Merge remote-tracking branch 'spark-upstream/master' into HEAD

Conflicts:
	README.md
	core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala
	core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
	core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala
	pom.xml
	project/SparkBuild.scala
	repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala

1 files changed, 156 insertions, 19 deletions

diff --git a/docs/configuration.md b/docs/configuration.md
index 97183bafdb..1d6c3d1633 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -3,26 +3,37 @@ layout: global
 title: Spark Configuration
 ---
 
-Spark provides three main locations to configure the system:
+Spark provides three locations to configure the system:
 
-* [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`.
+* [Spark properties](#spark-properties) control most application parameters and can be set by passing
+  a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java
+  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.
+* [Logging](#configuring-logging) can be configured through `log4j.properties`.
 
 
-# System Properties
+# Spark 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:
+Spark properties control most application settings and are configured separately for each application.
+The preferred way to set them is by passing a [SparkConf](api/core/index.html#org.apache.spark.SparkConf)
+class to your SparkContext constructor.
+Alternatively, Spark will also load them from Java system properties (for compatibility with old versions
+of Spark) and from a [`spark.conf` file](#configuration-files) on your classpath.
+
+SparkConf lets you configure most of the common properties to initialize a cluster (e.g., master URL and
+application name), as well as arbitrary key-value pairs through the `set()` method. For example, we could
+initialize an application as follows:
 
 {% highlight scala %}
-System.setProperty("spark.cores.max", "5")
-val sc = new SparkContext(...)
+val conf = new SparkConf()
+             .setMaster("local")
+             .setAppName("My application")
+             .set("spark.executor.memory", "1g")
+val sc = new SparkContext(conf)
 {% endhighlight %}
 
-Most of the configurable system properties control internal settings that have reasonable default values. However,
+Most of the properties control internal settings that have reasonable default values. However,
 there are at least five properties that you will commonly want to control:
 
 <table class="table">
@@ -66,12 +77,14 @@ there are at least five properties that you will commonly want to control:
 </tr>
 <tr>
   <td>spark.cores.max</td>
-  <td>(infinite)</td>
+  <td>(not set)</td>
   <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
-    offered by the cluster manager.
+    sharing mode</a>, the maximum amount of CPU cores to request for the application from
+    across the cluster (not from each machine). If not set, the default will be
+    <code>spark.deploy.defaultCores</code> on Spark's standalone cluster manager, or
+    infinite (all available cores) on Mesos.
   </td>
 </tr>
 </table>
@@ -275,12 +288,33 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.akka.timeout</td>
-  <td>20</td>
+  <td>100</td>
   <td>
     Communication timeout between Spark nodes, in seconds.
   </td>
 </tr>
 <tr>
+  <td>spark.akka.heartbeat.pauses</td>
+  <td>600</td>
+  <td>
+     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` if you need to.
+  </td>
+</tr>
+<tr>
+  <td>spark.akka.failure-detector.threshold</td>
+  <td>300.0</td>
+  <td>
+     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). This maps to akka's `akka.remote.transport-failure-detector.threshold`. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
+  </td>
+</tr>
+<tr>
+  <td>spark.akka.heartbeat.interval</td>
+  <td>1000</td>
+  <td>
+    This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure detector can be, a sensistive failure detector can help evict rogue executors really quick. However this is usually not the case as gc pauses and network lags are expected in a real spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes leading to flooding the network with those. 
+  </td>
+</tr>
+<tr>
   <td>spark.driver.host</td>
   <td>(local hostname)</td>
   <td>
@@ -327,14 +361,116 @@ Apart from these, the following properties are also available, and may be useful
     Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, <code>BlockManager</code> might take a performance hit.
   </td>
 </tr>
+<tr>
+  <td>akka.x.y....</td>
+  <td>value</td>
+  <td>
+    An arbitrary akka configuration can be set directly on spark conf and it is applied for all the ActorSystems created spark wide for that SparkContext and its assigned executors as well.
+  </td>
+</tr>
 
+<tr>
+  <td>spark.shuffle.consolidateFiles</td>
+  <td>false</td>
+  <td>
+    If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations.
+  </td>
+</tr>
+<tr>
+  <td>spark.speculation</td>
+  <td>false</td>
+  <td>
+    If set to "true", performs speculative execution of tasks. This means if one or more tasks are running slowly in a stage, they will be re-launched.
+  </td>
+</tr>
+<tr>
+  <td>spark.speculation.interval</td>
+  <td>100</td>
+  <td>
+    How often Spark will check for tasks to speculate, in milliseconds.
+  </td>
+</tr>
+<tr>
+  <td>spark.speculation.quantile</td>
+  <td>0.75</td>
+  <td>
+    Percentage of tasks which must be complete before speculation is enabled for a particular stage.
+  </td>
+</tr>
+<tr>
+  <td>spark.speculation.multiplier</td>
+  <td>1.5</td>
+  <td>
+    How many times slower a task is than the median to be considered for speculation.
+  </td>
+</tr>
+<tr>
+  <td>spark.logConf</td>
+  <td>false</td>
+  <td>
+    Log the supplied SparkConf as INFO at start of spark context.
+  </td>
+</tr>
+<tr>
+  <td>spark.deploy.spreadOut</td>
+  <td>true</td>
+  <td>
+    Whether the standalone cluster manager should spread applications out across nodes or try
+    to consolidate them onto as few nodes as possible. Spreading out is usually better for
+    data locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/>
+    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual
+    applications; you can set it through <code>SPARK_JAVA_OPTS</code> in <code>spark-env.sh</code>.
+  </td>
+</tr>
+<tr>
+  <td>spark.deploy.defaultCores</td>
+  <td>(infinite)</td>
+  <td>
+    Default number of cores to give to applications in Spark's standalone mode if they don't
+    set <code>spark.cores.max</code>. If not set, applications always get all available
+    cores unless they configure <code>spark.cores.max</code> themselves.
+    Set this lower on a shared cluster to prevent users from grabbing
+    the whole cluster by default. <br/>
+    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual
+    applications; you can set it through <code>SPARK_JAVA_OPTS</code> in <code>spark-env.sh</code>.
+  </td>
+</tr>
 </table>
 
+## Viewing Spark Properties
+
+The application web UI at `http://<driver>:4040` lists Spark properties in the "Environment" tab.
+This is a useful place to check to make sure that your properties have been set correctly.
+
+## Configuration Files
+
+You can also configure Spark properties through a `spark.conf` file on your Java classpath.
+Because these properties are usually application-specific, we recommend putting this fine *only* on your
+application's classpath, and not in a global Spark classpath.
+
+The `spark.conf` file uses Typesafe Config's [HOCON format](https://github.com/typesafehub/config#json-superset),
+which is a superset of Java properties files and JSON. For example, the following is a simple config file:
+
+{% highlight awk %}
+# Comments are allowed
+spark.executor.memory = 512m
+spark.serializer = org.apache.spark.serializer.KryoSerializer
+{% endhighlight %}
+
+The format also allows hierarchical nesting, as follows:
+
+{% highlight awk %}
+spark.akka {
+  threads = 8
+  timeout = 200
+}
+{% endhighlight %}
+
 # Environment Variables
 
-Certain Spark settings can also be configured through environment variables, which are read from the `conf/spark-env.sh`
+Certain Spark settings can 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
+as library search paths. While Spark properties can also be set there through `SPARK_JAVA_OPTS`, for per-application settings, we recommend setting
 these properties within the application instead of in `spark-env.sh` so that different applications can use different
 settings.
 
@@ -351,7 +487,8 @@ The following variables can be set in `spark-env.sh`:
    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`). 
+   properties that you'd like to pass with `-D`. One use case is to set some Spark properties differently on this
+   machine, 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.