apache
diff --git a/‎docs/_layouts/global.html
Lines changed: 7 additions & 6 deletions b/‎docs/_layouts/global.html
Lines changed: 7 additions & 6 deletions
diff --git a/‎docs/configuration.md
Lines changed: 74 additions & 59 deletions b/‎docs/configuration.md
Lines changed: 74 additions & 59 deletions
@@ -33,7 +33,7 @@
         <!-- Google analytics script -->
         <script type="text/javascript">
           var _gaq = _gaq || [];
-          _gaq.push(['_setAccount', 'UA-32518208-1']);
+          _gaq.push(['_setAccount', 'UA-32518208-2']);
           _gaq.push(['_trackPageview']);
 
           (function() {
@@ -79,9 +79,9 @@
                         <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/scala/index.html#org.apache.spark.package">Scaladoc</a></li>
-                                <li><a href="api/java/index.html">Javadoc</a></li>
-                                <li><a href="api/python/index.html">Python API</a></li>
+                                <li><a href="api/scala/index.html#org.apache.spark.package">Scala</a></li>
+                                <li><a href="api/java/index.html">Java</a></li>
+                                <li><a href="api/python/index.html">Python</a></li>
                             </ul>
                         </li>
 
@@ -91,10 +91,11 @@
                                 <li><a href="cluster-overview.html">Overview</a></li>
                                 <li><a href="submitting-applications.html">Submitting Applications</a></li>
                                 <li class="divider"></li>
-                                <li><a href="ec2-scripts.html">Amazon EC2</a></li>
-                                <li><a href="spark-standalone.html">Standalone Mode</a></li>
+                                <li><a href="spark-standalone.html">Spark Standalone</a></li>
                                 <li><a href="running-on-mesos.html">Mesos</a></li>
                                 <li><a href="running-on-yarn.html">YARN</a></li>
+                                <li class="divider"></li>
+                                <li><a href="ec2-scripts.html">Amazon EC2</a></li>
                             </ul>
                         </li>
 
 
@@ -8,7 +8,7 @@ title: Spark Configuration
 Spark provides three locations to configure the system:
 
 * [Spark properties](#spark-properties) control most application parameters and can be set by using
-  a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object, or through Java
+  a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object, or through Java
   system properties.
 * [Environment variables](#environment-variables) can be used to set per-machine settings, such as
   the IP address, through the `conf/spark-env.sh` script on each node.
@@ -23,8 +23,8 @@ application. These properties can be set directly on a
 (e.g. master URL and application name), as well as arbitrary key-value pairs through the
 `set()` method. For example, we could initialize an application with two threads as follows:
 
-Note that we run with local[2], meaning two threads - which represents "minimal" parallelism, 
-which can help detect bugs that only exist when we run in a distributed context. 
+Note that we run with local[2], meaning two threads - which represents "minimal" parallelism,
+which can help detect bugs that only exist when we run in a distributed context.
 
 {% highlight scala %}
 val conf = new SparkConf()
@@ -35,7 +35,7 @@ val sc = new SparkContext(conf)
 {% endhighlight %}
 
 Note that we can have more than 1 thread in local mode, and in cases like spark streaming, we may actually
-require one to prevent any sort of starvation issues.  
+require one to prevent any sort of starvation issues.
 
 ## Dynamically Loading Spark Properties
 In some cases, you may want to avoid hard-coding certain configurations in a `SparkConf`. For
@@ -48,8 +48,8 @@ val sc = new SparkContext(new SparkConf())
 
 Then, you can supply configuration values at runtime:
 {% highlight bash %}
-./bin/spark-submit --name "My app" --master local[4] --conf spark.shuffle.spill=false 
-  --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar 
+./bin/spark-submit --name "My app" --master local[4] --conf spark.shuffle.spill=false
+  --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar
 {% endhighlight %}
 
 The Spark shell and [`spark-submit`](submitting-applications.html)
@@ -123,7 +123,7 @@ of the most common options to set are:
   <td>
     Limit of total size of serialized results of all partitions for each Spark action (e.g. collect).
     Should be at least 1M, or 0 for unlimited. Jobs will be aborted if the total size
-    is above this limit. 
+    is above this limit.
     Having a high limit may cause out-of-memory errors in driver (depends on spark.driver.memory
     and memory overhead of objects in JVM). Setting a proper limit can protect the driver from
     out-of-memory errors.
@@ -217,6 +217,45 @@ Apart from these, the following properties are also available, and may be useful
     Set a special library path to use when launching executor JVM's.
   </td>
 </tr>
+<tr>
+  <td><code>spark.executor.logs.rolling.strategy</code></td>
+  <td>(none)</td>
+  <td>
+    Set the strategy of rolling of executor logs. By default it is disabled. It can
+    be set to "time" (time-based rolling) or "size" (size-based rolling). For "time",
+    use <code>spark.executor.logs.rolling.time.interval</code> to set the rolling interval.
+    For "size", use <code>spark.executor.logs.rolling.size.maxBytes</code> to set
+    the maximum file size for rolling.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.executor.logs.rolling.time.interval</code></td>
+  <td>daily</td>
+  <td>
+    Set the time interval by which the executor logs will be rolled over.
+    Rolling is disabled by default. Valid values are `daily`, `hourly`, `minutely` or
+    any interval in seconds. See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
+    for automatic cleaning of old logs.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.executor.logs.rolling.size.maxBytes</code></td>
+  <td>(none)</td>
+  <td>
+    Set the max size of the file by which the executor logs will be rolled over.
+    Rolling is disabled by default. Value is set in terms of bytes.
+    See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
+    for automatic cleaning of old logs.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.executor.logs.rolling.maxRetainedFiles</code></td>
+  <td>(none)</td>
+  <td>
+    Sets the number of latest rolling log files that are going to be retained by the system.
+    Older log files will be deleted. Disabled by default.
+  </td>
+</tr>
 <tr>
   <td><code>spark.files.userClassPathFirst</code></td>
   <td>false</td>
@@ -250,10 +289,11 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.python.profile.dump</code></td>
   <td>(none)</td>
   <td>
-    The directory which is used to dump the profile result before driver exiting. 
+    The directory which is used to dump the profile result before driver exiting.
     The results will be dumped as separated file for each RDD. They can be loaded
     by ptats.Stats(). If this is specified, the profile result will not be displayed
     automatically.
+  </td>
 </tr>
 <tr>
   <td><code>spark.python.worker.reuse</code></td>
@@ -269,8 +309,8 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.executorEnv.[EnvironmentVariableName]</code></td>
   <td>(none)</td>
   <td>
-    Add the environment variable specified by <code>EnvironmentVariableName</code> to the Executor 
-    process. The user can specify multiple of these and to set multiple environment variables. 
+    Add the environment variable specified by <code>EnvironmentVariableName</code> to the Executor
+    process. The user can specify multiple of these and to set multiple environment variables.
   </td>
 </tr>
 <tr>
@@ -475,9 +515,9 @@ Apart from these, the following properties are also available, and may be useful
   <td>
     The codec used to compress internal data such as RDD partitions, broadcast variables and
     shuffle outputs. By default, Spark provides three codecs: <code>lz4</code>, <code>lzf</code>,
-    and <code>snappy</code>. You can also use fully qualified class names to specify the codec, 
-    e.g. 
-    <code>org.apache.spark.io.LZ4CompressionCodec</code>,    
+    and <code>snappy</code>. You can also use fully qualified class names to specify the codec,
+    e.g.
+    <code>org.apache.spark.io.LZ4CompressionCodec</code>,
     <code>org.apache.spark.io.LZFCompressionCodec</code>,
     and <code>org.apache.spark.io.SnappyCompressionCodec</code>.
   </td>
@@ -945,7 +985,7 @@ Apart from these, the following properties are also available, and may be useful
     (resources are executors in yarn mode, CPU cores in standalone mode)
     to wait for before scheduling begins. Specified as a double between 0.0 and 1.0.
     Regardless of whether the minimum ratio of resources has been reached,
-    the maximum amount of time it will wait before scheduling begins is controlled by config 
+    the maximum amount of time it will wait before scheduling begins is controlled by config
     <code>spark.scheduler.maxRegisteredResourcesWaitingTime</code>.
   </td>
 </tr>
@@ -954,7 +994,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>30000</td>
   <td>
     Maximum amount of time to wait for resources to register before scheduling begins
-    (in milliseconds).  
+    (in milliseconds).
   </td>
 </tr>
 <tr>
@@ -1023,7 +1063,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>false</td>
   <td>
     Whether Spark acls should are enabled. If enabled, this checks to see if the user has
-    access permissions to view or modify the job.  Note this requires the user to be known, 
+    access permissions to view or modify the job.  Note this requires the user to be known,
     so if the user comes across as null no checks are done. Filters can be used with the UI
     to authenticate and set the user.
   </td>
@@ -1062,17 +1102,31 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.streaming.blockInterval</code></td>
   <td>200</td>
   <td>
-    Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced
-    into blocks of data before storing them in Spark.
+    Interval (milliseconds) at which data received by Spark Streaming receivers is chunked
+    into blocks of data before storing them in Spark. Minimum recommended - 50 ms. See the
+    <a href="streaming-programming-guide.html#level-of-parallelism-in-data-receiving">performance
+     tuning</a> section in the Spark Streaming programing guide for more details.
   </td>
 </tr>
 <tr>
   <td><code>spark.streaming.receiver.maxRate</code></td>
   <td>infinite</td>
   <td>
-    Maximum rate (per second) at which each receiver will push data into blocks. Effectively,
-    each stream will consume at most this number of records per second.
+    Maximum number records per second at which each receiver will receive data.
+    Effectively, each stream will consume at most this number of records per second.
     Setting this configuration to 0 or a negative number will put no limit on the rate.
+    See the <a href="streaming-programming-guide.html#deploying-applications">deployment guide</a>
+    in the Spark Streaming programing guide for mode details.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.streaming.receiver.writeAheadLogs.enable</code></td>
+  <td>false</td>
+  <td>
+    Enable write ahead logs for receivers. All the input data received through receivers
+    will be saved to write ahead logs that will allow it to be recovered after driver failures.
+    See the <a href="streaming-programming-guide.html#deploying-applications">deployment guide</a>
+    in the Spark Streaming programing guide for more details.
   </td>
 </tr>
 <tr>
@@ -1086,45 +1140,6 @@ Apart from these, the following properties are also available, and may be useful
     higher memory usage in Spark.
   </td>
 </tr>
-<tr>
-  <td><code>spark.executor.logs.rolling.strategy</code></td>
-  <td>(none)</td>
-  <td>
-    Set the strategy of rolling of executor logs. By default it is disabled. It can
-    be set to "time" (time-based rolling) or "size" (size-based rolling). For "time",
-    use <code>spark.executor.logs.rolling.time.interval</code> to set the rolling interval.
-    For "size", use <code>spark.executor.logs.rolling.size.maxBytes</code> to set
-    the maximum file size for rolling.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.executor.logs.rolling.time.interval</code></td>
-  <td>daily</td>
-  <td>
-    Set the time interval by which the executor logs will be rolled over.
-    Rolling is disabled by default. Valid values are `daily`, `hourly`, `minutely` or
-    any interval in seconds. See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
-    for automatic cleaning of old logs.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.executor.logs.rolling.size.maxBytes</code></td>
-  <td>(none)</td>
-  <td>
-    Set the max size of the file by which the executor logs will be rolled over.
-    Rolling is disabled by default. Value is set in terms of bytes.
-    See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
-    for automatic cleaning of old logs.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.executor.logs.rolling.maxRetainedFiles</code></td>
-  <td>(none)</td>
-  <td>
-    Sets the number of latest rolling log files that are going to be retained by the system.
-    Older log files will be deleted. Disabled by default.
-  </td>
-</tr>
 </table>
 
 #### Cluster Managers