      case ExecutorGained(execId, hostPort) =>
-        handleExecutorGained(execId, hostPort)
+      case ExecutorGained(execId, host) =>
+        handleExecutorGained(execId, host)
 
       case ExecutorLost(execId) =>
         handleExecutorLost(execId)
 
       case begin: BeginEvent =>
-        sparkListeners.foreach(_.onTaskStart(SparkListenerTaskStart(begin.task, begin.taskInfo)))
+        listenerBus.post(SparkListenerTaskStart(begin.task, begin.taskInfo))
 
       case completion: CompletionEvent =>
-        sparkListeners.foreach(_.onTaskEnd(SparkListenerTaskEnd(completion.task,
-                               completion.reason, completion.taskInfo, completion.taskMetrics)))
+        listenerBus.post(SparkListenerTaskEnd(
+          completion.task, completion.reason, completion.taskInfo, completion.taskMetrics))
         handleTaskCompletion(completion)
 
       case TaskSetFailed(taskSet, reason) =>
-        abortStage(idToStage(taskSet.stageId), reason)
+        abortStage(stageIdToStage(taskSet.stageId), reason)
 
       case StopDAGScheduler =>
         // Cancel any active jobs
         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, None))))
+          listenerBus.post(SparkListenerJobEnd(job, JobFailed(error, None)))
         }
         return true
     }
-    return false
+    false
   }
 
   /**
@@ -379,7 +398,7 @@ class DAGScheduler(
     clearCacheLocs()
     val failed2 = failed.toArray
     failed.clear()
-    for (stage <- failed2.sortBy(_.priority)) {
+    for (stage <- failed2.sortBy(_.jobId)) {
       submitStage(stage)
     }
   }
@@ -397,7 +416,7 @@ class DAGScheduler(
     logTrace("failed: " + failed)
     val waiting2 = waiting.toArray
     waiting.clear()
-    for (stage <- waiting2.sortBy(_.priority)) {
+    for (stage <- waiting2.sortBy(_.jobId)) {
       submitStage(stage)
     }
   }
@@ -444,7 +463,7 @@ class DAGScheduler(
    */
   protected def runLocally(job: ActiveJob) {
     logInfo("Computing the requested partition locally")
-    new Thread("Local computation of job " + job.runId) {
+    new Thread("Local computation of job " + job.jobId) {
       override def run() {
         runLocallyWithinThread(job)
       }
@@ -504,12 +523,17 @@ class DAGScheduler(
     } else {
       // This is a final stage; figure out its job's missing partitions
       val job = resultStageToJob(stage)
-      for (id <- 0 until job.numPartitions if (!job.finished(id))) {
+      for (id <- 0 until job.numPartitions if !job.finished(id)) {
         val partition = job.partitions(id)
         val locs = getPreferredLocs(stage.rdd, partition)
         tasks += new ResultTask(stage.id, stage.rdd, job.func, partition, locs, id)
       }
     }
+    // must be run listener before possible NotSerializableException
+    // should be "StageSubmitted" first and then "JobEnded"
+    val properties = idToActiveJob(stage.jobId).properties
+    listenerBus.post(SparkListenerStageSubmitted(stage, tasks.size, properties))
+
     if (tasks.size > 0) {
       // Preemptively serialize a task to make sure it can be serialized. We are catching this
       // exception here because it would be fairly hard to catch the non-serializable exception
@@ -524,13 +548,11 @@ class DAGScheduler(
           return
       }
 
-      sparkListeners.foreach(_.onStageSubmitted(SparkListenerStageSubmitted(stage, tasks.size)))
       logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
       myPending ++= tasks
       logDebug("New pending tasks: " + myPending)
-      val properties = idToActiveJob(stage.priority).properties
       taskSched.submitTasks(
-        new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.priority, properties))
+        new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties))
       if (!stage.submissionTime.isDefined) {
         stage.submissionTime = Some(System.currentTimeMillis())
       }
@@ -547,7 +569,7 @@ class DAGScheduler(
    */
   private def handleTaskCompletion(event: CompletionEvent) {
     val task = event.task
-    val stage = idToStage(task.stageId)
+    val stage = stageIdToStage(task.stageId)
 
     def markStageAsFinished(stage: Stage) = {
       val serviceTime = stage.submissionTime match {
@@ -556,8 +578,7 @@ class DAGScheduler(
       }
       logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime))
       stage.completionTime = Some(System.currentTimeMillis)
-      val stageComp = StageCompleted(stageToInfos(stage))
-      sparkListeners.foreach{_.onStageCompleted(stageComp)}
+      listenerBus.post(StageCompleted(stageToInfos(stage)))
       running -= stage
     }
     event.reason match {
@@ -577,11 +598,11 @@ class DAGScheduler(
                   job.numFinished += 1
                   // If the whole job has finished, remove it
                   if (job.numFinished == job.numPartitions) {
-                    idToActiveJob -= stage.priority
+                    idToActiveJob -= stage.jobId
                     activeJobs -= job
                     resultStageToJob -= stage
                     markStageAsFinished(stage)
-                    sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobSucceeded)))
+                    listenerBus.post(SparkListenerJobEnd(job, JobSucceeded))
                   }
                   job.listener.taskSucceeded(rt.outputId, event.result)
                 }
@@ -593,7 +614,7 @@ class DAGScheduler(
             val status = event.result.asInstanceOf[MapStatus]
             val execId = status.location.executorId
             logDebug("ShuffleMapTask finished on " + execId)
-            if (failedGeneration.contains(execId) && smt.generation <= failedGeneration(execId)) {
+            if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) {
               logInfo("Ignoring possibly bogus ShuffleMapTask completion from " + execId)
             } else {
               stage.addOutputLoc(smt.partition, status)
@@ -605,16 +626,16 @@ class DAGScheduler(
               logInfo("waiting: " + waiting)
               logInfo("failed: " + failed)
               if (stage.shuffleDep != None) {
-                // We supply true to increment the generation number here in case this is a
+                // We supply true to increment the epoch number here in case this is a
                 // recomputation of the map outputs. In that case, some nodes may have cached
                 // locations with holes (from when we detected the error) and will need the
-                // generation incremented to refetch them.
-                // TODO: Only increment the generation number if this is not the first time
+                // epoch incremented to refetch them.
+                // TODO: Only increment the epoch number if this is not the first time
                 //       we registered these map outputs.
                 mapOutputTracker.registerMapOutputs(
                   stage.shuffleDep.get.shuffleId,
                   stage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray,
-                  true)
+                  changeEpoch = true)
               }
               clearCacheLocs()
               if (stage.outputLocs.count(_ == Nil) != 0) {
@@ -648,7 +669,7 @@ class DAGScheduler(
 
       case FetchFailed(bmAddress, shuffleId, mapId, reduceId) =>
         // Mark the stage that the reducer was in as unrunnable
-        val failedStage = idToStage(task.stageId)
+        val failedStage = stageIdToStage(task.stageId)
         running -= failedStage
         failed += failedStage
         // TODO: Cancel running tasks in the stage
@@ -668,7 +689,7 @@ class DAGScheduler(
         lastFetchFailureTime = System.currentTimeMillis() // TODO: Use pluggable clock
         // TODO: mark the executor as failed only if there were lots of fetch failures on it
         if (bmAddress != null) {
-          handleExecutorLost(bmAddress.executorId, Some(task.generation))
+          handleExecutorLost(bmAddress.executorId, Some(task.epoch))
         }
 
       case ExceptionFailure(className, description, stackTrace, metrics) =>
@@ -676,7 +697,7 @@ class DAGScheduler(
 
       case other =>
         // Unrecognized failure - abort all jobs depending on this stage
-        abortStage(idToStage(task.stageId), task + " failed: " + other)
+        abortStage(stageIdToStage(task.stageId), task + " failed: " + other)
     }
   }
 
@@ -684,36 +705,36 @@ class DAGScheduler(
    * Responds to an executor being lost. This is called inside the event loop, so it assumes it can
    * modify the scheduler's internal state. Use executorLost() to post a loss event from outside.
    *
-   * Optionally the generation during which the failure was caught can be passed to avoid allowing
+   * Optionally the epoch during which the failure was caught can be passed to avoid allowing
    * stray fetch failures from possibly retriggering the detection of a node as lost.
    */
-  private def handleExecutorLost(execId: String, maybeGeneration: Option[Long] = None) {
-    val currentGeneration = maybeGeneration.getOrElse(mapOutputTracker.getGeneration)
-    if (!failedGeneration.contains(execId) || failedGeneration(execId) < currentGeneration) {
-      failedGeneration(execId) = currentGeneration
-      logInfo("Executor lost: %s (generation %d)".format(execId, currentGeneration))
+  private def handleExecutorLost(execId: String, maybeEpoch: Option[Long] = None) {
+    val currentEpoch = maybeEpoch.getOrElse(mapOutputTracker.getEpoch)
+    if (!failedEpoch.contains(execId) || failedEpoch(execId) < currentEpoch) {
+      failedEpoch(execId) = currentEpoch
+      logInfo("Executor lost: %s (epoch %d)".format(execId, currentEpoch))
       blockManagerMaster.removeExecutor(execId)
       // TODO: This will be really slow if we keep accumulating shuffle map stages
       for ((shuffleId, stage) <- shuffleToMapStage) {
         stage.removeOutputsOnExecutor(execId)
         val locs = stage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray
-        mapOutputTracker.registerMapOutputs(shuffleId, locs, true)
+        mapOutputTracker.registerMapOutputs(shuffleId, locs, changeEpoch = true)
       }
       if (shuffleToMapStage.isEmpty) {
-        mapOutputTracker.incrementGeneration()
+        mapOutputTracker.incrementEpoch()
       }
       clearCacheLocs()
     } else {
       logDebug("Additional executor lost message for " + execId +
-               "(generation " + currentGeneration + ")")
+               "(epoch " + currentEpoch + ")")
     }
   }
 
-  private def handleExecutorGained(execId: String, hostPort: String) {
-    // remove from failedGeneration(execId) ?
-    if (failedGeneration.contains(execId)) {
-      logInfo("Host gained which was in lost list earlier: " + hostPort)
-      failedGeneration -= execId
+  private def handleExecutorGained(execId: String, host: String) {
+    // remove from failedEpoch(execId) ?
+    if (failedEpoch.contains(execId)) {
+      logInfo("Host gained which was in lost list earlier: " + host)
+      failedEpoch -= execId
     }
   }
 
@@ -728,8 +749,8 @@ class DAGScheduler(
       val job = resultStageToJob(resultStage)
       val error = new SparkException("Job failed: " + reason)
       job.listener.jobFailed(error)
-      sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage)))))
-      idToActiveJob -= resultStage.priority
+      listenerBus.post(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage))))
+      idToActiveJob -= resultStage.jobId
       activeJobs -= job
       resultStageToJob -= resultStage
     }
@@ -753,7 +774,7 @@ class DAGScheduler(
         for (dep <- rdd.dependencies) {
           dep match {
             case shufDep: ShuffleDependency[_,_] =>
-              val mapStage = getShuffleMapStage(shufDep, stage.priority)
+              val mapStage = getShuffleMapStage(shufDep, stage.jobId)
               if (!mapStage.isAvailable) {
                 visitedStages += mapStage
                 visit(mapStage.rdd)
@@ -768,16 +789,16 @@ class DAGScheduler(
     visitedRdds.contains(target.rdd)
   }
 
-  private def getPreferredLocs(rdd: RDD[_], partition: Int): List[String] = {
+  private def getPreferredLocs(rdd: RDD[_], partition: Int): Seq[TaskLocation] = {
     // If the partition is cached, return the cache locations
     val cached = getCacheLocs(rdd)(partition)
-    if (cached != Nil) {
+    if (!cached.isEmpty) {
       return cached
     }
     // If the RDD has some placement preferences (as is the case for input RDDs), get those
     val rddPrefs = rdd.preferredLocations(rdd.partitions(partition)).toList
-    if (rddPrefs != Nil) {
-      return rddPrefs
+    if (!rddPrefs.isEmpty) {
+      return rddPrefs.map(host => TaskLocation(host))
     }
     // If the RDD has narrow dependencies, pick the first partition of the first narrow dep
     // that has any placement preferences. Ideally we would choose based on transfer sizes,
@@ -791,13 +812,13 @@ class DAGScheduler(
         }
       case _ =>
     })
-    return Nil
+    Nil
   }
 
   private def cleanup(cleanupTime: Long) {
-    var sizeBefore = idToStage.size
-    idToStage.clearOldValues(cleanupTime)
-    logInfo("idToStage " + sizeBefore + " --> " + idToStage.size)
+    var sizeBefore = stageIdToStage.size
+    stageIdToStage.clearOldValues(cleanupTime)
+    logInfo("stageIdToStage " + sizeBefore + " --> " + stageIdToStage.size)
 
     sizeBefore = shuffleToMapStage.size
     shuffleToMapStage.clearOldValues(cleanupTime)
diff --git a/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala
index 3b4ee6287aa20cffa7cd7277a7b4309087da9f87..b8ba0e92394c257aa5432253f1e9f6030ba141f1 100644
--- a/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala
@@ -54,9 +54,7 @@ private[spark] case class CompletionEvent(
     taskMetrics: TaskMetrics)
   extends DAGSchedulerEvent
 
-private[spark] case class ExecutorGained(execId: String, hostPort: String) extends DAGSchedulerEvent {
-  Utils.checkHostPort(hostPort, "Required hostport")
-}
+private[spark] case class ExecutorGained(execId: String, host: String) extends DAGSchedulerEvent
 
 private[spark] case class ExecutorLost(execId: String) extends DAGSchedulerEvent
 
diff --git a/core/src/main/scala/spark/scheduler/DAGSchedulerSource.scala b/core/src/main/scala/spark/scheduler/DAGSchedulerSource.scala
index 87d27cc70d77fe4abd123ce2663e8897f4e1ab4a..98c4fb7e59a03755cebefb21fe6a99f0a2a89f82 100644
--- a/core/src/main/scala/spark/scheduler/DAGSchedulerSource.scala
+++ b/core/src/main/scala/spark/scheduler/DAGSchedulerSource.scala
@@ -21,7 +21,7 @@ private[spark] class DAGSchedulerSource(val dagScheduler: DAGScheduler) extends
   })
 
   metricRegistry.register(MetricRegistry.name("job", "allJobs", "number"), new Gauge[Int] {
-    override def getValue: Int = dagScheduler.nextRunId.get()
+    override def getValue: Int = dagScheduler.nextJobId.get()
   })
 
   metricRegistry.register(MetricRegistry.name("job", "activeJobs", "number"), new Gauge[Int] {
diff --git a/core/src/main/scala/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/spark/scheduler/InputFormatInfo.scala
index 65f8c3200e43722b6fa442022d18fab7cfc2cf14..8f1b9b29b54c3f8c309b9f09fbe827fb9eb20b51 100644
--- a/core/src/main/scala/spark/scheduler/InputFormatInfo.scala
+++ b/core/src/main/scala/spark/scheduler/InputFormatInfo.scala
@@ -17,7 +17,7 @@
 
 package spark.scheduler
 
-import spark.Logging
+import spark.{Logging, SparkEnv}
 import scala.collection.immutable.Set
 import org.apache.hadoop.mapred.{FileInputFormat, JobConf}
 import org.apache.hadoop.security.UserGroupInformation
@@ -26,7 +26,6 @@ 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
 
 
 /**
@@ -88,8 +87,9 @@ 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 env = SparkEnv.get
     val conf = new JobConf(configuration)
-    SparkHadoopUtil.addCredentials(conf);
+    env.hadoop.addCredentials(conf)
     FileInputFormat.setInputPaths(conf, path)
 
     val instance: org.apache.hadoop.mapreduce.InputFormat[_, _] =
@@ -108,8 +108,9 @@ 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 env = SparkEnv.get
     val jobConf = new JobConf(configuration)
-    SparkHadoopUtil.addCredentials(jobConf);
+    env.hadoop.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 f7565b8c57e14c503da6fcca0af23afa868c88bb..1bc9fabdffdcb83eda48fa341eb87a9dd90f9f0b 100644
--- a/core/src/main/scala/spark/scheduler/JobLogger.scala
+++ b/core/src/main/scala/spark/scheduler/JobLogger.scala
@@ -23,8 +23,10 @@ import java.io.FileNotFoundException
 import java.text.SimpleDateFormat
 import java.util.{Date, Properties}
 import java.util.concurrent.LinkedBlockingQueue
+
 import scala.collection.mutable.{Map, HashMap, ListBuffer}
 import scala.io.Source
+
 import spark._
 import spark.executor.TaskMetrics
 import spark.scheduler.cluster.TaskInfo
@@ -53,31 +55,6 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging {
   def getJobIDToStages = jobIDToStages
   def getEventQueue = eventQueue
   
-  new Thread("JobLogger") {
-    setDaemon(true)
-    override def run() {
-      while (true) {
-        val event = eventQueue.take
-        logDebug("Got event of type " + event.getClass.getName)
-        event match {
-          case SparkListenerJobStart(job, properties) =>
-            processJobStartEvent(job, properties)
-          case SparkListenerStageSubmitted(stage, taskSize) =>
-            processStageSubmittedEvent(stage, taskSize)
-          case StageCompleted(stageInfo) =>
-            processStageCompletedEvent(stageInfo)
-          case SparkListenerJobEnd(job, result) =>
-            processJobEndEvent(job, result)
-          case SparkListenerTaskStart(task, taskInfo) =>
-            processTaskStartEvent(task, taskInfo)
-          case SparkListenerTaskEnd(task, reason, taskInfo, taskMetrics) =>
-            processTaskEndEvent(task, reason, taskInfo, taskMetrics)
-          case _ =>
-        }
-      }
-    }
-  }.start()
-
   // Create a folder for log files, the folder's name is the creation time of the jobLogger
   protected def createLogDir() {
     val dir = new File(logDir + "/" + logDirName + "/")
@@ -125,7 +102,7 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging {
     stageIDToJobID.get(stageID).foreach(jobID => jobLogInfo(jobID, info, withTime))
 
   protected def buildJobDep(jobID: Int, stage: Stage) {
-    if (stage.priority == jobID) {
+    if (stage.jobId == jobID) {
       jobIDToStages.get(jobID) match {
         case Some(stageList) => stageList += stage
         case None => val stageList = new  ListBuffer[Stage]
@@ -201,12 +178,12 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging {
     }else{
       stageInfo = "STAGE_ID=" + stage.id + " RESULT_STAGE"
     }
-    if (stage.priority == jobID) {
+    if (stage.jobId == jobID) {
       jobLogInfo(jobID, indentString(indent) + stageInfo, false)
       recordRddInStageGraph(jobID, stage.rdd, indent)
       stage.parents.foreach(recordStageDepGraph(jobID, _, indent + 2))
     } else
-      jobLogInfo(jobID, indentString(indent) + stageInfo + " JOB_ID=" + stage.priority, false)
+      jobLogInfo(jobID, indentString(indent) + stageInfo + " JOB_ID=" + stage.jobId, false)
   }
   
   // Record task metrics into job log files
@@ -238,49 +215,32 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging {
   }
   
   override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) {
-    eventQueue.put(stageSubmitted)
-  }
-
-  protected def processStageSubmittedEvent(stage: Stage, taskSize: Int) {
-    stageLogInfo(stage.id, "STAGE_ID=" + stage.id + " STATUS=SUBMITTED" + " TASK_SIZE=" + taskSize)
+    stageLogInfo(
+      stageSubmitted.stage.id,
+      "STAGE_ID=%d STATUS=SUBMITTED TASK_SIZE=%d".format(
+        stageSubmitted.stage.id, stageSubmitted.taskSize))
   }
   
   override def onStageCompleted(stageCompleted: StageCompleted) {
-    eventQueue.put(stageCompleted)
-  }
-
-  protected def processStageCompletedEvent(stageInfo: StageInfo) {
-    stageLogInfo(stageInfo.stage.id, "STAGE_ID=" + 
-                 stageInfo.stage.id + " STATUS=COMPLETED")
+    stageLogInfo(
+      stageCompleted.stageInfo.stage.id,
+      "STAGE_ID=%d STATUS=COMPLETED".format(stageCompleted.stageInfo.stage.id))
     
   }
 
-  override def onTaskStart(taskStart: SparkListenerTaskStart) {
-    eventQueue.put(taskStart)
-  }
-
-  protected def processTaskStartEvent(task: Task[_], taskInfo: TaskInfo) {
-    var taskStatus = ""
-    task match {
-      case resultTask: ResultTask[_, _] => taskStatus = "TASK_TYPE=RESULT_TASK"
-      case shuffleMapTask: ShuffleMapTask => taskStatus = "TASK_TYPE=SHUFFLE_MAP_TASK"
-    }
-  }
+  override def onTaskStart(taskStart: SparkListenerTaskStart) { }
 
   override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
-    eventQueue.put(taskEnd)
-  }
-
-  protected def processTaskEndEvent(task: Task[_], reason: TaskEndReason, 
-      taskInfo: TaskInfo, taskMetrics: TaskMetrics) {
+    val task = taskEnd.task
+    val taskInfo = taskEnd.taskInfo
     var taskStatus = ""
     task match {
       case resultTask: ResultTask[_, _] => taskStatus = "TASK_TYPE=RESULT_TASK"
       case shuffleMapTask: ShuffleMapTask => taskStatus = "TASK_TYPE=SHUFFLE_MAP_TASK"
     }
-    reason match {
+    taskEnd.reason match {
       case Success => taskStatus += " STATUS=SUCCESS"
-        recordTaskMetrics(task.stageId, taskStatus, taskInfo, taskMetrics)
+        recordTaskMetrics(task.stageId, taskStatus, taskInfo, taskEnd.taskMetrics)
       case Resubmitted => 
         taskStatus += " STATUS=RESUBMITTED TID=" + taskInfo.taskId + 
                       " STAGE_ID=" + task.stageId
@@ -299,39 +259,34 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging {
   }
   
   override def onJobEnd(jobEnd: SparkListenerJobEnd) {
-    eventQueue.put(jobEnd)
-  }
-
-  protected def processJobEndEvent(job: ActiveJob, reason: JobResult) {
-    var info = "JOB_ID=" + job.runId
-    reason match {
+    val job = jobEnd.job
+    var info = "JOB_ID=" + job.jobId
+    jobEnd.jobResult match {
       case JobSucceeded => info += " STATUS=SUCCESS"
       case JobFailed(exception, _) =>
         info += " STATUS=FAILED REASON="
         exception.getMessage.split("\\s+").foreach(info += _ + "_")
       case _ =>
     }
-    jobLogInfo(job.runId, info.substring(0, info.length - 1).toUpperCase)
-    closeLogWriter(job.runId)
+    jobLogInfo(job.jobId, info.substring(0, info.length - 1).toUpperCase)
+    closeLogWriter(job.jobId)
   }
 
   protected def recordJobProperties(jobID: Int, properties: Properties) {
     if(properties != null) {
-      val annotation = properties.getProperty("spark.job.annotation", "")
-      jobLogInfo(jobID, annotation, false)
+      val description = properties.getProperty(SparkContext.SPARK_JOB_DESCRIPTION, "")
+      jobLogInfo(jobID, description, false)
     }
   }
 
   override def onJobStart(jobStart: SparkListenerJobStart) {
-    eventQueue.put(jobStart)
-  }
- 
-  protected def processJobStartEvent(job: ActiveJob, properties: Properties) {
-    createLogWriter(job.runId)
-    recordJobProperties(job.runId, properties)
-    buildJobDep(job.runId, job.finalStage)
-    recordStageDep(job.runId)
-    recordStageDepGraph(job.runId, job.finalStage) 
-    jobLogInfo(job.runId, "JOB_ID=" + job.runId + " STATUS=STARTED")
+    val job = jobStart.job
+    val properties = jobStart.properties
+    createLogWriter(job.jobId)
+    recordJobProperties(job.jobId, properties)
+    buildJobDep(job.jobId, job.finalStage)
+    recordStageDep(job.jobId)
+    recordStageDepGraph(job.jobId, job.finalStage)
+    jobLogInfo(job.jobId, "JOB_ID=" + job.jobId + " STATUS=STARTED")
   }
 }
diff --git a/core/src/main/scala/spark/scheduler/ResultTask.scala b/core/src/main/scala/spark/scheduler/ResultTask.scala
index 361b1e6b91e36791d2f1d87f7f8d51e7266c72a1..d066df5dc1dddfc288efc1deed9610bda6d30d9b 100644
--- a/core/src/main/scala/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/spark/scheduler/ResultTask.scala
@@ -51,15 +51,13 @@ private[spark] object ResultTask {
   }
 
   def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], (TaskContext, Iterator[_]) => _) = {
-    synchronized {
-      val loader = Thread.currentThread.getContextClassLoader
-      val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
-      val ser = SparkEnv.get.closureSerializer.newInstance
-      val objIn = ser.deserializeStream(in)
-      val rdd = objIn.readObject().asInstanceOf[RDD[_]]
-      val func = objIn.readObject().asInstanceOf[(TaskContext, Iterator[_]) => _]
-      return (rdd, func)
-    }
+    val loader = Thread.currentThread.getContextClassLoader
+    val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
+    val ser = SparkEnv.get.closureSerializer.newInstance
+    val objIn = ser.deserializeStream(in)
+    val rdd = objIn.readObject().asInstanceOf[RDD[_]]
+    val func = objIn.readObject().asInstanceOf[(TaskContext, Iterator[_]) => _]
+    return (rdd, func)
   }
 
   def clearCache() {
@@ -75,7 +73,7 @@ private[spark] class ResultTask[T, U](
     var rdd: RDD[T],
     var func: (TaskContext, Iterator[T]) => U,
     var partition: Int,
-    @transient locs: Seq[String],
+    @transient locs: Seq[TaskLocation],
     val outputId: Int)
   extends Task[U](stageId) with Externalizable {
 
@@ -87,11 +85,8 @@ private[spark] class ResultTask[T, U](
     rdd.partitions(partition)
   }
 
-  private val preferredLocs: Seq[String] = if (locs == null) Nil else locs.toSet.toSeq
-
-  {
-    // DEBUG code
-    preferredLocs.foreach (hostPort => Utils.checkHost(Utils.parseHostPort(hostPort)._1, "preferredLocs : " + preferredLocs))
+  @transient private val preferredLocs: Seq[TaskLocation] = {
+    if (locs == null) Nil else locs.toSet.toSeq
   }
 
   override def run(attemptId: Long): U = {
@@ -104,7 +99,7 @@ private[spark] class ResultTask[T, U](
     }
   }
 
-  override def preferredLocations: Seq[String] = preferredLocs
+  override def preferredLocations: Seq[TaskLocation] = preferredLocs
 
   override def toString = "ResultTask(" + stageId + ", " + partition + ")"
 
@@ -118,6 +113,7 @@ private[spark] class ResultTask[T, U](
       out.write(bytes)
       out.writeInt(partition)
       out.writeInt(outputId)
+      out.writeLong(epoch)
       out.writeObject(split)
     }
   }
@@ -132,6 +128,7 @@ private[spark] class ResultTask[T, U](
     func = func_.asInstanceOf[(TaskContext, Iterator[T]) => U]
     partition = in.readInt()
     val outputId = in.readInt()
+    epoch = in.readLong()
     split = in.readObject().asInstanceOf[Partition]
   }
 }
diff --git a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
index 1c25605f75c5e916ff10350eb82f49bf312b5e3b..f2a038576b722cce8c8d2737d7856eeef8091c32 100644
--- a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala
@@ -18,16 +18,9 @@
 package spark.scheduler
 
 import java.io._
-import java.util.{HashMap => JHashMap}
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
-import scala.collection.mutable.{ArrayBuffer, HashMap}
-import scala.collection.JavaConversions._
-
-import it.unimi.dsi.fastutil.io.FastBufferedOutputStream
-
-import com.ning.compress.lzf.LZFInputStream
-import com.ning.compress.lzf.LZFOutputStream
+import scala.collection.mutable.HashMap
 
 import spark._
 import spark.executor.ShuffleWriteMetrics
@@ -95,25 +88,18 @@ private[spark] class ShuffleMapTask(
     var rdd: RDD[_],
     var dep: ShuffleDependency[_,_],
     var partition: Int,
-    @transient private var locs: Seq[String])
+    @transient private var locs: Seq[TaskLocation])
   extends Task[MapStatus](stageId)
   with Externalizable
   with Logging {
 
   protected def this() = this(0, null, null, 0, null)
 
-  @transient private val preferredLocs: Seq[String] = if (locs == null) Nil else locs.toSet.toSeq
-
-  {
-    // DEBUG code
-    preferredLocs.foreach (hostPort => Utils.checkHost(Utils.parseHostPort(hostPort)._1, "preferredLocs : " + preferredLocs))
+  @transient private val preferredLocs: Seq[TaskLocation] = {
+    if (locs == null) Nil else locs.toSet.toSeq
   }
 
-  var split = if (rdd == null) {
-    null
-  } else {
-    rdd.partitions(partition)
-  }
+  var split = if (rdd == null) null else rdd.partitions(partition)
 
   override def writeExternal(out: ObjectOutput) {
     RDDCheckpointData.synchronized {
@@ -123,7 +109,7 @@ private[spark] class ShuffleMapTask(
       out.writeInt(bytes.length)
       out.write(bytes)
       out.writeInt(partition)
-      out.writeLong(generation)
+      out.writeLong(epoch)
       out.writeObject(split)
     }
   }
@@ -137,7 +123,7 @@ private[spark] class ShuffleMapTask(
     rdd = rdd_
     dep = dep_
     partition = in.readInt()
-    generation = in.readLong()
+    epoch = in.readLong()
     split = in.readObject().asInstanceOf[Partition]
   }
 
@@ -159,7 +145,7 @@ private[spark] class ShuffleMapTask(
 
       // Write the map output to its associated buckets.
       for (elem <- rdd.iterator(split, taskContext)) {
-        val pair = elem.asInstanceOf[(Any, Any)]
+        val pair = elem.asInstanceOf[Product2[Any, Any]]
         val bucketId = dep.partitioner.getPartition(pair._1)
         buckets.writers(bucketId).write(pair)
       }
@@ -197,7 +183,7 @@ private[spark] class ShuffleMapTask(
     }
   }
 
-  override def preferredLocations: Seq[String] = preferredLocs
+  override def preferredLocations: Seq[TaskLocation] = preferredLocs
 
   override def toString = "ShuffleMapTask(%d, %d)".format(stageId, partition)
 }
diff --git a/core/src/main/scala/spark/scheduler/SparkListener.scala b/core/src/main/scala/spark/scheduler/SparkListener.scala
index 4eb7e4e6a5f8329b2e5df1466a93451a8dd66ee5..e5531011c2b87f71762cc8a4276cade0deb2c797 100644
--- a/core/src/main/scala/spark/scheduler/SparkListener.scala
+++ b/core/src/main/scala/spark/scheduler/SparkListener.scala
@@ -25,7 +25,8 @@ import spark.executor.TaskMetrics
 
 sealed trait SparkListenerEvents
 
-case class SparkListenerStageSubmitted(stage: Stage, taskSize: Int) extends SparkListenerEvents
+case class SparkListenerStageSubmitted(stage: Stage, taskSize: Int, properties: Properties)
+     extends SparkListenerEvents
 
 case class StageCompleted(val stageInfo: StageInfo) extends SparkListenerEvents
 
@@ -34,10 +35,10 @@ case class SparkListenerTaskStart(task: Task[_], taskInfo: TaskInfo) extends Spa
 case class SparkListenerTaskEnd(task: Task[_], reason: TaskEndReason, taskInfo: TaskInfo,
      taskMetrics: TaskMetrics) extends SparkListenerEvents
 
-case class SparkListenerJobStart(job: ActiveJob, properties: Properties = null) 
+case class SparkListenerJobStart(job: ActiveJob, properties: Properties = null)
      extends SparkListenerEvents
 
-case class SparkListenerJobEnd(job: ActiveJob, jobResult: JobResult) 
+case class SparkListenerJobEnd(job: ActiveJob, jobResult: JobResult)
      extends SparkListenerEvents
 
 trait SparkListener {
@@ -45,7 +46,7 @@ trait SparkListener {
    * Called when a stage is completed, with information on the completed stage
    */
   def onStageCompleted(stageCompleted: StageCompleted) { }
-  
+
   /**
    * Called when a stage is submitted
    */
@@ -65,12 +66,12 @@ trait SparkListener {
    * Called when a job starts
    */
   def onJobStart(jobStart: SparkListenerJobStart) { }
-  
+
   /**
    * Called when a job ends
    */
   def onJobEnd(jobEnd: SparkListenerJobEnd) { }
-  
+
 }
 
 /**
@@ -152,7 +153,7 @@ object StatsReportListener extends Logging {
   }
 
   def showBytesDistribution(heading: String, dist: Distribution) {
-    showDistribution(heading, dist, (d => Utils.memoryBytesToString(d.toLong)): Double => String)
+    showDistribution(heading, dist, (d => Utils.bytesToString(d.toLong)): Double => String)
   }
 
   def showMillisDistribution(heading: String, dOpt: Option[Distribution]) {
diff --git a/core/src/main/scala/spark/scheduler/SparkListenerBus.scala b/core/src/main/scala/spark/scheduler/SparkListenerBus.scala
new file mode 100644
index 0000000000000000000000000000000000000000..f55ed455ed3f700cc5e6cc7664c1a7d39d40699b
--- /dev/null
+++ b/core/src/main/scala/spark/scheduler/SparkListenerBus.scala
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.scheduler
+
+import java.util.concurrent.LinkedBlockingQueue
+
+import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
+
+import spark.Logging
+
+/** Asynchronously passes SparkListenerEvents to registered SparkListeners. */
+private[spark] class SparkListenerBus() extends Logging {
+  private val sparkListeners = new ArrayBuffer[SparkListener]() with SynchronizedBuffer[SparkListener]
+
+  /* Cap the capacity of the SparkListenerEvent queue so we get an explicit error (rather than
+   * an OOM exception) if it's perpetually being added to more quickly than it's being drained. */
+  private val EVENT_QUEUE_CAPACITY = 10000 
+  private val eventQueue = new LinkedBlockingQueue[SparkListenerEvents](EVENT_QUEUE_CAPACITY)
+  private var queueFullErrorMessageLogged = false
+
+  new Thread("SparkListenerBus") {
+    setDaemon(true)
+    override def run() {
+      while (true) {
+        val event = eventQueue.take
+        event match {
+          case stageSubmitted: SparkListenerStageSubmitted =>
+            sparkListeners.foreach(_.onStageSubmitted(stageSubmitted))
+          case stageCompleted: StageCompleted =>
+            sparkListeners.foreach(_.onStageCompleted(stageCompleted))
+          case jobStart: SparkListenerJobStart =>
+            sparkListeners.foreach(_.onJobStart(jobStart))
+          case jobEnd: SparkListenerJobEnd =>
+            sparkListeners.foreach(_.onJobEnd(jobEnd))
+          case taskStart: SparkListenerTaskStart =>
+            sparkListeners.foreach(_.onTaskStart(taskStart))
+          case taskEnd: SparkListenerTaskEnd =>
+            sparkListeners.foreach(_.onTaskEnd(taskEnd))
+          case _ =>
+        }
+      }
+    }
+  }.start()
+
+  def addListener(listener: SparkListener) {
+    sparkListeners += listener
+  }
+
+  def post(event: SparkListenerEvents) {
+    val eventAdded = eventQueue.offer(event)
+    if (!eventAdded && !queueFullErrorMessageLogged) {
+      logError("Dropping SparkListenerEvent because no remaining room in event queue. " +
+        "This likely means one of the SparkListeners is too slow and cannot keep up with the " +
+        "rate at which tasks are being started by the scheduler.")
+      queueFullErrorMessageLogged = true
+    }
+  }
+}
+
diff --git a/core/src/main/scala/spark/scheduler/Stage.scala b/core/src/main/scala/spark/scheduler/Stage.scala
index 5428daeb94771833702bfd9c0357d78dc55a2b7c..c599c00ac4406cb7e91357f4a3c0eb6dbc24eda1 100644
--- a/core/src/main/scala/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/spark/scheduler/Stage.scala
@@ -33,15 +33,16 @@ import spark.storage.BlockManagerId
  * initiated a job (e.g. count(), save(), etc). For shuffle map stages, we also track the nodes
  * that each output partition is on.
  *
- * Each Stage also has a priority, which is (by default) based on the job it was submitted in.
- * This allows Stages from earlier jobs to be computed first or recovered faster on failure.
+ * Each Stage also has a jobId, identifying the job that first submitted the stage.  When FIFO
+ * scheduling is used, this allows Stages from earlier jobs to be computed first or recovered
+ * faster on failure.
  */
 private[spark] class Stage(
     val id: Int,
     val rdd: RDD[_],
     val shuffleDep: Option[ShuffleDependency[_,_]],  // Output shuffle if stage is a map stage
     val parents: List[Stage],
-    val priority: Int,
+    val jobId: Int,
     callSite: Option[String])
   extends Logging {
 
diff --git a/core/src/main/scala/spark/scheduler/Task.scala b/core/src/main/scala/spark/scheduler/Task.scala
index 50768d43e02baf953d7e6773dc1fb1f9b877a498..0ab2ae6cfe9f655ea74420a693f1938c8352beea 100644
--- a/core/src/main/scala/spark/scheduler/Task.scala
+++ b/core/src/main/scala/spark/scheduler/Task.scala
@@ -30,9 +30,9 @@ import spark.executor.TaskMetrics
  */
 private[spark] abstract class Task[T](val stageId: Int) extends Serializable {
   def run(attemptId: Long): T
-  def preferredLocations: Seq[String] = Nil
+  def preferredLocations: Seq[TaskLocation] = Nil
 
-  var generation: Long = -1   // Map output tracker generation. Will be set by TaskScheduler.
+  var epoch: Long = -1   // Map output tracker epoch. Will be set by TaskScheduler.
 
   var metrics: Option[TaskMetrics] = None
 
diff --git a/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala b/core/src/main/scala/spark/scheduler/TaskLocation.scala
similarity index 54%
rename from core/src/hadoop2-yarn/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala
rename to core/src/main/scala/spark/scheduler/TaskLocation.scala
index 1a7cdf4788112658c9c61336978040ab91a67919..fea117e956d454e7eaa1bea3aa2c7506a6423f08 100644
--- a/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala
+++ b/core/src/main/scala/spark/scheduler/TaskLocation.scala
@@ -15,16 +15,20 @@
  * limitations under the License.
  */
 
-package org.apache.hadoop.mapreduce
+package spark.scheduler
 
-import org.apache.hadoop.conf.Configuration
-import task.{TaskAttemptContextImpl, JobContextImpl}
-
-trait HadoopMapReduceUtil {
-  def newJobContext(conf: Configuration, jobId: JobID): JobContext = new JobContextImpl(conf, jobId)
+/**
+ * A location where a task should run. This can either be a host or a (host, executorID) pair.
+ * In the latter case, we will prefer to launch the task on that executorID, but our next level
+ * of preference will be executors on the same host if this is not possible.
+ */
+private[spark]
+class TaskLocation private (val host: String, val executorId: Option[String]) extends Serializable {
+  override def toString: String = "TaskLocation(" + host + ", " + executorId + ")"
+}
 
-  def newTaskAttemptContext(conf: Configuration, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContextImpl(conf, attemptId)
+private[spark] object TaskLocation {
+  def apply(host: String, executorId: String) = new TaskLocation(host, Some(executorId))
 
-  def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) =
-    new TaskAttemptID(jtIdentifier, jobId, if (isMap) TaskType.MAP else TaskType.REDUCE, taskId, attemptId)
+  def apply(host: String) = new TaskLocation(host, None)
 }
diff --git a/core/src/main/scala/spark/scheduler/TaskResult.scala b/core/src/main/scala/spark/scheduler/TaskResult.scala
index dc0621ea7ba5fb20ec6be60627d20d602790a1de..fc4856756b127d4cee32e34231c4b55365b15f54 100644
--- a/core/src/main/scala/spark/scheduler/TaskResult.scala
+++ b/core/src/main/scala/spark/scheduler/TaskResult.scala
@@ -21,16 +21,26 @@ import java.io._
 
 import scala.collection.mutable.Map
 import spark.executor.TaskMetrics
+import spark.{Utils, SparkEnv}
+import java.nio.ByteBuffer
 
 // Task result. Also contains updates to accumulator variables.
 // TODO: Use of distributed cache to return result is a hack to get around
 // what seems to be a bug with messages over 60KB in libprocess; fix it
 private[spark]
-class TaskResult[T](var value: T, var accumUpdates: Map[Long, Any], var metrics: TaskMetrics) extends Externalizable {
+class TaskResult[T](var value: T, var accumUpdates: Map[Long, Any], var metrics: TaskMetrics)
+  extends Externalizable
+{
   def this() = this(null.asInstanceOf[T], null, null)
 
   override def writeExternal(out: ObjectOutput) {
-    out.writeObject(value)
+
+    val objectSer = SparkEnv.get.serializer.newInstance()
+    val bb = objectSer.serialize(value)
+
+    out.writeInt(bb.remaining())
+    Utils.writeByteBuffer(bb, out)
+
     out.writeInt(accumUpdates.size)
     for ((key, value) <- accumUpdates) {
       out.writeLong(key)
@@ -40,7 +50,14 @@ class TaskResult[T](var value: T, var accumUpdates: Map[Long, Any], var metrics:
   }
 
   override def readExternal(in: ObjectInput) {
-    value = in.readObject().asInstanceOf[T]
+
+    val objectSer = SparkEnv.get.serializer.newInstance()
+
+    val blen = in.readInt()
+    val byteVal = new Array[Byte](blen)
+    in.readFully(byteVal)
+    value = objectSer.deserialize(ByteBuffer.wrap(byteVal))
+
     val numUpdates = in.readInt
     if (numUpdates == 0) {
       accumUpdates = null
diff --git a/core/src/main/scala/spark/scheduler/TaskScheduler.scala b/core/src/main/scala/spark/scheduler/TaskScheduler.scala
index 51883080069f34eb7cf54c5a7feab1b973ef8356..4943d58e254034f43c2f4ce6b305455b5cf40e1a 100644
--- a/core/src/main/scala/spark/scheduler/TaskScheduler.scala
+++ b/core/src/main/scala/spark/scheduler/TaskScheduler.scala
@@ -17,6 +17,8 @@
 
 package spark.scheduler
 
+import spark.scheduler.cluster.Pool
+import spark.scheduler.cluster.SchedulingMode.SchedulingMode
 /**
  * Low-level task scheduler interface, implemented by both ClusterScheduler and LocalScheduler.
  * These schedulers get sets of tasks submitted to them from the DAGScheduler for each stage,
@@ -25,6 +27,11 @@ package spark.scheduler
  * the TaskSchedulerListener interface.
  */
 private[spark] trait TaskScheduler {
+
+  def rootPool: Pool
+
+  def schedulingMode: SchedulingMode
+
   def start(): Unit
 
   // Invoked after system has successfully initialized (typically in spark context).
diff --git a/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala b/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala
index 2cdeb1c8c0c6ca874b8e79dcda9285cab04db280..64be50b2d02b8e13b2795024c933379f5312830c 100644
--- a/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala
+++ b/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala
@@ -35,7 +35,7 @@ private[spark] trait TaskSchedulerListener {
                 taskInfo: TaskInfo, taskMetrics: TaskMetrics): Unit
 
   // A node was added to the cluster.
-  def executorGained(execId: String, hostPort: String): Unit
+  def executorGained(execId: String, host: String): Unit
 
   // A node was lost from the cluster.
   def executorLost(execId: String): Unit
diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala
index 7c10074dc761fad68116bda60c287dcb7bed0358..679d899b472a280805f29abcb901548ddef253c8 100644
--- a/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala
@@ -26,51 +26,34 @@ import scala.collection.mutable.HashSet
 import spark._
 import spark.TaskState.TaskState
 import spark.scheduler._
+import spark.scheduler.cluster.SchedulingMode.SchedulingMode
 import java.nio.ByteBuffer
 import java.util.concurrent.atomic.AtomicLong
 import java.util.{TimerTask, Timer}
 
 /**
  * The main TaskScheduler implementation, for running tasks on a cluster. Clients should first call
- * start(), then submit task sets through the runTasks method.
+ * initialize() and start(), then submit task sets through the runTasks method.
+ *
+ * This class can work with multiple types of clusters by acting through a SchedulerBackend.
+ * It handles common logic, like determining a scheduling order across jobs, waking up to launch
+ * speculative tasks, etc.
+ *
+ * THREADING: SchedulerBackends and task-submitting clients can call this class from multiple
+ * threads, so it needs locks in public API methods to maintain its state. In addition, some
+ * SchedulerBackends sycnchronize on themselves when they want to send events here, and then
+ * acquire a lock on us, so we need to make sure that we don't try to lock the backend while
+ * we are holding a lock on ourselves.
  */
 private[spark] class ClusterScheduler(val sc: SparkContext)
   extends TaskScheduler
-  with Logging {
-
+  with Logging
+{
   // How often to check for speculative tasks
   val SPECULATION_INTERVAL = System.getProperty("spark.speculation.interval", "100").toLong
+
   // Threshold above which we warn user initial TaskSet may be starved
   val STARVATION_TIMEOUT = System.getProperty("spark.starvation.timeout", "15000").toLong
-  // How often to revive offers in case there are pending tasks - that is how often to try to get
-  // tasks scheduled in case there are nodes available : default 0 is to disable it - to preserve existing behavior
-  // Note that this is required due to delayed scheduling due to data locality waits, etc.
-  // TODO: rename property ?
-  val TASK_REVIVAL_INTERVAL = System.getProperty("spark.tasks.revive.interval", "0").toLong
-
-  /*
-   This property controls how aggressive we should be to modulate waiting for node local task scheduling.
-   To elaborate, currently there is a time limit (3 sec def) to ensure that spark attempts to wait for node locality of tasks before
-   scheduling on other nodes. We have modified this in yarn branch such that offers to task set happen in prioritized order :
-   node-local, rack-local and then others
-   But once all available node local (and no pref) tasks are scheduled, instead of waiting for 3 sec before
-   scheduling to other nodes (which degrades performance for time sensitive tasks and on larger clusters), we can
-   modulate that : to also allow rack local nodes or any node. The default is still set to HOST - so that previous behavior is
-   maintained. This is to allow tuning the tension between pulling rdd data off node and scheduling computation asap.
-
-   TODO: rename property ? The value is one of
-   - NODE_LOCAL (default, no change w.r.t current behavior),
-   - RACK_LOCAL and
-   - ANY
-
-   Note that this property makes more sense when used in conjugation with spark.tasks.revive.interval > 0 : else it is not very effective.
-
-   Additional Note: For non trivial clusters, there is a 4x - 5x reduction in running time (in some of our experiments) based on whether
-   it is left at default NODE_LOCAL, RACK_LOCAL (if cluster is configured to be rack aware) or ANY.
-   If cluster is rack aware, then setting it to RACK_LOCAL gives best tradeoff and a 3x - 4x performance improvement while minimizing IO impact.
-   Also, it brings down the variance in running time drastically.
-    */
-  val TASK_SCHEDULING_AGGRESSION = TaskLocality.parse(System.getProperty("spark.tasks.schedule.aggression", "NODE_LOCAL"))
 
   val activeTaskSets = new HashMap[String, TaskSetManager]
 
@@ -88,16 +71,11 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
   // Which executor IDs we have executors on
   val activeExecutorIds = new HashSet[String]
 
-  // TODO: We might want to remove this and merge it with execId datastructures - but later.
-  // Which hosts in the cluster are alive (contains hostPort's) - used for process local and node local task locality.
-  private val hostPortsAlive = new HashSet[String]
-  private val hostToAliveHostPorts = new HashMap[String, HashSet[String]]
-
   // The set of executors we have on each host; this is used to compute hostsAlive, which
   // in turn is used to decide when we can attain data locality on a given host
-  private val executorsByHostPort = new HashMap[String, HashSet[String]]
+  private val executorsByHost = new HashMap[String, HashSet[String]]
 
-  private val executorIdToHostPort = new HashMap[String, String]
+  private val executorIdToHost = new HashMap[String, String]
 
   // JAR server, if any JARs were added by the user to the SparkContext
   var jarServer: HttpServer = null
@@ -114,6 +92,9 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
 
   var schedulableBuilder: SchedulableBuilder = null
   var rootPool: Pool = null
+  // default scheduler is FIFO
+  val schedulingMode: SchedulingMode = SchedulingMode.withName(
+    System.getProperty("spark.cluster.schedulingmode", "FIFO"))
 
   override def setListener(listener: TaskSchedulerListener) {
     this.listener = listener
@@ -121,36 +102,25 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
 
   def initialize(context: SchedulerBackend) {
     backend = context
-    //default scheduler is FIFO
-    val schedulingMode = System.getProperty("spark.cluster.schedulingmode", "FIFO")
-    //temporarily set rootPool name to empty
-    rootPool = new Pool("", SchedulingMode.withName(schedulingMode), 0, 0)
+    // temporarily set rootPool name to empty
+    rootPool = new Pool("", schedulingMode, 0, 0)
     schedulableBuilder = {
       schedulingMode match {
-        case "FIFO" =>
+        case SchedulingMode.FIFO =>
           new FIFOSchedulableBuilder(rootPool)
-        case "FAIR" =>
+        case SchedulingMode.FAIR =>
           new FairSchedulableBuilder(rootPool)
       }
     }
     schedulableBuilder.buildPools()
-    // resolve executorId to hostPort mapping.
-    def executorToHostPort(executorId: String, defaultHostPort: String): String = {
-      executorIdToHostPort.getOrElse(executorId, defaultHostPort)
-    }
-
-    // Unfortunately, this means that SparkEnv is indirectly referencing ClusterScheduler
-    // Will that be a design violation ?
-    SparkEnv.get.executorIdToHostPort = Some(executorToHostPort)
   }
 
-
   def newTaskId(): Long = nextTaskId.getAndIncrement()
 
   override def start() {
     backend.start()
 
-    if (JBoolean.getBoolean("spark.speculation")) {
+    if (System.getProperty("spark.speculation", "false").toBoolean) {
       new Thread("ClusterScheduler speculation check") {
         setDaemon(true)
 
@@ -167,27 +137,6 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
         }
       }.start()
     }
-
-
-    // Change to always run with some default if TASK_REVIVAL_INTERVAL <= 0 ?
-    if (TASK_REVIVAL_INTERVAL > 0) {
-      new Thread("ClusterScheduler task offer revival check") {
-        setDaemon(true)
-
-        override def run() {
-          logInfo("Starting speculative task offer revival thread")
-          while (true) {
-            try {
-              Thread.sleep(TASK_REVIVAL_INTERVAL)
-            } catch {
-              case e: InterruptedException => {}
-            }
-
-            if (hasPendingTasks()) backend.reviveOffers()
-          }
-        }
-      }.start()
-    }
   }
 
   override def submitTasks(taskSet: TaskSet) {
@@ -199,19 +148,20 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
       schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
       taskSetTaskIds(taskSet.id) = new HashSet[Long]()
 
-      if (hasReceivedTask == false) {
+      if (!hasReceivedTask) {
         starvationTimer.scheduleAtFixedRate(new TimerTask() {
           override def run() {
             if (!hasLaunchedTask) {
               logWarning("Initial job has not accepted any resources; " +
-                "check your cluster UI to ensure that workers are registered")
+                "check your cluster UI to ensure that workers are registered " +
+                "and have sufficient memory")
             } else {
               this.cancel()
             }
           }
         }, STARVATION_TIMEOUT, STARVATION_TIMEOUT)
       }
-      hasReceivedTask = true;
+      hasReceivedTask = true
     }
     backend.reviveOffers()
   }
@@ -232,170 +182,55 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
    * sets for tasks in order of priority. We fill each node with tasks in a round-robin manner so
    * that tasks are balanced across the cluster.
    */
-  def resourceOffers(offers: Seq[WorkerOffer]): Seq[Seq[TaskDescription]] = {
-    synchronized {
-      SparkEnv.set(sc.env)
-      // Mark each slave as alive and remember its hostname
-      for (o <- offers) {
-        // DEBUG Code
-        Utils.checkHostPort(o.hostPort)
-
-        executorIdToHostPort(o.executorId) = o.hostPort
-        if (! executorsByHostPort.contains(o.hostPort)) {
-          executorsByHostPort(o.hostPort) = new HashSet[String]()
-        }
-
-        hostPortsAlive += o.hostPort
-        hostToAliveHostPorts.getOrElseUpdate(Utils.parseHostPort(o.hostPort)._1, new HashSet[String]).add(o.hostPort)
-        executorGained(o.executorId, o.hostPort)
-      }
-      // Build a list of tasks to assign to each slave
-      val tasks = offers.map(o => new ArrayBuffer[TaskDescription](o.cores))
-      // merge availableCpus into nodeToAvailableCpus block ?
-      val availableCpus = offers.map(o => o.cores).toArray
-      val nodeToAvailableCpus = {
-        val map = new HashMap[String, Int]()
-        for (offer <- offers) {
-          val hostPort = offer.hostPort
-          val cores = offer.cores
-          // DEBUG code
-          Utils.checkHostPort(hostPort)
-
-          val host = Utils.parseHostPort(hostPort)._1
-
-          map.put(host, map.getOrElse(host, 0) + cores)
-        }
-
-        map
+  def resourceOffers(offers: Seq[WorkerOffer]): Seq[Seq[TaskDescription]] = synchronized {
+    SparkEnv.set(sc.env)
+
+    // Mark each slave as alive and remember its hostname
+    for (o <- offers) {
+      executorIdToHost(o.executorId) = o.host
+      if (!executorsByHost.contains(o.host)) {
+        executorsByHost(o.host) = new HashSet[String]()
+        executorGained(o.executorId, o.host)
       }
-      var launchedTask = false
-      val sortedTaskSetQueue = rootPool.getSortedTaskSetQueue()
-      for (manager <- sortedTaskSetQueue)
-      {
-        logInfo("parentName:%s,name:%s,runningTasks:%s".format(manager.parent.name, manager.name, manager.runningTasks))
-      }
-      for (manager <- sortedTaskSetQueue) {
+    }
 
-        // Split offers based on node local, rack local and off-rack tasks.
-        val processLocalOffers = new HashMap[String, ArrayBuffer[Int]]()
-        val nodeLocalOffers = new HashMap[String, ArrayBuffer[Int]]()
-        val rackLocalOffers = new HashMap[String, ArrayBuffer[Int]]()
-        val otherOffers = new HashMap[String, ArrayBuffer[Int]]()
+    // Build a list of tasks to assign to each worker
+    val tasks = offers.map(o => new ArrayBuffer[TaskDescription](o.cores))
+    val availableCpus = offers.map(o => o.cores).toArray
+    val sortedTaskSets = rootPool.getSortedTaskSetQueue()
+    for (taskSet <- sortedTaskSets) {
+      logDebug("parentName: %s, name: %s, runningTasks: %s".format(
+        taskSet.parent.name, taskSet.name, taskSet.runningTasks))
+    }
 
+    // Take each TaskSet in our scheduling order, and then offer it each node in increasing order
+    // of locality levels so that it gets a chance to launch local tasks on all of them.
+    var launchedTask = false
+    for (taskSet <- sortedTaskSets; maxLocality <- TaskLocality.values) {
+      do {
+        launchedTask = false
         for (i <- 0 until offers.size) {
-          val hostPort = offers(i).hostPort
-          // DEBUG code
-          Utils.checkHostPort(hostPort)
-
-          val numProcessLocalTasks =  math.max(0, math.min(manager.numPendingTasksForHostPort(hostPort), availableCpus(i)))
-          if (numProcessLocalTasks > 0){
-            val list = processLocalOffers.getOrElseUpdate(hostPort, new ArrayBuffer[Int])
-            for (j <- 0 until numProcessLocalTasks) list += i
+          val execId = offers(i).executorId
+          val host = offers(i).host
+          for (task <- taskSet.resourceOffer(execId, host, availableCpus(i), maxLocality)) {
+            tasks(i) += task
+            val tid = task.taskId
+            taskIdToTaskSetId(tid) = taskSet.taskSet.id
+            taskSetTaskIds(taskSet.taskSet.id) += tid
+            taskIdToExecutorId(tid) = execId
+            activeExecutorIds += execId
+            executorsByHost(host) += execId
+            availableCpus(i) -= 1
+            launchedTask = true
           }
-
-          val host = Utils.parseHostPort(hostPort)._1
-          val numNodeLocalTasks =  math.max(0,
-            // Remove process local tasks (which are also host local btw !) from this
-            math.min(manager.numPendingTasksForHost(hostPort) - numProcessLocalTasks, nodeToAvailableCpus(host)))
-          if (numNodeLocalTasks > 0){
-            val list = nodeLocalOffers.getOrElseUpdate(host, new ArrayBuffer[Int])
-            for (j <- 0 until numNodeLocalTasks) list += i
-          }
-
-          val numRackLocalTasks =  math.max(0,
-            // Remove node local tasks (which are also rack local btw !) from this
-            math.min(manager.numRackLocalPendingTasksForHost(hostPort) - numProcessLocalTasks - numNodeLocalTasks, nodeToAvailableCpus(host)))
-          if (numRackLocalTasks > 0){
-            val list = rackLocalOffers.getOrElseUpdate(host, new ArrayBuffer[Int])
-            for (j <- 0 until numRackLocalTasks) list += i
-          }
-          if (numNodeLocalTasks <= 0 && numRackLocalTasks <= 0){
-            // add to others list - spread even this across cluster.
-            val list = otherOffers.getOrElseUpdate(host, new ArrayBuffer[Int])
-            list += i
-          }
-        }
-
-        val offersPriorityList = new ArrayBuffer[Int](
-          processLocalOffers.size + nodeLocalOffers.size + rackLocalOffers.size + otherOffers.size)
-
-        // First process local, then host local, then rack, then others
-
-        // numNodeLocalOffers contains count of both process local and host offers.
-        val numNodeLocalOffers = {
-          val processLocalPriorityList = ClusterScheduler.prioritizeContainers(processLocalOffers)
-          offersPriorityList ++= processLocalPriorityList
-
-          val nodeLocalPriorityList = ClusterScheduler.prioritizeContainers(nodeLocalOffers)
-          offersPriorityList ++= nodeLocalPriorityList
-
-          processLocalPriorityList.size + nodeLocalPriorityList.size
-        }
-        val numRackLocalOffers = {
-          val rackLocalPriorityList = ClusterScheduler.prioritizeContainers(rackLocalOffers)
-          offersPriorityList ++= rackLocalPriorityList
-          rackLocalPriorityList.size
-        }
-        offersPriorityList ++= ClusterScheduler.prioritizeContainers(otherOffers)
-
-        var lastLoop = false
-        val lastLoopIndex = TASK_SCHEDULING_AGGRESSION match {
-          case TaskLocality.NODE_LOCAL => numNodeLocalOffers
-          case TaskLocality.RACK_LOCAL => numRackLocalOffers + numNodeLocalOffers
-          case TaskLocality.ANY => offersPriorityList.size
         }
+      } while (launchedTask)
+    }
 
-        do {
-          launchedTask = false
-          var loopCount = 0
-          for (i <- offersPriorityList) {
-            val execId = offers(i).executorId
-            val hostPort = offers(i).hostPort
-
-            // If last loop and within the lastLoopIndex, expand scope - else use null (which will use default/existing)
-            val overrideLocality = if (lastLoop && loopCount < lastLoopIndex) TASK_SCHEDULING_AGGRESSION else null
-
-            // If last loop, override waiting for host locality - we scheduled all local tasks already and there might be more available ...
-            loopCount += 1
-
-            manager.slaveOffer(execId, hostPort, availableCpus(i), overrideLocality) match {
-              case Some(task) =>
-                tasks(i) += task
-                val tid = task.taskId
-                taskIdToTaskSetId(tid) = manager.taskSet.id
-                taskSetTaskIds(manager.taskSet.id) += tid
-                taskIdToExecutorId(tid) = execId
-                activeExecutorIds += execId
-                executorsByHostPort(hostPort) += execId
-                availableCpus(i) -= 1
-                launchedTask = true
-
-              case None => {}
-              }
-            }
-          // Loop once more - when lastLoop = true, then we try to schedule task on all nodes irrespective of
-          // data locality (we still go in order of priority : but that would not change anything since
-          // if data local tasks had been available, we would have scheduled them already)
-          if (lastLoop) {
-            // prevent more looping
-            launchedTask = false
-          } else if (!lastLoop && !launchedTask) {
-            // Do this only if TASK_SCHEDULING_AGGRESSION != NODE_LOCAL
-            if (TASK_SCHEDULING_AGGRESSION != TaskLocality.NODE_LOCAL) {
-              // fudge launchedTask to ensure we loop once more
-              launchedTask = true
-              // dont loop anymore
-              lastLoop = true
-            }
-          }
-        } while (launchedTask)
-      }
-
-      if (tasks.size > 0) {
-        hasLaunchedTask = true
-      }
-      return tasks
+    if (tasks.size > 0) {
+      hasLaunchedTask = true
     }
+    return tasks
   }
 
   def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) {
@@ -443,7 +278,6 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
       backend.reviveOffers()
     }
     if (taskFailed) {
-
       // Also revive offers if a task had failed for some reason other than host lost
       backend.reviveOffers()
     }
@@ -498,7 +332,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
   }
 
   // Check for pending tasks in all our active jobs.
-  def hasPendingTasks(): Boolean = {
+  def hasPendingTasks: Boolean = {
     synchronized {
       rootPool.hasPendingTasks()
     }
@@ -509,7 +343,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
 
     synchronized {
       if (activeExecutorIds.contains(executorId)) {
-        val hostPort = executorIdToHostPort(executorId)
+        val hostPort = executorIdToHost(executorId)
         logError("Lost executor %s on %s: %s".format(executorId, hostPort, reason))
         removeExecutor(executorId)
         failedExecutor = Some(executorId)
@@ -531,88 +365,63 @@ private[spark] class ClusterScheduler(val sc: SparkContext)
   /** Remove an executor from all our data structures and mark it as lost */
   private def removeExecutor(executorId: String) {
     activeExecutorIds -= executorId
-    val hostPort = executorIdToHostPort(executorId)
-    if (hostPortsAlive.contains(hostPort)) {
-      // DEBUG Code
-      Utils.checkHostPort(hostPort)
-
-      hostPortsAlive -= hostPort
-      hostToAliveHostPorts.getOrElseUpdate(Utils.parseHostPort(hostPort)._1, new HashSet[String]).remove(hostPort)
-    }
-
-    val execs = executorsByHostPort.getOrElse(hostPort, new HashSet)
+    val host = executorIdToHost(executorId)
+    val execs = executorsByHost.getOrElse(host, new HashSet)
     execs -= executorId
     if (execs.isEmpty) {
-      executorsByHostPort -= hostPort
+      executorsByHost -= host
     }
-    executorIdToHostPort -= executorId
-    rootPool.executorLost(executorId, hostPort)
+    executorIdToHost -= executorId
+    rootPool.executorLost(executorId, host)
   }
 
-  def executorGained(execId: String, hostPort: String) {
-    listener.executorGained(execId, hostPort)
+  def executorGained(execId: String, host: String) {
+    listener.executorGained(execId, host)
   }
 
-  def getExecutorsAliveOnHost(host: String): Option[Set[String]] = {
-    Utils.checkHost(host)
-
-    val retval = hostToAliveHostPorts.get(host)
-    if (retval.isDefined) {
-      return Some(retval.get.toSet)
-    }
+  def getExecutorsAliveOnHost(host: String): Option[Set[String]] = synchronized {
+    executorsByHost.get(host).map(_.toSet)
+  }
 
-    None
+  def hasExecutorsAliveOnHost(host: String): Boolean = synchronized {
+    executorsByHost.contains(host)
   }
 
-  def isExecutorAliveOnHostPort(hostPort: String): Boolean = {
-    // Even if hostPort is a host, it does not matter - it is just a specific check.
-    // But we do have to ensure that only hostPort get into hostPortsAlive !
-    // So no check against Utils.checkHostPort
-    hostPortsAlive.contains(hostPort)
+  def isExecutorAlive(execId: String): Boolean = synchronized {
+    activeExecutorIds.contains(execId)
   }
 
   // By default, rack is unknown
   def getRackForHost(value: String): Option[String] = None
-
-  // By default, (cached) hosts for rack is unknown
-  def getCachedHostsForRack(rack: String): Option[Set[String]] = None
 }
 
 
 object ClusterScheduler {
-
-  // Used to 'spray' available containers across the available set to ensure too many containers on same host
-  // are not used up. Used in yarn mode and in task scheduling (when there are multiple containers available
-  // to execute a task)
-  // For example: yarn can returns more containers than we would have requested under ANY, this method
-  // prioritizes how to use the allocated containers.
-  // flatten the map such that the array buffer entries are spread out across the returned value.
-  // given <host, list[container]> == <h1, [c1 .. c5]>, <h2, [c1 .. c3]>, <h3, [c1, c2]>, <h4, c1>, <h5, c1>, i
-  // the return value would be something like : h1c1, h2c1, h3c1, h4c1, h5c1, h1c2, h2c2, h3c2, h1c3, h2c3, h1c4, h1c5
-  // We then 'use' the containers in this order (consuming only the top K from this list where
-  // K = number to be user). This is to ensure that if we have multiple eligible allocations,
-  // they dont end up allocating all containers on a small number of hosts - increasing probability of
-  // multiple container failure when a host goes down.
-  // Note, there is bias for keys with higher number of entries in value to be picked first (by design)
-  // Also note that invocation of this method is expected to have containers of same 'type'
-  // (host-local, rack-local, off-rack) and not across types : so that reordering is simply better from
-  // the available list - everything else being same.
-  // That is, we we first consume data local, then rack local and finally off rack nodes. So the
-  // prioritization from this method applies to within each category
+  /**
+   * Used to balance containers across hosts.
+   *
+   * Accepts a map of hosts to resource offers for that host, and returns a prioritized list of
+   * resource offers representing the order in which the offers should be used.  The resource
+   * offers are ordered such that we'll allocate one container on each host before allocating a
+   * second container on any host, and so on, in order to reduce the damage if a host fails.
+   *
+   * For example, given <h1, [o1, o2, o3]>, <h2, [o4]>, <h1, [o5, o6]>, returns
+   * [o1, o5, o4, 02, o6, o3]
+   */
   def prioritizeContainers[K, T] (map: HashMap[K, ArrayBuffer[T]]): List[T] = {
     val _keyList = new ArrayBuffer[K](map.size)
     _keyList ++= map.keys
 
     // order keyList based on population of value in map
     val keyList = _keyList.sortWith(
-      (left, right) => map.get(left).getOrElse(Set()).size > map.get(right).getOrElse(Set()).size
+      (left, right) => map(left).size > map(right).size
     )
 
     val retval = new ArrayBuffer[T](keyList.size * 2)
     var index = 0
     var found = true
 
-    while (found){
+    while (found) {
       found = false
       for (key <- keyList) {
         val containerList: ArrayBuffer[T] = map.get(key).getOrElse(null)
diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala
index 860a38e9f8a01d35230e05f99ebe8221ceb350c8..a4d6880abb55d7b6d2c35c98fb6574a31c45439c 100644
--- a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala
@@ -29,70 +29,44 @@ import scala.math.min
 import spark.{FetchFailed, Logging, Resubmitted, SparkEnv, Success, TaskEndReason, TaskState, Utils}
 import spark.{ExceptionFailure, SparkException, TaskResultTooBigFailure}
 import spark.TaskState.TaskState
-import spark.scheduler.{ShuffleMapTask, Task, TaskResult, TaskSet}
+import spark.scheduler._
+import scala.Some
+import spark.FetchFailed
+import spark.ExceptionFailure
+import spark.TaskResultTooBigFailure
+import spark.util.{SystemClock, Clock}
 
 
-private[spark] object TaskLocality
-  extends Enumeration("PROCESS_LOCAL", "NODE_LOCAL", "RACK_LOCAL", "ANY") with Logging {
-
-  // process local is expected to be used ONLY within tasksetmanager for now.
-  val PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY = Value
-
-  type TaskLocality = Value
-
-  def isAllowed(constraint: TaskLocality, condition: TaskLocality): Boolean = {
-
-    // Must not be the constraint.
-    assert (constraint != TaskLocality.PROCESS_LOCAL)
-
-    constraint match {
-      case TaskLocality.NODE_LOCAL =>
-        condition == TaskLocality.NODE_LOCAL
-      case TaskLocality.RACK_LOCAL =>
-        condition == TaskLocality.NODE_LOCAL || condition == TaskLocality.RACK_LOCAL
-      // For anything else, allow
-      case _ => true
-    }
-  }
-
-  def parse(str: String): TaskLocality = {
-    // better way to do this ?
-    try {
-      val retval = TaskLocality.withName(str)
-      // Must not specify PROCESS_LOCAL !
-      assert (retval != TaskLocality.PROCESS_LOCAL)
-      retval
-    } catch {
-      case nEx: NoSuchElementException => {
-        logWarning("Invalid task locality specified '" + str + "', defaulting to NODE_LOCAL")
-        // default to preserve earlier behavior
-        NODE_LOCAL
-      }
-    }
-  }
-}
-
 /**
- * Schedules the tasks within a single TaskSet in the ClusterScheduler.
+ * Schedules the tasks within a single TaskSet in the ClusterScheduler. This class keeps track of
+ * the status of each task, retries tasks if they fail (up to a limited number of times), and
+ * handles locality-aware scheduling for this TaskSet via delay scheduling. The main interfaces
+ * to it are resourceOffer, which asks the TaskSet whether it wants to run a task on one node,
+ * and statusUpdate, which tells it that one of its tasks changed state (e.g. finished).
+ *
+ * THREADING: This class is designed to only be called from code with a lock on the
+ * ClusterScheduler (e.g. its event handlers). It should not be called from other threads.
  */
-private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet: TaskSet)
-  extends TaskSetManager with Logging {
-
-  // Maximum time to wait to run a task in a preferred location (in ms)
-  val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong
-
+private[spark] class ClusterTaskSetManager(
+    sched: ClusterScheduler,
+    val taskSet: TaskSet,
+    clock: Clock = SystemClock)
+  extends TaskSetManager
+  with Logging
+{
   // CPUs to request per task
-  val CPUS_PER_TASK = System.getProperty("spark.task.cpus", "1").toDouble
+  val CPUS_PER_TASK = System.getProperty("spark.task.cpus", "1").toInt
 
   // Maximum times a task is allowed to fail before failing the job
-  val MAX_TASK_FAILURES = 4
+  val MAX_TASK_FAILURES = System.getProperty("spark.task.maxFailures", "4").toInt
 
   // Quantile of tasks at which to start speculation
   val SPECULATION_QUANTILE = System.getProperty("spark.speculation.quantile", "0.75").toDouble
   val SPECULATION_MULTIPLIER = System.getProperty("spark.speculation.multiplier", "1.5").toDouble
 
   // Serializer for closures and tasks.
-  val ser = SparkEnv.get.closureSerializer.newInstance()
+  val env = SparkEnv.get
+  val ser = env.closureSerializer.newInstance()
 
   val tasks = taskSet.tasks
   val numTasks = tasks.length
@@ -107,34 +81,29 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
   var runningTasks = 0
   var priority = taskSet.priority
   var stageId = taskSet.stageId
-  var name = "TaskSet_" + taskSet.stageId.toString
+  var name = "TaskSet_"+taskSet.stageId.toString
   var parent: Schedulable = null
 
-  // Last time when we launched a preferred task (for delay scheduling)
-  var lastPreferredLaunchTime = System.currentTimeMillis
-
-  // List of pending tasks for each node (process local to container).
-  // These collections are actually
+  // Set of pending tasks for each executor. These collections are actually
   // treated as stacks, in which new tasks are added to the end of the
   // ArrayBuffer and removed from the end. This makes it faster to detect
   // tasks that repeatedly fail because whenever a task failed, it is put
   // back at the head of the stack. They are also only cleaned up lazily;
   // when a task is launched, it remains in all the pending lists except
   // the one that it was launched from, but gets removed from them later.
-  private val pendingTasksForHostPort = new HashMap[String, ArrayBuffer[Int]]
+  private val pendingTasksForExecutor = new HashMap[String, ArrayBuffer[Int]]
 
-  // List of pending tasks for each node.
-  // Essentially, similar to pendingTasksForHostPort, except at host level
+  // Set of pending tasks for each host. Similar to pendingTasksForExecutor,
+  // but at host level.
   private val pendingTasksForHost = new HashMap[String, ArrayBuffer[Int]]
 
-  // List of pending tasks for each node based on rack locality.
-  // Essentially, similar to pendingTasksForHost, except at rack level
-  private val pendingRackLocalTasksForHost = new HashMap[String, ArrayBuffer[Int]]
+  // Set of pending tasks for each rack -- similar to the above.
+  private val pendingTasksForRack = new HashMap[String, ArrayBuffer[Int]]
 
-  // List containing pending tasks with no locality preferences
+  // Set containing pending tasks with no locality preferences.
   val pendingTasksWithNoPrefs = new ArrayBuffer[Int]
 
-  // List containing all pending tasks (also used as a stack, as above)
+  // Set containing all pending tasks (also used as a stack, as above).
   val allPendingTasks = new ArrayBuffer[Int]
 
   // Tasks that can be speculated. Since these will be a small fraction of total
@@ -144,25 +113,24 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
   // Task index, start and finish time for each task attempt (indexed by task ID)
   val taskInfos = new HashMap[Long, TaskInfo]
 
-  // Did the job fail?
+  // Did the TaskSet fail?
   var failed = false
   var causeOfFailure = ""
 
   // How frequently to reprint duplicate exceptions in full, in milliseconds
   val EXCEPTION_PRINT_INTERVAL =
     System.getProperty("spark.logging.exceptionPrintInterval", "10000").toLong
-  // Map of recent exceptions (identified by string representation and
-  // top stack frame) to duplicate count (how many times the same
-  // exception has appeared) and time the full exception was
-  // printed. This should ideally be an LRU map that can drop old
-  // exceptions automatically.
+
+  // Map of recent exceptions (identified by string representation and top stack frame) to
+  // duplicate count (how many times the same exception has appeared) and time the full exception
+  // was printed. This should ideally be an LRU map that can drop old exceptions automatically.
   val recentExceptions = HashMap[String, (Int, Long)]()
 
-  // Figure out the current map output tracker generation and set it on all tasks
-  val generation = sched.mapOutputTracker.getGeneration
-  logDebug("Generation for " + taskSet.id + ": " + generation)
+  // Figure out the current map output tracker epoch and set it on all tasks
+  val epoch = sched.mapOutputTracker.getEpoch
+  logDebug("Epoch for " + taskSet + ": " + epoch)
   for (t <- tasks) {
-    t.generation = generation
+    t.epoch = epoch
   }
 
   // Add all our tasks to the pending lists. We do this in reverse order
@@ -171,166 +139,86 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
     addPendingTask(i)
   }
 
-  // Note that it follows the hierarchy.
-  // if we search for NODE_LOCAL, the output will include PROCESS_LOCAL and
-  // if we search for RACK_LOCAL, it will include PROCESS_LOCAL & NODE_LOCAL
-  private def findPreferredLocations(
-      _taskPreferredLocations: Seq[String],
-      scheduler: ClusterScheduler,
-      taskLocality: TaskLocality.TaskLocality): HashSet[String] =
-  {
-    if (TaskLocality.PROCESS_LOCAL == taskLocality) {
-      // straight forward comparison ! Special case it.
-      val retval = new HashSet[String]()
-      scheduler.synchronized {
-        for (location <- _taskPreferredLocations) {
-          if (scheduler.isExecutorAliveOnHostPort(location)) {
-            retval += location
-          }
-        }
-      }
-
-      return retval
-    }
-
-    val taskPreferredLocations = {
-      if (TaskLocality.NODE_LOCAL == taskLocality) {
-        _taskPreferredLocations
-      } else {
-        assert (TaskLocality.RACK_LOCAL == taskLocality)
-        // Expand set to include all 'seen' rack local hosts.
-        // This works since container allocation/management happens within master -
-        //  so any rack locality information is updated in msater.
-        // Best case effort, and maybe sort of kludge for now ... rework it later ?
-        val hosts = new HashSet[String]
-        _taskPreferredLocations.foreach(h => {
-          val rackOpt = scheduler.getRackForHost(h)
-          if (rackOpt.isDefined) {
-            val hostsOpt = scheduler.getCachedHostsForRack(rackOpt.get)
-            if (hostsOpt.isDefined) {
-              hosts ++= hostsOpt.get
-            }
-          }
+  // Figure out which locality levels we have in our TaskSet, so we can do delay scheduling
+  val myLocalityLevels = computeValidLocalityLevels()
+  val localityWaits = myLocalityLevels.map(getLocalityWait) // Time to wait at each level
 
-          // Ensure that irrespective of what scheduler says, host is always added !
-          hosts += h
-        })
+  // Delay scheduling variables: we keep track of our current locality level and the time we
+  // last launched a task at that level, and move up a level when localityWaits[curLevel] expires.
+  // We then move down if we manage to launch a "more local" task.
+  var currentLocalityIndex = 0    // Index of our current locality level in validLocalityLevels
+  var lastLaunchTime = clock.getTime()  // Time we last launched a task at this level
 
-        hosts
+  /**
+   * Add a task to all the pending-task lists that it should be on. If readding is set, we are
+   * re-adding the task so only include it in each list if it's not already there.
+   */
+  private def addPendingTask(index: Int, readding: Boolean = false) {
+    // Utility method that adds `index` to a list only if readding=false or it's not already there
+    def addTo(list: ArrayBuffer[Int]) {
+      if (!readding || !list.contains(index)) {
+        list += index
       }
     }
 
-    val retval = new HashSet[String]
-    scheduler.synchronized {
-      for (prefLocation <- taskPreferredLocations) {
-        val aliveLocationsOpt = scheduler.getExecutorsAliveOnHost(Utils.parseHostPort(prefLocation)._1)
-        if (aliveLocationsOpt.isDefined) {
-          retval ++= aliveLocationsOpt.get
+    var hadAliveLocations = false
+    for (loc <- tasks(index).preferredLocations) {
+      for (execId <- loc.executorId) {
+        if (sched.isExecutorAlive(execId)) {
+          addTo(pendingTasksForExecutor.getOrElseUpdate(execId, new ArrayBuffer))
+          hadAliveLocations = true
         }
       }
-    }
-
-    retval
-  }
-
-  // Add a task to all the pending-task lists that it should be on.
-  private def addPendingTask(index: Int) {
-    // We can infer hostLocalLocations from rackLocalLocations by joining it against
-    // tasks(index).preferredLocations (with appropriate hostPort <-> host conversion).
-    // But not doing it for simplicity sake. If this becomes a performance issue, modify it.
-    val locs = tasks(index).preferredLocations
-    val processLocalLocations = findPreferredLocations(locs, sched, TaskLocality.PROCESS_LOCAL)
-    val hostLocalLocations = findPreferredLocations(locs, sched, TaskLocality.NODE_LOCAL)
-    val rackLocalLocations = findPreferredLocations(locs, sched, TaskLocality.RACK_LOCAL)
-
-    if (rackLocalLocations.size == 0) {
-      // Current impl ensures this.
-      assert (processLocalLocations.size == 0)
-      assert (hostLocalLocations.size == 0)
-      pendingTasksWithNoPrefs += index
-    } else {
-
-      // process local locality
-      for (hostPort <- processLocalLocations) {
-        // DEBUG Code
-        Utils.checkHostPort(hostPort)
-
-        val hostPortList = pendingTasksForHostPort.getOrElseUpdate(hostPort, ArrayBuffer())
-        hostPortList += index
-      }
-
-      // host locality (includes process local)
-      for (hostPort <- hostLocalLocations) {
-        // DEBUG Code
-        Utils.checkHostPort(hostPort)
-
-        val host = Utils.parseHostPort(hostPort)._1
-        val hostList = pendingTasksForHost.getOrElseUpdate(host, ArrayBuffer())
-        hostList += index
+      if (sched.hasExecutorsAliveOnHost(loc.host)) {
+        addTo(pendingTasksForHost.getOrElseUpdate(loc.host, new ArrayBuffer))
+        for (rack <- sched.getRackForHost(loc.host)) {
+          addTo(pendingTasksForRack.getOrElseUpdate(rack, new ArrayBuffer))
+        }
+        hadAliveLocations = true
       }
+    }
 
-      // rack locality (includes process local and host local)
-      for (rackLocalHostPort <- rackLocalLocations) {
-        // DEBUG Code
-        Utils.checkHostPort(rackLocalHostPort)
-
-        val rackLocalHost = Utils.parseHostPort(rackLocalHostPort)._1
-        val list = pendingRackLocalTasksForHost.getOrElseUpdate(rackLocalHost, ArrayBuffer())
-        list += index
-      }
+    if (!hadAliveLocations) {
+      // Even though the task might've had preferred locations, all of those hosts or executors
+      // are dead; put it in the no-prefs list so we can schedule it elsewhere right away.
+      addTo(pendingTasksWithNoPrefs)
     }
 
-    allPendingTasks += index
+    if (!readding) {
+      allPendingTasks += index  // No point scanning this whole list to find the old task there
+    }
   }
 
-  // Return the pending tasks list for a given host port (process local), or an empty list if
-  // there is no map entry for that host
-  private def getPendingTasksForHostPort(hostPort: String): ArrayBuffer[Int] = {
-    // DEBUG Code
-    Utils.checkHostPort(hostPort)
-    pendingTasksForHostPort.getOrElse(hostPort, ArrayBuffer())
+  /**
+   * Return the pending tasks list for a given executor ID, or an empty list if
+   * there is no map entry for that host
+   */
+  private def getPendingTasksForExecutor(executorId: String): ArrayBuffer[Int] = {
+    pendingTasksForExecutor.getOrElse(executorId, ArrayBuffer())
   }
 
-  // Return the pending tasks list for a given host, or an empty list if
-  // there is no map entry for that host
-  private def getPendingTasksForHost(hostPort: String): ArrayBuffer[Int] = {
-    val host = Utils.parseHostPort(hostPort)._1
+  /**
+   * Return the pending tasks list for a given host, or an empty list if
+   * there is no map entry for that host
+   */
+  private def getPendingTasksForHost(host: String): ArrayBuffer[Int] = {
     pendingTasksForHost.getOrElse(host, ArrayBuffer())
   }
 
-  // Return the pending tasks (rack level) list for a given host, or an empty list if
-  // there is no map entry for that host
-  private def getRackLocalPendingTasksForHost(hostPort: String): ArrayBuffer[Int] = {
-    val host = Utils.parseHostPort(hostPort)._1
-    pendingRackLocalTasksForHost.getOrElse(host, ArrayBuffer())
-  }
-
-  // Number of pending tasks for a given host Port (which would be process local)
-  override def numPendingTasksForHostPort(hostPort: String): Int = {
-    getPendingTasksForHostPort(hostPort).count { index =>
-      copiesRunning(index) == 0 && !finished(index)
-    }
-  }
-
-  // Number of pending tasks for a given host (which would be data local)
-  override def numPendingTasksForHost(hostPort: String): Int = {
-    getPendingTasksForHost(hostPort).count { index =>
-      copiesRunning(index) == 0 && !finished(index)
-    }
-  }
-
-  // Number of pending rack local tasks for a given host
-  override def numRackLocalPendingTasksForHost(hostPort: String): Int = {
-    getRackLocalPendingTasksForHost(hostPort).count { index =>
-      copiesRunning(index) == 0 && !finished(index)
-    }
+  /**
+   * Return the pending rack-local task list for a given rack, or an empty list if
+   * there is no map entry for that rack
+   */
+  private def getPendingTasksForRack(rack: String): ArrayBuffer[Int] = {
+    pendingTasksForRack.getOrElse(rack, ArrayBuffer())
   }
 
-
-  // Dequeue a pending task from the given list and return its index.
-  // Return None if the list is empty.
-  // This method also cleans up any tasks in the list that have already
-  // been launched, since we want that to happen lazily.
+  /**
+   * Dequeue a pending task from the given list and return its index.
+   * Return None if the list is empty.
+   * This method also cleans up any tasks in the list that have already
+   * been launched, since we want that to happen lazily.
+   */
   private def findTaskFromList(list: ArrayBuffer[Int]): Option[Int] = {
     while (!list.isEmpty) {
       val index = list.last
@@ -342,191 +230,158 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
     return None
   }
 
-  // Return a speculative task for a given host if any are available. The task should not have an
-  // attempt running on this host, in case the host is slow. In addition, if locality is set, the
-  // task must have a preference for this host/rack/no preferred locations at all.
-  private def findSpeculativeTask(hostPort: String, locality: TaskLocality.TaskLocality): Option[Int] = {
+  /** Check whether a task is currently running an attempt on a given host */
+  private def hasAttemptOnHost(taskIndex: Int, host: String): Boolean = {
+    !taskAttempts(taskIndex).exists(_.host == host)
+  }
 
-    assert (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL))
+  /**
+   * Return a speculative task for a given executor if any are available. The task should not have
+   * an attempt running on this host, in case the host is slow. In addition, the task should meet
+   * the given locality constraint.
+   */
+  private def findSpeculativeTask(execId: String, host: String, locality: TaskLocality.Value)
+    : Option[(Int, TaskLocality.Value)] =
+  {
     speculatableTasks.retain(index => !finished(index)) // Remove finished tasks from set
 
-    if (speculatableTasks.size > 0) {
-      val localTask = speculatableTasks.find { index =>
-        val locations = findPreferredLocations(tasks(index).preferredLocations, sched,
-          TaskLocality.NODE_LOCAL)
-        val attemptLocs = taskAttempts(index).map(_.hostPort)
-        (locations.size == 0 || locations.contains(hostPort)) && !attemptLocs.contains(hostPort)
+    if (!speculatableTasks.isEmpty) {
+      // Check for process-local or preference-less tasks; note that tasks can be process-local
+      // on multiple nodes when we replicate cached blocks, as in Spark Streaming
+      for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) {
+        val prefs = tasks(index).preferredLocations
+        val executors = prefs.flatMap(_.executorId)
+        if (prefs.size == 0 || executors.contains(execId)) {
+          speculatableTasks -= index
+          return Some((index, TaskLocality.PROCESS_LOCAL))
+        }
       }
 
-      if (localTask != None) {
-        speculatableTasks -= localTask.get
-        return localTask
+      // Check for node-local tasks
+      if (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) {
+        for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) {
+          val locations = tasks(index).preferredLocations.map(_.host)
+          if (locations.contains(host)) {
+            speculatableTasks -= index
+            return Some((index, TaskLocality.NODE_LOCAL))
+          }
+        }
       }
 
-      // check for rack locality
+      // Check for rack-local tasks
       if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) {
-        val rackTask = speculatableTasks.find { index =>
-          val locations = findPreferredLocations(tasks(index).preferredLocations, sched,
-            TaskLocality.RACK_LOCAL)
-          val attemptLocs = taskAttempts(index).map(_.hostPort)
-          locations.contains(hostPort) && !attemptLocs.contains(hostPort)
-        }
-
-        if (rackTask != None) {
-          speculatableTasks -= rackTask.get
-          return rackTask
+        for (rack <- sched.getRackForHost(host)) {
+          for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) {
+            val racks = tasks(index).preferredLocations.map(_.host).map(sched.getRackForHost)
+            if (racks.contains(rack)) {
+              speculatableTasks -= index
+              return Some((index, TaskLocality.RACK_LOCAL))
+            }
+          }
         }
       }
 
-      // Any task ...
+      // Check for non-local tasks
       if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) {
-        // Check for attemptLocs also ?
-        val nonLocalTask = speculatableTasks.find { i =>
-          !taskAttempts(i).map(_.hostPort).contains(hostPort)
-        }
-        if (nonLocalTask != None) {
-          speculatableTasks -= nonLocalTask.get
-          return nonLocalTask
+        for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) {
+          speculatableTasks -= index
+          return Some((index, TaskLocality.ANY))
         }
       }
     }
+
     return None
   }
 
-  // Dequeue a pending task for a given node and return its index.
-  // If localOnly is set to false, allow non-local tasks as well.
-  private def findTask(hostPort: String, locality: TaskLocality.TaskLocality): Option[Int] = {
-    val processLocalTask = findTaskFromList(getPendingTasksForHostPort(hostPort))
-    if (processLocalTask != None) {
-      return processLocalTask
+  /**
+   * Dequeue a pending task for a given node and return its index and locality level.
+   * Only search for tasks matching the given locality constraint.
+   */
+  private def findTask(execId: String, host: String, locality: TaskLocality.Value)
+    : Option[(Int, TaskLocality.Value)] =
+  {
+    for (index <- findTaskFromList(getPendingTasksForExecutor(execId))) {
+      return Some((index, TaskLocality.PROCESS_LOCAL))
     }
 
-    val localTask = findTaskFromList(getPendingTasksForHost(hostPort))
-    if (localTask != None) {
-      return localTask
+    if (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) {
+      for (index <- findTaskFromList(getPendingTasksForHost(host))) {
+        return Some((index, TaskLocality.NODE_LOCAL))
+      }
     }
 
     if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) {
-      val rackLocalTask = findTaskFromList(getRackLocalPendingTasksForHost(hostPort))
-      if (rackLocalTask != None) {
-        return rackLocalTask
+      for {
+        rack <- sched.getRackForHost(host)
+        index <- findTaskFromList(getPendingTasksForRack(rack))
+      } {
+        return Some((index, TaskLocality.RACK_LOCAL))
       }
     }
 
-    // Look for no pref tasks AFTER rack local tasks - this has side effect that we will get to
-    // failed tasks later rather than sooner.
-    // TODO: That code path needs to be revisited (adding to no prefs list when host:port goes down).
-    val noPrefTask = findTaskFromList(pendingTasksWithNoPrefs)
-    if (noPrefTask != None) {
-      return noPrefTask
+    // Look for no-pref tasks after rack-local tasks since they can run anywhere.
+    for (index <- findTaskFromList(pendingTasksWithNoPrefs)) {
+      return Some((index, TaskLocality.PROCESS_LOCAL))
     }
 
     if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) {
-      val nonLocalTask = findTaskFromList(allPendingTasks)
-      if (nonLocalTask != None) {
-        return nonLocalTask
+      for (index <- findTaskFromList(allPendingTasks)) {
+        return Some((index, TaskLocality.ANY))
       }
     }
 
     // Finally, if all else has failed, find a speculative task
-    return findSpeculativeTask(hostPort, locality)
-  }
-
-  private def isProcessLocalLocation(task: Task[_], hostPort: String): Boolean = {
-    Utils.checkHostPort(hostPort)
-
-    val locs = task.preferredLocations
-
-    locs.contains(hostPort)
-  }
-
-  private def isHostLocalLocation(task: Task[_], hostPort: String): Boolean = {
-    val locs = task.preferredLocations
-
-    // If no preference, consider it as host local
-    if (locs.isEmpty) return true
-
-    val host = Utils.parseHostPort(hostPort)._1
-    locs.find(h => Utils.parseHostPort(h)._1 == host).isDefined
+    return findSpeculativeTask(execId, host, locality)
   }
 
-  // Does a host count as a rack local preferred location for a task?
-  // (assumes host is NOT preferred location).
-  // This is true if either the task has preferred locations and this host is one, or it has
-  // no preferred locations (in which we still count the launch as preferred).
-  private def isRackLocalLocation(task: Task[_], hostPort: String): Boolean = {
-
-    val locs = task.preferredLocations
-
-    val preferredRacks = new HashSet[String]()
-    for (preferredHost <- locs) {
-      val rack = sched.getRackForHost(preferredHost)
-      if (None != rack) preferredRacks += rack.get
-    }
-
-    if (preferredRacks.isEmpty) return false
-
-    val hostRack = sched.getRackForHost(hostPort)
-
-    return None != hostRack && preferredRacks.contains(hostRack.get)
-  }
-
-  // Respond to an offer of a single slave from the scheduler by finding a task
-  override def slaveOffer(
+  /**
+   * Respond to an offer of a single slave from the scheduler by finding a task
+   */
+  override def resourceOffer(
       execId: String,
-      hostPort: String,
-      availableCpus: Double,
-      overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] =
+      host: String,
+      availableCpus: Int,
+      maxLocality: TaskLocality.TaskLocality)
+    : Option[TaskDescription] =
   {
     if (tasksFinished < numTasks && availableCpus >= CPUS_PER_TASK) {
-      // If explicitly specified, use that
-      val locality = if (overrideLocality != null) overrideLocality else {
-        // expand only if we have waited for more than LOCALITY_WAIT for a host local task ...
-        val time = System.currentTimeMillis
-        if (time - lastPreferredLaunchTime < LOCALITY_WAIT) {
-          TaskLocality.NODE_LOCAL
-        } else {
-          TaskLocality.ANY
-        }
+      val curTime = clock.getTime()
+
+      var allowedLocality = getAllowedLocalityLevel(curTime)
+      if (allowedLocality > maxLocality) {
+        allowedLocality = maxLocality   // We're not allowed to search for farther-away tasks
       }
 
-      findTask(hostPort, locality) match {
-        case Some(index) => {
-          // Found a task; do some bookkeeping and return a Mesos task for it
+      findTask(execId, host, allowedLocality) match {
+        case Some((index, taskLocality)) => {
+          // Found a task; do some bookkeeping and return a task description
           val task = tasks(index)
           val taskId = sched.newTaskId()
           // Figure out whether this should count as a preferred launch
-          val taskLocality =
-            if (isProcessLocalLocation(task, hostPort)) TaskLocality.PROCESS_LOCAL
-            else if (isHostLocalLocation(task, hostPort)) TaskLocality.NODE_LOCAL
-            else if (isRackLocalLocation(task, hostPort)) TaskLocality.RACK_LOCAL
-            else  TaskLocality.ANY
-          val prefStr = taskLocality.toString
           logInfo("Starting task %s:%d as TID %s on slave %s: %s (%s)".format(
-            taskSet.id, index, taskId, execId, hostPort, prefStr))
+            taskSet.id, index, taskId, execId, host, taskLocality))
           // Do various bookkeeping
           copiesRunning(index) += 1
-          val time = System.currentTimeMillis
-          val info = new TaskInfo(taskId, index, time, execId, hostPort, taskLocality)
+          val info = new TaskInfo(taskId, index, curTime, execId, host, taskLocality)
           taskInfos(taskId) = info
           taskAttempts(index) = info :: taskAttempts(index)
-          if (taskLocality == TaskLocality.PROCESS_LOCAL || taskLocality == TaskLocality.NODE_LOCAL) {
-            lastPreferredLaunchTime = time
-          }
+          // Update our locality level for delay scheduling
+          currentLocalityIndex = getLocalityIndex(taskLocality)
+          lastLaunchTime = curTime
           // Serialize and return the task
-          val startTime = System.currentTimeMillis
+          val startTime = clock.getTime()
           // We rely on the DAGScheduler to catch non-serializable closures and RDDs, so in here
           // we assume the task can be serialized without exceptions.
           val serializedTask = Task.serializeWithDependencies(
             task, sched.sc.addedFiles, sched.sc.addedJars, ser)
-          val timeTaken = System.currentTimeMillis - startTime
+          val timeTaken = clock.getTime() - startTime
           increaseRunningTasks(1)
           logInfo("Serialized task %s:%d as %d bytes in %d ms".format(
             taskSet.id, index, serializedTask.limit, timeTaken))
           val taskName = "task %s:%d".format(taskSet.id, index)
           if (taskAttempts(index).size == 1)
             taskStarted(task,info)
-          return Some(new TaskDescription(taskId, execId, taskName, serializedTask))
+          return Some(new TaskDescription(taskId, execId, taskName, index, serializedTask))
         }
         case _ =>
       }
@@ -534,7 +389,37 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
     return None
   }
 
+  /**
+   * Get the level we can launch tasks according to delay scheduling, based on current wait time.
+   */
+  private def getAllowedLocalityLevel(curTime: Long): TaskLocality.TaskLocality = {
+    while (curTime - lastLaunchTime >= localityWaits(currentLocalityIndex) &&
+        currentLocalityIndex < myLocalityLevels.length - 1)
+    {
+      // Jump to the next locality level, and remove our waiting time for the current one since
+      // we don't want to count it again on the next one
+      lastLaunchTime += localityWaits(currentLocalityIndex)
+      currentLocalityIndex += 1
+    }
+    myLocalityLevels(currentLocalityIndex)
+  }
+
+  /**
+   * Find the index in myLocalityLevels for a given locality. This is also designed to work with
+   * localities that are not in myLocalityLevels (in case we somehow get those) by returning the
+   * next-biggest level we have. Uses the fact that the last value in myLocalityLevels is ANY.
+   */
+  def getLocalityIndex(locality: TaskLocality.TaskLocality): Int = {
+    var index = 0
+    while (locality > myLocalityLevels(index)) {
+      index += 1
+    }
+    index
+  }
+
+  /** Called by cluster scheduler when one of our tasks changes state */
   override def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) {
+    SparkEnv.set(env)
     state match {
       case TaskState.FINISHED =>
         taskFinished(tid, state, serializedData)
@@ -564,8 +449,8 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
     decreaseRunningTasks(1)
     if (!finished(index)) {
       tasksFinished += 1
-      logInfo("Finished TID %s in %d ms (progress: %d/%d)".format(
-        tid, info.duration, tasksFinished, numTasks))
+      logInfo("Finished TID %s in %d ms on %s (progress: %d/%d)".format(
+        tid, info.duration, info.host, tasksFinished, numTasks))
       // Deserialize task result and pass it to the scheduler
       try {
         val result = ser.deserialize[TaskResult[_]](serializedData)
@@ -625,7 +510,7 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
           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 now = clock.getTime()
             val (printFull, dupCount) = {
               if (recentExceptions.contains(key)) {
                 val (dupCount, printTime) = recentExceptions(key)
@@ -697,44 +582,33 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
     }
   }
 
-  // TODO: for now we just find Pool not TaskSetManager,
-  // we can extend this function in future if needed
   override def getSchedulableByName(name: String): Schedulable = {
     return null
   }
 
-  override def addSchedulable(schedulable:Schedulable) {
-    //nothing
-  }
+  override def addSchedulable(schedulable: Schedulable) {}
 
-  override def removeSchedulable(schedulable:Schedulable) {
-    //nothing
-  }
+  override def removeSchedulable(schedulable: Schedulable) {}
 
   override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = {
-    var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager]
+    var sortedTaskSetQueue = ArrayBuffer[TaskSetManager](this)
     sortedTaskSetQueue += this
     return sortedTaskSetQueue
   }
 
-  override def executorLost(execId: String, hostPort: String) {
+  /** Called by cluster scheduler when an executor is lost so we can re-enqueue our tasks */
+  override def executorLost(execId: String, host: String) {
     logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id)
 
-    // If some task has preferred locations only on hostname, and there are no more executors there,
-    // put it in the no-prefs list to avoid the wait from delay scheduling
-
-    // host local tasks - should we push this to rack local or no pref list ? For now, preserving
-    // behavior and moving to no prefs list. Note, this was done due to impliations related to
-    // 'waiting' for data local tasks, etc.
-    // Note: NOT checking process local list - since host local list is super set of that. We need
-    // to ad to no prefs only if there is no host local node for the task (not if there is no
-    // process local node for the task)
-    for (index <- getPendingTasksForHost(Utils.parseHostPort(hostPort)._1)) {
-      val newLocs = findPreferredLocations(
-        tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL)
-      if (newLocs.isEmpty) {
-        pendingTasksWithNoPrefs += index
-      }
+    // Re-enqueue pending tasks for this host based on the status of the cluster -- for example, a
+    // task that used to have locations on only this host might now go to the no-prefs list. Note
+    // that it's okay if we add a task to the same queue twice (if it had multiple preferred
+    // locations), because findTaskFromList will skip already-running tasks.
+    for (index <- getPendingTasksForExecutor(execId)) {
+      addPendingTask(index, readding=true)
+    }
+    for (index <- getPendingTasksForHost(host)) {
+      addPendingTask(index, readding=true)
     }
 
     // Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage
@@ -774,7 +648,7 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
     val minFinishedForSpeculation = (SPECULATION_QUANTILE * numTasks).floor.toInt
     logDebug("Checking for speculative tasks: minFinished = " + minFinishedForSpeculation)
     if (tasksFinished >= minFinishedForSpeculation) {
-      val time = System.currentTimeMillis()
+      val time = clock.getTime()
       val durations = taskInfos.values.filter(_.successful).map(_.duration).toArray
       Arrays.sort(durations)
       val medianDuration = durations(min((0.5 * numTasks).round.toInt, durations.size - 1))
@@ -788,7 +662,7 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
           !speculatableTasks.contains(index)) {
           logInfo(
             "Marking task %s:%d (on %s) as speculatable because it ran more than %.0f ms".format(
-              taskSet.id, index, info.hostPort, threshold))
+              taskSet.id, index, info.host, threshold))
           speculatableTasks += index
           foundTasks = true
         }
@@ -800,4 +674,39 @@ private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet:
   override def hasPendingTasks(): Boolean = {
     numTasks > 0 && tasksFinished < numTasks
   }
+
+  private def getLocalityWait(level: TaskLocality.TaskLocality): Long = {
+    val defaultWait = System.getProperty("spark.locality.wait", "3000")
+    level match {
+      case TaskLocality.PROCESS_LOCAL =>
+        System.getProperty("spark.locality.wait.process", defaultWait).toLong
+      case TaskLocality.NODE_LOCAL =>
+        System.getProperty("spark.locality.wait.node", defaultWait).toLong
+      case TaskLocality.RACK_LOCAL =>
+        System.getProperty("spark.locality.wait.rack", defaultWait).toLong
+      case TaskLocality.ANY =>
+        0L
+    }
+  }
+
+  /**
+   * Compute the locality levels used in this TaskSet. Assumes that all tasks have already been
+   * added to queues using addPendingTask.
+   */
+  private def computeValidLocalityLevels(): Array[TaskLocality.TaskLocality] = {
+    import TaskLocality.{PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY}
+    val levels = new ArrayBuffer[TaskLocality.TaskLocality]
+    if (!pendingTasksForExecutor.isEmpty && getLocalityWait(PROCESS_LOCAL) != 0) {
+      levels += PROCESS_LOCAL
+    }
+    if (!pendingTasksForHost.isEmpty && getLocalityWait(NODE_LOCAL) != 0) {
+      levels += NODE_LOCAL
+    }
+    if (!pendingTasksForRack.isEmpty && getLocalityWait(RACK_LOCAL) != 0) {
+      levels += RACK_LOCAL
+    }
+    levels += ANY
+    logDebug("Valid locality levels for " + taskSet + ": " + levels.mkString(", "))
+    levels.toArray
+  }
 }
diff --git a/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala b/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala
index f557b142c4eb7861b64626bc3e89b194d245e587..e77e8e4162e1a435d33a6a84117920b71d60ba74 100644
--- a/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala
@@ -17,14 +17,18 @@
 
 package spark.scheduler.cluster
 
-import scala.collection.mutable.ArrayBuffer
+import spark.scheduler.cluster.SchedulingMode.SchedulingMode
 
+import scala.collection.mutable.ArrayBuffer
 /**
  * An interface for schedulable entities.
  * there are two type of Schedulable entities(Pools and TaskSetManagers)
  */
 private[spark] trait Schedulable {
   var parent: Schedulable
+  // child queues
+  def schedulableQueue: ArrayBuffer[Schedulable]
+  def schedulingMode: SchedulingMode
   def weight: Int
   def minShare: Int
   def runningTasks: Int
diff --git a/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala
index 95554023c02c0efcbb6b7fe063a7bc13075372df..2fc8a76a0575b868fad3f076bac068879fafdb42 100644
--- a/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala
@@ -17,19 +17,14 @@
 
 package spark.scheduler.cluster
 
-import java.io.{File, FileInputStream, FileOutputStream}
+import java.io.{File, FileInputStream, FileOutputStream, FileNotFoundException}
+import java.util.Properties
 
-import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.HashMap
-import scala.collection.mutable.HashSet
-import scala.util.control.Breaks._
-import scala.xml._
+import scala.xml.XML
 
 import spark.Logging
 import spark.scheduler.cluster.SchedulingMode.SchedulingMode
 
-import java.util.Properties
 
 /**
  * An interface to build Schedulable tree
@@ -41,10 +36,11 @@ private[spark] trait SchedulableBuilder {
   def addTaskSetManager(manager: Schedulable, properties: Properties)
 }
 
-private[spark] class FIFOSchedulableBuilder(val rootPool: Pool) extends SchedulableBuilder with Logging {
+private[spark] class FIFOSchedulableBuilder(val rootPool: Pool)
+  extends SchedulableBuilder with Logging {
 
   override def buildPools() {
-    //nothing
+    // nothing
   }
 
   override def addTaskSetManager(manager: Schedulable, properties: Properties) {
@@ -52,9 +48,10 @@ private[spark] class FIFOSchedulableBuilder(val rootPool: Pool) extends Schedula
   }
 }
 
-private[spark] class FairSchedulableBuilder(val rootPool: Pool) extends SchedulableBuilder with Logging {
+private[spark] class FairSchedulableBuilder(val rootPool: Pool)
+  extends SchedulableBuilder with Logging {
 
-  val schedulerAllocFile = System.getProperty("spark.fairscheduler.allocation.file","unspecified")
+  val schedulerAllocFile = System.getProperty("spark.fairscheduler.allocation.file")
   val FAIR_SCHEDULER_PROPERTIES = "spark.scheduler.cluster.fair.pool"
   val DEFAULT_POOL_NAME = "default"
   val MINIMUM_SHARES_PROPERTY = "minShare"
@@ -67,47 +64,53 @@ private[spark] class FairSchedulableBuilder(val rootPool: Pool) extends Schedula
   val DEFAULT_WEIGHT = 1
 
   override def buildPools() {
+    if (schedulerAllocFile != null) {
     val file = new File(schedulerAllocFile)
-    if (file.exists()) {
-      val xml = XML.loadFile(file)
-      for (poolNode <- (xml \\ POOLS_PROPERTY)) {
-
-        val poolName = (poolNode \ POOL_NAME_PROPERTY).text
-        var schedulingMode = DEFAULT_SCHEDULING_MODE
-        var minShare = DEFAULT_MINIMUM_SHARE
-        var weight = DEFAULT_WEIGHT
-
-        val xmlSchedulingMode = (poolNode \ SCHEDULING_MODE_PROPERTY).text
-        if (xmlSchedulingMode != "") {
-          try {
-            schedulingMode = SchedulingMode.withName(xmlSchedulingMode)
-          } catch {
-            case e: Exception => logInfo("Error xml schedulingMode, using default schedulingMode")
+      if (file.exists()) {
+        val xml = XML.loadFile(file)
+        for (poolNode <- (xml \\ POOLS_PROPERTY)) {
+
+          val poolName = (poolNode \ POOL_NAME_PROPERTY).text
+          var schedulingMode = DEFAULT_SCHEDULING_MODE
+          var minShare = DEFAULT_MINIMUM_SHARE
+          var weight = DEFAULT_WEIGHT
+
+          val xmlSchedulingMode = (poolNode \ SCHEDULING_MODE_PROPERTY).text
+          if (xmlSchedulingMode != "") {
+            try {
+              schedulingMode = SchedulingMode.withName(xmlSchedulingMode)
+            } catch {
+              case e: Exception => logInfo("Error xml schedulingMode, using default schedulingMode")
+            }
           }
-        }
 
-        val xmlMinShare = (poolNode \ MINIMUM_SHARES_PROPERTY).text
-        if (xmlMinShare != "") {
-          minShare = xmlMinShare.toInt
-        }
+          val xmlMinShare = (poolNode \ MINIMUM_SHARES_PROPERTY).text
+          if (xmlMinShare != "") {
+            minShare = xmlMinShare.toInt
+          }
 
-        val xmlWeight = (poolNode \ WEIGHT_PROPERTY).text
-        if (xmlWeight != "") {
-          weight = xmlWeight.toInt
-        }
+          val xmlWeight = (poolNode \ WEIGHT_PROPERTY).text
+          if (xmlWeight != "") {
+            weight = xmlWeight.toInt
+          }
 
-        val pool = new Pool(poolName, schedulingMode, minShare, weight)
-        rootPool.addSchedulable(pool)
-        logInfo("Create new pool with name:%s,schedulingMode:%s,minShare:%d,weight:%d".format(
-          poolName, schedulingMode, minShare, weight))
+          val pool = new Pool(poolName, schedulingMode, minShare, weight)
+          rootPool.addSchedulable(pool)
+          logInfo("Created pool %s, schedulingMode: %s, minShare: %d, weight: %d".format(
+            poolName, schedulingMode, minShare, weight))
+        }
+      } else {
+        throw new java.io.FileNotFoundException(
+          "Fair scheduler allocation file not found: " + schedulerAllocFile)
       }
     }
 
-    //finally create "default" pool
+    // finally create "default" pool
     if (rootPool.getSchedulableByName(DEFAULT_POOL_NAME) == null) {
-      val pool = new Pool(DEFAULT_POOL_NAME, DEFAULT_SCHEDULING_MODE, DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT)
+      val pool = new Pool(DEFAULT_POOL_NAME, DEFAULT_SCHEDULING_MODE,
+        DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT)
       rootPool.addSchedulable(pool)
-      logInfo("Create default pool with name:%s,schedulingMode:%s,minShare:%d,weight:%d".format(
+      logInfo("Created default pool %s, schedulingMode: %s, minShare: %d, weight: %d".format(
         DEFAULT_POOL_NAME, DEFAULT_SCHEDULING_MODE, DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT))
     }
   }
@@ -119,10 +122,12 @@ private[spark] class FairSchedulableBuilder(val rootPool: Pool) extends Schedula
       poolName = properties.getProperty(FAIR_SCHEDULER_PROPERTIES, DEFAULT_POOL_NAME)
       parentPool = rootPool.getSchedulableByName(poolName)
       if (parentPool == null) {
-        //we will create a new pool that user has configured in app instead of being defined in xml file
-        parentPool = new Pool(poolName,DEFAULT_SCHEDULING_MODE, DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT)
+        // we will create a new pool that user has configured in app
+        // instead of being defined in xml file
+        parentPool = new Pool(poolName, DEFAULT_SCHEDULING_MODE,
+          DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT)
         rootPool.addSchedulable(parentPool)
-        logInfo("Create pool with name:%s,schedulingMode:%s,minShare:%d,weight:%d".format(
+        logInfo("Created pool %s, schedulingMode: %s, minShare: %d, weight: %d".format(
           poolName, DEFAULT_SCHEDULING_MODE, DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT))
       }
     }
diff --git a/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala
index 4b3e3e50e13babcccd854c5aadcd0783b75db34d..55cdf4791f985725d7bf05f431da100ba281bb60 100644
--- a/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala
@@ -17,8 +17,13 @@
 
 package spark.scheduler.cluster
 
-object SchedulingMode extends Enumeration("FAIR","FIFO"){
+/**
+ *  "FAIR" and "FIFO" determines which policy is used
+ *    to order tasks amongst a Schedulable's sub-queues
+ *  "NONE" is used when the a Schedulable has no sub-queues.
+ */
+object SchedulingMode extends Enumeration("FAIR", "FIFO", "NONE") {
 
   type SchedulingMode = Value
-  val FAIR,FIFO = Value
+  val FAIR,FIFO,NONE = Value
 }
diff --git a/core/src/main/scala/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 55d6c0a47ef07fb7c02417e3243a96bfb05ad19c..42c3b4a6cf633773b8278ddcd1a1f8b23ac01b2b 100644
--- a/core/src/main/scala/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -77,7 +77,7 @@ private[spark] class SparkDeploySchedulerBackend(
 
   override def executorAdded(executorId: String, workerId: String, hostPort: String, cores: Int, memory: Int) {
     logInfo("Granted executor ID %s on hostPort %s with %d cores, %s RAM".format(
-       executorId, hostPort, cores, Utils.memoryMegabytesToString(memory)))
+       executorId, hostPort, cores, Utils.megabytesToString(memory)))
   }
 
   override def executorRemoved(executorId: String, message: String, exitStatus: Option[Int]) {
diff --git a/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala b/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala
index ac9e5ef94d33347fc6c0b5b69e25fa54e7a726c7..05c29eb72f65f573cc1d76334c07cf22f69322c0 100644
--- a/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala
@@ -17,46 +17,47 @@
 
 package spark.scheduler.cluster
 
-import spark.TaskState.TaskState
 import java.nio.ByteBuffer
-import spark.util.SerializableBuffer
+
+import spark.TaskState.TaskState
 import spark.Utils
+import spark.util.SerializableBuffer
+
 
 private[spark] sealed trait StandaloneClusterMessage extends Serializable
 
-// Driver to executors
-private[spark]
-case class LaunchTask(task: TaskDescription) extends StandaloneClusterMessage
+private[spark] object StandaloneClusterMessages {
 
-private[spark]
-case class RegisteredExecutor(sparkProperties: Seq[(String, String)])
-  extends StandaloneClusterMessage
+  // Driver to executors
+  case class LaunchTask(task: TaskDescription) extends StandaloneClusterMessage
 
-private[spark]
-case class RegisterExecutorFailed(message: String) extends StandaloneClusterMessage
+  case class RegisteredExecutor(sparkProperties: Seq[(String, String)])
+    extends StandaloneClusterMessage
 
-// Executors to driver
-private[spark]
-case class RegisterExecutor(executorId: String, hostPort: String, cores: Int)
-  extends StandaloneClusterMessage {
-  Utils.checkHostPort(hostPort, "Expected host port")
-}
+  case class RegisterExecutorFailed(message: String) extends StandaloneClusterMessage
 
-private[spark]
-case class StatusUpdate(executorId: String, taskId: Long, state: TaskState, data: SerializableBuffer)
-  extends StandaloneClusterMessage
+  // Executors to driver
+  case class RegisterExecutor(executorId: String, hostPort: String, cores: Int)
+    extends StandaloneClusterMessage {
+    Utils.checkHostPort(hostPort, "Expected host port")
+  }
+
+  case class StatusUpdate(executorId: String, taskId: Long, state: TaskState,
+    data: SerializableBuffer) extends StandaloneClusterMessage
 
-private[spark]
-object StatusUpdate {
-  /** Alternate factory method that takes a ByteBuffer directly for the data field */
-  def apply(executorId: String, taskId: Long, state: TaskState, data: ByteBuffer): StatusUpdate = {
-    StatusUpdate(executorId, taskId, state, new SerializableBuffer(data))
+  object StatusUpdate {
+    /** Alternate factory method that takes a ByteBuffer directly for the data field */
+    def apply(executorId: String, taskId: Long, state: TaskState, data: ByteBuffer)
+      : StatusUpdate = {
+      StatusUpdate(executorId, taskId, state, new SerializableBuffer(data))
+    }
   }
-}
 
-// Internal messages in driver
-private[spark] case object ReviveOffers extends StandaloneClusterMessage
-private[spark] case object StopDriver extends StandaloneClusterMessage
+  // Internal messages in driver
+  case object ReviveOffers extends StandaloneClusterMessage
 
-private[spark] case class RemoveExecutor(executorId: String, reason: String)
-  extends StandaloneClusterMessage
+  case object StopDriver extends StandaloneClusterMessage
+
+  case class RemoveExecutor(executorId: String, reason: String) extends StandaloneClusterMessage
+
+}
diff --git a/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala b/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala
index 03a64e01921e4ea9acda24c59dc6645599bb5e31..3203be10294d375d860e0e14ed24912a2edb31d0 100644
--- a/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala
@@ -17,17 +17,19 @@
 
 package spark.scheduler.cluster
 
+import java.util.concurrent.atomic.AtomicInteger
+
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
 import akka.actor._
-import akka.util.duration._
+import akka.dispatch.Await
 import akka.pattern.ask
+import akka.remote.{RemoteClientShutdown, RemoteClientDisconnected, RemoteClientLifeCycleEvent}
 import akka.util.Duration
+import akka.util.duration._
 
 import spark.{Utils, SparkException, Logging, TaskState}
-import akka.dispatch.Await
-import java.util.concurrent.atomic.AtomicInteger
-import akka.remote.{RemoteClientShutdown, RemoteClientDisconnected, RemoteClientLifeCycleEvent}
+import spark.scheduler.cluster.StandaloneClusterMessages._
 
 /**
  * A standalone scheduler backend, which waits for standalone executors to connect to it through
@@ -36,15 +38,15 @@ import akka.remote.{RemoteClientShutdown, RemoteClientDisconnected, RemoteClient
  */
 private[spark]
 class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: ActorSystem)
-  extends SchedulerBackend with Logging {
-
+  extends SchedulerBackend with Logging
+{
   // Use an atomic variable to track total number of cores in the cluster for simplicity and speed
   var totalCoreCount = new AtomicInteger(0)
 
   class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor {
     private val executorActor = new HashMap[String, ActorRef]
     private val executorAddress = new HashMap[String, Address]
-    private val executorHostPort = new HashMap[String, String]
+    private val executorHost = new HashMap[String, String]
     private val freeCores = new HashMap[String, Int]
     private val actorToExecutorId = new HashMap[ActorRef, String]
     private val addressToExecutorId = new HashMap[Address, String]
@@ -52,6 +54,10 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor
     override def preStart() {
       // Listen for remote client disconnection events, since they don't go through Akka's watch()
       context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent])
+
+      // Periodically revive offers to allow delay scheduling to work
+      val reviveInterval = System.getProperty("spark.scheduler.revive.interval", "1000").toLong
+      context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers)
     }
 
     def receive = {
@@ -64,7 +70,7 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor
           sender ! RegisteredExecutor(sparkProperties)
           context.watch(sender)
           executorActor(executorId) = sender
-          executorHostPort(executorId) = hostPort
+          executorHost(executorId) = Utils.parseHostPort(hostPort)._1
           freeCores(executorId) = cores
           executorAddress(executorId) = sender.path.address
           actorToExecutorId(sender) = executorId
@@ -104,13 +110,13 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor
     // Make fake resource offers on all executors
     def makeOffers() {
       launchTasks(scheduler.resourceOffers(
-        executorHostPort.toArray.map {case (id, hostPort) => new WorkerOffer(id, hostPort, freeCores(id))}))
+        executorHost.toArray.map {case (id, host) => new WorkerOffer(id, host, freeCores(id))}))
     }
 
     // Make fake resource offers on just one executor
     def makeOffers(executorId: String) {
       launchTasks(scheduler.resourceOffers(
-        Seq(new WorkerOffer(executorId, executorHostPort(executorId), freeCores(executorId)))))
+        Seq(new WorkerOffer(executorId, executorHost(executorId), freeCores(executorId)))))
     }
 
     // Launch tasks returned by a set of resource offers
@@ -129,9 +135,8 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor
         actorToExecutorId -= executorActor(executorId)
         addressToExecutorId -= executorAddress(executorId)
         executorActor -= executorId
-        executorHostPort -= executorId
+        executorHost -= executorId
         freeCores -= executorId
-        executorHostPort -= executorId
         totalCoreCount.addAndGet(-numCores)
         scheduler.executorLost(executorId, SlaveLost(reason))
       }
diff --git a/core/src/main/scala/spark/scheduler/cluster/TaskDescription.scala b/core/src/main/scala/spark/scheduler/cluster/TaskDescription.scala
index 761fdf691982dac161232aab1023249edb51a48e..187553233fb42a28ec85c95fc4f8710eccb08c2a 100644
--- a/core/src/main/scala/spark/scheduler/cluster/TaskDescription.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/TaskDescription.scala
@@ -24,6 +24,7 @@ private[spark] class TaskDescription(
     val taskId: Long,
     val executorId: String,
     val name: String,
+    val index: Int,    // Index within this task's TaskSet
     _serializedTask: ByteBuffer)
   extends Serializable {
 
@@ -31,4 +32,6 @@ private[spark] class TaskDescription(
   private val buffer = new SerializableBuffer(_serializedTask)
 
   def serializedTask: ByteBuffer = buffer.value
+
+  override def toString: String = "TaskDescription(TID=%d, index=%d)".format(taskId, index)
 }
diff --git a/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala b/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala
index c693b722ac3d6c604ed2b664da884d19ea873318..c2c5522686f74d62384a5acb0d25d50d7e988085 100644
--- a/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala
@@ -28,11 +28,9 @@ class TaskInfo(
     val index: Int,
     val launchTime: Long,
     val executorId: String,
-    val hostPort: String,
+    val host: String,
     val taskLocality: TaskLocality.TaskLocality) {
 
-  Utils.checkHostPort(hostPort, "Expected hostport")
-
   var finishTime: Long = 0
   var failed = false
 
diff --git a/core/src/hadoop1/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala b/core/src/main/scala/spark/scheduler/cluster/TaskLocality.scala
similarity index 64%
rename from core/src/hadoop1/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala
rename to core/src/main/scala/spark/scheduler/cluster/TaskLocality.scala
index 25386b2796ec80fd064892b637cb0e096139e90f..1c33e41f87e81d082bd3151c4f55fd39df32f32f 100644
--- a/core/src/hadoop1/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/TaskLocality.scala
@@ -15,13 +15,18 @@
  * limitations under the License.
  */
 
-package org.apache.hadoop.mapred
+package spark.scheduler.cluster
 
-trait HadoopMapRedUtil {
-  def newJobContext(conf: JobConf, jobId: JobID): JobContext = new JobContext(conf, jobId)
 
-  def newTaskAttemptContext(conf: JobConf, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContext(conf, attemptId)
+private[spark] object TaskLocality
+  extends Enumeration("PROCESS_LOCAL", "NODE_LOCAL", "RACK_LOCAL", "ANY")
+{
+  // process local is expected to be used ONLY within tasksetmanager for now.
+  val PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY = Value
 
-  def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = new TaskAttemptID(jtIdentifier,
-    jobId, isMap, taskId, attemptId)
+  type TaskLocality = Value
+
+  def isAllowed(constraint: TaskLocality, condition: TaskLocality): Boolean = {
+    condition <= constraint
+  }
 }
diff --git a/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala
index 7978a5df7464d316f195b3423983130af1c192a7..0248830b7a24c250dbdd6b41d2c9a4068f692f88 100644
--- a/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala
@@ -22,21 +22,28 @@ import java.nio.ByteBuffer
 import spark.TaskState.TaskState
 import spark.scheduler.TaskSet
 
+/**
+ * Tracks and schedules the tasks within a single TaskSet. This class keeps track of the status of
+ * each task and is responsible for retries on failure and locality. The main interfaces to it
+ * are resourceOffer, which asks the TaskSet whether it wants to run a task on one node, and
+ * statusUpdate, which tells it that one of its tasks changed state (e.g. finished).
+ *
+ * THREADING: This class is designed to only be called from code with a lock on the TaskScheduler
+ * (e.g. its event handlers). It should not be called from other threads.
+ */
 private[spark] trait TaskSetManager extends Schedulable {
-
+  def schedulableQueue = null
+  
+  def schedulingMode = SchedulingMode.NONE
+  
   def taskSet: TaskSet
 
-  def slaveOffer(
+  def resourceOffer(
       execId: String,
-      hostPort: String,
-      availableCpus: Double,
-      overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription]
-
-  def numPendingTasksForHostPort(hostPort: String): Int
-
-  def numRackLocalPendingTasksForHost(hostPort: String): Int
-
-  def numPendingTasksForHost(hostPort: String): Int
+      host: String,
+      availableCpus: Int,
+      maxLocality: TaskLocality.TaskLocality)
+    : Option[TaskDescription]
 
   def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer)
 
diff --git a/core/src/main/scala/spark/scheduler/cluster/WorkerOffer.scala b/core/src/main/scala/spark/scheduler/cluster/WorkerOffer.scala
index 06d1203f70b0fd00a8397fc76e34f3a52bac70c3..1d09bd9b034193f71bf22e2aa8bb5c44424c2994 100644
--- a/core/src/main/scala/spark/scheduler/cluster/WorkerOffer.scala
+++ b/core/src/main/scala/spark/scheduler/cluster/WorkerOffer.scala
@@ -21,5 +21,4 @@ package spark.scheduler.cluster
  * Represents free resources available on an executor.
  */
 private[spark]
-class WorkerOffer(val executorId: String, val hostPort: String, val cores: Int) {
-}
+class WorkerOffer(val executorId: String, val host: String, val cores: Int)
diff --git a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala
index edd83d4cb49639c236d0df50ee33fe66fc6b4d4d..5be4dbd9f0b527489eeba69ac234cbc8b8593ad8 100644
--- a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala
+++ b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala
@@ -18,8 +18,11 @@
 package spark.scheduler.local
 
 import java.io.File
+import java.lang.management.ManagementFactory
 import java.util.concurrent.atomic.AtomicInteger
 import java.nio.ByteBuffer
+
+import scala.collection.JavaConversions._
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashMap
 import scala.collection.mutable.HashSet
@@ -29,6 +32,7 @@ import spark.TaskState.TaskState
 import spark.executor.ExecutorURLClassLoader
 import spark.scheduler._
 import spark.scheduler.cluster._
+import spark.scheduler.cluster.SchedulingMode.SchedulingMode
 import akka.actor._
 
 /**
@@ -85,6 +89,8 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc:
 
   var schedulableBuilder: SchedulableBuilder = null
   var rootPool: Pool = null
+  val schedulingMode: SchedulingMode = SchedulingMode.withName(
+    System.getProperty("spark.cluster.schedulingmode", "FIFO"))
   val activeTaskSets = new HashMap[String, TaskSetManager]
   val taskIdToTaskSetId = new HashMap[Long, String]
   val taskSetTaskIds = new HashMap[String, HashSet[Long]]
@@ -92,15 +98,13 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc:
   var localActor: ActorRef = null
 
   override def start() {
-    //default scheduler is FIFO
-    val schedulingMode = System.getProperty("spark.cluster.schedulingmode", "FIFO")
-    //temporarily set rootPool name to empty
-    rootPool = new Pool("", SchedulingMode.withName(schedulingMode), 0, 0)
+    // temporarily set rootPool name to empty
+    rootPool = new Pool("", schedulingMode, 0, 0)
     schedulableBuilder = {
       schedulingMode match {
-        case "FIFO" =>
+        case SchedulingMode.FIFO =>
           new FIFOSchedulableBuilder(rootPool)
-        case "FAIR" =>
+        case SchedulingMode.FAIR =>
           new FairSchedulableBuilder(rootPool)
       }
     }
@@ -137,7 +141,7 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc:
       for (manager <- sortedTaskSetQueue) {
         do {
           launchTask = false
-          manager.slaveOffer(null, null, freeCpuCores) match {
+          manager.resourceOffer(null, null, freeCpuCores, null) match {
             case Some(task) =>
               tasks += task
               taskIdToTaskSetId(task.taskId) = manager.taskSet.id
@@ -168,9 +172,13 @@ 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 objectSer = SparkEnv.get.serializer.newInstance()
+    var attemptedTask: Option[Task[_]] = None   
     val start = System.currentTimeMillis()
     var taskStart: Long = 0
+    def getTotalGCTime = ManagementFactory.getGarbageCollectorMXBeans.map(g => g.getCollectionTime).sum
+    val startGCTime = getTotalGCTime
+
     try {
       Accumulators.clear()
       Thread.currentThread().setContextClassLoader(classLoader)
@@ -192,14 +200,15 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc:
       // executor does. This is useful to catch serialization errors early
       // on in development (so when users move their local Spark programs
       // to the cluster, they don't get surprised by serialization errors).
-      val serResult = ser.serialize(result)
+      val serResult = objectSer.serialize(result)
       deserializedTask.metrics.get.resultSize = serResult.limit()
-      val resultToReturn = ser.deserialize[Any](serResult)
+      val resultToReturn = objectSer.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 = serviceTime.toInt
+      deserializedTask.metrics.get.jvmGCTime = getTotalGCTime - startGCTime
       deserializedTask.metrics.get.executorDeserializeTime = deserTime.toInt
       val taskResult = new TaskResult(result, accumUpdates, deserializedTask.metrics.getOrElse(null))
       val serializedResult = ser.serialize(taskResult)
@@ -208,7 +217,10 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc:
       case t: Throwable => {
         val serviceTime = System.currentTimeMillis() - taskStart
         val metrics = attemptedTask.flatMap(t => t.metrics)
-        metrics.foreach{m => m.executorRunTime = serviceTime.toInt}
+        for (m <- metrics) {
+          m.executorRunTime = serviceTime.toInt
+          m.jvmGCTime = getTotalGCTime - startGCTime
+        }
         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 b29740c886e572b7f82413dce53097fe710ec1d4..e237f289e367bd31062ad0e99eff8d7b571ea1b7 100644
--- a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala
+++ b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala
@@ -42,7 +42,8 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas
   val taskInfos = new HashMap[Long, TaskInfo]
   val numTasks = taskSet.tasks.size
   var numFinished = 0
-  val ser = SparkEnv.get.closureSerializer.newInstance()
+  val env = SparkEnv.get
+  val ser = env.closureSerializer.newInstance()
   val copiesRunning = new Array[Int](numTasks)
   val finished = new Array[Boolean](numTasks)
   val numFailures = new Array[Int](numTasks)
@@ -63,11 +64,11 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas
   }
 
   override def addSchedulable(schedulable: Schedulable): Unit = {
-    //nothing
+    // nothing
   }
 
   override def removeSchedulable(schedulable: Schedulable): Unit = {
-    //nothing
+    // nothing
   }
 
   override def getSchedulableByName(name: String): Schedulable = {
@@ -75,7 +76,7 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas
   }
 
   override def executorLost(executorId: String, host: String): Unit = {
-    //nothing
+    // nothing
   }
 
   override def checkSpeculatableTasks() = true
@@ -97,14 +98,15 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas
     return None
   }
 
-  override def slaveOffer(
+  override def resourceOffer(
       execId: String,
-      hostPort: String,
-      availableCpus: Double,
-      overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] =
+      host: String,
+      availableCpus: Int,
+      maxLocality: TaskLocality.TaskLocality)
+    : Option[TaskDescription] =
   {
     SparkEnv.set(sched.env)
-    logDebug("availableCpus:%d,numFinished:%d,numTasks:%d".format(
+    logDebug("availableCpus:%d, numFinished:%d, numTasks:%d".format(
       availableCpus.toInt, numFinished, numTasks))
     if (availableCpus > 0 && numFinished < numTasks) {
       findTask() match {
@@ -123,26 +125,15 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas
           copiesRunning(index) += 1
           increaseRunningTasks(1)
           taskStarted(task, info)
-          return Some(new TaskDescription(taskId, null, taskName, bytes))
+          return Some(new TaskDescription(taskId, null, taskName, index, bytes))
         case None => {}
       }
     }
     return None
   }
 
-  override def numPendingTasksForHostPort(hostPort: String): Int = {
-    return 0
-  }
-
-  override def numRackLocalPendingTasksForHost(hostPort :String): Int = {
-    return 0
-  }
-
-  override def numPendingTasksForHost(hostPort: String): Int = {
-    return 0
-  }
-
   override def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) {
+    SparkEnv.set(env)
     state match {
       case TaskState.FINISHED =>
         taskEnded(tid, state, serializedData)
diff --git a/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala
index 7bc60405440544c2da1350f1bfb814107f44daa6..6ebbb5ec9b2643964f4c338babbebee38ca08a8b 100644
--- a/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala
@@ -110,12 +110,6 @@ private[spark] class CoarseMesosSchedulerBackend(
   }
 
   def createCommand(offer: Offer, numCores: Int): CommandInfo = {
-    val runScript = new File(sparkHome, "run").getCanonicalPath
-    val driverUrl = "akka://spark@%s:%s/user/%s".format(
-      System.getProperty("spark.driver.host"), System.getProperty("spark.driver.port"),
-      StandaloneSchedulerBackend.ACTOR_NAME)
-    val command = "\"%s\" spark.executor.StandaloneExecutorBackend %s %s %s %d".format(
-      runScript, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores)
     val environment = Environment.newBuilder()
     sc.executorEnvs.foreach { case (key, value) =>
       environment.addVariables(Environment.Variable.newBuilder()
@@ -123,7 +117,26 @@ private[spark] class CoarseMesosSchedulerBackend(
         .setValue(value)
         .build())
     }
-    return CommandInfo.newBuilder().setValue(command).setEnvironment(environment).build()
+    val command = CommandInfo.newBuilder()
+      .setEnvironment(environment)
+    val driverUrl = "akka://spark@%s:%s/user/%s".format(
+      System.getProperty("spark.driver.host"),
+      System.getProperty("spark.driver.port"),
+      StandaloneSchedulerBackend.ACTOR_NAME)
+    val uri = System.getProperty("spark.executor.uri")
+    if (uri == null) {
+      val runScript = new File(sparkHome, "run").getCanonicalPath
+      command.setValue("\"%s\" spark.executor.StandaloneExecutorBackend %s %s %s %d".format(
+        runScript, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores))
+    } else {
+      // Grab everything to the first '.'. We'll use that and '*' to
+      // glob the directory "correctly".
+      val basename = uri.split('/').last.split('.').head
+      command.setValue("cd %s*; ./run spark.executor.StandaloneExecutorBackend %s %s %s %d".format(
+        basename, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores))
+      command.addUris(CommandInfo.URI.newBuilder().setValue(uri))
+    }
+    return command.build()
   }
 
   override def offerRescinded(d: SchedulerDriver, o: OfferID) {}
diff --git a/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala
index 75b8268b55baa8dc7d545d21be9b1ff0d5085614..f6069a5775e2ba6e3550091ab69ff7dff62f4776 100644
--- a/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala
@@ -89,7 +89,6 @@ private[spark] class MesosSchedulerBackend(
     val sparkHome = sc.getSparkHome().getOrElse(throw new SparkException(
       "Spark home is not set; set it through the spark.home system " +
       "property, the SPARK_HOME environment variable or the SparkContext constructor"))
-    val execScript = new File(sparkHome, "spark-executor").getCanonicalPath
     val environment = Environment.newBuilder()
     sc.executorEnvs.foreach { case (key, value) =>
       environment.addVariables(Environment.Variable.newBuilder()
@@ -97,15 +96,23 @@ private[spark] class MesosSchedulerBackend(
         .setValue(value)
         .build())
     }
+    val command = CommandInfo.newBuilder()
+      .setEnvironment(environment)
+    val uri = System.getProperty("spark.executor.uri")
+    if (uri == null) {
+      command.setValue(new File(sparkHome, "spark-executor").getCanonicalPath)
+    } else {
+      // Grab everything to the first '.'. We'll use that and '*' to
+      // glob the directory "correctly".
+      val basename = uri.split('/').last.split('.').head
+      command.setValue("cd %s*; ./spark-executor".format(basename))
+      command.addUris(CommandInfo.URI.newBuilder().setValue(uri))
+    }
     val memory = Resource.newBuilder()
       .setName("mem")
       .setType(Value.Type.SCALAR)
       .setScalar(Value.Scalar.newBuilder().setValue(executorMemory).build())
       .build()
-    val command = CommandInfo.newBuilder()
-      .setValue(execScript)
-      .setEnvironment(environment)
-      .build()
     ExecutorInfo.newBuilder()
       .setExecutorId(ExecutorID.newBuilder().setValue(execId).build())
       .setCommand(command)
diff --git a/core/src/main/scala/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/spark/storage/BlockFetcherIterator.scala
index 1965c5bc19b98bbf8e6ccdab91d3da3af9294712..568783d893134069277ed428e2288b900aa9bbde 100644
--- a/core/src/main/scala/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/spark/storage/BlockFetcherIterator.scala
@@ -111,7 +111,7 @@ object BlockFetcherIterator {
 
     protected def sendRequest(req: FetchRequest) {
       logDebug("Sending request for %d blocks (%s) from %s".format(
-        req.blocks.size, Utils.memoryBytesToString(req.size), req.address.hostPort))
+        req.blocks.size, Utils.bytesToString(req.size), req.address.hostPort))
       val cmId = new ConnectionManagerId(req.address.host, req.address.port)
       val blockMessageArray = new BlockMessageArray(req.blocks.map {
         case (blockId, size) => BlockMessage.fromGetBlock(GetBlock(blockId))
@@ -132,9 +132,10 @@ object BlockFetcherIterator {
                 "Unexpected message " + blockMessage.getType + " received from " + cmId)
             }
             val blockId = blockMessage.getId
+            val networkSize = blockMessage.getData.limit()
             results.put(new FetchResult(blockId, sizeMap(blockId),
               () => dataDeserialize(blockId, blockMessage.getData, serializer)))
-            _remoteBytesRead += req.size
+            _remoteBytesRead += networkSize
             logDebug("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime))
           }
         }
@@ -309,7 +310,7 @@ object BlockFetcherIterator {
       }
 
       logDebug("Sending request for %d blocks (%s) from %s".format(
-        req.blocks.size, Utils.memoryBytesToString(req.size), req.address.host))
+        req.blocks.size, Utils.bytesToString(req.size), req.address.host))
       val cmId = new ConnectionManagerId(req.address.host, req.address.nettyPort)
       val cpier = new ShuffleCopier
       cpier.getBlocks(cmId, req.blocks, putResult)
diff --git a/core/src/main/scala/spark/storage/BlockManager.scala b/core/src/main/scala/spark/storage/BlockManager.scala
index e4ffa57ad20b6b79e6ef3baddbef1b09413b530a..2a6ec2a55dddd893a2b14e2843fc1bf0e22dc63b 100644
--- a/core/src/main/scala/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/spark/storage/BlockManager.scala
@@ -27,11 +27,10 @@ import akka.dispatch.{Await, Future}
 import akka.util.Duration
 import akka.util.duration._
 
-import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream}
-
 import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream
 
 import spark.{Logging, SparkEnv, SparkException, Utils}
+import spark.io.CompressionCodec
 import spark.network._
 import spark.serializer.Serializer
 import spark.util.{ByteBufferInputStream, IdGenerator, MetadataCleaner, TimeStampedHashMap}
@@ -158,6 +157,13 @@ private[spark] class BlockManager(
   val metadataCleaner = new MetadataCleaner("BlockManager", this.dropOldBlocks)
   initialize()
 
+  // The compression codec to use. Note that the "lazy" val is necessary because we want to delay
+  // the initialization of the compression codec until it is first used. The reason is that a Spark
+  // program could be using a user-defined codec in a third party jar, which is loaded in
+  // Executor.updateDependencies. When the BlockManager is initialized, user level jars hasn't been
+  // loaded yet.
+  private lazy val compressionCodec: CompressionCodec = CompressionCodec.createCodec()
+
   /**
    * Construct a BlockManager with a memory limit set based on system properties.
    */
@@ -919,18 +925,14 @@ private[spark] class BlockManager(
    * Wrap an output stream for compression if block compression is enabled for its block type
    */
   def wrapForCompression(blockId: String, s: OutputStream): OutputStream = {
-    if (shouldCompress(blockId)) {
-      (new LZFOutputStream(s)).setFinishBlockOnFlush(true)
-    } else {
-      s
-    }
+    if (shouldCompress(blockId)) compressionCodec.compressedOutputStream(s) else s
   }
 
   /**
    * Wrap an input stream for compression if block compression is enabled for its block type
    */
   def wrapForCompression(blockId: String, s: InputStream): InputStream = {
-    if (shouldCompress(blockId)) new LZFInputStream(s) else s
+    if (shouldCompress(blockId)) compressionCodec.compressedInputStream(s) else s
   }
 
   def dataSerialize(
@@ -1002,43 +1004,43 @@ private[spark] object BlockManager extends Logging {
     }
   }
 
-  def blockIdsToExecutorLocations(blockIds: Array[String], env: SparkEnv, blockManagerMaster: BlockManagerMaster = null): HashMap[String, List[String]] = {
+  def blockIdsToBlockManagers(
+      blockIds: Array[String],
+      env: SparkEnv,
+      blockManagerMaster: BlockManagerMaster = null)
+  : Map[String, Seq[BlockManagerId]] =
+  {
     // env == null and blockManagerMaster != null is used in tests
     assert (env != null || blockManagerMaster != null)
-    val locationBlockIds: Seq[Seq[BlockManagerId]] =
-      if (env != null) {
-        env.blockManager.getLocationBlockIds(blockIds)
-      } else {
-        blockManagerMaster.getLocations(blockIds)
-      }
+    val blockLocations: Seq[Seq[BlockManagerId]] = if (env != null) {
+      env.blockManager.getLocationBlockIds(blockIds)
+    } else {
+      blockManagerMaster.getLocations(blockIds)
+    }
 
-    // Convert from block master locations to executor locations (we need that for task scheduling)
-    val executorLocations = new HashMap[String, List[String]]()
+    val blockManagers = new HashMap[String, Seq[BlockManagerId]]
     for (i <- 0 until blockIds.length) {
-      val blockId = blockIds(i)
-      val blockLocations = locationBlockIds(i)
-
-      val executors = new HashSet[String]()
-
-      if (env != null) {
-        for (bkLocation <- blockLocations) {
-          val executorHostPort = env.resolveExecutorIdToHostPort(bkLocation.executorId, bkLocation.host)
-          executors += executorHostPort
-          // logInfo("bkLocation = " + bkLocation + ", executorHostPort = " + executorHostPort)
-        }
-      } else {
-        // Typically while testing, etc - revert to simply using host.
-        for (bkLocation <- blockLocations) {
-          executors += bkLocation.host
-          // logInfo("bkLocation = " + bkLocation + ", executorHostPort = " + executorHostPort)
-        }
-      }
-
-      executorLocations.put(blockId, executors.toSeq.toList)
+      blockManagers(blockIds(i)) = blockLocations(i)
     }
+    blockManagers.toMap
+  }
 
-    executorLocations
+  def blockIdsToExecutorIds(
+      blockIds: Array[String],
+      env: SparkEnv,
+      blockManagerMaster: BlockManagerMaster = null)
+    : Map[String, Seq[String]] =
+  {
+    blockIdsToBlockManagers(blockIds, env, blockManagerMaster).mapValues(s => s.map(_.executorId))
   }
 
+  def blockIdsToHosts(
+      blockIds: Array[String],
+      env: SparkEnv,
+      blockManagerMaster: BlockManagerMaster = null)
+    : Map[String, Seq[String]] =
+  {
+    blockIdsToBlockManagers(blockIds, env, blockManagerMaster).mapValues(s => s.map(_.host))
+  }
 }
 
diff --git a/core/src/main/scala/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/spark/storage/BlockManagerMaster.scala
index 3186f7c85b16404e582906ba2ec1f89a57d6832e..76128e8cfffe9f0773f7807d950bc56554ebdc93 100644
--- a/core/src/main/scala/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/spark/storage/BlockManagerMaster.scala
@@ -23,6 +23,7 @@ import akka.pattern.ask
 import akka.util.Duration
 
 import spark.{Logging, SparkException}
+import spark.storage.BlockManagerMessages._
 
 
 private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Logging {
diff --git a/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala
index 244000d95276468088747863018723323b24a3c0..2a2e17855046c1320e9f60d57f0b97687ee6c454 100644
--- a/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala
+++ b/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala
@@ -29,6 +29,8 @@ import akka.util.Duration
 import akka.util.duration._
 
 import spark.{Logging, Utils, SparkException}
+import spark.storage.BlockManagerMessages._
+
 
 /**
  * BlockManagerMasterActor is an actor on the master node to track statuses of
@@ -330,7 +332,7 @@ object BlockManagerMasterActor {
     private val _blocks = new JHashMap[String, BlockStatus]
 
     logInfo("Registering block manager %s with %s RAM".format(
-      blockManagerId.hostPort, Utils.memoryBytesToString(maxMem)))
+      blockManagerId.hostPort, Utils.bytesToString(maxMem)))
 
     def updateLastSeenMs() {
       _lastSeenMs = System.currentTimeMillis()
@@ -356,12 +358,12 @@ object BlockManagerMasterActor {
         if (storageLevel.useMemory) {
           _remainingMem -= memSize
           logInfo("Added %s in memory on %s (size: %s, free: %s)".format(
-            blockId, blockManagerId.hostPort, Utils.memoryBytesToString(memSize),
-            Utils.memoryBytesToString(_remainingMem)))
+            blockId, blockManagerId.hostPort, Utils.bytesToString(memSize),
+            Utils.bytesToString(_remainingMem)))
         }
         if (storageLevel.useDisk) {
           logInfo("Added %s on disk on %s (size: %s)".format(
-            blockId, blockManagerId.hostPort, Utils.memoryBytesToString(diskSize)))
+            blockId, blockManagerId.hostPort, Utils.bytesToString(diskSize)))
         }
       } else if (_blocks.containsKey(blockId)) {
         // If isValid is not true, drop the block.
@@ -370,12 +372,12 @@ object BlockManagerMasterActor {
         if (blockStatus.storageLevel.useMemory) {
           _remainingMem += blockStatus.memSize
           logInfo("Removed %s on %s in memory (size: %s, free: %s)".format(
-            blockId, blockManagerId.hostPort, Utils.memoryBytesToString(memSize),
-            Utils.memoryBytesToString(_remainingMem)))
+            blockId, blockManagerId.hostPort, Utils.bytesToString(memSize),
+            Utils.bytesToString(_remainingMem)))
         }
         if (blockStatus.storageLevel.useDisk) {
           logInfo("Removed %s on %s on disk (size: %s)".format(
-            blockId, blockManagerId.hostPort, Utils.memoryBytesToString(diskSize)))
+            blockId, blockManagerId.hostPort, Utils.bytesToString(diskSize)))
         }
       }
     }
diff --git a/core/src/main/scala/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/spark/storage/BlockManagerMessages.scala
index 01de4ccb8f45face3494d4600cae2c9c0974def8..9375a9ca54825138d3988a6615af8d941ebff06a 100644
--- a/core/src/main/scala/spark/storage/BlockManagerMessages.scala
+++ b/core/src/main/scala/spark/storage/BlockManagerMessages.scala
@@ -22,102 +22,89 @@ import java.io.{Externalizable, ObjectInput, ObjectOutput}
 import akka.actor.ActorRef
 
 
-//////////////////////////////////////////////////////////////////////////////////
-// Messages from the master to slaves.
-//////////////////////////////////////////////////////////////////////////////////
-private[spark]
-sealed trait ToBlockManagerSlave
-
-// Remove a block from the slaves that have it. This can only be used to remove
-// blocks that the master knows about.
-private[spark]
-case class RemoveBlock(blockId: String) extends ToBlockManagerSlave
-
-// Remove all blocks belonging to a specific RDD.
-private[spark] case class RemoveRdd(rddId: Int) extends ToBlockManagerSlave
-
-
-//////////////////////////////////////////////////////////////////////////////////
-// Messages from slaves to the master.
-//////////////////////////////////////////////////////////////////////////////////
-private[spark]
-sealed trait ToBlockManagerMaster
-
-private[spark]
-case class RegisterBlockManager(
-    blockManagerId: BlockManagerId,
-    maxMemSize: Long,
-    sender: ActorRef)
-  extends ToBlockManagerMaster
-
-private[spark]
-case class HeartBeat(blockManagerId: BlockManagerId) extends ToBlockManagerMaster
-
-private[spark]
-class UpdateBlockInfo(
-    var blockManagerId: BlockManagerId,
-    var blockId: String,
-    var storageLevel: StorageLevel,
-    var memSize: Long,
-    var diskSize: Long)
-  extends ToBlockManagerMaster
-  with Externalizable {
-
-  def this() = this(null, null, null, 0, 0)  // For deserialization only
-
-  override def writeExternal(out: ObjectOutput) {
-    blockManagerId.writeExternal(out)
-    out.writeUTF(blockId)
-    storageLevel.writeExternal(out)
-    out.writeLong(memSize)
-    out.writeLong(diskSize)
+private[storage] object BlockManagerMessages {
+  //////////////////////////////////////////////////////////////////////////////////
+  // Messages from the master to slaves.
+  //////////////////////////////////////////////////////////////////////////////////
+  sealed trait ToBlockManagerSlave
+
+  // Remove a block from the slaves that have it. This can only be used to remove
+  // blocks that the master knows about.
+  case class RemoveBlock(blockId: String) extends ToBlockManagerSlave
+
+  // Remove all blocks belonging to a specific RDD.
+  case class RemoveRdd(rddId: Int) extends ToBlockManagerSlave
+
+
+  //////////////////////////////////////////////////////////////////////////////////
+  // Messages from slaves to the master.
+  //////////////////////////////////////////////////////////////////////////////////
+  sealed trait ToBlockManagerMaster
+
+  case class RegisterBlockManager(
+      blockManagerId: BlockManagerId,
+      maxMemSize: Long,
+      sender: ActorRef)
+    extends ToBlockManagerMaster
+
+  case class HeartBeat(blockManagerId: BlockManagerId) extends ToBlockManagerMaster
+
+  class UpdateBlockInfo(
+      var blockManagerId: BlockManagerId,
+      var blockId: String,
+      var storageLevel: StorageLevel,
+      var memSize: Long,
+      var diskSize: Long)
+    extends ToBlockManagerMaster
+    with Externalizable {
+
+    def this() = this(null, null, null, 0, 0)  // For deserialization only
+
+    override def writeExternal(out: ObjectOutput) {
+      blockManagerId.writeExternal(out)
+      out.writeUTF(blockId)
+      storageLevel.writeExternal(out)
+      out.writeLong(memSize)
+      out.writeLong(diskSize)
+    }
+
+    override def readExternal(in: ObjectInput) {
+      blockManagerId = BlockManagerId(in)
+      blockId = in.readUTF()
+      storageLevel = StorageLevel(in)
+      memSize = in.readLong()
+      diskSize = in.readLong()
+    }
   }
 
-  override def readExternal(in: ObjectInput) {
-    blockManagerId = BlockManagerId(in)
-    blockId = in.readUTF()
-    storageLevel = StorageLevel(in)
-    memSize = in.readLong()
-    diskSize = in.readLong()
+  object UpdateBlockInfo {
+    def apply(blockManagerId: BlockManagerId,
+        blockId: String,
+        storageLevel: StorageLevel,
+        memSize: Long,
+        diskSize: Long): UpdateBlockInfo = {
+      new UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize)
+    }
+
+    // For pattern-matching
+    def unapply(h: UpdateBlockInfo): Option[(BlockManagerId, String, StorageLevel, Long, Long)] = {
+      Some((h.blockManagerId, h.blockId, h.storageLevel, h.memSize, h.diskSize))
+    }
   }
-}
 
-private[spark]
-object UpdateBlockInfo {
-  def apply(blockManagerId: BlockManagerId,
-      blockId: String,
-      storageLevel: StorageLevel,
-      memSize: Long,
-      diskSize: Long): UpdateBlockInfo = {
-    new UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize)
-  }
+  case class GetLocations(blockId: String) extends ToBlockManagerMaster
 
-  // For pattern-matching
-  def unapply(h: UpdateBlockInfo): Option[(BlockManagerId, String, StorageLevel, Long, Long)] = {
-    Some((h.blockManagerId, h.blockId, h.storageLevel, h.memSize, h.diskSize))
-  }
-}
+  case class GetLocationsMultipleBlockIds(blockIds: Array[String]) extends ToBlockManagerMaster
 
-private[spark]
-case class GetLocations(blockId: String) extends ToBlockManagerMaster
+  case class GetPeers(blockManagerId: BlockManagerId, size: Int) extends ToBlockManagerMaster
 
-private[spark]
-case class GetLocationsMultipleBlockIds(blockIds: Array[String]) extends ToBlockManagerMaster
+  case class RemoveExecutor(execId: String) extends ToBlockManagerMaster
 
-private[spark]
-case class GetPeers(blockManagerId: BlockManagerId, size: Int) extends ToBlockManagerMaster
+  case object StopBlockManagerMaster extends ToBlockManagerMaster
 
-private[spark]
-case class RemoveExecutor(execId: String) extends ToBlockManagerMaster
+  case object GetMemoryStatus extends ToBlockManagerMaster
 
-private[spark]
-case object StopBlockManagerMaster extends ToBlockManagerMaster
+  case object ExpireDeadHosts extends ToBlockManagerMaster
 
-private[spark]
-case object GetMemoryStatus extends ToBlockManagerMaster
-
-private[spark]
-case object ExpireDeadHosts extends ToBlockManagerMaster
-
-private[spark]
-case object GetStorageStatus extends ToBlockManagerMaster
+  case object GetStorageStatus extends ToBlockManagerMaster
+}
diff --git a/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala b/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala
index 45cffad810ef5a74222f06ac13108007f64aa604..6e5fb43732905e66deedb0ea6e2bb17272e908a2 100644
--- a/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala
+++ b/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala
@@ -19,7 +19,7 @@ package spark.storage
 
 import akka.actor.Actor
 
-import spark.{Logging, SparkException, Utils}
+import spark.storage.BlockManagerMessages._
 
 
 /**
diff --git a/core/src/main/scala/spark/storage/BlockManagerSource.scala b/core/src/main/scala/spark/storage/BlockManagerSource.scala
index 4faa715c9475fce4035e02804ba4a075a5cf13aa..2aecd1ea7167662c9ef858abb646a72ed856a9c3 100644
--- a/core/src/main/scala/spark/storage/BlockManagerSource.scala
+++ b/core/src/main/scala/spark/storage/BlockManagerSource.scala
@@ -3,7 +3,7 @@ package spark.storage
 import com.codahale.metrics.{Gauge,MetricRegistry}
 
 import spark.metrics.source.Source
-import spark.storage._
+
 
 private[spark] class BlockManagerSource(val blockManager: BlockManager) extends Source {
   val metricRegistry = new MetricRegistry()
diff --git a/core/src/main/scala/spark/storage/BlockMessage.scala b/core/src/main/scala/spark/storage/BlockMessage.scala
index ab72dbb62bad14aa44149ba0e88340f2da0d944e..bcce26b7c14e94316b6818b9eef47156f0503cce 100644
--- a/core/src/main/scala/spark/storage/BlockMessage.scala
+++ b/core/src/main/scala/spark/storage/BlockMessage.scala
@@ -22,7 +22,6 @@ import java.nio.ByteBuffer
 import scala.collection.mutable.StringBuilder
 import scala.collection.mutable.ArrayBuffer
 
-import spark._
 import spark.network._
 
 private[spark] case class GetBlock(id: String)
diff --git a/core/src/main/scala/spark/storage/BlockMessageArray.scala b/core/src/main/scala/spark/storage/BlockMessageArray.scala
index b0229d6124d63b40462cb0c8bb44644865e8422b..ee2fc167d5b5198bfafebe634f6f3d1b8c2d8fc4 100644
--- a/core/src/main/scala/spark/storage/BlockMessageArray.scala
+++ b/core/src/main/scala/spark/storage/BlockMessageArray.scala
@@ -19,7 +19,6 @@ package spark.storage
 
 import java.nio.ByteBuffer
 
-import scala.collection.mutable.StringBuilder
 import scala.collection.mutable.ArrayBuffer
 
 import spark._
@@ -113,7 +112,7 @@ private[spark] object BlockMessageArray {
   
   def main(args: Array[String]) {
     val blockMessages = 
-      (0 until 10).map(i => {
+      (0 until 10).map { i =>
         if (i % 2 == 0) {
           val buffer =  ByteBuffer.allocate(100)
           buffer.clear
@@ -121,7 +120,7 @@ private[spark] object BlockMessageArray {
         } else {
           BlockMessage.fromGetBlock(GetBlock(i.toString))
         }
-      })
+      }
     val blockMessageArray = new BlockMessageArray(blockMessages)
     println("Block message array created")
     
diff --git a/core/src/main/scala/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/spark/storage/BlockObjectWriter.scala
index 01ed6e8c1fa321f4f02aeee9e81e6a570412aaf5..3812009ca16709bda71e60366180cda77a2b2536 100644
--- a/core/src/main/scala/spark/storage/BlockObjectWriter.scala
+++ b/core/src/main/scala/spark/storage/BlockObjectWriter.scala
@@ -17,8 +17,6 @@
 
 package spark.storage
 
-import java.nio.ByteBuffer
-
 
 /**
  * An interface for writing JVM objects to some underlying storage. This interface allows
diff --git a/core/src/main/scala/spark/storage/DiskStore.scala b/core/src/main/scala/spark/storage/DiskStore.scala
index 3495d653bd21222b725f7a0a02a6417aa225e0b5..b14497157e9e2123c14aaee58499bfb54bd290e5 100644
--- a/core/src/main/scala/spark/storage/DiskStore.scala
+++ b/core/src/main/scala/spark/storage/DiskStore.scala
@@ -66,7 +66,6 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String)
     override def close() {
       if (initialized) {
         objOut.close()
-        bs.close()
         channel = null
         bs = null
         objOut = null
@@ -148,7 +147,7 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String)
     channel.close()
     val finishTime = System.currentTimeMillis
     logDebug("Block %s stored as %s file on disk in %d ms".format(
-      blockId, Utils.memoryBytesToString(bytes.limit), (finishTime - startTime)))
+      blockId, Utils.bytesToString(bytes.limit), (finishTime - startTime)))
   }
 
   private def getFileBytes(file: File): ByteBuffer = {
@@ -182,7 +181,7 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String)
 
     val timeTaken = System.currentTimeMillis - startTime
     logDebug("Block %s stored as %s file on disk in %d ms".format(
-      blockId, Utils.memoryBytesToString(length), timeTaken))
+      blockId, Utils.bytesToString(length), timeTaken))
 
     if (returnValues) {
       // Return a byte buffer for the contents of the file
diff --git a/core/src/main/scala/spark/storage/MemoryStore.scala b/core/src/main/scala/spark/storage/MemoryStore.scala
index b5a86b85a770222cce63524f2ed4988ce3378340..5a51f5cf313fa01a6de197a8c8337b4da72fba52 100644
--- a/core/src/main/scala/spark/storage/MemoryStore.scala
+++ b/core/src/main/scala/spark/storage/MemoryStore.scala
@@ -38,7 +38,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
   // blocks from the memory store.
   private val putLock = new Object()
 
-  logInfo("MemoryStore started with capacity %s.".format(Utils.memoryBytesToString(maxMemory)))
+  logInfo("MemoryStore started with capacity %s.".format(Utils.bytesToString(maxMemory)))
 
   def freeMemory: Long = maxMemory - currentMemory
 
@@ -164,10 +164,10 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
         currentMemory += size
         if (deserialized) {
           logInfo("Block %s stored as values to memory (estimated size %s, free %s)".format(
-            blockId, Utils.memoryBytesToString(size), Utils.memoryBytesToString(freeMemory)))
+            blockId, Utils.bytesToString(size), Utils.bytesToString(freeMemory)))
         } else {
           logInfo("Block %s stored as bytes to memory (size %s, free %s)".format(
-            blockId, Utils.memoryBytesToString(size), Utils.memoryBytesToString(freeMemory)))
+            blockId, Utils.bytesToString(size), Utils.bytesToString(freeMemory)))
         }
         true
       } else {
diff --git a/core/src/main/scala/spark/storage/StorageUtils.scala b/core/src/main/scala/spark/storage/StorageUtils.scala
index 2aeed4ea3cf42099fe2828abbfb1585ecba351a6..123b8f634571f9af7636ff8339881a9e57204a47 100644
--- a/core/src/main/scala/spark/storage/StorageUtils.scala
+++ b/core/src/main/scala/spark/storage/StorageUtils.scala
@@ -42,9 +42,9 @@ case class RDDInfo(id: Int, name: String, storageLevel: StorageLevel,
   numCachedPartitions: Int, numPartitions: Int, memSize: Long, diskSize: Long)
   extends Ordered[RDDInfo] {
   override def toString = {
-    import Utils.memoryBytesToString
+    import Utils.bytesToString
     "RDD \"%s\" (%d) Storage: %s; CachedPartitions: %d; TotalPartitions: %d; MemorySize: %s; DiskSize: %s".format(name, id,
-      storageLevel.toString, numCachedPartitions, numPartitions, memoryBytesToString(memSize), memoryBytesToString(diskSize))
+      storageLevel.toString, numCachedPartitions, numPartitions, bytesToString(memSize), bytesToString(diskSize))
   }
 
   override def compare(that: RDDInfo) = {
diff --git a/core/src/main/scala/spark/ui/JettyUtils.scala b/core/src/main/scala/spark/ui/JettyUtils.scala
index ca6088ad93a5f8672d58b3e34007ded60c1ff09c..ba58f3572991517157bc0f7548069404bd1143fd 100644
--- a/core/src/main/scala/spark/ui/JettyUtils.scala
+++ b/core/src/main/scala/spark/ui/JettyUtils.scala
@@ -17,21 +17,20 @@
 
 package spark.ui
 
-import annotation.tailrec
-
 import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
 
-import net.liftweb.json.{JValue, pretty, render}
+import scala.annotation.tailrec
+import scala.util.{Try, Success, Failure}
+import scala.util.parsing.json.JSONType
+import scala.xml.Node
 
 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
@@ -39,8 +38,8 @@ private[spark] object JettyUtils extends Logging {
   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 jsonResponderToHandler(responder: Responder[JSONType]): Handler =
+    createHandler(responder, "text/json", (in: JSONType) => in.toString)
 
   implicit def htmlResponderToHandler(responder: Responder[Seq[Node]]): Handler =
     createHandler(responder, "text/html", (in: Seq[Node]) => "<!DOCTYPE html>" + in.toString)
@@ -48,7 +47,7 @@ private[spark] object JettyUtils extends Logging {
   implicit def textResponderToHandler(responder: Responder[String]): Handler =
     createHandler(responder, "text/plain")
 
-  private def createHandler[T <% AnyRef](responder: Responder[T], contentType: String,
+  def createHandler[T <% AnyRef](responder: Responder[T], contentType: String,
                                  extractFn: T => String = (in: Any) => in.toString): Handler = {
     new AbstractHandler {
       def handle(target: String,
diff --git a/core/src/main/scala/spark/ui/SparkUI.scala b/core/src/main/scala/spark/ui/SparkUI.scala
index 7599f82a94414558f182c775a5a191a67c177ca7..23ded44ba383de802aabbecaf136bfd53cbc09d4 100644
--- a/core/src/main/scala/spark/ui/SparkUI.scala
+++ b/core/src/main/scala/spark/ui/SparkUI.scala
@@ -21,7 +21,7 @@ import javax.servlet.http.HttpServletRequest
 
 import org.eclipse.jetty.server.{Handler, Server}
 
-import spark.{Logging, SparkContext, Utils}
+import spark.{Logging, SparkContext, SparkEnv, Utils}
 import spark.ui.env.EnvironmentUI
 import spark.ui.exec.ExecutorsUI
 import spark.ui.storage.BlockManagerUI
@@ -30,7 +30,7 @@ import spark.ui.JettyUtils._
 
 /** Top level user interface for Spark */
 private[spark] class SparkUI(sc: SparkContext) extends Logging {
-  val host = Utils.localHostName()
+  val host = Option(System.getenv("SPARK_PUBLIC_DNS")).getOrElse(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
@@ -43,8 +43,12 @@ private[spark] class SparkUI(sc: SparkContext) extends Logging {
   val jobs = new JobProgressUI(sc)
   val env = new EnvironmentUI(sc)
   val exec = new ExecutorsUI(sc)
+
+  // Add MetricsServlet handlers by default
+  val metricsServletHandlers = SparkEnv.get.metricsSystem.getServletHandlers
+
   val allHandlers = storage.getHandlers ++ jobs.getHandlers ++ env.getHandlers ++
-    exec.getHandlers ++ handlers
+    exec.getHandlers ++ metricsServletHandlers ++ handlers
 
   /** Bind the HTTP server which backs this web interface */
   def bind() {
@@ -54,9 +58,9 @@ private[spark] class SparkUI(sc: SparkContext) extends Logging {
       server = Some(srv)
       boundPort = Some(usedPort)
     } catch {
-    case e: Exception =>
-      logError("Failed to create Spark JettyUtils", e)
-      System.exit(1)
+      case e: Exception =>
+        logError("Failed to create Spark JettyUtils", e)
+        System.exit(1)
     }
   }
 
@@ -78,6 +82,6 @@ private[spark] class SparkUI(sc: SparkContext) extends Logging {
 }
 
 private[spark] object SparkUI {
-  val DEFAULT_PORT = "33000"
+  val DEFAULT_PORT = "3030"
   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
index e33c80282a5fbbfae534c6123e8ab0f10840d82a..fe2afc11299e47d8210566762491bf682c36ef7b 100644
--- a/core/src/main/scala/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/spark/ui/UIUtils.scala
@@ -28,14 +28,14 @@ private[spark] object UIUtils {
   /** 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>
     }
+    val storage = page match {
+      case Storage => <li class="active"><a href="/storage">Storage</a></li>
+      case _ => <li><a href="/storage">Storage</a></li>
+    }
     val environment = page match {
       case Environment => <li class="active"><a href="/environment">Environment</a></li>
       case _ => <li><a href="/environment">Environment</a></li>
@@ -65,18 +65,14 @@ private[spark] object UIUtils {
               <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}
+                    <a href="/" class="brand"><img src="/static/spark-logo-77x50px-hd.png" /></a>
+                    <ul class="nav nav-pills">
                       {jobs}
+                      {storage}
                       {environment}
                       {executors}
                     </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>
+                    <p class="navbar-text pull-right">Application: <strong>{sc.appName}</strong></p>
                   </div>
                 </div>
               </div>
@@ -85,9 +81,9 @@ private[spark] object UIUtils {
 
           <div class="row" style="padding-top: 5px;">
             <div class="span12">
-              <h1 style="vertical-align: bottom; display: inline-block;">
+              <h3 style="vertical-align: bottom; display: inline-block;">
                 {title}
-              </h1>
+              </h3>
             </div>
           </div>
           <hr/>
@@ -117,9 +113,9 @@ private[spark] object UIUtils {
               <img src="/static/spark_logo.png" />
             </div>
             <div class="span10">
-              <h1 style="vertical-align: bottom; margin-top: 40px; display: inline-block;">
+              <h3 style="vertical-align: bottom; margin-top: 40px; display: inline-block;">
                 {title}
-              </h1>
+              </h3>
             </div>
           </div>
           {content}
@@ -129,9 +125,21 @@ private[spark] object UIUtils {
   }
 
   /** 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>
+  def listingTable[T](
+      headers: Seq[String],
+      makeRow: T => Seq[Node],
+      rows: Seq[T],
+      fixedWidth: Boolean = false): Seq[Node] = {
+
+    val colWidth = 100.toDouble / headers.size
+    val colWidthAttr = if (fixedWidth) colWidth + "%" else ""
+    var tableClass = "table table-bordered table-striped table-condensed sortable"
+    if (fixedWidth) {
+      tableClass += " table-fixed"
+    }
+
+    <table class={tableClass}>
+      <thead>{headers.map(h => <th width={colWidthAttr}>{h}</th>)}</thead>
       <tbody>
         {rows.map(r => makeRow(r))}
       </tbody>
diff --git a/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala
index a80e2d7002689873849b7e510f3bda9fd5ac7d24..f96419520fb8bf19a0ebfab5680f2310ceaf77e2 100644
--- a/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala
+++ b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala
@@ -21,6 +21,8 @@ import scala.util.Random
 
 import spark.SparkContext
 import spark.SparkContext._
+import spark.scheduler.cluster.SchedulingMode
+
 
 /**
  * Continuously generates jobs that expose various features of the WebUI (internal testing tool).
@@ -29,18 +31,29 @@ import spark.SparkContext._
  */
 private[spark] object UIWorkloadGenerator {
   val NUM_PARTITIONS = 100
-  val INTER_JOB_WAIT_MS = 500
+  val INTER_JOB_WAIT_MS = 5000
 
   def main(args: Array[String]) {
+    if (args.length < 2) {
+      println("usage: ./run spark.ui.UIWorkloadGenerator [master] [FIFO|FAIR]")
+      System.exit(1)
+    }
     val master = args(0)
+    val schedulingMode = SchedulingMode.withName(args(1))
     val appName = "Spark UI Tester"
+
+    if (schedulingMode == SchedulingMode.FAIR) {
+      System.setProperty("spark.cluster.schedulingmode", "FAIR")
+    }
     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)
+    def setProperties(s: String) = {
+      if(schedulingMode == SchedulingMode.FAIR) {
+        sc.setLocalProperty("spark.scheduler.cluster.fair.pool", s)
+      }
+      sc.setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, s)
     }
+
     val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS)
     def nextFloat() = (new Random()).nextFloat()
 
@@ -73,14 +86,18 @@ private[spark] object UIWorkloadGenerator {
 
     while (true) {
       for ((desc, job) <- jobs) {
-        try {
-          setName(desc)
-          job()
-          println("Job funished: " + desc)
-        } catch {
-          case e: Exception =>
-            println("Job Failed: " + desc)
-        }
+        new Thread {
+          override def run() {
+            try {
+              setProperties(desc)
+              job()
+              println("Job funished: " + desc)
+            } catch {
+              case e: Exception =>
+                println("Job Failed: " + desc)
+            }
+          }
+        }.start
         Thread.sleep(INTER_JOB_WAIT_MS)
       }
     }
diff --git a/core/src/main/scala/spark/ui/env/EnvironmentUI.scala b/core/src/main/scala/spark/ui/env/EnvironmentUI.scala
index 5ae7935ed487a19a6c85c1508fe71929d91ccc5d..b3e28ce317b0b4cffd10ff7c014c936b86f51184 100644
--- a/core/src/main/scala/spark/ui/env/EnvironmentUI.scala
+++ b/core/src/main/scala/spark/ui/env/EnvironmentUI.scala
@@ -19,18 +19,17 @@ package spark.ui.env
 
 import javax.servlet.http.HttpServletRequest
 
-import org.eclipse.jetty.server.Handler
-
 import scala.collection.JavaConversions._
 import scala.util.Properties
+import scala.xml.Node
+
+import org.eclipse.jetty.server.Handler
 
 import spark.ui.JettyUtils._
-import spark.ui.UIUtils.headerSparkPage
+import spark.ui.UIUtils
 import spark.ui.Page.Environment
 import spark.SparkContext
-import spark.ui.UIUtils
 
-import scala.xml.Node
 
 private[spark] class EnvironmentUI(sc: SparkContext) {
 
@@ -44,22 +43,24 @@ private[spark] class EnvironmentUI(sc: SparkContext) {
       ("Java Home", Properties.javaHome),
       ("Scala Version", Properties.versionString),
       ("Scala Home", Properties.scalaHome)
-    )
+    ).sorted
     def jvmRow(kv: (String, String)) = <tr><td>{kv._1}</td><td>{kv._2}</td></tr>
-    def jvmTable = UIUtils.listingTable(Seq("Name", "Value"), jvmRow, jvmInformation)
+    def jvmTable =
+      UIUtils.listingTable(Seq("Name", "Value"), jvmRow, jvmInformation, fixedWidth = true)
 
     val properties = System.getProperties.iterator.toSeq
-    val classPathProperty = properties
-        .filter{case (k, v) => k.contains("java.class.path")}
-        .headOption
-        .getOrElse("", "")
-    val sparkProperties = properties.filter(_._1.startsWith("spark"))
-    val otherProperties = properties.diff(sparkProperties :+ classPathProperty)
+    val classPathProperty = properties.find { case (k, v) =>
+      k.contains("java.class.path")
+    }.getOrElse(("", ""))
+    val sparkProperties = properties.filter(_._1.startsWith("spark")).sorted
+    val otherProperties = properties.diff(sparkProperties :+ classPathProperty).sorted
 
     val propertyHeaders = Seq("Name", "Value")
     def propertyRow(kv: (String, String)) = <tr><td>{kv._1}</td><td>{kv._2}</td></tr>
-    val sparkPropertyTable = UIUtils.listingTable(propertyHeaders, propertyRow, sparkProperties)
-    val otherPropertyTable = UIUtils.listingTable(propertyHeaders, propertyRow, otherProperties)
+    val sparkPropertyTable =
+      UIUtils.listingTable(propertyHeaders, propertyRow, sparkProperties, fixedWidth = true)
+    val otherPropertyTable =
+      UIUtils.listingTable(propertyHeaders, propertyRow, otherProperties, fixedWidth = true)
 
     val classPathEntries = classPathProperty._2
         .split(System.getProperty("path.separator", ":"))
@@ -67,20 +68,27 @@ private[spark] class EnvironmentUI(sc: SparkContext) {
         .map(e => (e, "System Classpath"))
     val addedJars = sc.addedJars.iterator.toSeq.map{case (path, time) => (path, "Added By User")}
     val addedFiles = sc.addedFiles.iterator.toSeq.map{case (path, time) => (path, "Added By User")}
-    val classPath = addedJars ++ addedFiles ++ classPathEntries
+    val classPath = (addedJars ++ addedFiles ++ classPathEntries).sorted
 
     val classPathHeaders = Seq("Resource", "Source")
     def classPathRow(data: (String, String)) = <tr><td>{data._1}</td><td>{data._2}</td></tr>
-    val classPathTable = UIUtils.listingTable(classPathHeaders, classPathRow, classPath)
+    val classPathTable =
+      UIUtils.listingTable(classPathHeaders, classPathRow, classPath, fixedWidth = true)
 
     val content =
       <span>
-        <h2>Runtime Information</h2> {jvmTable}
-        <h2>Spark Properties</h2> {sparkPropertyTable}
-        <h2>System Properties</h2> {otherPropertyTable}
-        <h2>Classpath Entries</h2> {classPathTable}
+        <h4>Runtime Information</h4> {jvmTable}
+        <hr/>
+        <h4>{sparkProperties.size} Spark Properties</h4>
+        {sparkPropertyTable}
+        <hr/>
+        <h4>{otherProperties.size} System Properties</h4>
+        {otherPropertyTable}
+        <hr/>
+        <h4>{classPath.size} Classpath Entries</h4>
+        {classPathTable}
       </span>
 
-    headerSparkPage(content, sc, "Environment", Environment)
+    UIUtils.headerSparkPage(content, sc, "Environment", Environment)
   }
 }
diff --git a/core/src/main/scala/spark/ui/exec/ExecutorsUI.scala b/core/src/main/scala/spark/ui/exec/ExecutorsUI.scala
index db1c902955151d38f002fee1ac802f1d43da6936..f97860013e1d4b1967577669bd808b27ec37541d 100644
--- a/core/src/main/scala/spark/ui/exec/ExecutorsUI.scala
+++ b/core/src/main/scala/spark/ui/exec/ExecutorsUI.scala
@@ -1,25 +1,20 @@
 package spark.ui.exec
 
-
 import javax.servlet.http.HttpServletRequest
 
-import org.eclipse.jetty.server.Handler
+import scala.collection.mutable.{HashMap, HashSet}
+import scala.xml.Node
 
-import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
-import scala.util.Properties
+import org.eclipse.jetty.server.Handler
 
-import spark.{ExceptionFailure, Logging, SparkContext, Success, Utils}
+import spark.{ExceptionFailure, Logging, Utils, SparkContext}
 import spark.executor.TaskMetrics
 import spark.scheduler.cluster.TaskInfo
-import spark.scheduler._
-import spark.SparkContext
-import spark.storage.{StorageStatus, StorageUtils}
+import spark.scheduler.{SparkListenerTaskStart, SparkListenerTaskEnd, SparkListener}
 import spark.ui.JettyUtils._
 import spark.ui.Page.Executors
-import spark.ui.UIUtils.headerSparkPage
 import spark.ui.UIUtils
 
-import scala.xml.{Node, XML}
 
 private[spark] class ExecutorsUI(val sc: SparkContext) {
 
@@ -38,32 +33,32 @@ private[spark] class ExecutorsUI(val sc: SparkContext) {
   def render(request: HttpServletRequest): Seq[Node] = {
     val storageStatusList = sc.getExecutorStorageStatus
 
-    val maxMem = storageStatusList.map(_.maxMem).reduce(_+_)
-    val memUsed = storageStatusList.map(_.memUsed()).reduce(_+_)
-    val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize))
-      .reduceOption(_+_).getOrElse(0L)
+    val maxMem = storageStatusList.map(_.maxMem).fold(0L)(_+_)
+    val memUsed = storageStatusList.map(_.memUsed()).fold(0L)(_+_)
+    val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize)).fold(0L)(_+_)
 
     val execHead = Seq("Executor ID", "Address", "RDD blocks", "Memory used", "Disk used",
       "Active tasks", "Failed tasks", "Complete tasks", "Total tasks")
-    def execRow(kv: Seq[String]) =
+
+    def execRow(kv: Seq[String]) = {
       <tr>
         <td>{kv(0)}</td>
         <td>{kv(1)}</td>
         <td>{kv(2)}</td>
         <td sorttable_customkey={kv(3)}>
-          {Utils.memoryBytesToString(kv(3).toLong)} / {Utils.memoryBytesToString(kv(4).toLong)}
+          {Utils.bytesToString(kv(3).toLong)} / {Utils.bytesToString(kv(4).toLong)}
         </td>
         <td sorttable_customkey={kv(5)}>
-          {Utils.memoryBytesToString(kv(5).toLong)}
+          {Utils.bytesToString(kv(5).toLong)}
         </td>
         <td>{kv(6)}</td>
         <td>{kv(7)}</td>
         <td>{kv(8)}</td>
         <td>{kv(9)}</td>
       </tr>
-    val execInfo =
-      for (b <- 0 until storageStatusList.size)
-        yield getExecInfo(b)
+    }
+
+    val execInfo = for (b <- 0 until storageStatusList.size) yield getExecInfo(b)
     val execTable = UIUtils.listingTable(execHead, execRow, execInfo)
 
     val content =
@@ -71,9 +66,9 @@ private[spark] class ExecutorsUI(val sc: SparkContext) {
         <div class="span12">
           <ul class="unstyled">
             <li><strong>Memory:</strong>
-              {Utils.memoryBytesToString(memUsed)} Used
-              ({Utils.memoryBytesToString(maxMem)} Total) </li>
-            <li><strong>Disk:</strong> {Utils.memoryBytesToString(diskSpaceUsed)} Used </li>
+              {Utils.bytesToString(memUsed)} Used
+              ({Utils.bytesToString(maxMem)} Total) </li>
+            <li><strong>Disk:</strong> {Utils.bytesToString(diskSpaceUsed)} Used </li>
           </ul>
         </div>
       </div>
@@ -83,7 +78,7 @@ private[spark] class ExecutorsUI(val sc: SparkContext) {
         </div>
       </div>;
 
-    headerSparkPage(content, sc, "Executors", Executors)
+    UIUtils.headerSparkPage(content, sc, execInfo.size + " Executors", Executors)
   }
 
   def getExecInfo(a: Int): Seq[String] = {
@@ -93,10 +88,10 @@ private[spark] class ExecutorsUI(val sc: SparkContext) {
     val memUsed = sc.getExecutorStorageStatus(a).memUsed().toString
     val maxMem = sc.getExecutorStorageStatus(a).maxMem.toString
     val diskUsed = sc.getExecutorStorageStatus(a).diskUsed().toString
-    val activeTasks = listener.executorToTasksActive.getOrElse(a.toString, Seq[Long]()).size.toString
-    val failedTasks = listener.executorToTasksFailed.getOrElse(a.toString, 0).toString
-    val completedTasks = listener.executorToTasksComplete.getOrElse(a.toString, 0).toString
-    val totalTasks = listener.executorToTaskInfos(a.toString).size.toString
+    val activeTasks = listener.executorToTasksActive.get(a.toString).map(l => l.size).getOrElse(0)
+    val failedTasks = listener.executorToTasksFailed.getOrElse(a.toString, 0)
+    val completedTasks = listener.executorToTasksComplete.getOrElse(a.toString, 0)
+    val totalTasks = activeTasks + failedTasks + completedTasks
 
     Seq(
       execId,
@@ -105,36 +100,28 @@ private[spark] class ExecutorsUI(val sc: SparkContext) {
       memUsed,
       maxMem,
       diskUsed,
-      activeTasks,
-      failedTasks,
-      completedTasks,
-      totalTasks
+      activeTasks.toString,
+      failedTasks.toString,
+      completedTasks.toString,
+      totalTasks.toString
     )
   }
 
   private[spark] class ExecutorsListener extends SparkListener with Logging {
-    val executorToTasksActive = HashMap[String, HashSet[Long]]()
+    val executorToTasksActive = HashMap[String, HashSet[TaskInfo]]()
     val executorToTasksComplete = HashMap[String, Int]()
     val executorToTasksFailed = HashMap[String, Int]()
-    val executorToTaskInfos =
-      HashMap[String, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]]()
 
     override def onTaskStart(taskStart: SparkListenerTaskStart) {
       val eid = taskStart.taskInfo.executorId
-      if (!executorToTasksActive.contains(eid))
-        executorToTasksActive(eid) = HashSet[Long]()
-      executorToTasksActive(eid) += taskStart.taskInfo.taskId
-      val taskList = executorToTaskInfos.getOrElse(
-        eid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]())
-      taskList += ((taskStart.taskInfo, None, None))
-      executorToTaskInfos(eid) = taskList
+      val activeTasks = executorToTasksActive.getOrElseUpdate(eid, new HashSet[TaskInfo]())
+      activeTasks += taskStart.taskInfo
     }
 
     override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
       val eid = taskEnd.taskInfo.executorId
-      if (!executorToTasksActive.contains(eid))
-        executorToTasksActive(eid) = HashSet[Long]()
-      executorToTasksActive(eid) -= taskEnd.taskInfo.taskId
+      val activeTasks = executorToTasksActive.getOrElseUpdate(eid, new HashSet[TaskInfo]())
+      activeTasks -= taskEnd.taskInfo
       val (failureInfo, metrics): (Option[ExceptionFailure], Option[TaskMetrics]) =
         taskEnd.reason match {
           case e: ExceptionFailure =>
@@ -142,13 +129,8 @@ private[spark] class ExecutorsUI(val sc: SparkContext) {
             (Some(e), e.metrics)
           case _ =>
             executorToTasksComplete(eid) = executorToTasksComplete.getOrElse(eid, 0) + 1
-            (None, Some(taskEnd.taskMetrics))
+            (None, Option(taskEnd.taskMetrics))
         }
-      val taskList = executorToTaskInfos.getOrElse(
-        eid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]())
-      taskList -= ((taskEnd.taskInfo, None, None))
-      taskList += ((taskEnd.taskInfo, metrics, failureInfo))
-      executorToTaskInfos(eid) = taskList
     }
   }
 }
diff --git a/core/src/main/scala/spark/ui/jobs/IndexPage.scala b/core/src/main/scala/spark/ui/jobs/IndexPage.scala
index f31af3cda6e9292c70abb601544687646fb7699f..cda6addd22a241a77e7a6a0b2644311c791c96fa 100644
--- a/core/src/main/scala/spark/ui/jobs/IndexPage.scala
+++ b/core/src/main/scala/spark/ui/jobs/IndexPage.scala
@@ -17,113 +17,78 @@
 
 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.scheduler.cluster.SchedulingMode
 import spark.ui.Page._
-import spark.storage.StorageLevel
+import spark.ui.UIUtils._
 
-/** Page showing list of all ongoing and recently finished stages */
+
+/** Page showing list of all ongoing and recently finished stages and pools*/
 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>
-          <th>Duration</th>
-          <th colspan="2">Tasks: Complete/Total</th>
-          <th>Shuffle Activity</th>
-          <th>Stored RDD</th>
-        </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"
+    listener.synchronized {
+      val activeStages = listener.activeStages.toSeq
+      val completedStages = listener.completedStages.reverse.toSeq
+      val failedStages = listener.failedStages.reverse.toSeq
+      val now = System.currentTimeMillis()
+
+      var activeTime = 0L
+      for (tasks <- listener.stageToTasksActive.values; t <- tasks) {
+        activeTime += t.timeRunning(now)
+      }
+
+      val activeStagesTable = new StageTable(activeStages.sortBy(_.submissionTime).reverse, parent)
+      val completedStagesTable = new StageTable(completedStages.sortBy(_.submissionTime).reverse, parent)
+      val failedStagesTable = new StageTable(failedStages.sortBy(_.submissionTime).reverse, parent)
+
+      val pools = listener.sc.getAllPools
+      val poolTable = new PoolTable(pools, listener)
+      val summary: NodeSeq =
+       <div>
+         <ul class="unstyled">
+           <li>
+             <strong>Duration: </strong>
+             {parent.formatDuration(now - listener.sc.startTime)}
+           </li>
+           <li>
+              <strong>CPU Time: </strong>
+              {parent.formatDuration(listener.totalTime + activeTime)}
+           </li>
+           <li><strong>Scheduling Mode:</strong> {parent.sc.getSchedulingMode}</li>
+           <li>
+             <a href="#active"><strong>Active Stages:</strong></a>
+             {activeStages.size}
+           </li>
+           <li>
+             <a href="#completed"><strong>Completed Stages:</strong></a>
+             {completedStages.size}
+           </li>
+           <li>
+             <a href="#failed"><strong>Failed Stages:</strong></a>
+             {failedStages.size}
+           </li>
+         </ul>
+       </div>
+
+      val content = summary ++
+        {if (listener.sc.getSchedulingMode == SchedulingMode.FAIR) {
+           <hr/><h4>{pools.size} Fair Scheduler Pools</h4> ++ poolTable.toNodeSeq
+        } else {
+          Seq()
+        }} ++
+        <hr/><h4 id="active">{activeStages.size} Active Stages</h4> ++
+        activeStagesTable.toNodeSeq++
+        <hr/><h4 id="completed">{completedStages.size} Completed Stages</h4> ++
+        completedStagesTable.toNodeSeq++
+        <hr/><h4 id ="failed">{failedStages.size} Failed Stages</h4> ++
+        failedStagesTable.toNodeSeq
+
+      headerSparkPage(content, parent.sc, "Spark Stages", Jobs)
     }
-    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.name}</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/JobProgressListener.scala b/core/src/main/scala/spark/ui/jobs/JobProgressListener.scala
new file mode 100644
index 0000000000000000000000000000000000000000..1d9767a83c8f65a75a7897eee406ae19a91b12f9
--- /dev/null
+++ b/core/src/main/scala/spark/ui/jobs/JobProgressListener.scala
@@ -0,0 +1,156 @@
+package spark.ui.jobs
+
+import scala.Seq
+import scala.collection.mutable.{ListBuffer, HashMap, HashSet}
+
+import spark.{ExceptionFailure, SparkContext, Success, Utils}
+import spark.scheduler._
+import spark.scheduler.cluster.TaskInfo
+import spark.executor.TaskMetrics
+import collection.mutable
+
+/**
+ * Tracks task-level information to be displayed in the UI.
+ *
+ * All access to the data structures in this class must be synchronized on the
+ * class, since the UI thread and the DAGScheduler event loop may otherwise
+ * be reading/updating the internal data structures concurrently.
+ */
+private[spark] class JobProgressListener(val sc: SparkContext) extends SparkListener {
+  // How many stages to remember
+  val RETAINED_STAGES = System.getProperty("spark.ui.retained_stages", "1000").toInt
+  val DEFAULT_POOL_NAME = "default"
+
+  val stageToPool = new HashMap[Stage, String]()
+  val stageToDescription = new HashMap[Stage, String]()
+  val poolToActiveStages = new HashMap[String, HashSet[Stage]]()
+
+  val activeStages = HashSet[Stage]()
+  val completedStages = ListBuffer[Stage]()
+  val failedStages = ListBuffer[Stage]()
+
+  // Total metrics reflect metrics only for completed tasks
+  var totalTime = 0L
+  var totalShuffleRead = 0L
+  var totalShuffleWrite = 0L
+
+  val stageToTime = HashMap[Int, Long]()
+  val stageToShuffleRead = HashMap[Int, Long]()
+  val stageToShuffleWrite = HashMap[Int, Long]()
+  val stageToTasksActive = HashMap[Int, HashSet[TaskInfo]]()
+  val stageToTasksComplete = HashMap[Int, Int]()
+  val stageToTasksFailed = HashMap[Int, Int]()
+  val stageToTaskInfos =
+    HashMap[Int, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]]()
+
+  override def onJobStart(jobStart: SparkListenerJobStart) {}
+
+  override def onStageCompleted(stageCompleted: StageCompleted) = synchronized {
+    val stage = stageCompleted.stageInfo.stage
+    poolToActiveStages(stageToPool(stage)) -= stage
+    activeStages -= stage
+    completedStages += stage
+    trimIfNecessary(completedStages)
+  }
+
+  /** If stages is too large, remove and garbage collect old stages */
+  def trimIfNecessary(stages: ListBuffer[Stage]) = synchronized {
+    if (stages.size > RETAINED_STAGES) {
+      val toRemove = RETAINED_STAGES / 10
+      stages.takeRight(toRemove).foreach( s => {
+        stageToTaskInfos.remove(s.id)
+        stageToTime.remove(s.id)
+        stageToShuffleRead.remove(s.id)
+        stageToShuffleWrite.remove(s.id)
+        stageToTasksActive.remove(s.id)
+        stageToTasksComplete.remove(s.id)
+        stageToTasksFailed.remove(s.id)
+        stageToPool.remove(s)
+        if (stageToDescription.contains(s)) {stageToDescription.remove(s)}
+      })
+      stages.trimEnd(toRemove)
+    }
+  }
+
+  /** For FIFO, all stages are contained by "default" pool but "default" pool here is meaningless */
+  override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = synchronized {
+    val stage = stageSubmitted.stage
+    activeStages += stage
+
+    val poolName = Option(stageSubmitted.properties).map {
+      p => p.getProperty("spark.scheduler.cluster.fair.pool", DEFAULT_POOL_NAME)
+    }.getOrElse(DEFAULT_POOL_NAME)
+    stageToPool(stage) = poolName
+
+    val description = Option(stageSubmitted.properties).flatMap {
+      p => Option(p.getProperty(SparkContext.SPARK_JOB_DESCRIPTION))
+    }
+    description.map(d => stageToDescription(stage) = d)
+
+    val stages = poolToActiveStages.getOrElseUpdate(poolName, new HashSet[Stage]())
+    stages += stage
+  }
+  
+  override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized {
+    val sid = taskStart.task.stageId
+    val tasksActive = stageToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]())
+    tasksActive += taskStart.taskInfo
+    val taskList = stageToTaskInfos.getOrElse(
+      sid, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]())
+    taskList += ((taskStart.taskInfo, None, None))
+    stageToTaskInfos(sid) = taskList
+  }
+ 
+  override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized {
+    val sid = taskEnd.task.stageId
+    val tasksActive = stageToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]())
+    tasksActive -= taskEnd.taskInfo
+    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, Option(taskEnd.taskMetrics))
+      }
+
+    stageToTime.getOrElseUpdate(sid, 0L)
+    val time = metrics.map(m => m.executorRunTime).getOrElse(0)
+    stageToTime(sid) += time
+    totalTime += time
+
+    stageToShuffleRead.getOrElseUpdate(sid, 0L)
+    val shuffleRead = metrics.flatMap(m => m.shuffleReadMetrics).map(s =>
+      s.remoteBytesRead).getOrElse(0L)
+    stageToShuffleRead(sid) += shuffleRead
+    totalShuffleRead += shuffleRead
+
+    stageToShuffleWrite.getOrElseUpdate(sid, 0L)
+    val shuffleWrite = metrics.flatMap(m => m.shuffleWriteMetrics).map(s =>
+      s.shuffleBytesWritten).getOrElse(0L)
+    stageToShuffleWrite(sid) += shuffleWrite
+    totalShuffleWrite += shuffleWrite
+
+    val taskList = stageToTaskInfos.getOrElse(
+      sid, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]())
+    taskList -= ((taskEnd.taskInfo, None, None))
+    taskList += ((taskEnd.taskInfo, metrics, failureInfo))
+    stageToTaskInfos(sid) = taskList
+  }
+
+  override def onJobEnd(jobEnd: SparkListenerJobEnd) = synchronized {
+    jobEnd match {
+      case end: SparkListenerJobEnd =>
+        end.jobResult match {
+          case JobFailed(ex, Some(stage)) =>
+            activeStages -= stage
+            poolToActiveStages(stageToPool(stage)) -= stage
+            failedStages += stage
+            trimIfNecessary(failedStages)
+          case _ =>
+        }
+      case _ =>
+    }
+  }
+}
diff --git a/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala
index 6e332415dbcf85eaaf50e2a335c3ee1c68751e6b..c83f102ff32ff024dc1c776741209176bdeaf1f3 100644
--- a/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala
+++ b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala
@@ -31,9 +31,9 @@ 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
+import spark.scheduler.cluster.SchedulingMode
+import spark.scheduler.cluster.SchedulingMode.SchedulingMode
 
 /** Web UI showing progress status of all jobs in the given SparkContext. */
 private[spark] class JobProgressUI(val sc: SparkContext) {
@@ -43,9 +43,10 @@ private[spark] class JobProgressUI(val sc: SparkContext) {
 
   private val indexPage = new IndexPage(this)
   private val stagePage = new StagePage(this)
+  private val poolPage = new PoolPage(this)
 
   def start() {
-    _listener = Some(new JobProgressListener)
+    _listener = Some(new JobProgressListener(sc))
     sc.addSparkListener(listener)
   }
 
@@ -53,108 +54,7 @@ private[spark] class JobProgressUI(val sc: SparkContext) {
 
   def getHandlers = Seq[(String, Handler)](
     ("/stages/stage", (request: HttpServletRequest) => stagePage.render(request)),
+    ("/stages/pool", (request: HttpServletRequest) => poolPage.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 stageToTasksActive = HashMap[Int, HashSet[Long]]()
-  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 onTaskStart(taskStart: SparkListenerTaskStart) {
-    val sid = taskStart.task.stageId
-    if (!stageToTasksActive.contains(sid))
-      stageToTasksActive(sid) = HashSet[Long]()
-    stageToTasksActive(sid) += taskStart.taskInfo.taskId
-    val taskList = stageToTaskInfos.getOrElse(
-      sid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]())
-    taskList += ((taskStart.taskInfo, None, None))
-    stageToTaskInfos(sid) = taskList
-  }
-
-  override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
-    val sid = taskEnd.task.stageId
-    if (!stageToTasksActive.contains(sid))
-      stageToTasksActive(sid) = HashSet[Long]()
-    stageToTasksActive(sid) -= taskEnd.taskInfo.taskId
-    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, None, None))
-    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/PoolPage.scala b/core/src/main/scala/spark/ui/jobs/PoolPage.scala
new file mode 100644
index 0000000000000000000000000000000000000000..e8f80ebfce7bed6626b7642b734026b5501e3c1b
--- /dev/null
+++ b/core/src/main/scala/spark/ui/jobs/PoolPage.scala
@@ -0,0 +1,33 @@
+package spark.ui.jobs
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.{NodeSeq, Node}
+import scala.collection.mutable.HashSet
+
+import spark.scheduler.Stage
+import spark.ui.UIUtils._
+import spark.ui.Page._
+
+/** Page showing specific pool details */
+private[spark] class PoolPage(parent: JobProgressUI) {
+  def listener = parent.listener
+
+  def render(request: HttpServletRequest): Seq[Node] = {
+    listener.synchronized {
+      val poolName = request.getParameter("poolname")
+      val poolToActiveStages = listener.poolToActiveStages
+      val activeStages = poolToActiveStages.get(poolName).toSeq.flatten
+      val activeStagesTable = new StageTable(activeStages.sortBy(_.submissionTime).reverse, parent)
+
+      val pool = listener.sc.getPoolForName(poolName).get
+      val poolTable = new PoolTable(Seq(pool), listener)
+
+      val content = <h4>Summary </h4> ++ poolTable.toNodeSeq() ++
+                    <hr/>
+                    <h4>{activeStages.size} Active Stages</h4> ++ activeStagesTable.toNodeSeq()
+
+      headerSparkPage(content, parent.sc, "Fair Scheduler Pool: " + poolName, Jobs)
+    }
+  }
+}
diff --git a/core/src/main/scala/spark/ui/jobs/PoolTable.scala b/core/src/main/scala/spark/ui/jobs/PoolTable.scala
new file mode 100644
index 0000000000000000000000000000000000000000..621828f9c3ea44899b0fee7b6bc6ceba9cc1837c
--- /dev/null
+++ b/core/src/main/scala/spark/ui/jobs/PoolTable.scala
@@ -0,0 +1,55 @@
+package spark.ui.jobs
+
+import scala.collection.mutable.HashMap
+import scala.collection.mutable.HashSet
+import scala.xml.Node
+
+import spark.scheduler.Stage
+import spark.scheduler.cluster.Schedulable
+
+/** Table showing list of pools */
+private[spark] class PoolTable(pools: Seq[Schedulable], listener: JobProgressListener) {
+
+  var poolToActiveStages: HashMap[String, HashSet[Stage]] = listener.poolToActiveStages
+
+  def toNodeSeq(): Seq[Node] = {
+    listener.synchronized {
+      poolTable(poolRow, pools)
+    }
+  }
+
+  private def poolTable(makeRow: (Schedulable, HashMap[String, HashSet[Stage]]) => Seq[Node],
+    rows: Seq[Schedulable]
+    ): Seq[Node] = {
+    <table class="table table-bordered table-striped table-condensed sortable table-fixed">
+      <thead>
+        <th>Pool Name</th>
+        <th>Minimum Share</th>
+        <th>Pool Weight</th>
+        <th>Active Stages</th>
+        <th>Running Tasks</th>
+        <th>SchedulingMode</th>
+      </thead>
+      <tbody>
+        {rows.map(r => makeRow(r, poolToActiveStages))}
+      </tbody>
+    </table>
+  }
+
+  private def poolRow(p: Schedulable, poolToActiveStages: HashMap[String, HashSet[Stage]])
+    : Seq[Node] = {
+    val activeStages = poolToActiveStages.get(p.name) match {
+      case Some(stages) => stages.size
+      case None => 0
+    }
+    <tr>
+      <td><a href={"/stages/pool?poolname=%s".format(p.name)}>{p.name}</a></td>
+      <td>{p.minShare}</td>
+      <td>{p.weight}</td>
+      <td>{activeStages}</td>
+      <td>{p.runningTasks}</td>
+      <td>{p.schedulingMode}</td>
+    </tr>
+  }
+}
+
diff --git a/core/src/main/scala/spark/ui/jobs/StagePage.scala b/core/src/main/scala/spark/ui/jobs/StagePage.scala
index 654f3477235405cbc995cb3a41de817f9efb9c1d..6948ea4dd907bbc26f15f25bbf88244df1029634 100644
--- a/core/src/main/scala/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/spark/ui/jobs/StagePage.scala
@@ -36,75 +36,112 @@ private[spark] class StagePage(parent: JobProgressUI) {
   val dateFmt = parent.dateFmt
 
   def render(request: HttpServletRequest): Seq[Node] = {
-    val stageId = request.getParameter("id").toInt
+    listener.synchronized {
+      val stageId = request.getParameter("id").toInt
+      val now = System.currentTimeMillis()
+
+      if (!listener.stageToTaskInfos.contains(stageId)) {
+        val content =
+          <div>
+            <h4>Summary Metrics</h4> No tasks have started yet
+            <h4>Tasks</h4> No tasks have started yet
+          </div>
+        return headerSparkPage(content, parent.sc, "Details for Stage %s".format(stageId), Jobs)
+      }
 
-    if (!listener.stageToTaskInfos.contains(stageId)) {
-      val content =
-        <div>
-          <h2>Summary Metrics</h2> No tasks have started yet
-          <h2>Tasks</h2> No tasks have started yet
-        </div>
-      return headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs)
-    }
+      val tasks = listener.stageToTaskInfos(stageId).toSeq.sortBy(_._1.launchTime)
 
-    val tasks = listener.stageToTaskInfos(stageId)
+      val numCompleted = tasks.count(_._1.finished)
+      val shuffleReadBytes = listener.stageToShuffleRead.getOrElse(stageId, 0L)
+      val hasShuffleRead = shuffleReadBytes > 0
+      val shuffleWriteBytes = listener.stageToShuffleWrite.getOrElse(stageId, 0L)
+      val hasShuffleWrite = shuffleWriteBytes > 0
 
-    val shuffleRead = listener.hasShuffleRead(stageId)
-    val shuffleWrite = listener.hasShuffleWrite(stageId)
+      var activeTime = 0L
+      listener.stageToTasksActive(stageId).foreach(activeTime += _.timeRunning(now))
 
-    val taskHeaders: Seq[String] =
-      Seq("Task ID", "Status", "Duration", "Locality Level", "Worker", "Launch Time") ++
-        {if (shuffleRead) Seq("Shuffle Read")  else Nil} ++
-        {if (shuffleWrite) Seq("Shuffle Write") else Nil} ++
-      Seq("Details")
+      val summary =
+        <div>
+          <ul class="unstyled">
+            <li>
+              <strong>CPU time: </strong>
+              {parent.formatDuration(listener.stageToTime.getOrElse(stageId, 0L) + activeTime)}
+            </li>
+            {if (hasShuffleRead)
+              <li>
+                <strong>Shuffle read: </strong>
+                {Utils.bytesToString(shuffleReadBytes)}
+              </li>
+            }
+            {if (hasShuffleWrite)
+              <li>
+                <strong>Shuffle write: </strong>
+                {Utils.bytesToString(shuffleWriteBytes)}
+              </li>
+            }
+          </ul>
+        </div>
 
-    val taskTable = listingTable(taskHeaders, taskRow, tasks)
+      val taskHeaders: Seq[String] =
+        Seq("Task ID", "Status", "Locality Level", "Executor", "Launch Time", "Duration") ++
+        Seq("GC Time") ++
+        {if (hasShuffleRead) Seq("Shuffle Read")  else Nil} ++
+        {if (hasShuffleWrite) Seq("Shuffle Write") else Nil} ++
+        Seq("Errors")
 
-    // Excludes tasks which failed and have incomplete metrics
-    val validTasks = tasks.filter(t => t._1.status == "SUCCESS" && (Option(t._2).isDefined))
+      val taskTable = listingTable(taskHeaders, taskRow(hasShuffleRead, hasShuffleWrite), tasks)
 
-    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)
+      // Excludes tasks which failed and have incomplete metrics
+      val validTasks = tasks.filter(t => t._1.status == "SUCCESS" && (t._2.isDefined))
 
-        val shuffleWriteSizes = validTasks.map {
-          case(info, metrics, exception) =>
-            metrics.get.shuffleWriteMetrics.map(_.shuffleBytesWritten).getOrElse(0L).toDouble
+      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.bytesToString(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 (hasShuffleRead) shuffleReadQuantiles else Nil,
+            if (hasShuffleWrite) shuffleWriteQuantiles else Nil)
+
+          val quantileHeaders = Seq("Metric", "Min", "25th percentile",
+            "Median", "75th percentile", "Max")
+          def quantileRow(data: Seq[String]): Seq[Node] = <tr> {data.map(d => <td>{d}</td>)} </tr>
+          Some(listingTable(quantileHeaders, quantileRow, listings, fixedWidth = true))
         }
-        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;
+      val content =
+        summary ++
+        <h4>Summary Metrics for {numCompleted} Completed Tasks</h4> ++
+        <div>{summaryTable.getOrElse("No tasks have reported metrics yet.")}</div> ++
+        <hr/><h4>Tasks</h4> ++ taskTable;
 
-    headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs)
+      headerSparkPage(content, parent.sc, "Details for Stage %d".format(stageId), Jobs)
+    }
   }
 
 
-  def taskRow(taskData: (TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])): Seq[Node] = {
+  def taskRow(shuffleRead: Boolean, shuffleWrite: Boolean)
+             (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
@@ -113,20 +150,28 @@ private[spark] class StagePage(parent: JobProgressUI) {
       else metrics.map(m => m.executorRunTime).getOrElse(1)
     val formatDuration = if (info.status == "RUNNING") parent.formatDuration(duration)
       else metrics.map(m => parent.formatDuration(m.executorRunTime)).getOrElse("")
+    val gcTime = metrics.map(m => m.jvmGCTime).getOrElse(0L)
 
     <tr>
       <td>{info.taskId}</td>
       <td>{info.status}</td>
+      <td>{info.taskLocality}</td>
+      <td>{info.host}</td>
+      <td>{dateFmt.format(new Date(info.launchTime))}</td>
       <td sorttable_customkey={duration.toString}>
         {formatDuration}
       </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 sorttable_customkey={gcTime.toString}>
+        {if (gcTime > 0) parent.formatDuration(gcTime) else ""}
+      </td>
+      {if (shuffleRead) {
+        <td>{metrics.flatMap{m => m.shuffleReadMetrics}.map{s =>
+          Utils.bytesToString(s.remoteBytesRead)}.getOrElse("")}</td>
+      }}
+      {if (shuffleWrite) {
+        <td>{metrics.flatMap{m => m.shuffleWriteMetrics}.map{s =>
+          Utils.bytesToString(s.shuffleBytesWritten)}.getOrElse("")}</td>
+      }}
       <td>{exception.map(e =>
         <span>
           {e.className} ({e.description})<br/>
diff --git a/core/src/main/scala/spark/ui/jobs/StageTable.scala b/core/src/main/scala/spark/ui/jobs/StageTable.scala
new file mode 100644
index 0000000000000000000000000000000000000000..b31f4abc26d65d39e8c30f22ff19286c3f5ad3b9
--- /dev/null
+++ b/core/src/main/scala/spark/ui/jobs/StageTable.scala
@@ -0,0 +1,107 @@
+package spark.ui.jobs
+
+import java.util.Date
+
+import scala.xml.Node
+import scala.collection.mutable.HashSet
+
+import spark.Utils
+import spark.scheduler.cluster.{SchedulingMode, TaskInfo}
+import spark.scheduler.Stage
+
+
+/** Page showing list of all ongoing and recently finished stages */
+private[spark] class StageTable(val stages: Seq[Stage], val parent: JobProgressUI) {
+
+  val listener = parent.listener
+  val dateFmt = parent.dateFmt
+  val isFairScheduler = listener.sc.getSchedulingMode == SchedulingMode.FAIR
+
+  def toNodeSeq(): Seq[Node] = {
+    listener.synchronized {
+      stageTable(stageRow, stages)
+    }
+  }
+
+  /** Special table which merges two header cells. */
+  private 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>
+        {if (isFairScheduler) {<th>Pool Name</th>} else {}}
+        <th>Description</th>
+        <th>Submitted</th>
+        <th>Duration</th>
+        <th>Tasks: Succeeded/Total</th>
+        <th>Shuffle Read</th>
+        <th>Shuffle Write</th>
+      </thead>
+      <tbody>
+        {rows.map(r => makeRow(r))}
+      </tbody>
+    </table>
+  }
+
+  private def makeProgressBar(started: Int, completed: Int, failed: String, total: Int): Seq[Node] = {
+    val completeWidth = "width: %s%%".format((completed.toDouble/total)*100)
+    val startWidth = "width: %s%%".format((started.toDouble/total)*100)
+
+    <div class="progress" style="height: 15px; margin-bottom: 0px; position: relative">
+      <span style="text-align:center; position:absolute; width:100%;">
+        {completed}/{total} {failed}
+      </span>
+      <div class="bar bar-completed" style={completeWidth}></div>
+      <div class="bar bar-running" style={startWidth}></div>
+    </div>
+  }
+
+
+  private def stageRow(s: Stage): Seq[Node] = {
+    val submissionTime = s.submissionTime match {
+      case Some(t) => dateFmt.format(new Date(t))
+      case None => "Unknown"
+    }
+
+    val shuffleRead = listener.stageToShuffleRead.getOrElse(s.id, 0L) match {
+      case 0 => ""
+      case b => Utils.bytesToString(b)
+    }
+    val shuffleWrite = listener.stageToShuffleWrite.getOrElse(s.id, 0L) match {
+      case 0 => ""
+      case b => Utils.bytesToString(b)
+    }
+
+    val startedTasks = listener.stageToTasksActive.getOrElse(s.id, HashSet[TaskInfo]()).size
+    val completedTasks = listener.stageToTasksComplete.getOrElse(s.id, 0)
+    val failedTasks = listener.stageToTasksFailed.getOrElse(s.id, 0) match {
+        case f if f > 0 => "(%s failed)".format(f)
+        case _ => ""
+    }
+    val totalTasks = s.numPartitions
+
+    val poolName = listener.stageToPool.get(s)
+
+    val nameLink = <a href={"/stages/stage?id=%s".format(s.id)}>{s.name}</a>
+    val description = listener.stageToDescription.get(s)
+      .map(d => <div><em>{d}</em></div><div>{nameLink}</div>).getOrElse(nameLink)
+    val finishTime = s.completionTime.getOrElse(System.currentTimeMillis())
+    val duration =  s.submissionTime.map(t => finishTime - t)
+
+    <tr>
+      <td>{s.id}</td>
+      {if (isFairScheduler) {
+        <td><a href={"/stages/pool?poolname=%s".format(poolName.get)}>{poolName.get}</a></td>}
+      }
+      <td>{description}</td>
+      <td valign="middle">{submissionTime}</td>
+      <td sorttable_customkey={duration.getOrElse(-1).toString}>
+        {duration.map(d => parent.formatDuration(d)).getOrElse("Unknown")}
+      </td>
+      <td class="progress-cell">
+        {makeProgressBar(startedTasks, completedTasks, failedTasks, totalTasks)}
+      </td>
+      <td>{shuffleRead}</td>
+      <td>{shuffleWrite}</td>
+    </tr>
+  }
+}
diff --git a/core/src/main/scala/spark/ui/storage/IndexPage.scala b/core/src/main/scala/spark/ui/storage/IndexPage.scala
index f76192eba84e777b2773016f21fc0ef6de9f71bd..0751f9e8f9102ad2abbd6d463140544b480e8157 100644
--- a/core/src/main/scala/spark/ui/storage/IndexPage.scala
+++ b/core/src/main/scala/spark/ui/storage/IndexPage.scala
@@ -58,8 +58,8 @@ private[spark] class IndexPage(parent: BlockManagerUI) {
       </td>
       <td>{rdd.numCachedPartitions}</td>
       <td>{rdd.numCachedPartitions / rdd.numPartitions.toDouble}</td>
-      <td>{Utils.memoryBytesToString(rdd.memSize)}</td>
-      <td>{Utils.memoryBytesToString(rdd.diskSize)}</td>
+      <td>{Utils.bytesToString(rdd.memSize)}</td>
+      <td>{Utils.bytesToString(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
index 003be54ad8ec9f44f79de7a8906a925ad2c4a5f3..f0b711e6ecc88d66850f6e5756d5a0059336cf95 100644
--- a/core/src/main/scala/spark/ui/storage/RDDPage.scala
+++ b/core/src/main/scala/spark/ui/storage/RDDPage.scala
@@ -21,12 +21,13 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import spark.storage.{StorageStatus, StorageUtils}
-import spark.ui.UIUtils._
 import spark.Utils
+import spark.storage.{StorageStatus, StorageUtils}
 import spark.storage.BlockManagerMasterActor.BlockStatus
+import spark.ui.UIUtils._
 import spark.ui.Page._
 
+
 /** Page showing storage details for a given RDD */
 private[spark] class RDDPage(parent: BlockManagerUI) {
   val sc = parent.sc
@@ -44,7 +45,7 @@ private[spark] class RDDPage(parent: BlockManagerUI) {
     val workerTable = listingTable(workerHeaders, workerRow, workers)
 
     val blockHeaders = Seq("Block Name", "Storage Level", "Size in Memory", "Size on Disk",
-      "Locations")
+      "Executors")
 
     val blockStatuses = filteredStorageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1)
     val blockLocations = StorageUtils.blockLocationsFromStorageStatus(filteredStorageStatusList)
@@ -71,11 +72,11 @@ private[spark] class RDDPage(parent: BlockManagerUI) {
             </li>
             <li>
               <strong>Memory Size:</strong>
-              {Utils.memoryBytesToString(rddInfo.memSize)}
+              {Utils.bytesToString(rddInfo.memSize)}
             </li>
             <li>
               <strong>Disk Size:</strong>
-              {Utils.memoryBytesToString(rddInfo.diskSize)}
+              {Utils.bytesToString(rddInfo.diskSize)}
             </li>
           </ul>
         </div>
@@ -83,18 +84,19 @@ private[spark] class RDDPage(parent: BlockManagerUI) {
       <hr/>
       <div class="row">
         <div class="span12">
+          <h4> Data Distribution on {workers.size} Executors </h4>
           {workerTable}
         </div>
       </div>
       <hr/>
       <div class="row">
         <div class="span12">
-          <h3> RDD Summary </h3>
+          <h4> {blocks.size} Partitions </h4>
           {blockTable}
         </div>
       </div>;
 
-    headerSparkPage(content, parent.sc, "RDD Info: " + rddInfo.name, Jobs)
+    headerSparkPage(content, parent.sc, "RDD Storage Info for " + rddInfo.name, Storage)
   }
 
   def blockRow(row: (String, BlockStatus, Seq[String])): Seq[Node] = {
@@ -105,10 +107,10 @@ private[spark] class RDDPage(parent: BlockManagerUI) {
         {block.storageLevel.description}
       </td>
       <td sorttable_customkey={block.memSize.toString}>
-        {Utils.memoryBytesToString(block.memSize)}
+        {Utils.bytesToString(block.memSize)}
       </td>
       <td sorttable_customkey={block.diskSize.toString}>
-        {Utils.memoryBytesToString(block.diskSize)}
+        {Utils.bytesToString(block.diskSize)}
       </td>
       <td>
         {locations.map(l => <span>{l}<br/></span>)}
@@ -121,10 +123,10 @@ private[spark] class RDDPage(parent: BlockManagerUI) {
     <tr>
       <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td>
       <td>
-        {Utils.memoryBytesToString(status.memUsed(prefix))}
-        ({Utils.memoryBytesToString(status.memRemaining)} Total Available)
+        {Utils.bytesToString(status.memUsed(prefix))}
+        ({Utils.bytesToString(status.memRemaining)} Remaining)
       </td>
-      <td>{Utils.memoryBytesToString(status.diskUsed(prefix))}</td>
+      <td>{Utils.bytesToString(status.diskUsed(prefix))}</td>
     </tr>
   }
 }
diff --git a/core/src/main/scala/spark/SoftReferenceCache.scala b/core/src/main/scala/spark/util/Clock.scala
similarity index 62%
rename from core/src/main/scala/spark/SoftReferenceCache.scala
rename to core/src/main/scala/spark/util/Clock.scala
index f41a379582a16f186a2851b77541138c39d68e4f..aa71a5b4424640bff202c2efbc9d631bd0c9ee95 100644
--- a/core/src/main/scala/spark/SoftReferenceCache.scala
+++ b/core/src/main/scala/spark/util/Clock.scala
@@ -15,21 +15,15 @@
  * limitations under the License.
  */
 
-package spark
-
-import com.google.common.collect.MapMaker
+package spark.util
 
 /**
- * An implementation of Cache that uses soft references.
+ * An interface to represent clocks, so that they can be mocked out in unit tests.
  */
-private[spark] class SoftReferenceCache extends Cache {
-  val map = new MapMaker().softValues().makeMap[Any, Any]()
-
-  override def get(datasetId: Any, partition: Int): Any =
-    map.get((datasetId, partition))
+private[spark] trait Clock {
+  def getTime(): Long
+}
 
-  override def put(datasetId: Any, partition: Int, value: Any): CachePutResponse = {
-    map.put((datasetId, partition), value)
-    return CachePutSuccess(0)
-  }
+private[spark] object SystemClock extends Clock {
+  def getTime(): Long = System.currentTimeMillis()
 }
diff --git a/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala b/core/src/main/scala/spark/util/MutablePair.scala
similarity index 56%
rename from core/src/hadoop2-yarn/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala
rename to core/src/main/scala/spark/util/MutablePair.scala
index 0f972b7a0b7999ad692773a02ba8d6c8385541af..78d404e66b1e205e92064183a347f46d1eef2f03 100644
--- a/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala
+++ b/core/src/main/scala/spark/util/MutablePair.scala
@@ -1,4 +1,3 @@
-
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
@@ -16,15 +15,22 @@
  * limitations under the License.
  */
 
-package org.apache.hadoop.mapred
-
-import org.apache.hadoop.mapreduce.TaskType
+package spark.util
 
-trait HadoopMapRedUtil {
-  def newJobContext(conf: JobConf, jobId: JobID): JobContext = new JobContextImpl(conf, jobId)
 
-  def newTaskAttemptContext(conf: JobConf, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContextImpl(conf, attemptId)
+/**
+ * A tuple of 2 elements. This can be used as an alternative to Scala's Tuple2 when we want to
+ * minimize object allocation.
+ *
+ * @param  _1   Element 1 of this MutablePair
+ * @param  _2   Element 2 of this MutablePair
+ */
+case class MutablePair[@specialized(Int, Long, Double, Char, Boolean/*, AnyRef*/) T1,
+                      @specialized(Int, Long, Double, Char, Boolean/*, AnyRef*/) T2]
+  (var _1: T1, var _2: T2)
+  extends Product2[T1, T2]
+{
+  override def toString = "(" + _1 + "," + _2 + ")"
 
-  def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) =
-    new TaskAttemptID(jtIdentifier, jobId, if (isMap) TaskType.MAP else TaskType.REDUCE, taskId, attemptId)
+  override def canEqual(that: Any): Boolean = that.isInstanceOf[MutablePair[_,_]]
 }
diff --git a/core/src/main/scala/spark/util/Vector.scala b/core/src/main/scala/spark/util/Vector.scala
index ed49386f185ee2ffc13d8129444294f019d2adb9..a47cac3b96830301c62f1fc0cdba19204bc3f4b7 100644
--- a/core/src/main/scala/spark/util/Vector.scala
+++ b/core/src/main/scala/spark/util/Vector.scala
@@ -73,7 +73,6 @@ class Vector(val elements: Array[Double]) extends Serializable {
   def += (other: Vector): Vector = {
     if (length != other.length)
       throw new IllegalArgumentException("Vectors of different length")
-    var ans = 0.0
     var i = 0
     while (i < length) {
       elements(i) += other(i)
@@ -117,9 +116,7 @@ object Vector {
   def apply(elements: Double*) = new Vector(elements.toArray)
 
   def apply(length: Int, initializer: Int => Double): Vector = {
-    val elements = new Array[Double](length)
-    for (i <- 0 until length)
-      elements(i) = initializer(i)
+    val elements: Array[Double] = Array.tabulate(length)(initializer)
     return new Vector(elements)
   }
 
diff --git a/core/src/test/scala/spark/CheckpointSuite.scala b/core/src/test/scala/spark/CheckpointSuite.scala
index a84c89e3c9517605f726631523faf98a5a6974ad..966dede2be12a8ba626f49c69320e76057bb59d6 100644
--- a/core/src/test/scala/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/spark/CheckpointSuite.scala
@@ -99,7 +99,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
   test("ShuffledRDD") {
     testCheckpointing(rdd => {
       // Creating ShuffledRDD directly as PairRDDFunctions.combineByKey produces a MapPartitionedRDD
-      new ShuffledRDD(rdd.map(x => (x % 2, 1)), partitioner)
+      new ShuffledRDD[Int, Int, (Int, Int)](rdd.map(x => (x % 2, 1)), partitioner)
     })
   }
 
diff --git a/core/src/test/scala/spark/JavaAPISuite.java b/core/src/test/scala/spark/JavaAPISuite.java
index 5e2bf2d231d5c6b052e6eb70a73dbc0c67cd66cb..c337c49268ad759deaee29bf52f2fdbadd727e93 100644
--- a/core/src/test/scala/spark/JavaAPISuite.java
+++ b/core/src/test/scala/spark/JavaAPISuite.java
@@ -22,6 +22,7 @@ import java.io.IOException;
 import java.io.Serializable;
 import java.util.*;
 
+import com.google.common.base.Optional;
 import scala.Tuple2;
 
 import com.google.common.base.Charsets;
@@ -197,6 +198,35 @@ public class JavaAPISuite implements Serializable {
     cogrouped.collect();
   }
 
+  @Test
+  public void leftOuterJoin() {
+    JavaPairRDD<Integer, Integer> rdd1 = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<Integer, Integer>(1, 1),
+      new Tuple2<Integer, Integer>(1, 2),
+      new Tuple2<Integer, Integer>(2, 1),
+      new Tuple2<Integer, Integer>(3, 1)
+      ));
+    JavaPairRDD<Integer, Character> rdd2 = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<Integer, Character>(1, 'x'),
+      new Tuple2<Integer, Character>(2, 'y'),
+      new Tuple2<Integer, Character>(2, 'z'),
+      new Tuple2<Integer, Character>(4, 'w')
+    ));
+    List<Tuple2<Integer,Tuple2<Integer,Optional<Character>>>> joined =
+      rdd1.leftOuterJoin(rdd2).collect();
+    Assert.assertEquals(5, joined.size());
+    Tuple2<Integer,Tuple2<Integer,Optional<Character>>> firstUnmatched =
+      rdd1.leftOuterJoin(rdd2).filter(
+        new Function<Tuple2<Integer, Tuple2<Integer, Optional<Character>>>, Boolean>() {
+          @Override
+          public Boolean call(Tuple2<Integer, Tuple2<Integer, Optional<Character>>> tup)
+            throws Exception {
+            return !tup._2()._2().isPresent();
+          }
+      }).first();
+    Assert.assertEquals(3, firstUnmatched._1().intValue());
+  }
+
   @Test
   public void foldReduce() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13));
@@ -718,7 +748,7 @@ public class JavaAPISuite implements Serializable {
         }
       };
 
-    JavaRDD<Integer> sizes = rdd1.zipPartitions(sizesFn, rdd2);
+    JavaRDD<Integer> sizes = rdd1.zipPartitions(rdd2, sizesFn);
     Assert.assertEquals("[3, 2, 3, 2]", sizes.collect().toString());
   }
 
diff --git a/core/src/test/scala/spark/KryoSerializerSuite.scala b/core/src/test/scala/spark/KryoSerializerSuite.scala
index 30d2d5282bc84bf887d25c5233020be9e7d06499..7568a0bf6591cf4dd8ab5d42e0d32c119b3ef778 100644
--- a/core/src/test/scala/spark/KryoSerializerSuite.scala
+++ b/core/src/test/scala/spark/KryoSerializerSuite.scala
@@ -22,7 +22,9 @@ import scala.collection.mutable
 import org.scalatest.FunSuite
 import com.esotericsoftware.kryo._
 
-class KryoSerializerSuite extends FunSuite {
+import KryoTest._
+
+class KryoSerializerSuite extends FunSuite with SharedSparkContext {
   test("basic types") {
     val ser = (new KryoSerializer).newInstance()
     def check[T](t: T) {
@@ -124,6 +126,57 @@ class KryoSerializerSuite extends FunSuite {
 
     System.clearProperty("spark.kryo.registrator")
   }
+
+  test("kryo with collect") {
+    val control = 1 :: 2 :: Nil
+    val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)).collect().map(_.x)
+    assert(control === result.toSeq)
+  }
+
+  test("kryo with parallelize") {
+    val control = 1 :: 2 :: Nil
+    val result = sc.parallelize(control.map(new ClassWithoutNoArgConstructor(_))).map(_.x).collect()
+    assert (control === result.toSeq)
+  }
+
+  test("kryo with parallelize for specialized tuples") {
+    assert (sc.parallelize( Array((1, 11), (2, 22), (3, 33)) ).count === 3)
+  }
+
+  test("kryo with parallelize for primitive arrays") {
+    assert (sc.parallelize( Array(1, 2, 3) ).count === 3)
+  }
+
+  test("kryo with collect for specialized tuples") {
+    assert (sc.parallelize( Array((1, 11), (2, 22), (3, 33)) ).collect().head === (1, 11))
+  }
+
+  test("kryo with reduce") {
+    val control = 1 :: 2 :: Nil
+    val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_))
+        .reduce((t1, t2) => new ClassWithoutNoArgConstructor(t1.x + t2.x)).x
+    assert(control.sum === result)
+  }
+
+  // TODO: this still doesn't work
+  ignore("kryo with fold") {
+    val control = 1 :: 2 :: Nil
+    val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_))
+        .fold(new ClassWithoutNoArgConstructor(10))((t1, t2) => new ClassWithoutNoArgConstructor(t1.x + t2.x)).x
+    assert(10 + control.sum === result)
+  }
+
+  override def beforeAll() {
+    System.setProperty("spark.serializer", "spark.KryoSerializer")
+    System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName)
+    super.beforeAll()
+  }
+
+  override def afterAll() {
+    super.afterAll()
+    System.clearProperty("spark.kryo.registrator")
+    System.clearProperty("spark.serializer")
+  }
 }
 
 object KryoTest {
@@ -152,4 +205,4 @@ object KryoTest {
       k.register(classOf[java.util.HashMap[_, _]])
     }
   }
-}
\ No newline at end of file
+}
diff --git a/core/src/test/scala/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/spark/MapOutputTrackerSuite.scala
index ce6cec0451e3ed08ccc432344154b6db8462df15..c21f3331d0876b769092e3454f496b96b7cda49a 100644
--- a/core/src/test/scala/spark/MapOutputTrackerSuite.scala
+++ b/core/src/test/scala/spark/MapOutputTrackerSuite.scala
@@ -112,22 +112,22 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
         "akka://spark@localhost:" + boundPort + "/user/MapOutputTracker")
 
     masterTracker.registerShuffle(10, 1)
-    masterTracker.incrementGeneration()
-    slaveTracker.updateGeneration(masterTracker.getGeneration)
+    masterTracker.incrementEpoch()
+    slaveTracker.updateEpoch(masterTracker.getEpoch)
     intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) }
 
     val compressedSize1000 = MapOutputTracker.compressSize(1000L)
     val size1000 = MapOutputTracker.decompressSize(compressedSize1000)
     masterTracker.registerMapOutput(10, 0, new MapStatus(
       BlockManagerId("a", "hostA", 1000, 0), Array(compressedSize1000)))
-    masterTracker.incrementGeneration()
-    slaveTracker.updateGeneration(masterTracker.getGeneration)
+    masterTracker.incrementEpoch()
+    slaveTracker.updateEpoch(masterTracker.getEpoch)
     assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
            Seq((BlockManagerId("a", "hostA", 1000, 0), size1000)))
 
     masterTracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0))
-    masterTracker.incrementGeneration()
-    slaveTracker.updateGeneration(masterTracker.getGeneration)
+    masterTracker.incrementEpoch()
+    slaveTracker.updateEpoch(masterTracker.getEpoch)
     intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) }
 
     // failure should be cached
diff --git a/core/src/test/scala/spark/PairRDDFunctionsSuite.scala b/core/src/test/scala/spark/PairRDDFunctionsSuite.scala
index b102eaf4e65bcfdc03e7c479d725e6378fd18ade..328b3b5497ea884f4c9039c672f24398b4ca2fd5 100644
--- a/core/src/test/scala/spark/PairRDDFunctionsSuite.scala
+++ b/core/src/test/scala/spark/PairRDDFunctionsSuite.scala
@@ -21,16 +21,11 @@ import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashSet
 
 import org.scalatest.FunSuite
-import org.scalatest.prop.Checkers
-import org.scalacheck.Arbitrary._
-import org.scalacheck.Gen
-import org.scalacheck.Prop._
 
 import com.google.common.io.Files
-
-import spark.rdd.ShuffledRDD
 import spark.SparkContext._
 
+
 class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
   test("groupByKey") {
     val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)))
diff --git a/core/src/test/scala/spark/PartitionPruningRDDSuite.scala b/core/src/test/scala/spark/PartitionPruningRDDSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..88352b639f9b1d953246bc438a82a0e364a7f1bf
--- /dev/null
+++ b/core/src/test/scala/spark/PartitionPruningRDDSuite.scala
@@ -0,0 +1,28 @@
+package spark
+
+import org.scalatest.FunSuite
+import spark.SparkContext._
+import spark.rdd.PartitionPruningRDD
+
+
+class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext {
+
+  test("Pruned Partitions inherit locality prefs correctly") {
+    class TestPartition(i: Int) extends Partition {
+      def index = i
+    }
+    val rdd = new RDD[Int](sc, Nil) {
+      override protected def getPartitions = {
+        Array[Partition](
+            new TestPartition(1),
+            new TestPartition(2), 
+            new TestPartition(3))
+      }
+      def compute(split: Partition, context: TaskContext) = {Iterator()}
+    }
+    val prunedRDD = PartitionPruningRDD.create(rdd, {x => if (x==2) true else false})
+    val p = prunedRDD.partitions(0)
+    assert(p.index == 2)
+    assert(prunedRDD.partitions.length == 1)
+  }
+}
diff --git a/core/src/test/scala/spark/RDDSuite.scala b/core/src/test/scala/spark/RDDSuite.scala
index cbddf4e523d16a1872c3f93d61afdcadcdb13dbe..75778de1cc916dd52135d343def2951613ed2fbe 100644
--- a/core/src/test/scala/spark/RDDSuite.scala
+++ b/core/src/test/scala/spark/RDDSuite.scala
@@ -170,7 +170,7 @@ class RDDSuite extends FunSuite with SharedSparkContext {
 
     // we can optionally shuffle to keep the upstream parallel
     val coalesced5 = data.coalesce(1, shuffle = true)
-    assert(coalesced5.dependencies.head.rdd.dependencies.head.rdd.asInstanceOf[ShuffledRDD[_, _]] !=
+    assert(coalesced5.dependencies.head.rdd.dependencies.head.rdd.asInstanceOf[ShuffledRDD[_, _, _]] !=
       null)
   }
 
diff --git a/core/src/test/scala/spark/ShuffleSuite.scala b/core/src/test/scala/spark/ShuffleSuite.scala
index 752e4b85e67d2585f128a9e76b754aa49cafee1a..8745689c70bca55f8b11a6579cfdbc0ac9e00e2a 100644
--- a/core/src/test/scala/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/spark/ShuffleSuite.scala
@@ -17,20 +17,14 @@
 
 package spark
 
-import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.HashSet
-
 import org.scalatest.FunSuite
 import org.scalatest.matchers.ShouldMatchers
-import org.scalatest.prop.Checkers
-import org.scalacheck.Arbitrary._
-import org.scalacheck.Gen
-import org.scalacheck.Prop._
-
-import com.google.common.io.Files
 
-import spark.rdd.ShuffledRDD
 import spark.SparkContext._
+import spark.ShuffleSuite.NonJavaSerializableClass
+import spark.rdd.{SubtractedRDD, CoGroupedRDD, OrderedRDDFunctions, ShuffledRDD}
+import spark.util.MutablePair
+
 
 class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
   test("groupByKey without compression") {
@@ -55,12 +49,12 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
 
     val a = sc.parallelize(1 to 10, 2)
     val b = a.map { x =>
-      (x, new ShuffleSuite.NonJavaSerializableClass(x * 2))
+      (x, new NonJavaSerializableClass(x * 2))
     }
     // If the Kryo serializer is not used correctly, the shuffle would fail because the
     // default Java serializer cannot handle the non serializable class.
-    val c = new ShuffledRDD(b, new HashPartitioner(NUM_BLOCKS),
-      classOf[spark.KryoSerializer].getName)
+    val c = new ShuffledRDD[Int, NonJavaSerializableClass, (Int, NonJavaSerializableClass)](
+      b, new HashPartitioner(NUM_BLOCKS)).setSerializer(classOf[spark.KryoSerializer].getName)
     val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
 
     assert(c.count === 10)
@@ -77,11 +71,12 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
     sc = new SparkContext("local-cluster[2,1,512]", "test")
     val a = sc.parallelize(1 to 10, 2)
     val b = a.map { x =>
-      (x, new ShuffleSuite.NonJavaSerializableClass(x * 2))
+      (x, new NonJavaSerializableClass(x * 2))
     }
     // If the Kryo serializer is not used correctly, the shuffle would fail because the
     // default Java serializer cannot handle the non serializable class.
-    val c = new ShuffledRDD(b, new HashPartitioner(3), classOf[spark.KryoSerializer].getName)
+    val c = new ShuffledRDD[Int, NonJavaSerializableClass, (Int, NonJavaSerializableClass)](
+      b, new HashPartitioner(3)).setSerializer(classOf[spark.KryoSerializer].getName)
     assert(c.count === 10)
   }
 
@@ -96,7 +91,8 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
 
     // NOTE: The default Java serializer doesn't create zero-sized blocks.
     //       So, use Kryo
-    val c = new ShuffledRDD(b, new HashPartitioner(10), classOf[spark.KryoSerializer].getName)
+    val c = new ShuffledRDD[Int, Int, (Int, Int)](b, new HashPartitioner(10))
+      .setSerializer(classOf[spark.KryoSerializer].getName)
 
     val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
     assert(c.count === 4)
@@ -121,7 +117,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
     val b = a.map(x => (x, x*2))
 
     // NOTE: The default Java serializer should create zero-sized blocks
-    val c = new ShuffledRDD(b, new HashPartitioner(10))
+    val c = new ShuffledRDD[Int, Int, (Int, Int)](b, new HashPartitioner(10))
 
     val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
     assert(c.count === 4)
@@ -135,6 +131,72 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
     // We should have at most 4 non-zero sized partitions
     assert(nonEmptyBlocks.size <= 4)
   }
+
+  test("shuffle using mutable pairs") {
+    // Use a local cluster with 2 processes to make sure there are both local and remote blocks
+    sc = new SparkContext("local-cluster[2,1,512]", "test")
+    def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2)
+    val data = Array(p(1, 1), p(1, 2), p(1, 3), p(2, 1))
+    val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2)
+    val results = new ShuffledRDD[Int, Int, MutablePair[Int, Int]](pairs, new HashPartitioner(2))
+      .collect()
+
+    data.foreach { pair => results should contain (pair) }
+  }
+
+  test("sorting using mutable pairs") {
+    // This is not in SortingSuite because of the local cluster setup.
+    // Use a local cluster with 2 processes to make sure there are both local and remote blocks
+    sc = new SparkContext("local-cluster[2,1,512]", "test")
+    def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2)
+    val data = Array(p(1, 11), p(3, 33), p(100, 100), p(2, 22))
+    val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2)
+    val results = new OrderedRDDFunctions[Int, Int, MutablePair[Int, Int]](pairs)
+      .sortByKey().collect()
+    results(0) should be (p(1, 11))
+    results(1) should be (p(2, 22))
+    results(2) should be (p(3, 33))
+    results(3) should be (p(100, 100))
+  }
+
+  test("cogroup using mutable pairs") {
+    // Use a local cluster with 2 processes to make sure there are both local and remote blocks
+    sc = new SparkContext("local-cluster[2,1,512]", "test")
+    def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2)
+    val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1))
+    val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"), p(3, "3"))
+    val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2)
+    val pairs2: RDD[MutablePair[Int, String]] = sc.parallelize(data2, 2)
+    val results = new CoGroupedRDD[Int](Seq(pairs1, pairs2), new HashPartitioner(2)).collectAsMap()
+
+    assert(results(1)(0).length === 3)
+    assert(results(1)(0).contains(1))
+    assert(results(1)(0).contains(2))
+    assert(results(1)(0).contains(3))
+    assert(results(1)(1).length === 2)
+    assert(results(1)(1).contains("11"))
+    assert(results(1)(1).contains("12"))
+    assert(results(2)(0).length === 1)
+    assert(results(2)(0).contains(1))
+    assert(results(2)(1).length === 1)
+    assert(results(2)(1).contains("22"))
+    assert(results(3)(0).length === 0)
+    assert(results(3)(1).contains("3"))
+  }
+
+  test("subtract mutable pairs") {
+    // Use a local cluster with 2 processes to make sure there are both local and remote blocks
+    sc = new SparkContext("local-cluster[2,1,512]", "test")
+    def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2)
+    val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1), p(3, 33))
+    val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"))
+    val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2)
+    val pairs2: RDD[MutablePair[Int, String]] = sc.parallelize(data2, 2)
+    val results = new SubtractedRDD(pairs1, pairs2, new HashPartitioner(2)).collect()
+    results should have length (1)
+    // substracted rdd return results as Tuple2
+    results(0) should be ((3, 33))
+  }
 }
 
 object ShuffleSuite {
diff --git a/core/src/test/scala/spark/UtilsSuite.scala b/core/src/test/scala/spark/UtilsSuite.scala
index 31c3b25c502791cc5725b4506911a72461681ca7..98a6c1a1c9e336189fdca53c27cb59b5b6cb52d6 100644
--- a/core/src/test/scala/spark/UtilsSuite.scala
+++ b/core/src/test/scala/spark/UtilsSuite.scala
@@ -26,14 +26,14 @@ import scala.util.Random
 
 class UtilsSuite extends FunSuite {
 
-  test("memoryBytesToString") {
-    assert(Utils.memoryBytesToString(10) === "10.0 B")
-    assert(Utils.memoryBytesToString(1500) === "1500.0 B")
-    assert(Utils.memoryBytesToString(2000000) === "1953.1 KB")
-    assert(Utils.memoryBytesToString(2097152) === "2.0 MB")
-    assert(Utils.memoryBytesToString(2306867) === "2.2 MB")
-    assert(Utils.memoryBytesToString(5368709120L) === "5.0 GB")
-    assert(Utils.memoryBytesToString(5L * 1024L * 1024L * 1024L * 1024L) === "5.0 TB")
+  test("bytesToString") {
+    assert(Utils.bytesToString(10) === "10.0 B")
+    assert(Utils.bytesToString(1500) === "1500.0 B")
+    assert(Utils.bytesToString(2000000) === "1953.1 KB")
+    assert(Utils.bytesToString(2097152) === "2.0 MB")
+    assert(Utils.bytesToString(2306867) === "2.2 MB")
+    assert(Utils.bytesToString(5368709120L) === "5.0 GB")
+    assert(Utils.bytesToString(5L * 1024L * 1024L * 1024L * 1024L) === "5.0 TB")
   }
 
   test("copyStream") {
diff --git a/core/src/test/scala/spark/ZippedPartitionsSuite.scala b/core/src/test/scala/spark/ZippedPartitionsSuite.scala
index 5e6d7b09d8d36cd69cc6dc0418c7e9bd030ec71d..bb5d379273b3db9ffa2f7aa0ae3d1f093daff7d3 100644
--- a/core/src/test/scala/spark/ZippedPartitionsSuite.scala
+++ b/core/src/test/scala/spark/ZippedPartitionsSuite.scala
@@ -40,7 +40,7 @@ class ZippedPartitionsSuite extends FunSuite with SharedSparkContext {
     val data2 = sc.makeRDD(Array("1", "2", "3", "4", "5", "6"), 2)
     val data3 = sc.makeRDD(Array(1.0, 2.0), 2)
 
-    val zippedRDD = data1.zipPartitions(ZippedPartitionsSuite.procZippedData, data2, data3)
+    val zippedRDD = data1.zipPartitions(data2, data3)(ZippedPartitionsSuite.procZippedData)
 
     val obtainedSizes = zippedRDD.collect()
     val expectedSizes = Array(2, 3, 1, 2, 3, 1)
diff --git a/core/src/test/scala/spark/io/CompressionCodecSuite.scala b/core/src/test/scala/spark/io/CompressionCodecSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..1ba82fe2b92b0b53d27c4ab0a7d1cc9cf1b6b828
--- /dev/null
+++ b/core/src/test/scala/spark/io/CompressionCodecSuite.scala
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.io
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+
+import org.scalatest.FunSuite
+
+
+class CompressionCodecSuite extends FunSuite {
+
+  def testCodec(codec: CompressionCodec) {
+    // Write 1000 integers to the output stream, compressed.
+    val outputStream = new ByteArrayOutputStream()
+    val out = codec.compressedOutputStream(outputStream)
+    for (i <- 1 until 1000) {
+      out.write(i % 256)
+    }
+    out.close()
+
+    // Read the 1000 integers back.
+    val inputStream = new ByteArrayInputStream(outputStream.toByteArray)
+    val in = codec.compressedInputStream(inputStream)
+    for (i <- 1 until 1000) {
+      assert(in.read() === i % 256)
+    }
+    in.close()
+  }
+
+  test("default compression codec") {
+    val codec = CompressionCodec.createCodec()
+    assert(codec.getClass === classOf[SnappyCompressionCodec])
+    testCodec(codec)
+  }
+
+  test("lzf compression codec") {
+    val codec = CompressionCodec.createCodec(classOf[LZFCompressionCodec].getName)
+    assert(codec.getClass === classOf[LZFCompressionCodec])
+    testCodec(codec)
+  }
+
+  test("snappy compression codec") {
+    val codec = CompressionCodec.createCodec(classOf[SnappyCompressionCodec].getName)
+    assert(codec.getClass === classOf[SnappyCompressionCodec])
+    testCodec(codec)
+  }
+}
diff --git a/core/src/test/scala/spark/metrics/MetricsConfigSuite.scala b/core/src/test/scala/spark/metrics/MetricsConfigSuite.scala
index 87cd2ffad25ba11b8a2c377276c5a516d70a3c5d..b0213b62d9c993168a4e3385d801134e239df4e7 100644
--- a/core/src/test/scala/spark/metrics/MetricsConfigSuite.scala
+++ b/core/src/test/scala/spark/metrics/MetricsConfigSuite.scala
@@ -1,12 +1,24 @@
-package spark.metrics
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 
-import java.util.Properties
-import java.io.{File, FileOutputStream}
+package spark.metrics
 
 import org.scalatest.{BeforeAndAfter, FunSuite}
 
-import spark.metrics._
-
 class MetricsConfigSuite extends FunSuite with BeforeAndAfter {
   var filePath: String = _
 
@@ -18,11 +30,14 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter {
     val conf = new MetricsConfig(Option("dummy-file"))
     conf.initialize()
 
-    assert(conf.properties.size() === 0)
+    assert(conf.properties.size() === 5)
     assert(conf.properties.getProperty("test-for-dummy") === null)
 
     val property = conf.getInstance("random")
-    assert(property.size() === 0)
+    assert(property.size() === 3)
+    assert(property.getProperty("sink.servlet.class") === "spark.metrics.sink.MetricsServlet")
+    assert(property.getProperty("sink.servlet.uri") === "/metrics/json")
+    assert(property.getProperty("sink.servlet.sample") === "false")
   }
 
   test("MetricsConfig with properties set") {
@@ -30,16 +45,22 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter {
     conf.initialize()
 
     val masterProp = conf.getInstance("master")
-    assert(masterProp.size() === 3)
+    assert(masterProp.size() === 6)
     assert(masterProp.getProperty("sink.console.period") === "20")
     assert(masterProp.getProperty("sink.console.unit") === "minutes")
     assert(masterProp.getProperty("source.jvm.class") === "spark.metrics.source.JvmSource")
+    assert(masterProp.getProperty("sink.servlet.class") === "spark.metrics.sink.MetricsServlet")
+    assert(masterProp.getProperty("sink.servlet.uri") === "/metrics/master/json")
+    assert(masterProp.getProperty("sink.servlet.sample") === "false")
 
     val workerProp = conf.getInstance("worker")
-    assert(workerProp.size() === 3)
+    assert(workerProp.size() === 6)
     assert(workerProp.getProperty("sink.console.period") === "10")
     assert(workerProp.getProperty("sink.console.unit") === "seconds")
-    assert(masterProp.getProperty("source.jvm.class") === "spark.metrics.source.JvmSource")
+    assert(workerProp.getProperty("source.jvm.class") === "spark.metrics.source.JvmSource")
+    assert(workerProp.getProperty("sink.servlet.class") === "spark.metrics.sink.MetricsServlet")
+    assert(workerProp.getProperty("sink.servlet.uri") === "/metrics/json")
+    assert(workerProp.getProperty("sink.servlet.sample") === "false")
   }
 
   test("MetricsConfig with subProperties") {
@@ -47,7 +68,7 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter {
     conf.initialize()
 
     val propCategories = conf.propertyCategories
-    assert(propCategories.size === 2)
+    assert(propCategories.size === 3)
 
     val masterProp = conf.getInstance("master")
     val sourceProps = conf.subProperties(masterProp, MetricsSystem.SOURCE_REGEX)
@@ -55,10 +76,14 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter {
     assert(sourceProps("jvm").getProperty("class") === "spark.metrics.source.JvmSource")
 
     val sinkProps = conf.subProperties(masterProp, MetricsSystem.SINK_REGEX)
-    assert(sinkProps.size === 1)
+    assert(sinkProps.size === 2)
     assert(sinkProps.contains("console"))
+    assert(sinkProps.contains("servlet"))
 
     val consoleProps = sinkProps("console")
     assert(consoleProps.size() === 2)
+
+    val servletProps = sinkProps("servlet")
+    assert(servletProps.size() === 3)
   }
 }
diff --git a/core/src/test/scala/spark/metrics/MetricsSystemSuite.scala b/core/src/test/scala/spark/metrics/MetricsSystemSuite.scala
index c1899964172fa235d4525e7958e3110794e20bb6..dc65ac6994b74e8d5e7290ee34860d6667512a07 100644
--- a/core/src/test/scala/spark/metrics/MetricsSystemSuite.scala
+++ b/core/src/test/scala/spark/metrics/MetricsSystemSuite.scala
@@ -1,12 +1,24 @@
-package spark.metrics
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 
-import java.util.Properties
-import java.io.{File, FileOutputStream}
+package spark.metrics
 
 import org.scalatest.{BeforeAndAfter, FunSuite}
 
-import spark.metrics._
-
 class MetricsSystemSuite extends FunSuite with BeforeAndAfter {
   var filePath: String = _
 
@@ -22,6 +34,7 @@ class MetricsSystemSuite extends FunSuite with BeforeAndAfter {
 
     assert(sources.length === 0)
     assert(sinks.length === 0)
+    assert(!metricsSystem.getServletHandlers.isEmpty)
   }
 
   test("MetricsSystem with sources add") {
@@ -31,6 +44,7 @@ class MetricsSystemSuite extends FunSuite with BeforeAndAfter {
 
     assert(sources.length === 0)
     assert(sinks.length === 1)
+    assert(!metricsSystem.getServletHandlers.isEmpty)
 
     val source = new spark.deploy.master.MasterSource(null)
     metricsSystem.registerSource(source)
diff --git a/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala
index a8b88d7936e4f5413d540c5c56c0fa094286aaff..3b4a0d52fc79e98ba4bf0d8e28078dae53cf07d1 100644
--- a/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala
@@ -32,6 +32,10 @@ import spark.{Dependency, ShuffleDependency, OneToOneDependency}
 import spark.{FetchFailed, Success, TaskEndReason}
 import spark.storage.{BlockManagerId, BlockManagerMaster}
 
+import spark.scheduler.cluster.Pool
+import spark.scheduler.cluster.SchedulingMode
+import spark.scheduler.cluster.SchedulingMode.SchedulingMode
+
 /**
  * Tests for DAGScheduler. These tests directly call the event processing functions in DAGScheduler
  * rather than spawning an event loop thread as happens in the real code. They use EasyMock
@@ -49,11 +53,13 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
   /** Set of TaskSets the DAGScheduler has requested executed. */
   val taskSets = scala.collection.mutable.Buffer[TaskSet]()
   val taskScheduler = new TaskScheduler() {
+    override def rootPool: Pool = null
+    override def schedulingMode: SchedulingMode = SchedulingMode.NONE
     override def start() = {}
     override def stop() = {}
     override def submitTasks(taskSet: TaskSet) = {
       // normally done by TaskSetManager
-      taskSet.tasks.foreach(_.generation = mapOutputTracker.getGeneration)
+      taskSet.tasks.foreach(_.epoch = mapOutputTracker.getEpoch)
       taskSets += taskSet
     }
     override def setListener(listener: TaskSchedulerListener) = {}
@@ -293,10 +299,10 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
     val reduceRdd = makeRdd(2, List(shuffleDep))
     submit(reduceRdd, Array(0, 1))
     // pretend we were told hostA went away
-    val oldGeneration = mapOutputTracker.getGeneration
+    val oldEpoch = mapOutputTracker.getEpoch
     runEvent(ExecutorLost("exec-hostA"))
-    val newGeneration = mapOutputTracker.getGeneration
-    assert(newGeneration > oldGeneration)
+    val newEpoch = mapOutputTracker.getEpoch
+    assert(newEpoch > oldEpoch)
     val noAccum = Map[Long, Any]()
     val taskSet = taskSets(0)
     // should be ignored for being too old
@@ -305,8 +311,8 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
     runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostB", 1), noAccum, null, null))
     // should be ignored for being too old
     runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA", 1), noAccum, null, null))
-    // should work because it's a new generation
-    taskSet.tasks(1).generation = newGeneration
+    // should work because it's a new epoch
+    taskSet.tasks(1).epoch = newEpoch
     runEvent(CompletionEvent(taskSet.tasks(1), Success, makeMapStatus("hostA", 1), noAccum, null, null))
     assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) ===
            Array(makeBlockManagerId("hostB"), makeBlockManagerId("hostA")))
@@ -395,12 +401,14 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
     assert(results === Map(0 -> 42))
   }
 
-  /** Assert that the supplied TaskSet has exactly the given preferredLocations. Note, converts taskSet's locations to host only. */
-  private def assertLocations(taskSet: TaskSet, locations: Seq[Seq[String]]) {
-    assert(locations.size === taskSet.tasks.size)
-    for ((expectLocs, taskLocs) <-
-            taskSet.tasks.map(_.preferredLocations).zip(locations)) {
-      assert(expectLocs.map(loc => spark.Utils.parseHostPort(loc)._1) === taskLocs)
+  /**
+   * Assert that the supplied TaskSet has exactly the given hosts as its preferred locations.
+   * Note that this checks only the host and not the executor ID.
+   */
+  private def assertLocations(taskSet: TaskSet, hosts: Seq[Seq[String]]) {
+    assert(hosts.size === taskSet.tasks.size)
+    for ((taskLocs, expectedLocs) <- taskSet.tasks.map(_.preferredLocations).zip(hosts)) {
+      assert(taskLocs.map(_.host) === expectedLocs)
     }
   }
 
diff --git a/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala b/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala
index 0f855c38da4f49f7abc3e5e119cc9f1bdf09afd3..bb9e715f95e217bd2c2b7c5f7a3bcbe90c782ae9 100644
--- a/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala
+++ b/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala
@@ -57,7 +57,7 @@ class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers
     val shuffleMapStage = new Stage(1, parentRdd, Some(shuffleDep), Nil, jobID, None)
     val rootStage = new Stage(0, rootRdd, None, List(shuffleMapStage), jobID, None)
     
-    joblogger.onStageSubmitted(SparkListenerStageSubmitted(rootStage, 4))
+    joblogger.onStageSubmitted(SparkListenerStageSubmitted(rootStage, 4, null))
     joblogger.getRddNameTest(parentRdd) should be (parentRdd.getClass.getName)
     parentRdd.setName("MyRDD")
     joblogger.getRddNameTest(parentRdd) should be ("MyRDD")
diff --git a/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/cluster/ClusterSchedulerSuite.scala
similarity index 92%
rename from core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala
rename to core/src/test/scala/spark/scheduler/cluster/ClusterSchedulerSuite.scala
index 05afcd656760bff60f54c1111cea4f03cc4b22b9..abfdabf5fe634f5c135a7f1c04467120a4c8cc4c 100644
--- a/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala
+++ b/core/src/test/scala/spark/scheduler/cluster/ClusterSchedulerSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package spark.scheduler
+package spark.scheduler.cluster
 
 import org.scalatest.FunSuite
 import org.scalatest.BeforeAndAfter
@@ -27,7 +27,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import java.util.Properties
 
-class DummyTaskSetManager(
+class FakeTaskSetManager(
     initPriority: Int,
     initStageId: Int,
     initNumTasks: Int,
@@ -72,10 +72,16 @@ class DummyTaskSetManager(
   override def executorLost(executorId: String, host: String): Unit = {
   }
 
-  override def slaveOffer(execId: String, host: String, avaiableCpus: Double, overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] = {
+  override def resourceOffer(
+      execId: String,
+      host: String,
+      availableCpus: Int,
+      maxLocality: TaskLocality.TaskLocality)
+    : Option[TaskDescription] =
+  {
     if (tasksFinished + runningTasks < numTasks) {
       increaseRunningTasks(1)
-      return Some(new TaskDescription(0, execId, "task 0:0", null))
+      return Some(new TaskDescription(0, execId, "task 0:0", 0, null))
     }
     return None
   }
@@ -98,17 +104,10 @@ class DummyTaskSetManager(
   }
 }
 
-class DummyTask(stageId: Int) extends Task[Int](stageId)
-{
-  def run(attemptId: Long): Int = {
-    return 0
-  }
-}
-
 class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging {
 
-  def createDummyTaskSetManager(priority: Int, stage: Int, numTasks: Int, cs: ClusterScheduler, taskSet: TaskSet): DummyTaskSetManager = {
-    new DummyTaskSetManager(priority, stage, numTasks, cs , taskSet)
+  def createDummyTaskSetManager(priority: Int, stage: Int, numTasks: Int, cs: ClusterScheduler, taskSet: TaskSet): FakeTaskSetManager = {
+    new FakeTaskSetManager(priority, stage, numTasks, cs , taskSet)
   }
 
   def resourceOffer(rootPool: Pool): Int = {
@@ -118,7 +117,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging
        logInfo("parentName:%s, parent running tasks:%d, name:%s,runningTasks:%d".format(manager.parent.name, manager.parent.runningTasks, manager.name, manager.runningTasks))
     }
     for (taskSet <- taskSetQueue) {
-      taskSet.slaveOffer("execId_1", "hostname_1", 1) match {
+      taskSet.resourceOffer("execId_1", "hostname_1", 1, TaskLocality.ANY) match {
         case Some(task) =>
           return taskSet.stageId
         case None => {}
@@ -135,7 +134,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging
     sc = new SparkContext("local", "ClusterSchedulerSuite")
     val clusterScheduler = new ClusterScheduler(sc)
     var tasks = ArrayBuffer[Task[_]]()
-    val task = new DummyTask(0)
+    val task = new FakeTask(0)
     tasks += task
     val taskSet = new TaskSet(tasks.toArray,0,0,0,null)
 
@@ -162,7 +161,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging
     sc = new SparkContext("local", "ClusterSchedulerSuite")
     val clusterScheduler = new ClusterScheduler(sc)
     var tasks = ArrayBuffer[Task[_]]()
-    val task = new DummyTask(0)
+    val task = new FakeTask(0)
     tasks += task
     val taskSet = new TaskSet(tasks.toArray,0,0,0,null)
 
@@ -219,7 +218,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging
     sc = new SparkContext("local", "ClusterSchedulerSuite")
     val clusterScheduler = new ClusterScheduler(sc)
     var tasks = ArrayBuffer[Task[_]]()
-    val task = new DummyTask(0)
+    val task = new FakeTask(0)
     tasks += task
     val taskSet = new TaskSet(tasks.toArray,0,0,0,null)
 
diff --git a/core/src/test/scala/spark/scheduler/cluster/ClusterTaskSetManagerSuite.scala b/core/src/test/scala/spark/scheduler/cluster/ClusterTaskSetManagerSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..5a0b949ef5f65e87595b294d18df0e7e14e19ebd
--- /dev/null
+++ b/core/src/test/scala/spark/scheduler/cluster/ClusterTaskSetManagerSuite.scala
@@ -0,0 +1,273 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.scheduler.cluster
+
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.mutable
+
+import org.scalatest.FunSuite
+
+import spark._
+import spark.scheduler._
+import spark.executor.TaskMetrics
+import java.nio.ByteBuffer
+import spark.util.FakeClock
+
+/**
+ * A mock ClusterScheduler implementation that just remembers information about tasks started and
+ * feedback received from the TaskSetManagers. Note that it's important to initialize this with
+ * a list of "live" executors and their hostnames for isExecutorAlive and hasExecutorsAliveOnHost
+ * to work, and these are required for locality in ClusterTaskSetManager.
+ */
+class FakeClusterScheduler(sc: SparkContext, liveExecutors: (String, String)* /* execId, host */)
+  extends ClusterScheduler(sc)
+{
+  val startedTasks = new ArrayBuffer[Long]
+  val endedTasks = new mutable.HashMap[Long, TaskEndReason]
+  val finishedManagers = new ArrayBuffer[TaskSetManager]
+
+  val executors = new mutable.HashMap[String, String] ++ liveExecutors
+
+  listener = new TaskSchedulerListener {
+    def taskStarted(task: Task[_], taskInfo: TaskInfo) {
+      startedTasks += taskInfo.index
+    }
+
+    def taskEnded(
+        task: Task[_],
+        reason: TaskEndReason,
+        result: Any,
+        accumUpdates: mutable.Map[Long, Any],
+        taskInfo: TaskInfo,
+        taskMetrics: TaskMetrics)
+    {
+      endedTasks(taskInfo.index) = reason
+    }
+
+    def executorGained(execId: String, host: String) {}
+
+    def executorLost(execId: String) {}
+
+    def taskSetFailed(taskSet: TaskSet, reason: String) {}
+  }
+
+  def removeExecutor(execId: String): Unit = executors -= execId
+
+  override def taskSetFinished(manager: TaskSetManager): Unit = finishedManagers += manager
+
+  override def isExecutorAlive(execId: String): Boolean = executors.contains(execId)
+
+  override def hasExecutorsAliveOnHost(host: String): Boolean = executors.values.exists(_ == host)
+}
+
+class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
+  import TaskLocality.{ANY, PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL}
+
+  val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong
+
+  test("TaskSet with no preferences") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeClusterScheduler(sc, ("exec1", "host1"))
+    val taskSet = createTaskSet(1)
+    val manager = new ClusterTaskSetManager(sched, taskSet)
+
+    // Offer a host with no CPUs
+    assert(manager.resourceOffer("exec1", "host1", 0, ANY) === None)
+
+    // Offer a host with process-local as the constraint; this should work because the TaskSet
+    // above won't have any locality preferences
+    val taskOption = manager.resourceOffer("exec1", "host1", 2, TaskLocality.PROCESS_LOCAL)
+    assert(taskOption.isDefined)
+    val task = taskOption.get
+    assert(task.executorId === "exec1")
+    assert(sched.startedTasks.contains(0))
+
+    // Re-offer the host -- now we should get no more tasks
+    assert(manager.resourceOffer("exec1", "host1", 2, PROCESS_LOCAL) === None)
+
+    // Tell it the task has finished
+    manager.statusUpdate(0, TaskState.FINISHED, createTaskResult(0))
+    assert(sched.endedTasks(0) === Success)
+    assert(sched.finishedManagers.contains(manager))
+  }
+
+  test("multiple offers with no preferences") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeClusterScheduler(sc, ("exec1", "host1"))
+    val taskSet = createTaskSet(3)
+    val manager = new ClusterTaskSetManager(sched, taskSet)
+
+    // First three offers should all find tasks
+    for (i <- 0 until 3) {
+      val taskOption = manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL)
+      assert(taskOption.isDefined)
+      val task = taskOption.get
+      assert(task.executorId === "exec1")
+    }
+    assert(sched.startedTasks.toSet === Set(0, 1, 2))
+
+    // Re-offer the host -- now we should get no more tasks
+    assert(manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL) === None)
+
+    // Finish the first two tasks
+    manager.statusUpdate(0, TaskState.FINISHED, createTaskResult(0))
+    manager.statusUpdate(1, TaskState.FINISHED, createTaskResult(1))
+    assert(sched.endedTasks(0) === Success)
+    assert(sched.endedTasks(1) === Success)
+    assert(!sched.finishedManagers.contains(manager))
+
+    // Finish the last task
+    manager.statusUpdate(2, TaskState.FINISHED, createTaskResult(2))
+    assert(sched.endedTasks(2) === Success)
+    assert(sched.finishedManagers.contains(manager))
+  }
+
+  test("basic delay scheduling") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeClusterScheduler(sc, ("exec1", "host1"), ("exec2", "host2"))
+    val taskSet = createTaskSet(4,
+      Seq(TaskLocation("host1", "exec1")),
+      Seq(TaskLocation("host2", "exec2")),
+      Seq(TaskLocation("host1"), TaskLocation("host2", "exec2")),
+      Seq()   // Last task has no locality prefs
+    )
+    val clock = new FakeClock
+    val manager = new ClusterTaskSetManager(sched, taskSet, clock)
+
+    // First offer host1, exec1: first task should be chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0)
+
+    // Offer host1, exec1 again: the last task, which has no prefs, should be chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 3)
+
+    // Offer host1, exec1 again, at PROCESS_LOCAL level: nothing should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL) === None)
+
+    clock.advance(LOCALITY_WAIT)
+
+    // Offer host1, exec1 again, at PROCESS_LOCAL level: nothing should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL) === None)
+
+    // Offer host1, exec1 again, at NODE_LOCAL level: we should choose task 2
+    assert(manager.resourceOffer("exec1", "host1", 1, NODE_LOCAL).get.index == 2)
+
+    // Offer host1, exec1 again, at NODE_LOCAL level: nothing should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, NODE_LOCAL) === None)
+
+    // Offer host1, exec1 again, at ANY level: nothing should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None)
+
+    clock.advance(LOCALITY_WAIT)
+
+    // Offer host1, exec1 again, at ANY level: task 1 should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 1)
+
+    // Offer host1, exec1 again, at ANY level: nothing should be chosen as we've launched all tasks
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None)
+  }
+
+  test("delay scheduling with fallback") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeClusterScheduler(sc,
+      ("exec1", "host1"), ("exec2", "host2"), ("exec3", "host3"))
+    val taskSet = createTaskSet(5,
+      Seq(TaskLocation("host1")),
+      Seq(TaskLocation("host2")),
+      Seq(TaskLocation("host2")),
+      Seq(TaskLocation("host3")),
+      Seq(TaskLocation("host2"))
+    )
+    val clock = new FakeClock
+    val manager = new ClusterTaskSetManager(sched, taskSet, clock)
+
+    // First offer host1: first task should be chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0)
+
+    // Offer host1 again: nothing should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None)
+
+    clock.advance(LOCALITY_WAIT)
+
+    // Offer host1 again: second task (on host2) should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 1)
+
+    // Offer host1 again: third task (on host2) should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 2)
+
+    // Offer host2: fifth task (also on host2) should get chosen
+    assert(manager.resourceOffer("exec2", "host2", 1, ANY).get.index === 4)
+
+    // Now that we've launched a local task, we should no longer launch the task for host3
+    assert(manager.resourceOffer("exec2", "host2", 1, ANY) === None)
+
+    clock.advance(LOCALITY_WAIT)
+
+    // After another delay, we can go ahead and launch that task non-locally
+    assert(manager.resourceOffer("exec2", "host2", 1, ANY).get.index === 3)
+  }
+
+  test("delay scheduling with failed hosts") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeClusterScheduler(sc, ("exec1", "host1"), ("exec2", "host2"))
+    val taskSet = createTaskSet(3,
+      Seq(TaskLocation("host1")),
+      Seq(TaskLocation("host2")),
+      Seq(TaskLocation("host3"))
+    )
+    val clock = new FakeClock
+    val manager = new ClusterTaskSetManager(sched, taskSet, clock)
+
+    // First offer host1: first task should be chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0)
+
+    // Offer host1 again: third task should be chosen immediately because host3 is not up
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 2)
+
+    // After this, nothing should get chosen
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None)
+
+    // Now mark host2 as dead
+    sched.removeExecutor("exec2")
+    manager.executorLost("exec2", "host2")
+
+    // Task 1 should immediately be launched on host1 because its original host is gone
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 1)
+
+    // Now that all tasks have launched, nothing new should be launched anywhere else
+    assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None)
+    assert(manager.resourceOffer("exec2", "host2", 1, ANY) === None)
+  }
+
+  /**
+   * Utility method to create a TaskSet, potentially setting a particular sequence of preferred
+   * locations for each task (given as varargs) if this sequence is not empty.
+   */
+  def createTaskSet(numTasks: Int, prefLocs: Seq[TaskLocation]*): TaskSet = {
+    if (prefLocs.size != 0 && prefLocs.size != numTasks) {
+      throw new IllegalArgumentException("Wrong number of task locations")
+    }
+    val tasks = Array.tabulate[Task[_]](numTasks) { i =>
+      new FakeTask(i, if (prefLocs.size != 0) prefLocs(i) else Nil)
+    }
+    new TaskSet(tasks, 0, 0, 0, null)
+  }
+
+  def createTaskResult(id: Int): ByteBuffer = {
+    ByteBuffer.wrap(Utils.serialize(new TaskResult[Int](id, mutable.Map.empty, new TaskMetrics)))
+  }
+}
diff --git a/core/src/hadoop2/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala b/core/src/test/scala/spark/scheduler/cluster/FakeTask.scala
similarity index 63%
rename from core/src/hadoop2/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala
rename to core/src/test/scala/spark/scheduler/cluster/FakeTask.scala
index 4b3d84670c2bde43c79a3e8fb40151595aee82c0..de9e66be20ae6116828bae17650dd0d0bb06f768 100644
--- a/core/src/hadoop2/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala
+++ b/core/src/test/scala/spark/scheduler/cluster/FakeTask.scala
@@ -15,13 +15,12 @@
  * limitations under the License.
  */
 
-package org.apache.hadoop.mapred
+package spark.scheduler.cluster
 
-trait HadoopMapRedUtil {
-  def newJobContext(conf: JobConf, jobId: JobID): JobContext = new JobContextImpl(conf, jobId)
+import spark.scheduler.{TaskLocation, Task}
 
-  def newTaskAttemptContext(conf: JobConf, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContextImpl(conf, attemptId)
+class FakeTask(stageId: Int, prefLocs: Seq[TaskLocation] = Nil) extends Task[Int](stageId) {
+  override def run(attemptId: Long): Int = 0
 
-  def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = new TaskAttemptID(jtIdentifier,
-    jobId, isMap, taskId, attemptId)
+  override def preferredLocations: Seq[TaskLocation] = prefLocs
 }
diff --git a/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/local/LocalSchedulerSuite.scala
similarity index 95%
rename from core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala
rename to core/src/test/scala/spark/scheduler/local/LocalSchedulerSuite.scala
index 14bb58731b7449a06a1d4a56c7099e016269090e..d28ee47fa335ef81e512b943f674c8a4f6ea5a8d 100644
--- a/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala
+++ b/core/src/test/scala/spark/scheduler/local/LocalSchedulerSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package spark.scheduler
+package spark.scheduler.local
 
 import org.scalatest.FunSuite
 import org.scalatest.BeforeAndAfter
@@ -57,23 +57,23 @@ object TaskThreadInfo {
  * 1. each thread contains one job.
  * 2. each job contains one stage.
  * 3. each stage only contains one task.
- * 4. each task(launched) must be lanched orderly(using threadToStarted) to make sure 
- *    it will get cpu core resource, and will wait to finished after user manually 
- *    release "Lock" and then cluster will contain another free cpu cores. 
- * 5. each task(pending) must use "sleep" to  make sure it has been added to taskSetManager queue, 
+ * 4. each task(launched) must be lanched orderly(using threadToStarted) to make sure
+ *    it will get cpu core resource, and will wait to finished after user manually
+ *    release "Lock" and then cluster will contain another free cpu cores.
+ * 5. each task(pending) must use "sleep" to  make sure it has been added to taskSetManager queue,
  *    thus it will be scheduled later when cluster has free cpu cores.
  */
 class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
 
   def createThread(threadIndex: Int, poolName: String, sc: SparkContext, sem: Semaphore) {
-   
+
     TaskThreadInfo.threadToRunning(threadIndex) = false
     val nums = sc.parallelize(threadIndex to threadIndex, 1)
     TaskThreadInfo.threadToLock(threadIndex) = new Lock()
     TaskThreadInfo.threadToStarted(threadIndex) = new CountDownLatch(1)
     new Thread {
       if (poolName != null) {
-        sc.addLocalProperties("spark.scheduler.cluster.fair.pool",poolName)
+        sc.setLocalProperty("spark.scheduler.cluster.fair.pool", poolName)
       }
       override def run() {
         val ans = nums.map(number => {
@@ -88,7 +88,7 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
       }
     }.start()
   }
-  
+
   test("Local FIFO scheduler end-to-end test") {
     System.setProperty("spark.cluster.schedulingmode", "FIFO")
     sc = new SparkContext("local[4]", "test")
@@ -103,8 +103,8 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
     createThread(4,null,sc,sem)
     TaskThreadInfo.threadToStarted(4).await()
     // thread 5 and 6 (stage pending)must meet following two points
-    // 1. stages (taskSetManager) of jobs in thread 5 and 6 should be add to taskSetManager 
-    //    queue before executing TaskThreadInfo.threadToLock(1).jobFinished() 
+    // 1. stages (taskSetManager) of jobs in thread 5 and 6 should be add to taskSetManager
+    //    queue before executing TaskThreadInfo.threadToLock(1).jobFinished()
     // 2. priority of stage in thread 5 should be prior to priority of stage in thread 6
     // So I just use "sleep" 1s here for each thread.
     // TODO: any better solution?
@@ -112,24 +112,24 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
     Thread.sleep(1000)
     createThread(6,null,sc,sem)
     Thread.sleep(1000)
-    
+
     assert(TaskThreadInfo.threadToRunning(1) === true)
     assert(TaskThreadInfo.threadToRunning(2) === true)
     assert(TaskThreadInfo.threadToRunning(3) === true)
     assert(TaskThreadInfo.threadToRunning(4) === true)
     assert(TaskThreadInfo.threadToRunning(5) === false)
     assert(TaskThreadInfo.threadToRunning(6) === false)
-    
+
     TaskThreadInfo.threadToLock(1).jobFinished()
     TaskThreadInfo.threadToStarted(5).await()
-    
+
     assert(TaskThreadInfo.threadToRunning(1) === false)
     assert(TaskThreadInfo.threadToRunning(2) === true)
     assert(TaskThreadInfo.threadToRunning(3) === true)
     assert(TaskThreadInfo.threadToRunning(4) === true)
     assert(TaskThreadInfo.threadToRunning(5) === true)
     assert(TaskThreadInfo.threadToRunning(6) === false)
-    
+
     TaskThreadInfo.threadToLock(3).jobFinished()
     TaskThreadInfo.threadToStarted(6).await()
 
@@ -139,7 +139,7 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
     assert(TaskThreadInfo.threadToRunning(4) === true)
     assert(TaskThreadInfo.threadToRunning(5) === true)
     assert(TaskThreadInfo.threadToRunning(6) === true)
-   
+
     TaskThreadInfo.threadToLock(2).jobFinished()
     TaskThreadInfo.threadToLock(4).jobFinished()
     TaskThreadInfo.threadToLock(5).jobFinished()
@@ -160,18 +160,18 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
     TaskThreadInfo.threadToStarted(20).await()
     createThread(30,"3",sc,sem)
     TaskThreadInfo.threadToStarted(30).await()
-   
+
     assert(TaskThreadInfo.threadToRunning(10) === true)
     assert(TaskThreadInfo.threadToRunning(20) === true)
     assert(TaskThreadInfo.threadToRunning(30) === true)
-    
+
     createThread(11,"1",sc,sem)
     TaskThreadInfo.threadToStarted(11).await()
     createThread(21,"2",sc,sem)
     TaskThreadInfo.threadToStarted(21).await()
     createThread(31,"3",sc,sem)
     TaskThreadInfo.threadToStarted(31).await()
-    
+
     assert(TaskThreadInfo.threadToRunning(11) === true)
     assert(TaskThreadInfo.threadToRunning(21) === true)
     assert(TaskThreadInfo.threadToRunning(31) === true)
@@ -185,19 +185,19 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
     assert(TaskThreadInfo.threadToRunning(12) === true)
     assert(TaskThreadInfo.threadToRunning(22) === true)
     assert(TaskThreadInfo.threadToRunning(32) === false)
-    
+
     TaskThreadInfo.threadToLock(10).jobFinished()
     TaskThreadInfo.threadToStarted(32).await()
-    
+
     assert(TaskThreadInfo.threadToRunning(32) === true)
 
-    //1. Similar with above scenario, sleep 1s for stage of 23 and 33 to be added to taskSetManager 
+    //1. Similar with above scenario, sleep 1s for stage of 23 and 33 to be added to taskSetManager
     //   queue so that cluster will assign free cpu core to stage 23 after stage 11 finished.
     //2. priority of 23 and 33 will be meaningless as using fair scheduler here.
     createThread(23,"2",sc,sem)
     createThread(33,"3",sc,sem)
     Thread.sleep(1000)
-    
+
     TaskThreadInfo.threadToLock(11).jobFinished()
     TaskThreadInfo.threadToStarted(23).await()
 
@@ -206,7 +206,7 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
 
     TaskThreadInfo.threadToLock(12).jobFinished()
     TaskThreadInfo.threadToStarted(33).await()
-    
+
     assert(TaskThreadInfo.threadToRunning(33) === true)
 
     TaskThreadInfo.threadToLock(20).jobFinished()
@@ -217,7 +217,7 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext {
     TaskThreadInfo.threadToLock(31).jobFinished()
     TaskThreadInfo.threadToLock(32).jobFinished()
     TaskThreadInfo.threadToLock(33).jobFinished()
-    
-    sem.acquire(11) 
+
+    sem.acquire(11)
   }
 }
diff --git a/core/src/test/scala/spark/ui/UISuite.scala b/core/src/test/scala/spark/ui/UISuite.scala
index 56c1fed6ad05318b1da0d89c35ee69a07ee4639f..735a794396fa247fe967b300d39ad3428e6e7556 100644
--- a/core/src/test/scala/spark/ui/UISuite.scala
+++ b/core/src/test/scala/spark/ui/UISuite.scala
@@ -24,14 +24,15 @@ import org.eclipse.jetty.server.Server
 
 class UISuite extends FunSuite {
   test("jetty port increases under contention") {
-    val startPort = 33333
+    val startPort = 3030
     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)
+    // Allow some wiggle room in case ports on the machine are under contention
+    assert(boundPort1 > startPort && boundPort1 < startPort + 10)
+    assert(boundPort2 > boundPort1 && boundPort2 < boundPort1 + 10)
   }
 
   test("jetty binds to port 0 correctly") {
diff --git a/core/src/test/scala/spark/util/FakeClock.scala b/core/src/test/scala/spark/util/FakeClock.scala
new file mode 100644
index 0000000000000000000000000000000000000000..236706317e351c7b5fadbf90754473a82cc6e95d
--- /dev/null
+++ b/core/src/test/scala/spark/util/FakeClock.scala
@@ -0,0 +1,26 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.util
+
+class FakeClock extends Clock {
+  private var time = 0L
+
+  def advance(millis: Long): Unit = time += millis
+
+  def getTime(): Long = time
+}
diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index f06ab2d5b08ccf70cbca7c6f7a7ba34d0e0b9945..a76346f42862e12f1bc1a1f6815b77bde5dc3d78 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -74,6 +74,7 @@
                                 <li><a href="api/core/index.html">Spark Java/Scala (Scaladoc)</a></li>
                                 <li><a href="api/pyspark/index.html">Spark Python (Epydoc)</a></li>
                                 <li><a href="api/streaming/index.html">Spark Streaming Java/Scala (Scaladoc) </a></li>
+                                <li><a href="api/mllib/index.html">Spark ML Library (Scaladoc) </a></li>
                             </ul>
                         </li>
 
diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb
index 45ef4bba82f7addf391c8ce7d5b848e3ab9ec0ce..217254c59f83ef64b640277ab57a24c270a58886 100644
--- a/docs/_plugins/copy_api_dirs.rb
+++ b/docs/_plugins/copy_api_dirs.rb
@@ -20,7 +20,7 @@ include FileUtils
 
 if ENV['SKIP_API'] != '1'
   # Build Scaladoc for Java/Scala
-  projects = ["core", "examples", "repl", "bagel", "streaming"]
+  projects = ["core", "examples", "repl", "bagel", "streaming", "mllib"]
 
   puts "Moving to project root and building scaladoc."
   curr_dir = pwd
diff --git a/docs/configuration.md b/docs/configuration.md
index 5c06897cae428fffe635dfaa35ed4c33a463776b..dff08a06f5651b5de2a8bf08f93d4abcccd80b7a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -35,7 +35,7 @@ for these variables.
 * `SPARK_JAVA_OPTS`, to add JVM options. This includes any system properties that you'd like to pass with `-D`.
 * `SPARK_CLASSPATH`, to add elements to Spark's classpath.
 * `SPARK_LIBRARY_PATH`, to add search directories for native libraries.
-* `SPARK_MEM`, to set the amount of memory used per node. This should be in the same format as the 
+* `SPARK_MEM`, to set the amount of memory used per node. This should be in the same format as the
    JVM's -Xmx option, e.g. `300m` or `1g`. Note that this option will soon be deprecated in favor of
    the `spark.executor.memory` system property, so we recommend using that in new code.
 
@@ -77,7 +77,7 @@ there are at least five properties that you will commonly want to control:
     Class to use for serializing objects that will be sent over the network or need to be cached
     in serialized form. The default of Java serialization works with any Serializable Java object but is
     quite slow, so we recommend <a href="tuning.html">using <code>spark.KryoSerializer</code>
-    and configuring Kryo serialization</a> when speed is necessary. Can be any subclass of 
+    and configuring Kryo serialization</a> when speed is necessary. Can be any subclass of
     <a href="api/core/index.html#spark.Serializer"><code>spark.Serializer</code></a>).
   </td>
 </tr>
@@ -86,7 +86,7 @@ there are at least five properties that you will commonly want to control:
   <td>(none)</td>
   <td>
     If you use Kryo serialization, set this class to register your custom classes with Kryo.
-    You need to set it to a class that extends 
+    You need to set it to a class that extends
     <a href="api/core/index.html#spark.KryoRegistrator"><code>spark.KryoRegistrator</code></a>).
     See the <a href="tuning.html#data-serialization">tuning guide</a> for more details.
   </td>
@@ -180,6 +180,21 @@ Apart from these, the following properties are also available, and may be useful
     Can save substantial space at the cost of some extra CPU time.
   </td>
 </tr>
+<tr>
+  <td>spark.io.compression.codec</td>
+  <td>spark.io.SnappyCompressionCodec</td>
+  <td>
+    The compression codec class to use for various compressions. By default, Spark provides two
+    codecs: <code>spark.io.LZFCompressionCodec</code> and <code>spark.io.SnappyCompressionCodec</code>.
+  </td>
+</tr>
+<tr>
+  <td>spark.io.compression.snappy.block.size</td>
+  <td>32768</td>
+  <td>
+    Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is used.
+  </td>
+</tr>
 <tr>
   <td>spark.reducer.maxMbInFlight</td>
   <td>48</td>
@@ -228,8 +243,34 @@ Apart from these, the following properties are also available, and may be useful
   <td>3000</td>
   <td>
     Number of milliseconds to wait to launch a data-local task before giving up and launching it
-    in a non-data-local location. You should increase this if your tasks are long and you are seeing
-    poor data locality, but the default generally works well.
+    on a less-local node. The same wait will be used to step through multiple locality levels
+    (process-local, node-local, rack-local and then any). It is also possible to customize the
+    waiting time for each level by setting <code>spark.locality.wait.node</code>, etc.
+    You should increase this setting if your tasks are long and see poor locality, but the
+    default usually works well.
+  </td>
+</tr>
+<tr>
+  <td>spark.locality.wait.process</td>
+  <td>spark.locality.wait</td>
+  <td>
+    Customize the locality wait for process locality. This affects tasks that attempt to access
+    cached data in a particular executor process.
+  </td>
+</tr>
+<tr>
+  <td>spark.locality.wait.node</td>
+  <td>spark.locality.wait</td>
+  <td>
+    Customize the locality wait for node locality. For example, you can set this to 0 to skip
+    node locality and search immediately for rack locality (if your cluster has rack information).
+  </td>
+</tr>
+<tr>
+  <td>spark.locality.wait.rack</td>
+  <td>spark.locality.wait</td>
+  <td>
+    Customize the locality wait for rack locality.
   </td>
 </tr>
 <tr>
@@ -295,6 +336,14 @@ Apart from these, the following properties are also available, and may be useful
     Duration (milliseconds) of how long to batch new objects coming from network receivers.
   </td>
 </tr>
+<tr>
+  <td>spark.task.maxFailures</td>
+  <td>4</td>
+  <td>
+    Number of individual task failures before giving up on the job.
+    Should be greater than or equal to 1. Number of allowed retries = this value - 1.
+  </td>
+</tr>
 
 </table>
 
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index e8aaac74d030ec1e688952d33d780afbc8c13879..794bff56474c6b7331ba6906a09f64fa93de5b39 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -10,6 +10,7 @@ To learn the basics of Spark, we recommend reading through the
 easy to follow even if you don't know Scala.
 This guide will show how to use the Spark features described there in Python.
 
+
 # Key Differences in the Python API
 
 There are a few key differences between the Python and Scala APIs:
@@ -50,6 +51,7 @@ PySpark will automatically ship these functions to workers, along with any objec
 Instances of classes will be serialized and shipped to workers by PySpark, but classes themselves cannot be automatically distributed to workers.
 The [Standalone Use](#standalone-use) section describes how to ship code dependencies to workers.
 
+
 # Installing and Configuring PySpark
 
 PySpark requires Python 2.6 or higher.
@@ -81,16 +83,41 @@ The Python shell can be used explore data interactively and is a simple way to l
 >>> help(pyspark) # Show all pyspark functions
 {% endhighlight %}
 
-By default, the `pyspark` shell creates SparkContext that runs jobs locally.
-To connect to a non-local cluster, set the `MASTER` environment variable.
+By default, the `pyspark` shell creates SparkContext that runs jobs locally on a single core.
+To connect to a non-local cluster, or use multiple cores, set the `MASTER` environment variable.
 For example, to use the `pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
 
 {% highlight bash %}
 $ MASTER=spark://IP:PORT ./pyspark
 {% endhighlight %}
 
+Or, to use four cores on the local machine:
+
+{% highlight bash %}
+$ MASTER=local[4] ./pyspark
+{% endhighlight %}
+
+
+## IPython
+
+It is also possible to launch PySpark in [IPython](http://ipython.org), the enhanced Python interpreter.
+To do this, simply set the `IPYTHON` variable to `1` when running `pyspark`:
+
+{% highlight bash %}
+$ IPYTHON=1 ./pyspark
+{% endhighlight %}
+
+Alternatively, you can customize the `ipython` command by setting `IPYTHON_OPTS`. For example, to launch
+the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing support:
 
-# Standalone Use
+{% highlight bash %}
+$ IPYTHON_OPTS="notebook --pylab inline" ./pyspark
+{% endhighlight %}
+
+IPython also works on a cluster or on multiple cores if you set the `MASTER` environment variable.
+
+
+# Standalone Programs
 
 PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `pyspark`.
 The Quick Start guide includes a [complete example](quick-start.html#a-standalone-job-in-python) of a standalone Python job.
@@ -105,6 +132,7 @@ sc = SparkContext("local", "Job Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg
 Files listed here will be added to the `PYTHONPATH` and shipped to remote worker machines.
 Code dependencies can be added to an existing SparkContext using its `addPyFile()` method.
 
+
 # Where to Go from Here
 
 PySpark includes several sample programs in the [`python/examples` folder](https://github.com/mesos/spark/tree/master/python/examples).
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 66fb8d73e80f3dacd94283bdab120c77c0d807be..9c2cedfd886f5360f925e8cd8682899860be2307 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -55,7 +55,7 @@ This would be used to connect to the cluster, write to the dfs and submit jobs t
 
 The command to launch the YARN Client is as follows:
 
-    SPARK_JAR=<SPARK_YAR_FILE> ./run spark.deploy.yarn.Client \
+    SPARK_JAR=<SPARK_YARN_JAR_FILE> ./run spark.deploy.yarn.Client \
       --jar <YOUR_APP_JAR_FILE> \
       --class <APP_MAIN_CLASS> \
       --args <APP_MAIN_ARGUMENTS> \
@@ -68,7 +68,7 @@ The command to launch the YARN Client is as follows:
 
 For example:
 
-    SPARK_JAR=./core/target/spark-core-assembly-{{site.SPARK_VERSION}}.jar ./run spark.deploy.yarn.Client \
+    SPARK_JAR=./yarn/target/spark-yarn-assembly-{{site.SPARK_VERSION}}.jar ./run spark.deploy.yarn.Client \
       --jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}.jar \
       --class spark.examples.SparkPi \
       --args yarn-standalone \
diff --git a/docs/spark-simple-tutorial.md b/docs/spark-simple-tutorial.md
deleted file mode 100644
index fbdbc7d19d9c80d9771b8fa2efeb961e2514b01e..0000000000000000000000000000000000000000
--- a/docs/spark-simple-tutorial.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-layout: global
-title: Tutorial - Running a Simple Spark Application
----
-
-1. Create directory for spark demo:
-
-        ~$ mkdir SparkTest
-
-2. Copy the sbt files in ~/spark/sbt directory:
-
-        ~/SparkTest$ cp -r ../spark/sbt .
-
-3. Edit the ~/SparkTest/sbt/sbt file to look like this:
-
-        #!/usr/bin/env bash
-        java -Xmx800M -XX:MaxPermSize=150m -jar $(dirname $0)/sbt-launch-*.jar "$@"
-
-4. To build a Spark application, you need Spark and its dependencies in a single Java archive (JAR) file. Create this JAR in Spark's main directory with sbt as:
-
-        ~/spark$ sbt/sbt assembly
-
-5. create a source file in ~/SparkTest/src/main/scala directory:
-
-        ~/SparkTest/src/main/scala$ vi Test1.scala
-
-6. Make the contain of the Test1.scala file like this:
-
-        import spark.SparkContext
-        import spark.SparkContext._
-        object Test1 {
-          def main(args: Array[String]) {
-            val sc = new SparkContext("local", "SparkTest")
-            println(sc.parallelize(1 to 10).reduce(_ + _))
-            System.exit(0)
-          }
-        }
-
-7. Run the Test1.scala file:
-
-        ~/SparkTest$ sbt/sbt run
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 3986c0c79dceaecb0da46adbf07241b64229f650..7463844a4e36aba77ff99f63fbdabbebe32d2df0 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -43,7 +43,7 @@ Finally, the following configuration options can be passed to the master and wor
   </tr>
   <tr>
     <td><code>-p PORT</code>, <code>--port PORT</code></td>
-    <td>IP address or DNS name to listen on (default: 7077 for master, random for worker)</td>
+    <td>Port for service to listen on (default: 7077 for master, random for worker)</td>
   </tr>
   <tr>
     <td><code>--webui-port PORT</code></td>
diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 6c48d9765f62f67dcd5df7146a57f37368a34b61..30253a94b845c4b75bf7c52e40846c8712c15f61 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -9,9 +9,9 @@
 # to you under the Apache License, Version 2.0 (the
 # "License"); you may not use this file except in compliance
 # with the License.  You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -52,7 +52,7 @@ def parse_args():
       help="Seconds to wait for nodes to start (default: 120)")
   parser.add_option("-k", "--key-pair",
       help="Key pair to use on instances")
-  parser.add_option("-i", "--identity-file", 
+  parser.add_option("-i", "--identity-file",
       help="SSH private key file to use for logging into instances")
   parser.add_option("-t", "--instance-type", default="m1.large",
       help="Type of instance to launch (default: m1.large). " +
@@ -65,9 +65,7 @@ def parse_args():
       help="Availability zone to launch instances in, or 'all' to spread " +
            "slaves across multiple (an additional $0.01/Gb for bandwidth" +
            "between zones applies)")
-  parser.add_option("-a", "--ami",
-      help="Amazon Machine Image ID to use")
-
+  parser.add_option("-a", "--ami", help="Amazon Machine Image ID to use")
   parser.add_option("-v", "--spark-version", default="0.7.3",
       help="Version of Spark to use: 'X.Y.Z' or a specific git hash")
   parser.add_option("--spark-git-repo", 
@@ -75,7 +73,6 @@ def parse_args():
       help="Github repo from which to checkout supplied commit hash")
   parser.add_option("--hadoop-major-version", default="1",
       help="Major version of Hadoop (default: 1)")
-
   parser.add_option("-D", metavar="[ADDRESS:]PORT", dest="proxy_port", 
       help="Use SSH dynamic port forwarding to create a SOCKS proxy at " +
             "the given local address (for use with login)")
@@ -100,7 +97,7 @@ def parse_args():
       help="The SSH user you want to connect as (default: root)")
   parser.add_option("--delete-groups", action="store_true", default=False,
       help="When destroying a cluster, delete the security groups that were created")
-            
+
   (opts, args) = parser.parse_args()
   if len(args) != 2:
     parser.print_help()
@@ -222,6 +219,7 @@ def launch_cluster(conn, opts, cluster_name):
     master_group.authorize('tcp', 50030, 50030, '0.0.0.0/0')
     master_group.authorize('tcp', 50070, 50070, '0.0.0.0/0')
     master_group.authorize('tcp', 60070, 60070, '0.0.0.0/0')
+    master_group.authorize('tcp', 3030, 3035, '0.0.0.0/0')
     if opts.ganglia:
       master_group.authorize('tcp', 5080, 5080, '0.0.0.0/0')
   if slave_group.rules == []: # Group was just now created
@@ -284,7 +282,7 @@ def launch_cluster(conn, opts, cluster_name):
           block_device_map = block_map)
       my_req_ids += [req.id for req in slave_reqs]
       i += 1
-    
+
     print "Waiting for spot instances to be granted..."
     try:
       while True:
@@ -422,7 +420,7 @@ def setup_standalone_cluster(master, slave_nodes, opts):
   slave_ips = '\n'.join([i.public_dns_name for i in slave_nodes])
   ssh(master, opts, "echo \"%s\" > spark/conf/slaves" % (slave_ips))
   ssh(master, opts, "/root/spark/bin/start-all.sh")
-  
+
 def setup_spark_cluster(master, opts):
   ssh(master, opts, "chmod u+x spark-ec2/setup.sh")
   ssh(master, opts, "spark-ec2/setup.sh")
@@ -538,7 +536,7 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules):
               dest.write(text)
               dest.close()
   # rsync the whole directory over to the master machine
-  command = (("rsync -rv -e 'ssh -o StrictHostKeyChecking=no -i %s' " + 
+  command = (("rsync -rv -e 'ssh -o StrictHostKeyChecking=no -i %s' " +
       "'%s/' '%s@%s:/'") % (opts.identity_file, tmp_dir, opts.user, active_master))
   subprocess.check_call(command, shell=True)
   # Remove the temp directory we created above
@@ -567,9 +565,9 @@ def ssh(host, opts, command):
       print "Error connecting to host {0}, sleeping 30".format(e)
       time.sleep(30)
       tries = tries + 1
-    
-    
-    
+
+
+
 
 
 # Gets a list of zones to launch instances in
@@ -645,7 +643,7 @@ def main():
                            from_port=rule.from_port,
                            to_port=rule.to_port,
                            src_group=grant)
-          
+
           # Sleep for AWS eventual-consistency to catch up, and for instances
           # to terminate
           time.sleep(30)  # Yes, it does have to be this long :-(
@@ -656,13 +654,13 @@ def main():
             except boto.exception.EC2ResponseError:
               success = False;
               print "Failed to delete security group " + group.name
-          
+
           # Unfortunately, group.revoke() returns True even if a rule was not
           # deleted, so this needs to be rerun if something fails
           if success: break;
-          
+
           attempt += 1
-          
+
         if not success:
           print "Failed to delete all security groups after 3 tries."
           print "Try re-running in a few minutes."
@@ -685,7 +683,7 @@ def main():
   elif action == "stop":
     response = raw_input("Are you sure you want to stop the cluster " +
         cluster_name + "?\nDATA ON EPHEMERAL DISKS WILL BE LOST, " +
-        "BUT THE CLUSTER WILL KEEP USING SPACE ON\n" + 
+        "BUT THE CLUSTER WILL KEEP USING SPACE ON\n" +
         "AMAZON EBS IF IT IS EBS-BACKED!!\n" +
         "Stop cluster " + cluster_name + " (y/N): ")
     if response == "y":
diff --git a/examples/pom.xml b/examples/pom.xml
index 7a8d08fadec71b77147bdf807084867561eabb6a..0db52b8691cbe2499b8f756529e3016abf3cd4ed 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -32,6 +32,36 @@
   <url>http://spark-project.org/</url>
 
   <dependencies>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-core</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-streaming</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-mllib</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hbase</groupId>
+      <artifactId>hbase</artifactId>
+      <version>0.94.6</version>
+      <exclusions>
+        <exclusion>
+          <groupId>asm</groupId>
+          <artifactId>asm</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.jboss.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
     <dependency>
       <groupId>org.scala-lang</groupId>
       <artifactId>scala-library</artifactId>
@@ -55,41 +85,41 @@
       <artifactId>scalacheck_${scala.version}</artifactId>
       <scope>test</scope>
     </dependency>
-      <dependency>
-        <groupId>org.apache.cassandra</groupId>
-        <artifactId>cassandra-all</artifactId>
-        <version>1.2.5</version>
-        <exclusions>
-            <exclusion>
-                <groupId>com.google.guava</groupId>
-                <artifactId>guava</artifactId>
-            </exclusion>
-            <exclusion>
-                <groupId>com.googlecode.concurrentlinkedhashmap</groupId>
-                <artifactId>concurrentlinkedhashmap-lru</artifactId>
-            </exclusion>
-            <exclusion>
-                <groupId>com.ning</groupId>
-                <artifactId>compress-lzf</artifactId>
-            </exclusion>
-            <exclusion>
-                <groupId>io.netty</groupId>
-                <artifactId>netty</artifactId>
-            </exclusion>
-            <exclusion>
-                <groupId>jline</groupId>
-                <artifactId>jline</artifactId>
-            </exclusion>
-            <exclusion>
-                <groupId>log4j</groupId>
-                <artifactId>log4j</artifactId>
-            </exclusion>
-            <exclusion>
-                <groupId>org.apache.cassandra.deps</groupId>
-                <artifactId>avro</artifactId>
-            </exclusion>
-        </exclusions>
-      </dependency>
+    <dependency>
+      <groupId>org.apache.cassandra</groupId>
+      <artifactId>cassandra-all</artifactId>
+      <version>1.2.5</version>
+      <exclusions>
+        <exclusion>
+          <groupId>com.google.guava</groupId>
+          <artifactId>guava</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>com.googlecode.concurrentlinkedhashmap</groupId>
+          <artifactId>concurrentlinkedhashmap-lru</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>com.ning</groupId>
+          <artifactId>compress-lzf</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>io.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>jline</groupId>
+          <artifactId>jline</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>log4j</groupId>
+          <artifactId>log4j</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.apache.cassandra.deps</groupId>
+          <artifactId>avro</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.version}/classes</outputDirectory>
@@ -101,136 +131,4 @@
       </plugin>
     </plugins>
   </build>
-
-  <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-streaming</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hbase</groupId>
-          <artifactId>hbase</artifactId>
-          <version>0.94.6</version>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop1</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-streaming</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hbase</groupId>
-          <artifactId>hbase</artifactId>
-          <version>0.94.6</version>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2-yarn</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-streaming</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-api</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-common</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hbase</groupId>
-          <artifactId>hbase</artifactId>
-          <version>0.94.6</version>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2-yarn</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-  </profiles>
 </project>
diff --git a/examples/src/main/java/spark/examples/JavaPageRank.java b/examples/src/main/java/spark/examples/JavaPageRank.java
new file mode 100644
index 0000000000000000000000000000000000000000..75df1af2e30b84fbc6ddd69b6189d8ebe2eeeedd
--- /dev/null
+++ b/examples/src/main/java/spark/examples/JavaPageRank.java
@@ -0,0 +1,115 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.examples;
+
+import scala.Tuple2;
+import spark.api.java.JavaPairRDD;
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.FlatMapFunction;
+import spark.api.java.function.Function;
+import spark.api.java.function.Function2;
+import spark.api.java.function.PairFlatMapFunction;
+import spark.api.java.function.PairFunction;
+
+import java.util.List;
+import java.util.ArrayList;
+
+/**
+ * Computes the PageRank of URLs from an input file. Input file should
+ * be in format of:
+ * URL         neighbor URL
+ * URL         neighbor URL
+ * URL         neighbor URL
+ * ...
+ * where URL and their neighbors are separated by space(s).
+ */
+public class JavaPageRank {
+  private static class Sum extends Function2<Double, Double, Double> {
+    @Override
+    public Double call(Double a, Double b) {
+      return a + b;
+    }
+  }
+
+  public static void main(String[] args) throws Exception {
+    if (args.length < 3) {
+      System.err.println("Usage: JavaPageRank <master> <file> <number_of_iterations>");
+      System.exit(1);
+    }
+
+    JavaSparkContext ctx = new JavaSparkContext(args[0], "JavaPageRank",
+      System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
+
+    // Loads in input file. It should be in format of:
+    //     URL         neighbor URL
+    //     URL         neighbor URL
+    //     URL         neighbor URL
+    //     ...
+    JavaRDD<String> lines = ctx.textFile(args[1], 1);
+
+    // Loads all URLs from input file and initialize their neighbors.
+    JavaPairRDD<String, List<String>> links = lines.map(new PairFunction<String, String, String>() {
+      @Override
+      public Tuple2<String, String> call(String s) {
+        String[] parts = s.split("\\s+");
+        return new Tuple2<String, String>(parts[0], parts[1]);
+      }
+    }).distinct().groupByKey().cache();
+
+    // Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one.
+    JavaPairRDD<String, Double> ranks = links.mapValues(new Function<List<String>, Double>() {
+      @Override
+      public Double call(List<String> rs) throws Exception {
+        return 1.0;
+      }
+    });
+
+    // Calculates and updates URL ranks continuously using PageRank algorithm.
+    for (int current = 0; current < Integer.parseInt(args[2]); current++) {
+      // Calculates URL contributions to the rank of other URLs.
+      JavaPairRDD<String, Double> contribs = links.join(ranks).values()
+        .flatMap(new PairFlatMapFunction<Tuple2<List<String>, Double>, String, Double>() {
+          @Override
+          public Iterable<Tuple2<String, Double>> call(Tuple2<List<String>, Double> s) {
+            List<Tuple2<String, Double>> results = new ArrayList<Tuple2<String, Double>>();
+            for (String n : s._1) {
+              results.add(new Tuple2<String, Double>(n, s._2 / s._1.size()));
+            }
+            return results;
+          }
+      });
+
+      // Re-calculates URL ranks based on neighbor contributions.
+      ranks = contribs.reduceByKey(new Sum()).mapValues(new Function<Double, Double>() {
+        @Override
+        public Double call(Double sum) throws Exception {
+          return 0.15 + sum * 0.85;
+        }
+      });
+    }
+
+    // Collects all URL ranks and dump them to console.
+    List<Tuple2<String, Double>> output = ranks.collect();
+    for (Tuple2 tuple : output) {
+        System.out.println(tuple._1 + " has rank: " + tuple._2 + ".");
+    }
+
+    System.exit(0);
+  }
+}
diff --git a/examples/src/main/java/spark/mllib/examples/JavaALS.java b/examples/src/main/java/spark/mllib/examples/JavaALS.java
new file mode 100644
index 0000000000000000000000000000000000000000..b48f459cb7ee2d4f5c385ea5b10b58ad90c8b309
--- /dev/null
+++ b/examples/src/main/java/spark/mllib/examples/JavaALS.java
@@ -0,0 +1,87 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.examples;
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.Function;
+
+import spark.mllib.recommendation.ALS;
+import spark.mllib.recommendation.MatrixFactorizationModel;
+import spark.mllib.recommendation.Rating;
+
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.StringTokenizer;
+
+import scala.Tuple2;
+
+/**
+ * Example using MLLib ALS from Java.
+ */
+public class  JavaALS {
+
+  static class ParseRating extends Function<String, Rating> {
+    public Rating call(String line) {
+      StringTokenizer tok = new StringTokenizer(line, ",");
+      int x = Integer.parseInt(tok.nextToken());
+      int y = Integer.parseInt(tok.nextToken());
+      double rating = Double.parseDouble(tok.nextToken());
+      return new Rating(x, y, rating);
+    }
+  }
+
+  static class FeaturesToString extends Function<Tuple2<Object, double[]>, String> {
+    public String call(Tuple2<Object, double[]> element) {
+      return element._1().toString() + "," + Arrays.toString(element._2());
+    }
+  }
+
+  public static void main(String[] args) {
+
+    if (args.length != 5 && args.length != 6) {
+      System.err.println(
+          "Usage: JavaALS <master> <ratings_file> <rank> <iterations> <output_dir> [<blocks>]");
+      System.exit(1);
+    }
+
+    int rank = Integer.parseInt(args[2]);
+    int iterations = Integer.parseInt(args[3]);
+    String outputDir = args[4];
+    int blocks = -1;
+    if (args.length == 6) {
+      blocks = Integer.parseInt(args[5]);
+    }
+
+    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaALS",
+        System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
+    JavaRDD<String> lines = sc.textFile(args[1]);
+
+    JavaRDD<Rating> ratings = lines.map(new ParseRating());
+
+    MatrixFactorizationModel model = ALS.train(ratings.rdd(), rank, iterations, 0.01, blocks);
+
+    model.userFeatures().toJavaRDD().map(new FeaturesToString()).saveAsTextFile(
+        outputDir + "/userFeatures");
+    model.productFeatures().toJavaRDD().map(new FeaturesToString()).saveAsTextFile(
+        outputDir + "/productFeatures");
+    System.out.println("Final user/product features written to " + outputDir);
+
+    System.exit(0);
+  }
+}
diff --git a/examples/src/main/java/spark/mllib/examples/JavaKMeans.java b/examples/src/main/java/spark/mllib/examples/JavaKMeans.java
new file mode 100644
index 0000000000000000000000000000000000000000..02f40438b82c3667a076d4a51bf8c70e7aa29131
--- /dev/null
+++ b/examples/src/main/java/spark/mllib/examples/JavaKMeans.java
@@ -0,0 +1,81 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.examples;
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.Function;
+
+import spark.mllib.clustering.KMeans;
+import spark.mllib.clustering.KMeansModel;
+
+import java.util.Arrays;
+import java.util.StringTokenizer;
+
+/**
+ * Example using MLLib KMeans from Java.
+ */
+public class JavaKMeans {
+
+  static class ParsePoint extends Function<String, double[]> {
+    public double[] call(String line) {
+      StringTokenizer tok = new StringTokenizer(line, " ");
+      int numTokens = tok.countTokens();
+      double[] point = new double[numTokens];
+      for (int i = 0; i < numTokens; ++i) {
+        point[i] = Double.parseDouble(tok.nextToken());
+      }
+      return point;
+    }
+  }
+
+  public static void main(String[] args) {
+
+    if (args.length < 4) {
+      System.err.println(
+          "Usage: JavaKMeans <master> <input_file> <k> <max_iterations> [<runs>]");
+      System.exit(1);
+    }
+
+    String inputFile = args[1];
+    int k = Integer.parseInt(args[2]);
+    int iterations = Integer.parseInt(args[3]);
+    int runs = 1;
+
+    if (args.length >= 5) {
+      runs = Integer.parseInt(args[4]);
+    }
+
+    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaKMeans",
+        System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
+    JavaRDD<String> lines = sc.textFile(args[1]);
+
+    JavaRDD<double[]> points = lines.map(new ParsePoint());
+
+    KMeansModel model = KMeans.train(points.rdd(), k, iterations, runs);
+
+    System.out.println("Cluster centers:");
+    for (double[] center : model.clusterCenters()) {
+      System.out.println(" " + Arrays.toString(center));
+    }
+    double cost = model.computeCost(points.rdd());
+    System.out.println("Cost: " + cost);
+
+    System.exit(0);
+  }
+}
diff --git a/examples/src/main/java/spark/mllib/examples/JavaLR.java b/examples/src/main/java/spark/mllib/examples/JavaLR.java
new file mode 100644
index 0000000000000000000000000000000000000000..bf4aeaf40f63e411a39353b0e49a3d9aeffd3904
--- /dev/null
+++ b/examples/src/main/java/spark/mllib/examples/JavaLR.java
@@ -0,0 +1,85 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.examples;
+
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.Function;
+
+import spark.mllib.classification.LogisticRegressionWithSGD;
+import spark.mllib.classification.LogisticRegressionModel;
+import spark.mllib.regression.LabeledPoint;
+
+import java.util.Arrays;
+import java.util.StringTokenizer;
+
+/**
+ * Logistic regression based classification using ML Lib.
+ */
+public class JavaLR {
+
+  static class ParsePoint extends Function<String, LabeledPoint> {
+    public LabeledPoint call(String line) {
+      String[] parts = line.split(",");
+      double y = Double.parseDouble(parts[0]);
+      StringTokenizer tok = new StringTokenizer(parts[1], " ");
+      int numTokens = tok.countTokens();
+      double[] x = new double[numTokens];
+      for (int i = 0; i < numTokens; ++i) {
+        x[i] = Double.parseDouble(tok.nextToken());
+      }
+      return new LabeledPoint(y, x);
+    }
+  }
+
+  public static void printWeights(double[] a) {
+    System.out.println(Arrays.toString(a));
+  }
+
+  public static void main(String[] args) {
+    if (args.length != 4) {
+      System.err.println("Usage: JavaLR <master> <input_dir> <step_size> <niters>");
+      System.exit(1);
+    }
+
+    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaLR",
+        System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
+    JavaRDD<String> lines = sc.textFile(args[1]);
+    JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache();
+    double stepSize = Double.parseDouble(args[2]);
+    int iterations = Integer.parseInt(args[3]);
+
+    // Another way to configure LogisticRegression
+    //
+    // LogisticRegressionWithSGD lr = new LogisticRegressionWithSGD();
+    // lr.optimizer().setNumIterations(iterations)
+    //               .setStepSize(stepSize)
+    //               .setMiniBatchFraction(1.0);
+    // lr.setIntercept(true);
+    // LogisticRegressionModel model = lr.train(points.rdd());
+
+    LogisticRegressionModel model = LogisticRegressionWithSGD.train(points.rdd(),
+        iterations, stepSize);
+
+    System.out.print("Final w: ");
+    printWeights(model.weights());
+
+    System.exit(0);
+  }
+}
diff --git a/examples/src/main/scala/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/spark/examples/SparkHdfsLR.scala
index ef6e09a8e845b463a5baa524c0581cb5bdc24cac..43c91156640c899aaf622c87efdbe646b4adf398 100644
--- a/examples/src/main/scala/spark/examples/SparkHdfsLR.scala
+++ b/examples/src/main/scala/spark/examples/SparkHdfsLR.scala
@@ -21,7 +21,6 @@ import java.util.Random
 import scala.math.exp
 import spark.util.Vector
 import spark._
-import spark.deploy.SparkHadoopUtil
 import spark.scheduler.InputFormatInfo
 
 /**
@@ -52,7 +51,7 @@ object SparkHdfsLR {
       System.exit(1)
     }
     val inputPath = args(1)
-    val conf = SparkHadoopUtil.newConfiguration()
+    val conf = SparkEnv.get.hadoop.newConfiguration()
     val sc = new SparkContext(args(0), "SparkHdfsLR",
       System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")), Map(), 
       InputFormatInfo.computePreferredLocations(
diff --git a/examples/src/main/scala/spark/examples/SparkPageRank.scala b/examples/src/main/scala/spark/examples/SparkPageRank.scala
new file mode 100644
index 0000000000000000000000000000000000000000..dedbbd01a3a9c3b0ba6d181721e1e3dfb3812d1f
--- /dev/null
+++ b/examples/src/main/scala/spark/examples/SparkPageRank.scala
@@ -0,0 +1,46 @@
+package spark.examples
+
+import spark.SparkContext._
+import spark.SparkContext
+
+
+/**
+ * Computes the PageRank of URLs from an input file. Input file should
+ * be in format of:
+ * URL         neighbor URL
+ * URL         neighbor URL
+ * URL         neighbor URL
+ * ...
+ * where URL and their neighbors are separated by space(s).
+ */
+object SparkPageRank {
+  def main(args: Array[String]) {
+    if (args.length < 3) {
+      System.err.println("Usage: PageRank <master> <file> <number_of_iterations>")
+      System.exit(1)
+    }
+    var iters = args(2).toInt
+    val ctx = new SparkContext(args(0), "PageRank",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
+    val lines = ctx.textFile(args(1), 1)
+    val links = lines.map{ s =>
+      val parts = s.split("\\s+")
+      (parts(0), parts(1))
+    }.distinct().groupByKey().cache()
+    var ranks = links.mapValues(v => 1.0)
+
+    for (i <- 1 to iters) {
+      val contribs = links.join(ranks).values.flatMap{ case (urls, rank) =>
+        val size = urls.size
+        urls.map(url => (url, rank / size))
+      }
+      ranks = contribs.reduceByKey(_ + _).mapValues(0.15 + 0.85 * _)
+    }
+
+    val output = ranks.collect()
+    output.foreach(tup => println(tup._1 + " has rank: " + tup._2 + "."))
+
+    System.exit(0)
+  }
+}
+
diff --git a/make-distribution.sh b/make-distribution.sh
index 4374e0e8c4e06d0f64364a341de06f08047b356b..55dc22b99270c099d760f69d5b25f378b84a8bc9 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -24,9 +24,10 @@
 # so it is completely self contained.
 # It does not contain source or *.class files.
 #
-# Arguments
-#   (none): Creates dist/ directory
-#      tgz: Additionally creates spark-$VERSION-bin.tar.gz
+# Optional Arguments
+#      --tgz: Additionally creates spark-$VERSION-bin.tar.gz
+#      --hadoop VERSION: Builds against specified version of Hadoop.
+#      --with-yarn: Enables support for Hadoop YARN.
 #
 # Recommended deploy/testing procedure (standalone mode):
 # 1) Rsync / deploy the dist/ dir to one host
@@ -44,20 +45,50 @@ DISTDIR="$FWDIR/dist"
 export TERM=dumb   # Prevents color codes in SBT output
 VERSION=$($FWDIR/sbt/sbt "show version" | tail -1 | cut -f 2 | sed 's/^\([a-zA-Z0-9.-]*\).*/\1/')
 
-if [ "$1" == "tgz" ]; then
-	echo "Making spark-$VERSION-bin.tar.gz"
+# Initialize defaults
+SPARK_HADOOP_VERSION=1.2.1
+SPARK_WITH_YARN=false
+MAKE_TGZ=false
+
+# Parse arguments
+while (( "$#" )); do
+  case $1 in
+    --hadoop)
+      SPARK_HADOOP_VERSION="$2"
+      shift
+      ;;
+    --with-yarn)
+      SPARK_WITH_YARN=true
+      ;;
+    --tgz)
+      MAKE_TGZ=true
+      ;;
+  esac
+  shift
+done
+
+if [ "$MAKE_TGZ" == "true" ]; then
+	echo "Making spark-$VERSION-hadoop_$SPARK_HADOOP_VERSION-bin.tar.gz"
 else
 	echo "Making distribution for Spark $VERSION in $DISTDIR..."
 fi
 
+echo "Hadoop version set to $SPARK_HADOOP_VERSION"
+if [ "$SPARK_WITH_YARN" == "true" ]; then
+  echo "YARN enabled"
+else
+  echo "YARN disabled"
+fi
 
 # Build fat JAR
-$FWDIR/sbt/sbt "repl/assembly"
+export SPARK_HADOOP_VERSION
+export SPARK_WITH_YARN
+"$FWDIR/sbt/sbt" "repl/assembly"
 
 # Make directories
 rm -rf "$DISTDIR"
 mkdir -p "$DISTDIR/jars"
-echo "$VERSION" >$DISTDIR/RELEASE
+echo "$VERSION" > "$DISTDIR/RELEASE"
 
 # Copy jars
 cp $FWDIR/repl/target/*.jar "$DISTDIR/jars/"
@@ -66,11 +97,12 @@ cp $FWDIR/repl/target/*.jar "$DISTDIR/jars/"
 cp -r "$FWDIR/bin" "$DISTDIR"
 cp -r "$FWDIR/conf" "$DISTDIR"
 cp "$FWDIR/run" "$FWDIR/spark-shell" "$DISTDIR"
+cp "$FWDIR/spark-executor" "$DISTDIR"
 
 
-if [ "$1" == "tgz" ]; then
+if [ "$MAKE_TGZ" == "true" ]; then
   TARDIR="$FWDIR/spark-$VERSION"
-  cp -r $DISTDIR $TARDIR
-  tar -zcf spark-$VERSION-bin.tar.gz -C $FWDIR spark-$VERSION
-  rm -rf $TARDIR
+  cp -r "$DISTDIR" "$TARDIR"
+  tar -zcf "spark-$VERSION-hadoop_$SPARK_HADOOP_VERSION-bin.tar.gz" -C "$FWDIR" "spark-$VERSION"
+  rm -rf "$TARDIR"
 fi
diff --git a/mllib/pom.xml b/mllib/pom.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ab31d5734e42a249e83f3b97c751c2d3fc981c98
--- /dev/null
+++ b/mllib/pom.xml
@@ -0,0 +1,75 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.spark-project</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>0.8.0-SNAPSHOT</version>
+    <relativePath>../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.spark-project</groupId>
+  <artifactId>spark-mllib</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project ML Library</name>
+  <url>http://spark-project.org/</url>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-core</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.eclipse.jetty</groupId>
+      <artifactId>jetty-server</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.jblas</groupId>
+      <artifactId>jblas</artifactId>
+      <version>1.2.3</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/mllib/src/main/scala/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/spark/mllib/classification/ClassificationModel.scala
new file mode 100644
index 0000000000000000000000000000000000000000..70fae8c15a27549a7f935fc4b655fd571cff526e
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/classification/ClassificationModel.scala
@@ -0,0 +1,21 @@
+package spark.mllib.classification
+
+import spark.RDD
+
+trait ClassificationModel extends Serializable {
+  /**
+   * Predict values for the given data set using the model trained.
+   *
+   * @param testData RDD representing data points to be predicted
+   * @return RDD[Int] 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 Int prediction from the trained model
+   */
+  def predict(testData: Array[Double]): Double
+}
diff --git a/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala
new file mode 100644
index 0000000000000000000000000000000000000000..30ee0ab0ff869c4e7198bcfa3cb16e01316dc228
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala
@@ -0,0 +1,179 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.classification
+
+import spark.{Logging, RDD, SparkContext}
+import spark.mllib.optimization._
+import spark.mllib.regression._
+import spark.mllib.util.MLUtils
+
+import scala.math.round
+
+import org.jblas.DoubleMatrix
+
+/**
+ * Classification model trained using Logistic Regression.
+ *
+ * @param weights Weights computed for every feature.
+ * @param intercept Intercept computed for this model.
+ */
+class LogisticRegressionModel(
+    override val weights: Array[Double],
+    override val intercept: Double)
+  extends GeneralizedLinearModel(weights, intercept)
+  with ClassificationModel with Serializable {
+
+  override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
+      intercept: Double) = {
+    val margin = dataMatrix.mmul(weightMatrix).get(0) + intercept
+    round(1.0/ (1.0 + math.exp(margin * -1)))
+  }
+}
+
+/**
+ * Train a classification model for Logistic Regression using Stochastic Gradient Descent.
+ */
+class LogisticRegressionWithSGD private (
+    var stepSize: Double,
+    var numIterations: Int,
+    var regParam: Double,
+    var miniBatchFraction: Double,
+    var addIntercept: Boolean)
+  extends GeneralizedLinearAlgorithm[LogisticRegressionModel]
+  with Serializable {
+
+  val gradient = new LogisticGradient()
+  val updater = new SimpleUpdater()
+  val optimizer = new GradientDescent(gradient, updater).setStepSize(stepSize)
+                                                        .setNumIterations(numIterations)
+                                                        .setRegParam(regParam)
+                                                        .setMiniBatchFraction(miniBatchFraction)
+  /**
+   * Construct a LogisticRegression object with default parameters
+   */
+  def this() = this(1.0, 100, 0.0, 1.0, true)
+
+  def createModel(weights: Array[Double], intercept: Double) = {
+    new LogisticRegressionModel(weights, intercept)
+  }
+}
+
+/**
+ * Top-level methods for calling Logistic Regression.
+ */
+object LogisticRegressionWithSGD {
+  // NOTE(shivaram): We use multiple train methods instead of default arguments to support
+  // Java programs.
+
+  /**
+   * 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. The weights used in
+   * gradient descent are initialized using the initial weights provided.
+   *
+   * @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.
+   * @param initialWeights Initial set of weights to be used. Array should be equal in size to 
+   *        the number of features in the data.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int,
+      stepSize: Double,
+      miniBatchFraction: Double,
+      initialWeights: Array[Double])
+    : LogisticRegressionModel =
+  {
+    new LogisticRegressionWithSGD(stepSize, numIterations, 0.0, miniBatchFraction, true).run(
+      input, initialWeights)
+  }
+
+  /**
+   * 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[LabeledPoint],
+      numIterations: Int,
+      stepSize: Double,
+      miniBatchFraction: Double)
+    : LogisticRegressionModel =
+  {
+    new LogisticRegressionWithSGD(stepSize, numIterations, 0.0, miniBatchFraction, true).run(
+      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[LabeledPoint],
+      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[LabeledPoint],
+      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 = LogisticRegressionWithSGD.train(data, args(3).toInt, args(2).toDouble)
+
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/spark/mllib/classification/SVM.scala
new file mode 100644
index 0000000000000000000000000000000000000000..f799cb28298b7803f276b107e48ec634ee4009cd
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/classification/SVM.scala
@@ -0,0 +1,176 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.classification
+
+import scala.math.signum
+import spark.{Logging, RDD, SparkContext}
+import spark.mllib.optimization._
+import spark.mllib.regression._
+import spark.mllib.util.MLUtils
+
+import org.jblas.DoubleMatrix
+
+/**
+ * Model built using SVM.
+ *
+ * @param weights Weights computed for every feature.
+ * @param intercept Intercept computed for this model.
+ */
+class SVMModel(
+    override val weights: Array[Double],
+    override val intercept: Double)
+  extends GeneralizedLinearModel(weights, intercept)
+  with ClassificationModel with Serializable {
+
+  override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
+      intercept: Double) = {
+    signum(dataMatrix.dot(weightMatrix) + intercept)
+  }
+}
+
+/**
+ * Train an SVM using Stochastic Gradient Descent.
+ */
+class SVMWithSGD private (
+    var stepSize: Double,
+    var numIterations: Int,
+    var regParam: Double,
+    var miniBatchFraction: Double,
+    var addIntercept: Boolean)
+  extends GeneralizedLinearAlgorithm[SVMModel] with Serializable {
+
+  val gradient = new HingeGradient()
+  val updater = new SquaredL2Updater()
+  val optimizer = new GradientDescent(gradient, updater).setStepSize(stepSize)
+                                                        .setNumIterations(numIterations)
+                                                        .setRegParam(regParam)
+                                                        .setMiniBatchFraction(miniBatchFraction)
+  /**
+   * Construct a SVM object with default parameters
+   */
+  def this() = this(1.0, 100, 1.0, 1.0, true)
+
+  def createModel(weights: Array[Double], intercept: Double) = {
+    new SVMModel(weights, intercept)
+  }
+}
+
+/**
+ * Top-level methods for calling SVM.
+ */
+object SVMWithSGD {
+
+  /**
+   * Train a SVM 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. The weights used in
+   * gradient descent are initialized using the initial weights provided.
+   *
+   * @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 regParam Regularization parameter.
+   * @param miniBatchFraction Fraction of data to be used per iteration.
+   * @param initialWeights Initial set of weights to be used. Array should be equal in size to 
+   *        the number of features in the data.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int,
+      stepSize: Double,
+      regParam: Double,
+      miniBatchFraction: Double,
+      initialWeights: Array[Double])
+    : SVMModel =
+  {
+    new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(input,
+      initialWeights)
+  }
+
+  /**
+   * Train a SVM 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 regParam Regularization parameter.
+   * @param miniBatchFraction Fraction of data to be used per iteration.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int,
+      stepSize: Double,
+      regParam: Double,
+      miniBatchFraction: Double)
+    : SVMModel =
+  {
+    new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(input)
+  }
+
+  /**
+   * Train a SVM 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 regParam Regularization parameter.
+   * @param numIterations Number of iterations of gradient descent to run.
+   * @return a SVMModel which has the weights and offset from training.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int,
+      stepSize: Double,
+      regParam: Double)
+    : SVMModel =
+  {
+    train(input, numIterations, stepSize, regParam, 1.0)
+  }
+
+  /**
+   * Train a SVM 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 SVMModel which has the weights and offset from training.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int)
+    : SVMModel =
+  {
+    train(input, numIterations, 1.0, 1.0, 1.0)
+  }
+
+  def main(args: Array[String]) {
+    if (args.length != 5) {
+      println("Usage: SVM <master> <input_dir> <step_size> <regularization_parameter> <niters>")
+      System.exit(1)
+    }
+    val sc = new SparkContext(args(0), "SVM")
+    val data = MLUtils.loadLabeledData(sc, args(1))
+    val model = SVMWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
+
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala
index d875d6de50a2018140e328c7b5cc0ed9b7db7f9d..97e3d110ae5e3a44c34abd49dd496dddd7aaa014 100644
--- a/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala
@@ -112,7 +112,7 @@ class KMeans private (
    * 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 = {
+  def run(data: RDD[Array[Double]]): KMeansModel = {
     // TODO: check whether data is persistent; this needs RDD.storageLevel to be publicly readable
 
     val sc = data.sparkContext
@@ -194,8 +194,8 @@ class KMeans private (
    */
   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))
+    val sample = data.takeSample(true, runs * k, new Random().nextInt()).toSeq
+    Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k).toArray)
   }
 
   /**
@@ -210,7 +210,7 @@ class KMeans private (
   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 sample = data.takeSample(true, runs, seed).toSeq
     val centers = Array.tabulate(runs)(r => ArrayBuffer(sample(r)))
 
     // On each step, sample 2 * k points on average for each run with probability proportional
@@ -271,7 +271,7 @@ object KMeans {
                 .setMaxIterations(maxIterations)
                 .setRuns(runs)
                 .setInitializationMode(initializationMode)
-                .train(data)
+                .run(data)
   }
 
   def train(data: RDD[Array[Double]], k: Int, maxIterations: Int, runs: Int): KMeansModel = {
@@ -315,14 +315,15 @@ object KMeans {
   }
 
   def main(args: Array[String]) {
-    if (args.length != 4) {
-      println("Usage: KMeans <master> <input_file> <k> <max_iterations>")
+    if (args.length < 4) {
+      println("Usage: KMeans <master> <input_file> <k> <max_iterations> [<runs>]")
       System.exit(1)
     }
     val (master, inputFile, k, iters) = (args(0), args(1), args(2).toInt, args(3).toInt)
+    val runs = if (args.length >= 5) args(4).toInt else 1
     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 data = sc.textFile(inputFile).map(line => line.split(' ').map(_.toDouble)).cache()
+    val model = KMeans.train(data, k, iters, runs)
     val cost = model.computeCost(data)
     println("Cluster centers:")
     for (c <- model.clusterCenters) {
diff --git a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala
index d5338360c83f6ae5c0360088a9070d5a16b64f6f..e72b8b3a92eda03d1bb40cd46188549586a85c5a 100644
--- a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala
+++ b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala
@@ -19,14 +19,74 @@ package spark.mllib.optimization
 
 import org.jblas.DoubleMatrix
 
+/**
+ * Class used to compute the gradient for a loss function, given a single data point.
+ */
 abstract class Gradient extends Serializable {
   /**
-   * Compute the gradient for a given row of data.
+   * Compute the gradient and loss given features of a single data point.
    *
-   * @param data - One row of data. Row matrix of size 1xn where n is the number of features.
+   * @param data - Feature values for one data point. Column matrix of size nx1
+   *               where n is the number of features.
    * @param label - Label for this data item.
    * @param weights - Column matrix containing weights for every feature.
+   *
+   * @return A tuple of 2 elements. The first element is a column matrix containing the computed
+   *         gradient and the second element is the loss computed at this data point.
+   *
    */
   def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): 
       (DoubleMatrix, Double)
 }
+
+/**
+ * Compute gradient and loss for a logistic loss function.
+ */
+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)
+  }
+}
+
+/**
+ * Compute gradient and loss for a Least-squared loss function.
+ */
+class SquaredGradient extends Gradient {
+  override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): 
+      (DoubleMatrix, Double) = {
+    val diff: Double = data.dot(weights) - label
+
+    val loss = 0.5 * diff * diff
+    val gradient = data.mul(diff)
+
+    (gradient, loss)
+  }
+}
+
+/**
+ * Compute gradient and loss for a Hinge loss function.
+ */
+class HingeGradient extends Gradient {
+  override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): 
+      (DoubleMatrix, Double) = {
+
+    val dotProduct = data.dot(weights)
+
+    if (1.0 > label * dotProduct)
+      (data.mul(-label), 1.0 - label * dotProduct)
+    else
+      (DoubleMatrix.zeros(1,weights.length), 0.0)
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala
index 185a2a24f637f73a66d5b09aed6a2e1748322e39..31917df7e87124a9873b238481452fbd1bc078af 100644
--- a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala
@@ -24,18 +24,95 @@ import org.jblas.DoubleMatrix
 
 import scala.collection.mutable.ArrayBuffer
 
+/**
+ * Class used to solve an optimization problem using Gradient Descent.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every iteration.
+ */
+class GradientDescent(var gradient: Gradient, var updater: Updater) extends Optimizer {
+
+  private var stepSize: Double = 1.0
+  private var numIterations: Int = 100
+  private var regParam: Double = 0.0
+  private var miniBatchFraction: Double = 1.0
+
+  /**
+   * Set the step size per-iteration of SGD. Default 1.0.
+   */
+  def setStepSize(step: Double): this.type = {
+    this.stepSize = step
+    this
+  }
+
+  /**
+   * Set fraction of data to be used for each SGD iteration. Default 1.0.
+   */
+  def setMiniBatchFraction(fraction: Double): this.type = {
+    this.miniBatchFraction = fraction
+    this
+  }
+
+  /**
+   * Set the number of iterations for SGD. Default 100.
+   */
+  def setNumIterations(iters: Int): this.type = {
+    this.numIterations = iters
+    this
+  }
+
+  /**
+   * Set the regularization parameter used for SGD. Default 0.0.
+   */
+  def setRegParam(regParam: Double): this.type = {
+    this.regParam = regParam
+    this
+  }
+
+  /**
+   * Set the gradient function to be used for SGD.
+   */
+  def setGradient(gradient: Gradient): this.type = {
+    this.gradient = gradient
+    this
+  }
+
+
+  /**
+   * Set the updater function to be used for SGD.
+   */
+  def setUpdater(updater: Updater): this.type = {
+    this.updater = updater
+    this
+  }
+
+  def optimize(data: RDD[(Double, Array[Double])], initialWeights: Array[Double])
+    : Array[Double] = {
 
-object GradientDescent {
+    val (weights, stochasticLossHistory) = GradientDescent.runMiniBatchSGD(
+        data,
+        gradient,
+        updater,
+        stepSize,
+        numIterations,
+        regParam,
+        miniBatchFraction,
+        initialWeights)
+    weights
+  }
+
+}
 
+// Top-level method to run gradient descent.
+object GradientDescent extends Logging {
   /**
    * 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 numIterations - number of iterations that SGD should be run.
+   * @param regParam - regularization parameter
    * @param miniBatchFraction - fraction of the input data set that should be used for
    *                            one iteration of SGD. Default value 1.0.
    *
@@ -48,33 +125,42 @@ object GradientDescent {
     gradient: Gradient,
     updater: Updater,
     stepSize: Double,
-    numIters: Int,
-    initialWeights: Array[Double],
-    miniBatchFraction: Double=1.0) : (Array[Double], Array[Double]) = {
+    numIterations: Int,
+    regParam: Double,
+    miniBatchFraction: Double,
+    initialWeights: Array[Double]) : (Array[Double], Array[Double]) = {
 
-    val stochasticLossHistory = new ArrayBuffer[Double](numIters)
+    val stochasticLossHistory = new ArrayBuffer[Double](numIterations)
 
     val nexamples: Long = data.count()
     val miniBatchSize = nexamples * miniBatchFraction
 
     // Initialize weights as a column vector
     var weights = new DoubleMatrix(initialWeights.length, 1, initialWeights:_*)
-    var reg_val = 0.0
+    var regVal = 0.0
 
-    for (i <- 1 to numIters) {
+    for (i <- 1 to numIterations) {
       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)
+          val featuresCol = new DoubleMatrix(features.length, 1, features:_*)
+          val (grad, loss) = gradient.compute(featuresCol, y, weights)
           (grad, loss)
       }.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2))
 
-      stochasticLossHistory.append(lossSum / miniBatchSize + reg_val)
-      val update = updater.compute(weights, gradientSum.div(miniBatchSize), stepSize, i)
+      /**
+       * NOTE(Xinghao): lossSum is computed using the weights from the previous iteration
+       * and regVal is the regularization value computed in the previous iteration as well.
+       */
+      stochasticLossHistory.append(lossSum / miniBatchSize + regVal)
+      val update = updater.compute(
+        weights, gradientSum.div(miniBatchSize), stepSize, i, regParam)
       weights = update._1
-      reg_val = update._2
+      regVal = update._2
     }
 
+    logInfo("GradientDescent finished. Last 10 stochastic losses %s".format(
+      stochasticLossHistory.takeRight(10).mkString(", ")))
+
     (weights.toArray, stochasticLossHistory.toArray)
   }
 }
diff --git a/mllib/src/main/scala/spark/mllib/optimization/Optimizer.scala b/mllib/src/main/scala/spark/mllib/optimization/Optimizer.scala
new file mode 100644
index 0000000000000000000000000000000000000000..76a519c338847ea12a87168057516c7c58197fc3
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/optimization/Optimizer.scala
@@ -0,0 +1,29 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.optimization
+
+import spark.RDD
+
+trait Optimizer {
+
+  /**
+   * Solve the provided convex optimization problem. 
+   */
+  def optimize(data: RDD[(Double, Array[Double])], initialWeights: Array[Double]): Array[Double]
+
+}
diff --git a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala
index 18cb5f3a95ce1e9a1b74efd5eae1ad38e9b45dd3..db67d6b0bcd7d7c11d7c0fdc08d72c710e00ba73 100644
--- a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala
+++ b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala
@@ -17,28 +17,83 @@
 
 package spark.mllib.optimization
 
+import scala.math._
 import org.jblas.DoubleMatrix
 
+/**
+ * Class used to update weights used in Gradient Descent.
+ */
 abstract class Updater extends Serializable {
   /**
-   * Compute an updated value for weights given the gradient, stepSize and iteration number.
+   * Compute an updated value for weights given the gradient, stepSize, iteration number and
+   * regularization parameter. Also returns the regularization value computed using the
+   * *updated* weights.
    *
-   * @param weightsOlds - Column matrix of size nx1 where n is the number of features.
+   * @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
+   * @param regParam - Regularization parameter
    *
    * @return A tuple of 2 elements. The first element is a column matrix containing updated weights,
-   *         and the second element is the regularization value.
+   *         and the second element is the regularization value computed using updated weights.
    */
-  def compute(weightsOlds: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int):
-      (DoubleMatrix, Double)
+  def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int,
+      regParam: Double): (DoubleMatrix, Double)
 }
 
+/**
+ * A simple updater that adaptively adjusts the learning rate the
+ * square root of the number of iterations. Does not perform any regularization.
+ */
 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))
+      stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = {
+    val thisIterStepSize = stepSize / math.sqrt(iter)
+    val normGradient = gradient.mul(thisIterStepSize)
     (weightsOld.sub(normGradient), 0)
   }
 }
+
+/**
+ * Updater that adjusts learning rate and performs L1 regularization.
+ *
+ * The corresponding proximal operator used is the soft-thresholding function.
+ * That is, each weight component is shrunk towards 0 by shrinkageVal.
+ *
+ * If w >  shrinkageVal, set weight component to w-shrinkageVal.
+ * If w < -shrinkageVal, set weight component to w+shrinkageVal.
+ * If -shrinkageVal < w < shrinkageVal, set weight component to 0.
+ *
+ * Equivalently, set weight component to signum(w) * max(0.0, abs(w) - shrinkageVal)
+ */
+class L1Updater extends Updater {
+  override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix,
+      stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = {
+    val thisIterStepSize = stepSize / math.sqrt(iter)
+    val normGradient = gradient.mul(thisIterStepSize)
+    // Take gradient step
+    val newWeights = weightsOld.sub(normGradient)
+    // Soft thresholding
+    val shrinkageVal = regParam * thisIterStepSize
+    (0 until newWeights.length).foreach { i =>
+      val wi = newWeights.get(i)
+      newWeights.put(i, signum(wi) * max(0.0, abs(wi) - shrinkageVal))
+    }
+    (newWeights, newWeights.norm1 * regParam)
+  }
+}
+
+/**
+ * Updater that adjusts the learning rate and performs L2 regularization
+ */
+class SquaredL2Updater extends Updater {
+  override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix,
+      stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = {
+    val thisIterStepSize = stepSize / math.sqrt(iter)
+    val normGradient = gradient.mul(thisIterStepSize)
+    val newWeights = weightsOld.sub(normGradient).div(2.0 * thisIterStepSize * regParam + 1.0)
+    (newWeights, pow(newWeights.norm2, 2.0) * regParam)
+  }
+}
+
diff --git a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
index 7da96397a62bee4207409e50ede50f7cc40cdc84..6c71dc1f320c8a77b10f10f7e3bf54e358830c0b 100644
--- a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
@@ -35,8 +35,7 @@ import org.jblas.{DoubleMatrix, SimpleBlas, Solve}
  * 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])
+private[recommendation] case class OutLinkBlock(elementIds: Array[Int], shouldSend: Array[BitSet])
 
 
 /**
@@ -56,8 +55,7 @@ private[recommendation] case class InLinkBlock(
 /**
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
  */
-private[recommendation] case class Rating(user: Int, product: Int, rating: Double)
-
+case class Rating(val user: Int, val product: Int, val rating: Double)
 
 /**
  * Alternating Least Squares matrix factorization.
@@ -105,10 +103,10 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
   }
 
   /**
-   * Run ALS with the configured parmeters on an input RDD of (user, product, rating) triples.
+   * Run ALS with the configured parameters 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 = {
+  def run(ratings: RDD[Rating]): MatrixFactorizationModel = {
     val numBlocks = if (this.numBlocks == -1) {
       math.max(ratings.context.defaultParallelism, ratings.partitions.size / 2)
     } else {
@@ -117,8 +115,10 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
 
     val partitioner = new HashPartitioner(numBlocks)
 
-    val ratingsByUserBlock = ratings.map{ case (u, p, r) => (u % numBlocks, Rating(u, p, r)) }
-    val ratingsByProductBlock = ratings.map{ case (u, p, r) => (p % numBlocks, Rating(p, u, r)) }
+    val ratingsByUserBlock = ratings.map{ rating => (rating.user % numBlocks, rating) }
+    val ratingsByProductBlock = ratings.map{ rating =>
+      (rating.product % numBlocks, Rating(rating.product, rating.user, rating.rating))
+    }
 
     val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock)
     val (productInLinks, productOutLinks) = makeLinkRDDs(numBlocks, ratingsByProductBlock)
@@ -357,14 +357,14 @@ object ALS {
    * @param blocks     level of parallelism to split computation into
    */
   def train(
-      ratings: RDD[(Int, Int, Double)],
+      ratings: RDD[Rating],
       rank: Int,
       iterations: Int,
       lambda: Double,
       blocks: Int)
     : MatrixFactorizationModel =
   {
-    new ALS(blocks, rank, iterations, lambda).train(ratings)
+    new ALS(blocks, rank, iterations, lambda).run(ratings)
   }
 
   /**
@@ -379,7 +379,7 @@ object ALS {
    * @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)
+  def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double)
     : MatrixFactorizationModel =
   {
     train(ratings, rank, iterations, lambda, -1)
@@ -396,7 +396,7 @@ object ALS {
    * @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)
+  def train(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel =
   {
     train(ratings, rank, iterations, 0.01, -1)
@@ -419,11 +419,12 @@ object ALS {
     System.setProperty("spark.serializer", "spark.KryoSerializer")
     System.setProperty("spark.kryo.registrator", classOf[ALSRegistrator].getName)
     System.setProperty("spark.kryo.referenceTracking", "false")
+    System.setProperty("spark.kryoserializer.buffer.mb", "8")
     System.setProperty("spark.locality.wait", "10000")
     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)
+      Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
     }
     val model = ALS.train(ratings, rank, iters, 0.01, blocks)
     model.userFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") }
diff --git a/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 38637b3dd137677994da6ed1f6c4b00b92ae92ee..5e21717da525f5d9b89b819449670a931e20b497 100644
--- a/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -22,6 +22,15 @@ import spark.SparkContext._
 
 import org.jblas._
 
+/**
+ * Model representing the result of matrix factorization.
+ *
+ * @param rank Rank for the features in this model.
+ * @param userFeatures RDD of tuples where each tuple represents the userId and
+ *                     the features computed for this user.
+ * @param productFeatures RDD of tuples where each tuple represents the productId
+ *                        and the features computed for this product.
+ */
 class MatrixFactorizationModel(
     val rank: Int,
     val userFeatures: RDD[(Int, Array[Double])],
diff --git a/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
new file mode 100644
index 0000000000000000000000000000000000000000..4ecafff08b3281f6df4fab5d41001b123ac871b8
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -0,0 +1,142 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.regression
+
+import spark.{Logging, RDD}
+import spark.mllib.optimization._
+
+import org.jblas.DoubleMatrix
+
+/**
+ * GeneralizedLinearModel (GLM) represents a model trained using 
+ * GeneralizedLinearAlgorithm. GLMs consist of a weight vector and
+ * an intercept.
+ *
+ * @param weights Weights computed for every feature.
+ * @param intercept Intercept computed for this model.
+ */
+abstract class GeneralizedLinearModel(val weights: Array[Double], val intercept: Double)
+  extends Serializable {
+
+  // Create a column vector that can be used for predictions
+  private val weightsMatrix = new DoubleMatrix(weights.length, 1, weights:_*)
+
+  /**
+   * Predict the result given a data point and the weights learned.
+   * 
+   * @param dataMatrix Row vector containing the features for this data point
+   * @param weightMatrix Column vector containing the weights of the model
+   * @param intercept Intercept of the model.
+   */
+  def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
+    intercept: Double): Double
+
+  /**
+   * 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: spark.RDD[Array[Double]]): RDD[Double] = {
+    // A small optimization to avoid serializing the entire model. Only the weightsMatrix
+    // and intercept is needed.
+    val localWeights = weightsMatrix
+    val localIntercept = intercept
+
+    testData.map { x =>
+      val dataMatrix = new DoubleMatrix(1, x.length, x:_*)
+      predictPoint(dataMatrix, localWeights, localIntercept)
+    }
+  }
+
+  /**
+   * 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 = {
+    val dataMat = new DoubleMatrix(1, testData.length, testData:_*)
+    predictPoint(dataMat, weightsMatrix, intercept)
+  }
+}
+
+/**
+ * GeneralizedLinearAlgorithm implements methods to train a Genearalized Linear Model (GLM).
+ * This class should be extended with an Optimizer to create a new GLM.
+ */
+abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
+  extends Logging with Serializable {
+
+  val optimizer: Optimizer
+
+  /**
+   * Create a model given the weights and intercept
+   */
+  protected def createModel(weights: Array[Double], intercept: Double): M
+
+  protected var addIntercept: Boolean
+
+  /**
+   * Set if the algorithm should add an intercept. Default true.
+   */
+  def setIntercept(addIntercept: Boolean): this.type = {
+    this.addIntercept = addIntercept
+    this
+  }
+
+  /**
+   * Run the algorithm with the configured parameters on an input
+   * RDD of LabeledPoint entries.
+   */
+  def run(input: RDD[LabeledPoint]) : M = {
+    val nfeatures: Int = input.first().features.length
+    val initialWeights = Array.fill(nfeatures)(1.0)
+    run(input, initialWeights)
+  }
+
+  /**
+   * Run the algorithm with the configured parameters on an input RDD
+   * of LabeledPoint entries starting from the initial weights provided.
+   */
+  def run(input: RDD[LabeledPoint], initialWeights: Array[Double]) : M = {
+
+    // Add a extra variable consisting of all 1.0's for the intercept.
+    val data = if (addIntercept) {
+      input.map(labeledPoint => (labeledPoint.label, Array(1.0, labeledPoint.features:_*)))
+    } else {
+      input.map(labeledPoint => (labeledPoint.label, labeledPoint.features))
+    }
+
+    val initialWeightsWithIntercept = if (addIntercept) {
+      Array(1.0, initialWeights:_*)
+    } else {
+      initialWeights
+    }
+
+    val weights = optimizer.optimize(data, initialWeightsWithIntercept)
+    val intercept = weights(0)
+    val weightsScaled = weights.tail
+
+    val model = createModel(weightsScaled, intercept)
+
+    logInfo("Final model weights " + model.weights.mkString(","))
+    logInfo("Final model intercept " + model.intercept)
+    model
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/spark/mllib/regression/LabeledPoint.scala
new file mode 100644
index 0000000000000000000000000000000000000000..3de60482c54cf75bbba7241c90c8c5041826cbc0
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/regression/LabeledPoint.scala
@@ -0,0 +1,26 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.regression
+
+/**
+ * Class that represents the features and labels of a data point.
+ *
+ * @param label Label for this data point.
+ * @param features List of features for this data point.
+ */
+case class LabeledPoint(val label: Double, val features: Array[Double])
diff --git a/mllib/src/main/scala/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/spark/mllib/regression/Lasso.scala
new file mode 100644
index 0000000000000000000000000000000000000000..6bbc990a5a02fbaaef83c9aab04d02a94a4c8312
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/regression/Lasso.scala
@@ -0,0 +1,176 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.regression
+
+import spark.{Logging, RDD, SparkContext}
+import spark.mllib.optimization._
+import spark.mllib.util.MLUtils
+
+import org.jblas.DoubleMatrix
+
+/**
+ * Regression model trained using Lasso.
+ *
+ * @param weights Weights computed for every feature.
+ * @param intercept Intercept computed for this model.
+ */
+class LassoModel(
+    override val weights: Array[Double],
+    override val intercept: Double)
+  extends GeneralizedLinearModel(weights, intercept)
+  with RegressionModel with Serializable {
+
+  override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
+      intercept: Double) = {
+    dataMatrix.dot(weightMatrix) + intercept
+  }
+}
+
+/**
+ * Train a regression model with L1-regularization using Stochastic Gradient Descent.
+ */
+class LassoWithSGD private (
+    var stepSize: Double,
+    var numIterations: Int,
+    var regParam: Double,
+    var miniBatchFraction: Double,
+    var addIntercept: Boolean)
+  extends GeneralizedLinearAlgorithm[LassoModel]
+  with Serializable {
+
+  val gradient = new SquaredGradient()
+  val updater = new L1Updater()
+  val optimizer = new GradientDescent(gradient, updater).setStepSize(stepSize)
+                                                        .setNumIterations(numIterations)
+                                                        .setRegParam(regParam)
+                                                        .setMiniBatchFraction(miniBatchFraction)
+
+  /**
+   * Construct a Lasso object with default parameters
+   */
+  def this() = this(1.0, 100, 1.0, 1.0, true)
+
+  def createModel(weights: Array[Double], intercept: Double) = {
+    new LassoModel(weights, intercept)
+  }
+}
+
+/**
+ * Top-level methods for calling Lasso.
+ */
+object LassoWithSGD {
+
+  /**
+   * Train a Lasso 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. The weights used in
+   * gradient descent are initialized using the initial weights provided.
+   *
+   * @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 regParam Regularization parameter.
+   * @param miniBatchFraction Fraction of data to be used per iteration.
+   * @param initialWeights Initial set of weights to be used. Array should be equal in size to 
+   *        the number of features in the data.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int,
+      stepSize: Double,
+      regParam: Double,
+      miniBatchFraction: Double,
+      initialWeights: Array[Double])
+    : LassoModel =
+  {
+    new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(input,
+        initialWeights)
+  }
+
+  /**
+   * Train a Lasso 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 regParam Regularization parameter.
+   * @param miniBatchFraction Fraction of data to be used per iteration.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int,
+      stepSize: Double,
+      regParam: Double,
+      miniBatchFraction: Double)
+    : LassoModel =
+  {
+    new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(input)
+  }
+
+  /**
+   * Train a Lasso 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 regParam Regularization parameter.
+   * @param numIterations Number of iterations of gradient descent to run.
+   * @return a LassoModel which has the weights and offset from training.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int,
+      stepSize: Double,
+      regParam: Double)
+    : LassoModel =
+  {
+    train(input, numIterations, stepSize, regParam, 1.0)
+  }
+
+  /**
+   * Train a Lasso 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 LassoModel which has the weights and offset from training.
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      numIterations: Int)
+    : LassoModel =
+  {
+    train(input, numIterations, 1.0, 1.0, 1.0)
+  }
+
+  def main(args: Array[String]) {
+    if (args.length != 5) {
+      println("Usage: Lasso <master> <input_dir> <step_size> <regularization_parameter> <niters>")
+      System.exit(1)
+    }
+    val sc = new SparkContext(args(0), "Lasso")
+    val data = MLUtils.loadLabeledData(sc, args(1))
+    val model = LassoWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
+
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala
deleted file mode 100644
index 4b2254601706a484253865e7f5def99a18ccbe71..0000000000000000000000000000000000000000
--- a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala
+++ /dev/null
@@ -1,243 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package 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: Array[Double],
-  val intercept: Double,
-  val stochasticLosses: Array[Double]) extends RegressionModel {
-
-  // Create a column vector that can be used for predictions
-  private val weightsMatrix = new DoubleMatrix(weights.length, 1, weights:_*)
-
-  override def predict(testData: spark.RDD[Array[Double]]) = {
-    // A small optimization to avoid serializing the entire model. Only the weightsMatrix
-    // and intercept is needed.
-    val localWeights = weightsMatrix
-    val localIntercept = intercept
-    testData.map { x =>
-      val margin = new DoubleMatrix(1, x.length, x:_*).mmul(localWeights).get(0) + localIntercept
-      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(weightsMatrix).get(0) + this.intercept
-    1.0/ (1.0 + math.exp(margin * -1))
-  }
-}
-
-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)
-  }
-}
-
-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 = {
-    val nfeatures: Int = input.take(1)(0)._2.length
-    val initialWeights = Array.fill(nfeatures)(1.0)
-    train(input, initialWeights)
-  }
-
-  def train(
-    input: RDD[(Double, Array[Double])],
-    initialWeights: 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 initalWeightsWithIntercept = Array(1.0, initialWeights:_*)
-
-    val (weights, stochasticLosses) = GradientDescent.runMiniBatchSGD(
-      data,
-      new LogisticGradient(),
-      new SimpleUpdater(),
-      stepSize,
-      numIters,
-      initalWeightsWithIntercept,
-      miniBatchFraction)
-
-    val intercept = weights(0)
-    val weightsScaled = weights.tail
-
-    val model = new LogisticRegressionModel(weightsScaled, intercept, stochasticLosses)
-
-    logInfo("Final model weights " + model.weights.mkString(","))
-    logInfo("Final model intercept " + model.intercept)
-    logInfo("Last 10 stochastic losses " + model.stochasticLosses.takeRight(10).mkString(", "))
-    model
-  }
-}
-
-/**
- * Top-level methods for calling Logistic Regression.
- * NOTE(shivaram): We use multiple train methods instead of default arguments to support 
- *                 Java programs.
- */
-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. The weights used in
-   * gradient descent are initialized using the initial weights provided.
-   *
-   * @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.
-   * @param initialWeights Initial set of weights to be used. Array should be equal in size to 
-   *        the number of features in the data.
-   */
-  def train(
-      input: RDD[(Double, Array[Double])],
-      numIterations: Int,
-      stepSize: Double,
-      miniBatchFraction: Double,
-      initialWeights: Array[Double])
-    : LogisticRegressionModel =
-  {
-    new LogisticRegression(stepSize, miniBatchFraction, numIterations).train(input, initialWeights)
-  }
-
-  /**
-   * 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/Regression.scala b/mllib/src/main/scala/spark/mllib/regression/RegressionModel.scala
similarity index 100%
rename from mllib/src/main/scala/spark/mllib/regression/Regression.scala
rename to mllib/src/main/scala/spark/mllib/regression/RegressionModel.scala
diff --git a/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala
index 6ba141e8fb332beac21e0af7f741ec0013461807..b42d94af4168c9003cebb43315ba4ec1ee22983c 100644
--- a/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala
@@ -71,7 +71,8 @@ class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double)
     this
   }
 
-  def train(input: RDD[(Double, Array[Double])]): RidgeRegressionModel = {
+  def train(inputLabeled: RDD[LabeledPoint]): RidgeRegressionModel = {
+    val input = inputLabeled.map(labeledPoint => (labeledPoint.label, labeledPoint.features))
     val nfeatures: Int = input.take(1)(0)._2.length
     val nexamples: Long = input.count()
 
@@ -167,10 +168,10 @@ class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double)
 
 /**
  * Top-level methods for calling Ridge Regression.
- * NOTE(shivaram): We use multiple train methods instead of default arguments to support 
- *                 Java programs.
  */
 object RidgeRegression {
+  // NOTE(shivaram): We use multiple train methods instead of default arguments to support
+  // Java programs.
 
   /**
    * Train a ridge regression model given an RDD of (response, features) pairs.
@@ -183,7 +184,7 @@ object RidgeRegression {
    * @param lambdaHigh upper bound used in binary search for lambda
    */
   def train(
-      input: RDD[(Double, Array[Double])],
+      input: RDD[LabeledPoint],
       lambdaLow: Double,
       lambdaHigh: Double)
     : RidgeRegressionModel =
@@ -199,7 +200,7 @@ object RidgeRegression {
    *
    * @param input RDD of (response, array of features) pairs.
    */
-  def train(input: RDD[(Double, Array[Double])]) : RidgeRegressionModel = {
+  def train(input: RDD[LabeledPoint]) : RidgeRegressionModel = {
     train(input, 0.0, 100.0)
   }
 
diff --git a/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala
new file mode 100644
index 0000000000000000000000000000000000000000..672b63f65ab9d35681ab0d17eee1d15bda481a5f
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.util
+
+import scala.util.Random
+
+import spark.{RDD, SparkContext}
+
+/**
+ * Generate test data for KMeans. This class first chooses k cluster centers
+ * from a d-dimensional Gaussian distribution scaled by factor r and then creates a Gaussian
+ * cluster with scale 1 around each center.
+ */
+
+object KMeansDataGenerator {
+
+  /**
+   * Generate an RDD containing test data for KMeans.
+   *
+   * @param sc SparkContext to use for creating the RDD
+   * @param numPoints Number of points that will be contained in the RDD
+   * @param k Number of clusters
+   * @param d Number of dimensions
+   * @param r Scaling factor for the distribution of the initial centers
+   * @param numPartitions Number of partitions of the generated RDD; default 2
+   */
+  def generateKMeansRDD(
+      sc: SparkContext,
+      numPoints: Int,
+      k: Int,
+      d: Int,
+      r: Double,
+      numPartitions: Int = 2)
+    : RDD[Array[Double]] =
+  {
+    // First, generate some centers
+    val rand = new Random(42)
+    val centers = Array.fill(k)(Array.fill(d)(rand.nextGaussian() * r))
+    // Then generate points around each center
+    sc.parallelize(0 until numPoints, numPartitions).map { idx =>
+      val center = centers(idx % k)
+      val rand2 = new Random(42 + idx)
+      Array.tabulate(d)(i => center(i) + rand2.nextGaussian())
+    }
+  }
+
+  def main(args: Array[String]) {
+    if (args.length < 6) {
+      println("Usage: KMeansGenerator " +
+        "<master> <output_dir> <num_points> <k> <d> <r> [<num_partitions>]")
+      System.exit(1)
+    }
+
+    val sparkMaster = args(0)
+    val outputPath = args(1)
+    val numPoints = args(2).toInt
+    val k = args(3).toInt
+    val d = args(4).toInt
+    val r = args(5).toDouble
+    val parts = if (args.length >= 7) args(6).toInt else 2
+
+    val sc = new SparkContext(sparkMaster, "KMeansDataGenerator")
+    val data = generateKMeansRDD(sc, numPoints, k, d, r, parts)
+    data.map(_.mkString(" ")).saveAsTextFile(outputPath)
+
+    System.exit(0)
+  }
+}
+
diff --git a/mllib/src/main/scala/spark/mllib/util/LassoDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/LassoDataGenerator.scala
new file mode 100644
index 0000000000000000000000000000000000000000..eeb14fc4e32f9f4ac960c4c615f814429a033f3a
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/util/LassoDataGenerator.scala
@@ -0,0 +1,48 @@
+package spark.mllib.util
+
+import scala.util.Random
+
+import org.jblas.DoubleMatrix
+
+import spark.{RDD, SparkContext}
+import spark.mllib.regression.LabeledPoint
+
+/**
+ * Generate sample data used for Lasso Regression. This class generates uniform random values
+ * for the features and adds Gaussian noise with weight 0.1 to generate response variables.
+ */
+object LassoDataGenerator {
+
+  def main(args: Array[String]) {
+    if (args.length < 2) {
+      println("Usage: LassoGenerator " +
+        "<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 sc = new SparkContext(sparkMaster, "LassoGenerator")
+
+    val globalRnd = new Random(94720)
+    val trueWeights = new DoubleMatrix(1, nfeatures+1,
+      Array.fill[Double](nfeatures + 1) { globalRnd.nextGaussian() }:_*)
+
+    val data: RDD[LabeledPoint] = sc.parallelize(0 until nexamples, parts).map { idx =>
+      val rnd = new Random(42 + idx)
+
+      val x = Array.fill[Double](nfeatures) {
+        rnd.nextDouble() * 2.0 - 1.0
+      }
+      val y = (new DoubleMatrix(1, x.length, x:_*)).dot(trueWeights) + rnd.nextGaussian() * 0.1
+      LabeledPoint(y, x)
+    }
+
+    MLUtils.saveLabeledData(data, outputPath)
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.scala
index 8d659cd97c6502ec60c879cb00b54ba7e87ba313..d6402f23e2d94dd5724593622e00b97c52162b72 100644
--- a/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.scala
+++ b/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.scala
@@ -20,12 +20,17 @@ package spark.mllib.util
 import scala.util.Random
 
 import spark.{RDD, SparkContext}
+import spark.mllib.regression.LabeledPoint
+
+/**
+ * Generate test data for LogisticRegression. This class chooses positive labels
+ * with probability `probOne` and scales features for positive examples by `eps`.
+ */
 
 object LogisticRegressionDataGenerator {
 
   /**
-   * Generate an RDD containing test data for LogisticRegression. This function chooses
-   * positive labels with probability `probOne` and scales positive examples by `eps`.
+   * Generate an RDD containing test data for LogisticRegression.
    *
    * @param sc SparkContext to use for creating the RDD.
    * @param nexamples Number of examples that will be contained in the RDD.
@@ -40,7 +45,7 @@ object LogisticRegressionDataGenerator {
     nfeatures: Int,
     eps: Double,
     nparts: Int = 2,
-    probOne: Double = 0.5): RDD[(Double, Array[Double])] = {
+    probOne: Double = 0.5): RDD[LabeledPoint] = {
     val data = sc.parallelize(0 until nexamples, nparts).map { idx =>
       val rnd = new Random(42 + idx)
 
@@ -48,7 +53,7 @@ object LogisticRegressionDataGenerator {
       val x = Array.fill[Double](nfeatures) {
         rnd.nextGaussian() + (y * eps)
       }
-      (y, x)
+      LabeledPoint(y, x)
     }
     data
   }
diff --git a/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala
new file mode 100644
index 0000000000000000000000000000000000000000..88992cde0cf8ed81a488673b74467e90d535ebd7
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala
@@ -0,0 +1,113 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.recommendation
+
+import scala.util.Random
+
+import org.jblas.DoubleMatrix
+
+import spark.{RDD, SparkContext}
+import spark.mllib.util.MLUtils
+
+/**
+* Generate RDD(s) containing data for Matrix Factorization.
+*
+* This method samples training entries according to the oversampling factor
+* 'trainSampFact', which is a multiplicative factor of the number of
+* degrees of freedom of the matrix: rank*(m+n-rank).
+* 
+* It optionally samples entries for a testing matrix using 
+* 'testSampFact', the percentage of the number of training entries 
+* to use for testing.
+*
+* This method takes the following inputs:
+*   sparkMaster    (String) The master URL.
+*   outputPath     (String) Directory to save output.
+*   m              (Int) Number of rows in data matrix.
+*   n              (Int) Number of columns in data matrix.
+*   rank           (Int) Underlying rank of data matrix.
+*   trainSampFact  (Double) Oversampling factor.
+*   noise          (Boolean) Whether to add gaussian noise to training data.
+*   sigma          (Double) Standard deviation of added gaussian noise.
+*   test           (Boolean) Whether to create testing RDD.
+*   testSampFact   (Double) Percentage of training data to use as test data.
+*/
+
+object MFDataGenerator{
+
+  def main(args: Array[String]) {
+    if (args.length < 2) {
+      println("Usage: MFDataGenerator " +
+        "<master> <outputDir> [m] [n] [rank] [trainSampFact] [noise] [sigma] [test] [testSampFact]")
+      System.exit(1)
+    }
+
+    val sparkMaster: String = args(0)
+    val outputPath: String = args(1)
+    val m: Int = if (args.length > 2) args(2).toInt else 100
+    val n: Int = if (args.length > 3) args(3).toInt else 100
+    val rank: Int = if (args.length > 4) args(4).toInt else 10
+    val trainSampFact: Double = if (args.length > 5) args(5).toDouble else 1.0
+    val noise: Boolean = if (args.length > 6) args(6).toBoolean else false
+    val sigma: Double = if (args.length > 7) args(7).toDouble else 0.1
+    val test: Boolean = if (args.length > 8) args(8).toBoolean else false
+    val testSampFact: Double = if (args.length > 9) args(9).toDouble else 0.1
+
+    val sc = new SparkContext(sparkMaster, "MFDataGenerator")
+
+    val A = DoubleMatrix.randn(m, rank)
+    val B = DoubleMatrix.randn(rank, n)
+    val z = 1 / (scala.math.sqrt(scala.math.sqrt(rank)))
+    A.mmuli(z)
+    B.mmuli(z)
+    val fullData = A.mmul(B)
+
+    val df = rank * (m + n - rank)
+    val sampSize = scala.math.min(scala.math.round(trainSampFact * df),
+      scala.math.round(.99 * m * n)).toInt
+    val rand = new Random()
+    val mn = m * n
+    val shuffled = rand.shuffle(1 to mn toIterable)
+
+    val omega = shuffled.slice(0, sampSize)
+    val ordered = omega.sortWith(_ < _).toArray
+    val trainData: RDD[(Int, Int, Double)] = sc.parallelize(ordered)
+      .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1)))
+
+    // optionally add gaussian noise
+    if (noise) { 
+      trainData.map(x => (x._1, x._2, x._3 + rand.nextGaussian * sigma))
+    }
+
+    trainData.map(x => x._1 + "," + x._2 + "," + x._3).saveAsTextFile(outputPath)
+
+    // optionally generate testing data
+    if (test) {
+      val testSampSize = scala.math
+        .min(scala.math.round(sampSize * testSampFact),scala.math.round(mn - sampSize)).toInt
+      val testOmega = shuffled.slice(sampSize, sampSize + testSampSize)
+      val testOrdered = testOmega.sortWith(_ < _).toArray
+      val testData: RDD[(Int, Int, Double)] = sc.parallelize(testOrdered)
+        .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1)))
+      testData.map(x => x._1 + "," + x._2 + "," + x._3).saveAsTextFile(outputPath)
+    }
+        
+    sc.stop()
+  
+  }
+}
\ No newline at end of file
diff --git a/mllib/src/main/scala/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala
index b5e564df6d40be548c2e4eb9b6c21fead51edfc7..4e030a81b48f9f37af138bcc93bbdd9a71d70327 100644
--- a/mllib/src/main/scala/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala
@@ -21,32 +21,42 @@ import spark.{RDD, SparkContext}
 import spark.SparkContext._
 
 import org.jblas.DoubleMatrix
+import spark.mllib.regression.LabeledPoint
 
 /**
- * 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.
+ * Helper methods to load, save and pre-process data used in ML Lib.
  */
 object MLUtils {
 
   /**
+   * Load labeled data from a file. The data format used here is
+   * <L>, <f1> <f2> ...
+   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
+   *
    * @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).
+   * @return An RDD of LabeledPoint. Each labeled point has two elements: 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])] = {
+  def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
     sc.textFile(dir).map { line =>
-      val parts = line.split(",")
+      val parts = line.split(',')
       val label = parts(0).toDouble
-      val features = parts(1).trim().split(" ").map(_.toDouble)
-      (label, features)
+      val features = parts(1).trim().split(' ').map(_.toDouble)
+      LabeledPoint(label, features)
     }
   }
 
-  def saveLabeledData(data: RDD[(Double, Array[Double])], dir: String) {
-    val dataStr = data.map(x => x._1 + "," + x._2.mkString(" "))
+  /**
+   * Save labeled data to a file. The data format used here is
+   * <L>, <f1> <f2> ...
+   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
+   *
+   * @param data An RDD of LabeledPoints containing data to be saved.
+   * @param dir Directory to save the data.
+   */
+  def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
+    val dataStr = data.map(x => x.label + "," + x.features.mkString(" "))
     dataStr.saveAsTextFile(dir)
   }
 
diff --git a/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.scala
index c5b8a29942c34da980bd5978181421d207346c9d..4d329168bedee49fcdd9b8922ab2cf0b5606aa5e 100644
--- a/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.scala
+++ b/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.scala
@@ -22,33 +22,40 @@ import scala.util.Random
 import org.jblas.DoubleMatrix
 
 import spark.{RDD, SparkContext}
+import spark.mllib.regression.LabeledPoint
 
+/**
+ * Generate sample data used for RidgeRegression. This class generates
+ * uniformly random values for every feature and adds Gaussian noise with mean `eps` to the
+ * response variable `Y`.
+ *
+ */
 object RidgeRegressionDataGenerator {
 
   /**
-   * Generate an RDD containing test data used for RidgeRegression. This function generates
-   * uniformly random values for every feature and adds Gaussian noise with mean `eps` to the
-   * response variable `Y`.
+   * Generate an RDD containing sample data for RidgeRegression.
    *
    * @param sc SparkContext to be used for generating the RDD.
    * @param nexamples Number of examples that will be contained in the RDD.
    * @param nfeatures Number of features to generate for each example.
    * @param eps Epsilon factor by which examples are scaled.
    * @param nparts Number of partitions in the RDD. Default value is 2.
+   *
+   * @return RDD of LabeledPoint containing sample data.
    */
   def generateRidgeRDD(
     sc: SparkContext,
     nexamples: Int,
     nfeatures: Int,
     eps: Double,
-    nparts: Int = 2) : RDD[(Double, Array[Double])] = {
+    nparts: Int = 2) : RDD[LabeledPoint] = {
     org.jblas.util.Random.seed(42)
     // 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 nparts, nparts).flatMap { p =>
+    val data: RDD[LabeledPoint] = sc.parallelize(0 until nparts, nparts).flatMap { p =>
       org.jblas.util.Random.seed(42 + p)
       val examplesInPartition = nexamples / nparts
 
@@ -61,16 +68,16 @@ object RidgeRegressionDataGenerator {
       val yObs = new DoubleMatrix(normalValues).addi(y)
 
       Iterator.tabulate(examplesInPartition) { i =>
-        (yObs.get(i, 0), X.getRow(i).toArray)
+        LabeledPoint(yObs.get(i, 0), X.getRow(i).toArray)
       }
     }
     data
   }
 
   def main(args: Array[String]) {
-    if (args.length != 5) {
+    if (args.length < 2) {
       println("Usage: RidgeRegressionGenerator " +
-        "<master> <output_dir> <num_examples> <num_features> <num_partitions>")
+        "<master> <output_dir> [num_examples] [num_features] [num_partitions]")
       System.exit(1)
     }
 
diff --git a/mllib/src/main/scala/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/SVMDataGenerator.scala
new file mode 100644
index 0000000000000000000000000000000000000000..e02bd190f6cb5e9a5a7a2983c3411a2b27f85bde
--- /dev/null
+++ b/mllib/src/main/scala/spark/mllib/util/SVMDataGenerator.scala
@@ -0,0 +1,49 @@
+package spark.mllib.util
+
+import scala.util.Random
+import scala.math.signum
+
+import spark.{RDD, SparkContext}
+
+import org.jblas.DoubleMatrix
+import spark.mllib.regression.LabeledPoint
+
+/**
+ * Generate sample data used for SVM. This class generates uniform random values
+ * for the features and adds Gaussian noise with weight 0.1 to generate labels.
+ */
+object SVMDataGenerator {
+
+  def main(args: Array[String]) {
+    if (args.length < 2) {
+      println("Usage: SVMGenerator " +
+        "<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 sc = new SparkContext(sparkMaster, "SVMGenerator")
+
+    val globalRnd = new Random(94720)
+    val trueWeights = new DoubleMatrix(1, nfeatures+1,
+      Array.fill[Double](nfeatures + 1) { globalRnd.nextGaussian() }:_*)
+
+    val data: RDD[LabeledPoint] = sc.parallelize(0 until nexamples, parts).map { idx =>
+      val rnd = new Random(42 + idx)
+
+      val x = Array.fill[Double](nfeatures) {
+        rnd.nextDouble() * 2.0 - 1.0
+      }
+      val y = signum((new DoubleMatrix(1, x.length, x:_*)).dot(trueWeights) + rnd.nextGaussian() * 0.1)
+      LabeledPoint(y, x)
+    }
+
+    MLUtils.saveLabeledData(data, outputPath)
+    sc.stop()
+  }
+}
diff --git a/mllib/src/test/java/spark/mllib/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/spark/mllib/classification/JavaLogisticRegressionSuite.java
new file mode 100644
index 0000000000000000000000000000000000000000..e0ebd45cd8d7ceb8326904674a076ad74421b340
--- /dev/null
+++ b/mllib/src/test/java/spark/mllib/classification/JavaLogisticRegressionSuite.java
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.classification;
+
+import java.io.Serializable;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+
+import spark.mllib.regression.LabeledPoint;
+
+public class JavaLogisticRegressionSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+    System.clearProperty("spark.driver.port");
+  }
+
+  int validatePrediction(List<LabeledPoint> validationData, LogisticRegressionModel model) {
+    int numAccurate = 0;
+    for (LabeledPoint point: validationData) {
+      Double prediction = model.predict(point.features());
+      if (prediction == point.label()) {
+        numAccurate++;
+      }
+    }
+    return numAccurate;
+  }
+
+  @Test
+  public void runLRUsingConstructor() {
+    int nPoints = 10000;
+    double A = 2.0;
+    double B = -1.5;
+
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(
+        LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
+    List<LabeledPoint> validationData =
+        LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 17);
+
+    LogisticRegressionWithSGD lrImpl = new LogisticRegressionWithSGD();
+    lrImpl.optimizer().setStepSize(1.0)
+                      .setRegParam(1.0)
+                      .setNumIterations(100);
+    LogisticRegressionModel model = lrImpl.run(testRDD.rdd());
+
+    int numAccurate = validatePrediction(validationData, model);
+    Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
+  }
+
+  @Test
+  public void runLRUsingStaticMethods() {
+    int nPoints = 10000;
+    double A = 2.0;
+    double B = -1.5;
+
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(
+        LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
+    List<LabeledPoint> validationData =
+        LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 17);
+
+    LogisticRegressionModel model = LogisticRegressionWithSGD.train(
+        testRDD.rdd(), 100, 1.0, 1.0);
+
+    int numAccurate = validatePrediction(validationData, model);
+    Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
+  }
+
+}
diff --git a/mllib/src/test/java/spark/mllib/classification/JavaSVMSuite.java b/mllib/src/test/java/spark/mllib/classification/JavaSVMSuite.java
new file mode 100644
index 0000000000000000000000000000000000000000..7881b3c38f01589fcee0e29a1f96947176a9edc0
--- /dev/null
+++ b/mllib/src/test/java/spark/mllib/classification/JavaSVMSuite.java
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.classification;
+
+
+import java.io.Serializable;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+
+import spark.mllib.regression.LabeledPoint;
+
+public class JavaSVMSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaSVMSuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+    System.clearProperty("spark.driver.port");
+  }
+
+  int validatePrediction(List<LabeledPoint> validationData, SVMModel model) {
+    int numAccurate = 0;
+    for (LabeledPoint point: validationData) {
+      Double prediction = model.predict(point.features());
+      if (prediction == point.label()) {
+        numAccurate++;
+      }
+    }
+    return numAccurate;
+  }
+
+  @Test
+  public void runSVMUsingConstructor() {
+    int nPoints = 10000;
+    double A = 2.0;
+    double[] weights = {-1.5, 1.0};
+
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(SVMSuite.generateSVMInputAsList(A,
+        weights, nPoints, 42), 2).cache();
+    List<LabeledPoint> validationData =
+        SVMSuite.generateSVMInputAsList(A, weights, nPoints, 17);
+
+    SVMWithSGD svmSGDImpl = new SVMWithSGD();
+    svmSGDImpl.optimizer().setStepSize(1.0)
+                          .setRegParam(1.0)
+                          .setNumIterations(100);
+    SVMModel model = svmSGDImpl.run(testRDD.rdd());
+
+    int numAccurate = validatePrediction(validationData, model);
+    Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
+  }
+
+  @Test
+  public void runSVMUsingStaticMethods() {
+    int nPoints = 10000;
+    double A = 2.0;
+    double[] weights = {-1.5, 1.0};
+
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(SVMSuite.generateSVMInputAsList(A,
+        weights, nPoints, 42), 2).cache();
+    List<LabeledPoint> validationData =
+        SVMSuite.generateSVMInputAsList(A, weights, nPoints, 17);
+
+    SVMModel model = SVMWithSGD.train(testRDD.rdd(), 100, 1.0, 1.0, 1.0);
+
+    int numAccurate = validatePrediction(validationData, model);
+    Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
+  }
+
+}
diff --git a/mllib/src/test/java/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/spark/mllib/clustering/JavaKMeansSuite.java
new file mode 100644
index 0000000000000000000000000000000000000000..3f2d82bfb4a13fcb9a1bcbf7135dd2cccc85e4b5
--- /dev/null
+++ b/mllib/src/test/java/spark/mllib/clustering/JavaKMeansSuite.java
@@ -0,0 +1,115 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.clustering;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+
+public class JavaKMeansSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaKMeans");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+    System.clearProperty("spark.driver.port");
+  }
+
+  // L1 distance between two points
+  double distance1(double[] v1, double[] v2) {
+    double distance = 0.0;
+    for (int i = 0; i < v1.length; ++i) {
+      distance = Math.max(distance, Math.abs(v1[i] - v2[i]));
+    }
+    return distance;
+  }
+
+  // Assert that two sets of points are equal, within EPSILON tolerance
+  void assertSetsEqual(double[][] v1, double[][] v2) {
+    double EPSILON = 1e-4;
+    Assert.assertTrue(v1.length == v2.length);
+    for (int i = 0; i < v1.length; ++i) {
+      double minDistance = Double.MAX_VALUE;
+      for (int j = 0; j < v2.length; ++j) {
+        minDistance = Math.min(minDistance, distance1(v1[i], v2[j]));
+      }
+      Assert.assertTrue(minDistance <= EPSILON);
+    }
+
+    for (int i = 0; i < v2.length; ++i) {
+      double minDistance = Double.MAX_VALUE;
+      for (int j = 0; j < v1.length; ++j) {
+        minDistance = Math.min(minDistance, distance1(v2[i], v1[j]));
+      }
+      Assert.assertTrue(minDistance <= EPSILON);
+    }
+  }
+
+
+  @Test
+  public void runKMeansUsingStaticMethods() {
+    List<double[]> points = new ArrayList();
+    points.add(new double[]{1.0, 2.0, 6.0});
+    points.add(new double[]{1.0, 3.0, 0.0});
+    points.add(new double[]{1.0, 4.0, 6.0});
+
+    double[][] expectedCenter = { {1.0, 3.0, 4.0} };
+
+    JavaRDD<double[]> data = sc.parallelize(points, 2);
+    KMeansModel model = KMeans.train(data.rdd(), 1, 1);
+    assertSetsEqual(model.clusterCenters(), expectedCenter);
+
+    model = KMeans.train(data.rdd(), 1, 1, 1, KMeans.RANDOM());
+    assertSetsEqual(model.clusterCenters(), expectedCenter);
+  }
+
+  @Test
+  public void runKMeansUsingConstructor() {
+    List<double[]> points = new ArrayList();
+    points.add(new double[]{1.0, 2.0, 6.0});
+    points.add(new double[]{1.0, 3.0, 0.0});
+    points.add(new double[]{1.0, 4.0, 6.0});
+
+    double[][] expectedCenter = { {1.0, 3.0, 4.0} };
+
+    JavaRDD<double[]> data = sc.parallelize(points, 2);
+    KMeansModel model = new KMeans().setK(1).setMaxIterations(5).run(data.rdd());
+    assertSetsEqual(model.clusterCenters(), expectedCenter);
+
+    model = new KMeans().setK(1)
+                        .setMaxIterations(1)
+                        .setRuns(1)
+                        .setInitializationMode(KMeans.RANDOM())
+                        .run(data.rdd());
+    assertSetsEqual(model.clusterCenters(), expectedCenter);
+  }
+}
diff --git a/mllib/src/test/java/spark/mllib/recommendation/JavaALSSuite.java b/mllib/src/test/java/spark/mllib/recommendation/JavaALSSuite.java
new file mode 100644
index 0000000000000000000000000000000000000000..7993629a6d5447fe9a75de51563a85d2481638d8
--- /dev/null
+++ b/mllib/src/test/java/spark/mllib/recommendation/JavaALSSuite.java
@@ -0,0 +1,110 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.recommendation;
+
+import java.io.Serializable;
+import java.util.List;
+
+import scala.Tuple2;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+
+import org.jblas.DoubleMatrix;
+
+public class JavaALSSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaALS");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+    System.clearProperty("spark.driver.port");
+  }
+
+  void validatePrediction(MatrixFactorizationModel model, int users, int products, int features, 
+      DoubleMatrix trueRatings, double matchThreshold) {
+    DoubleMatrix predictedU = new DoubleMatrix(users, features);
+    List<scala.Tuple2<Object, double[]>> userFeatures = model.userFeatures().toJavaRDD().collect();
+    for (int i = 0; i < features; ++i) {
+      for (scala.Tuple2<Object, double[]> userFeature : userFeatures) {
+        predictedU.put((Integer)userFeature._1(), i, userFeature._2()[i]);
+      }
+    }
+    DoubleMatrix predictedP = new DoubleMatrix(products, features);
+
+    List<scala.Tuple2<Object, double[]>> productFeatures =
+      model.productFeatures().toJavaRDD().collect();
+    for (int i = 0; i < features; ++i) {
+      for (scala.Tuple2<Object, double[]> productFeature : productFeatures) {
+        predictedP.put((Integer)productFeature._1(), i, productFeature._2()[i]);
+      }
+    }
+
+    DoubleMatrix predictedRatings = predictedU.mmul(predictedP.transpose());
+
+    for (int u = 0; u < users; ++u) {
+      for (int p = 0; p < products; ++p) {
+        double prediction = predictedRatings.get(u, p);
+        double correct = trueRatings.get(u, p);
+        Assert.assertTrue(Math.abs(prediction - correct) < matchThreshold);
+      }
+    }
+  }
+
+  @Test
+  public void runALSUsingStaticMethods() {
+    int features = 1;
+    int iterations = 15;
+    int users = 10;
+    int products = 10;
+    scala.Tuple2<List<Rating>, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList(
+        users, products, features, 0.7);
+
+    JavaRDD<Rating> data = sc.parallelize(testData._1());
+    MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations);
+    validatePrediction(model, users, products, features, testData._2(), 0.3);
+  }
+
+  @Test
+  public void runALSUsingConstructor() {
+    int features = 2;
+    int iterations = 15;
+    int users = 20;
+    int products = 30;
+    scala.Tuple2<List<Rating>, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList(
+        users, products, features, 0.7);
+
+    JavaRDD<Rating> data = sc.parallelize(testData._1());
+
+    MatrixFactorizationModel model = new ALS().setRank(features)
+                                              .setIterations(iterations)
+                                              .run(data.rdd());
+    validatePrediction(model, users, products, features, testData._2(), 0.3);
+  }
+}
diff --git a/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java b/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java
new file mode 100644
index 0000000000000000000000000000000000000000..e26d7b385c704b3050e04c7abe6ed95231c0341a
--- /dev/null
+++ b/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java
@@ -0,0 +1,96 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.regression;
+
+import java.io.Serializable;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+
+public class JavaLassoSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaLassoSuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+    System.clearProperty("spark.driver.port");
+  }
+
+  int validatePrediction(List<LabeledPoint> validationData, LassoModel model) {
+    int numAccurate = 0;
+    for (LabeledPoint point: validationData) {
+      Double prediction = model.predict(point.features());
+      // A prediction is off if the prediction is more than 0.5 away from expected value.
+      if (Math.abs(prediction - point.label()) <= 0.5) {
+        numAccurate++;
+      }
+    }
+    return numAccurate;
+  }
+
+  @Test
+  public void runLassoUsingConstructor() {
+    int nPoints = 10000;
+    double A = 2.0;
+    double[] weights = {-1.5, 1.0e-2};
+
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(LassoSuite.generateLassoInputAsList(A,
+        weights, nPoints, 42), 2).cache();
+    List<LabeledPoint> validationData =
+        LassoSuite.generateLassoInputAsList(A, weights, nPoints, 17);
+
+    LassoWithSGD svmSGDImpl = new LassoWithSGD();
+    svmSGDImpl.optimizer().setStepSize(1.0)
+                          .setRegParam(0.01)
+                          .setNumIterations(20);
+    LassoModel model = svmSGDImpl.run(testRDD.rdd());
+
+    int numAccurate = validatePrediction(validationData, model);
+    Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
+  }
+
+  @Test
+  public void runLassoUsingStaticMethods() {
+    int nPoints = 10000;
+    double A = 2.0;
+    double[] weights = {-1.5, 1.0e-2};
+
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(LassoSuite.generateLassoInputAsList(A,
+        weights, nPoints, 42), 2).cache();
+    List<LabeledPoint> validationData =
+        LassoSuite.generateLassoInputAsList(A, weights, nPoints, 17);
+
+    LassoModel model = LassoWithSGD.train(testRDD.rdd(), 100, 1.0, 0.01, 1.0);
+
+    int numAccurate = validatePrediction(validationData, model);
+    Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
+  }
+
+}
diff --git a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
similarity index 62%
rename from mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
rename to mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
index 0a99b78cf859b0f388fc1fc98c7d0f80a06c366e..bd87c528c35db3daf0f95d05024c0dbf07f74595 100644
--- a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -15,22 +15,26 @@
  * limitations under the License.
  */
 
-package spark.mllib.regression
+package spark.mllib.classification
 
 import scala.util.Random
+import scala.collection.JavaConversions._
 
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
+import org.scalatest.matchers.ShouldMatchers
 
 import spark.SparkContext
+import spark.mllib.regression._
 
+object LogisticRegressionSuite {
 
-class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
-  val sc = new SparkContext("local", "test")
-
-  override def afterAll() {
-    sc.stop()
-    System.clearProperty("spark.driver.port")
+  def generateLogisticInputAsList(
+    offset: Double,
+    scale: Double,
+    nPoints: Int,
+    seed: Int): java.util.List[LabeledPoint] = {
+    seqAsJavaList(generateLogisticInput(offset, scale, nPoints, seed))
   }
 
   // Generate input of the form Y = logistic(offset + scale*X)
@@ -38,7 +42,7 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
       offset: Double,
       scale: Double,
       nPoints: Int,
-      seed: Int): Seq[(Double, Array[Double])]  = {
+      seed: Int): Seq[LabeledPoint]  = {
     val rnd = new Random(seed)
     val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
 
@@ -51,22 +55,36 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
 
     // y <- A + B*x + rLogis()
     // y <- as.numeric(y > 0)
-    val y: Seq[Double] = (0 until nPoints).map { i =>
+    val y: Seq[Int] = (0 until nPoints).map { i =>
       val yVal = offset + scale * x1(i) + rLogis(i)
-      if (yVal > 0) 1.0 else 0.0
+      if (yVal > 0) 1 else 0
     }
 
-    val testData = (0 until nPoints).map(i => (y(i), Array(x1(i))))
+    val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Array(x1(i))))
     testData
   }
 
-  def validatePrediction(predictions: Seq[Double], input: Seq[(Double, Array[Double])]) {
-    val numOffPredictions = predictions.zip(input).filter { case (prediction, (expected, _)) =>
-      // A prediction is off if the prediction is more than 0.5 away from expected value.
-      math.abs(prediction - expected) > 0.5
+}
+
+class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll with ShouldMatchers {
+  @transient private var sc: SparkContext = _
+
+  override def beforeAll() {
+    sc = new SparkContext("local", "test")
+  }
+
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
+    val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) =>
+      (prediction != expected.label)
     }.size
-    // At least 80% of the predictions should be on.
-    assert(numOffPredictions < input.length / 5)
+    // At least 83% of the predictions should be on.
+    ((input.length - numOffPredictions).toDouble / input.length) should be > 0.83
   }
 
   // Test if we can correctly learn A, B where Y = logistic(A + B*X)
@@ -75,26 +93,27 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
     val A = 2.0
     val B = -1.5
 
-    val testData = generateLogisticInput(A, B, nPoints, 42)
+    val testData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 42)
 
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
-    val lr = new LogisticRegression().setStepSize(10.0).setNumIterations(20)
+    val lr = new LogisticRegressionWithSGD()
+    lr.optimizer.setStepSize(10.0).setNumIterations(20)
 
-    val model = lr.train(testRDD)
+    val model = lr.run(testRDD)
 
     // Test the weights
     val weight0 = model.weights(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]")
 
-    val validationData = generateLogisticInput(A, B, nPoints, 17)
+    val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
     val validationRDD = sc.parallelize(validationData, 2)
     // Test prediction on RDD.
-    validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData)
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
 
     // Test prediction on Array.
-    validatePrediction(validationData.map(row => model.predict(row._2)), validationData)
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
 
   test("logistic regression with initial weights") {
@@ -102,7 +121,7 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
     val A = 2.0
     val B = -1.5
 
-    val testData = generateLogisticInput(A, B, nPoints, 42)
+    val testData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 42)
 
     val initialB = -1.0
     val initialWeights = Array(initialB)
@@ -111,20 +130,21 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
     testRDD.cache()
 
     // Use half as many iterations as the previous test.
-    val lr = new LogisticRegression().setStepSize(10.0).setNumIterations(10)
+    val lr = new LogisticRegressionWithSGD()
+    lr.optimizer.setStepSize(10.0).setNumIterations(10)
 
-    val model = lr.train(testRDD, initialWeights)
+    val model = lr.run(testRDD, initialWeights)
 
     val weight0 = model.weights(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]")
 
-    val validationData = generateLogisticInput(A, B, nPoints, 17)
+    val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
     val validationRDD = sc.parallelize(validationData, 2)
     // Test prediction on RDD.
-    validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData)
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
 
     // Test prediction on Array.
-    validatePrediction(validationData.map(row => model.predict(row._2)), validationData)
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
 }
diff --git a/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..04f631d80f824246a5666a64629ade3276edf1ae
--- /dev/null
+++ b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala
@@ -0,0 +1,142 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.classification
+
+import scala.util.Random
+import scala.math.signum
+import scala.collection.JavaConversions._
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+
+import spark.SparkContext
+import spark.mllib.regression._
+
+import org.jblas.DoubleMatrix
+
+object SVMSuite {
+
+  def generateSVMInputAsList(
+    intercept: Double,
+    weights: Array[Double],
+    nPoints: Int,
+    seed: Int): java.util.List[LabeledPoint] = {
+    seqAsJavaList(generateSVMInput(intercept, weights, nPoints, seed))
+  }
+
+  // Generate noisy input of the form Y = signum(x.dot(weights) + intercept + noise)
+  def generateSVMInput(
+    intercept: Double,
+    weights: Array[Double],
+    nPoints: Int,
+    seed: Int): Seq[LabeledPoint] = {
+    val rnd = new Random(seed)
+    val weightsMat = new DoubleMatrix(1, weights.length, weights:_*)
+    val x = Array.fill[Array[Double]](nPoints)(
+        Array.fill[Double](weights.length)(rnd.nextGaussian()))
+    val y = x.map { xi =>
+      signum(
+        (new DoubleMatrix(1, xi.length, xi:_*)).dot(weightsMat) +
+        intercept +
+        0.1 * rnd.nextGaussian()
+      ).toInt
+    }
+    y.zip(x).map(p => LabeledPoint(p._1, p._2))
+  }
+
+}
+
+class SVMSuite extends FunSuite with BeforeAndAfterAll {
+  @transient private var sc: SparkContext = _
+
+  override def beforeAll() {
+    sc = new SparkContext("local", "test")
+  }
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
+    val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) =>
+      (prediction != expected.label)
+    }.size
+    // At least 80% of the predictions should be on.
+    assert(numOffPredictions < input.length / 5)
+  }
+
+
+  test("SVM using local random SGD") {
+    val nPoints = 10000
+
+    val A = 2.0
+    val B = -1.5
+    val C = 1.0
+
+    val testData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 42)
+
+    val testRDD = sc.parallelize(testData, 2)
+    testRDD.cache()
+
+    val svm = new SVMWithSGD()
+    svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100)
+
+    val model = svm.run(testRDD)
+
+    val validationData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 17)
+    val validationRDD  = sc.parallelize(validationData,2)
+
+    // Test prediction on RDD.
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
+
+    // Test prediction on Array.
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
+  }
+
+  test("SVM local random SGD with initial weights") {
+    val nPoints = 10000
+
+    val A = 2.0
+    val B = -1.5
+    val C = 1.0
+
+    val testData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 42)
+
+    val initialB = -1.0
+    val initialC = -1.0
+    val initialWeights = Array(initialB,initialC)
+
+    val testRDD = sc.parallelize(testData, 2)
+    testRDD.cache()
+
+    val svm = new SVMWithSGD()
+    svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100)
+
+    val model = svm.run(testRDD, initialWeights)
+
+    val validationData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 17)
+    val validationRDD  = sc.parallelize(validationData,2)
+
+    // Test prediction on RDD.
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
+
+    // Test prediction on Array.
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
+  }
+}
diff --git a/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala
index bebade9afbc61b7ee9945b014c92e323d26c1c60..d5d95c86393d21c345a9f9b164dead08b7689c2e 100644
--- a/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala
@@ -27,9 +27,12 @@ import spark.SparkContext._
 
 import org.jblas._
 
-
 class KMeansSuite extends FunSuite with BeforeAndAfterAll {
-  val sc = new SparkContext("local", "test")
+  @transient private var sc: SparkContext = _
+
+  override def beforeAll() {
+    sc = new SparkContext("local", "test")
+  }
 
   override def afterAll() {
     sc.stop()
diff --git a/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala
index f98590b8d962a3b7ddfadda9fd75f5084363f168..15a60efda6ec785fa802621f56c6562a756fd6d6 100644
--- a/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala
@@ -17,6 +17,7 @@
 
 package spark.mllib.recommendation
 
+import scala.collection.JavaConversions._
 import scala.util.Random
 
 import org.scalatest.BeforeAndAfterAll
@@ -27,9 +28,49 @@ import spark.SparkContext._
 
 import org.jblas._
 
+object ALSSuite {
+
+  def generateRatingsAsJavaList(
+      users: Int,
+      products: Int,
+      features: Int,
+      samplingRate: Double): (java.util.List[Rating], DoubleMatrix) = {
+    val (sampledRatings, trueRatings) = generateRatings(users, products, features, samplingRate)
+    (seqAsJavaList(sampledRatings), trueRatings)
+  }
+
+  def generateRatings(
+      users: Int,
+      products: Int,
+      features: Int,
+      samplingRate: Double): (Seq[Rating], DoubleMatrix) = {
+    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 Rating(u, p, trueRatings.get(u, p))
+    }
+
+    (sampledRatings, trueRatings)
+  }
+
+}
+
 
 class ALSSuite extends FunSuite with BeforeAndAfterAll {
-  val sc = new SparkContext("local", "test")
+  @transient private var sc: SparkContext = _
+
+  override def beforeAll() {
+    sc = new SparkContext("local", "test")
+  }
 
   override def afterAll() {
     sc.stop()
@@ -57,21 +98,8 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll {
   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 (sampledRatings, trueRatings) = ALSSuite.generateRatings(users, products,
+      features, samplingRate)
     val model = ALS.train(sc.parallelize(sampledRatings), features, iterations)
 
     val predictedU = new DoubleMatrix(users, features)
diff --git a/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..55a738f1e493a3df1be29f5faba6aeab3c18ea2c
--- /dev/null
+++ b/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package spark.mllib.regression
+
+import scala.collection.JavaConversions._
+import scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+
+import spark.SparkContext
+
+import org.jblas.DoubleMatrix
+
+object LassoSuite {
+
+  def generateLassoInputAsList(
+    intercept: Double,
+    weights: Array[Double],
+    nPoints: Int,
+    seed: Int): java.util.List[LabeledPoint] = {
+    seqAsJavaList(generateLassoInput(intercept, weights, nPoints, seed))
+  }
+
+
+  // Generate noisy input of the form Y = x.dot(weights) + intercept + noise
+  def generateLassoInput(
+    intercept: Double,
+    weights: Array[Double],
+    nPoints: Int,
+    seed: Int): Seq[LabeledPoint] = {
+    val rnd = new Random(seed)
+    val weightsMat = new DoubleMatrix(1, weights.length, weights:_*)
+    val x = Array.fill[Array[Double]](nPoints)(
+      Array.fill[Double](weights.length)(rnd.nextGaussian()))
+    val y = x.map(xi =>
+      (new DoubleMatrix(1, xi.length, xi:_*)).dot(weightsMat) + intercept + 0.1 * rnd.nextGaussian()
+    )
+    y.zip(x).map(p => LabeledPoint(p._1, p._2))
+  }
+
+}
+
+class LassoSuite extends FunSuite with BeforeAndAfterAll {
+  @transient private var sc: SparkContext = _
+
+  override def beforeAll() {
+    sc = new SparkContext("local", "test")
+  }
+
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
+    val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) =>
+      // A prediction is off if the prediction is more than 0.5 away from expected value.
+      math.abs(prediction - expected.label) > 0.5
+    }.size
+    // At least 80% of the predictions should be on.
+    assert(numOffPredictions < input.length / 5)
+  }
+
+  test("Lasso local random SGD") {
+    val nPoints = 10000
+
+    val A = 2.0
+    val B = -1.5
+    val C = 1.0e-2
+
+    val testData = LassoSuite.generateLassoInput(A, Array[Double](B,C), nPoints, 42)
+
+    val testRDD = sc.parallelize(testData, 2)
+    testRDD.cache()
+
+    val ls = new LassoWithSGD()
+    ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(20)
+
+    val model = ls.run(testRDD)
+
+    val weight0 = model.weights(0)
+    val weight1 = model.weights(1)
+    assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
+    assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
+    assert(weight1 >= -1.0e-3 && weight1 <= 1.0e-3, weight1 + " not in [-0.001, 0.001]")
+
+    val validationData = LassoSuite.generateLassoInput(A, Array[Double](B,C), nPoints, 17)
+    val validationRDD  = sc.parallelize(validationData, 2)
+
+    // Test prediction on RDD.
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
+
+    // Test prediction on Array.
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
+  }
+
+  test("Lasso local random SGD with initial weights") {
+    val nPoints = 10000
+
+    val A = 2.0
+    val B = -1.5
+    val C = 1.0e-2
+
+    val testData = LassoSuite.generateLassoInput(A, Array[Double](B,C), nPoints, 42)
+
+    val initialB = -1.0
+    val initialC = -1.0
+    val initialWeights = Array(initialB,initialC)
+
+    val testRDD = sc.parallelize(testData, 2)
+    testRDD.cache()
+
+    val ls = new LassoWithSGD()
+    ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(20)
+
+    val model = ls.run(testRDD, initialWeights)
+
+    val weight0 = model.weights(0)
+    val weight1 = model.weights(1)
+    assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
+    assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
+    assert(weight1 >= -1.0e-3 && weight1 <= 1.0e-3, weight1 + " not in [-0.001, 0.001]")
+
+    val validationData = LassoSuite.generateLassoInput(A, Array[Double](B,C), nPoints, 17)
+    val validationRDD  = sc.parallelize(validationData,2)
+
+    // Test prediction on RDD.
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
+
+    // Test prediction on Array.
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
+  }
+}
diff --git a/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala
index 3c588c61620c508ba556aafdf0608e89b2fdc145..e2b244894d4ed72855bed70c73b12c6b716ab407 100644
--- a/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala
@@ -27,7 +27,11 @@ import spark.SparkContext._
 
 
 class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll {
-  val sc = new SparkContext("local", "test")
+  @transient private var sc: SparkContext = _
+
+  override def beforeAll() {
+    sc = new SparkContext("local", "test")
+  }
 
   override def afterAll() {
     sc.stop()
@@ -47,7 +51,7 @@ class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll {
     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 testData = (0 until 20).map(i => LabeledPoint(y(i), xMat(i))).toArray
 
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
diff --git a/pagerank_data.txt b/pagerank_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..95755ab8f5af8b27377a60cbe3edb8ab403e8465
--- /dev/null
+++ b/pagerank_data.txt
@@ -0,0 +1,6 @@
+1 2
+1 3
+1 4
+2 1
+3 1
+4 1
diff --git a/pom.xml b/pom.xml
index 44729bd4225ea28deed249aaeb972aaf5e80644c..fc0b3140701a159b416d5200647f2e50f86d572c 100644
--- a/pom.xml
+++ b/pom.xml
@@ -58,6 +58,7 @@
     <module>core</module>
     <module>bagel</module>
     <module>examples</module>
+    <module>mllib</module>
     <module>tools</module>
     <module>streaming</module>
     <module>repl</module>
@@ -69,11 +70,12 @@
 
     <java.version>1.5</java.version>
     <scala.version>2.9.3</scala.version>
-    <mesos.version>0.9.0-incubating</mesos.version>
+    <mesos.version>0.12.1</mesos.version>
     <akka.version>2.0.3</akka.version>
     <slf4j.version>1.7.2</slf4j.version>
-    <cdh.version>4.1.2</cdh.version>
     <log4j.version>1.2.17</log4j.version>
+    <hadoop.version>1.2.1</hadoop.version>
+    <!-- <hadoop.version>2.0.0-mr1-cdh4.1.2</hadoop.version> -->
 
     <PermGen>64m</PermGen>
     <MaxPermGen>512m</MaxPermGen>
@@ -182,6 +184,11 @@
         <artifactId>compress-lzf</artifactId>
         <version>0.8.4</version>
       </dependency>
+      <dependency>
+        <groupId>org.xerial.snappy</groupId>
+        <artifactId>snappy-java</artifactId>
+        <version>1.0.5</version>
+      </dependency>
       <dependency>
         <groupId>org.ow2.asm</groupId>
         <artifactId>asm</artifactId>
@@ -195,12 +202,12 @@
       <dependency>
         <groupId>com.twitter</groupId>
         <artifactId>chill_2.9.3</artifactId>
-        <version>0.3.0</version>
+        <version>0.3.1</version>
       </dependency>
       <dependency>
         <groupId>com.twitter</groupId>
         <artifactId>chill-java</artifactId>
-        <version>0.3.0</version>
+        <version>0.3.1</version>
       </dependency>
       <dependency>
         <groupId>com.typesafe.akka</groupId>
@@ -248,11 +255,6 @@
         <version>10.4.2.0</version>
         <scope>test</scope>
       </dependency>
-      <dependency>
-        <groupId>net.liftweb</groupId>
-        <artifactId>lift-json_2.9.2</artifactId>
-        <version>2.5</version>
-      </dependency>
       <dependency>
         <groupId>com.codahale.metrics</groupId>
         <artifactId>metrics-core</artifactId>
@@ -263,6 +265,11 @@
         <artifactId>metrics-jvm</artifactId>
         <version>3.0.0</version>
       </dependency>
+      <dependency>
+        <groupId>com.codahale.metrics</groupId>
+        <artifactId>metrics-json</artifactId>
+        <version>3.0.0</version>
+      </dependency>
       <dependency>
         <groupId>org.scala-lang</groupId>
         <artifactId>scala-compiler</artifactId>
@@ -314,6 +321,54 @@
         <version>0.8</version>
         <scope>test</scope>
       </dependency>
+      <dependency>
+        <groupId>org.apache.hadoop</groupId>
+        <artifactId>hadoop-client</artifactId>
+        <version>${hadoop.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>asm</groupId>
+            <artifactId>asm</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.jboss.netty</groupId>
+            <artifactId>netty</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.codehaus.jackson</groupId>
+            <artifactId>jackson-core-asl</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.codehaus.jackson</groupId>
+            <artifactId>jackson-mapper-asl</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.codehaus.jackson</groupId>
+            <artifactId>jackson-jaxrs</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.codehaus.jackson</groupId>
+            <artifactId>jackson-xc</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <!-- Specify Avro version because Kafka also has it as a dependency -->
+      <dependency>
+        <groupId>org.apache.avro</groupId>
+        <artifactId>avro</artifactId>
+        <version>1.7.4</version>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.avro</groupId>
+        <artifactId>avro-ipc</artifactId>
+        <version>1.7.4</version>
+        <exclusions>
+          <exclusion>
+            <groupId>org.jboss.netty</groupId>
+            <artifactId>netty</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
     </dependencies>
   </dependencyManagement>
 
@@ -519,69 +574,19 @@
   </build>
 
   <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <properties>
-        <hadoop.major.version>1</hadoop.major.version>
-      </properties>
-      <dependencyManagement>
-        <dependencies>
-          <dependency>
-            <groupId>org.apache.hadoop</groupId>
-            <artifactId>hadoop-core</artifactId>
-            <version>1.0.4</version>
-          </dependency>
-        </dependencies>
-      </dependencyManagement>
-    </profile>
-
-    <profile>
-      <id>hadoop2</id>
-      <properties>
-        <hadoop.major.version>2</hadoop.major.version>
-      </properties>
-      <dependencyManagement>
-        <dependencies>
-          <dependency>
-            <groupId>org.apache.hadoop</groupId>
-            <artifactId>hadoop-core</artifactId>
-            <version>2.0.0-mr1-cdh${cdh.version}</version>
-          </dependency>
-          <dependency>
-            <groupId>org.apache.hadoop</groupId>
-            <artifactId>hadoop-client</artifactId>
-            <version>2.0.0-mr1-cdh${cdh.version}</version>
-          </dependency>
-          <!-- Specify Avro version because Kafka also has it as a dependency -->
-          <dependency>
-            <groupId>org.apache.avro</groupId>
-            <artifactId>avro</artifactId>
-            <version>1.7.4</version>
-          </dependency>
-          <dependency>
-            <groupId>org.apache.avro</groupId>
-            <artifactId>avro-ipc</artifactId>
-            <version>1.7.4</version>
-            <exclusions>
-              <exclusion>
-                <groupId>org.jboss.netty</groupId>
-                <artifactId>netty</artifactId>
-              </exclusion>
-            </exclusions>
-          </dependency>
-        </dependencies>
-      </dependencyManagement>
-    </profile>
-
     <profile>
       <id>hadoop2-yarn</id>
       <properties>
         <hadoop.major.version>2</hadoop.major.version>
         <!-- 0.23.* is same as 2.0.* - except hardened to run production jobs -->
         <!-- <yarn.version>0.23.7</yarn.version> -->
-        <yarn.version>2.0.2-alpha</yarn.version>
+        <yarn.version>2.0.5-alpha</yarn.version>
       </properties>
 
+      <modules>
+        <module>yarn</module>
+      </modules>
+
       <repositories>
         <repository>
           <id>maven-root</id>
@@ -603,32 +608,125 @@
             <groupId>org.apache.hadoop</groupId>
             <artifactId>hadoop-client</artifactId>
             <version>${yarn.version}</version>
+            <exclusions>
+              <exclusion>
+                <groupId>asm</groupId>
+                <artifactId>asm</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.jboss.netty</groupId>
+                <artifactId>netty</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-core-asl</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-mapper-asl</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-jaxrs</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-xc</artifactId>
+              </exclusion>
+            </exclusions>
           </dependency>
           <dependency>
             <groupId>org.apache.hadoop</groupId>
             <artifactId>hadoop-yarn-api</artifactId>
             <version>${yarn.version}</version>
+            <exclusions>
+              <exclusion>
+                <groupId>asm</groupId>
+                <artifactId>asm</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.jboss.netty</groupId>
+                <artifactId>netty</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-core-asl</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-mapper-asl</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-jaxrs</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-xc</artifactId>
+              </exclusion>
+            </exclusions>
           </dependency>
           <dependency>
             <groupId>org.apache.hadoop</groupId>
             <artifactId>hadoop-yarn-common</artifactId>
             <version>${yarn.version}</version>
+            <exclusions>
+              <exclusion>
+                <groupId>asm</groupId>
+                <artifactId>asm</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.jboss.netty</groupId>
+                <artifactId>netty</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-core-asl</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-mapper-asl</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-jaxrs</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-xc</artifactId>
+              </exclusion>
+            </exclusions>
           </dependency>
           <dependency>
             <groupId>org.apache.hadoop</groupId>
             <artifactId>hadoop-yarn-client</artifactId>
             <version>${yarn.version}</version>
-          </dependency>
-          <!-- Specify Avro version because Kafka also has it as a dependency -->
-          <dependency>
-            <groupId>org.apache.avro</groupId>
-            <artifactId>avro</artifactId>
-            <version>1.7.4</version>
-          </dependency>
-          <dependency>
-            <groupId>org.apache.avro</groupId>
-            <artifactId>avro-ipc</artifactId>
-            <version>1.7.4</version>
+            <exclusions>
+              <exclusion>
+                <groupId>asm</groupId>
+                <artifactId>asm</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.jboss.netty</groupId>
+                <artifactId>netty</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-core-asl</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-mapper-asl</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-jaxrs</artifactId>
+              </exclusion>
+              <exclusion>
+                <groupId>org.codehaus.jackson</groupId>
+                <artifactId>jackson-xc</artifactId>
+              </exclusion>
+            </exclusions>
           </dependency>
         </dependencies>
       </dependencyManagement>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 9920e00a674fec3666d7ee6c276dd8b7a36c4e80..831bfbed78f643cc66baaf4c42fd2288c0cee6e9 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -26,27 +26,21 @@ import AssemblyKeys._
 object SparkBuild extends Build {
   // Hadoop version to build against. For example, "0.20.2", "0.20.205.0", or
   // "1.0.4" for Apache releases, or "0.20.2-cdh3u5" for Cloudera Hadoop.
-  val HADOOP_VERSION = "1.0.4"
-  val HADOOP_MAJOR_VERSION = "1"
-  val HADOOP_YARN = false
+  // Note that these variables can be set through the environment variables
+  // SPARK_HADOOP_VERSION and SPARK_WITH_YARN.
+  val DEFAULT_HADOOP_VERSION = "1.2.1"
+  val DEFAULT_WITH_YARN = false
 
-  // For Hadoop 2 versions such as "2.0.0-mr1-cdh4.1.1", set the HADOOP_MAJOR_VERSION to "2"
-  //val HADOOP_VERSION = "2.0.0-mr1-cdh4.1.1"
-  //val HADOOP_MAJOR_VERSION = "2"
-  //val HADOOP_YARN = false
+  // HBase version; set as appropriate.
+  val HBASE_VERSION = "0.94.6"
 
-  // For Hadoop 2 YARN support
-  //val HADOOP_VERSION = "2.0.2-alpha"
-  //val HADOOP_MAJOR_VERSION = "2"
-  //val HADOOP_YARN = true
-
-  lazy val root = Project("root", file("."), settings = rootSettings) aggregate(core, repl, examples, bagel, streaming, mllib, tools)
+  lazy val root = Project("root", file("."), settings = rootSettings) aggregate(allProjects:_*)
 
   lazy val core = Project("core", file("core"), settings = coreSettings)
 
-  lazy val repl = Project("repl", file("repl"), settings = replSettings) dependsOn (core)
+  lazy val repl = Project("repl", file("repl"), settings = replSettings) dependsOn(core) dependsOn(bagel) dependsOn(mllib) dependsOn(maybeYarn:_*)
 
-  lazy val examples = Project("examples", file("examples"), settings = examplesSettings) dependsOn (core) dependsOn (streaming)
+  lazy val examples = Project("examples", file("examples"), settings = examplesSettings) dependsOn (core) dependsOn (streaming) dependsOn(mllib)
 
   lazy val tools = Project("tools", file("tools"), settings = examplesSettings) dependsOn (core) dependsOn (streaming)
 
@@ -56,10 +50,24 @@ object SparkBuild extends Build {
 
   lazy val mllib = Project("mllib", file("mllib"), settings = mllibSettings) dependsOn (core)
 
+  lazy val yarn = Project("yarn", file("yarn"), settings = yarnSettings) 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")
 
+  // Allows build configuration to be set through environment variables
+  lazy val hadoopVersion = scala.util.Properties.envOrElse("SPARK_HADOOP_VERSION", DEFAULT_HADOOP_VERSION)
+  lazy val isYarnMode = scala.util.Properties.envOrNone("SPARK_WITH_YARN") match {
+    case None => DEFAULT_WITH_YARN
+    case Some(v) => v.toBoolean
+  }
+
+  // Conditionally include the yarn sub-project
+  lazy val maybeYarn = if(isYarnMode) Seq[ClasspathDependency](yarn) else Seq[ClasspathDependency]()
+  lazy val maybeYarnRef = if(isYarnMode) Seq[ProjectReference](yarn) else Seq[ProjectReference]()
+  lazy val allProjects = Seq[ProjectReference](core, repl, examples, bagel, streaming, mllib, tools) ++ maybeYarnRef
+
   def sharedSettings = Defaults.defaultSettings ++ Seq(
     organization := "org.spark-project",
     version := "0.8.0-SNAPSHOT",
@@ -151,6 +159,7 @@ object SparkBuild extends Build {
   val excludeJackson = ExclusionRule(organization = "org.codehaus.jackson")
   val excludeNetty = ExclusionRule(organization = "org.jboss.netty")
   val excludeAsm = ExclusionRule(organization = "asm")
+  val excludeSnappy = ExclusionRule(organization = "org.xerial.snappy")
 
   def coreSettings = sharedSettings ++ Seq(
     name := "spark-core",
@@ -168,6 +177,7 @@ object SparkBuild extends Build {
       "org.slf4j" % "slf4j-log4j12" % slf4jVersion,
       "commons-daemon" % "commons-daemon" % "1.0.10",
       "com.ning" % "compress-lzf" % "0.8.4",
+      "org.xerial.snappy" % "snappy-java" % "1.0.5",
       "org.ow2.asm" % "asm" % "4.0",
       "com.google.protobuf" % "protobuf-java" % "2.4.1",
       "com.typesafe.akka" % "akka-actor" % "2.0.5" excludeAll(excludeNetty),
@@ -175,40 +185,18 @@ object SparkBuild extends Build {
       "com.typesafe.akka" % "akka-slf4j" % "2.0.5" excludeAll(excludeNetty),
       "it.unimi.dsi" % "fastutil" % "6.4.4",
       "colt" % "colt" % "1.2.0",
-      "net.liftweb" % "lift-json_2.9.2" % "2.5",
-      "org.apache.mesos" % "mesos" % "0.9.0-incubating",
+      "org.apache.mesos" % "mesos" % "0.12.1",
       "io.netty" % "netty-all" % "4.0.0.Beta2",
       "org.apache.derby" % "derby" % "10.4.2.0" % "test",
+      "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm),
+      "org.apache.avro" % "avro" % "1.7.4",
+      "org.apache.avro" % "avro-ipc" % "1.7.4" excludeAll(excludeNetty),
       "com.codahale.metrics" % "metrics-core" % "3.0.0",
       "com.codahale.metrics" % "metrics-jvm" % "3.0.0",
-      "com.twitter" % "chill_2.9.3" % "0.3.0",
-      "com.twitter" % "chill-java" % "0.3.0"
-    ) ++ (
-      if (HADOOP_MAJOR_VERSION == "2") {
-        if (HADOOP_YARN) {
-          Seq(
-            // Exclude rule required for all ?
-            "org.apache.hadoop" % "hadoop-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm),
-            "org.apache.hadoop" % "hadoop-yarn-api" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm),
-            "org.apache.hadoop" % "hadoop-yarn-common" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm),
-            "org.apache.hadoop" % "hadoop-yarn-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm)
-          )
-        } else {
-          Seq(
-            "org.apache.hadoop" % "hadoop-core" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm),
-            "org.apache.hadoop" % "hadoop-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm)
-          )
-        }
-      } else {
-        Seq("org.apache.hadoop" % "hadoop-core" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty) )
-      }),
-    unmanagedSourceDirectories in Compile <+= baseDirectory{ _ /
-      ( if (HADOOP_YARN && HADOOP_MAJOR_VERSION == "2") {
-        "src/hadoop2-yarn/scala"
-      } else {
-        "src/hadoop" + HADOOP_MAJOR_VERSION + "/scala"
-      } )
-    }
+      "com.codahale.metrics" % "metrics-json" % "3.0.0",
+      "com.twitter" % "chill_2.9.3" % "0.3.1",
+      "com.twitter" % "chill-java" % "0.3.1"
+    )
   ) ++ assemblySettings ++ extraAssemblySettings
 
   def rootSettings = sharedSettings ++ Seq(
@@ -225,7 +213,7 @@ object SparkBuild extends Build {
     libraryDependencies ++= Seq(
       "com.twitter" % "algebird-core_2.9.2" % "0.1.11",
 
-      "org.apache.hbase" % "hbase" % "0.94.6" excludeAll(excludeNetty, excludeAsm),
+      "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeNetty, excludeAsm),
 
       "org.apache.cassandra" % "cassandra-all" % "1.2.5"
         exclude("com.google.guava", "guava")
@@ -235,6 +223,7 @@ object SparkBuild extends Build {
         exclude("jline","jline")
         exclude("log4j","log4j")
         exclude("org.apache.cassandra.deps", "avro")
+        excludeAll(excludeSnappy)
     )
   )
 
@@ -242,7 +231,9 @@ object SparkBuild extends Build {
     name := "spark-tools"
   )
 
-  def bagelSettings = sharedSettings ++ Seq(name := "spark-bagel")
+  def bagelSettings = sharedSettings ++ Seq(
+    name := "spark-bagel"
+  )
 
   def mllibSettings = sharedSettings ++ Seq(
     name := "spark-mllib",
@@ -257,13 +248,24 @@ object SparkBuild extends Build {
       "Akka Repository" at "http://repo.akka.io/releases/"
     ),
     libraryDependencies ++= Seq(
-      "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty),
+      "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty, excludeSnappy),
       "com.github.sgroschupf" % "zkclient" % "0.1" excludeAll(excludeNetty),
       "org.twitter4j" % "twitter4j-stream" % "3.0.3" excludeAll(excludeNetty),
       "com.typesafe.akka" % "akka-zeromq" % "2.0.5" excludeAll(excludeNetty)
     )
   ) ++ assemblySettings ++ extraAssemblySettings
 
+  def yarnSettings = sharedSettings ++ Seq(
+    name := "spark-yarn",
+    libraryDependencies ++= Seq(
+      // Exclude rule required for all ?
+      "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm),
+      "org.apache.hadoop" % "hadoop-yarn-api" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm),
+      "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm),
+      "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm)
+    )
+  ) ++ assemblySettings ++ extraAssemblySettings
+
   def extraAssemblySettings() = Seq(test in assembly := {}) ++ Seq(
     mergeStrategy in assembly := {
       case m if m.toLowerCase.endsWith("manifest.mf") => MergeStrategy.discard
diff --git a/pyspark b/pyspark
index 37a355462e7f77340db1ea033da03ad657fedaba..801239c108b6672f90da71bdfa5dc550054e5b03 100755
--- a/pyspark
+++ b/pyspark
@@ -53,9 +53,13 @@ if [[ "$SPARK_LAUNCH_WITH_SCALA" != "0" ]] ; then
     export SPARK_LAUNCH_WITH_SCALA=1
 fi
 
+if [ -n "$IPYTHON_OPTS" ]; then
+  IPYTHON=1
+fi
+
 if [[ "$IPYTHON" = "1" ]] ; then
-    export PYSPARK_PYTHON="ipython"
-    exec "$PYSPARK_PYTHON" -i -c "%run $PYTHONSTARTUP"
+  IPYTHON_OPTS=${IPYTHON_OPTS:--i}
+  exec ipython "$IPYTHON_OPTS" -c "%run $PYTHONSTARTUP"
 else
-    exec "$PYSPARK_PYTHON" "$@"
+  exec "$PYSPARK_PYTHON" "$@"
 fi
diff --git a/python/examples/als.py b/python/examples/als.py
index f2b2eee64cdf87367abc3e02276d3661a105bf76..a77dfb2577835f6093a31781465733b3504efedb 100755
--- a/python/examples/als.py
+++ b/python/examples/als.py
@@ -48,8 +48,7 @@ def update(i, vec, mat, ratings):
 
 if __name__ == "__main__":
     if len(sys.argv) < 2:
-        print >> sys.stderr, \
-            "Usage: PythonALS <master> <M> <U> <F> <iters> <slices>"
+        print >> sys.stderr, "Usage: als <master> <M> <U> <F> <iters> <slices>"
         exit(-1)
     sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)])
     M = int(sys.argv[2]) if len(sys.argv) > 2 else 100
@@ -84,5 +83,5 @@ if __name__ == "__main__":
         usb = sc.broadcast(us)
 
         error = rmse(R, ms, us)
-        print "Iteration %d:" % i 
+        print "Iteration %d:" % i
         print "\nRMSE: %5.4f\n" % error
diff --git a/python/examples/kmeans.py b/python/examples/kmeans.py
old mode 100644
new mode 100755
index c670556f2b848a46a6b2c33c0a97661c346acff4..ba31af92fca256e89b3f6fd3e81d515c8c4a5710
--- a/python/examples/kmeans.py
+++ b/python/examples/kmeans.py
@@ -41,8 +41,7 @@ def closestPoint(p, centers):
 
 if __name__ == "__main__":
     if len(sys.argv) < 5:
-        print >> sys.stderr, \
-            "Usage: PythonKMeans <master> <file> <k> <convergeDist>"
+        print >> sys.stderr, "Usage: kmeans <master> <file> <k> <convergeDist>"
         exit(-1)
     sc = SparkContext(sys.argv[1], "PythonKMeans")
     lines = sc.textFile(sys.argv[2])
diff --git a/python/examples/logistic_regression.py b/python/examples/logistic_regression.py
index 54d227d0d33cc7a6fb9677fe9e86db1892126ade..1117dea5380e764a82174e21ca1b0bfbf9b9b974 100755
--- a/python/examples/logistic_regression.py
+++ b/python/examples/logistic_regression.py
@@ -16,7 +16,8 @@
 #
 
 """
-This example requires numpy (http://www.numpy.org/)
+A logistic regression implementation that uses NumPy (http://www.numpy.org) to act on batches
+of input data using efficient matrix operations.
 """
 from collections import namedtuple
 from math import exp
@@ -27,48 +28,45 @@ import numpy as np
 from pyspark import SparkContext
 
 
-N = 100000  # Number of data points
 D = 10  # Number of dimensions
-R = 0.7   # Scaling factor
-ITERATIONS = 5
-np.random.seed(42)
 
 
-DataPoint = namedtuple("DataPoint", ['x', 'y'])
-from lr import DataPoint  # So that DataPoint is properly serialized
-
-
-def generateData():
-    def generatePoint(i):
-        y = -1 if i % 2 == 0 else 1
-        x = np.random.normal(size=D) + (y * R)
-        return DataPoint(x, y)
-    return [generatePoint(i) for i in range(N)]
-
+# Read a batch of points from the input file into a NumPy matrix object. We operate on batches to
+# make further computations faster.
+# The data file contains lines of the form <label> <x1> <x2> ... <xD>. We load each block of these
+# into a NumPy array of size numLines * (D + 1) and pull out column 0 vs the others in gradient().
+def readPointBatch(iterator):
+    strs = list(iterator)
+    matrix = np.zeros((len(strs), D + 1))
+    for i in xrange(len(strs)):
+        matrix[i] = np.fromstring(strs[i].replace(',', ' '), dtype=np.float32, sep=' ')
+    return [matrix]
 
 if __name__ == "__main__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, \
-            "Usage: PythonLR <master> [<slices>]"
+    if len(sys.argv) != 4:
+        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
         exit(-1)
     sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
-    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
-    points = sc.parallelize(generateData(), slices).cache()
+    points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache()
+    iterations = int(sys.argv[3])
 
     # Initialize w to a random value
     w = 2 * np.random.ranf(size=D) - 1
     print "Initial w: " + str(w)
 
+    # Compute logistic regression gradient for a matrix of data points
+    def gradient(matrix, w):
+        Y = matrix[:,0]    # point labels (first column of input file)
+        X = matrix[:,1:]   # point coordinates
+        # For each point (x, y), compute gradient function, then sum these up
+        return ((1.0 / (1.0 + np.exp(-Y * X.dot(w))) - 1.0) * Y * X.T).sum(1)
+
     def add(x, y):
         x += y
         return x
 
-    for i in range(1, ITERATIONS + 1):
-        print "On iteration %i" % i
-
-        gradient = points.map(lambda p:
-            (1.0 / (1.0 + exp(-p.y * np.dot(w, p.x)))) * p.y * p.x
-        ).reduce(add)
-        w -= gradient
+    for i in range(iterations):
+        print "On iteration %i" % (i + 1)
+        w -= points.map(lambda m: gradient(m, w)).reduce(add)
 
     print "Final w: " + str(w)
diff --git a/python/examples/pagerank.py b/python/examples/pagerank.py
new file mode 100755
index 0000000000000000000000000000000000000000..cd774cf3a319faea51ea824fea1400c02628e7c2
--- /dev/null
+++ b/python/examples/pagerank.py
@@ -0,0 +1,70 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#!/usr/bin/env python
+
+import re, sys
+from operator import add
+
+from pyspark import SparkContext
+
+
+def computeContribs(urls, rank):
+    """Calculates URL contributions to the rank of other URLs."""
+    num_urls = len(urls)
+    for url in urls: yield (url, rank / num_urls)
+
+
+def parseNeighbors(urls):
+    """Parses a urls pair string into urls pair."""
+    parts = re.split(r'\s+', urls)
+    return parts[0], parts[1]
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print >> sys.stderr, "Usage: pagerank <master> <file> <number_of_iterations>"
+        exit(-1)
+
+    # Initialize the spark context.
+    sc = SparkContext(sys.argv[1], "PythonPageRank")
+
+    # Loads in input file. It should be in format of:
+    #     URL         neighbor URL
+    #     URL         neighbor URL
+    #     URL         neighbor URL
+    #     ...
+    lines = sc.textFile(sys.argv[2], 1)
+
+    # Loads all URLs from input file and initialize their neighbors.
+    links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()
+
+    # Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one.
+    ranks = links.map(lambda (url, neighbors): (url, 1.0))
+
+    # Calculates and updates URL ranks continuously using PageRank algorithm.
+    for iteration in xrange(int(sys.argv[3])):
+        # Calculates URL contributions to the rank of other URLs.
+        contribs = links.join(ranks).flatMap(lambda (url, (urls, rank)):
+            computeContribs(urls, rank))
+
+        # Re-calculates URL ranks based on neighbor contributions.
+        ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15)
+
+    # Collects all URL ranks and dump them to console.
+    for (link, rank) in ranks.collect():
+        print "%s has rank: %s." % (link, rank)
diff --git a/python/examples/pi.py b/python/examples/pi.py
old mode 100644
new mode 100755
index 33c026e824bae7e569495e48b98e65d49e40979d..ab0645fc2f3264686411301b38fb79cfd21bd66a
--- a/python/examples/pi.py
+++ b/python/examples/pi.py
@@ -24,8 +24,7 @@ from pyspark import SparkContext
 
 if __name__ == "__main__":
     if len(sys.argv) == 1:
-        print >> sys.stderr, \
-            "Usage: PythonPi <master> [<slices>]"
+        print >> sys.stderr, "Usage: pi <master> [<slices>]"
         exit(-1)
     sc = SparkContext(sys.argv[1], "PythonPi")
     slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
diff --git a/python/examples/transitive_closure.py b/python/examples/transitive_closure.py
old mode 100644
new mode 100755
index 40be3b500077e45cd9281549f0586c30983dabbe..744cce6651607d7f81bf9aa339718c200b229108
--- a/python/examples/transitive_closure.py
+++ b/python/examples/transitive_closure.py
@@ -37,10 +37,9 @@ def generateGraph():
 
 if __name__ == "__main__":
     if len(sys.argv) == 1:
-        print >> sys.stderr, \
-            "Usage: PythonTC <master> [<slices>]"
+        print >> sys.stderr, "Usage: transitive_closure <master> [<slices>]"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonTC")
+    sc = SparkContext(sys.argv[1], "PythonTransitiveClosure")
     slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
     tc = sc.parallelize(generateGraph(), slices).cache()
 
diff --git a/python/examples/wordcount.py b/python/examples/wordcount.py
old mode 100644
new mode 100755
index 41c846ba79b6b9ec860cf491973ee7a3a0654eae..a6de22766ab1451759ea94439da931d412e14bd2
--- a/python/examples/wordcount.py
+++ b/python/examples/wordcount.py
@@ -23,8 +23,7 @@ from pyspark import SparkContext
 
 if __name__ == "__main__":
     if len(sys.argv) < 3:
-        print >> sys.stderr, \
-            "Usage: PythonWordCount <master> <file>"
+        print >> sys.stderr, "Usage: wordcount <master> <file>"
         exit(-1)
     sc = SparkContext(sys.argv[1], "PythonWordCount")
     lines = sc.textFile(sys.argv[2], 1)
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 2f741cb345191682429ca507ac7cea9842aa0db8..2803ce90f3ed2629a5b1e7934ee65e55b94a8f5a 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -46,6 +46,7 @@ class SparkContext(object):
     _next_accum_id = 0
     _active_spark_context = None
     _lock = Lock()
+    _python_includes = None # zip and egg files that need to be added to PYTHONPATH
 
     def __init__(self, master, jobName, sparkHome=None, pyFiles=None,
         environment=None, batchSize=1024):
@@ -103,11 +104,14 @@ class SparkContext(object):
         # send.
         self._pickled_broadcast_vars = set()
 
+        SparkFiles._sc = self
+        root_dir = SparkFiles.getRootDirectory()
+        sys.path.append(root_dir)
+
         # Deploy any code dependencies specified in the constructor
+        self._python_includes = list()
         for path in (pyFiles or []):
             self.addPyFile(path)
-        SparkFiles._sc = self
-        sys.path.append(SparkFiles.getRootDirectory())
 
         # Create a temporary directory inside spark.local.dir:
         local_dir = self._jvm.spark.Utils.getLocalDir()
@@ -141,14 +145,21 @@ class SparkContext(object):
     def parallelize(self, c, numSlices=None):
         """
         Distribute a local Python collection to form an RDD.
+
+        >>> sc.parallelize(range(5), 5).glom().collect()
+        [[0], [1], [2], [3], [4]]
         """
         numSlices = numSlices or self.defaultParallelism
         # Calling the Java parallelize() method with an ArrayList is too slow,
         # because it sends O(n) Py4J commands.  As an alternative, serialized
         # objects are written to a file and loaded through textFile().
         tempFile = NamedTemporaryFile(delete=False, dir=self._temp_dir)
-        if self.batchSize != 1:
-            c = batched(c, self.batchSize)
+        # Make sure we distribute data evenly if it's smaller than self.batchSize
+        if "__len__" not in dir(c):
+            c = list(c)    # Make it a list so we can compute its length
+        batchSize = min(len(c) // numSlices, self.batchSize)
+        if batchSize > 1:
+            c = batched(c, batchSize)
         for x in c:
             write_with_length(dump_pickle(x), tempFile)
         tempFile.close()
@@ -250,7 +261,11 @@ class SparkContext(object):
         HTTP, HTTPS or FTP URI.
         """
         self.addFile(path)
-        filename = path.split("/")[-1]
+        (dirname, filename) = os.path.split(path) # dirname may be directory or HDFS/S3 prefix
+
+        if filename.endswith('.zip') or filename.endswith('.ZIP') or filename.endswith('.egg'):
+            self._python_includes.append(filename)
+            sys.path.append(os.path.join(SparkFiles.getRootDirectory(), filename)) # for tests in local mode
 
     def setCheckpointDir(self, dirName, useExisting=False):
         """
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index c6a6b24c5a6de9cfa1f63e7808a60d9669a62417..99f5967a8edc932824ca6413ba111a8db03895a0 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -160,7 +160,7 @@ class RDD(object):
         >>> sorted(sc.parallelize([1, 1, 2, 3]).distinct().collect())
         [1, 2, 3]
         """
-        return self.map(lambda x: (x, "")) \
+        return self.map(lambda x: (x, None)) \
                    .reduceByKey(lambda x, _: x) \
                    .map(lambda (x, _): x)
 
@@ -267,7 +267,11 @@ class RDD(object):
         >>> def f(x): print x
         >>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f)
         """
-        self.map(f).collect()  # Force evaluation
+        def processPartition(iterator):
+            for x in iterator:
+                f(x)
+            yield None
+        self.mapPartitions(processPartition).collect()  # Force evaluation
 
     def collect(self):
         """
@@ -386,13 +390,16 @@ class RDD(object):
         >>> sc.parallelize([2, 3, 4, 5, 6]).take(10)
         [2, 3, 4, 5, 6]
         """
+        def takeUpToNum(iterator):
+            taken = 0
+            while taken < num:
+                yield next(iterator)
+                taken += 1
+        # Take only up to num elements from each partition we try
+        mapped = self.mapPartitions(takeUpToNum)
         items = []
-        for partition in range(self._jrdd.splits().size()):
-            iterator = self.ctx._takePartition(self._jrdd.rdd(), partition)
-            # Each item in the iterator is a string, Python object, batch of
-            # Python objects.  Regardless, it is sufficient to take `num`
-            # of these objects in order to collect `num` Python objects:
-            iterator = iterator.take(num)
+        for partition in range(mapped._jrdd.splits().size()):
+            iterator = self.ctx._takePartition(mapped._jrdd.rdd(), partition)
             items.extend(self._collect_iterator_through_file(iterator))
             if len(items) >= num:
                 break
@@ -749,11 +756,12 @@ class PipelinedRDD(RDD):
             self.ctx._gateway._gateway_client)
         self.ctx._pickled_broadcast_vars.clear()
         class_manifest = self._prev_jrdd.classManifest()
-        env = copy.copy(self.ctx.environment)
-        env['PYTHONPATH'] = os.environ.get("PYTHONPATH", "")
-        env = MapConverter().convert(env, self.ctx._gateway._gateway_client)
+        env = MapConverter().convert(self.ctx.environment,
+                                     self.ctx._gateway._gateway_client)
+        includes = ListConverter().convert(self.ctx._python_includes,
+                                     self.ctx._gateway._gateway_client)
         python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(),
-            pipe_command, env, self.preservesPartitioning, self.ctx.pythonExec,
+            pipe_command, env, includes, self.preservesPartitioning, self.ctx.pythonExec,
             broadcast_vars, self.ctx._javaAccumulator, class_manifest)
         self._jrdd_val = python_rdd.asJavaRDD()
         return self._jrdd_val
diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index cc8cd9e3c411bfa5e52b7cdb4f40fc8c381b14e4..9b4b4e78cb7276e939c5e31dcafc930fbd98b136 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -24,10 +24,15 @@ import os
 import pyspark
 from pyspark.context import SparkContext
 
+# this is the equivalent of ADD_JARS
+add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") != None else None
 
-sc = SparkContext(os.environ.get("MASTER", "local"), "PySparkShell")
+sc = SparkContext(os.environ.get("MASTER", "local"), "PySparkShell", pyFiles=add_files)
 print "Spark context avaiable as sc."
 
+if add_files != None:
+    print "Adding files: [%s]" % ", ".join(add_files)
+
 # The ./pyspark script stores the old PYTHONSTARTUP value in OLD_PYTHONSTARTUP,
 # which allows us to execute the user's PYTHONSTARTUP file:
 _pythonstartup = os.environ.get('OLD_PYTHONSTARTUP')
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index dfd841b10a0dfa31b0c32c08adf690a9e7405538..29d6a128f6a9b83dc742c676ef010a90f54ab73e 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -64,7 +64,7 @@ class TestCheckpoint(PySparkTestCase):
         flatMappedRDD = parCollection.flatMap(lambda x: range(1, x + 1))
 
         self.assertFalse(flatMappedRDD.isCheckpointed())
-        self.assertIsNone(flatMappedRDD.getCheckpointFile())
+        self.assertTrue(flatMappedRDD.getCheckpointFile() is None)
 
         flatMappedRDD.checkpoint()
         result = flatMappedRDD.collect()
@@ -79,13 +79,13 @@ class TestCheckpoint(PySparkTestCase):
         flatMappedRDD = parCollection.flatMap(lambda x: [x])
 
         self.assertFalse(flatMappedRDD.isCheckpointed())
-        self.assertIsNone(flatMappedRDD.getCheckpointFile())
+        self.assertTrue(flatMappedRDD.getCheckpointFile() is None)
 
         flatMappedRDD.checkpoint()
         flatMappedRDD.count()  # forces a checkpoint to be computed
         time.sleep(1)  # 1 second
 
-        self.assertIsNotNone(flatMappedRDD.getCheckpointFile())
+        self.assertTrue(flatMappedRDD.getCheckpointFile() is not None)
         recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile())
         self.assertEquals([1, 2, 3, 4], recovered.collect())
 
@@ -125,6 +125,17 @@ class TestAddFile(PySparkTestCase):
         from userlibrary import UserClass
         self.assertEqual("Hello World!", UserClass().hello())
 
+    def test_add_egg_file_locally(self):
+        # To ensure that we're actually testing addPyFile's effects, check that
+        # this fails due to `userlibrary` not being on the Python path:
+        def func():
+            from userlib import UserClass
+        self.assertRaises(ImportError, func)
+        path = os.path.join(SPARK_HOME, "python/test_support/userlib-0.1-py2.7.egg")
+        self.sc.addPyFile(path)
+        from userlib import UserClass
+        self.assertEqual("Hello World from inside a package!", UserClass().hello())
+
 
 class TestIO(PySparkTestCase):
 
@@ -164,9 +175,12 @@ class TestDaemon(unittest.TestCase):
         time.sleep(1)
 
         # daemon should no longer accept connections
-        with self.assertRaises(EnvironmentError) as trap:
+        try:
             self.connect(port)
-        self.assertEqual(trap.exception.errno, ECONNREFUSED)
+        except EnvironmentError as exception:
+            self.assertEqual(exception.errno, ECONNREFUSED)
+        else:
+            self.fail("Expected EnvironmentError to be raised")
 
     def test_termination_stdin(self):
         """Ensure that daemon and workers terminate when stdin is closed."""
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 75d692beeb073c05315eaefb87a5d86864c57cfb..695f6dfb8444c1ef72f1649438854d402c5e0011 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -49,15 +49,26 @@ def main(infile, outfile):
     split_index = read_int(infile)
     if split_index == -1:  # for unit tests
         return
+
+    # fetch name of workdir
     spark_files_dir = load_pickle(read_with_length(infile))
     SparkFiles._root_directory = spark_files_dir
     SparkFiles._is_running_on_worker = True
-    sys.path.append(spark_files_dir)
+
+    # fetch names and values of broadcast variables
     num_broadcast_variables = read_int(infile)
     for _ in range(num_broadcast_variables):
         bid = read_long(infile)
         value = read_with_length(infile)
         _broadcastRegistry[bid] = Broadcast(bid, load_pickle(value))
+
+    # fetch names of includes (*.zip and *.egg files) and construct PYTHONPATH
+    sys.path.append(spark_files_dir) # *.py files that were added will be copied here
+    num_python_includes =  read_int(infile)
+    for _ in range(num_python_includes):
+        sys.path.append(os.path.join(spark_files_dir, load_pickle(read_with_length(infile))))
+
+    # now load function
     func = load_obj(infile)
     bypassSerializer = load_obj(infile)
     if bypassSerializer:
diff --git a/python/run-tests b/python/run-tests
index 1ee947d4145ed0e71aa3eeefea01f0db2255a32f..cbc554ea9db0d2cdd18323a408879d98f80810cb 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -21,22 +21,23 @@
 # Figure out where the Spark framework is installed
 FWDIR="$(cd `dirname $0`; cd ../; pwd)"
 
-FAILED=0
-
-$FWDIR/pyspark pyspark/rdd.py
-FAILED=$(($?||$FAILED))
+# CD into the python directory to find things on the right path
+cd "$FWDIR/python"
 
-$FWDIR/pyspark pyspark/context.py
-FAILED=$(($?||$FAILED))
+FAILED=0
 
-$FWDIR/pyspark -m doctest pyspark/broadcast.py
-FAILED=$(($?||$FAILED))
+rm -f unit-tests.log
 
-$FWDIR/pyspark -m doctest pyspark/accumulators.py
-FAILED=$(($?||$FAILED))
+function run_test() {
+    $FWDIR/pyspark $1 2>&1 | tee -a unit-tests.log
+    FAILED=$((PIPESTATUS[0]||$FAILED))
+}
 
-$FWDIR/pyspark -m unittest pyspark.tests
-FAILED=$(($?||$FAILED))
+run_test "pyspark/rdd.py"
+run_test "pyspark/context.py"
+run_test "-m doctest pyspark/broadcast.py"
+run_test "-m doctest pyspark/accumulators.py"
+run_test "pyspark/tests.py"
 
 if [[ $FAILED != 0 ]]; then
     echo -en "\033[31m"  # Red
diff --git a/python/test_support/userlib-0.1-py2.7.egg b/python/test_support/userlib-0.1-py2.7.egg
new file mode 100644
index 0000000000000000000000000000000000000000..1674c9cb2227e160aec55c74e9ba1ddba850f580
Binary files /dev/null and b/python/test_support/userlib-0.1-py2.7.egg differ
diff --git a/repl-bin/pom.xml b/repl-bin/pom.xml
index 7c4e722cc157ea72a73c86b64022ff3999fa1b6a..919e35f24041a52531562662a83745882971d2b3 100644
--- a/repl-bin/pom.xml
+++ b/repl-bin/pom.xml
@@ -32,11 +32,31 @@
   <url>http://spark-project.org/</url>
 
   <properties>
-    <deb.pkg.name>spark-${classifier}</deb.pkg.name>
-    <deb.install.path>/usr/share/spark-${classifier}</deb.install.path>
+    <deb.pkg.name>spark</deb.pkg.name>
+    <deb.install.path>/usr/share/spark</deb.install.path>
     <deb.user>root</deb.user>
   </properties>
 
+  <dependencies>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-core</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-bagel</artifactId>
+      <version>${project.version}</version>
+      <scope>runtime</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-repl</artifactId>
+      <version>${project.version}</version>
+      <scope>runtime</scope>
+    </dependency>
+  </dependencies>
+
   <build>
     <plugins>
       <plugin>
@@ -44,7 +64,7 @@
         <artifactId>maven-shade-plugin</artifactId>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
-          <outputFile>${project.build.directory}/${project.artifactId}-${project.version}-shaded-${classifier}.jar</outputFile>
+          <outputFile>${project.build.directory}/${project.artifactId}-${project.version}-shaded.jar</outputFile>
           <artifactSet>
             <includes>
               <include>*:*</include>
@@ -85,143 +105,13 @@
   </build>
 
   <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <properties>
-        <classifier>hadoop1</classifier>
-      </properties>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-bagel</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-examples</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-repl</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>runtime</scope>
-        </dependency>
-      </dependencies>
-    </profile>
-    <profile>
-      <id>hadoop2</id>
-      <properties>
-        <classifier>hadoop2</classifier>
-      </properties>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-bagel</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-examples</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-repl</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>runtime</scope>
-        </dependency>
-      </dependencies>
-    </profile>
     <profile>
       <id>hadoop2-yarn</id>
-      <properties>
-        <classifier>hadoop2-yarn</classifier>
-      </properties>
       <dependencies>
         <dependency>
           <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-bagel</artifactId>
+          <artifactId>spark-yarn</artifactId>
           <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-examples</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-repl</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-api</artifactId>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-common</artifactId>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-client</artifactId>
-          <scope>runtime</scope>
         </dependency>
       </dependencies>
     </profile>
@@ -261,7 +151,7 @@
                   <compression>gzip</compression>
                   <dataSet>
                     <data>
-                      <src>${project.build.directory}/${project.artifactId}-${project.version}-shaded-${classifier}.jar</src>
+                      <src>${project.build.directory}/${project.artifactId}-${project.version}-shaded.jar</src>
                       <type>file</type>
                       <mapper>
                         <type>perm</type>
diff --git a/repl/pom.xml b/repl/pom.xml
index 7d8da03254b41bc2e991f6cf36a7b85e1f6135aa..5bc9a99c5c45a39bd6754a9c90c1aa56bef4c225 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -37,6 +37,17 @@
   </properties>
 
   <dependencies>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-core</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-bagel</artifactId>
+      <version>${project.version}</version>
+      <scope>runtime</scope>
+    </dependency>
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-server</artifactId>
@@ -57,7 +68,6 @@
       <groupId>org.slf4j</groupId>
       <artifactId>slf4j-log4j12</artifactId>
     </dependency>
-
     <dependency>
       <groupId>org.scalatest</groupId>
       <artifactId>scalatest_${scala.version}</artifactId>
@@ -73,6 +83,35 @@
     <outputDirectory>target/scala-${scala.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory>
     <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-antrun-plugin</artifactId>
+        <executions>
+          <execution>
+            <phase>test</phase>
+            <goals>
+              <goal>run</goal>
+            </goals>
+            <configuration>
+              <exportAntProperties>true</exportAntProperties>
+              <tasks>
+                <property name="spark.classpath" refid="maven.test.classpath"/>
+                <property environment="env"/>
+                <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry.">
+                  <condition>
+                    <not>
+                      <or>
+                        <isset property="env.SCALA_HOME"/>
+                        <isset property="env.SCALA_LIBRARY_PATH"/>
+                      </or>
+                    </not>
+                  </condition>
+                </fail>
+              </tasks>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
       <plugin>
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
@@ -80,186 +119,22 @@
           <environmentVariables>
             <SPARK_HOME>${basedir}/..</SPARK_HOME>
             <SPARK_TESTING>1</SPARK_TESTING>
+            <SPARK_CLASSPATH>${spark.classpath}</SPARK_CLASSPATH>
           </environmentVariables>
         </configuration>
       </plugin>
     </plugins>
   </build>
-
   <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <properties>
-        <classifier>hadoop1</classifier>
-      </properties>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-bagel</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-examples</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop1</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2</id>
-      <properties>
-        <classifier>hadoop2</classifier>
-      </properties>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-bagel</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-examples</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro-ipc</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
     <profile>
       <id>hadoop2-yarn</id>
-      <properties>
-        <classifier>hadoop2-yarn</classifier>
-      </properties>
       <dependencies>
         <dependency>
           <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-bagel</artifactId>
+          <artifactId>spark-yarn</artifactId>
           <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-examples</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-streaming</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-          <scope>runtime</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-api</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-common</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro-ipc</artifactId>
-          <scope>provided</scope>
         </dependency>
       </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2-yarn</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
     </profile>
   </profiles>
 </project>
diff --git a/repl/src/main/scala/spark/repl/SparkILoop.scala b/repl/src/main/scala/spark/repl/SparkILoop.scala
index 59f9d05683dd04601d6b1bcbb1722967ddefd3d0..0cecbd71adc9f8f63d023cf5e0b21aaee0543fb9 100644
--- a/repl/src/main/scala/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/spark/repl/SparkILoop.scala
@@ -831,6 +831,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master:
   var sparkContext: SparkContext = null
 
   def createSparkContext(): SparkContext = {
+    val uri = System.getenv("SPARK_EXECUTOR_URI")
+    if (uri != null) {
+      System.setProperty("spark.executor.uri", uri)
+    }
     val master = this.master match {
       case Some(m) => m
       case None => {
diff --git a/run b/run
index 4cffda470899f01b7ca839befafe92ed19447b29..3868332c90cdd1c2ca4ffe037008ed78d0a95c4b 100755
--- a/run
+++ b/run
@@ -72,7 +72,10 @@ esac
 # hard to kill the child with stuff like Process.destroy(). However, for
 # the Spark shell, the wrapper is necessary to properly reset the terminal
 # when we exit, so we allow it to set a variable to launch with scala.
-if [ "$SPARK_LAUNCH_WITH_SCALA" == "1" ]; then
+# We still fall back on java for the shell if this is a "release" created
+# from make-distribution.sh since it's possible scala is not installed
+# but we have everything we need to run the shell.
+if [[ "$SPARK_LAUNCH_WITH_SCALA" == "1" && ! -f "$FWDIR/RELEASE" ]]; then
   if [ "$SCALA_HOME" ]; then
     RUNNER="${SCALA_HOME}/bin/scala"
   else
@@ -161,4 +164,12 @@ else
   # The JVM doesn't read JAVA_OPTS by default so we need to pass it in
   EXTRA_ARGS="$JAVA_OPTS"
 fi
-exec "$RUNNER" -cp "$CLASSPATH" $EXTRA_ARGS "$@"
+
+command="$RUNNER -cp \"$CLASSPATH\" $EXTRA_ARGS $@"
+if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then
+  echo "Spark Command: $command"
+  echo "========================================"
+  echo
+fi
+
+exec $command
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 7e6b06d772fc0cc883b5a588bb32f1db78b66fb0..5c0582d6fb631d80c604cab85f789d19d549a2d4 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -40,6 +40,11 @@
   </repositories>
 
   <dependencies>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-core</artifactId>
+      <version>${project.version}</version>
+    </dependency>
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-server</artifactId>
@@ -115,103 +120,4 @@
       </plugin>
     </plugins>
   </build>
-
-  <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop1</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2-yarn</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-api</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-common</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2-yarn</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-  </profiles>
 </project>
diff --git a/streaming/src/main/scala/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/spark/streaming/Checkpoint.scala
index 1e4c1e3742bb7da2753589376859f9f511321719..070d930b5eb8f5bf0783f42007bc4cfd99813625 100644
--- a/streaming/src/main/scala/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/spark/streaming/Checkpoint.scala
@@ -17,16 +17,17 @@
 
 package spark.streaming
 
-import spark.{Logging, Utils}
-
-import org.apache.hadoop.fs.{FileUtil, Path}
-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
 
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.conf.Configuration
+
+import spark.Logging
+import spark.io.CompressionCodec
+
+
 private[streaming]
 class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
   extends Logging with Serializable {
@@ -49,6 +50,7 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
   }
 }
 
+
 /**
  * Convenience class to speed up the writing of graph checkpoint to file
  */
@@ -66,6 +68,8 @@ class CheckpointWriter(checkpointDir: String) extends Logging {
   val maxAttempts = 3
   val executor = Executors.newFixedThreadPool(1)
 
+  private val compressionCodec = CompressionCodec.createCodec()
+
   // Removed code which validates whether there is only one CheckpointWriter per path 'file' since 
   // I did not notice any errors - reintroduce it ?
 
@@ -103,7 +107,7 @@ class CheckpointWriter(checkpointDir: String) extends Logging {
 
   def write(checkpoint: Checkpoint) {
     val bos = new ByteArrayOutputStream()
-    val zos = new LZFOutputStream(bos)
+    val zos = compressionCodec.compressedOutputStream(bos)
     val oos = new ObjectOutputStream(zos)
     oos.writeObject(checkpoint)
     oos.close()
@@ -137,6 +141,8 @@ object CheckpointReader extends Logging {
     val fs = new Path(path).getFileSystem(new Configuration())
     val attempts = Seq(new Path(path, "graph"), new Path(path, "graph.bk"), new Path(path), new Path(path + ".bk"))
 
+    val compressionCodec = CompressionCodec.createCodec()
+
     attempts.foreach(file => {
       if (fs.exists(file)) {
         logInfo("Attempting to load checkpoint from file '" + file + "'")
@@ -147,7 +153,7 @@ object CheckpointReader extends Logging {
           // of ObjectInputStream is used to explicitly use the current thread's default class
           // loader to find and load classes. This is a well know Java issue and has popped up
           // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627)
-          val zis = new LZFInputStream(fis)
+          val zis = compressionCodec.compressedInputStream(fis)
           val ois = new ObjectInputStreamWithLoader(zis, Thread.currentThread().getContextClassLoader)
           val cp = ois.readObject.asInstanceOf[Checkpoint]
           ois.close()
@@ -170,7 +176,9 @@ object CheckpointReader extends Logging {
 }
 
 private[streaming]
-class ObjectInputStreamWithLoader(inputStream_ : InputStream, loader: ClassLoader) extends ObjectInputStream(inputStream_) {
+class ObjectInputStreamWithLoader(inputStream_ : InputStream, loader: ClassLoader)
+  extends ObjectInputStream(inputStream_) {
+
   override def resolveClass(desc: ObjectStreamClass): Class[_] = {
     try {
       return loader.loadClass(desc.getName())
diff --git a/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala
index ccd15563b07008e413d19ad79c055cfb5e5885a6..ea08fb38267941807a524d3f38330798bfc0b4ff 100644
--- a/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala
+++ b/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala
@@ -29,7 +29,7 @@ import spark.{RDD, Partitioner}
 import org.apache.hadoop.mapred.{JobConf, OutputFormat}
 import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat}
 import org.apache.hadoop.conf.Configuration
-import spark.api.java.{JavaRDD, JavaPairRDD}
+import spark.api.java.{JavaUtils, JavaRDD, JavaPairRDD}
 import spark.storage.StorageLevel
 import com.google.common.base.Optional
 import spark.RDD
@@ -401,10 +401,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
   (Seq[V], Option[S]) => Option[S] = {
     val scalaFunc: (Seq[V], Option[S]) => Option[S] = (values, state) => {
       val list: JList[V] = values
-      val scalaState: Optional[S] = state match {
-        case Some(s) => Optional.of(s)
-        case _ => Optional.absent()
-      }
+      val scalaState: Optional[S] = JavaUtils.optionToOptional(state)
       val result: Optional[S] = in.apply(list, scalaState)
       result.isPresent match {
         case true => Some(result.get())
diff --git a/tools/pom.xml b/tools/pom.xml
index 1125aba4f17ba301d06a8617f11d1e7a52b929ad..95b5e80e5b1bc8c9839afc15747aa7940420cb11 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -30,6 +30,24 @@
   <name>Spark Project Tools</name>
   <url>http://spark-project.org/</url>
 
+  <dependencies>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-core</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project</groupId>
+      <artifactId>spark-streaming</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+
   <build>
     <outputDirectory>target/scala-${scala.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory>
@@ -48,121 +66,4 @@
       </plugin>
     </plugins>
   </build>
-
-  <profiles>
-    <profile>
-      <id>hadoop1</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-streaming</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop1</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop1</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-streaming</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-core</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-    <profile>
-      <id>hadoop2-yarn</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-core</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.spark-project</groupId>
-          <artifactId>spark-streaming</artifactId>
-          <version>${project.version}</version>
-          <classifier>hadoop2-yarn</classifier>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-client</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-api</artifactId>
-          <scope>provided</scope>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.hadoop</groupId>
-          <artifactId>hadoop-yarn-common</artifactId>
-          <scope>provided</scope>
-        </dependency>
-      </dependencies>
-      <build>
-        <plugins>
-          <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-jar-plugin</artifactId>
-            <configuration>
-              <classifier>hadoop2-yarn</classifier>
-            </configuration>
-          </plugin>
-        </plugins>
-      </build>
-    </profile>
-  </profiles>
 </project>
diff --git a/tools/src/main/scala/spark/tools/JavaAPICompletenessChecker.scala b/tools/src/main/scala/spark/tools/JavaAPICompletenessChecker.scala
index 3a55f5081287c4a98c450e6471f4e84529189cdf..f45d0b281cf0a1a38def95993c6c6569f00abc42 100644
--- a/tools/src/main/scala/spark/tools/JavaAPICompletenessChecker.scala
+++ b/tools/src/main/scala/spark/tools/JavaAPICompletenessChecker.scala
@@ -17,13 +17,15 @@
 
 package spark.tools
 
-import spark._
 import java.lang.reflect.Method
+
 import scala.collection.mutable.ArrayBuffer
+
+import spark._
 import spark.api.java._
+import spark.rdd.OrderedRDDFunctions
 import spark.streaming.{PairDStreamFunctions, DStream, StreamingContext}
 import spark.streaming.api.java.{JavaPairDStream, JavaDStream, JavaStreamingContext}
-import scala.Tuple2
 
 
 private[spark] abstract class SparkType(val name: String)
@@ -121,7 +123,7 @@ object JavaAPICompletenessChecker {
     SparkMethod(name, returnType, parameters)
   }
 
-  private def toJavaType(scalaType: SparkType): SparkType = {
+  private def toJavaType(scalaType: SparkType, isReturnType: Boolean): SparkType = {
     val renameSubstitutions = Map(
       "scala.collection.Map" -> "java.util.Map",
       // TODO: the JavaStreamingContext API accepts Array arguments
@@ -140,40 +142,43 @@ object JavaAPICompletenessChecker {
             case "spark.RDD" =>
               if (parameters(0).name == classOf[Tuple2[_, _]].getName) {
                 val tupleParams =
-                  parameters(0).asInstanceOf[ParameterizedType].parameters.map(toJavaType)
+                  parameters(0).asInstanceOf[ParameterizedType].parameters.map(applySubs)
                 ParameterizedType(classOf[JavaPairRDD[_, _]].getName, tupleParams)
               } else {
-                ParameterizedType(classOf[JavaRDD[_]].getName, parameters.map(toJavaType))
+                ParameterizedType(classOf[JavaRDD[_]].getName, parameters.map(applySubs))
               }
             case "spark.streaming.DStream" =>
               if (parameters(0).name == classOf[Tuple2[_, _]].getName) {
                 val tupleParams =
-                  parameters(0).asInstanceOf[ParameterizedType].parameters.map(toJavaType)
+                  parameters(0).asInstanceOf[ParameterizedType].parameters.map(applySubs)
                 ParameterizedType("spark.streaming.api.java.JavaPairDStream", tupleParams)
               } else {
                 ParameterizedType("spark.streaming.api.java.JavaDStream",
-                  parameters.map(toJavaType))
+                  parameters.map(applySubs))
+              }
+            case "scala.Option" => {
+              if (isReturnType) {
+                ParameterizedType("com.google.common.base.Optional", parameters.map(applySubs))
+              } else {
+                applySubs(parameters(0))
               }
-            // TODO: Spark Streaming uses Guava's Optional in place of Option, leading to some
-            // false-positives here:
-            case "scala.Option" =>
-              toJavaType(parameters(0))
+            }
             case "scala.Function1" =>
               val firstParamName = parameters.last.name
               if (firstParamName.startsWith("scala.collection.Traversable") ||
                 firstParamName.startsWith("scala.collection.Iterator")) {
                 ParameterizedType("spark.api.java.function.FlatMapFunction",
                   Seq(parameters(0),
-                    parameters.last.asInstanceOf[ParameterizedType].parameters(0)).map(toJavaType))
+                    parameters.last.asInstanceOf[ParameterizedType].parameters(0)).map(applySubs))
               } else if (firstParamName == "scala.runtime.BoxedUnit") {
                 ParameterizedType("spark.api.java.function.VoidFunction",
-                  parameters.dropRight(1).map(toJavaType))
+                  parameters.dropRight(1).map(applySubs))
               } else {
-                ParameterizedType("spark.api.java.function.Function", parameters.map(toJavaType))
+                ParameterizedType("spark.api.java.function.Function", parameters.map(applySubs))
               }
             case _ =>
               ParameterizedType(renameSubstitutions.getOrElse(name, name),
-                parameters.map(toJavaType))
+                parameters.map(applySubs))
           }
         case BaseType(name) =>
           if (renameSubstitutions.contains(name)) {
@@ -194,8 +199,9 @@ object JavaAPICompletenessChecker {
 
   private def toJavaMethod(method: SparkMethod): SparkMethod = {
     val params = method.parameters
-      .filterNot(_.name == "scala.reflect.ClassManifest").map(toJavaType)
-    SparkMethod(method.name, toJavaType(method.returnType), params)
+      .filterNot(_.name == "scala.reflect.ClassManifest")
+      .map(toJavaType(_, isReturnType = false))
+    SparkMethod(method.name, toJavaType(method.returnType, isReturnType = true), params)
   }
 
   private def isExcludedByName(method: Method): Boolean = {
@@ -332,7 +338,7 @@ object JavaAPICompletenessChecker {
     println()
 
     println("Missing OrderedRDD methods")
-    printMissingMethods(classOf[OrderedRDDFunctions[_, _]], classOf[JavaPairRDD[_, _]])
+    printMissingMethods(classOf[OrderedRDDFunctions[_, _, _]], classOf[JavaPairRDD[_, _]])
     println()
 
     println("Missing SparkContext methods")
diff --git a/yarn/pom.xml b/yarn/pom.xml
new file mode 100644
index 0000000000000000000000000000000000000000..07dd170eae54fcbe58d97cbe50acc78e6814a244
--- /dev/null
+++ b/yarn/pom.xml
@@ -0,0 +1,111 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!-- ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.spark-project</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>0.8.0-SNAPSHOT</version>
+    <relativePath>../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.spark-project</groupId>
+  <artifactId>spark-yarn</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project YARN Support</name>
+  <url>http://spark-project.org/</url>
+
+  <build>
+    <outputDirectory>target/scala-${scala.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-shade-plugin</artifactId>
+        <configuration>
+          <shadedArtifactAttached>false</shadedArtifactAttached>
+          <outputFile>${project.build.directory}/${project.artifactId}-${project.version}-shaded.jar</outputFile>
+          <artifactSet>
+            <includes>
+              <include>*:*</include>
+            </includes>
+          </artifactSet>
+          <filters>
+            <filter>
+              <artifact>*:*</artifact>
+              <excludes>
+                <exclude>META-INF/*.SF</exclude>
+                <exclude>META-INF/*.DSA</exclude>
+                <exclude>META-INF/*.RSA</exclude>
+              </excludes>
+            </filter>
+          </filters>
+        </configuration>
+        <executions>
+          <execution>
+            <phase>package</phase>
+            <goals>
+              <goal>shade</goal>
+            </goals>
+            <configuration>
+              <transformers>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                  <resource>reference.conf</resource>
+                </transformer>
+              </transformers>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
+  </build>
+
+  <profiles>
+    <profile>
+      <id>hadoop2-yarn</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.spark-project</groupId>
+          <artifactId>spark-core</artifactId>
+          <version>${project.version}</version>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-yarn-api</artifactId>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-yarn-common</artifactId>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-yarn-client</artifactId>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.avro</groupId>
+          <artifactId>avro</artifactId>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.avro</groupId>
+          <artifactId>avro-ipc</artifactId>
+        </dependency>
+      </dependencies>
+    </profile>
+  </profiles>
+</project>
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/spark/deploy/yarn/ApplicationMaster.scala
similarity index 97%
rename from core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala
rename to yarn/src/main/scala/spark/deploy/yarn/ApplicationMaster.scala
index 1b06169739c0ccacd1b77251f7079ba2fde3f2f7..15dbd1c0fb293cf89089f02c35dbc6ae65f11109 100644
--- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/spark/deploy/yarn/ApplicationMaster.scala
@@ -124,18 +124,20 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e
   private def waitForSparkMaster() {
     logInfo("Waiting for spark driver to be reachable.")
     var driverUp = false
-    while(!driverUp) {
+    var tries = 0
+    while(!driverUp && tries < 10) {
       val driverHost = System.getProperty("spark.driver.host")
       val driverPort = System.getProperty("spark.driver.port")
       try {
         val socket = new Socket(driverHost, driverPort.toInt)
         socket.close()
-        logInfo("Master now available: " + driverHost + ":" + driverPort)
+        logInfo("Driver now available: " + driverHost + ":" + driverPort)
         driverUp = true
       } catch {
         case e: Exception =>
-          logError("Failed to connect to driver at " + driverHost + ":" + driverPort)
+          logWarning("Failed to connect to driver at " + driverHost + ":" + driverPort + ", retrying")
         Thread.sleep(100)
+        tries = tries + 1
       }
     }
   }
@@ -176,7 +178,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e
       var sparkContext: SparkContext = null
       ApplicationMaster.sparkContextRef.synchronized {
         var count = 0
-        while (ApplicationMaster.sparkContextRef.get() == null) {
+        while (ApplicationMaster.sparkContextRef.get() == null && count < 10) {
           logInfo("Waiting for spark context initialization ... " + count)
           count = count + 1
           ApplicationMaster.sparkContextRef.wait(10000L)
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/spark/deploy/yarn/ApplicationMasterArguments.scala
similarity index 100%
rename from core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMasterArguments.scala
rename to yarn/src/main/scala/spark/deploy/yarn/ApplicationMasterArguments.scala
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/spark/deploy/yarn/Client.scala
similarity index 98%
rename from core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala
rename to yarn/src/main/scala/spark/deploy/yarn/Client.scala
index 8bcbfc273517ce8ef54d1f4aec37cf01787fb591..9d3860b863b8fe9024bed65bde6cb8ef88c5c87a 100644
--- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/spark/deploy/yarn/Client.scala
@@ -165,7 +165,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
     Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./*")
     Apps.addToEnvironment(env, Environment.CLASSPATH.name, "$CLASSPATH")
     Client.populateHadoopClasspath(yarnConf, env)
-    SparkHadoopUtil.setYarnMode(env)
+    env("SPARK_YARN_MODE") = "true"
     env("SPARK_YARN_JAR_PATH") = 
       localResources("spark.jar").getResource().getScheme.toString() + "://" +
       localResources("spark.jar").getResource().getFile().toString()
@@ -313,8 +313,11 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
 
 object Client {
   def main(argStrings: Array[String]) {
+    // Set an env variable indicating we are running in YARN mode.
+    // Note that anything with SPARK prefix gets propagated to all (remote) processes
+    System.setProperty("SPARK_YARN_MODE", "true")
+
     val args = new ClientArguments(argStrings)
-    SparkHadoopUtil.setYarnMode()
     new Client(args).run
   }
 
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/spark/deploy/yarn/ClientArguments.scala
similarity index 100%
rename from core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala
rename to yarn/src/main/scala/spark/deploy/yarn/ClientArguments.scala
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala b/yarn/src/main/scala/spark/deploy/yarn/WorkerRunnable.scala
similarity index 100%
rename from core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala
rename to yarn/src/main/scala/spark/deploy/yarn/WorkerRunnable.scala
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/src/main/scala/spark/deploy/yarn/YarnAllocationHandler.scala
similarity index 100%
rename from core/src/hadoop2-yarn/scala/spark/deploy/yarn/YarnAllocationHandler.scala
rename to yarn/src/main/scala/spark/deploy/yarn/YarnAllocationHandler.scala
diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala b/yarn/src/main/scala/spark/deploy/yarn/YarnSparkHadoopUtil.scala
similarity index 58%
rename from core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala
rename to yarn/src/main/scala/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index 6122fdced0da8fc8c7f1463a62ce9deb670a5e68..77c4ee7f3f67f9afec64bc3aa978c09ce43e35be 100644
--- a/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala
+++ b/yarn/src/main/scala/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -15,8 +15,9 @@
  * limitations under the License.
  */
 
-package spark.deploy
+package spark.deploy.yarn
 
+import spark.deploy.SparkHadoopUtil
 import collection.mutable.HashMap
 import org.apache.hadoop.mapred.JobConf
 import org.apache.hadoop.security.UserGroupInformation
@@ -28,48 +29,17 @@ import java.security.PrivilegedExceptionAction
 /**
  * Contains util methods to interact with Hadoop from spark.
  */
-object SparkHadoopUtil {
-
-  val yarnConf = newConfiguration()
-
-  def getUserNameFromEnvironment(): String = {
-    // defaulting to env if -D is not present ...
-    val retval = System.getProperty(Environment.USER.name, System.getenv(Environment.USER.name))
-
-    // If nothing found, default to user we are running as
-    if (retval == null) System.getProperty("user.name") else retval
-  }
-
-  def runAsUser(func: (Product) => Unit, args: Product) {
-    runAsUser(func, args, getUserNameFromEnvironment())
-  }
-
-  def runAsUser(func: (Product) => Unit, args: Product, user: String) {
-    func(args)
-  }
+class YarnSparkHadoopUtil extends SparkHadoopUtil {
 
   // 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.
-  def isYarnMode(): Boolean = {
-    val yarnMode = System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))
-    java.lang.Boolean.valueOf(yarnMode)
-  }
-
-  // Set an env variable indicating we are running in YARN mode.
-  // Note that anything with SPARK prefix gets propagated to all (remote) processes
-  def setYarnMode() {
-    System.setProperty("SPARK_YARN_MODE", "true")
-  }
-
-  def setYarnMode(env: HashMap[String, String]) {
-    env("SPARK_YARN_MODE") = "true"
-  }
+  override def isYarnMode(): Boolean = { true }
 
   // 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())
+  override 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) {
+  override def addCredentials(conf: JobConf) {
     val jobCreds = conf.getCredentials();
     jobCreds.mergeAll(UserGroupInformation.getCurrentUser().getCredentials())
   }
diff --git a/core/src/hadoop2-yarn/scala/spark/scheduler/cluster/YarnClusterScheduler.scala b/yarn/src/main/scala/spark/scheduler/cluster/YarnClusterScheduler.scala
similarity index 90%
rename from core/src/hadoop2-yarn/scala/spark/scheduler/cluster/YarnClusterScheduler.scala
rename to yarn/src/main/scala/spark/scheduler/cluster/YarnClusterScheduler.scala
index 307d96111c8cd9ce269b55f5c3426deef7f0f59d..bb58353e0cc68e657b20c5e06478d12545fb08ee 100644
--- a/core/src/hadoop2-yarn/scala/spark/scheduler/cluster/YarnClusterScheduler.scala
+++ b/yarn/src/main/scala/spark/scheduler/cluster/YarnClusterScheduler.scala
@@ -41,13 +41,6 @@ private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
     if (retval != null) Some(retval) else None
   }
 
-  // By default, if rack is unknown, return nothing
-  override def getCachedHostsForRack(rack: String): Option[Set[String]] = {
-    if (rack == None || rack == null) return None
-
-    YarnAllocationHandler.fetchCachedHostsForRack(rack)
-  }
-
   override def postStartHook() {
     val sparkContextInitialized = ApplicationMaster.sparkContextInitialized(sc)
     if (sparkContextInitialized){