Merge remote-tracking branch 'mesos/master' into yarnUILink

Conflicts: core/src/main/scala/org/apache/spark/ui/UIUtils.scala core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala docs/running-on-yarn.md
author: Y.CORP.YAHOO.COM\tgraves <tgraves@thatenemy-lm.champ.corp.yahoo.com> 2013-09-03 08:36:59 -0500
committer: Y.CORP.YAHOO.COM\tgraves <tgraves@thatenemy-lm.champ.corp.yahoo.com> 2013-09-03 08:36:59 -0500
commit: 547fc4a4120a2bb3a6aeca66dd0cd568534ba65a (patch)
tree: bfb00a0a5925ca9a08d6e7a05cc33a411a5873c1 /docs
parent: 96452eea56c289e25a711ca54c812723a5059739 (diff)
parent: 59218bdd4996a13116009e3669b1b875be23a694 (diff)
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spark-547fc4a4120a2bb3a6aeca66dd0cd568534ba65a.tar.bz2
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20 files changed, 428 insertions, 301 deletions
diff --git a/docs/README.md b/docs/README.md
index c2b3497bb3..dfcf753553 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,6 +1,6 @@
 Welcome to the Spark documentation!
 
-This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at http://spark-project.org/documentation.html.
+This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at http://spark.incubator.apache.org/documentation.html.
 
 Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the documentation yourself. Why build it yourself? So that you have the docs that corresponds to whichever version of Spark you currently have checked out of revision control.
 
diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index a76346f428..84749fda4e 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -61,20 +61,25 @@
                             <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">Scala</a></li>
-                                <li><a href="java-programming-guide.html">Java</a></li>
-                                <li><a href="python-programming-guide.html">Python</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 class="divider"></li>
                                 <li><a href="streaming-programming-guide.html">Spark Streaming</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>
                             </ul>
                         </li>
                         
                         <li class="dropdown">
                             <a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a>
                             <ul class="dropdown-menu">
-                                <li><a href="api/core/index.html">Spark Java/Scala (Scaladoc)</a></li>
-                                <li><a href="api/pyspark/index.html">Spark Python (Epydoc)</a></li>
-                                <li><a href="api/streaming/index.html">Spark Streaming Java/Scala (Scaladoc) </a></li>
-                                <li><a href="api/mllib/index.html">Spark ML Library (Scaladoc) </a></li>
+                                <li><a href="api/core/index.html">Spark Core for Java/Scala</a></li>
+                                <li><a href="api/pyspark/index.html">Spark Core for Python</a></li>
+                                <li class="divider"></li>
+                                <li><a href="api/streaming/index.html">Spark Streaming</a></li>
+                                <li><a href="api/mllib/index.html">MLlib (Machine Learning)</a></li>
+                                <li><a href="api/bagel/index.html">Bagel (Pregel on Spark)</a></li>
                             </ul>
                         </li>
 
@@ -91,10 +96,10 @@
                         <li class="dropdown">
                             <a href="api.html" class="dropdown-toggle" data-toggle="dropdown">More<b class="caret"></b></a>
                             <ul class="dropdown-menu">
-                                <li><a href="building-with-maven.html">Building Spark with Maven</a></li>
                                 <li><a href="configuration.html">Configuration</a></li>
                                 <li><a href="tuning.html">Tuning Guide</a></li>
-                                <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
+                                <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
+                                <li><a href="building-with-maven.html">Building Spark with Maven</a></li>
                                 <li><a href="contributing-to-spark.html">Contributing to Spark</a></li>
                             </ul>
                         </li>
@@ -136,9 +141,15 @@
 
             <hr>-->
 
-            <!--<footer>
-                <p></p>
-            </footer>-->
+            <footer>
+              <hr>
+              <p style="text-align: center; veritcal-align: middle; color: #999;">
+                Apache Spark is an effort undergoing incubation at the Apache Software Foundation.
+                <a href="http://incubator.apache.org">
+                  <img style="margin-left: 20px;" src="img/incubator-logo.png" />
+                </a>
+              </p>
+            </footer>
 
         </div> <!-- /container -->
 
diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index c526da3ca0..140190a38c 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -3,22 +3,21 @@ layout: global
 title: Bagel Programming Guide
 ---
 
-**Bagel** is a Spark implementation of Google's [Pregel](http://portal.acm.org/citation.cfm?id=1807184) graph processing framework. Bagel currently supports basic graph computation, combiners, and aggregators.
+Bagel is a Spark implementation of Google's [Pregel](http://portal.acm.org/citation.cfm?id=1807184) graph processing framework. Bagel currently supports basic graph computation, combiners, and aggregators.
 
 In the Pregel programming model, jobs run as a sequence of iterations called _supersteps_. In each superstep, each vertex in the graph runs a user-specified function that can update state associated with the vertex and send messages to other vertices for use in the *next* iteration.
 
 This guide shows the programming model and features of Bagel by walking through an example implementation of PageRank on Bagel.
 
-## Linking with Bagel
+# Linking with Bagel
 
-To write a Bagel application, you will need to add Spark, its dependencies, and Bagel to your CLASSPATH:
+To use Bagel in your program, add the following SBT or Maven dependency:
 
-1. Run `sbt/sbt update` to fetch Spark's dependencies, if you haven't already done so.
-2. Run `sbt/sbt assembly` to build Spark and its dependencies into one JAR (`core/target/spark-core-assembly-{{site.SPARK_VERSION}}.jar`) 
-3. Run `sbt/sbt package` build the Bagel JAR (`bagel/target/scala_{{site.SCALA_VERSION}}/spark-bagel_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}.jar`).
-4. Add these two JARs to your CLASSPATH.
+    groupId = org.apache.spark
+    artifactId = spark-bagel_{{site.SCALA_VERSION}}
+    version = {{site.SPARK_VERSION}}
 
-## Programming Model
+# 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.
 
@@ -29,8 +28,8 @@ representing the current PageRank of the vertex, and similarly extend
 the `Message` and `Edge` classes. Note that these need to be marked `@serializable` to allow Spark to transfer them across machines. We also import the Bagel types and implicit conversions.
 
 {% highlight scala %}
-import spark.bagel._
-import spark.bagel.Bagel._
+import org.apache.spark.bagel._
+import org.apache.spark.bagel.Bagel._
 
 @serializable class PREdge(val targetId: String) extends Edge
 
@@ -89,7 +88,7 @@ Finally, we print the results.
 println(result.map(v => "%s\t%s\n".format(v.id, v.rank)).collect.mkString)
 {% endhighlight %}
 
-### Combiners
+## Combiners
 
 Sending a message to another vertex generally involves expensive communication over the network. For certain algorithms, it's possible to reduce the amount of communication using _combiners_. For example, if the compute function receives integer messages and only uses their sum, it's possible for Bagel to combine multiple messages to the same vertex by summing them.
 
@@ -97,7 +96,7 @@ For combiner support, Bagel can optionally take a set of combiner functions that
 
 _Example: PageRank with combiners_
 
-### Aggregators
+## Aggregators
 
 Aggregators perform a reduce across all vertices after each superstep, and provide the result to each vertex in the next superstep.
 
@@ -105,11 +104,11 @@ For aggregator support, Bagel can optionally take an aggregator function that re
 
 _Example_
 
-### Operations
+## Operations
 
-Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/mesos/spark/blob/master/bagel/src/main/scala/spark/bagel/Bagel.scala) for details.
+Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/apache/incubator-spark/blob/master/bagel/src/main/scala/spark/bagel/Bagel.scala) for details.
 
-#### Actions
+### Actions
 
 {% highlight scala %}
 /*** Full form ***/
@@ -133,7 +132,7 @@ Bagel.run(sc, vertices, messages, numSplits)(compute)
 // and returns (newVertex: V, outMessages: Array[M])
 {% endhighlight %}
 
-#### Types
+### Types
 
 {% highlight scala %}
 trait Combiner[M, C] {
@@ -156,6 +155,10 @@ trait Message[K] {
 }
 {% endhighlight %}
 
-## Where to Go from Here
+# Where to Go from Here
 
-Two example jobs, PageRank and shortest path, are included in `examples/src/main/scala/spark/examples/bagel`. You can run them by passing the class name to the `run-example` script included in Spark -- for example, `./run-example spark.examples.bagel.WikipediaPageRank`. Each example program prints usage help when run without any arguments.
+Two example jobs, PageRank and shortest path, are included in `examples/src/main/scala/org/apache/spark/examples/bagel`. You can run them by passing the class name to the `run-example` script included in Spark; e.g.:
+
+    ./run-example org.apache.spark.examples.bagel.WikipediaPageRank
+
+Each example program prints usage help when run without any arguments.
diff --git a/docs/configuration.md b/docs/configuration.md
index b125eeb03c..310e78a9eb 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -5,50 +5,14 @@ title: Spark Configuration
 
 Spark provides three main locations to configure the system:
 
-* [Environment variables](#environment-variables) for launching Spark workers, which can
-  be set either in your driver program or in the `conf/spark-env.sh` script.
-* [Java system properties](#system-properties), which control internal configuration parameters and can be set either
-  programmatically (by calling `System.setProperty` *before* creating a `SparkContext`) or through the
-  `SPARK_JAVA_OPTS` environment variable in `spark-env.sh`.
+* [Java system properties](#system-properties), which control internal configuration parameters and can be set
+  either programmatically (by calling `System.setProperty` *before* creating a `SparkContext`) or through
+  JVM arguments.
+* [Environment variables](#environment-variables) for configuring per-machine settings such as the IP address,
+  which can be set in the `conf/spark-env.sh` script.
 * [Logging configuration](#configuring-logging), which is done through `log4j.properties`.
 
 
-# Environment Variables
-
-Spark determines how to initialize the JVM on worker nodes, or even on the local node when you run `spark-shell`,
-by running the `conf/spark-env.sh` script in the directory where it is installed. This script does not exist by default
-in the Git repository, but but you can create it by copying `conf/spark-env.sh.template`. Make sure that you make
-the copy executable.
-
-Inside `spark-env.sh`, you *must* set at least the following two variables:
-
-* `SCALA_HOME`, to point to your Scala installation, or `SCALA_LIBRARY_PATH` to point to the directory for Scala
-  library JARs (if you install Scala as a Debian or RPM package, there is no `SCALA_HOME`, but these libraries
-  are in a separate path, typically /usr/share/java; look for `scala-library.jar`).
-* `MESOS_NATIVE_LIBRARY`, if you are [running on a Mesos cluster](running-on-mesos.html).
-
-In addition, there are four other variables that control execution. These should be set *in the environment that
-launches the job's driver program* instead of `spark-env.sh`, because they will be automatically propagated to
-workers. Setting these per-job instead of in `spark-env.sh` ensures that different jobs can have different settings
-for these variables.
-
-* `SPARK_JAVA_OPTS`, to add JVM options. This includes any system properties that you'd like to pass with `-D`.
-* `SPARK_CLASSPATH`, to add elements to Spark's classpath.
-* `SPARK_LIBRARY_PATH`, to add search directories for native libraries.
-* `SPARK_MEM`, to set the amount of memory used per node. This should be in the same format as the
-   JVM's -Xmx option, e.g. `300m` or `1g`. Note that this option will soon be deprecated in favor of
-   the `spark.executor.memory` system property, so we recommend using that in new code.
-
-Beware that if you do set these variables in `spark-env.sh`, they will override the values set by user programs,
-which is undesirable; if you prefer, you can choose to have `spark-env.sh` set them only if the user program
-hasn't, as follows:
-
-{% highlight bash %}
-if [ -z "$SPARK_JAVA_OPTS" ] ; then
-  SPARK_JAVA_OPTS="-verbose:gc"
-fi
-{% endhighlight %}
-
 # System Properties
 
 To set a system property for configuring Spark, you need to either pass it with a -D flag to the JVM (for example `java -Dspark.cores.max=5 MyProgram`) or call `System.setProperty` in your code *before* creating your Spark context, as follows:
@@ -67,18 +31,18 @@ there are at least five properties that you will commonly want to control:
   <td>spark.executor.memory</td>
   <td>512m</td>
   <td>
-    Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. `512m`, `2g`).
+    Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. <code>512m</code>, <code>2g</code>).
   </td>
 </tr>
 <tr>
   <td>spark.serializer</td>
-  <td>spark.JavaSerializer</td>
+  <td>org.apache.spark.serializer.<br />JavaSerializer</td>
   <td>
     Class to use for serializing objects that will be sent over the network or need to be cached
     in serialized form. The default of Java serialization works with any Serializable Java object but is
-    quite slow, so we recommend <a href="tuning.html">using <code>spark.KryoSerializer</code>
+    quite slow, so we recommend <a href="tuning.html">using <code>org.apache.spark.serializer.KryoSerializer</code>
     and configuring Kryo serialization</a> when speed is necessary. Can be any subclass of
-    <a href="api/core/index.html#spark.Serializer"><code>spark.Serializer</code></a>).
+    <a href="api/core/index.html#org.apache.spark.serializer.Serializer"><code>org.apache.spark.Serializer</code></a>.
   </td>
 </tr>
 <tr>
@@ -86,8 +50,8 @@ there are at least five properties that you will commonly want to control:
   <td>(none)</td>
   <td>
     If you use Kryo serialization, set this class to register your custom classes with Kryo.
-    You need to set it to a class that extends
-    <a href="api/core/index.html#spark.KryoRegistrator"><code>spark.KryoRegistrator</code></a>).
+    It should be set to a class that extends
+    <a href="api/core/index.html#org.apache.spark.serializer.KryoRegistrator"><code>KryoRegistrator</code></a>.
     See the <a href="tuning.html#data-serialization">tuning guide</a> for more details.
   </td>
 </tr>
@@ -97,7 +61,7 @@ there are at least five properties that you will commonly want to control:
   <td>
     Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored
     on disk. This should be on a fast, local disk in your system. It can also be a comma-separated
-    list of multiple directories.
+    list of multiple directories on different disks.
   </td>
 </tr>
 <tr>
@@ -106,7 +70,8 @@ there are at least five properties that you will commonly want to control:
   <td>
     When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a
     <a href="running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained"
-    sharing mode</a>, how many CPU cores to request at most. The default will use all available cores.
+    sharing mode</a>, how many CPU cores to request at most. The default will use all available cores
+    offered by the cluster manager.
   </td>
 </tr>
 </table>
@@ -182,10 +147,10 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.io.compression.codec</td>
-  <td>spark.io.SnappyCompressionCodec</td>
+  <td>org.apache.spark.io.<br />LZFCompressionCodec</td>
   <td>
     The compression codec class to use for various compressions. By default, Spark provides two
-    codecs: <code>spark.io.LZFCompressionCodec</code> and <code>spark.io.SnappyCompressionCodec</code>.
+    codecs: <code>org.apache.spark.io.LZFCompressionCodec</code> and <code>org.apache.spark.io.SnappyCompressionCodec</code>.
   </td>
 </tr>
 <tr>
@@ -206,7 +171,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.closure.serializer</td>
-  <td>spark.JavaSerializer</td>
+  <td>org.apache.spark.serializer.<br />JavaSerializer</td>
   <td>
     Serializer class to use for closures. Generally Java is fine unless your distributed functions
     (e.g. map functions) reference large objects in the driver program.
@@ -233,7 +198,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.broadcast.factory</td>
-  <td>spark.broadcast.HttpBroadcastFactory</td>
+  <td>org.apache.spark.broadcast.<br />HttpBroadcastFactory</td>
   <td>
     Which broadcast implementation to use.
   </td>
@@ -321,7 +286,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.cleaner.ttl</td>
-  <td>(disable)</td>
+  <td>(infinite)</td>
   <td>
     Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.).
     Periodic cleanups will ensure that metadata older than this duration will be forgetten. This is
@@ -347,6 +312,34 @@ Apart from these, the following properties are also available, and may be useful
 
 </table>
 
+# Environment Variables
+
+Certain Spark settings can also be configured through environment variables, which are read from the `conf/spark-env.sh`
+script in the directory where Spark is installed (or `conf/spark-env.cmd` on Windows). These variables are meant to be for machine-specific settings, such
+as library search paths. While Java system properties can also be set here, for application settings, we recommend setting
+these properties within the application instead of in `spark-env.sh` so that different applications can use different
+settings.
+
+Note that `conf/spark-env.sh` does not exist by default when Spark is installed. However, you can copy
+`conf/spark-env.sh.template` to create it. Make sure you make the copy executable.
+
+The following variables can be set in `spark-env.sh`:
+
+* `JAVA_HOME`, the location where Java is installed (if it's not on your default `PATH`)
+* `PYSPARK_PYTHON`, the Python binary to use for PySpark
+* `SPARK_LOCAL_IP`, to configure which IP address of the machine to bind to.
+* `SPARK_LIBRARY_PATH`, to add search directories for native libraries.
+* `SPARK_CLASSPATH`, to add elements to Spark's classpath that you want to be present for _all_ applications.
+   Note that applications can also add dependencies for themselves through `SparkContext.addJar` -- we recommend
+   doing that when possible.
+* `SPARK_JAVA_OPTS`, to add JVM options. This includes Java options like garbage collector settings and any system
+   properties that you'd like to pass with `-D` (e.g., `-Dspark.local.dir=/disk1,/disk2`). 
+* Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores
+  to use on each machine and maximum memory.
+
+Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might
+compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface.
+
 # Configuring Logging
 
 Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can configure it by adding a `log4j.properties`
diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css
index eb48138e08..ccb529eaba 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 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:20px;color:#333;background-color:#fff}a{color:#08c;text-decoration:none}a:hover{color:#005580;text-decoration:underline}.img-rounded{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px 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diff --git a/docs/hardware-provisioning.md b/docs/hardware-provisioning.md
new file mode 100644
index 0000000000..e5f054cb14
--- /dev/null
+++ b/docs/hardware-provisioning.md
@@ -0,0 +1,69 @@
+---
+layout: global
+title: Hardware Provisioning
+---
+
+A common question received by Spark developers is how to configure hardware for it. While the right
+hardware will depend on the situation, we make the following recommendations.
+
+# Storage Systems
+
+Because most Spark jobs will likely have to read input data from an external storage system (e.g.
+the Hadoop File System, or HBase), it is important to place it **as close to this system as
+possible**. We recommend the following:
+
+* If at all possible, run Spark on the same nodes as HDFS. The simplest way is to set up a Spark
+[standalone mode cluster](spark-standalone.html) on the same nodes, and configure Spark and
+Hadoop's memory and CPU usage to avoid interference (for Hadoop, the relevant options are
+`mapred.child.java.opts` for the per-task memory and `mapred.tasktracker.map.tasks.maximum`
+and `mapred.tasktracker.reduce.tasks.maximum` for number of tasks). Alternatively, you can run
+Hadoop and Spark on a common cluster manager like [Mesos](running-on-mesos.html) or
+[Hadoop YARN](running-on-yarn.html).
+
+* If this is not possible, run Spark on different nodes in the same local-area network as HDFS.
+
+* For low-latency data stores like HBase, it may be preferrable to run computing jobs on different
+nodes than the storage system to avoid interference.
+
+# Local Disks
+
+While Spark can perform a lot of its computation in memory, it still uses local disks to store
+data that doesn't fit in RAM, as well as to preserve intermediate output between stages. We
+recommend having **4-8 disks** per node, configured _without_ RAID (just as separate mount points).
+In Linux, mount the disks with the [`noatime` option](http://www.centos.org/docs/5/html/Global_File_System/s2-manage-mountnoatime.html)
+to reduce unnecessary writes. In Spark, [configure](configuration.html) the `spark.local.dir`
+variable to be a comma-separated list of the local disks. If you are running HDFS, it's fine to
+use the same disks as HDFS.
+
+# Memory
+
+In general, Spark can run well with anywhere from **8 GB to hundreds of gigabytes** of memory per
+machine. In all cases, we recommend allocating only at most 75% of the memory for Spark; leave the
+rest for the operating system and buffer cache.
+
+How much memory you will need will depend on your application. To determine how much your
+application uses for a certain dataset size, load part of your dataset in a Spark RDD and use the
+Storage tab of Spark's monitoring UI (`http://<driver-node>:3030`) to see its size in memory.
+Note that memory usage is greatly affected by storage level and serialization format -- see
+the [tuning guide](tuning.html) for tips on how to reduce it.
+
+Finally, note that the Java VM does not always behave well with more than 200 GB of RAM. If you
+purchase machines with more RAM than this, you can run _multiple worker JVMs per node_. In
+Spark's [standalone mode](spark-standalone.html), you can set the number of workers per node
+with the `SPARK_WORKER_INSTANCES` variable in `conf/spark-env.sh`, and the number of cores
+per worker with `SPARK_WORKER_CORES`.
+
+# Network
+
+In our experience, when the data is in memory, a lot of Spark applications are network-bound.
+Using a **10 Gigabit** or higher network is the best way to make these applications faster.
+This is especially true for "distributed reduce" applications such as group-bys, reduce-bys, and
+SQL joins. In any given application, you can see how much data Spark shuffles across the network
+from the application's monitoring UI (`http://<driver-node>:3030`).
+
+# CPU Cores
+
+Spark scales well to tens of CPU cores per machine because it performes minimal sharing between
+threads. You should likely provision at least **8-16 cores** per machine. Depending on the CPU
+cost of your workload, you may also need more: once data is in memory, most applications are
+either CPU- or network-bound.
diff --git a/docs/img/incubator-logo.png b/docs/img/incubator-logo.png
new file mode 100644
index 0000000000..33ca7f6227
--- /dev/null
+++ b/docs/img/incubator-logo.png
diff --git a/docs/index.md b/docs/index.md
index ec9c7dd4f3..7d73929940 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -3,50 +3,65 @@ layout: global
 title: Spark Overview
 ---
 
-Spark is a MapReduce-like cluster computing framework designed for low-latency iterative jobs and interactive use from an interpreter.
-It provides clean, language-integrated APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html), with a rich array of parallel operators.
-Spark can run on the Apache Mesos cluster manager, Hadoop YARN, Amazon EC2, or without an independent resource manager ("standalone mode").
+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, [Bagel](bagel-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
 
 # Downloading
 
-Get Spark by visiting the [downloads page](http://spark-project.org/downloads.html) of the Spark website. This documentation is for Spark version {{site.SPARK_VERSION}}.
+Get Spark by visiting the [downloads page](http://spark.incubator.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}.
 
-# Building
+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 requires [Scala {{site.SCALA_VERSION}}](http://www.scala-lang.org/). You will need to have Scala's `bin` directory in your `PATH`,
-or you will need to set the `SCALA_HOME` environment variable to point
-to where you've installed Scala. Scala must also be accessible through one
-of these methods on slave nodes on your cluster.
+# Building
 
 Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with it. To compile the code, go into the top-level Spark directory and run
 
     sbt/sbt assembly
 
-Spark also supports building using Maven. If you would like to build using Maven, see the [instructions for building Spark with Maven](building-with-maven.html).
+For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you will need to use this same version of Scala in your own program -- newer major versions may not work. You can get the right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
 
 # Testing the Build
 
-Spark comes with a number of sample programs in the `examples` directory.
+Spark comes with several sample programs in the `examples` directory.
 To run one of the samples, use `./run-example <class> <params>` in the top-level Spark directory
-(the `run` script sets up the appropriate paths and launches that program).
-For example, `./run-example spark.examples.SparkPi` will run a sample program that estimates Pi. Each of the
-examples prints usage help if no params are given.
+(the `run-example` script sets up the appropriate paths and launches that program).
+For example, try `./run-example org.apache.spark.examples.SparkPi local`.
+Each example prints usage help when run with no parameters.
 
 Note that all of the sample programs take a `<master>` parameter specifying the cluster URL
 to connect to. This can be a [URL for a distributed cluster](scala-programming-guide.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.
 
-Finally, Spark can be used interactively from a modified version of the Scala interpreter that you can start through
-`./spark-shell`. This is a great way to learn Spark.
+Finally, Spark can be used interactively through modified versions of the Scala shell (`./spark-shell`) or
+Python interpreter (`./pyspark`). These are a great way to learn Spark.
+
+# Running on a Cluster
+
+Spark supports several options for deployment:
+
+* [Amazon EC2](ec2-scripts.html): our scripts let you launch a cluster in about 5 minutes
+* [Standalone Deploy Mode](spark-standalone.html): simplest way to deploy Spark on a private cluster
+* [Apache Mesos](running-on-mesos.html)
+* [Hadoop YARN](running-on-yarn.html)
 
 # A Note About Hadoop Versions
 
-Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
+Spark uses the Hadoop-client library to talk to HDFS and other Hadoop-supported
 storage systems. Because the HDFS protocol has changed in different versions of
-Hadoop, you must build Spark against the same version that your cluster runs.
-You can change the version by setting the `HADOOP_VERSION` variable at the top
-of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
+Hadoop, you must build Spark against the same version that your cluster uses.
+By default, Spark links to Hadoop 1.0.4. You can change this by setting the
+`SPARK_HADOOP_VERSION` variable when compiling:
+
+    SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly
+
+In addition, if you wish to run Spark on [YARN](running-on-yarn.md), set
+`SPARK_YARN` to `true`:
+
+    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
+
+(Note that on Windows, you need to set the environment variables on separate lines, e.g., `set SPARK_HADOOP_VERSION=1.2.1`.)
 
 # Where to Go from Here
 
@@ -54,15 +69,20 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
 
 * [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 Guide](streaming-programming-guide.html): using the alpha release of Spark Streaming
+  * [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): using the alpha release of Spark Streaming
+* [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
 
 **API Docs:**
 
-* [Spark Java/Scala (Scaladoc)](api/core/index.html)
-* [Spark Python (Epydoc)](api/pyspark/index.html)
-* [Spark Streaming Java/Scala (Scaladoc)](api/streaming/index.html)
+* [Spark for Java/Scala (Scaladoc)](api/core/index.html)
+* [Spark for Python (Epydoc)](api/pyspark/index.html)
+* [Spark Streaming for Java/Scala (Scaladoc)](api/streaming/index.html)
+* [MLlib (Machine Learning) for Java/Scala (Scaladoc)](api/mllib/index.html)
+* [Bagel (Pregel on Spark) for Scala (Scaladoc)](api/bagel/index.html)
+
 
 **Deployment guides:**
 
@@ -74,27 +94,28 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
 
 **Other documents:**
 
-* [Building Spark With Maven](building-with-maven.html): Build Spark using the Maven build tool
 * [Configuration](configuration.html): customize Spark via its configuration system
 * [Tuning Guide](tuning.html): best practices to optimize performance and memory use
-* [Bagel](bagel-programming-guide.html): an implementation of Google's Pregel on Spark
+* [Hardware Provisioning](hardware-provisioning.html): recommendations for cluster hardware
+* [Building Spark with Maven](building-with-maven.html): Build Spark using the Maven build tool
 * [Contributing to Spark](contributing-to-spark.html)
 
 **External resources:**
 
-* [Spark Homepage](http://www.spark-project.org)
-* [Mailing List](http://groups.google.com/group/spark-users): ask questions about Spark here
-* [AMP Camp](http://ampcamp.berkeley.edu/): a two-day training camp at UC Berkeley that featured talks and exercises
-  about Spark, Shark, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/agenda-2012),
+* [Spark Homepage](http://spark.incubator.apache.org)
+* [Shark](http://shark.cs.berkeley.edu): Apache Hive over Spark
+* [Mailing Lists](http://spark.incubator.apache.org/mailing-lists.html): ask questions about Spark here
+* [AMP Camps](http://ampcamp.berkeley.edu/): a series of training camps at UC Berkeley that featured talks and
+  exercises about Spark, Shark, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/agenda-2012),
   [slides](http://ampcamp.berkeley.edu/agenda-2012) and [exercises](http://ampcamp.berkeley.edu/exercises-2012) are
   available online for free.
-* [Code Examples](http://spark-project.org/examples.html): more are also available in the [examples subfolder](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/examples) of Spark
+* [Code Examples](http://spark.incubator.apache.org/examples.html): more are also available in the [examples subfolder](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/) 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)
 
 # Community
 
-To get help using Spark or keep up with Spark development, sign up for the [spark-users mailing list](http://groups.google.com/group/spark-users).
+To get help using Spark or keep up with Spark development, sign up for the [user mailing list](http://spark.incubator.apache.org/mailing-lists.html).
 
 If you're in the San Francisco Bay Area, there's a regular [Spark meetup](http://www.meetup.com/spark-users/) every few weeks. Come by to meet the developers and other users.
 
diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md
index dd19a5f0c9..53085cc671 100644
--- a/docs/java-programming-guide.md
+++ b/docs/java-programming-guide.md
@@ -10,9 +10,9 @@ 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
-[`spark.api.java`](api/core/index.html#spark.api.java.package) package, and includes
-a [`JavaSparkContext`](api/core/index.html#spark.api.java.JavaSparkContext) for
-initializing Spark and [`JavaRDD`](api/core/index.html#spark.api.java.JavaRDD) classes,
+[`org.apache.spark.api.java`](api/core/index.html#org.apache.spark.api.java.package) package, and includes
+a [`JavaSparkContext`](api/core/index.html#org.apache.spark.api.java.JavaSparkContext) for
+initializing Spark and [`JavaRDD`](api/core/index.html#org.apache.spark.api.java.JavaRDD) 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.
@@ -23,12 +23,12 @@ There are a few key differences between the Java and Scala APIs:
 
 * Java does not support anonymous or first-class functions, so functions must
   be implemented by extending the
-  [`spark.api.java.function.Function`](api/core/index.html#spark.api.java.function.Function),
-  [`Function2`](api/core/index.html#spark.api.java.function.Function2), etc.
+  [`org.apache.spark.api.java.function.Function`](api/core/index.html#org.apache.spark.api.java.function.Function),
+  [`Function2`](api/core/index.html#org.apache.spark.api.java.function.Function2), etc.
   classes.
 * To maintain type safety, the Java API defines specialized Function and RDD
   classes for key-value pairs and doubles. For example, 
-  [`JavaPairRDD`](api/core/index.html#spark.api.java.JavaPairRDD)
+  [`JavaPairRDD`](api/core/index.html#org.apache.spark.api.java.JavaPairRDD)
   stores key-value pairs.
 * RDD methods like `collect()` and `countByKey()` return Java collections types,
   such as `java.util.List` and `java.util.Map`.
@@ -44,8 +44,8 @@ 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/core/index.html#spark.api.java.JavaPairRDD)
-and [`JavaDoubleRDD`](api/core/index.html#spark.api.java.JavaDoubleRDD)
+[`JavaPairRDD`](api/core/index.html#org.apache.spark.api.java.JavaPairRDD)
+and [`JavaDoubleRDD`](api/core/index.html#org.apache.spark.api.java.JavaDoubleRDD)
 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
@@ -75,7 +75,7 @@ class has a single abstract method, `call()`, that must be implemented.
 ## Storage Levels
 
 RDD [storage level](scala-programming-guide.html#rdd-persistence) constants, such as `MEMORY_AND_DISK`, are
-declared in the [spark.api.java.StorageLevels](api/core/index.html#spark.api.java.StorageLevels) class. To
+declared in the [org.apache.spark.api.java.StorageLevels](api/core/index.html#org.apache.spark.api.java.StorageLevels) class. To
 define your own storage level, you can use StorageLevels.create(...). 
 
 
@@ -92,8 +92,8 @@ The Java API supports other Spark features, including
 As an example, we will implement word count using the Java API.
 
 {% highlight java %}
-import spark.api.java.*;
-import spark.api.java.function.*;
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.*;
 
 JavaSparkContext sc = new JavaSparkContext(...);
 JavaRDD<String> lines = ctx.textFile("hdfs://...");
@@ -179,7 +179,7 @@ just a matter of style.
 # Javadoc
 
 We currently provide documentation for the Java API as Scaladoc, in the
-[`spark.api.java` package](api/core/index.html#spark.api.java.package), because
+[`org.apache.spark.api.java` package](api/core/index.html#org.apache.spark.api.java.package), because
 some of the classes are implemented in Scala. The main downside is that the types and function
 definitions show Scala syntax (for example, `def reduce(func: Function2[T, T]): T` instead of
 `T reduce(Function2<T, T> func)`). 
@@ -189,7 +189,10 @@ We hope to generate documentation with Java-style syntax in the future.
 # Where to Go from Here
 
 Spark includes several sample programs using the Java API in
-[`examples/src/main/java`](https://github.com/mesos/spark/tree/master/examples/src/main/java/spark/examples).  You can run them by passing the class name to the
-`run-example` script included in Spark -- for example, `./run-example
-spark.examples.JavaWordCount`.  Each example program prints usage help when run
+[`examples/src/main/java`](https://github.com/apache/incubator-spark/tree/master/examples/src/main/java/org/apache/spark/examples).  You can run them by passing the class name to the
+`run-example` script included in Spark; for example:
+
+    ./run-example org.apache.spark.examples.JavaWordCount
+
+Each example program prints usage help when run
 without any arguments.
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
new file mode 100644
index 0000000000..c897f8b36c
--- /dev/null
+++ b/docs/mllib-guide.md
@@ -0,0 +1,6 @@
+---
+layout: global
+title: Machine Learning Library (MLlib)
+---
+
+Coming soon.
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 15d3ebfcae..8c33a953a4 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -4,7 +4,7 @@ title: Python Programming Guide
 ---
 
 
-The Spark Python API (PySpark) exposes most of the Spark features available in the Scala version to Python.
+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.
@@ -15,12 +15,8 @@ This guide will show how to use the Spark features described there in Python.
 
 There are a few key differences between the Python and Scala APIs:
 
-* Python is dynamically typed, so RDDs can hold objects of different types.
-* PySpark does not currently support the following Spark features:
-    - Special functions on RDDs of doubles, such as `mean` and `stdev`
-    - `lookup`, `sample` and `sort`
-    - `persist` at storage levels other than `MEMORY_ONLY`
-    - Execution on Windows -- this is slated for a future release
+* Python is dynamically typed, so RDDs can hold objects of multiple types.
+* PySpark does not yet support a few API calls, such as `lookup`, `sort`, and `persist` at custom storage levels. See the [API docs](api/pyspark/index.html) for details.
 
 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:
@@ -30,7 +26,7 @@ logData = sc.textFile(logFile).cache()
 errors = logData.filter(lambda line: "ERROR" in line)
 {% endhighlight %}
 
-You can also pass functions that are defined using the `def` keyword; this is useful for more complicated functions that cannot be expressed using `lambda`:
+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):
@@ -38,7 +34,7 @@ def is_error(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 to other tasks:
+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"]
@@ -51,17 +47,20 @@ PySpark will automatically ship these functions to workers, along with any objec
 Instances of classes will be serialized and shipped to workers by PySpark, but classes themselves cannot be automatically distributed to workers.
 The [Standalone Use](#standalone-use) section describes how to ship code dependencies to workers.
 
+In addition, PySpark fully supports interactive use---simply run `./pyspark` to launch an interactive shell.
+
 
 # Installing and Configuring PySpark
 
 PySpark requires Python 2.6 or higher.
-PySpark jobs are executed using a standard cPython interpreter in order to support Python modules that use C extensions.
+PySpark jobs 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's scripts will run programs using `python`; an alternate Python executable may be specified by setting the `PYSPARK_PYTHON` environment variable in `conf/spark-env.sh`.
+
+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.
 
-Standalone PySpark jobs should be run using the `pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh`.
+Standalone PySpark jobs should be run using the `pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh` or `.cmd`.
 The script automatically adds the `pyspark` package to the `PYTHONPATH`.
 
 
@@ -101,7 +100,7 @@ $ MASTER=local[4] ./pyspark
 ## IPython
 
 It is also possible to launch PySpark in [IPython](http://ipython.org), the enhanced Python interpreter.
-To do this, simply set the `IPYTHON` variable to `1` when running `pyspark`:
+To do this, set the `IPYTHON` variable to `1` when running `pyspark`:
 
 {% highlight bash %}
 $ IPYTHON=1 ./pyspark
@@ -132,12 +131,16 @@ sc = SparkContext("local", "Job Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg
 Files listed here will be added to the `PYTHONPATH` and shipped to remote worker machines.
 Code dependencies can be added to an existing SparkContext using its `addPyFile()` method.
 
+# API Docs
+
+[API documentation](api/pyspark/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.
 
 # Where to Go from Here
 
-PySpark includes several sample programs in the [`python/examples` folder](https://github.com/mesos/spark/tree/master/python/examples).
-You can run them by passing the files to the `pyspark` script -- for example `./pyspark python/examples/wordcount.py`.
-Each program prints usage help when run without arguments.
+PySpark also includes several sample programs in the [`python/examples` folder](https://github.com/apache/incubator-spark/tree/master/python/examples).
+You can run them by passing the files to `pyspark`; e.g.:
+
+    ./pyspark python/examples/wordcount.py
 
-We currently provide [API documentation](api/pyspark/index.html) for the Python API as Epydoc.
-Many of the RDD method descriptions contain [doctests](http://docs.python.org/2/library/doctest.html) that provide additional usage examples.
+Each program prints usage help when run without arguments.
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 4e9deadbaa..70c3df8095 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -53,7 +53,7 @@ scala> textFile.filter(line => line.contains("Spark")).count() // How many lines
 res3: Long = 15
 {% endhighlight %}
 
-## More On RDD Operations
+## More on RDD Operations
 RDD actions and transformations can be used for more complex computations. Let's say we want to find the line with the most words:
 
 {% highlight scala %}
@@ -108,8 +108,8 @@ We'll create a very simple Spark job in Scala. So simple, in fact, that it's nam
 
 {% highlight scala %}
 /*** SimpleJob.scala ***/
-import spark.SparkContext
-import SparkContext._
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
 
 object SimpleJob {
   def main(args: Array[String]) {
@@ -126,7 +126,7 @@ object SimpleJob {
 
 This job simply counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the job, the directory where Spark is installed, and a name for the jar file containing the job's sources. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.
 
-This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds two repositories which host Spark dependencies:
+This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds a repository that Spark depends on:
 
 {% highlight scala %}
 name := "Simple Project"
@@ -135,14 +135,18 @@ version := "1.0"
 
 scalaVersion := "{{site.SCALA_VERSION}}"
 
-libraryDependencies += "org.spark-project" %% "spark-core" % "{{site.SPARK_VERSION}}"
+libraryDependencies += "org.apache.spark" %% "spark-core" % "{{site.SPARK_VERSION}}"
 
-resolvers ++= Seq(
-  "Akka Repository" at "http://repo.akka.io/releases/",
-  "Spray Repository" at "http://repo.spray.cc/")
+resolvers += "Akka Repository" at "http://repo.akka.io/releases/"
 {% endhighlight %}
 
-Of course, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the job's code, then use `sbt run` to execute our example job.
+If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS:
+
+{% highlight scala %}
+libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "<your-hdfs-version>"
+{% endhighlight %}
+
+Finally, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the job's code, then use `sbt run` to execute our example job.
 
 {% highlight bash %}
 $ find .
@@ -159,8 +163,6 @@ $ sbt run
 Lines with a: 46, Lines with b: 23
 {% endhighlight %}
 
-This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.
-
 # A Standalone Job In Java
 Now say we wanted to write a standalone job using the Java API. We will walk through doing this with Maven. If you are using other build systems, consider using the Spark assembly JAR described in the developer guide.
 
@@ -168,8 +170,8 @@ We'll create a very simple Spark job, `SimpleJob.java`:
 
 {% highlight java %}
 /*** SimpleJob.java ***/
-import spark.api.java.*;
-import spark.api.java.function.Function;
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
 
 public class SimpleJob {
   public static void main(String[] args) {
@@ -205,17 +207,13 @@ To build the job, we also write a Maven `pom.xml` file that lists Spark as a dep
   <version>1.0</version>
   <repositories>
     <repository>
-      <id>Spray.cc repository</id>
-      <url>http://repo.spray.cc</url>
-    </repository>
-    <repository>
       <id>Akka repository</id>
       <url>http://repo.akka.io/releases</url>
     </repository>
   </repositories>
   <dependencies>
     <dependency> <!-- Spark dependency -->
-      <groupId>org.spark-project</groupId>
+      <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_{{site.SCALA_VERSION}}</artifactId>
       <version>{{site.SPARK_VERSION}}</version>
     </dependency>
@@ -223,6 +221,16 @@ To build the job, we also write a Maven `pom.xml` file that lists Spark as a dep
 </project>
 {% endhighlight %}
 
+If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS:
+
+{% highlight xml %}
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+      <version>...</version>
+    </dependency>
+{% endhighlight %}
+
 We lay out these files according to the canonical Maven directory structure:
 {% highlight bash %}
 $ find .
@@ -242,8 +250,6 @@ $ mvn exec:java -Dexec.mainClass="SimpleJob"
 Lines with a: 46, Lines with b: 23
 {% endhighlight %}
 
-This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.
-
 # A Standalone Job In Python
 Now we will show how to write a standalone job using the Python API (PySpark).
 
@@ -280,4 +286,33 @@ $ ./pyspark SimpleJob.py
 Lines with a: 46, Lines with b: 23
 {% endhighlight python %}
 
-This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.
+# Running Jobs on a Cluster
+
+There are a few additional considerations when running jobs on a 
+[Spark](spark-standalone.html), [YARN](running-on-yarn.html), or 
+[Mesos](running-on-mesos.html) cluster.
+
+### Including Your Dependencies
+If your code depends on other projects, you will need to ensure they are also
+present on the slave nodes. A popular approach is 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-assembly-plugin/) 
+have assembly plugins. When creating assembly jars, list Spark 
+itself as a `provided` dependency; it need not be bundled since it is 
+already present on the slaves. Once you have an assembled jar, 
+add it to the SparkContext as shown here. It is also possible to submit 
+your dependent jars one-by-one when creating a SparkContext.
+
+### Setting Configuration Options
+Spark includes several configuration options which influence the behavior
+of your job. These should be set as 
+[JVM system properties](configuration.html#system-properties) in your 
+program. The options will be captured and shipped to all slave nodes.
+
+### Accessing Hadoop Filesystems
+
+The examples here access a local file. To read data from a distributed
+filesystem, such as HDFS, include 
+[Hadoop version information](index.html#a-note-about-hadoop-versions)
+in your build file. By default, Spark builds against HDFS 1.0.4.
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index f4a3eb667c..b31f78e8bf 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -9,9 +9,8 @@ Spark can run on private clusters managed by the [Apache Mesos](http://incubator
 2. Download Mesos {{site.MESOS_VERSION}} from a [mirror](http://www.apache.org/dyn/closer.cgi/incubator/mesos/mesos-{{site.MESOS_VERSION}}/).
 3. Configure Mesos using the `configure` script, passing the location of your `JAVA_HOME` using `--with-java-home`. Mesos comes with "template" configure scripts for different platforms, such as `configure.macosx`, that you can run. See the README file in Mesos for other options. **Note:** If you want to run Mesos without installing it into the default paths on your system (e.g. if you don't have administrative privileges to install it), you should also pass the `--prefix` option to `configure` to tell it where to install. For example, pass `--prefix=/home/user/mesos`. By default the prefix is `/usr/local`.
 4. Build Mesos using `make`, and then install it using `make install`.
-5. Create a file called `spark-env.sh` in Spark's `conf` directory, by copying `conf/spark-env.sh.template`, and add the following lines in it:
+5. Create a file called `spark-env.sh` in Spark's `conf` directory, by copying `conf/spark-env.sh.template`, and add the following lines it:
    * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is usually `<prefix>/lib/libmesos.so` (where the prefix is `/usr/local` by default). Also, on Mac OS X, the library is called `libmesos.dylib` instead of `.so`.
-   * `export SCALA_HOME=<path to Scala directory>`.
 6. Copy Spark and Mesos to the _same_ paths on all the nodes in the cluster (or, for Mesos, `make install` on every node).
 7. Configure Mesos for deployment:
    * On your master node, edit `<prefix>/var/mesos/deploy/masters` to list your master and `<prefix>/var/mesos/deploy/slaves` to list the slaves, where `<prefix>` is the prefix where you installed Mesos (`/usr/local` by default).
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 2f0d71fc35..fded896185 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -3,51 +3,34 @@ layout: global
 title: Launching Spark on YARN
 ---
 
-Experimental support for running over a [YARN (Hadoop
+Support for running on [YARN (Hadoop
 NextGen)](http://hadoop.apache.org/docs/r2.0.2-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html)
-cluster was added to Spark in version 0.6.0.  This was merged into master as part of 0.7 effort.
-To build spark with YARN support, please use the hadoop2-yarn profile.
-Ex:  mvn -Phadoop2-yarn clean install
+was added to Spark in version 0.6.0, and improved in 0.7.0 and 0.8.0.
 
-# Building spark core consolidated jar.
+# Building a YARN-Enabled Assembly JAR
 
-We need a consolidated spark core jar (which bundles all the required dependencies) to run Spark jobs on a yarn cluster.
-This can be built either through sbt or via maven.
+We need a consolidated Spark JAR (which bundles all the required dependencies) to run Spark jobs on a YARN cluster.
+This can be built by setting the Hadoop version and `SPARK_YARN` environment variable, as follows:
 
--   Building spark assembled jar via sbt.
-Enable YARN support by setting `SPARK_YARN=true` when invoking sbt:
+    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly
 
-    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt clean assembly
-
-The assembled jar would typically be something like :
-`./yarn/target/spark-yarn-assembly-0.8.0-SNAPSHOT.jar`
-
-
--   Building spark assembled jar via Maven.
-    Use the hadoop2-yarn profile and execute the package target.
-
-Something like this. Ex:
-
-    mvn -Phadoop2-yarn -Dhadoop.version=2.0.5-alpha clean package -DskipTests=true
-
-
-This will build the shaded (consolidated) jar. Typically something like :
-`./yarn/target/spark-yarn-bin-<VERSION>-shaded.jar`
+The assembled JAR will be something like this:
+`./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.
 
 
 # Preparations
 
-- Building spark-yarn assembly (see above).
+- Building a YARN-enabled assembly (see above).
 - Your application code must be packaged into a separate JAR file.
 
-If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt package`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
+If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
 
 # Configuration
 
 Most of the configs are the same for Spark on YARN as other deploys. See the Configuration page for more information on those.  These are configs that are specific to SPARK on YARN.
 
 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. ie SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar"
+* `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.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.
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index db584d2096..f7768e55fc 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -17,19 +17,27 @@ This guide shows each of these features and walks through some samples. It assum
 
 # Linking with Spark
 
-To write a Spark application, you will need to add both Spark and its dependencies to your CLASSPATH. If you use sbt or Maven, Spark is available through Maven Central at:
+Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you'll need to use this same version of Scala in your program -- newer major versions may not work.
 
-    groupId = org.spark-project
+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_VERSION}}
     version = {{site.SPARK_VERSION}} 
 
-For other build systems or environments, you can run `sbt/sbt assembly` to build both Spark and its dependencies into one JAR (`core/target/spark-core-assembly-0.6.0.jar`), then add this to your CLASSPATH.
+In addition, if you wish to access an HDFS cluster, you need to add a dependency on `hadoop-client` for your version of HDFS:
+
+    groupId = org.apache.hadoop
+    artifactId = hadoop-client
+    version = <your-hdfs-version>
+
+For other build systems, you can run `sbt/sbt assembly` to pack Spark and its dependencies into one JAR (`assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop*.jar`), then add this to your CLASSPATH. Set the HDFS version as described [here](index.html#a-note-about-hadoop-versions).
 
-In addition, you'll need to import some Spark classes and implicit conversions. Add the following lines at the top of your program:
+Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
 
 {% highlight scala %}
-import spark.SparkContext
-import SparkContext._
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
 {% endhighlight %}
 
 # Initializing Spark
@@ -134,7 +142,7 @@ All transformations in Spark are <i>lazy</i>, in that they do not compute their
 
 By default, each transformed RDD is 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/core/index.html#spark.RDD) for details):
+The following tables list the transformations and actions currently supported (see also the [RDD API doc](api/core/index.html#org.apache.spark.RDD) for details):
 
 ### Transformations
 
@@ -203,7 +211,7 @@ The following tables list the transformations and actions currently supported (s
 </tr>
 </table>
 
-A complete list of transformations is available in the [RDD API doc](api/core/index.html#spark.RDD).
+A complete list of transformations is available in the [RDD API doc](api/core/index.html#org.apache.spark.RDD).
 
 ### Actions
 
@@ -251,7 +259,7 @@ A complete list of transformations is available in the [RDD API doc](api/core/in
 </tr>
 </table>
 
-A complete list of actions is available in the [RDD API doc](api/core/index.html#spark.RDD).
+A complete list of actions is available in the [RDD API doc](api/core/index.html#org.apache.spark.RDD).
 
 ## RDD Persistence
 
@@ -259,7 +267,7 @@ One of the most important capabilities in Spark is *persisting* (or *caching*) a
 
 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 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 even replicate it across nodes. These levels are chosen by passing a [`spark.storage.StorageLevel`](api/core/index.html#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:
+In addition, each 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 even replicate it across nodes. These levels are chosen by passing a [`org.apache.spark.storage.StorageLevel`](api/core/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>
@@ -310,7 +318,7 @@ We recommend going through the following process to select one:
   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/core/index.html#spark.storage.StorageLevel$) singleton object.  
+If you want to define your own storage level (say, with replication factor of 3 instead of 2), then use the function factor method `apply()` of the [`StorageLevel`](api/core/index.html#org.apache.spark.storage.StorageLevel$) singleton object.  
 
 # Shared Variables
 
@@ -356,7 +364,11 @@ res2: Int = 10
 # Where to Go from Here
 
 You can see some [example Spark programs](http://www.spark-project.org/examples.html) on the Spark website.
-In addition, Spark includes several sample programs 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 `run-example` script included in Spark -- for example, `./run-example spark.examples.SparkPi`. Each example program prints usage help when run without any arguments.
+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 `run-example` script included in Spark; for example:
+
+    ./run-example org.apache.spark.examples.SparkPi
+
+Each example program prints usage help when run without any arguments.
 
 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
diff --git a/docs/spark-debugger.md b/docs/spark-debugger.md
index f6f0988858..d6315d97f4 100644
--- a/docs/spark-debugger.md
+++ b/docs/spark-debugger.md
@@ -2,7 +2,7 @@
 layout: global
 title: The Spark Debugger
 ---
-**Summary:** The Spark debugger provides replay debugging for deterministic (logic) errors in Spark programs. It's currently in development, but you can try it out in the [arthur branch](https://github.com/mesos/spark/tree/arthur).
+**Summary:** The Spark debugger provides replay debugging for deterministic (logic) errors in Spark programs. It's currently in development, but you can try it out in the [arthur branch](https://github.com/apache/incubator-spark/tree/arthur).
 
 ## Introduction
 
@@ -19,7 +19,7 @@ For deterministic errors, debugging a Spark program is now as easy as debugging
 
 ## Approach
 
-As your Spark program runs, the slaves report key events back to the master -- for example, RDD creations, RDD contents, and uncaught exceptions. (A full list of event types is in [EventLogging.scala](https://github.com/mesos/spark/blob/arthur/core/src/main/scala/spark/EventLogging.scala).) The master logs those events, and you can load the event log into the debugger after your program is done running.
+As your Spark program runs, the slaves report key events back to the master -- for example, RDD creations, RDD contents, and uncaught exceptions. (A full list of event types is in [EventLogging.scala](https://github.com/apache/incubator-spark/blob/arthur/core/src/main/scala/spark/EventLogging.scala).) The master logs those events, and you can load the event log into the debugger after your program is done running.
 
 _A note on nondeterminism:_ For fault recovery, Spark requires RDD transformations (for example, the function passed to `RDD.map`) to be deterministic. The Spark debugger also relies on this property, and it can also warn you if your transformation is nondeterministic. This works by checksumming the contents of each RDD and comparing the checksums from the original execution to the checksums after recomputing the RDD in the debugger.
 
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index bb8be276c5..994a96f2c9 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -3,32 +3,21 @@ layout: global
 title: Spark Standalone Mode
 ---
 
-{% comment %}
-TODO(andyk):
-  - Add a table of contents
-  - Move configuration towards the end so that it doesn't come first
-  - Say the scripts will guess the resource amounts (i.e. # cores) automatically
-{% endcomment %}
-
-In addition to running on top of [Mesos](https://github.com/mesos/mesos), Spark also supports a standalone mode, consisting of one Spark master and several Spark worker processes. You can run the Spark standalone mode either locally (for testing) or on a cluster. If you wish to run on a cluster, we have provided [a set of deploy scripts](#cluster-launch-scripts) to launch a whole cluster.
-
-# Getting Started
-
-Compile Spark with `sbt package` as described in the [Getting Started Guide](index.html). You do not need to install Mesos on your machine if you are using the standalone mode.
+In addition to running on the Mesos or YARN cluster managers, Spark also provides a simple standalone deploy mode. You can launch a standalone cluster either manually, by starting a master and workers by hand, or use our provided [deploy scripts](#cluster-launch-scripts). It is also possible to run these daemons on a single machine for testing.
 
 # Starting a Cluster Manually
 
 You can start a standalone master server by executing:
 
-    ./spark-class spark.deploy.master.Master
+    ./spark-class org.apache.spark.deploy.master.Master
 
-Once started, the master will print out a `spark://IP:PORT` URL for itself, which you can use to connect workers to it,
-or pass as the "master" argument to `SparkContext` to connect a job to the cluster. You can also find this URL on
+Once started, the master will print out a `spark://HOST:PORT` URL for itself, which you can use to connect workers to it,
+or pass as the "master" argument to `SparkContext`. You can also find this URL on
 the master's web UI, which is [http://localhost:8080](http://localhost:8080) by default.
 
 Similarly, you can start one or more workers and connect them to the master via:
 
-    ./spark-class spark.deploy.worker.Worker spark://IP:PORT
+    ./spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT
 
 Once you have started a worker, look at the master's web UI ([http://localhost:8080](http://localhost:8080) by default).
 You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS).
@@ -68,7 +57,7 @@ Finally, the following configuration options can be passed to the master and wor
 
 To launch a Spark standalone cluster with the deploy scripts, you need to create a file called `conf/slaves` in your Spark directory, which should contain the hostnames of all the machines where you would like to start Spark workers, one per line. The master machine must be able to access each of the slave machines via password-less `ssh` (using a private key). For testing, you can just put `localhost` in this file.
 
-Once you've set up this fine, you can launch or stop your cluster with the following shell scripts, based on Hadoop's deploy scripts, and available in `SPARK_HOME/bin`:
+Once you've set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop's deploy scripts, and available in `SPARK_HOME/bin`:
 
 - `bin/start-master.sh` - Starts a master instance on the machine the script is executed on.
 - `bin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file.
@@ -85,47 +74,56 @@ You can optionally configure the cluster further by setting environment variable
   <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr>
   <tr>
     <td><code>SPARK_MASTER_IP</code></td>
-    <td>Bind the master to a specific IP address, for example a public one</td>
+    <td>Bind the master to a specific IP address, for example a public one.</td>
   </tr>
   <tr>
     <td><code>SPARK_MASTER_PORT</code></td>
-    <td>Start the master on a different port (default: 7077)</td>
+    <td>Start the master on a different port (default: 7077).</td>
   </tr>
   <tr>
     <td><code>SPARK_MASTER_WEBUI_PORT</code></td>
-    <td>Port for the master web UI (default: 8080)</td>
+    <td>Port for the master web UI (default: 8080).</td>
   </tr>
   <tr>
     <td><code>SPARK_WORKER_PORT</code></td>
-    <td>Start the Spark worker on a specific port (default: random)</td>
+    <td>Start the Spark worker on a specific port (default: random).</td>
   </tr>
   <tr>
     <td><code>SPARK_WORKER_DIR</code></td>
-    <td>Directory to run jobs in, which will include both logs and scratch space (default: SPARK_HOME/work)</td>
+    <td>Directory to run jobs in, which will include both logs and scratch space (default: SPARK_HOME/work).</td>
   </tr>
   <tr>
     <td><code>SPARK_WORKER_CORES</code></td>
-    <td>Total number of cores to allow Spark jobs to use on the machine (default: all available cores)</td>
+    <td>Total number of cores to allow Spark jobs to use on the machine (default: all available cores).</td>
   </tr>
   <tr>
     <td><code>SPARK_WORKER_MEMORY</code></td>
-    <td>Total amount of memory to allow Spark jobs to use on the machine, e.g. 1000M, 2G (default: total memory minus 1 GB); note that each job's <i>individual</i> memory is configured using <code>SPARK_MEM</code></td>
+    <td>Total amount of memory to allow Spark jobs to use on the machine, e.g. <code>1000m</code>, <code>2g</code> (default: total memory minus 1 GB); note that each job's <i>individual</i> memory is configured using its <code>spark.executor.memory</code> property.</td>
   </tr>
   <tr>
     <td><code>SPARK_WORKER_WEBUI_PORT</code></td>
-    <td>Port for the worker web UI (default: 8081)</td>
+    <td>Port for the worker web UI (default: 8081).</td>
+  </tr>
+  <tr>
+    <td><code>SPARK_WORKER_INSTANCES</code></td>
+    <td>
+      Number of worker instances to run on each machine (default: 1). You can make this more than 1 if
+      you have have very large machines and would like multiple Spark worker processes. If you do set
+      this, make sure to also set <code>SPARK_WORKER_CORES</code> explicitly to limit the cores per worker,
+      or else each worker will try to use all the cores.
+    </td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_MEMORY</code></td>
-    <td>Memory to allocate to the Spark master and worker daemons themselves (default: 512m)</td>
+    <td>Memory to allocate to the Spark master and worker daemons themselves (default: 512m).</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_JAVA_OPTS</code></td>
-    <td>JVM options for the Spark master and worker daemons themselves (default: none)</td>
+    <td>JVM options for the Spark master and worker daemons themselves (default: none).</td>
   </tr>
 </table>
 
-
+**Note:** The launch scripts do not currently support Windows. To run a Spark cluster on Windows, start the master and workers by hand.
 
 # Connecting a Job to the Cluster
 
@@ -155,5 +153,5 @@ In addition, detailed log output for each job is also written to the work direct
 
 # Running Alongside Hadoop
 
-You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on).
+You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on).
 
diff --git a/docs/streaming-custom-receivers.md b/docs/streaming-custom-receivers.md
index dfa343bf94..4e27d6559c 100644
--- a/docs/streaming-custom-receivers.md
+++ b/docs/streaming-custom-receivers.md
@@ -1,24 +1,22 @@
 ---
 layout: global
-title: Tutorial - Spark Streaming, Plugging in a custom receiver.
+title: Spark Streaming Custom Receivers
 ---
 
 A "Spark Streaming" receiver can be a simple network stream, streams of messages from a message queue, files etc. A receiver can also assume roles more than just receiving data like filtering, preprocessing, to name a few of the possibilities. The api to plug-in any user defined custom receiver is thus provided to encourage development of receivers which may be well suited to ones specific need.
 
 This guide shows the programming model and features by walking through a simple sample receiver and corresponding Spark Streaming application.
 
-### Write a simple receiver
+### Writing a Simple Receiver
 
-This starts with implementing [NetworkReceiver](#References)
+This starts with implementing [NetworkReceiver](api/streaming/index.html#org.apache.spark.streaming.dstream.NetworkReceiver).
 
-Following is a simple socket text-stream receiver.
+The following is a simple socket text-stream receiver.
 
 {% highlight scala %}
-
-       class SocketTextStreamReceiver(host: String,
-                                      port: Int
-                                       ) extends NetworkReceiver[String] {
-
+       class SocketTextStreamReceiver(host: String, port: Int(
+         extends NetworkReceiver[String]
+       {
          protected lazy val blocksGenerator: BlockGenerator =
            new BlockGenerator(StorageLevel.MEMORY_ONLY_SER_2)
 
@@ -36,23 +34,20 @@ Following is a simple socket text-stream receiver.
          protected def onStop() {
            blocksGenerator.stop()
          }
-
        }
-
 {% endhighlight %}
 
 
 All we did here is extended NetworkReceiver and called blockGenerator's API method (i.e. +=) to push our blocks of data. Please refer to scala-docs of NetworkReceiver for more details.
 
 
-### An Actor as Receiver.
+### An Actor as Receiver
 
 This starts with implementing [Actor](#References)
 
 Following is a simple socket text-stream receiver, which is appearently overly simplified using Akka's socket.io api.
 
 {% highlight scala %}
-
        class SocketTextStreamReceiver (host:String,
          port:Int,
          bytesToString: ByteString => String) extends Actor with Receiver {
@@ -64,52 +59,41 @@ Following is a simple socket text-stream receiver, which is appearently overly s
          }
 
        }
-
-
 {% endhighlight %}
 
 All we did here is mixed in trait Receiver and called pushBlock api method to push our blocks of data. Please refer to scala-docs of Receiver for more details.
 
-### A sample spark application
+### A Sample Spark Application
 
 * First create a Spark streaming context with master url and batchduration.
 
 {% highlight scala %}
-
     val ssc = new StreamingContext(master, "WordCountCustomStreamSource",
       Seconds(batchDuration))
-
 {% endhighlight %}
 
 * Plug-in the custom receiver into the spark streaming context and create a DStream.
 
 {% highlight scala %}
-
     val lines = ssc.networkStream[String](new SocketTextStreamReceiver(
       "localhost", 8445))
-
 {% endhighlight %}
 
 * OR Plug-in the actor as receiver into the spark streaming context and create a DStream.
 
 {% highlight scala %}
-
     val lines = ssc.actorStream[String](Props(new SocketTextStreamReceiver(
       "localhost",8445, z => z.utf8String)),"SocketReceiver")
-
 {% endhighlight %}
 
 * Process it.
 
 {% highlight scala %}
-
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
 
     wordCounts.print()
     ssc.start()
-
-
 {% endhighlight %}
 
 * After processing it, stream can be tested using the netcat utility.
@@ -119,12 +103,11 @@ All we did here is mixed in trait Receiver and called pushBlock api method to pu
      hello hello
 
 
-## Multiple homogeneous/heterogeneous receivers.
+## Multiple Homogeneous/Heterogeneous Receivers.
 
 A DStream union operation is provided for taking union on multiple input streams.
 
 {% highlight scala %}
-
     val lines = ssc.actorStream[String](Props(new SocketTextStreamReceiver(
       "localhost",8445, z => z.utf8String)),"SocketReceiver")
 
@@ -133,7 +116,6 @@ A DStream union operation is provided for taking union on multiple input streams
       "localhost",8446, z => z.utf8String)),"SocketReceiver")
 
     val union = lines.union(lines2)
-
 {% endhighlight %}
 
 Above stream can be easily process as described earlier.
@@ -143,4 +125,4 @@ _A more comprehensive example is provided in the spark streaming examples_
 ## References
 
 1.[Akka Actor documentation](http://doc.akka.io/docs/akka/2.0.5/scala/actors.html)
-2.[NetworkReceiver](http://spark-project.org/docs/latest/api/streaming/index.html#spark.streaming.dstream.NetworkReceiver)
+2.[NetworkReceiver](api/streaming/index.html#org.apache.spark.streaming.dstream.NetworkReceiver)
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 3330e63598..c7df172024 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -13,6 +13,14 @@ A Spark Streaming application is very similar to a Spark application; it consist
 
 This guide shows some how to start programming with DStreams. 
 
+# Linking with Spark Streaming
+
+Add the following SBT or Maven dependency to your project to use Spark Streaming:
+
+    groupId = org.apache.spark
+    artifactId = spark-streaming_{{site.SCALA_VERSION}}
+    version = {{site.SPARK_VERSION}}
+
 # Initializing Spark Streaming
 The first thing a Spark Streaming program must do is create a `StreamingContext` object, which tells Spark how to access a cluster. A `StreamingContext` can be created by using
 
@@ -34,7 +42,7 @@ ssc.textFileStream(directory)      // Creates a stream by monitoring and process
 ssc.socketStream(hostname, port)   // Creates a stream that uses a TCP socket to read data from hostname:port
 {% endhighlight %}
 
-We also provide a input streams for Kafka, Flume, Akka actor, etc. For a complete list of input streams, take a look at the [StreamingContext API documentation](api/streaming/index.html#spark.streaming.StreamingContext).
+We also provide a input streams for Kafka, Flume, Akka actor, etc. For a complete list of input streams, take a look at the [StreamingContext API documentation](api/streaming/index.html#org.apache.spark.streaming.StreamingContext).
 
 
 
@@ -156,7 +164,7 @@ Spark Streaming features windowed computations, which allow you to apply transfo
 
 </table>
 
-A complete list of DStream operations is available in the API documentation of [DStream](api/streaming/index.html#spark.streaming.DStream) and [PairDStreamFunctions](api/streaming/index.html#spark.streaming.PairDStreamFunctions).
+A complete list of DStream operations is available in the API documentation of [DStream](api/streaming/index.html#org.apache.spark.streaming.DStream) and [PairDStreamFunctions](api/streaming/index.html#org.apache.spark.streaming.PairDStreamFunctions).
 
 ## Output Operations
 When an output operator is called, it triggers the computation of a stream. Currently the following output operators are defined:
@@ -203,11 +211,11 @@ ssc.stop()
 {% endhighlight %}
 
 # Example
-A simple example to start off is the [NetworkWordCount](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala). This example counts the words received from a network server every second. Given below is the relevant sections of the source code. You can find the full source code in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/NetworkWordCount.scala` .
+A simple example to start off is the [NetworkWordCount](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala). This example counts the words received from a network server every second. Given below is the relevant sections of the source code. You can find the full source code in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/NetworkWordCount.scala` .
 
 {% highlight scala %}
-import spark.streaming.{Seconds, StreamingContext}
-import spark.streaming.StreamingContext._
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+import StreamingContext._
 ...
 
 // Create the context and set up a network input stream to receive from a host:port
@@ -234,7 +242,7 @@ $ nc -lk 9999
 Then, in a different terminal, you can start NetworkWordCount by using
 
 {% highlight bash %}
-$ ./run-example spark.streaming.examples.NetworkWordCount local[2] localhost 9999
+$ ./run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999
 {% endhighlight %}
 
 This will make NetworkWordCount connect to the netcat server. Any lines typed in the terminal running the netcat server will be counted and printed on screen.
@@ -272,7 +280,7 @@ Time: 1357008430000 ms
 </td>
 </table>
 
-You can find more examples in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/`. They can be run in the similar manner using `./run-example spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files.
+You can find more examples in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/`. They can be run in the similar manner using `./run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files.
 
 # DStream Persistence
 Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, using `persist()` method on a DStream would automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple operations on the same data). For window-based operations like `reduceByWindow` and `reduceByKeyAndWindow` and state-based operations like `updateStateByKey`, this is implicitly true. Hence, DStreams generated by window-based operations are automatically persisted in memory, without the developer calling `persist()`.
@@ -301,8 +309,8 @@ dstream.checkpoint(checkpointInterval) // checkpointInterval must be a multiple
 For DStreams that must be checkpointed (that is, DStreams created by `updateStateByKey` and `reduceByKeyAndWindow` with inverse function), the checkpoint interval of the DStream is by default set to a multiple of the DStream's sliding interval such that its at least 10 seconds.
 
 
-## Customizing Receiver
-Spark comes with a built in support for most common usage scenarios where input stream source can be either a network socket stream to support for a few message queues. Apart from that it is also possible to supply your own custom receiver via a convenient API. Find more details at [Custom Receiver Guide](streaming-custom-receivers.html)
+## Custom Receivers
+Spark comes with a built in support for most common usage scenarios where input stream source can be either a network socket stream to support for a few message queues. Apart from that it is also possible to supply your own custom receiver via a convenient API. Find more details at [Custom Receiver Guide](streaming-custom-receivers.html).
 
 # Performance Tuning
 Getting the best performance of a Spark Streaming application on a cluster requires a bit of tuning. This section explains a number of the parameters and configurations that can tuned to improve the performance of you application. At a high level, you need to consider two things:
@@ -315,7 +323,7 @@ Getting the best performance of a Spark Streaming application on a cluster requi
 There are a number of optimizations that can be done in Spark to minimize the processing time of each batch. These have been discussed in detail in [Tuning Guide](tuning.html). This section highlights some of the most important ones.
 
 ### Level of Parallelism
-Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of parallelism as an argument (see the [`spark.PairDStreamFunctions`](api/streaming/index.html#spark.PairDStreamFunctions) documentation), or set the system property `spark.default.parallelism` to change the default.
+Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of parallelism as an argument (see the [`PairDStreamFunctions`](api/streaming/index.html#org.apache.spark.PairDStreamFunctions) documentation), or set the system property `spark.default.parallelism` to change the default.
 
 ### Data Serialization
 The overhead of data serialization can be significant, especially when sub-second batch sizes are to be achieved. There are two aspects to it.
@@ -345,7 +353,7 @@ This value is closely tied with any window operation that is being used. Any win
 ## Memory Tuning
 Tuning the memory usage and GC behavior of Spark applications have been discussed in great detail in the [Tuning Guide](tuning.html). It is recommended that you read that. In this section, we highlight a few customizations that are strongly recommended to minimize GC related pauses in Spark Streaming applications and achieving more consistent batch processing times.
 
-* **Default persistence level of DStreams**: Unlike RDDs, the default persistence level of DStreams serializes the data in memory (that is, [StorageLevel.MEMORY_ONLY_SER](api/core/index.html#spark.storage.StorageLevel$) for DStream compared to [StorageLevel.MEMORY_ONLY](api/core/index.html#spark.storage.StorageLevel$) for RDDs). Even though keeping the data serialized incurs a higher serialization overheads, it significantly reduces GC pauses.
+* **Default persistence level of DStreams**: Unlike RDDs, the default persistence level of DStreams serializes the data in memory (that is, [StorageLevel.MEMORY_ONLY_SER](api/core/index.html#org.apache.spark.storage.StorageLevel$) for DStream compared to [StorageLevel.MEMORY_ONLY](api/core/index.html#org.apache.spark.storage.StorageLevel$) for RDDs). Even though keeping the data serialized incurs a higher serialization overheads, it significantly reduces GC pauses.
 
 * **Concurrent garbage collector**: Using the concurrent mark-and-sweep GC further minimizes the variability of GC pauses. Even though concurrent GC is known to reduce the overall processing throughput of the system, its use is still recommended to achieve more consistent batch processing times.
 
@@ -467,10 +475,10 @@ If the driver had crashed in the middle of the processing of time 3, then it wil
 
 # Java API
 
-Similar to [Spark's Java API](java-programming-guide.html), we also provide a Java API for Spark Streaming which allows all its features to be accessible from a Java program. This is defined in [spark.streaming.api.java] (api/streaming/index.html#spark.streaming.api.java.package) package and includes [JavaStreamingContext](api/streaming/index.html#spark.streaming.api.java.JavaStreamingContext) and [JavaDStream](api/streaming/index.html#spark.streaming.api.java.JavaDStream) classes that provide the same methods as their Scala counterparts, but take Java functions (that is, Function, and Function2) and return Java data and collection types. Some of the key points to note are:
+Similar to [Spark's Java API](java-programming-guide.html), we also provide a Java API for Spark Streaming which allows all its features to be accessible from a Java program. This is defined in [org.apache.spark.streaming.api.java] (api/streaming/index.html#org.apache.spark.streaming.api.java.package) package and includes [JavaStreamingContext](api/streaming/index.html#org.apache.spark.streaming.api.java.JavaStreamingContext) and [JavaDStream](api/streaming/index.html#org.apache.spark.streaming.api.java.JavaDStream) classes that provide the same methods as their Scala counterparts, but take Java functions (that is, Function, and Function2) and return Java data and collection types. Some of the key points to note are:
 
-1. Functions for transformations must be implemented as subclasses of [Function](api/core/index.html#spark.api.java.function.Function) and [Function2](api/core/index.html#spark.api.java.function.Function2)
-1. Unlike the Scala API, the Java API handles DStreams for key-value pairs using a separate [JavaPairDStream](api/streaming/index.html#spark.streaming.api.java.JavaPairDStream) class(similar to [JavaRDD and JavaPairRDD](java-programming-guide.html#rdd-classes). DStream functions like `map` and `filter` are implemented separately by JavaDStreams and JavaPairDStream to return DStreams of appropriate types.
+1. Functions for transformations must be implemented as subclasses of [Function](api/core/index.html#org.apache.spark.api.java.function.Function) and [Function2](api/core/index.html#org.apache.spark.api.java.function.Function2)
+1. Unlike the Scala API, the Java API handles DStreams for key-value pairs using a separate [JavaPairDStream](api/streaming/index.html#org.apache.spark.streaming.api.java.JavaPairDStream) class(similar to [JavaRDD and JavaPairRDD](java-programming-guide.html#rdd-classes). DStream functions like `map` and `filter` are implemented separately by JavaDStreams and JavaPairDStream to return DStreams of appropriate types.
 
 Spark's [Java Programming Guide](java-programming-guide.html) gives more ideas about using the Java API. To extends the ideas presented for the RDDs to DStreams, we present parts of the Java version of the same NetworkWordCount example presented above. The full source code is given at `<spark repo>/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java`
 
@@ -482,7 +490,7 @@ JavaDStream<String> lines = ssc.socketTextStream(ip, port);
 {% endhighlight %}
 
 
-Then the `lines` are split into words by using the `flatMap` function and [FlatMapFunction](api/core/index.html#spark.api.java.function.FlatMapFunction).
+Then the `lines` are split into words by using the `flatMap` function and [FlatMapFunction](api/core/index.html#org.apache.spark.api.java.function.FlatMapFunction).
 
 {% highlight java %}
 JavaDStream<String> words = lines.flatMap(
@@ -494,7 +502,7 @@ JavaDStream<String> words = lines.flatMap(
   });
 {% endhighlight %}
 
-The `words` is then mapped to a [JavaPairDStream](api/streaming/index.html#spark.streaming.api.java.JavaPairDStream) of `(word, 1)` pairs using `map` and [PairFunction](api/core/index.html#spark.api.java.function.PairFunction). This is  reduced by using `reduceByKey` and [Function2](api/core/index.html#spark.api.java.function.Function2).
+The `words` is then mapped to a [JavaPairDStream](api/streaming/index.html#org.apache.spark.streaming.api.java.JavaPairDStream) of `(word, 1)` pairs using `map` and [PairFunction](api/core/index.html#org.apache.spark.api.java.function.PairFunction). This is  reduced by using `reduceByKey` and [Function2](api/core/index.html#org.apache.spark.api.java.function.Function2).
 
 {% highlight java %}
 JavaPairDStream<String, Integer> wordCounts = words.map(
@@ -513,8 +521,8 @@ JavaPairDStream<String, Integer> wordCounts = words.map(
 {% endhighlight %}
 
 
-
 # Where to Go from Here
-* API docs - [Scala](api/streaming/index.html#spark.streaming.package) and [Java](api/streaming/index.html#spark.streaming.api.java.package)
-* More examples - [Scala](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/streaming/examples) and [Java](https://github.com/mesos/spark/tree/master/examples/src/main/java/spark/streaming/examples)
+
+* API docs - [Scala](api/streaming/index.html#org.apache.spark.streaming.package) and [Java](api/streaming/index.html#org.apache.spark.streaming.api.java.package)
+* More examples - [Scala](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/spark/streaming/examples) and [Java](https://github.com/apache/incubator-spark/tree/master/examples/src/main/java/spark/streaming/examples)
 * [Paper describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)
diff --git a/docs/tuning.md b/docs/tuning.md
index 5ffca54481..28d88a2659 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -32,24 +32,25 @@ in your operations) and performance. It provides two serialization libraries:
   [`java.io.Externalizable`](http://docs.oracle.com/javase/6/docs/api/java/io/Externalizable.html).
   Java serialization is flexible but often quite slow, and leads to large
   serialized formats for many classes.
-* [Kryo serialization](http://code.google.com/p/kryo/wiki/V1Documentation): Spark can also use
+* [Kryo serialization](http://code.google.com/p/kryo/): Spark can also use
   the Kryo library (version 2) to serialize objects more quickly. Kryo is significantly
   faster and more compact than Java serialization (often as much as 10x), but does not support all
   `Serializable` types and requires you to *register* the classes you'll use in the program in advance
   for best performance.
 
-You can switch to using Kryo by calling `System.setProperty("spark.serializer", "spark.KryoSerializer")`
+You can switch to using Kryo by calling `System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer")`
 *before* creating your SparkContext. The only reason it is not the default is because of the custom
 registration requirement, but we recommend trying it in any network-intensive application.
 
 Finally, to register your classes with Kryo, create a public class that extends
-[`spark.KryoRegistrator`](api/core/index.html#spark.KryoRegistrator) and set the
+[`org.apache.spark.serializer.KryoRegistrator`](api/core/index.html#org.apache.spark.serializer.KryoRegistrator) and set the
 `spark.kryo.registrator` system property to point to it, as follows:
 
 {% highlight scala %}
 import com.esotericsoftware.kryo.Kryo
+import org.apache.spark.serializer.KryoRegistrator
 
-class MyRegistrator extends spark.KryoRegistrator {
+class MyRegistrator extends KryoRegistrator {
   override def registerClasses(kryo: Kryo) {
     kryo.register(classOf[MyClass1])
     kryo.register(classOf[MyClass2])
@@ -57,7 +58,7 @@ class MyRegistrator extends spark.KryoRegistrator {
 }
 
 // Make sure to set these properties *before* creating a SparkContext!
-System.setProperty("spark.serializer", "spark.KryoSerializer")
+System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
 System.setProperty("spark.kryo.registrator", "mypackage.MyRegistrator")
 val sc = new SparkContext(...)
 {% endhighlight %}
@@ -216,7 +217,7 @@ enough. Spark automatically sets the number of "map" tasks to run on each file a
 (though you can control it through optional parameters to `SparkContext.textFile`, etc), and for
 distributed "reduce" operations, such as `groupByKey` and `reduceByKey`, it uses the largest
 parent RDD's number of partitions. You can pass the level of parallelism as a second argument
-(see the [`spark.PairRDDFunctions`](api/core/index.html#spark.PairRDDFunctions) documentation),
+(see the [`spark.PairRDDFunctions`](api/core/index.html#org.apache.spark.rdd.PairRDDFunctions) documentation),
 or set the system property `spark.default.parallelism` to change the default.
 In general, we recommend 2-3 tasks per CPU core in your cluster.
author	Y.CORP.YAHOO.COM\tgraves <tgraves@thatenemy-lm.champ.corp.yahoo.com>	2013-09-03 08:36:59 -0500
committer	Y.CORP.YAHOO.COM\tgraves <tgraves@thatenemy-lm.champ.corp.yahoo.com>	2013-09-03 08:36:59 -0500
commit	547fc4a4120a2bb3a6aeca66dd0cd568534ba65a (patch)
tree	bfb00a0a5925ca9a08d6e7a05cc33a411a5873c1 /docs
parent	96452eea56c289e25a711ca54c812723a5059739 (diff)
parent	59218bdd4996a13116009e3669b1b875be23a694 (diff)
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