diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index fb808129bb..4ba20e590f 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -9,6 +9,11 @@
         <title>{{ page.title }} - Spark {{site.SPARK_VERSION_SHORT}} Documentation</title>
         <meta name="description" content="">
 
+        {% if page.redirect %}
+          <meta http-equiv="refresh" content="0; url={{page.redirect}}">
+          <link rel="canonical" href="{{page.redirect}}" />
+        {% endif %}
+
         <link rel="stylesheet" href="css/bootstrap.min.css">
         <style>
             body {
@@ -61,15 +66,13 @@
                             <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a>
                             <ul class="dropdown-menu">
                                 <li><a href="quick-start.html">Quick Start</a></li>
-                                <li><a href="scala-programming-guide.html">Spark in Scala</a></li>
-                                <li><a href="java-programming-guide.html">Spark in Java</a></li>
-                                <li><a href="python-programming-guide.html">Spark in Python</a></li>
+                                <li><a href="programming-guide.html">Spark Programming Guide</a></li>
                                 <li class="divider"></li>
                                 <li><a href="streaming-programming-guide.html">Spark Streaming</a></li>
                                 <li><a href="sql-programming-guide.html">Spark SQL</a></li>
                                 <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li>
-                                <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
                                 <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li>
+                                <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
                             </ul>
                         </li>
 
@@ -86,6 +89,8 @@
                             <a href="#" class="dropdown-toggle" data-toggle="dropdown">Deploying<b class="caret"></b></a>
                             <ul class="dropdown-menu">
                                 <li><a href="cluster-overview.html">Overview</a></li>
+                                <li><a href="submitting-applications.html">Submitting Applications</a></li>
+                                <li class="divider"></li>
                                 <li><a href="ec2-scripts.html">Amazon EC2</a></li>
                                 <li><a href="spark-standalone.html">Standalone Mode</a></li>
                                 <li><a href="running-on-mesos.html">Mesos</a></li>
@@ -99,9 +104,10 @@
                                 <li><a href="configuration.html">Configuration</a></li>
                                 <li><a href="monitoring.html">Monitoring</a></li>
                                 <li><a href="tuning.html">Tuning Guide</a></li>
-                                <li><a href="hadoop-third-party-distributions.html">Running with CDH/HDP</a></li>
-                                <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
                                 <li><a href="job-scheduling.html">Job Scheduling</a></li>
+                                <li><a href="security.html">Security</a></li>
+                                <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
+                                <li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li>
                                 <li class="divider"></li>
                                 <li><a href="building-with-maven.html">Building Spark with Maven</a></li>
                                 <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li>
diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index 14f43cb6d3..b280df0c8e 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -21,7 +21,7 @@ To use Bagel in your program, add the following SBT or Maven dependency:
 
 # Programming Model
 
-Bagel operates on a graph represented as a [distributed dataset](scala-programming-guide.html) of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages.
+Bagel operates on a graph represented as a [distributed dataset](programming-guide.html) of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages.
 
 For example, we can use Bagel to implement PageRank. Here, vertices represent pages, edges represent links between pages, and messages represent shares of PageRank sent to the pages that a particular page links to.
 
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index 8b44535d82..55a9e37dfe 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -6,14 +6,16 @@ title: Building Spark with Maven
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-Building Spark using Maven requires Maven 3.0.4 or newer and Java 1.6 or newer.
+Building Spark using Maven requires Maven 3.0.4 or newer and Java 6+.
 
 
-## Setting up Maven's Memory Usage ##
+# Setting up Maven's Memory Usage
 
 You'll need to configure Maven to use more memory than usual by setting `MAVEN_OPTS`. We recommend the following settings:
 
-    export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
+{% highlight bash %}
+export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
+{% endhighlight %}
 
 If you don't run this, you may see errors like the following:
 
@@ -25,9 +27,9 @@ If you don't run this, you may see errors like the following:
 
 You can fix this by setting the `MAVEN_OPTS` variable as discussed before.
 
-*Note: For Java 1.8 and above this step is not required.*
+**Note:** *For Java 8 and above this step is not required.*
 
-## Specifying the Hadoop version ##
+# Specifying the Hadoop Version
 
 Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the "hadoop.version" property. If unset, Spark will build against Hadoop 1.0.4 by default. Note that certain build profiles are required for particular Hadoop versions:
 
@@ -46,14 +48,16 @@ Because HDFS is not protocol-compatible across versions, if you want to read fro
 
 For Apache Hadoop versions 1.x, Cloudera CDH "mr1" distributions, and other Hadoop versions without YARN, use:
 
-    # Apache Hadoop 1.2.1
-    $ mvn -Dhadoop.version=1.2.1 -DskipTests clean package
+{% highlight bash %}
+# Apache Hadoop 1.2.1
+mvn -Dhadoop.version=1.2.1 -DskipTests clean package
 
-    # Cloudera CDH 4.2.0 with MapReduce v1
-    $ mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
+# Cloudera CDH 4.2.0 with MapReduce v1
+mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
 
-    # Apache Hadoop 0.23.x
-    $ mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
+# Apache Hadoop 0.23.x
+mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
+{% endhighlight %}
 
 For Apache Hadoop 2.x, 0.23.x, Cloudera CDH, and other Hadoop versions with YARN, you can enable the "yarn-alpha" or "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". The additional build profile required depends on the YARN version:
 
@@ -69,75 +73,77 @@ For Apache Hadoop 2.x, 0.23.x, Cloudera CDH, and other Hadoop versions with YARN
 
 Examples:
 
-    # Apache Hadoop 2.0.5-alpha
-    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -DskipTests clean package
+{% highlight bash %}
+# Apache Hadoop 2.0.5-alpha
+mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -DskipTests clean package
 
-    # Cloudera CDH 4.2.0
-    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -DskipTests clean package
+# Cloudera CDH 4.2.0
+mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -DskipTests clean package
 
-    # Apache Hadoop 0.23.x
-    $ mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
+# Apache Hadoop 0.23.x
+mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
 
-    # Apache Hadoop 2.2.X
-    $ mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
+# Apache Hadoop 2.2.X
+mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
 
-    # Apache Hadoop 2.3.X
-    $ mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package
+# Apache Hadoop 2.3.X
+mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package
 
-    # Apache Hadoop 2.4.X
-    $ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
+# Apache Hadoop 2.4.X
+mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
 
-    # Different versions of HDFS and YARN.
-    $ mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package
+# Different versions of HDFS and YARN.
+mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package
+{% endhighlight %}
 
-## Spark Tests in Maven ##
+# Spark Tests in Maven
 
 Tests are run by default via the [ScalaTest Maven plugin](http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin). Some of the require Spark to be packaged first, so always run `mvn package` with `-DskipTests` the first time. You can then run the tests with `mvn -Dhadoop.version=... test`.
 
 The ScalaTest plugin also supports running only a specific test suite as follows:
 
-    $ mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test
+    mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test
 
 
-## Continuous Compilation ##
+# Continuous Compilation
 
 We use the scala-maven-plugin which supports incremental and continuous compilation. E.g.
 
-    $ mvn scala:cc
+    mvn scala:cc
 
 should run continuous compilation (i.e. wait for changes). However, this has not been tested extensively.
 
-## Using With IntelliJ IDEA ##
+# Using With IntelliJ IDEA
 
 This setup works fine in IntelliJ IDEA 11.1.4. After opening the project via the pom.xml file in the project root folder, you only need to activate either the hadoop1 or hadoop2 profile in the "Maven Properties" popout. We have not tried Eclipse/Scala IDE with this.
 
-## Building Spark Debian Packages ##
+# Building Spark Debian Packages
 
-The maven build includes support for building a Debian package containing the assembly 'fat-jar', PySpark, and the necessary scripts and configuration files. This can be created by specifying the following:
+The Maven build includes support for building a Debian package containing the assembly 'fat-jar', PySpark, and the necessary scripts and configuration files. This can be created by specifying the following:
 
-    $ mvn -Pdeb -DskipTests clean package
+    mvn -Pdeb -DskipTests clean package
 
 The debian package can then be found under assembly/target. We added the short commit hash to the file name so that we can distinguish individual packages built for SNAPSHOT versions.
 
-## Running java 8 test suites.
+# Running Java 8 Test Suites
 
-Running only java 8 tests and nothing else.
+Running only Java 8 tests and nothing else.
 
-    $ mvn install -DskipTests -Pjava8-tests
+    mvn install -DskipTests -Pjava8-tests
     
-Java 8 tests are run when -Pjava8-tests profile is enabled, they will run in spite of -DskipTests. 
+Java 8 tests are run when `-Pjava8-tests` profile is enabled, they will run in spite of `-DskipTests`. 
 For these tests to run your system must have a JDK 8 installation. 
 If you have JDK 8 installed but it is not the system default, you can set JAVA_HOME to point to JDK 8 before running the tests.
 
-## Building for PySpark on YARN ##
+# Building for PySpark on YARN
 
-PySpark on YARN is only supported if the jar is built with maven. Further, there is a known problem
-with building this assembly jar on Red Hat based operating systems (see SPARK-1753). If you wish to
+PySpark on YARN is only supported if the jar is built with Maven. Further, there is a known problem
+with building this assembly jar on Red Hat based operating systems (see [SPARK-1753](https://issues.apache.org/jira/browse/SPARK-1753)). If you wish to
 run PySpark on a YARN cluster with Red Hat installed, we recommend that you build the jar elsewhere,
 then ship it over to the cluster. We are investigating the exact cause for this.
 
-## Packaging without Hadoop dependencies for deployment on YARN ##
+# Packaging without Hadoop Dependencies for YARN
 
-The assembly jar produced by "mvn package" will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The "hadoop-provided" profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself. 
+The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself. 
 
 
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index f05a755de7..6a75d5c457 100644
--- a/docs/cluster-overview.md
+++ b/docs/cluster-overview.md
@@ -4,7 +4,8 @@ title: Cluster Mode Overview
 ---
 
 This document gives a short overview of how Spark runs on clusters, to make it easier to understand
-the components involved.
+the components involved. Read through the [application submission guide](submitting-applications.html)
+to submit applications to a cluster.
 
 # Components
 
@@ -50,107 +51,10 @@ The system currently supports three cluster managers:
 In addition, Spark's [EC2 launch scripts](ec2-scripts.html) make it easy to launch a standalone
 cluster on Amazon EC2.
 
-# Bundling and Launching Applications
+# Submitting Applications
 
-### Bundling Your Application's Dependencies
-If your code depends on other projects, you will need to package them alongside
-your application in order to distribute the code to a Spark cluster. To do this,
-to create an assembly jar (or "uber" jar) containing your code and its dependencies. Both
-[sbt](https://github.com/sbt/sbt-assembly) and
-[Maven](http://maven.apache.org/plugins/maven-shade-plugin/)
-have assembly plugins. When creating assembly jars, list Spark and Hadoop
-as `provided` dependencies; these need not be bundled since they are provided by
-the cluster manager at runtime. Once you have an assembled jar you can call the `bin/spark-submit`
-script as shown here while passing your jar.
-
-For Python, you can use the `pyFiles` argument of SparkContext
-or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed.
-
-### Launching Applications with Spark submit
-
-Once a user application is bundled, it can be launched using the `spark-submit` script located in
-the bin directory. This script takes care of setting up the classpath with Spark and its
-dependencies, and can support different cluster managers and deploy modes that Spark supports:
-
-    ./bin/spark-submit \
-      --class <main-class>
-      --master <master-url> \
-      --deploy-mode <deploy-mode> \
-      ... // other options
-      <application-jar>
-      [application-arguments]
-
-    main-class: The entry point for your application (e.g. org.apache.spark.examples.SparkPi)
-    master-url: The URL of the master node (e.g. spark://23.195.26.187:7077)
-    deploy-mode: Whether to deploy this application within the cluster or from an external client (e.g. client)
-    application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes.
-    application-arguments: Space delimited arguments passed to the main method of <main-class>, if any
-
-To enumerate all options available to `spark-submit` run it with the `--help` flag. Here are a few
-examples of common options:
-
-{% highlight bash %}
-# Run application locally
-./bin/spark-submit \
-  --class org.apache.spark.examples.SparkPi
-  --master local[8] \
-  /path/to/examples.jar \
-  100
-
-# Run on a Spark standalone cluster
-./bin/spark-submit \
-  --class org.apache.spark.examples.SparkPi
-  --master spark://207.184.161.138:7077 \
-  --executor-memory 20G \
-  --total-executor-cores 100 \
-  /path/to/examples.jar \
-  1000
-
-# Run on a YARN cluster
-HADOOP_CONF_DIR=XX ./bin/spark-submit \
-  --class org.apache.spark.examples.SparkPi
-  --master yarn-cluster \  # can also be `yarn-client` for client mode
-  --executor-memory 20G \
-  --num-executors 50 \
-  /path/to/examples.jar \
-  1000
-{% endhighlight %}
-
-### Loading Configurations from a File
-
-The `spark-submit` script can load default [Spark configuration values](configuration.html) from a
-properties file and pass them on to your application. By default it will read configuration options
-from `conf/spark-defaults.conf`. For more detail, see the section on
-[loading default configurations](configuration.html#loading-default-configurations).
-
-Loading default Spark configurations this way can obviate the need for certain flags to
-`spark-submit`. For instance, if the `spark.master` property is set, you can safely omit the
-`--master` flag from `spark-submit`. In general, configuration values explicitly set on a
-`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values in the
-defaults file.
-
-If you are ever unclear where configuration options are coming from, you can print out fine-grained
-debugging information by running `spark-submit` with the `--verbose` option.
-
-### Advanced Dependency Management
-When using `spark-submit`, the application jar along with any jars included with the `--jars` option
-will be automatically transferred to the cluster. Spark uses the following URL scheme to allow
-different strategies for disseminating jars:
-
-- **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and
-  every executor pulls the file from the driver HTTP server.
-- **hdfs:**, **http:**, **https:**, **ftp:** - these pull down files and JARs from the URI as expected
-- **local:** - a URI starting with local:/ is expected to exist as a local file on each worker node.  This
-  means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker,
-  or shared via NFS, GlusterFS, etc.
-
-Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes.
-This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup
-is handled automatically, and with Spark standalone, automatic cleanup can be configured with the
-`spark.worker.cleanup.appDataTtl` property.
-
-For python, the equivalent `--py-files` option can be used to distribute .egg and .zip libraries
-to executors.
+Applications can be submitted to a cluster of any type using the `spark-submit` script.
+The [application submission guide](submitting-applications.html) describes how to do this.
 
 # Monitoring
 
diff --git a/docs/configuration.md b/docs/configuration.md
index b6e7fd34ea..2fd691800e 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -7,8 +7,8 @@ title: Spark Configuration
 
 Spark provides three locations to configure the system:
 
-* [Spark properties](#spark-properties) control most application parameters and can be set by passing
-  a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java
+* [Spark properties](#spark-properties) control most application parameters and can be set by using
+  a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object, or through Java
   system properties.
 * [Environment variables](#environment-variables) can be used to set per-machine settings, such as
   the IP address, through the `conf/spark-env.sh` script on each node.
@@ -18,8 +18,8 @@ Spark provides three locations to configure the system:
 
 Spark properties control most application settings and are configured separately for each
 application. These properties can be set directly on a
-[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passed as an argument to your
-SparkContext. SparkConf allows you to configure some of the common properties
+[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) passed to your
+`SparkContext`. `SparkConf` allows you to configure some of the common properties
 (e.g. master URL and application name), as well as arbitrary key-value pairs through the
 `set()` method. For example, we could initialize an application as follows:
 
@@ -75,6 +75,7 @@ appear. For all other configuration properties, you can assume the default value
 Most of the properties that control internal settings have reasonable default values. Some
 of the most common options to set are:
 
+#### Application Properties
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
@@ -777,7 +778,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 </table>
 
-#### Cluster Managers (YARN, Mesos, Standalone)
+#### Cluster Managers
 Each cluster manager in Spark has additional configuration options. Configurations 
 can be found on the pages for each mode:
 
diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css
index 30ea440c00..3fa12ac2b2 100644
--- a/docs/css/bootstrap.min.css
+++ b/docs/css/bootstrap.min.css
@@ -6,4 +6,4 @@
  * http://www.apache.org/licenses/LICENSE-2.0
  *
  * Designed and built with all the love in the world @twitter by @mdo and @fat.
- */article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}a:hover,a:active{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{height:auto;max-width:100%;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}#map_canvas img{max-width:none}button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}button,input{*overflow:visible;line-height:normal}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}button,input[type="button"],input[type="reset"],input[type="submit"]{cursor:pointer;-webkit-appearance:button}input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}textarea{overflow:auto;vertical-align:top}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 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diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 42ab27bf55..fdb9f98e21 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -10,7 +10,7 @@ title: GraphX Programming Guide
   <img src="img/graphx_logo.png"
        title="GraphX Logo"
        alt="GraphX"
-       width="65%" />
+       width="60%" />
   <!-- Images are downsized intentionally to improve quality on retina displays -->
 </p>
 
@@ -25,6 +25,8 @@ operators (e.g., [subgraph](#structural_operators), [joinVertices](#join_operato
 addition, GraphX includes a growing collection of graph [algorithms](#graph_algorithms) and
 [builders](#graph_builders) to simplify graph analytics tasks.
 
+**GraphX is currently an alpha component. While we will minimize API changes, some APIs may change in future releases.**
+
 ## Background on Graph-Parallel Computation
 
 From social networks to language modeling, the growing scale and importance of
@@ -86,7 +88,7 @@ support the [Bagel API](api/scala/index.html#org.apache.spark.bagel.package) and
 [Bagel programming guide](bagel-programming-guide.html). However, we encourage Bagel users to
 explore the new GraphX API and comment on issues that may complicate the transition from Bagel.
 
-## Upgrade Guide from Spark 0.9.1
+## Migrating from Spark 0.9.1
 
 GraphX in Spark {{site.SPARK_VERSION}} contains one user-facing interface change from Spark 0.9.1. [`EdgeRDD`][EdgeRDD] may now store adjacent vertex attributes to construct the triplets, so it has gained a type parameter. The edges of a graph of type `Graph[VD, ED]` are of type `EdgeRDD[ED, VD]` rather than `EdgeRDD[ED]`.
 
@@ -690,7 +692,7 @@ class GraphOps[VD, ED] {
 
 In Spark, RDDs are not persisted in memory by default. To avoid recomputation, they must be explicitly cached when using them multiple times (see the [Spark Programming Guide][RDD Persistence]). Graphs in GraphX behave the same way. **When using a graph multiple times, make sure to call [`Graph.cache()`][Graph.cache] on it first.**
 
-[RDD Persistence]: scala-programming-guide.html#rdd-persistence
+[RDD Persistence]: programming-guide.html#rdd-persistence
 [Graph.cache]: api/scala/index.html#org.apache.spark.graphx.Graph@cache():Graph[VD,ED]
 
 In iterative computations, *uncaching* may also be necessary for best performance. By default, cached RDDs and graphs will remain in memory until memory pressure forces them to be evicted in LRU order. For iterative computation, intermediate results from previous iterations will fill up the cache. Though they will eventually be evicted, the unnecessary data stored in memory will slow down garbage collection. It would be more efficient to uncache intermediate results as soon as they are no longer necessary. This involves materializing (caching and forcing) a graph or RDD every iteration, uncaching all other datasets, and only using the materialized dataset in future iterations. However, because graphs are composed of multiple RDDs, it can be difficult to unpersist them correctly. **For iterative computation we recommend using the Pregel API, which correctly unpersists intermediate results.**
diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index a0aeab5727..32403bc695 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -1,6 +1,6 @@
 ---
 layout: global
-title: Running with Cloudera and HortonWorks
+title: Third-Party Hadoop Distributions
 ---
 
 Spark can run against all versions of Cloudera's Distribution Including Apache Hadoop (CDH) and
diff --git a/docs/index.md b/docs/index.md
index c9b10376cc..1a4ff3dbf5 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -4,23 +4,23 @@ title: Spark Overview
 ---
 
 Apache Spark is a fast and general-purpose cluster computing system.
-It provides high-level APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html) that make parallel jobs easy to write, and an optimized engine that supports general computation graphs.
-It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
+It provides high-level APIs in Java, Scala and Python,
+and an optimized engine that supports general execution graphs.
+It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [Spark SQL](sql-programming-guide.html) for structured data, [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
 
 # Downloading
 
-Get Spark by visiting the [downloads page](http://spark.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}. The downloads page 
+Get Spark from the [downloads page](http://spark.apache.org/downloads.html) of the project website. This documentation is for Spark version {{site.SPARK_VERSION}}. The downloads page 
 contains Spark packages for many popular HDFS versions. If you'd like to build Spark from 
-scratch, visit the [building with Maven](building-with-maven.html) page.
+scratch, visit [building Spark with Maven](building-with-maven.html).
 
-Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is 
-to have `java` to installed on your system `PATH`, or the `JAVA_HOME` environment variable 
-pointing to a Java installation.
+Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It's easy to run
+locally on one machine --- all you need is to have `java` installed on your system `PATH`,
+or the `JAVA_HOME` environment variable pointing to a Java installation.
 
-For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_BINARY_VERSION}}. 
-If you write applications in Scala, you will need to use a compatible Scala version 
-(e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work. You can get the 
-right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
+Spark runs on Java 6+ and Python 2.6+. For the Scala API, Spark {{site.SPARK_VERSION}} uses
+Scala {{site.SCALA_BINARY_VERSION}}. You will need to use a compatible Scala version 
+({{site.SCALA_BINARY_VERSION}}.x).
 
 # Running the Examples and Shell
 
@@ -28,24 +28,23 @@ Spark comes with several sample programs.  Scala, Java and Python examples are i
 `examples/src/main` directory. To run one of the Java or Scala sample programs, use
 `bin/run-example <class> [params]` in the top-level Spark directory. (Behind the scenes, this
 invokes the more general
-[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) for
+[`spark-submit` script](submitting-applications.html) for
 launching applications). For example,
 
     ./bin/run-example SparkPi 10
 
-You can also run Spark interactively through modified versions of the Scala shell. This is a
+You can also run Spark interactively through a modified version of the Scala shell. This is a
 great way to learn the framework.
 
     ./bin/spark-shell --master local[2]
 
 The `--master` option specifies the
-[master URL for a distributed cluster](scala-programming-guide.html#master-urls), or `local` to run
+[master URL for a distributed cluster](submitting-applications.html#master-urls), or `local` to run
 locally with one thread, or `local[N]` to run locally with N threads. You should start by using
 `local` for testing. For a full list of options, run Spark shell with the `--help` option.
 
-Spark also provides a Python interface. To run Spark interactively in a Python interpreter, use
-`bin/pyspark`. As in Spark shell, you can also pass in the `--master` option to configure your
-master URL.
+Spark also provides a Python API. To run Spark interactively in a Python interpreter, use
+`bin/pyspark`:
 
     ./bin/pyspark --master local[2]
 
@@ -66,17 +65,17 @@ options for deployment:
 
 # Where to Go from Here
 
-**Programming guides:**
+**Programming Guides:**
 
 * [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
-* [Spark Programming Guide](scala-programming-guide.html): an overview of Spark concepts, and details on the Scala API
-  * [Java Programming Guide](java-programming-guide.html): using Spark from Java
-  * [Python Programming Guide](python-programming-guide.html): using Spark from Python
-* [Spark Streaming](streaming-programming-guide.html): Spark's API for processing data streams
-* [Spark SQL](sql-programming-guide.html): Support for running relational queries on Spark
-* [MLlib (Machine Learning)](mllib-guide.html): Spark's built-in machine learning library
-* [Bagel (Pregel on Spark)](bagel-programming-guide.html): simple graph processing model
-* [GraphX (Graphs on Spark)](graphx-programming-guide.html): Spark's new API for graphs
+* [Spark Programming Guide](programming-guide.html): detailed overview of Spark
+  in all supported languages (Scala, Java, Python)
+* Modules built on Spark:
+  * [Spark Streaming](streaming-programming-guide.html): processing real-time data streams
+  * [Spark SQL](sql-programming-guide.html): support for structured data and relational queries
+  * [MLlib](mllib-guide.html): built-in machine learning library
+  * [GraphX](graphx-programming-guide.html): Spark's new API for graph processing
+  * [Bagel (Pregel on Spark)](bagel-programming-guide.html): older, simple graph processing model
 
 **API Docs:**
 
@@ -84,26 +83,30 @@ options for deployment:
 * [Spark Java API (Javadoc)](api/java/index.html)
 * [Spark Python API (Epydoc)](api/python/index.html)
 
-**Deployment guides:**
+**Deployment Guides:**
 
 * [Cluster Overview](cluster-overview.html): overview of concepts and components when running on a cluster
-* [Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
-* [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
-* [Mesos](running-on-mesos.html): deploy a private cluster using
-    [Apache Mesos](http://mesos.apache.org)
-* [YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
+* [Submitting Applications](submitting-applications.html): packaging and deploying applications
+* Deployment modes:
+  * [Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
+  * [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
+  * [Mesos](running-on-mesos.html): deploy a private cluster using
+      [Apache Mesos](http://mesos.apache.org)
+  * [YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
 
-**Other documents:**
+**Other Documents:**
 
 * [Configuration](configuration.html): customize Spark via its configuration system
+* [Monitoring](monitoring.html): track the behavior of your applications
 * [Tuning Guide](tuning.html): best practices to optimize performance and memory use
+* [Job Scheduling](job-scheduling.html): scheduling resources across and within Spark applications
 * [Security](security.html): Spark security support
 * [Hardware Provisioning](hardware-provisioning.html): recommendations for cluster hardware
-* [Job Scheduling](job-scheduling.html): scheduling resources across and within Spark applications
+* [3<sup>rd</sup> Party Hadoop Distributions](hadoop-third-party-distributions.html): using common Hadoop distributions
 * [Building Spark with Maven](building-with-maven.html): build Spark using the Maven system
 * [Contributing to Spark](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark)
 
-**External resources:**
+**External Resources:**
 
 * [Spark Homepage](http://spark.apache.org)
 * [Shark](http://shark.cs.berkeley.edu): Apache Hive over Spark
@@ -112,9 +115,9 @@ options for deployment:
   exercises about Spark, Shark, Spark Streaming, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/3/),
   [slides](http://ampcamp.berkeley.edu/3/) and [exercises](http://ampcamp.berkeley.edu/3/exercises/) are
   available online for free.
-* [Code Examples](http://spark.apache.org/examples.html): more are also available in the [examples subfolder](https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/) of Spark
-* [Paper Describing Spark](http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf)
-* [Paper Describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)
+* [Code Examples](http://spark.apache.org/examples.html): more are also available in the `examples` subfolder of Spark ([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+ [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
+ [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python))
 
 # Community
 
diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md
index 943fdd9d01..bb539582f6 100644
--- a/docs/java-programming-guide.md
+++ b/docs/java-programming-guide.md
@@ -1,218 +1,7 @@
 ---
 layout: global
 title: Java Programming Guide
+redirect: programming-guide.html
 ---
 
-The Spark Java API exposes all the Spark features available in the Scala version to Java.
-To learn the basics of Spark, we recommend reading through the
-[Scala programming guide](scala-programming-guide.html) first; it should be
-easy to follow even if you don't know Scala.
-This guide will show how to use the Spark features described there in Java.
-
-The Spark Java API is defined in the
-[`org.apache.spark.api.java`](api/java/index.html?org/apache/spark/api/java/package-summary.html) package, and includes
-a [`JavaSparkContext`](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) for
-initializing Spark and [`JavaRDD`](api/java/index.html?org/apache/spark/api/java/JavaRDD.html) classes,
-which support the same methods as their Scala counterparts but take Java functions and return
-Java data and collection types. The main differences have to do with passing functions to RDD
-operations (e.g. map) and handling RDDs of different types, as discussed next.
-
-# Key Differences in the Java API
-
-There are a few key differences between the Java and Scala APIs:
-
-* Java does not support anonymous or first-class functions, so functions are passed
-  using anonymous classes that implement the
-  [`org.apache.spark.api.java.function.Function`](api/java/index.html?org/apache/spark/api/java/function/Function.html),
-  [`Function2`](api/java/index.html?org/apache/spark/api/java/function/Function2.html), etc.
-  interfaces.
-* To maintain type safety, the Java API defines specialized Function and RDD
-  classes for key-value pairs and doubles. For example, 
-  [`JavaPairRDD`](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)
-  stores key-value pairs.
-* Some methods are defined on the basis of the passed function's return type.
-  For example `mapToPair()` returns
-  [`JavaPairRDD`](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html),
-  and `mapToDouble()` returns
-  [`JavaDoubleRDD`](api/java/index.html?org/apache/spark/api/java/JavaDoubleRDD.html).
-* RDD methods like `collect()` and `countByKey()` return Java collections types,
-  such as `java.util.List` and `java.util.Map`.
-* Key-value pairs, which are simply written as `(key, value)` in Scala, are represented
-  by the `scala.Tuple2` class, and need to be created using `new Tuple2<K, V>(key, value)`.
-
-## RDD Classes
-
-Spark defines additional operations on RDDs of key-value pairs and doubles, such
-as `reduceByKey`, `join`, and `stdev`.
-
-In the Scala API, these methods are automatically added using Scala's
-[implicit conversions](http://www.scala-lang.org/node/130) mechanism.
-
-In the Java API, the extra methods are defined in the
-[`JavaPairRDD`](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)
-and [`JavaDoubleRDD`](api/java/index.html?org/apache/spark/api/java/JavaDoubleRDD.html)
-classes.  RDD methods like `map` are overloaded by specialized `PairFunction`
-and `DoubleFunction` classes, allowing them to return RDDs of the appropriate
-types.  Common methods like `filter` and `sample` are implemented by
-each specialized RDD class, so filtering a `PairRDD` returns a new `PairRDD`,
-etc (this achieves the "same-result-type" principle used by the [Scala collections
-framework](http://docs.scala-lang.org/overviews/core/architecture-of-scala-collections.html)).
-
-## Function Interfaces
-
-The following table lists the function interfaces used by the Java API, located in the
-[`org.apache.spark.api.java.function`](api/java/index.html?org/apache/spark/api/java/function/package-summary.html)
-package. Each interface has a single abstract method, `call()`.
-
-<table class="table">
-<tr><th>Class</th><th>Function Type</th></tr>
-
-<tr><td>Function&lt;T, R&gt;</td><td>T =&gt; R </td></tr>
-<tr><td>DoubleFunction&lt;T&gt;</td><td>T =&gt; Double </td></tr>
-<tr><td>PairFunction&lt;T, K, V&gt;</td><td>T =&gt; Tuple2&lt;K, V&gt; </td></tr>
-
-<tr><td>FlatMapFunction&lt;T, R&gt;</td><td>T =&gt; Iterable&lt;R&gt; </td></tr>
-<tr><td>DoubleFlatMapFunction&lt;T&gt;</td><td>T =&gt; Iterable&lt;Double&gt; </td></tr>
-<tr><td>PairFlatMapFunction&lt;T, K, V&gt;</td><td>T =&gt; Iterable&lt;Tuple2&lt;K, V&gt;&gt; </td></tr>
-
-<tr><td>Function2&lt;T1, T2, R&gt;</td><td>T1, T2 =&gt; R (function of two arguments)</td></tr>
-</table>
-
-## Storage Levels
-
-RDD [storage level](scala-programming-guide.html#rdd-persistence) constants, such as `MEMORY_AND_DISK`, are
-declared in the [org.apache.spark.api.java.StorageLevels](api/java/index.html?org/apache/spark/api/java/StorageLevels.html) class. To
-define your own storage level, you can use StorageLevels.create(...). 
-
-# Other Features
-
-The Java API supports other Spark features, including
-[accumulators](scala-programming-guide.html#accumulators),
-[broadcast variables](scala-programming-guide.html#broadcast-variables), and
-[caching](scala-programming-guide.html#rdd-persistence).
-
-# Upgrading From Pre-1.0 Versions of Spark
-
-In version 1.0 of Spark the Java API was refactored to better support Java 8
-lambda expressions. Users upgrading from older versions of Spark should note
-the following changes:
-
-* All `org.apache.spark.api.java.function.*` have been changed from abstract
-  classes to interfaces. This means that concrete implementations of these 
-  `Function` classes will need to use `implements` rather than `extends`.
-* Certain transformation functions now have multiple versions depending
-  on the return type. In Spark core, the map functions (`map`, `flatMap`, and
-  `mapPartitions`) have type-specific versions, e.g.
-  [`mapToPair`](api/java/org/apache/spark/api/java/JavaRDDLike.html#mapToPair(org.apache.spark.api.java.function.PairFunction))
-  and [`mapToDouble`](api/java/org/apache/spark/api/java/JavaRDDLike.html#mapToDouble(org.apache.spark.api.java.function.DoubleFunction)).
-  Spark Streaming also uses the same approach, e.g. [`transformToPair`](api/java/org/apache/spark/streaming/api/java/JavaDStreamLike.html#transformToPair(org.apache.spark.api.java.function.Function)).
-
-# Example
-
-As an example, we will implement word count using the Java API.
-
-{% highlight java %}
-import org.apache.spark.api.java.*;
-import org.apache.spark.api.java.function.*;
-
-JavaSparkContext jsc = new JavaSparkContext(...);
-JavaRDD<String> lines = jsc.textFile("hdfs://...");
-JavaRDD<String> words = lines.flatMap(
-  new FlatMapFunction<String, String>() {
-    @Override public Iterable<String> call(String s) {
-      return Arrays.asList(s.split(" "));
-    }
-  }
-);
-{% endhighlight %}
-
-The word count program starts by creating a `JavaSparkContext`, which accepts
-the same parameters as its Scala counterpart.  `JavaSparkContext` supports the
-same data loading methods as the regular `SparkContext`; here, `textFile`
-loads lines from text files stored in HDFS.
-
-To split the lines into words, we use `flatMap` to split each line on
-whitespace.  `flatMap` is passed a `FlatMapFunction` that accepts a string and
-returns an `java.lang.Iterable` of strings.
-
-Here, the `FlatMapFunction` was created inline; another option is to subclass
-`FlatMapFunction` and pass an instance to `flatMap`:
-
-{% highlight java %}
-class Split extends FlatMapFunction<String, String> {
-  @Override public Iterable<String> call(String s) {
-    return Arrays.asList(s.split(" "));
-  }
-}
-JavaRDD<String> words = lines.flatMap(new Split());
-{% endhighlight %}
-
-Java 8+ users can also write the above `FlatMapFunction` in a more concise way using 
-a lambda expression:
-
-{% highlight java %}
-JavaRDD<String> words = lines.flatMap(s -> Arrays.asList(s.split(" ")));
-{% endhighlight %}
-
-This lambda syntax can be applied to all anonymous classes in Java 8.
-
-Continuing with the word count example, we map each word to a `(word, 1)` pair:
-
-{% highlight java %}
-import scala.Tuple2;
-JavaPairRDD<String, Integer> ones = words.mapToPair(
-  new PairFunction<String, String, Integer>() {
-    @Override public Tuple2<String, Integer> call(String s) {
-      return new Tuple2<String, Integer>(s, 1);
-    }
-  }
-);
-{% endhighlight %}
-
-Note that `mapToPair` was passed a `PairFunction<String, String, Integer>` and
-returned a `JavaPairRDD<String, Integer>`.
-
-To finish the word count program, we will use `reduceByKey` to count the
-occurrences of each word:
-
-{% highlight java %}
-JavaPairRDD<String, Integer> counts = ones.reduceByKey(
-  new Function2<Integer, Integer, Integer>() {
-    @Override public Integer call(Integer i1, Integer i2) {
-      return i1 + i2;
-    }
-  }
-);
-{% endhighlight %}
-
-Here, `reduceByKey` is passed a `Function2`, which implements a function with
-two arguments.  The resulting `JavaPairRDD` contains `(word, count)` pairs.
-
-In this example, we explicitly showed each intermediate RDD.  It is also
-possible to chain the RDD transformations, so the word count example could also
-be written as:
-
-{% highlight java %}
-JavaPairRDD<String, Integer> counts = lines.flatMapToPair(
-    ...
-  ).map(
-    ...
-  ).reduceByKey(
-    ...
-  );
-{% endhighlight %}
-
-There is no performance difference between these approaches; the choice is
-just a matter of style.
-
-# API Docs
-
-[API documentation](api/java/index.html) for Spark in Java is available in Javadoc format.
-
-# Where to Go from Here
-
-Spark includes several sample programs using the Java API in
-[`examples/src/main/java`](https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples).  You can run them by passing the class name to the
-`bin/run-example` script included in Spark; for example:
-
-    ./bin/run-example JavaWordCount README.md
+This document has been merged into the [Spark programming guide](programming-guide.html).
diff --git a/docs/js/api-docs.js b/docs/js/api-docs.js
index 1414b6d0b8..ce89d8943b 100644
--- a/docs/js/api-docs.js
+++ b/docs/js/api-docs.js
@@ -1,10 +1,27 @@
+/*
+ * 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.
+ */
+
 /* Dynamically injected post-processing code for the API docs */
 
 $(document).ready(function() {
   var annotations = $("dt:contains('Annotations')").next("dd").children("span.name");
-  addBadges(annotations, "AlphaComponent", ":: AlphaComponent ::", "<span class='alphaComponent badge'>Alpha Component</span>");
-  addBadges(annotations, "DeveloperApi", ":: DeveloperApi ::", "<span class='developer badge'>Developer API</span>");
-  addBadges(annotations, "Experimental", ":: Experimental ::", "<span class='experimental badge'>Experimental</span>");
+  addBadges(annotations, "AlphaComponent", ":: AlphaComponent ::", '<span class="alphaComponent badge">Alpha Component</span>');
+  addBadges(annotations, "DeveloperApi", ":: DeveloperApi ::", '<span class="developer badge">Developer API</span>');
+  addBadges(annotations, "Experimental", ":: Experimental ::", '<span class="experimental badge">Experimental</span>');
 });
 
 function addBadges(allAnnotations, name, tag, html) {
diff --git a/docs/js/main.js b/docs/js/main.js
index 5905546711..f1a90e47e8 100755
--- a/docs/js/main.js
+++ b/docs/js/main.js
@@ -1,3 +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.
+ */
+
+/* Custom JavaScript code in the MarkDown docs */
+
+// Enable language-specific code tabs
 function codeTabs() {
   var counter = 0;
   var langImages = {
@@ -62,6 +82,7 @@ function makeCollapsable(elt, accordionClass, accordionBodyId, title) {
   );
 }
 
+// Enable "view solution" sections (for exercises)
 function viewSolution() {
   var counter = 0
   $("div.solution").each(function() {
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 640ca83085..95ee6bc968 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -31,7 +31,7 @@ MLlib is a new component under active development.
 The APIs marked `Experimental`/`DeveloperApi` may change in future releases, 
 and we will provide migration guide between releases.
 
-## Dependencies
+# Dependencies
 
 MLlib uses linear algebra packages [Breeze](http://www.scalanlp.org/), which depends on
 [netlib-java](https://github.com/fommil/netlib-java), and
@@ -50,9 +50,9 @@ To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4
 
 ---
 
-## Migration guide
+# Migration Guide
 
-### From 0.9 to 1.0
+## From 0.9 to 1.0
 
 In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
 breaking changes.  If your data is sparse, please store it in a sparse format instead of dense to
@@ -84,9 +84,9 @@ val vector: Vector = Vectors.dense(array) // a dense vector
 <div data-lang="java" markdown="1">
 
 We used to represent a feature vector by `double[]`, which is replaced by
-[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
+[`Vector`](api/java/index.html?org/apache/spark/mllib/linalg/Vector.html) in v1.0. Algorithms that used
 to accept `RDD<double[]>` now take
-`RDD<Vector>`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint)
+`RDD<Vector>`. [`LabeledPoint`](api/java/index.html?org/apache/spark/mllib/regression/LabeledPoint.html)
 is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to
 `Vector` is straightforward:
 
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index a22980d03a..97e8f4e966 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -116,7 +116,7 @@ is a stochastic gradient. Here `$S$` is the sampled subset of size `$|S|=$ miniB
 $\cdot n$`.
 
 In each iteration, the sampling over the distributed dataset
-([RDD](scala-programming-guide.html#resilient-distributed-datasets-rdds)), as well as the
+([RDD](programming-guide.html#resilient-distributed-datasets-rdds)), as well as the
 computation of the sum of the partial results from each worker machine is performed by the
 standard spark routines.
 
diff --git a/docs/monitoring.md b/docs/monitoring.md
index fffc58ac39..2b9e9e5bd7 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -3,7 +3,7 @@ layout: global
 title: Monitoring and Instrumentation
 ---
 
-There are several ways to monitor Spark applications.
+There are several ways to monitor Spark applications: web UIs, metrics, and external instrumentation.
 
 # Web Interfaces
 
diff --git a/docs/programming-guide.md b/docs/programming-guide.md
new file mode 100644
index 0000000000..7d77e640d0
--- /dev/null
+++ b/docs/programming-guide.md
@@ -0,0 +1,1294 @@
+---
+layout: global
+title: Spark Programming Guide
+---
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+
+# Overview
+
+At a high level, every Spark application consists of a *driver program* that runs the user's `main` function and executes various *parallel operations* on a cluster. The main abstraction Spark provides is a *resilient distributed dataset* (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to *persist* an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.
+
+A second abstraction in Spark is *shared variables* that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: *broadcast variables*, which can be used to cache a value in memory on all nodes, and *accumulators*, which are variables that are only "added" to, such as counters and sums.
+
+This guide shows each of these features in each of Spark's supported languages. It is easiest to follow
+along with if you launch Spark's interactive shell -- either `bin/spark-shell` for the Scala shell or
+`bin/pyspark` for the Python one.
+
+# Linking with Spark
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_BINARY_VERSION}}. To write
+applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X).
+
+To write a Spark application, you need to add a Maven dependency on Spark. Spark is available through Maven Central at:
+
+    groupId = org.apache.spark
+    artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
+    version = {{site.SPARK_VERSION}}
+
+In addition, if you wish to access an HDFS cluster, you need to add a dependency on
+`hadoop-client` for your version of HDFS. Some common HDFS version tags are listed on the
+[third party distributions](hadoop-third-party-distributions.html) page.
+
+    groupId = org.apache.hadoop
+    artifactId = hadoop-client
+    version = <your-hdfs-version>
+
+Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+import org.apache.spark.SparkConf
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Spark {{site.SPARK_VERSION}} works with Java 6 and higher. If you are using Java 8, Spark supports
+[lambda expressions](http://docs.oracle.com/javase/tutorial/java/javaOO/lambdaexpressions.html)
+for concisely writing functions, otherwise you can use the classes in the
+[org.apache.spark.api.java.function](api/java/index.html?org/apache/spark/api/java/function/package-summary.html) package.
+
+To write a Spark application in Java, you need to add a dependency on Spark. Spark is available through Maven Central at:
+
+    groupId = org.apache.spark
+    artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
+    version = {{site.SPARK_VERSION}}
+
+In addition, if you wish to access an HDFS cluster, you need to add a dependency on
+`hadoop-client` for your version of HDFS. Some common HDFS version tags are listed on the
+[third party distributions](hadoop-third-party-distributions.html) page.
+
+    groupId = org.apache.hadoop
+    artifactId = hadoop-client
+    version = <your-hdfs-version>
+
+Finally, you need to import some Spark classes into your program. Add the following lines:
+
+{% highlight scala %}
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.SparkConf
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+Spark {{site.SPARK_VERSION}} works with Python 2.6 or higher (but not Python 3). It uses the standard CPython interpreter,
+so C libraries like NumPy can be used.
+
+To run Spark applications in Python, use the `bin/spark-submit` script located in the Spark directory.
+This script will load Spark's Java/Scala libraries and allow you to submit applications to a cluster.
+You can also use `bin/pyspark` to launch an interactive Python shell.
+
+If you wish to access HDFS data, you need to use a build of PySpark linking
+to your version of HDFS. Some common HDFS version tags are listed on the
+[third party distributions](hadoop-third-party-distributions.html) page.
+[Prebuilt packages](http://spark.apache.org/downloads.html) are also available on the Spark homepage
+for common HDFS versions.
+
+Finally, you need to import some Spark classes into your program. Add the following lines:
+
+{% highlight scala %}
+from pyspark import SparkContext, SparkConf
+{% endhighlight %}
+
+</div>
+
+</div>
+
+
+# Initializing Spark
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+The first thing a Spark program must do is to create a [SparkContext](api/scala/index.html#org.apache.spark.SparkContext) object, which tells Spark
+how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object
+that contains information about your application.
+
+{% highlight scala %}
+val conf = new SparkConf().setAppName(appName).setMaster(master)
+new SparkContext(conf)
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+The first thing a Spark program must do is to create a [JavaSparkContext](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) object, which tells Spark
+how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/java/index.html?org/apache/spark/SparkConf.html) object
+that contains information about your application.
+
+{% highlight java %}
+SparkConf conf = new SparkConf().setAppName(appName).setMaster(master);
+JavaSparkContext sc = new JavaSparkContext(conf);
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+The first thing a Spark program must do is to create a [SparkContext](api/python/pyspark.context.SparkContext-class.html) object, which tells Spark
+how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/python/pyspark.conf.SparkConf-class.html) object
+that contains information about your application.
+
+{% highlight python %}
+conf = SparkConf().setAppName(appName).setMaster(master)
+sc = SparkContext(conf)
+{% endhighlight %}
+
+</div>
+
+</div>
+
+The `appName` parameter is a name for your application to show on the cluster UI.
+`master` is a [Spark, Mesos or YARN cluster URL](submitting-applications.html#master-urls),
+or a special "local" string to run in local mode.
+In practice, when running on a cluster, you will not want to hardcode `master` in the program,
+but rather [launch the application with `spark-submit`](submitting-applications.html) and
+receive it there. However, for local testing and unit tests, you can pass "local" to run Spark
+in-process.
+
+
+## Using the Shell
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the
+variable called `sc`. Making your own SparkContext will not work. You can set which master the
+context connects to using the `--master` argument, and you can add JARs to the classpath
+by passing a comma-separated list to the `--jars` argument.
+For example, to run `bin/spark-shell` on exactly four cores, use:
+
+{% highlight bash %}
+$ ./bin/spark-shell --master local[4]
+{% endhighlight %}
+
+Or, to also add `code.jar` to its classpath, use:
+
+{% highlight bash %}
+$ ./bin/spark-shell --master local[4] --jars code.jar
+{% endhighlight %}
+
+For a complete list of options, run `spark-shell --help`. Behind the scenes,
+`spark-shell` invokes the more general [`spark-submit` script](submitting-applications.html).
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+In the PySpark shell, a special interpreter-aware SparkContext is already created for you, in the
+variable called `sc`. Making your own SparkContext will not work. You can set which master the
+context connects to using the `--master` argument, and you can add Python .zip, .egg or .py files
+to the runtime path by passing a comma-separated list to `--py-files`.
+For example, to run `bin/pyspark` on exactly four cores, use:
+
+{% highlight bash %}
+$ ./bin/pyspark --master local[4]
+{% endhighlight %}
+
+Or, to also add `code.py` to the search path (in order to later be able to `import code`), use:
+
+{% highlight bash %}
+$ ./bin/pyspark --master local[4] --py-files code.py
+{% endhighlight %}
+
+For a complete list of options, run `pyspark --help`. Behind the scenes,
+`pyspark` invokes the more general [`spark-submit` script](submitting-applications.html).
+
+It is also possible to launch the PySpark shell in [IPython](http://ipython.org), the
+enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To
+use IPython, set the `IPYTHON` variable to `1` when running `bin/pyspark`:
+
+{% highlight bash %}
+$ IPYTHON=1 ./bin/pyspark
+{% endhighlight %}
+
+You can customize the `ipython` command by setting `IPYTHON_OPTS`. For example, to launch
+the [IPython Notebook](http://ipython.org/notebook.html) with PyLab plot support:
+
+{% highlight bash %}
+$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark
+{% endhighlight %}
+
+</div>
+
+</div>
+
+# Resilient Distributed Datasets (RDDs)
+
+Spark revolves around the concept of a _resilient distributed dataset_ (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: *parallelizing*
+an existing collection in your driver program, or referencing a dataset in an external storage system, such as a
+shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.
+
+## Parallelized Collections
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+Parallelized collections are created by calling `SparkContext`'s `parallelize` method on an existing collection in your driver program (a Scala `Seq`). The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:
+
+{% highlight scala %}
+val data = Array(1, 2, 3, 4, 5)
+val distData = sc.parallelize(data)
+{% endhighlight %}
+
+Once created, the distributed dataset (`distData`) can be operated on in parallel. For example, we might call `distData.reduce((a, b) => a + b)` to add up the elements of the array. We describe operations on distributed datasets later on.
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Parallelized collections are created by calling `JavaSparkContext`'s `parallelize` method on an existing `Collection` in your driver program. The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:
+
+{% highlight java %}
+List<Integer> data = Arrays.asList(1, 2, 3, 4, 5);
+JavaRDD<Integer> distData = sc.parallelize(data);
+{% endhighlight %}
+
+Once created, the distributed dataset (`distData`) can be operated on in parallel. For example, we might call `distData.reduce((a, b) -> a + b)` to add up the elements of the list.
+We describe operations on distributed datasets later on.
+
+**Note:** *In this guide, we'll often use the concise Java 8 lambda syntax to specify Java functions, but
+in older versions of Java you can implement the interfaces in the
+[org.apache.spark.api.java.function](api/java/index.html?org/apache/spark/api/java/function/package-summary.html) package.
+We describe [passing functions to Spark](#passing-functions-to-spark) in more detail below.*
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+Parallelized collections are created by calling `SparkContext`'s `parallelize` method on an existing iterable or collection in your driver program. The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:
+
+{% highlight python %}
+data = [1, 2, 3, 4, 5]
+distData = sc.parallelize(data)
+{% endhighlight %}
+
+Once created, the distributed dataset (`distData`) can be operated on in parallel. For example, we can call `distData.reduce(lambda a, b: a + b)` to add up the elements of the list.
+We describe operations on distributed datasets later on.
+
+</div>
+
+</div>
+
+One important parameter for parallel collections is the number of *slices* to cut the dataset into. Spark will run one task for each slice of the cluster. Typically you want 2-4 slices for each CPU in your cluster. Normally, Spark tries to set the number of slices automatically based on your cluster. However, you can also set it manually by passing it as a second parameter to `parallelize` (e.g. `sc.parallelize(data, 10)`).
+
+## External Datasets
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc. Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop [InputFormat](http://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapred/InputFormat.html).
+
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+
+{% highlight scala %}
+scala> val distFile = sc.textFile("data.txt")
+distFile: RDD[String] = MappedRDD@1d4cee08
+{% endhighlight %}
+
+Once created, `distFile` can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the `map` and `reduce` operations as follows: `distFile.map(s => s.length).reduce((a, b) => a + b)`.
+
+Some notes on reading files with Spark:
+
+* If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.
+
+* All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
+
+* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+
+Apart from text files, Spark's Scala API also supports several other data formats:
+
+* `SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+
+* For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html). In addition, Spark allows you to specify native types for a few common Writables; for example, `sequenceFile[Int, String]` will automatically read IntWritables and Texts.
+
+* For other Hadoop InputFormats, you can use the `SparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `SparkContext.newHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`).
+
+* `RDD.saveAsObjectFile` and `SparkContext.objectFile` support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD.
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc. Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop [InputFormat](http://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapred/InputFormat.html).
+
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+
+{% highlight java %}
+JavaRDD<String> distFile = sc.textFile("data.txt");
+{% endhighlight %}
+
+Once created, `distFile` can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the `map` and `reduce` operations as follows: `distFile.map(s -> s.length()).reduce((a, b) -> a + b)`.
+
+Some notes on reading files with Spark:
+
+* If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.
+
+* All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
+
+* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+
+Apart from text files, Spark's Java API also supports several other data formats:
+
+* `JavaSparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+
+* For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html).
+
+* For other Hadoop InputFormats, you can use the `JavaSparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `JavaSparkContext.newHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`).
+
+* `JavaRDD.saveAsObjectFile` and `JavaSparkContext.objectFile` support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD.
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+PySpark can create distributed datasets from any file system supported by Hadoop, including your local file system, HDFS, KFS, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc.
+The current API is limited to text files, but support for binary Hadoop InputFormats is expected in future versions.
+
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+
+{% highlight python %}
+>>> distFile = sc.textFile("data.txt")
+{% endhighlight %}
+
+Once created, `distFile` can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the `map` and `reduce` operations as follows: `distFile.map(lambda s: len(s)).reduce(lambda a, b: a + b)`.
+
+Some notes on reading files with Spark:
+
+* If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.
+
+* All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
+
+* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+
+Apart reading files as a collection of lines,
+`SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+
+</div>
+
+
+</div>
+
+## RDD Operations
+
+RDDs support two types of operations: *transformations*, which create a new dataset from an existing one, and *actions*, which return a value to the driver program after running a computation on the dataset. For example, `map` is a transformation that passes each dataset element through a function and returns a new RDD representing the results. On the other hand, `reduce` is an action that aggregates all the elements of the RDD using some function and returns the final result to the driver program (although there is also a parallel `reduceByKey` that returns a distributed dataset).
+
+All transformations in Spark are <i>lazy</i>, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently -- for example, we can realize that a dataset created through `map` will be used in a `reduce` and return only the result of the `reduce` to the driver, rather than the larger mapped dataset.
+
+By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also *persist* an RDD in memory using the `persist` (or `cache`) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting RDDs on disk, or replicated across multiple nodes.
+
+### Basics
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+To illustrate RDD basics, consider the simple program below:
+
+{% highlight scala %}
+val lines = sc.textFile("data.txt")
+val lineLengths = lines.map(s => s.length)
+val totalLength = lineLengths.reduce((a, b) => a + b)
+{% endhighlight %}
+
+The first line defines a base RDD from an external file. This dataset is not loaded in memory or
+otherwise acted on: `lines` is merely a pointer to the file.
+The second line defines `lineLengths` as the result of a `map` transformation. Again, `lineLengths`
+is *not* immediately computed, due to laziness.
+Finally, we run `reduce`, which is an action. At this point Spark breaks the computation into tasks
+to run on separate machines, and each machine runs both its part of the map and a local reduction,
+returning only its answer to the driver program.
+
+If we also wanted to use `lineLengths` again later, we could add:
+
+{% highlight scala %}
+lineLengths.persist()
+{% endhighlight %}
+
+before the `reduce`, which would cause `lineLengths` to be saved in memory after the first time it is computed.
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+To illustrate RDD basics, consider the simple program below:
+
+{% highlight java %}
+JavaRDD<String> lines = sc.textFile("data.txt");
+JavaRDD<Integer> lineLengths = lines.map(s -> s.length());
+int totalLength = lineLengths.reduce((a, b) -> a + b);
+{% endhighlight %}
+
+The first line defines a base RDD from an external file. This dataset is not loaded in memory or
+otherwise acted on: `lines` is merely a pointer to the file.
+The second line defines `lineLengths` as the result of a `map` transformation. Again, `lineLengths`
+is *not* immediately computed, due to laziness.
+Finally, we run `reduce`, which is an action. At this point Spark breaks the computation into tasks
+to run on separate machines, and each machine runs both its part of the map and a local reduction,
+returning only its answer to the driver program.
+
+If we also wanted to use `lineLengths` again later, we could add:
+
+{% highlight java %}
+lineLengths.persist();
+{% endhighlight %}
+
+before the `reduce`, which would cause `lineLengths` to be saved in memory after the first time it is computed.
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+To illustrate RDD basics, consider the simple program below:
+
+{% highlight python %}
+lines = sc.textFile("data.txt")
+lineLengths = lines.map(lambda s: len(s))
+totalLength = lineLengths.reduce(lambda a, b: a + b)
+{% endhighlight %}
+
+The first line defines a base RDD from an external file. This dataset is not loaded in memory or
+otherwise acted on: `lines` is merely a pointer to the file.
+The second line defines `lineLengths` as the result of a `map` transformation. Again, `lineLengths`
+is *not* immediately computed, due to laziness.
+Finally, we run `reduce`, which is an action. At this point Spark breaks the computation into tasks
+to run on separate machines, and each machine runs both its part of the map and a local reduction,
+returning only its answer to the driver program.
+
+If we also wanted to use `lineLengths` again later, we could add:
+
+{% highlight python %}
+lineLengths.persist()
+{% endhighlight %}
+
+before the `reduce`, which would cause `lineLengths` to be saved in memory after the first time it is computed.
+
+</div>
+
+</div>
+
+### Passing Functions to Spark
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+Spark's API relies heavily on passing functions in the driver program to run on the cluster.
+There are two recommended ways to do this:
+
+* [Anonymous function syntax](http://docs.scala-lang.org/tutorials/tour/anonymous-function-syntax.html),
+  which can be used for short pieces of code.
+* Static methods in a global singleton object. For example, you can define `object MyFunctions` and then
+  pass `MyFunctions.func1`, as follows:
+
+{% highlight scala %}
+object MyFunctions {
+  def func1(s: String): String = { ... }
+}
+
+myRdd.map(MyFunctions.func1)
+{% endhighlight %}
+
+Note that while it is also possible to pass a reference to a method in a class instance (as opposed to
+a singleton object), this requires sending the object that contains that class along with the method.
+For example, consider:
+
+{% highlight scala %}
+class MyClass {
+  def func1(s: String): String = { ... }
+  def doStuff(rdd: RDD[String]): RDD[String] = { rdd.map(func1) }
+}
+{% endhighlight %}
+
+Here, if we create a `new MyClass` and call `doStuff` on it, the `map` inside there references the
+`func1` method *of that `MyClass` instance*, so the whole object needs to be sent to the cluster. It is
+similar to writing `rdd.map(x => this.func1(x))`.
+
+In a similar way, accessing fields of the outer object will reference the whole object:
+
+{% highlight scala %}
+class MyClass {
+  val field = "Hello"
+  def doStuff(rdd: RDD[String]): RDD[String] = { rdd.map(x => field + x) }
+}
+{% endhighlight %}
+
+is equilvalent to writing `rdd.map(x => this.field + x)`, which references all of `this`. To avoid this
+issue, the simplest way is to copy `field` into a local variable instead of accessing it externally:
+
+{% highlight scala %}
+def doStuff(rdd: RDD[String]): RDD[String] = {
+  val field_ = this.field
+  rdd.map(x => field_ + x)
+}
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Spark's API relies heavily on passing functions in the driver program to run on the cluster.
+In Java, functions are represented by classes implementing the interfaces in the
+[org.apache.spark.api.java.function](api/java/index.html?org/apache/spark/api/java/function/package-summary.html) package.
+There are two ways to create such functions:
+
+* Implement the Function interfaces in your own class, either as an anonymous inner class or a named one,
+  and pass an instance of it to Spark.
+* In Java 8, use [lambda expressions](http://docs.oracle.com/javase/tutorial/java/javaOO/lambdaexpressions.html)
+  to concisely define an implementation.
+
+While much of this guide uses lambda syntax for conciseness, it is easy to use all the same APIs
+in long-form. For example, we could have written our code above as follows:
+
+{% highlight java %}
+JavaRDD<String> lines = sc.textFile("data.txt");
+JavaRDD<Integer> lineLengths = lines.map(new Function<String, Integer>() {
+  public Integer call(String s) { return s.length(); }
+});
+int totalLength = lineLengths.reduce(new Function2<Integer, Integer, Integer>() {
+  public Integer call(Integer a, Integer b) { return a + b; }
+});
+{% endhighlight %}
+
+Or, if writing the functions inline is unwieldy:
+
+{% highlight java %}
+class GetLength implements Function<String, Integer> {
+  public Integer call(String s) { return s.length(); }
+}
+class Sum implements Function2<Integer, Integer, Integer> {
+  public Integer call(Integer a, Integer b) { return a + b; }
+}
+
+JavaRDD<String> lines = sc.textFile("data.txt");
+JavaRDD<Integer> lineLengths = lines.map(new GetLength());
+int totalLength = lineLengths.reduce(new Sum());
+{% endhighlight %}
+
+Note that anonymous inner classes in Java can also access variables in the enclosing scope as long
+as they are marked `final`. Spark will ship copies of these variables to each worker node as it does
+for other languages.
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+Spark's API relies heavily on passing functions in the driver program to run on the cluster.
+There are three recommended ways to do this:
+
+* [Lambda expressions](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions),
+  for simple functions that can be written as an expression. (Lambdas do not support multi-statement
+  functions or statements that do not return a value.)
+* Local `def`s inside the function calling into Spark, for longer code.
+* Top-level functions in a module.
+
+For example, to pass a longer function than can be supported using a `lambda`, consider
+the code below:
+
+{% highlight python %}
+"""MyScript.py"""
+if __name__ == "__main__":
+    def myFunc(s):
+        words = s.split(" ")
+        return len(words)
+
+    sc = SparkContext(...)
+    sc.textFile("file.txt").map(myFunc)
+{% endhighlight %}
+
+Note that while it is also possible to pass a reference to a method in a class instance (as opposed to
+a singleton object), this requires sending the object that contains that class along with the method.
+For example, consider:
+
+{% highlight python %}
+class MyClass(object):
+    def func(self, s):
+        return s
+    def doStuff(self, rdd):
+        return rdd.map(self.func)
+{% endhighlight %}
+
+Here, if we create a `new MyClass` and call `doStuff` on it, the `map` inside there references the
+`func` method *of that `MyClass` instance*, so the whole object needs to be sent to the cluster.
+
+In a similar way, accessing fields of the outer object will reference the whole object:
+
+{% highlight python %}
+class MyClass(object):
+    def __init__(self):
+        self.field = "Hello"
+    def doStuff(self, rdd):
+        return rdd.map(lambda s: self.field + x)
+{% endhighlight %}
+
+To avoid this issue, the simplest way is to copy `field` into a local variable instead
+of accessing it externally:
+
+{% highlight python %}
+def doStuff(self, rdd):
+    field = self.field
+    return rdd.map(lambda s: field + x)
+{% endhighlight %}
+
+</div>
+
+</div>
+
+### Working with Key-Value Pairs
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+While most Spark operations work on RDDs containing any type of objects, a few special operations are
+only available on RDDs of key-value pairs.
+The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+by a key.
+
+In Scala, these operations are automatically available on RDDs containing
+[Tuple2](http://www.scala-lang.org/api/{{site.SCALA_VERSION}}/index.html#scala.Tuple2) objects
+(the built-in tuples in the language, created by simply writing `(a, b)`), as long as you
+import `org.apache.spark.SparkContext._` in your program to enable Spark's implicit
+conversions. The key-value pair operations are available in the
+[PairRDDFunctions](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions) class,
+which automatically wraps around an RDD of tuples if you import the conversions.
+
+For example, the following code uses the `reduceByKey` operation on key-value pairs to count how
+many times each line of text occurs in a file:
+
+{% highlight scala %}
+val lines = sc.textFile("data.txt")
+val pairs = lines.map(s => (s, 1))
+val counts = pairs.reduceByKey((a, b) => a + b)
+{% endhighlight %}
+
+We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
+`counts.collect()` to bring them back to the driver program as an array of objects.
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+While most Spark operations work on RDDs containing any type of objects, a few special operations are
+only available on RDDs of key-value pairs.
+The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+by a key.
+
+In Java, key-value pairs are represented using the 
+[scala.Tuple2](http://www.scala-lang.org/api/{{site.SCALA_VERSION}}/index.html#scala.Tuple2) class
+from the Scala standard library. You can simply call `new Tuple2(a, b)` to create a tuple, and access
+its fields later with `tuple._1()` and `tuple._2()`.
+
+RDDs of key-value pairs are represented by the
+[JavaPairRDD](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html) class. You can construct
+JavaPairRDDs from JavaRDDs using special versions of the `map` operations, like
+`mapToPair` and `flatMapToPair`. The JavaPairRDD will have both standard RDD functions and special
+key-value ones.
+
+For example, the following code uses the `reduceByKey` operation on key-value pairs to count how
+many times each line of text occurs in a file:
+
+{% highlight scala %}
+JavaRDD<String> lines = sc.textFile("data.txt");
+JavaPairRDD<String, Integer> pairs = lines.mapToPair(s -> new Tuple2(s, 1));
+JavaPairRDD<String, Integer> counts = pairs.reduceByKey((a, b) -> a + b);
+{% endhighlight %}
+
+We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
+`counts.collect()` to bring them back to the driver program as an array of objects.
+
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+While most Spark operations work on RDDs containing any type of objects, a few special operations are
+only available on RDDs of key-value pairs.
+The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+by a key.
+
+In Python, these operations work on RDDs containing built-in Python tuples such as `(1, 2)`.
+Simply create such tuples and then call your desired operation.
+
+For example, the following code uses the `reduceByKey` operation on key-value pairs to count how
+many times each line of text occurs in a file:
+
+{% highlight python %}
+lines = sc.textFile("data.txt")
+pairs = lines.map(lambda s: (s, 1))
+counts = pairs.reduceByKey(lambda a, b: a + b)
+{% endhighlight %}
+
+We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
+`counts.collect()` to bring them back to the driver program as a list of objects.
+
+</div>
+
+</div>
+
+
+### Transformations
+
+The following table lists some of the common transformations supported by Spark. Refer to the
+RDD API doc
+([Scala](api/scala/index.html#org.apache.spark.rdd.RDD),
+ [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html),
+ [Python](api/python/pyspark.rdd.RDD-class.html))
+and pair RDD functions doc
+([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions),
+ [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html))
+for details.
+
+<table class="table">
+<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
+<tr>
+  <td> <b>map</b>(<i>func</i>) </td>
+  <td> Return a new distributed dataset formed by passing each element of the source through a function <i>func</i>. </td>
+</tr>
+<tr>
+  <td> <b>filter</b>(<i>func</i>) </td>
+  <td> Return a new dataset formed by selecting those elements of the source on which <i>func</i> returns true. </td>
+</tr>
+<tr>
+  <td> <b>flatMap</b>(<i>func</i>) </td>
+  <td> Similar to map, but each input item can be mapped to 0 or more output items (so <i>func</i> should return a Seq rather than a single item). </td>
+</tr>
+<tr>
+  <td> <b>mapPartitions</b>(<i>func</i>) </td>
+  <td> Similar to map, but runs separately on each partition (block) of the RDD, so <i>func</i> must be of type
+    Iterator&lt;T&gt; => Iterator&lt;U&gt; when running on an RDD of type T. </td>
+</tr>
+<tr>
+  <td> <b>mapPartitionsWithIndex</b>(<i>func</i>) </td>
+  <td> Similar to mapPartitions, but also provides <i>func</i> with an integer value representing the index of
+  the partition, so <i>func</i> must be of type (Int, Iterator&lt;T&gt;) => Iterator&lt;U&gt; when running on an RDD of type T.
+  </td>
+</tr>
+<tr>
+  <td> <b>sample</b>(<i>withReplacement</i>, <i>fraction</i>, <i>seed</i>) </td>
+  <td> Sample a fraction <i>fraction</i> of the data, with or without replacement, using a given random number generator seed. </td>
+</tr>
+<tr>
+  <td> <b>union</b>(<i>otherDataset</i>) </td>
+  <td> Return a new dataset that contains the union of the elements in the source dataset and the argument. </td>
+</tr>
+<tr>
+  <td> <b>intersection</b>(<i>otherDataset</i>) </td>
+  <td> Return a new RDD that contains the intersection of elements in the source dataset and the argument. </td>
+</tr>
+<tr>
+  <td> <b>distinct</b>([<i>numTasks</i>])) </td>
+  <td> Return a new dataset that contains the distinct elements of the source dataset.</td>
+</tr>
+<tr>
+  <td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
+  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable&lt;V&gt;) pairs. <br />
+    <b>Note:</b> If you are grouping in order to perform an aggregation (such as a sum or 
+      average) over each key, using <code>reduceByKey</code> or <code>combineByKey</code> will yield much better 
+      performance.
+    <br />
+    <b>Note:</b> By default, the level of parallelism in the output depends on the number of partitions of the parent RDD.
+      You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
+  </td>
+</tr>
+<tr>
+  <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
+  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
+</tr>
+<tr>
+  <td> <b>sortByKey</b>([<i>ascending</i>], [<i>numTasks</i>]) </td>
+  <td> When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean <code>ascending</code> argument.</td>
+</tr>
+<tr>
+  <td> <b>join</b>(<i>otherDataset</i>, [<i>numTasks</i>]) </td>
+  <td> When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key.
+    Outer joins are also supported through <code>leftOuterJoin</code> and <code>rightOuterJoin</code>.
+  </td>
+</tr>
+<tr>
+  <td> <b>cogroup</b>(<i>otherDataset</i>, [<i>numTasks</i>]) </td>
+  <td> When called on datasets of type (K, V) and (K, W), returns a dataset of (K, Iterable&lt;V&gt;, Iterable&lt;W&gt;) tuples. This operation is also called <code>groupWith</code>. </td>
+</tr>
+<tr>
+  <td> <b>cartesian</b>(<i>otherDataset</i>) </td>
+  <td> When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements). </td>
+</tr>
+<tr>
+  <td> <b>pipe</b>(<i>command</i>, <i>[envVars]</i>) </td>
+  <td> Pipe each partition of the RDD through a shell command, e.g. a Perl or bash script. RDD elements are written to the
+    process's stdin and lines output to its stdout are returned as an RDD of strings. </td>
+</tr>
+<tr>
+  <td> <b>coalesce</b>(<i>numPartitions</i>) </td>
+  <td> Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently
+    after filtering down a large dataset. </td>
+</tr>
+<tr>
+  <td> <b>repartition</b>(<i>numPartitions</i>) </td>
+  <td> Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them.
+    This always shuffles all data over the network. </td>
+</tr>
+</table>
+
+### Actions
+
+The following table lists some of the common actions supported by Spark. Refer to the
+RDD API doc
+([Scala](api/scala/index.html#org.apache.spark.rdd.RDD),
+ [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html),
+ [Python](api/python/pyspark.rdd.RDD-class.html))
+and pair RDD functions doc
+([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions),
+ [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html))
+for details.
+
+<table class="table">
+<tr><th>Action</th><th>Meaning</th></tr>
+<tr>
+  <td> <b>reduce</b>(<i>func</i>) </td>
+  <td> Aggregate the elements of the dataset using a function <i>func</i> (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel. </td>
+</tr>
+<tr>
+  <td> <b>collect</b>() </td>
+  <td> Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data. </td>
+</tr>
+<tr>
+  <td> <b>count</b>() </td>
+  <td> Return the number of elements in the dataset. </td>
+</tr>
+<tr>
+  <td> <b>first</b>() </td>
+  <td> Return the first element of the dataset (similar to take(1)). </td>
+</tr>
+<tr>
+  <td> <b>take</b>(<i>n</i>) </td>
+  <td> Return an array with the first <i>n</i> elements of the dataset. Note that this is currently not executed in parallel. Instead, the driver program computes all the elements. </td>
+</tr>
+<tr>
+  <td> <b>takeSample</b>(<i>withReplacement</i>, <i>num</i>, <i>seed</i>) </td>
+  <td> Return an array with a random sample of <i>num</i> elements of the dataset, with or without replacement, using the given random number generator seed. </td>
+</tr>
+<tr>
+  <td> <b>takeOrdered</b>(<i>n</i>, <i>[ordering]</i>) </td>
+  <td> Return the first <i>n</i> elements of the RDD using either their natural order or a custom comparator. </td>
+</tr>
+<tr>
+  <td> <b>saveAsTextFile</b>(<i>path</i>) </td>
+  <td> Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. </td>
+</tr>
+<tr>
+  <td> <b>saveAsSequenceFile</b>(<i>path</i>) <br /> (Java and Scala) </td>
+  <td> Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is available on RDDs of key-value pairs that either implement Hadoop's Writable interface. In Scala, it is also
+   available on types that are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc). </td>
+</tr>
+<tr>
+  <td> <b>saveAsObjectFile</b>(<i>path</i>) <br /> (Java and Scala) </td>
+  <td> Write the elements of the dataset in a simple format using Java serialization, which can then be loaded using
+    <code>SparkContext.objectFile()</code>. </td>
+</tr>
+<tr>
+  <td> <b>countByKey</b>() </td>
+  <td> Only available on RDDs of type (K, V). Returns a hashmap of (K, Int) pairs with the count of each key. </td>
+</tr>
+<tr>
+  <td> <b>foreach</b>(<i>func</i>) </td>
+  <td> Run a function <i>func</i> on each element of the dataset. This is usually done for side effects such as updating an accumulator variable (see below) or interacting with external storage systems. </td>
+</tr>
+</table>
+
+## RDD Persistence
+
+One of the most important capabilities in Spark is *persisting* (or *caching*) a dataset in memory
+across operations. When you persist an RDD, each node stores any partitions of it that it computes in
+memory and reuses them in other actions on that dataset (or datasets derived from it). This allows
+future actions to be much faster (often by more than 10x). Caching is a key tool for
+iterative algorithms and fast interactive use.
+
+You can mark an RDD to be persisted using the `persist()` or `cache()` methods on it. The first time
+it is computed in an action, it will be kept in memory on the nodes. Spark's cache is fault-tolerant --
+if any partition of an RDD is lost, it will automatically be recomputed using the transformations
+that originally created it.
+
+In addition, each persisted RDD can be stored using a different *storage level*, allowing you, for example,
+to persist the dataset on disk, persist it in memory but as serialized Java objects (to save space),
+replicate it across nodes, or store it off-heap in [Tachyon](http://tachyon-project.org/).
+These levels are set by passing a
+`StorageLevel` object ([Scala](api/scala/index.html#org.apache.spark.storage.StorageLevel),
+[Java](api/java/index.html?org/apache/spark/storage/StorageLevel.html),
+[Python](api/python/pyspark.storagelevel.StorageLevel-class.html))
+to `persist()`. The `cache()` method is a shorthand for using the default storage level,
+which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The full set of
+storage levels is:
+
+<table class="table">
+<tr><th style="width:23%">Storage Level</th><th>Meaning</th></tr>
+<tr>
+  <td> MEMORY_ONLY </td>
+  <td> Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will
+    not be cached and will be recomputed on the fly each time they're needed. This is the default level. </td>
+</tr>
+<tr>
+  <td> MEMORY_AND_DISK </td>
+  <td> Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the
+    partitions that don't fit on disk, and read them from there when they're needed. </td>
+</tr>
+<tr>
+  <td> MEMORY_ONLY_SER </td>
+  <td> Store RDD as <i>serialized</i> Java objects (one byte array per partition).
+    This is generally more space-efficient than deserialized objects, especially when using a
+    <a href="tuning.html">fast serializer</a>, but more CPU-intensive to read.
+  </td>
+</tr>
+<tr>
+  <td> MEMORY_AND_DISK_SER </td>
+  <td> Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of
+    recomputing them on the fly each time they're needed. </td>
+</tr>
+<tr>
+  <td> DISK_ONLY </td>
+  <td> Store the RDD partitions only on disk. </td>
+</tr>
+<tr>
+  <td> MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc.  </td>
+  <td> Same as the levels above, but replicate each partition on two cluster nodes. </td>
+</tr>
+<tr>
+  <td> OFF_HEAP (experimental) </td>
+  <td> Store RDD in serialized format in <a href="http://tachyon-project.org">Tachyon</a>.
+    Compared to MEMORY_ONLY_SER, OFF_HEAP reduces garbage collection overhead and allows executors
+    to be smaller and to share a pool of memory, making it attractive in environments with
+    large heaps or multiple concurrent applications.
+  </td>
+</tr>
+</table>
+
+**Note:** *In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, so it does not matter whether you choose a serialized level.*
+
+Spark also automatically persists some intermediate data in shuffle operations (e.g. `reduceByKey`), even without users calling `persist`. This is done to avoid recomputing the entire input if a node fails during the shuffle. We still recommend users call `persist` on the resulting RDD if they plan to reuse it.
+
+### Which Storage Level to Choose?
+
+Spark's storage levels are meant to provide different trade-offs between memory usage and CPU
+efficiency. We recommend going through the following process to select one:
+
+* If your RDDs fit comfortably with the default storage level (`MEMORY_ONLY`), leave them that way.
+  This is the most CPU-efficient option, allowing operations on the RDDs to run as fast as possible.
+
+* If not, try using `MEMORY_ONLY_SER` and [selecting a fast serialization library](tuning.html) to
+make the objects much more space-efficient, but still reasonably fast to access. 
+
+* Don't spill to disk unless the functions that computed your datasets are expensive, or they filter
+a large amount of the data. Otherwise, recomputing a partition may be as fast as reading it from
+disk.
+
+* Use the replicated storage levels if you want fast fault recovery (e.g. if using Spark to serve
+requests from a web application). *All* the storage levels provide full fault tolerance by
+recomputing lost data, but the replicated ones let you continue running tasks on the RDD without
+waiting to recompute a lost partition.
+
+* In environments with high amounts of memory or multiple applications, the experimental `OFF_HEAP`
+mode has several advantages:
+   * It allows multiple executors to share the same pool of memory in Tachyon.
+   * It significantly reduces garbage collection costs.
+   * Cached data is not lost if individual executors crash.
+
+### Removing Data
+
+Spark automatically monitors cache usage on each node and drops out old data partitions in a
+least-recently-used (LRU) fashion. If you would like to manually remove an RDD instead of waiting for
+it to fall out of the cache, use the `RDD.unpersist()` method.
+
+# Shared Variables
+
+Normally, when a function passed to a Spark operation (such as `map` or `reduce`) is executed on a
+remote cluster node, it works on separate copies of all the variables used in the function. These
+variables are copied to each machine, and no updates to the variables on the remote machine are
+propagated back to the driver program. Supporting general, read-write shared variables across tasks
+would be inefficient. However, Spark does provide two limited types of *shared variables* for two
+common usage patterns: broadcast variables and accumulators.
+
+## Broadcast Variables
+
+Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather
+than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a
+large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables
+using efficient broadcast algorithms to reduce communication cost.
+
+Broadcast variables are created from a variable `v` by calling `SparkContext.broadcast(v)`. The
+broadcast variable is a wrapper around `v`, and its value can be accessed by calling the `value`
+method. The code below shows this:
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+{% highlight scala %}
+scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))
+broadcastVar: spark.Broadcast[Array[Int]] = spark.Broadcast(b5c40191-a864-4c7d-b9bf-d87e1a4e787c)
+
+scala> broadcastVar.value
+res0: Array[Int] = Array(1, 2, 3)
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+{% highlight java %}
+Broadcast<int[]> broadcastVar = sc.broadcast(new int[] {1, 2, 3});
+
+broadcastVar.value();
+// returns [1, 2, 3]
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+{% highlight python %}
+>>> broadcastVar = sc.broadcast([1, 2, 3])
+<pyspark.broadcast.Broadcast object at 0x102789f10>
+
+>>> broadcastVar.value
+[1, 2, 3]
+{% endhighlight %}
+
+</div>
+
+</div>
+
+After the broadcast variable is created, it should be used instead of the value `v` in any functions
+run on the cluster so that `v` is not shipped to the nodes more than once. In addition, the object
+`v` should not be modified after it is broadcast in order to ensure that all nodes get the same
+value of the broadcast variable (e.g. if the variable is shipped to a new node later).
+
+## Accumulators
+
+Accumulators are variables that are only "added" to through an associative operation and can
+therefore be efficiently supported in parallel. They can be used to implement counters (as in
+MapReduce) or sums. Spark natively supports accumulators of numeric types, and programmers
+can add support for new types.
+
+An accumulator is created from an initial value `v` by calling `SparkContext.accumulator(v)`. Tasks
+running on the cluster can then add to it using the `add` method or the `+=` operator (in Scala and Python).
+However, they cannot read its value.
+Only the driver program can read the accumulator's value, using its `value` method.
+
+The code below shows an accumulator being used to add up the elements of an array:
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+{% highlight scala %}
+scala> val accum = sc.accumulator(0)
+accum: spark.Accumulator[Int] = 0
+
+scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x)
+...
+10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
+
+scala> accum.value
+res2: Int = 10
+{% endhighlight %}
+
+While this code used the built-in support for accumulators of type Int, programmers can also
+create their own types by subclassing [AccumulatorParam](api/scala/index.html#org.apache.spark.AccumulatorParam).
+The AccumulatorParam interface has two methods: `zero` for providing a "zero value" for your data
+type, and `addInPlace` for adding two values together. For example, supposing we had a `Vector` class
+representing mathematical vectors, we could write:
+
+{% highlight scala %}
+object VectorAccumulatorParam extends AccumulatorParam[Vector] {
+  def zero(initialValue: Vector): Vector = {
+    Vector.zeros(initialValue.size)
+  }
+  def addInPlace(v1: Vector, v2: Vector): Vector = {
+    v1 += v2
+  }
+}
+
+// Then, create an Accumulator of this type:
+val vecAccum = sc.accumulator(new Vector(...))(VectorAccumulatorParam)
+{% endhighlight %}
+
+In Scala, Spark also supports the more general [Accumulable](api/scala/index.html#org.apache.spark.Accumulable)
+interface to accumulate data where the resulting type is not the same as the elements added (e.g. build
+a list by collecting together elements), and the `SparkContext.accumulableCollection` method for accumulating
+common Scala collection types.
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+{% highlight java %}
+Accumulator<Integer> accum = sc.accumulator(0);
+
+sc.parallelize(Arrays.asList(1, 2, 3, 4)).foreach(x -> accum.add(x));
+// ...
+// 10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
+
+accum.value();
+// returns 10
+{% endhighlight %}
+
+While this code used the built-in support for accumulators of type Integer, programmers can also
+create their own types by subclassing [AccumulatorParam](api/java/index.html?org/apache/spark/AccumulatorParam.html).
+The AccumulatorParam interface has two methods: `zero` for providing a "zero value" for your data
+type, and `addInPlace` for adding two values together. For example, supposing we had a `Vector` class
+representing mathematical vectors, we could write:
+
+{% highlight java %}
+class VectorAccumulatorParam implements AccumulatorParam<Vector> {
+  public Vector zero(Vector initialValue) {
+    return Vector.zeros(initialValue.size());
+  }
+  public Vector addInPlace(Vector v1, Vector v2) {
+    v1.addInPlace(v2); return v1;
+  }
+}
+
+// Then, create an Accumulator of this type:
+Accumulator<Vector> vecAccum = sc.accumulator(new Vector(...), new VectorAccumulatorParam());
+{% endhighlight %}
+
+In Java, Spark also supports the more general [Accumulable](api/java/index.html?org/apache/spark/Accumulable.html)
+interface to accumulate data where the resulting type is not the same as the elements added (e.g. build
+a list by collecting together elements).
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+{% highlight python %}
+>>> accum = sc.accumulator(0)
+Accumulator<id=0, value=0>
+
+>>> sc.parallelize([1, 2, 3, 4]).foreach(lambda x: accum.add(x))
+...
+10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
+
+scala> accum.value
+10
+{% endhighlight %}
+
+While this code used the built-in support for accumulators of type Int, programmers can also
+create their own types by subclassing [AccumulatorParam](api/python/pyspark.accumulators.AccumulatorParam-class.html).
+The AccumulatorParam interface has two methods: `zero` for providing a "zero value" for your data
+type, and `addInPlace` for adding two values together. For example, supposing we had a `Vector` class
+representing mathematical vectors, we could write:
+
+{% highlight python %}
+class VectorAccumulatorParam(AccumulatorParam):
+    def zero(self, initialValue):
+        return Vector.zeros(initialValue.size)
+
+    def addInPlace(self, v1, v2):
+        v1 += v2
+        return v1
+
+# Then, create an Accumulator of this type:
+vecAccum = sc.accumulator(Vector(...), VectorAccumulatorParam())
+{% endhighlight %}
+
+</div>
+
+</div>
+
+# Deploying to a Cluster
+
+The [application submission guide](submitting-applications.html) describes how to submit applications to a cluster.
+In short, once you package your application into a JAR (for Java/Scala) or a set of `.py` or `.zip` files (for Python),
+the `bin/spark-submit` script lets you submit it to any supported cluster manager.
+
+# Unit Testing
+
+Spark is friendly to unit testing with any popular unit test framework.
+Simply create a `SparkContext` in your test with the master URL set to `local`, run your operations,
+and then call `SparkContext.stop()` to tear it down.
+Make sure you stop the context within a `finally` block or the test framework's `tearDown` method,
+as Spark does not support two contexts running concurrently in the same program.
+
+# Migrating from pre-1.0 Versions of Spark
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is
+not marked "experimental" or "developer API" will be supported in future versions.
+The only change for Scala users is that the grouping operations, e.g. `groupByKey`, `cogroup` and `join`,
+have changed from returning `(Key, Seq[Value])` pairs to `(Key, Iterable[Value])`.
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is
+not marked "experimental" or "developer API" will be supported in future versions.
+Several changes were made to the Java API:
+
+* The Function classes in `org.apache.spark.api.java.function` became interfaces in 1.0, meaning that old
+  code that `extends Function` should `implement Function` instead.
+* New variants of the `map` transformations, like `mapToPair` and `mapToDouble`, were added to create RDDs
+  of special data types.
+* Grouping operations like `groupByKey`, `cogroup` and `join` have changed from returning 
+  `(Key, List<Value>)` pairs to `(Key, Iterable<Value>)`.
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is
+not marked "experimental" or "developer API" will be supported in future versions.
+The only change for Python users is that the grouping operations, e.g. `groupByKey`, `cogroup` and `join`,
+have changed from returning (key, list of values) pairs to (key, iterable of values).
+
+</div>
+
+</div>
+
+Migration guides are also available for [Spark Streaming](streaming-programming-guide.html#migration-guide-from-091-or-below-to-1x),
+[MLlib](mllib-guide.html#migration-guide) and [GraphX](graphx-programming-guide.html#migrating-from-spark-091).
+
+
+# Where to Go from Here
+
+You can see some [example Spark programs](http://spark.apache.org/examples.html) on the Spark website.
+In addition, Spark includes several samples in the `examples` directory
+([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+ [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
+ [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python)).
+You can run Java and Scala examples by passing the class name to Spark's `bin/run-example` script; for instance:
+
+    ./bin/run-example SparkPi
+
+For Python examples, use `spark-submit` instead:
+
+    ./bin/spark-submit examples/src/main/python/pi.py
+
+For help on optimizing your programs, the [configuration](configuration.html) and
+[tuning](tuning.html) guides provide information on best practices. They are especially important for
+making sure that your data is stored in memory in an efficient format.
+For help on deploying, the [cluster mode overview](cluster-overview.html) describes the components involved
+in distributed operation and supported cluster managers.
+
+Finally, full API documentation is available in
+[Scala](api/scala/#org.apache.spark.package), [Java](api/java/) and [Python](api/python/).
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index b686bee1ae..68f04b50aa 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -1,171 +1,7 @@
 ---
 layout: global
 title: Python Programming Guide
+redirect: programming-guide.html
 ---
 
-
-The Spark Python API (PySpark) exposes the Spark programming model to Python.
-To learn the basics of Spark, we recommend reading through the
-[Scala programming guide](scala-programming-guide.html) first; it should be
-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:
-
-* Python is dynamically typed, so RDDs can hold objects of multiple types.
-* PySpark does not yet support a few API calls, such as `lookup` and non-text input files, though these will be added in future releases.
-
-In PySpark, RDDs support the same methods as their Scala counterparts but take Python functions and return Python collection types.
-Short functions can be passed to RDD methods using Python's [`lambda`](http://www.diveintopython.net/power_of_introspection/lambda_functions.html) syntax:
-
-{% highlight python %}
-logData = sc.textFile(logFile).cache()
-errors = logData.filter(lambda line: "ERROR" in line)
-{% endhighlight %}
-
-You can also pass functions that are defined with the `def` keyword; this is useful for longer functions that can't be expressed using `lambda`:
-
-{% highlight python %}
-def is_error(line):
-    return "ERROR" in line
-errors = logData.filter(is_error)
-{% endhighlight %}
-
-Functions can access objects in enclosing scopes, although modifications to those objects within RDD methods will not be propagated back:
-
-{% highlight python %}
-error_keywords = ["Exception", "Error"]
-def is_error(line):
-    return any(keyword in line for keyword in error_keywords)
-errors = logData.filter(is_error)
-{% endhighlight %}
-
-PySpark will automatically ship these functions to executors, along with any objects that they reference.
-Instances of classes will be serialized and shipped to executors by PySpark, but classes themselves cannot be automatically distributed to executors.
-The [Standalone Use](#standalone-programs) section describes how to ship code dependencies to executors.
-
-In addition, PySpark fully supports interactive use---simply run `./bin/pyspark` to launch an interactive shell.
-
-
-# Installing and Configuring PySpark
-
-PySpark requires Python 2.6 or higher.
-PySpark applications are executed using a standard CPython interpreter in order to support Python modules that use C extensions.
-We have not tested PySpark with Python 3 or with alternative Python interpreters, such as [PyPy](http://pypy.org/) or [Jython](http://www.jython.org/).
-
-By default, PySpark requires `python` to be available on the system `PATH` and use it to run programs; an alternate Python executable may be specified by setting the `PYSPARK_PYTHON` environment variable in `conf/spark-env.sh` (or `.cmd` on Windows).
-
-All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
-
-# Interactive Use
-
-The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line:
-
-{% highlight bash %}
-$ sbt/sbt assembly
-$ ./bin/pyspark
-{% endhighlight %}
-
-The Python shell can be used explore data interactively and is a simple way to learn the API:
-
-{% highlight python %}
->>> words = sc.textFile("/usr/share/dict/words")
->>> words.filter(lambda w: w.startswith("spar")).take(5)
-[u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass']
->>> help(pyspark) # Show all pyspark functions
-{% endhighlight %}
-
-By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on all of
-your machine's logical cores. To connect to a non-local cluster, or to specify a number of cores,
-set the `--master` flag. For example, to use the `bin/pyspark` shell with a
-[standalone Spark cluster](spark-standalone.html):
-
-{% highlight bash %}
-$ ./bin/pyspark --master spark://1.2.3.4:7077
-{% endhighlight %}
-
-Or, to use exactly four cores on the local machine:
-
-{% highlight bash %}
-$ ./bin/pyspark --master local[4]
-{% endhighlight %}
-
-Under the hood `bin/pyspark` is a wrapper around the
-[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit), so these
-two scripts share the same list of options. For a complete list of options, run `bin/pyspark` with
-the `--help` option.
-
-## IPython
-
-It is also possible to launch the PySpark shell in [IPython](http://ipython.org), the
-enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To
-use IPython, set the `IPYTHON` variable to `1` when running `bin/pyspark`:
-
-{% highlight bash %}
-$ IPYTHON=1 ./bin/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:
-
-{% highlight bash %}
-$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark
-{% endhighlight %}
-
-IPython also works on a cluster or on multiple cores if you set the `--master` flag.
-
-
-# Standalone Programs
-
-PySpark can also be used from standalone Python scripts by creating a SparkContext in your script
-and running the script using `bin/spark-submit`. The Quick Start guide includes a
-[complete example](quick-start.html#standalone-applications) of a standalone Python application.
-
-Code dependencies can be deployed by passing .zip or .egg files in the `--py-files` option of `spark-submit`:
-
-{% highlight bash %}
-./bin/spark-submit --py-files lib1.zip,lib2.zip my_script.py
-{% endhighlight %}
-
-Files listed here will be added to the `PYTHONPATH` and shipped to remote worker machines.
-Code dependencies can also be added to an existing SparkContext at runtime using its `addPyFile()` method.
-
-You can set [configuration properties](configuration.html#spark-properties) by passing a
-[SparkConf](api/python/pyspark.conf.SparkConf-class.html) object to SparkContext:
-
-{% highlight python %}
-from pyspark import SparkConf, SparkContext
-conf = (SparkConf()
-         .setMaster("local")
-         .setAppName("My app")
-         .set("spark.executor.memory", "1g"))
-sc = SparkContext(conf = conf)
-{% endhighlight %}
-
-`spark-submit` supports launching Python applications on standalone, Mesos or YARN clusters, through
-its `--master` argument. However, it currently requires the Python driver program to run on the local
-machine, not the cluster (i.e. the `--deploy-mode` parameter cannot be `cluster`).
-
-
-# API Docs
-
-[API documentation](api/python/index.html) for PySpark is available as Epydoc.
-Many of the methods also contain [doctests](http://docs.python.org/2/library/doctest.html) that provide additional usage examples.
-
-# Libraries
-
-[MLlib](mllib-guide.html) is also available in PySpark. To use it, you'll need
-[NumPy](http://www.numpy.org) version 1.4 or newer. The [MLlib guide](mllib-guide.html) contains
-some example applications.
-
-# Where to Go from Here
-
-PySpark also includes several sample programs in the [`examples/src/main/python` folder](https://github.com/apache/spark/tree/master/examples/src/main/python).
-You can run them by passing the files to `pyspark`; e.g.:
-
-    ./bin/spark-submit examples/src/main/python/wordcount.py README.md
-
-Each program prints usage help when run without the sufficient arguments.
+This document has been merged into the [Spark programming guide](programming-guide.html).
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 20e17ebf70..6402399477 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -9,7 +9,7 @@ title: Quick Start
 This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's
 interactive shell (in Python or Scala),
 then show how to write standalone applications in Java, Scala, and Python.
-See the [programming guide](scala-programming-guide.html) for a more complete reference.
+See the [programming guide](programming-guide.html) for a more complete reference.
 
 To follow along with this guide, first download a packaged release of Spark from the
 [Spark website](http://spark.apache.org/downloads.html). Since we won't be using HDFS,
@@ -35,7 +35,7 @@ scala> val textFile = sc.textFile("README.md")
 textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3
 {% endhighlight %}
 
-RDDs have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
+RDDs have _[actions](programming-guide.html#actions)_, which return values, and _[transformations](programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
 
 {% highlight scala %}
 scala> textFile.count() // Number of items in this RDD
@@ -45,7 +45,7 @@ scala> textFile.first() // First item in this RDD
 res1: String = # Apache Spark
 {% endhighlight %}
 
-Now let's use a transformation. We will use the [`filter`](scala-programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
+Now let's use a transformation. We will use the [`filter`](programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
 
 {% highlight scala %}
 scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
@@ -70,7 +70,7 @@ Spark's primary abstraction is a distributed collection of items called a Resili
 >>> textFile = sc.textFile("README.md")
 {% endhighlight %}
 
-RDDs have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
+RDDs have _[actions](programming-guide.html#actions)_, which return values, and _[transformations](programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
 
 {% highlight python %}
 >>> textFile.count() # Number of items in this RDD
@@ -80,7 +80,7 @@ RDDs have _[actions](scala-programming-guide.html#actions)_, which return values
 u'# Apache Spark'
 {% endhighlight %}
 
-Now let's use a transformation. We will use the [`filter`](scala-programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
+Now let's use a transformation. We will use the [`filter`](programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
 
 {% highlight python %}
 >>> linesWithSpark = textFile.filter(lambda line: "Spark" in line)
@@ -125,7 +125,7 @@ scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (w
 wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight scala %}
 scala> wordCounts.collect()
@@ -162,7 +162,7 @@ One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can i
 >>> wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight python %}
 >>> wordCounts.collect()
@@ -192,7 +192,7 @@ res9: Long = 15
 It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is
 that these same functions can be used on very large data sets, even when they are striped across
 tens or hundreds of nodes. You can also do this interactively by connecting `bin/spark-shell` to
-a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
+a cluster, as described in the [programming guide](programming-guide.html#initializing-spark).
 
 </div>
 <div data-lang="python" markdown="1">
@@ -210,7 +210,7 @@ a cluster, as described in the [programming guide](scala-programming-guide.html#
 It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is
 that these same functions can be used on very large data sets, even when they are striped across
 tens or hundreds of nodes. You can also do this interactively by connecting `bin/pyspark` to
-a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
+a cluster, as described in the [programming guide](programming-guide.html#initializing-spark).
 
 </div>
 </div>
@@ -336,7 +336,7 @@ As with the Scala example, we initialize a SparkContext, though we use the speci
 `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by
 `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes
 that extend `spark.api.java.function.Function`. The
-[Java programming guide](java-programming-guide.html) describes these differences in more detail.
+[Spark programming guide](programming-guide.html) describes these differences in more detail.
 
 To build the program, we also write a Maven `pom.xml` file that lists Spark as a dependency.
 Note that Spark artifacts are tagged with a Scala version.
@@ -442,6 +442,19 @@ Lines with a: 46, Lines with b: 23
 # Where to Go from Here
 Congratulations on running your first Spark application!
 
-* For an in-depth overview of the API see "Programming Guides" menu section.
-* For running applications on a cluster head to the [deployment overview](cluster-overview.html).
-* For configuration options available to Spark applications see the [configuration page](configuration.html).
+* For an in-depth overview of the API, start with the [Spark programming guide](programming-guide.html),
+  or see "Programming Guides" menu for other components.
+* For running applications on a cluster, head to the [deployment overview](cluster-overview.html).
+* Finally, Spark includes several samples in the `examples` directory
+([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+ [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
+ [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python)).
+You can run them as follows:
+
+{% highlight bash %}
+# For Scala and Java, use run-example:
+./bin/run-example SparkPi
+
+# For Python examples, use spark-submit directly:
+./bin/spark-submit examples/src/main/python/pi.py
+{% endhighlight %}
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index df8687f81f..e3c8922404 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -103,7 +103,7 @@ the `make-distribution.sh` script included in a Spark source tarball/checkout.
 ## Using a Mesos Master URL
 
 The Master URLs for Mesos are in the form `mesos://host:5050` for a single-master Mesos
-cluster, or `zk://host:2181` for a multi-master Mesos cluster using ZooKeeper.
+cluster, or `mesos://zk://host:2181` for a multi-master Mesos cluster using ZooKeeper.
 
 The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos:
 
@@ -116,7 +116,7 @@ The driver also needs some configuration in `spark-env.sh` to interact properly
 2. Also set `spark.executor.uri` to `<URL of spark-{{site.SPARK_VERSION}}.tar.gz>`.
 
 Now when starting a Spark application against the cluster, pass a `mesos://`
-or `zk://` URL as the master when creating a `SparkContext`. For example:
+URL as the master when creating a `SparkContext`. For example:
 
 {% highlight scala %}
 val conf = new SparkConf()
@@ -126,6 +126,9 @@ val conf = new SparkConf()
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
+(You can also use [`spark-submit`](submitting-applications.html) and configure `spark.executor.uri`
+in the [conf/spark-defaults.conf](configuration.html#loading-default-configurations) file.)
+
 When running a shell, the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`, so
 it does not need to be redundantly passed in as a system property.
 
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 66c330fdee..af1788f2aa 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -1,6 +1,6 @@
 ---
 layout: global
-title: Launching Spark on YARN
+title: Running Spark on YARN
 ---
 
 Support for running on [YARN (Hadoop
@@ -11,30 +11,69 @@ was added to Spark in version 0.6.0, and improved in subsequent releases.
 
 Running Spark-on-YARN requires a binary distribution of Spark which is built with YARN support.
 Binary distributions can be downloaded from the Spark project website. 
-To build Spark yourself, refer to the [building with maven guide](building-with-maven.html).
+To build Spark yourself, refer to the [building with Maven guide](building-with-maven.html).
 
 # Configuration
 
-Most of the configs are the same for Spark on YARN as for other deployment modes. See the Configuration page for more information on those.  These are configs that are specific to Spark on YARN.
+Most of the configs are the same for Spark on YARN as for other deployment modes. See the [configuration page](configuration.html) for more information on those.  These are configs that are specific to Spark on YARN.
 
-Environment variables:
+#### Environment Variables
 
 * `SPARK_YARN_USER_ENV`, to add environment variables to the Spark processes launched on YARN. This can be a comma separated list of environment variables, e.g. `SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar"`.
 
-System Properties:
+#### Spark Properties
 
-* `spark.yarn.applicationMaster.waitTries`, property to set the number of times the ApplicationMaster waits for the the spark master and then also the number of tries it waits for the Spark Context to be intialized. Default is 10.
-* `spark.yarn.submit.file.replication`, the HDFS replication level for the files uploaded into HDFS for the application. These include things like the spark jar, the app jar, and any distributed cache files/archives.
-* `spark.yarn.preserve.staging.files`, set to true to preserve the staged files(spark jar, app jar, distributed cache files) at the end of the job rather then delete them.
-* `spark.yarn.scheduler.heartbeat.interval-ms`, the interval in ms in which the Spark application master heartbeats into the YARN ResourceManager. Default is 5 seconds. 
-* `spark.yarn.max.executor.failures`, the maximum number of executor failures before failing the application. Default is the number of executors requested times 2 with minimum of 3.
-* `spark.yarn.historyServer.address`, the address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the Yarn ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI. 
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.yarn.applicationMaster.waitTries</code></td>
+  <td>10</td>
+  <td>
+    Set the number of times the ApplicationMaster waits for the the Spark master and then also the number of tries it waits for the SparkContext to be initialized
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.submit.file.replication</code></td>
+  <td>3</td>
+  <td>
+    HDFS replication level for the files uploaded into HDFS for the application. These include things like the Spark jar, the app jar, and any distributed cache files/archives.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.preserve.staging.files</code></td>
+  <td>false</td>
+  <td>
+    Set to true to preserve the staged files (Spark jar, app jar, distributed cache files) at the end of the job rather then delete them.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.scheduler.heartbeat.interval-ms</code></td>
+  <td>5000</td>
+  <td>
+    The interval in ms in which the Spark application master heartbeats into the YARN ResourceManager.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.max.executor.failures</code></td>
+  <td>2*numExecutors</td>
+  <td>
+    The maximum number of executor failures before failing the application.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.historyServer.address</code></td>
+  <td>(none)</td>
+  <td>
+    The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI.
+  </td>
+</tr>
+</table>
 
-By default, Spark on YARN will use a Spark jar installed locally, but the Spark jar can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a jar on HDFS, export SPARK_JAR=hdfs:///some/path.
+By default, Spark on YARN will use a Spark jar installed locally, but the Spark JAR can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a JAR on HDFS, `export SPARK_JAR=hdfs:///some/path`.
 
 # Launching Spark on YARN
 
-Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster.
+Ensure that `HADOOP_CONF_DIR` or `YARN_CONF_DIR` points to the directory which contains the (client side) configuration files for the Hadoop cluster.
 These configs are used to write to the dfs and connect to the YARN ResourceManager.
 
 There are two deploy modes that can be used to launch Spark applications on YARN. In yarn-cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In yarn-client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
@@ -62,9 +101,9 @@ To launch a Spark application in yarn-client mode, do the same, but replace "yar
 
     $ ./bin/spark-shell --master yarn-client
 
-## Adding additional jars
+## Adding Other JARs
 
-In yarn-cluster mode, the driver runs on a different machine than the client, so SparkContext.addJar won't work out of the box with files that are local to the client. To make files on the client available to SparkContext.addJar, include them with the `--jars` option in the launch command. 
+In yarn-cluster mode, the driver runs on a different machine than the client, so `SparkContext.addJar` won't work out of the box with files that are local to the client. To make files on the client available to `SparkContext.addJar`, include them with the `--jars` option in the launch command. 
 
     $ ./bin/spark-submit --class my.main.Class \
         --master yarn-cluster \
@@ -74,24 +113,24 @@ In yarn-cluster mode, the driver runs on a different machine than the client, so
 
 # Debugging your Application
 
-In YARN terminology, executors and application masters run inside "containers". YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the yarn.log-aggregation-enable config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the "yarn logs" command.
+In YARN terminology, executors and application masters run inside "containers". YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the `yarn.log-aggregation-enable` config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the "yarn logs" command.
 
     yarn logs -applicationId <app ID>
     
 will print out the contents of all log files from all containers from the given application.
 
-When log aggregation isn't turned on, logs are retained locally on each machine under YARN_APP_LOGS_DIR, which is usually configured to /tmp/logs or $HADOOP_HOME/logs/userlogs depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
+When log aggregation isn't turned on, logs are retained locally on each machine under `YARN_APP_LOGS_DIR`, which is usually configured to `/tmp/logs` or `$HADOOP_HOME/logs/userlogs` depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
 
-To review per-container launch environment, increase yarn.nodemanager.delete.debug-delay-sec to a
-large value (e.g. 36000), and then access the application cache through yarn.nodemanager.local-dirs
-on the nodes on which containers are launched. This directory contains the launch script, jars, and
+To review per-container launch environment, increase `yarn.nodemanager.delete.debug-delay-sec` to a
+large value (e.g. 36000), and then access the application cache through `yarn.nodemanager.local-dirs`
+on the nodes on which containers are launched. This directory contains the launch script, JARs, and
 all environment variables used for launching each container. This process is useful for debugging
 classpath problems in particular. (Note that enabling this requires admin privileges on cluster
 settings and a restart of all node managers. Thus, this is not applicable to hosted clusters).
 
-# Important notes
+# Important Notes
 
 - Before Hadoop 2.2, YARN does not support cores in container resource requests. Thus, when running against an earlier version, the numbers of cores given via command line arguments cannot be passed to YARN.  Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured.
-- The local directories used by Spark executors will be the local directories configured for YARN (Hadoop YARN config yarn.nodemanager.local-dirs). If the user specifies spark.local.dir, it will be ignored.
-- The --files and --archives options support specifying file names with the # similar to Hadoop. For example you can specify: --files localtest.txt#appSees.txt and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name appSees.txt, and your application should use the name as appSees.txt to reference it when running on YARN.
-- The --jars option allows the SparkContext.addJar function to work if you are using it with local files and running in yarn-cluster mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
+- The local directories used by Spark executors will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored.
+- The `--files` and `--archives` options support specifying file names with the # similar to Hadoop. For example you can specify: `--files localtest.txt#appSees.txt` and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name `appSees.txt`, and your application should use the name as `appSees.txt` to reference it when running on YARN.
+- The `--jars` option allows the `SparkContext.addJar` function to work if you are using it with local files and running in `yarn-cluster` mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index edaa7d0639..69ceb637dc 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -1,448 +1,7 @@
 ---
 layout: global
 title: Spark Programming Guide
+redirect: programming-guide.html
 ---
 
-* This will become a table of contents (this text will be scraped).
-{:toc}
-
-
-# Overview
-
-At a high level, every Spark application consists of a *driver program* that runs the user's `main` function and executes various *parallel operations* on a cluster. The main abstraction Spark provides is a *resilient distributed dataset* (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to *persist* an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.
-
-A second abstraction in Spark is *shared variables* that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: *broadcast variables*, which can be used to cache a value in memory on all nodes, and *accumulators*, which are variables that are only "added" to, such as counters and sums.
-
-This guide shows each of these features and walks through some samples. It assumes some familiarity with Scala, especially with the syntax for [closures](http://www.scala-lang.org/node/133). Note that you can also run Spark interactively using the `bin/spark-shell` script. We highly recommend doing that to follow along!
-
-# Linking with Spark
-
-Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_BINARY_VERSION}}. If you write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work.
-
-To write a Spark application, you need to add a dependency on Spark. If you use SBT or Maven, Spark is available through Maven Central at:
-
-    groupId = org.apache.spark
-    artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
-    version = {{site.SPARK_VERSION}}
-
-In addition, if you wish to access an HDFS cluster, you need to add a dependency on
-`hadoop-client` for your version of HDFS. Some common HDFS version tags are listed on the
-[third party distributions](hadoop-third-party-distributions.html) page.
-
-    groupId = org.apache.hadoop
-    artifactId = hadoop-client
-    version = <your-hdfs-version>
-
-Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
-
-{% highlight scala %}
-import org.apache.spark.SparkContext
-import org.apache.spark.SparkContext._
-import org.apache.spark.SparkConf
-{% endhighlight %}
-
-# Initializing Spark
-
-The first thing a Spark program must do is to create a `SparkContext` object, which tells Spark
-how to access a cluster. To create a `SparkContext` you first need to build a `SparkConf` object
-that contains information about your application.
-
-{% highlight scala %}
-val conf = new SparkConf().setAppName(appName).setMaster(master)
-new SparkContext(conf)
-{% endhighlight %}
-
-The `master` parameter is a string specifying a [Spark, Mesos or YARN cluster URL](#master-urls)
-to connect to, or a special "local" string to run in local mode, as described below. `appName` is
-a name for your application, which will be shown in the cluster web UI. It's also possible to set
-these variables [using a configuration file](cluster-overview.html#loading-configurations-from-a-file)
-which avoids hard-coding the master url in your application.
-
-In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the
-variable called `sc`. Making your own SparkContext will not work. You can set which master the
-context connects to using the `--master` argument, and you can add JARs to the classpath
-by passing a comma separated list to the `--jars` argument. For example, to run 
-`bin/spark-shell` on exactly four cores, use
-
-{% highlight bash %}
-$ ./bin/spark-shell --master local[4]
-{% endhighlight %}
-
-Or, to also add `code.jar` to its classpath, use:
-
-{% highlight bash %}
-$ ./bin/spark-shell --master local[4] --jars code.jar
-{% endhighlight %}
-
-For a complete list of options, run Spark shell with the `--help` option. Behind the scenes,
-Spark shell invokes the more general [Spark submit script](cluster-overview.html#launching-applications-with-spark-submit)
-used for launching applications, and passes on all of its parameters. As a result, these two scripts
-share the same parameters.
-
-### Master URLs
-
-The master URL passed to Spark can be in one of the following formats:
-
-<table class="table">
-<tr><th>Master URL</th><th>Meaning</th></tr>
-<tr><td> local </td><td> Run Spark locally with one worker thread (i.e. no parallelism at all). </td></tr>
-<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). </td></tr>
-<tr><td> local[*] </td><td> Run Spark locally with as many worker threads as logical cores on your machine.</td></tr>
-<tr><td> spark://HOST:PORT </td><td> Connect to the given <a href="spark-standalone.html">Spark standalone
-        cluster</a> master. The port must be whichever one your master is configured to use, which is 7077 by default.
-</td></tr>
-<tr><td> mesos://HOST:PORT </td><td> Connect to the given <a href="running-on-mesos.html">Mesos</a> cluster.
-        The host parameter is the hostname of the Mesos master. The port must be whichever one the master is configured to use,
-        which is 5050 by default.
-</td></tr>
-<tr><td> yarn-client </td><td> Connect to a <a href="running-on-yarn.html"> YARN </a> cluster in
-client mode. The cluster location will be inferred based on the local Hadoop configuration.
-</td></tr>
-<tr><td> yarn-cluster </td><td> Connect to a <a href="running-on-yarn.html"> YARN </a> cluster in
-cluster mode. The cluster location will be inferred based on the local Hadoop configuration.
-</td></tr>
-</table>
-
-If no master URL is specified, the spark shell defaults to `local[*]`.
-
-# Resilient Distributed Datasets (RDDs)
-
-Spark revolves around the concept of a _resilient distributed dataset_ (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are currently two types of RDDs: *parallelized collections*, which take an existing Scala collection and run functions on it in parallel, and *Hadoop datasets*, which run functions on each record of a file in Hadoop distributed file system or any other storage system supported by Hadoop. Both types of RDDs can be operated on through the same methods.
-
-## Parallelized Collections
-
-Parallelized collections are created by calling `SparkContext`'s `parallelize` method on an existing Scala collection (a `Seq` object). The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is some interpreter output showing how to create a parallel collection from an array:
-
-{% highlight scala %}
-scala> val data = Array(1, 2, 3, 4, 5)
-data: Array[Int] = Array(1, 2, 3, 4, 5)
-
-scala> val distData = sc.parallelize(data)
-distData: spark.RDD[Int] = spark.ParallelCollection@10d13e3e
-{% endhighlight %}
-
-Once created, the distributed dataset (`distData` here) can be operated on in parallel. For example, we might call `distData.reduce(_ + _)` to add up the elements of the array. We describe operations on distributed datasets later on.
-
-One important parameter for parallel collections is the number of *slices* to cut the dataset into. Spark will run one task for each slice of the cluster. Typically you want 2-4 slices for each CPU in your cluster. Normally, Spark tries to set the number of slices automatically based on your cluster. However, you can also set it manually by passing it as a second parameter to `parallelize` (e.g. `sc.parallelize(data, 10)`).
-
-## Hadoop Datasets
-
-Spark can create distributed datasets from any file stored in the Hadoop distributed file system (HDFS) or other storage systems supported by Hadoop (including your local file system, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), Hypertable, HBase, etc). Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop InputFormat.
-
-Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, `kfs://`, etc URI). Here is an example invocation:
-
-{% highlight scala %}
-scala> val distFile = sc.textFile("data.txt")
-distFile: spark.RDD[String] = spark.HadoopRDD@1d4cee08
-{% endhighlight %}
-
-Once created, `distFile` can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the `map` and `reduce` operations as follows: `distFile.map(_.size).reduce(_ + _)`.
-
-The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
-
-For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html). In addition, Spark allows you to specify native types for a few common Writables; for example, `sequenceFile[Int, String]` will automatically read IntWritables and Texts.
-
-Finally, for other Hadoop InputFormats, you can use the `SparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source.
-
-## RDD Operations
-
-RDDs support two types of operations: *transformations*, which create a new dataset from an existing one, and *actions*, which return a value to the driver program after running a computation on the dataset. For example, `map` is a transformation that passes each dataset element through a function and returns a new distributed dataset representing the results. On the other hand, `reduce` is an action that aggregates all the elements of the dataset using some function and returns the final result to the driver program (although there is also a parallel `reduceByKey` that returns a distributed dataset).
-
-All transformations in Spark are <i>lazy</i>, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently -- for example, we can realize that a dataset created through `map` will be used in a `reduce` and return only the result of the `reduce` to the driver, rather than the larger mapped dataset.
-
-By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also *persist* an RDD in memory using the `persist` (or `cache`) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting datasets on disk, or replicated across the cluster. The next section in this document describes these options.
-
-The following tables list the transformations and actions currently supported (see also the [RDD API doc](api/scala/index.html#org.apache.spark.rdd.RDD) for details):
-
-### Transformations
-
-<table class="table">
-<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
-<tr>
-  <td> <b>map</b>(<i>func</i>) </td>
-  <td> Return a new distributed dataset formed by passing each element of the source through a function <i>func</i>. </td>
-</tr>
-<tr>
-  <td> <b>filter</b>(<i>func</i>) </td>
-  <td> Return a new dataset formed by selecting those elements of the source on which <i>func</i> returns true. </td>
-</tr>
-<tr>
-  <td> <b>flatMap</b>(<i>func</i>) </td>
-  <td> Similar to map, but each input item can be mapped to 0 or more output items (so <i>func</i> should return a Seq rather than a single item). </td>
-</tr>
-<tr>
-  <td> <b>mapPartitions</b>(<i>func</i>) </td>
-  <td> Similar to map, but runs separately on each partition (block) of the RDD, so <i>func</i> must be of type
-    Iterator[T] => Iterator[U] when running on an RDD of type T. </td>
-</tr>
-<tr>
-  <td> <b>mapPartitionsWithIndex</b>(<i>func</i>) </td>
-  <td> Similar to mapPartitions, but also provides <i>func</i> with an integer value representing the index of
-  the partition, so <i>func</i> must be of type (Int, Iterator[T]) => Iterator[U] when running on an RDD of type T.
-  </td>
-</tr>
-<tr>
-  <td> <b>sample</b>(<i>withReplacement</i>, <i>fraction</i>, <i>seed</i>) </td>
-  <td> Sample a fraction <i>fraction</i> of the data, with or without replacement, using a given random number generator seed. </td>
-</tr>
-<tr>
-  <td> <b>union</b>(<i>otherDataset</i>) </td>
-  <td> Return a new dataset that contains the union of the elements in the source dataset and the argument. </td>
-</tr>
-<tr>
-  <td> <b>distinct</b>([<i>numTasks</i>])) </td>
-  <td> Return a new dataset that contains the distinct elements of the source dataset.</td>
-</tr>
-<tr>
-  <td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
-  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, Seq[V]) pairs. <br />
-<b>Note:</b> If you are grouping in order to perform an aggregation (such as a sum or 
-  average) over each key, using `reduceByKey` or `combineByKey` will yield much better 
-  performance.
-<br />
-<b>Note:</b> By default, if the RDD already has a partitioner, the task number is decided by the partition number of the partitioner, or else relies on the value of <code>spark.default.parallelism</code> if the property is set , otherwise depends on the partition number of the RDD. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
-  </td>
-</tr>
-<tr>
-  <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
-  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
-</tr>
-<tr>
-  <td> <b>sortByKey</b>([<i>ascending</i>], [<i>numTasks</i>]) </td>
-  <td> When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean <code>ascending</code> argument.</td>
-</tr>
-<tr>
-  <td> <b>join</b>(<i>otherDataset</i>, [<i>numTasks</i>]) </td>
-  <td> When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. </td>
-</tr>
-<tr>
-  <td> <b>cogroup</b>(<i>otherDataset</i>, [<i>numTasks</i>]) </td>
-  <td> When called on datasets of type (K, V) and (K, W), returns a dataset of (K, Seq[V], Seq[W]) tuples. This operation is also called <code>groupWith</code>. </td>
-</tr>
-<tr>
-  <td> <b>cartesian</b>(<i>otherDataset</i>) </td>
-  <td> When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements). </td>
-</tr>
-</table>
-
-A complete list of transformations is available in the [RDD API doc](api/scala/index.html#org.apache.spark.rdd.RDD).
-
-### Actions
-
-<table class="table">
-<tr><th>Action</th><th>Meaning</th></tr>
-<tr>
-  <td> <b>reduce</b>(<i>func</i>) </td>
-  <td> Aggregate the elements of the dataset using a function <i>func</i> (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel. </td>
-</tr>
-<tr>
-  <td> <b>collect</b>() </td>
-  <td> Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data. </td>
-</tr>
-<tr>
-  <td> <b>count</b>() </td>
-  <td> Return the number of elements in the dataset. </td>
-</tr>
-<tr>
-  <td> <b>first</b>() </td>
-  <td> Return the first element of the dataset (similar to take(1)). </td>
-</tr>
-<tr>
-  <td> <b>take</b>(<i>n</i>) </td>
-  <td> Return an array with the first <i>n</i> elements of the dataset. Note that this is currently not executed in parallel. Instead, the driver program computes all the elements. </td>
-</tr>
-<tr>
-  <td> <b>takeSample</b>(<i>withReplacement</i>, <i>num</i>, <i>seed</i>) </td>
-  <td> Return an array with a random sample of <i>num</i> elements of the dataset, with or without replacement, using the given random number generator seed. </td>
-</tr>
-<tr>
-  <td> <b>saveAsTextFile</b>(<i>path</i>) </td>
-  <td> Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. </td>
-</tr>
-<tr>
-  <td> <b>saveAsSequenceFile</b>(<i>path</i>) </td>
-  <td> Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is only available on RDDs of key-value pairs that either implement Hadoop's Writable interface or are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc). </td>
-</tr>
-<tr>
-  <td> <b>countByKey</b>() </td>
-  <td> Only available on RDDs of type (K, V). Returns a `Map` of (K, Int) pairs with the count of each key. </td>
-</tr>
-<tr>
-  <td> <b>foreach</b>(<i>func</i>) </td>
-  <td> Run a function <i>func</i> on each element of the dataset. This is usually done for side effects such as updating an accumulator variable (see below) or interacting with external storage systems. </td>
-</tr>
-</table>
-
-A complete list of actions is available in the [RDD API doc](api/scala/index.html#org.apache.spark.rdd.RDD).
-
-## RDD Persistence
-
-One of the most important capabilities in Spark is *persisting* (or *caching*) a dataset in memory
-across operations. When you persist an RDD, each node stores any slices of it that it computes in
-memory and reuses them in other actions on that dataset (or datasets derived from it). This allows
-future actions to be much faster (often by more than 10x). Caching is a key tool for building
-iterative algorithms with Spark and for interactive use from the interpreter.
-
-You can mark an RDD to be persisted using the `persist()` or `cache()` methods on it. The first time
-it is computed in an action, it will be kept in memory on the nodes. The cache is fault-tolerant --
-if any partition of an RDD is lost, it will automatically be recomputed using the transformations
-that originally created it.
-
-In addition, each persisted RDD can be stored using a different *storage level*, allowing you, for example,
-to persist the dataset on disk, or persist it in memory but as serialized Java objects (to save space),
-or replicate it across nodes, or store the data in off-heap memory in [Tachyon](http://tachyon-project.org/).
-These levels are chosen by passing a
-[`org.apache.spark.storage.StorageLevel`](api/scala/index.html#org.apache.spark.storage.StorageLevel)
-object to `persist()`. The `cache()` method is a shorthand for using the default storage level,
-which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The complete set of
-available storage levels is:
-
-<table class="table">
-<tr><th style="width:23%">Storage Level</th><th>Meaning</th></tr>
-<tr>
-  <td> MEMORY_ONLY </td>
-  <td> Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will
-    not be cached and will be recomputed on the fly each time they're needed. This is the default level. </td>
-</tr>
-<tr>
-  <td> MEMORY_AND_DISK </td>
-  <td> Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the
-    partitions that don't fit on disk, and read them from there when they're needed. </td>
-</tr>
-<tr>
-  <td> MEMORY_ONLY_SER </td>
-  <td> Store RDD as <i>serialized</i> Java objects (one byte array per partition).
-    This is generally more space-efficient than deserialized objects, especially when using a
-    <a href="tuning.html">fast serializer</a>, but more CPU-intensive to read.
-  </td>
-</tr>
-<tr>
-  <td> MEMORY_AND_DISK_SER </td>
-  <td> Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of
-    recomputing them on the fly each time they're needed. </td>
-</tr>
-<tr>
-  <td> OFF_HEAP  </td>
-  <td> Store RDD in a <i>serialized</i> format in Tachyon.
-    This is generally more space-efficient than deserialized objects, especially when using a
-    <a href="tuning.html">fast serializer</a>, but more CPU-intensive to read.
-    This also significantly reduces the overheads of GC.
-  </td>
-</tr>
-<tr>
-  <td> DISK_ONLY </td>
-  <td> Store the RDD partitions only on disk. </td>
-</tr>
-<tr>
-  <td> MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc.  </td>
-  <td> Same as the levels above, but replicate each partition on two cluster nodes. </td>
-</tr>
-</table>
-
-Spark sometimes automatically persists intermediate state from RDD operations, even without users calling persist() or cache(). In particular, if a shuffle happens when computing an RDD, Spark will keep the outputs from the map side of the shuffle on disk to avoid re-computing the entire dependency graph if an RDD is re-used. We still recommend users call persist() if they plan to re-use an RDD iteratively.
-
-### Which Storage Level to Choose?
-
-Spark's storage levels are meant to provide different trade-offs between memory usage and CPU
-efficiency. It allows uses to choose memory, disk, or Tachyon for storing data. We recommend going
-through the following process to select one:
-
-* If your RDDs fit comfortably with the default storage level (`MEMORY_ONLY`), leave them that way.
-  This is the most CPU-efficient option, allowing operations on the RDDs to run as fast as possible.
-
-* If not, try using `MEMORY_ONLY_SER` and [selecting a fast serialization library](tuning.html) to
-make the objects much more space-efficient, but still reasonably fast to access. You can also use
-`OFF_HEAP` mode to store the data off the heap in [Tachyon](http://tachyon-project.org/). This will
-significantly reduce JVM GC overhead.
-
-* Don't spill to disk unless the functions that computed your datasets are expensive, or they filter
-a large amount of the data. Otherwise, recomputing a partition is about as fast as reading it from
-disk.
-
-* Use the replicated storage levels if you want fast fault recovery (e.g. if using Spark to serve
-requests from a web application). *All* the storage levels provide full fault tolerance by
-recomputing lost data, but the replicated ones let you continue running tasks on the RDD without
-waiting to recompute a lost partition.
-
-If you want to define your own storage level (say, with replication factor of 3 instead of 2), then
-use the function factor method `apply()` of the
-[`StorageLevel`](api/scala/index.html#org.apache.spark.storage.StorageLevel$) singleton object.
-
-Spark has a block manager inside the Executors that let you chose memory, disk, or off-heap. The
-latter is for storing RDDs off-heap outside the Executor JVM on top of the memory management system
-[Tachyon](http://tachyon-project.org/). This mode has the following advantages:
-
-* Cached data will not be lost if individual executors crash.
-* Executors can have a smaller memory footprint, allowing you to run more executors on the same
-machine as the bulk of the memory will be inside Tachyon.
-* Reduced GC overhead since data is stored in Tachyon.
-
-# Shared Variables
-
-Normally, when a function passed to a Spark operation (such as `map` or `reduce`) is executed on a
-remote cluster node, it works on separate copies of all the variables used in the function. These
-variables are copied to each machine, and no updates to the variables on the remote machine are
-propagated back to the driver program. Supporting general, read-write shared variables across tasks
-would be inefficient. However, Spark does provide two limited types of *shared variables* for two
-common usage patterns: broadcast variables and accumulators.
-
-## Broadcast Variables
-
-Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather
-than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a
-large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables
-using efficient broadcast algorithms to reduce communication cost.
-
-Broadcast variables are created from a variable `v` by calling `SparkContext.broadcast(v)`. The
-broadcast variable is a wrapper around `v`, and its value can be accessed by calling the `value`
-method. The interpreter session below shows this:
-
-{% highlight scala %}
-scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))
-broadcastVar: spark.Broadcast[Array[Int]] = spark.Broadcast(b5c40191-a864-4c7d-b9bf-d87e1a4e787c)
-
-scala> broadcastVar.value
-res0: Array[Int] = Array(1, 2, 3)
-{% endhighlight %}
-
-After the broadcast variable is created, it should be used instead of the value `v` in any functions
-run on the cluster so that `v` is not shipped to the nodes more than once. In addition, the object
-`v` should not be modified after it is broadcast in order to ensure that all nodes get the same
-value of the broadcast variable (e.g. if the variable is shipped to a new node later).
-
-## Accumulators
-
-Accumulators are variables that are only "added" to through an associative operation and can
-therefore be efficiently supported in parallel. They can be used to implement counters (as in
-MapReduce) or sums. Spark natively supports accumulators of numeric value types and standard mutable
-collections, and programmers can add support for new types.
-
-An accumulator is created from an initial value `v` by calling `SparkContext.accumulator(v)`. Tasks
-running on the cluster can then add to it using the `+=` operator. However, they cannot read its
-value. Only the driver program can read the accumulator's value, using its `value` method.
-
-The interpreter session below shows an accumulator being used to add up the elements of an array:
-
-{% highlight scala %}
-scala> val accum = sc.accumulator(0)
-accum: spark.Accumulator[Int] = 0
-
-scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x)
-...
-10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
-
-scala> accum.value
-res2: Int = 10
-{% endhighlight %}
-
-
-# Where to Go from Here
-
-You can see some [example Spark programs](http://spark.apache.org/examples.html) on the Spark website.
-In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `bin/run-example` script included in Spark; for example:
-
-    ./bin/run-example SparkPi
-
-For help on optimizing your program, the [configuration](configuration.html) and
-[tuning](tuning.html) guides provide information on best practices. They are especially important for
-making sure that your data is stored in memory in an efficient format.
+This document has moved [here](programming-guide.html).
diff --git a/docs/security.md b/docs/security.md
index 90c69915f5..90ba678033 100644
--- a/docs/security.md
+++ b/docs/security.md
@@ -3,18 +3,18 @@ layout: global
 title: Spark Security
 ---
 
-Spark currently supports authentication via a shared secret. Authentication can be configured to be on via the `spark.authenticate` configuration parameter. This parameter controls whether the Spark communication protocols do authentication using the shared secret. This authentication is a basic handshake to make sure both sides have the same shared secret and are allowed to communicate. If the shared secret is not identical they will not be allowed to communicate.
+Spark currently supports authentication via a shared secret. Authentication can be configured to be on via the `spark.authenticate` configuration parameter. This parameter controls whether the Spark communication protocols do authentication using the shared secret. This authentication is a basic handshake to make sure both sides have the same shared secret and are allowed to communicate. If the shared secret is not identical they will not be allowed to communicate. The shared secret is created as follows:
 
-The Spark UI can also be secured by using javax servlet filters. A user may want to secure the UI if it has data that other users should not be allowed to see. The javax servlet filter specified by the user can authenticate the user and then once the user is logged in, Spark can compare that user versus the view acls to make sure they are authorized to view the UI. The configs 'spark.ui.acls.enable' and 'spark.ui.view.acls' control the behavior of the acls. Note that the person who started the application always has view access to the UI.
+* For Spark on [YARN](running-on-yarn.html) deployments, configuring `spark.authenticate` to `true` will automatically handle generating and distributing the shared secret. Each application will use a unique shared secret. 
+* For other types of Spark deployments, the Spark parameter `spark.authenticate.secret` should be configured on each of the nodes. This secret will be used by all the Master/Workers and applications.
 
-If your applications are using event logging, the directory where the event logs go (`spark.eventLog.dir`) should be manually created and have the proper permissions set on it. If you want those log files secure, the permissions should be set to drwxrwxrwxt for that directory. The owner of the directory should be the super user who is running the history server and the group permissions should be restricted to super user group. This will allow all users to write to the directory but will prevent unprivileged users from removing or renaming a file unless they own the file or directory. The event log files will be created by Spark with permissions such that only the user and group have read and write access.
+The Spark UI can also be secured by using [javax servlet filters](http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html) via the `spark.ui.filters` setting. A user may want to secure the UI if it has data that other users should not be allowed to see. The javax servlet filter specified by the user can authenticate the user and then once the user is logged in, Spark can compare that user versus the view ACLs to make sure they are authorized to view the UI. The configs `spark.ui.acls.enable` and `spark.ui.view.acls` control the behavior of the ACLs. Note that the user who started the application always has view access to the UI.
+On YARN, the Spark UI uses the standard YARN web application proxy mechanism and will authenticate via any installed Hadoop filters.
 
-For Spark on Yarn deployments, configuring `spark.authenticate` to true will automatically handle generating and distributing the shared secret. Each application will use a unique shared secret. The Spark UI uses the standard YARN web application proxy mechanism and will authenticate via any installed Hadoop filters. If an authentication filter is enabled, the acls controls can be used by control which users can via the Spark UI. 
+If your applications are using event logging, the directory where the event logs go (`spark.eventLog.dir`) should be manually created and have the proper permissions set on it. If you want those log files secured, the permissions should be set to `drwxrwxrwxt` for that directory. The owner of the directory should be the super user who is running the history server and the group permissions should be restricted to super user group. This will allow all users to write to the directory but will prevent unprivileged users from removing or renaming a file unless they own the file or directory. The event log files will be created by Spark with permissions such that only the user and group have read and write access.
 
-For other types of Spark deployments, the spark config `spark.authenticate.secret` should be configured on each of the nodes. This secret will be used by all the Master/Workers and applications. The UI can be secured using a javax servlet filter installed via `spark.ui.filters`. If an authentication filter is enabled, the acls controls can be used by control which users can via the Spark UI.
+**IMPORTANT NOTE:** *The experimental Netty shuffle path (`spark.shuffle.use.netty`) is not secured, so do not use Netty for shuffles if running with authentication.*
 
-IMPORTANT NOTE: The NettyBlockFetcherIterator is not secured so do not use netty for the shuffle is running with authentication on.
+See the [configuration page](configuration.html) for more details on the security configuration parameters.
 
-See [Spark Configuration](configuration.html) for more details on the security configs.
-
-See <a href="api/core/index.html#org.apache.spark.SecurityManager"><code>org.apache.spark.SecurityManager</code></a> for implementation details about security.
+See <a href="{{site.SPARK_GITHUB_URL}}/tree/master/core/src/main/scala/org/apache/spark/SecurityManager.scala"><code>org.apache.spark.SecurityManager</code></a> for implementation details about security.
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index dca80a9a69..3c1ce06083 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -222,7 +222,7 @@ SPARK_WORKER_OPTS supports the following system properties:
 # Connecting an Application to the Cluster
 
 To run an application on the Spark cluster, simply pass the `spark://IP:PORT` URL of the master as to the [`SparkContext`
-constructor](scala-programming-guide.html#initializing-spark).
+constructor](programming-guide.html#initializing-spark).
 
 To run an interactive Spark shell against the cluster, run the following command:
 
@@ -237,7 +237,7 @@ You can also pass an option `--cores <numCores>` to control the number of cores
 
 Spark supports two deploy modes: applications may run with the driver inside the client process or
 entirely inside the cluster. The
-[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) provides the
+[`spark-submit` script](submitting-applications.html) provides the
 most straightforward way to submit a compiled Spark application to the cluster in either deploy
 mode.
 
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 8a785450ad..a506457eba 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -2,7 +2,6 @@
 layout: global
 title: Spark SQL Programming Guide
 ---
-**Spark SQL is currently an Alpha component. Therefore, the APIs may be changed in future releases.**
 
 * This will become a table of contents (this text will be scraped).
 {:toc}
@@ -17,10 +16,10 @@ Spark.  At the core of this component is a new type of RDD,
 [SchemaRDD](api/scala/index.html#org.apache.spark.sql.SchemaRDD).  SchemaRDDs are composed
 [Row](api/scala/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
-in a traditional relational database.  A SchemaRDD can be created from an existing RDD, parquet
+in a traditional relational database.  A SchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 
-**All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`.**
+All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`.
 
 </div>
 
@@ -30,7 +29,7 @@ Spark.  At the core of this component is a new type of RDD,
 [JavaSchemaRDD](api/scala/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD).  JavaSchemaRDDs are composed
 [Row](api/scala/index.html#org.apache.spark.sql.api.java.Row) objects along with
 a schema that describes the data types of each column in the row.  A JavaSchemaRDD is similar to a table
-in a traditional relational database.  A JavaSchemaRDD can be created from an existing RDD, parquet
+in a traditional relational database.  A JavaSchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 </div>
 
@@ -41,13 +40,15 @@ Spark.  At the core of this component is a new type of RDD,
 [SchemaRDD](api/python/pyspark.sql.SchemaRDD-class.html).  SchemaRDDs are composed
 [Row](api/python/pyspark.sql.Row-class.html) objects along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
-in a traditional relational database.  A SchemaRDD can be created from an existing RDD, parquet
+in a traditional relational database.  A SchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 
-**All of the examples on this page use sample data included in the Spark distribution and can be run in the `pyspark` shell.**
+All of the examples on this page use sample data included in the Spark distribution and can be run in the `pyspark` shell.
 </div>
 </div>
 
+**Spark SQL is currently an alpha component. While we will minimize API changes, some APIs may change in future releases.**
+
 ***************************************************************************************************
 
 # Getting Started
@@ -240,8 +241,8 @@ Users that want a more complete dialect of SQL should look at the HiveQL support
 
 ## Using Parquet
 
-Parquet is a columnar format that is supported by many other data processing systems.  Spark SQL
-provides support for both reading and writing parquet files that automatically preserves the schema
+[Parquet](http://parquet.io) is a columnar format that is supported by many other data processing systems.
+Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema
 of the original data.  Using the data from the above example:
 
 <div class="codetabs">
@@ -254,11 +255,11 @@ import sqlContext._
 
 val people: RDD[Person] = ... // An RDD of case class objects, from the previous example.
 
-// The RDD is implicitly converted to a SchemaRDD, allowing it to be stored using parquet.
+// The RDD is implicitly converted to a SchemaRDD, allowing it to be stored using Parquet.
 people.saveAsParquetFile("people.parquet")
 
 // Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
-// The result of loading a parquet file is also a JavaSchemaRDD.
+// The result of loading a Parquet file is also a JavaSchemaRDD.
 val parquetFile = sqlContext.parquetFile("people.parquet")
 
 //Parquet files can also be registered as tables and then used in SQL statements.
@@ -275,10 +276,10 @@ teenagers.collect().foreach(println)
 
 JavaSchemaRDD schemaPeople = ... // The JavaSchemaRDD from the previous example.
 
-// JavaSchemaRDDs can be saved as parquet files, maintaining the schema information.
+// JavaSchemaRDDs can be saved as Parquet files, maintaining the schema information.
 schemaPeople.saveAsParquetFile("people.parquet");
 
-// Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
+// Read in the Parquet file created above.  Parquet files are self-describing so the schema is preserved.
 // The result of loading a parquet file is also a JavaSchemaRDD.
 JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
 
@@ -297,10 +298,10 @@ JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >=
 
 peopleTable # The SchemaRDD from the previous example.
 
-# SchemaRDDs can be saved as parquet files, maintaining the schema information.
+# SchemaRDDs can be saved as Parquet files, maintaining the schema information.
 peopleTable.saveAsParquetFile("people.parquet")
 
-# Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
+# Read in the Parquet file created above.  Parquet files are self-describing so the schema is preserved.
 # The result of loading a parquet file is also a SchemaRDD.
 parquetFile = sqlCtx.parquetFile("people.parquet")
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 972b660262..b95f818d0f 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -136,7 +136,7 @@ The complete code can be found in the Spark Streaming example
 <div data-lang="java" markdown="1">
 
 First, we create a
-[JavaStreamingContext](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html) object,
+[JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) object,
 which is the main entry point for all streaming
 functionality. Besides Spark's configuration, we specify that any DStream would be processed
 in 1 second batches.
@@ -215,7 +215,7 @@ jssc.awaitTermination();   // Wait for the computation to terminate
 {% endhighlight %}
 
 The complete code can be found in the Spark Streaming example
-[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
+[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/index.html?org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
 <br>
 
 </div>
@@ -360,12 +360,12 @@ new JavaStreamingContext(master, appName, batchInterval, [sparkHome], [jars])
 </div>
 </div>
 
-The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls)
+The `master` parameter is a standard [Spark cluster URL](programming-guide.html#master-urls)
 and can be "local" for local testing. The `appName` is a name of your program,
 which will be shown on your cluster's web UI. The `batchInterval` is the size of the batches,
 as explained earlier. Finally, the last two parameters are needed to deploy your code to a cluster
  if running in distributed mode, as described in the
- [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster).
+ [Spark programming guide](programming-guide.html#deploying-code-on-a-cluster).
  Additionally, the underlying SparkContext can be accessed as
 `ssc.sparkContext`.
 
@@ -813,10 +813,8 @@ output operators are defined:
 The complete list of DStream operations is available in the API documentation. For the Scala API,
 see [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream)
 and [PairDStreamFunctions](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions).
-For the Java API, see [JavaDStream](api/scala/index.html#org.apache.spark.streaming.api.java.dstream.DStream)
-and [JavaPairDStream](api/scala/index.html#org.apache.spark.streaming.api.java.JavaPairDStream).
-Specifically for the Java API, see [Spark's Java programming guide](java-programming-guide.html)
-for more information.
+For the Java API, see [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html)
+and [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html).
 
 ## Persistence
 Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is,
@@ -833,7 +831,7 @@ default persistence level is set to replicate the data to two nodes for fault-to
 Note that, unlike RDDs, the default persistence level of DStreams keeps the data serialized in
 memory. This is further discussed in the [Performance Tuning](#memory-tuning) section. More
 information on different persistence levels can be found in
-[Spark Programming Guide](scala-programming-guide.html#rdd-persistence).
+[Spark Programming Guide](programming-guide.html#rdd-persistence).
 
 ## RDD Checkpointing
 A _stateful operation_ is one which operates over multiple batches of data. This includes all
@@ -878,7 +876,7 @@ sending the data to two destinations (i.e., the earlier and upgraded application
 
 - The existing application is shutdown gracefully (see
 [`StreamingContext.stop(...)`](api/scala/index.html#org.apache.spark.streaming.StreamingContext)
-or [`JavaStreamingContext.stop(...)`](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html)
+or [`JavaStreamingContext.stop(...)`](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html)
 for graceful shutdown options) which ensure data that have been received is completely
 processed before shutdown. Then the
 upgraded application can be started, which will start processing from the same point where the earlier
@@ -1313,10 +1311,10 @@ This section elaborates the steps required to migrate your existing code to 1.0.
 `FlumeUtils.createStream`, etc.) now returns
 [InputDStream](api/scala/index.html#org.apache.spark.streaming.dstream.InputDStream) /
 [ReceiverInputDStream](api/scala/index.html#org.apache.spark.streaming.dstream.ReceiverInputDStream)
-(instead of DStream) for Scala, and [JavaInputDStream](api/java/org/apache/spark/streaming/api/java/JavaInputDStream.html) /
-[JavaPairInputDStream](api/java/org/apache/spark/streaming/api/java/JavaPairInputDStream.html) /
-[JavaReceiverInputDStream](api/java/org/apache/spark/streaming/api/java/JavaReceiverInputDStream.html) /
-[JavaPairReceiverInputDStream](api/java/org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html)
+(instead of DStream) for Scala, and [JavaInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaInputDStream.html) /
+[JavaPairInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairInputDStream.html) /
+[JavaReceiverInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaReceiverInputDStream.html) /
+[JavaPairReceiverInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html)
 (instead of JavaDStream) for Java. This ensures that functionality specific to input streams can
 be added to these classes in the future without breaking binary compatibility.
 Note that your existing Spark Streaming applications should not require any change
@@ -1367,14 +1365,14 @@ package and renamed for better clarity.
     [ZeroMQUtils](api/scala/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$), and
     [MQTTUtils](api/scala/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$)
   - Java docs
-    * [JavaStreamingContext](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html),
-    [JavaDStream](api/java/org/apache/spark/streaming/api/java/JavaDStream.html) and
-    [PairJavaDStream](api/java/org/apache/spark/streaming/api/java/PairJavaDStream.html)
-    * [KafkaUtils](api/java/org/apache/spark/streaming/kafka/KafkaUtils.html),
-    [FlumeUtils](api/java/org/apache/spark/streaming/flume/FlumeUtils.html),
-    [TwitterUtils](api/java/org/apache/spark/streaming/twitter/TwitterUtils.html),
-    [ZeroMQUtils](api/java/org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and
-    [MQTTUtils](api/java/org/apache/spark/streaming/mqtt/MQTTUtils.html)
+    * [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html),
+    [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html) and
+    [PairJavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/PairJavaDStream.html)
+    * [KafkaUtils](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html),
+    [FlumeUtils](api/java/index.html?org/apache/spark/streaming/flume/FlumeUtils.html),
+    [TwitterUtils](api/java/index.html?org/apache/spark/streaming/twitter/TwitterUtils.html),
+    [ZeroMQUtils](api/java/index.html?org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and
+    [MQTTUtils](api/java/index.html?org/apache/spark/streaming/mqtt/MQTTUtils.html)
 
 * More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming)
   and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming)
diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md
new file mode 100644
index 0000000000..d2864fe4c2
--- /dev/null
+++ b/docs/submitting-applications.md
@@ -0,0 +1,153 @@
+---
+layout: global
+title: Submitting Applications
+---
+
+The `spark-submit` script in Spark's `bin` directory is used to launch applications on a cluster.
+It can use all of Spark's supported [cluster managers](cluster-overview.html#cluster-manager-types)
+through a uniform interface so you don't have to configure your application specially for each one.
+
+# Bundling Your Application's Dependencies
+If your code depends on other projects, you will need to package them alongside
+your application in order to distribute the code to a Spark cluster. To do this,
+to create an assembly jar (or "uber" jar) containing your code and its dependencies. Both
+[sbt](https://github.com/sbt/sbt-assembly) and
+[Maven](http://maven.apache.org/plugins/maven-shade-plugin/)
+have assembly plugins. When creating assembly jars, list Spark and Hadoop
+as `provided` dependencies; these need not be bundled since they are provided by
+the cluster manager at runtime. Once you have an assembled jar you can call the `bin/spark-submit`
+script as shown here while passing your jar.
+
+For Python, you can use the `--py-files` argument of `spark-submit` to add `.py`, `.zip` or `.egg`
+files to be distributed with your application. If you depend on multiple Python files we recommend
+packaging them into a `.zip` or `.egg`.
+
+# Launching Applications with spark-submit
+
+Once a user application is bundled, it can be launched using the `bin/spark-submit` script.
+This script takes care of setting up the classpath with Spark and its
+dependencies, and can support different cluster managers and deploy modes that Spark supports:
+
+{% highlight bash %}
+./bin/spark-submit \
+  --class <main-class>
+  --master <master-url> \
+  --deploy-mode <deploy-mode> \
+  ... # other options
+  <application-jar> \
+  [application-arguments]
+{% endhighlight %}
+
+Some of the commonly used options are:
+
+* `--class`: The entry point for your application (e.g. `org.apache.spark.examples.SparkPi`)
+* `--master`: The [master URL](#master-urls) for the cluster (e.g. `spark://23.195.26.187:7077`)
+* `--deploy-mode`: Whether to deploy your driver program within the cluster or run it locally as an external client (either `cluster` or `client`)
+* `application-jar`: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes.
+* `application-arguments`: Arguments passed to the main method of your main class, if any
+
+For Python applications, simply pass a `.py` file in the place of `<application-jar>` instead of a JAR,
+and add Python `.zip`, `.egg` or `.py` files to the search path with `--py-files`.
+
+To enumerate all options available to `spark-submit` run it with `--help`. Here are a few
+examples of common options:
+
+{% highlight bash %}
+# Run application locally on 8 cores
+./bin/spark-submit \
+  --class org.apache.spark.examples.SparkPi \
+  --master local[8] \
+  /path/to/examples.jar \
+  100
+
+# Run on a Spark standalone cluster
+./bin/spark-submit \
+  --class org.apache.spark.examples.SparkPi \
+  --master spark://207.184.161.138:7077 \
+  --executor-memory 20G \
+  --total-executor-cores 100 \
+  /path/to/examples.jar \
+  1000
+
+# Run on a YARN cluster
+export HADOOP_CONF_DIR=XXX
+./bin/spark-submit \
+  --class org.apache.spark.examples.SparkPi \
+  --master yarn-cluster \  # can also be `yarn-client` for client mode
+  --executor-memory 20G \
+  --num-executors 50 \
+  /path/to/examples.jar \
+  1000
+
+# Run a Python application on a cluster
+./bin/spark-submit \
+  --master spark://207.184.161.138:7077 \
+  examples/src/main/python/pi.py \
+  1000
+{% endhighlight %}
+
+# Master URLs
+
+The master URL passed to Spark can be in one of the following formats:
+
+<table class="table">
+<tr><th>Master URL</th><th>Meaning</th></tr>
+<tr><td> local </td><td> Run Spark locally with one worker thread (i.e. no parallelism at all). </td></tr>
+<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). </td></tr>
+<tr><td> local[*] </td><td> Run Spark locally with as many worker threads as logical cores on your machine.</td></tr>
+<tr><td> spark://HOST:PORT </td><td> Connect to the given <a href="spark-standalone.html">Spark standalone
+        cluster</a> master. The port must be whichever one your master is configured to use, which is 7077 by default.
+</td></tr>
+<tr><td> mesos://HOST:PORT </td><td> Connect to the given <a href="running-on-mesos.html">Mesos</a> cluster.
+        The port must be whichever one your is configured to use, which is 5050 by default.
+        Or, for a Mesos cluster using ZooKeeper, use <code>mesos://zk://...</code>.
+</td></tr>
+<tr><td> yarn-client </td><td> Connect to a <a href="running-on-yarn.html"> YARN </a> cluster in
+client mode. The cluster location will be found based on the HADOOP_CONF_DIR variable.
+</td></tr>
+<tr><td> yarn-cluster </td><td> Connect to a <a href="running-on-yarn.html"> YARN </a> cluster in
+cluster mode. The cluster location will be found based on HADOOP_CONF_DIR.
+</td></tr>
+</table>
+
+
+# Loading Configuration from a File
+
+The `spark-submit` script can load default [Spark configuration values](configuration.html) from a
+properties file and pass them on to your application. By default it will read options
+from `conf/spark-defaults.conf` in the Spark directory. For more detail, see the section on
+[loading default configurations](configuration.html#loading-default-configurations).
+
+Loading default Spark configurations this way can obviate the need for certain flags to
+`spark-submit`. For instance, if the `spark.master` property is set, you can safely omit the
+`--master` flag from `spark-submit`. In general, configuration values explicitly set on a
+`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values in the
+defaults file.
+
+If you are ever unclear where configuration options are coming from, you can print out fine-grained
+debugging information by running `spark-submit` with the `--verbose` option.
+
+# Advanced Dependency Management
+When using `spark-submit`, the application jar along with any jars included with the `--jars` option
+will be automatically transferred to the cluster. Spark uses the following URL scheme to allow
+different strategies for disseminating jars:
+
+- **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and
+  every executor pulls the file from the driver HTTP server.
+- **hdfs:**, **http:**, **https:**, **ftp:** - these pull down files and JARs from the URI as expected
+- **local:** - a URI starting with local:/ is expected to exist as a local file on each worker node.  This
+  means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker,
+  or shared via NFS, GlusterFS, etc.
+
+Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes.
+This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup
+is handled automatically, and with Spark standalone, automatic cleanup can be configured with the
+`spark.worker.cleanup.appDataTtl` property.
+
+For python, the equivalent `--py-files` option can be used to distribute `.egg`, `.zip` and `.py` libraries
+to executors.
+
+# More Information
+
+Once you have deployed your application, the [cluster mode overview](cluster-overview.html) describes 
+the components involved in distributed execution, and how to monitor and debug applications.
diff --git a/docs/tuning.md b/docs/tuning.md
index 78e10770a8..c4230416e1 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -10,7 +10,7 @@ Because of the in-memory nature of most Spark computations, Spark programs can b
 by any resource in the cluster: CPU, network bandwidth, or memory.
 Most often, if the data fits in memory, the bottleneck is network bandwidth, but sometimes, you
 also need to do some tuning, such as
-[storing RDDs in serialized form](scala-programming-guide.html#rdd-persistence), to
+[storing RDDs in serialized form](programming-guide.html#rdd-persistence), to
 decrease memory usage.
 This guide will cover two main topics: data serialization, which is crucial for good network
 performance and can also reduce memory use, and memory tuning. We also sketch several smaller topics.
@@ -130,7 +130,7 @@ pointer-based data structures and wrapper objects. There are several ways to do
 
 When your objects are still too large to efficiently store despite this tuning, a much simpler way
 to reduce memory usage is to store them in *serialized* form, using the serialized StorageLevels in
-the [RDD persistence API](scala-programming-guide.html#rdd-persistence), such as `MEMORY_ONLY_SER`.
+the [RDD persistence API](programming-guide.html#rdd-persistence), such as `MEMORY_ONLY_SER`.
 Spark will then store each RDD partition as one large byte array.
 The only downside of storing data in serialized form is slower access times, due to having to
 deserialize each object on the fly.
@@ -239,7 +239,7 @@ number of cores in your clusters.
 
 ## Broadcasting Large Variables
 
-Using the [broadcast functionality](scala-programming-guide.html#broadcast-variables)
+Using the [broadcast functionality](programming-guide.html#broadcast-variables)
 available in `SparkContext` can greatly reduce the size of each serialized task, and the cost
 of launching a job over a cluster. If your tasks use any large object from the driver program
 inside of them (e.g. a static lookup table), consider turning it into a broadcast variable.