Merge branch 'mesos-master' into streaming

Conflicts:
	core/src/main/scala/spark/rdd/CheckpointRDD.scala
	streaming/src/main/scala/spark/streaming/dstream/ReducedWindowedDStream.scala

5 files changed, 20 insertions, 4 deletions

diff --git a/docs/configuration.md b/docs/configuration.md
index a7054b4321..f1ca77aa78 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -198,6 +198,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
+  <td>spark.worker.timeout</td>
+  <td>60</td>
+  <td>
+    Number of seconds after which the standalone deploy master considers a worker lost if it
+    receives no heartbeats.
+  </td>
+</tr>
+<tr>
   <td>spark.akka.frameSize</td>
   <td>10</td>
   <td>
@@ -218,7 +226,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>spark.akka.timeout</td>
   <td>20</td>
   <td>
-    Communication timeout between Spark nodes.
+    Communication timeout between Spark nodes, in seconds.
   </td>
 </tr>
 <tr>
diff --git a/docs/contributing-to-spark.md b/docs/contributing-to-spark.md
index c6e01c62d8..14d0dc856b 100644
--- a/docs/contributing-to-spark.md
+++ b/docs/contributing-to-spark.md
@@ -15,7 +15,7 @@ The Spark team welcomes contributions in the form of GitHub pull requests. Here
   But first, make sure that you have [configured a spark-env.sh](configuration.html) with at least
   `SCALA_HOME`, as some of the tests try to spawn subprocesses using this.
 - Add new unit tests for your code. We use [ScalaTest](http://www.scalatest.org/) for testing. Just add a new Suite in `core/src/test`, or methods to an existing Suite.
-- If you'd like to report a bug but don't have time to fix it, you can still post it to our [issues page](https://github.com/mesos/spark/issues), or email the [mailing list](http://www.spark-project.org/mailing-lists.html).
+- If you'd like to report a bug but don't have time to fix it, you can still post it to our [issue tracker](https://spark-project.atlassian.net), or email the [mailing list](http://www.spark-project.org/mailing-lists.html).
 
 # Licensing of Contributions
 
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index 301b330a79..b98718a553 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -203,7 +203,7 @@ A complete list of transformations is available in the [RDD API doc](api/core/in
 <tr><th>Action</th><th>Meaning</th></tr>
 <tr>
   <td> <b>reduce</b>(<i>func</i>) </td>
-  <td> Aggregate the elements of the dataset using a function <i>func</i> (which takes two arguments and returns one). The function should be associative so that it can be computed correctly in parallel. </td>
+  <td> Aggregate the elements of the dataset using a function <i>func</i> (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel. </td>
 </tr>
 <tr>
   <td> <b>collect</b>() </td>
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index bf296221b8..3986c0c79d 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -115,6 +115,14 @@ You can optionally configure the cluster further by setting environment variable
     <td><code>SPARK_WORKER_WEBUI_PORT</code></td>
     <td>Port for the worker web UI (default: 8081)</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>
+  </tr>
+  <tr>
+    <td><code>SPARK_DAEMON_JAVA_OPTS</code></td>
+    <td>JVM options for the Spark master and worker daemons themselves (default: none)</td>
+  </tr>
 </table>
 
 
diff --git a/docs/tuning.md b/docs/tuning.md
index 9aaa53cd65..738c530458 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -233,7 +233,7 @@ number of cores in your clusters.
 
 ## Broadcasting Large Variables
 
-Using the [broadcast functionality](scala-programming-guide#broadcast-variables)
+Using the [broadcast functionality](scala-programming-guide.html#broadcast-variables)
 available in `SparkContext` can greatly reduce the size of each serialized task, and the cost
 of launching a job over a cluster. If your tasks use any large object from the driver program
 inside of them (e.g. a static lookup table), consider turning it into a broadcast variable.