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authorPatrick Wendell <pwendell@gmail.com>2014-01-06 17:29:21 -0800
committerPatrick Wendell <pwendell@gmail.com>2014-01-06 17:29:21 -0800
commitc0498f9265e32ba82bcf48bf0df0f29c6cfea587 (patch)
tree55cce48bc1b7dcc4645715d06256c9923d6dd922 /docs
parentf236ddd1a245a587d5ee331fb67cf41456ed383c (diff)
parente4d6057b6692ca5f071819b4ec6eb5240a0a16bc (diff)
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Merge remote-tracking branch 'apache-github/master' into standalone-driver
Conflicts: core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala core/src/main/scala/org/apache/spark/deploy/master/Master.scala core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
Diffstat (limited to 'docs')
-rw-r--r--docs/README.md4
-rw-r--r--docs/_config.yml2
-rw-r--r--docs/_plugins/copy_api_dirs.rb4
-rw-r--r--docs/api.md2
-rw-r--r--docs/bagel-programming-guide.md4
-rw-r--r--docs/building-with-maven.md14
-rw-r--r--docs/configuration.md74
-rw-r--r--docs/css/bootstrap.min.css2
-rw-r--r--docs/hadoop-third-party-distributions.md2
-rw-r--r--docs/index.md16
-rw-r--r--docs/java-programming-guide.md4
-rw-r--r--docs/job-scheduling.md21
-rw-r--r--docs/mllib-guide.md2
-rw-r--r--docs/monitoring.md3
-rw-r--r--docs/python-programming-guide.md45
-rw-r--r--docs/quick-start.md62
-rw-r--r--docs/running-on-mesos.md19
-rw-r--r--docs/running-on-yarn.md17
-rw-r--r--docs/scala-programming-guide.md20
-rw-r--r--docs/spark-debugger.md2
-rw-r--r--docs/spark-standalone.md35
-rw-r--r--docs/streaming-programming-guide.md8
-rw-r--r--docs/tuning.md21
23 files changed, 228 insertions, 155 deletions
diff --git a/docs/README.md b/docs/README.md
index dfcf753553..e3d6c9a5bc 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -27,10 +27,10 @@ To mark a block of code in your markdown to be syntax highlighted by jekyll duri
## API Docs (Scaladoc and Epydoc)
-You can build just the Spark scaladoc by running `sbt/sbt doc` from the SPARK_PROJECT_ROOT directory.
+You can build just the Spark scaladoc by running `sbt doc` from the SPARK_PROJECT_ROOT directory.
Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the SPARK_PROJECT_ROOT/pyspark directory.
-When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc. The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
+When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc. The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 jekyll`.
diff --git a/docs/_config.yml b/docs/_config.yml
index 02067f9750..11d18f0ac2 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -4,7 +4,7 @@ markdown: kramdown
# These allow the documentation to be updated with nerw releases
# of Spark, Scala, and Mesos.
SPARK_VERSION: 0.9.0-incubating-SNAPSHOT
-SPARK_VERSION_SHORT: 0.9.0-SNAPSHOT
+SPARK_VERSION_SHORT: 0.9.0
SCALA_VERSION: 2.10
MESOS_VERSION: 0.13.0
SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net
diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb
index 431de909cb..ef9912c808 100644
--- a/docs/_plugins/copy_api_dirs.rb
+++ b/docs/_plugins/copy_api_dirs.rb
@@ -26,8 +26,8 @@ if not (ENV['SKIP_API'] == '1' or ENV['SKIP_SCALADOC'] == '1')
curr_dir = pwd
cd("..")
- puts "Running sbt/sbt doc from " + pwd + "; this may take a few minutes..."
- puts `sbt/sbt doc`
+ puts "Running sbt doc from " + pwd + "; this may take a few minutes..."
+ puts `sbt doc`
puts "Moving back into docs dir."
cd("docs")
diff --git a/docs/api.md b/docs/api.md
index e86d07770a..11e2c15324 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -3,7 +3,7 @@ layout: global
title: Spark API documentation (Scaladoc)
---
-Here you can find links to the Scaladoc generated for the Spark sbt subprojects. If the following links don't work, try running `sbt/sbt doc` from the Spark project home directory.
+Here you can find links to the Scaladoc generated for the Spark sbt subprojects. If the following links don't work, try running `sbt doc` from the Spark project home directory.
- [Spark](api/core/index.html)
- [Spark Examples](api/examples/index.html)
diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index de001e6c52..c4f1f6d6ad 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -157,8 +157,8 @@ trait Message[K] {
# Where to Go from Here
-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.:
+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 `bin/run-example` script included in Spark; e.g.:
- ./run-example org.apache.spark.examples.bagel.WikipediaPageRank
+ ./bin/run-example org.apache.spark.examples.bagel.WikipediaPageRank
Each example program prints usage help when run without any arguments.
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index c709001632..b9ff0af76f 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -37,20 +37,16 @@ For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions wit
# Cloudera CDH 4.2.0 with MapReduce v1
$ mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
-For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, you should enable the "hadoop2-yarn" profile and set the "yarn.version" property:
+For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, you should enable the "yarn-alpha" or "yarn" profile and set the "hadoop.version", "yarn.version" property:
# Apache Hadoop 2.0.5-alpha
- $ mvn -Phadoop2-yarn -Dhadoop.version=2.0.5-alpha -Dyarn.version=2.0.5-alpha -DskipTests clean package
+ $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -Dyarn.version=2.0.5-alpha -DskipTests clean package
# Cloudera CDH 4.2.0 with MapReduce v2
- $ mvn -Phadoop2-yarn -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-chd4.2.0 -DskipTests clean package
+ $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-chd4.2.0 -DskipTests clean package
-Hadoop versions 2.2.x and newer can be built by setting the ```new-yarn``` and the ```yarn.version``` as follows:
-
- # Apache Hadoop 2.2.X and newer
- $ mvn -Dyarn.version=2.2.0 -Dhadoop.version=2.2.0 -Pnew-yarn
-
-The build process handles Hadoop 2.2.x as a special case that uses the directory ```new-yarn```, which supports the new YARN API. Furthermore, for this version, the build depends on artifacts published by the spark-project to enable Akka 2.0.5 to work with protobuf 2.5.
+ # Apache Hadoop 2.2.X ( e.g. 2.2.0 as below ) and newer
+ $ mvn -Pyarn -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -DskipTests clean package
## Spark Tests in Maven ##
diff --git a/docs/configuration.md b/docs/configuration.md
index 677d182e50..09342fedfc 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">
@@ -70,7 +81,8 @@ 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>, the maximum amount of CPU cores to request for the application from
+ across the cluster (not from each machine). The default will use all available cores
offered by the cluster manager.
</td>
</tr>
@@ -385,11 +397,40 @@ Apart from these, the following properties are also available, and may be useful
</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.
@@ -406,7 +447,8 @@ 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.
diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css
index ccb529eaba..119bb5dba7 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 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diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index de6a2b0a43..141d475ba6 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -12,7 +12,7 @@ with these distributions:
When compiling Spark, you'll need to
[set the SPARK_HADOOP_VERSION flag](index.html#a-note-about-hadoop-versions):
- SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
+ SPARK_HADOOP_VERSION=1.0.4 sbt assembly
The table below lists the corresponding `SPARK_HADOOP_VERSION` code for each CDH/HDP release. Note that
some Hadoop releases are binary compatible across client versions. This means the pre-built Spark
diff --git a/docs/index.md b/docs/index.md
index d3ac696d1e..bf8d1c3375 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -17,16 +17,16 @@ Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you n
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
+ sbt assembly
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/).
# Running the Examples and Shell
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-example` script sets up the appropriate paths and launches that program).
-For example, try `./run-example org.apache.spark.examples.SparkPi local`.
+To run one of the samples, use `./bin/run-example <class> <params>` in the top-level Spark directory
+(the `bin/run-example` script sets up the appropriate paths and launches that program).
+For example, try `./bin/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
@@ -34,8 +34,8 @@ to connect to. This can be a [URL for a distributed cluster](scala-programming-g
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, you can run Spark interactively through modified versions of the Scala shell (`./spark-shell`) or
-Python interpreter (`./pyspark`). These are a great way to learn the framework.
+Finally, you can run Spark interactively through modified versions of the Scala shell (`./bin/spark-shell`) or
+Python interpreter (`./bin/pyspark`). These are a great way to learn the framework.
# Launching on a Cluster
@@ -56,12 +56,12 @@ 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=2.2.0 sbt/sbt assembly
+ SPARK_HADOOP_VERSION=2.2.0 sbt assembly
In addition, if you wish to run Spark on [YARN](running-on-yarn.html), set
`SPARK_YARN` to `true`:
- SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
+ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true 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`.
diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md
index 53085cc671..07732fa122 100644
--- a/docs/java-programming-guide.md
+++ b/docs/java-programming-guide.md
@@ -190,9 +190,9 @@ We hope to generate documentation with Java-style syntax in the future.
Spark includes several sample programs using the Java API in
[`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:
+`bin/run-example` script included in Spark; for example:
- ./run-example org.apache.spark.examples.JavaWordCount
+ ./bin/run-example org.apache.spark.examples.JavaWordCount
Each example program prints usage help when run
without any arguments.
diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md
index dbcb9ae343..5951155fe3 100644
--- a/docs/job-scheduling.md
+++ b/docs/job-scheduling.md
@@ -32,12 +32,12 @@ Resource allocation can be configured as follows, based on the cluster type:
* **Standalone mode:** By default, applications submitted to the standalone mode cluster will run in
FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit
- the number of nodes an application uses by setting the `spark.cores.max` system property in it. This
+ the number of nodes an application uses by setting the `spark.cores.max` configuration property in it. This
will allow multiple users/applications to run concurrently. For example, you might launch a long-running
server that uses 10 cores, and allow users to launch shells that use 20 cores each.
Finally, in addition to controlling cores, each application's `spark.executor.memory` setting controls
its memory use.
-* **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` system property to `true`,
+* **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`,
and optionally set `spark.cores.max` to limit each application's resource share as in the standalone mode.
You should also set `spark.executor.memory` to control the executor memory.
* **YARN:** The `--num-workers` option to the Spark YARN client controls how many workers it will allocate
@@ -78,11 +78,13 @@ of cluster resources. This means that short jobs submitted while a long job is r
resources right away and still get good response times, without waiting for the long job to finish. This
mode is best for multi-user settings.
-To enable the fair scheduler, simply set the `spark.scheduler.mode` to `FAIR` before creating
+To enable the fair scheduler, simply set the `spark.scheduler.mode` property to `FAIR` when configuring
a SparkContext:
{% highlight scala %}
-System.setProperty("spark.scheduler.mode", "FAIR")
+val conf = new SparkConf().setMaster(...).setAppName(...)
+conf.set("spark.scheduler.mode", "FAIR")
+val sc = new SparkContext(conf)
{% endhighlight %}
## Fair Scheduler Pools
@@ -98,8 +100,8 @@ adding the `spark.scheduler.pool` "local property" to the SparkContext in the th
This is done as follows:
{% highlight scala %}
-// Assuming context is your SparkContext variable
-context.setLocalProperty("spark.scheduler.pool", "pool1")
+// Assuming sc is your SparkContext variable
+sc.setLocalProperty("spark.scheduler.pool", "pool1")
{% endhighlight %}
After setting this local property, _all_ jobs submitted within this thread (by calls in this thread
@@ -108,7 +110,7 @@ it easy to have a thread run multiple jobs on behalf of the same user. If you'd
pool that a thread is associated with, simply call:
{% highlight scala %}
-context.setLocalProperty("spark.scheduler.pool", null)
+sc.setLocalProperty("spark.scheduler.pool", null)
{% endhighlight %}
## Default Behavior of Pools
@@ -138,10 +140,11 @@ properties:
of the cluster. By default, each pool's `minShare` is 0.
The pool properties can be set by creating an XML file, similar to `conf/fairscheduler.xml.template`,
-and setting the `spark.scheduler.allocation.file` property:
+and setting a `spark.scheduler.allocation.file` property in your
+[SparkConf](configuration.html#spark-properties).
{% highlight scala %}
-System.setProperty("spark.scheduler.allocation.file", "/path/to/file")
+conf.set("spark.scheduler.allocation.file", "/path/to/file")
{% endhighlight %}
The format of the XML file is simply a `<pool>` element for each pool, with different elements
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index c1ff9c417c..95537ef185 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -87,7 +87,7 @@ svmAlg.optimizer.setNumIterations(200)
val modelL1 = svmAlg.run(parsedData)
{% endhighlight %}
-Both of the code snippets above can be executed in `spark-shell` to generate a
+Both of the code snippets above can be executed in `bin/spark-shell` to generate a
classifier for the provided dataset.
Available algorithms for binary classification:
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 5ed0474477..0d5eb7065e 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -32,7 +32,8 @@ Spark has a configurable metrics system based on the
This allows users to report Spark metrics to a variety of sinks including HTTP, JMX, and CSV
files. The metrics system is configured via a configuration file that Spark expects to be present
at `$SPARK_HOME/conf/metrics.conf`. A custom file location can be specified via the
-`spark.metrics.conf` Java system property. Spark's metrics are decoupled into different
+`spark.metrics.conf` [configuration property](configuration.html#spark-properties).
+Spark's metrics are decoupled into different
_instances_ corresponding to Spark components. Within each instance, you can configure a
set of sinks to which metrics are reported. The following instances are currently supported:
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 55e39b1de1..5d48cb676a 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -47,7 +47,7 @@ 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.
+In addition, PySpark fully supports interactive use---simply run `./bin/pyspark` to launch an interactive shell.
# Installing and Configuring PySpark
@@ -60,17 +60,17 @@ By default, PySpark requires `python` to be available on the system `PATH` and u
All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
-Standalone PySpark applications 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`.
+Standalone PySpark applications should be run using the `bin/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 `bin/pyspark` package to the `PYTHONPATH`.
# Interactive Use
-The `pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options:
+The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options:
{% highlight bash %}
-$ sbt/sbt assembly
-$ ./pyspark
+$ sbt assembly
+$ ./bin/pyspark
{% endhighlight %}
The Python shell can be used explore data interactively and is a simple way to learn the API:
@@ -82,35 +82,35 @@ The Python shell can be used explore data interactively and is a simple way to l
>>> help(pyspark) # Show all pyspark functions
{% endhighlight %}
-By default, the `pyspark` shell creates SparkContext that runs applications locally on a single core.
+By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on a single core.
To connect to a non-local cluster, or use multiple cores, set the `MASTER` environment variable.
-For example, to use the `pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
+For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
{% highlight bash %}
-$ MASTER=spark://IP:PORT ./pyspark
+$ MASTER=spark://IP:PORT ./bin/pyspark
{% endhighlight %}
Or, to use four cores on the local machine:
{% highlight bash %}
-$ MASTER=local[4] ./pyspark
+$ MASTER=local[4] ./bin/pyspark
{% endhighlight %}
## IPython
It is also possible to launch PySpark in [IPython](http://ipython.org), the enhanced Python interpreter.
-To do this, set the `IPYTHON` variable to `1` when running `pyspark`:
+To do this, set the `IPYTHON` variable to `1` when running `bin/pyspark`:
{% highlight bash %}
-$ IPYTHON=1 ./pyspark
+$ IPYTHON=1 ./bin/pyspark
{% endhighlight %}
Alternatively, you can customize the `ipython` command by setting `IPYTHON_OPTS`. For example, to launch
the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing support:
{% highlight bash %}
-$ IPYTHON_OPTS="notebook --pylab inline" ./pyspark
+$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark
{% endhighlight %}
IPython also works on a cluster or on multiple cores if you set the `MASTER` environment variable.
@@ -118,7 +118,7 @@ IPython also works on a cluster or on multiple cores if you set the `MASTER` env
# Standalone Programs
-PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `pyspark`.
+PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/pyspark`.
The Quick Start guide includes a [complete example](quick-start.html#a-standalone-app-in-python) of a standalone Python application.
Code dependencies can be deployed by listing them in the `pyFiles` option in the SparkContext constructor:
@@ -131,15 +131,16 @@ sc = SparkContext("local", "App 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.
-You can set [system properties](configuration.html#system-properties)
-using `SparkContext.setSystemProperty()` class method *before*
-instantiating SparkContext. For example, to set the amount of memory
-per executor process:
+You can set [configuration properties](configuration.html#spark-properties) by passing a
+[SparkConf](api/pyspark/pyspark.conf.SparkConf-class.html) object to SparkContext:
{% highlight python %}
-from pyspark import SparkContext
-SparkContext.setSystemProperty('spark.executor.memory', '2g')
-sc = SparkContext("local", "App Name")
+from pyspark import SparkConf, SparkContext
+conf = (SparkConf()
+ .setMaster("local")
+ .setAppName("My app")
+ .set("spark.executor.memory", "1g"))
+sc = SparkContext(conf = conf)
{% endhighlight %}
# API Docs
@@ -152,6 +153,6 @@ Many of the methods also contain [doctests](http://docs.python.org/2/library/doc
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
+ ./bin/pyspark python/examples/wordcount.py
Each program prints usage help when run without arguments.
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 8f782db5b8..9b9261cfff 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -12,7 +12,7 @@ See the [programming guide](scala-programming-guide.html) for a more complete re
To follow along with this guide, you only need to have successfully built Spark on one machine. Simply go into your Spark directory and run:
{% highlight bash %}
-$ sbt/sbt assembly
+$ sbt assembly
{% endhighlight %}
# Interactive Analysis with the Spark Shell
@@ -20,7 +20,7 @@ $ sbt/sbt assembly
## Basics
Spark's interactive shell provides a simple way to learn the API, as well as a powerful tool to analyze datasets interactively.
-Start the shell by running `./spark-shell` in the Spark directory.
+Start the shell by running `./bin/spark-shell` in the Spark directory.
Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs. Let's make a new RDD from the text of the README file in the Spark source directory:
@@ -99,7 +99,7 @@ scala> linesWithSpark.count()
res9: Long = 15
{% endhighlight %}
-It may seem silly to use Spark to explore and cache a 30-line text file. The interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds of nodes. You can also do this interactively by connecting `spark-shell` to a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
+It may seem silly to use Spark to explore and cache a 30-line text file. The interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds of nodes. You can also do this interactively by connecting `bin/spark-shell` to a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
# A Standalone App in Scala
Now say we wanted to write a standalone application using the Spark API. We will walk through a simple application in both Scala (with SBT), Java (with Maven), and Python. If you are using other build systems, consider using the Spark assembly JAR described in the developer guide.
@@ -124,7 +124,7 @@ object SimpleApp {
}
{% endhighlight %}
-This program 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 proogram. 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 application, the directory where Spark is installed, and a name for the jar file containing the application's code. 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 program just 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 proogram. 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 application, the directory where Spark is installed, and a name for the jar file containing the application's code. 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 a repository that Spark depends on:
@@ -193,7 +193,7 @@ public class SimpleApp {
}
{% endhighlight %}
-This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail.
+This program just counts the number of lines containing 'a' and the number containing 'b' in a text file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail.
To build the program, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.
@@ -224,11 +224,11 @@ To build the program, we also write a Maven `pom.xml` file that lists Spark as a
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>
+<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:
@@ -270,18 +270,18 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs)
{% endhighlight %}
-This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file.
+This program just counts the number of lines containing 'a' and the number containing 'b' in a text file.
Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed.
As with the Scala and Java examples, we use a SparkContext to create RDDs.
We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference.
For applications that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide.html).
`SimpleApp` is simple enough that we do not need to specify any code dependencies.
-We can run this application using the `pyspark` script:
+We can run this application using the `bin/pyspark` script:
{% highlight python %}
$ cd $SPARK_HOME
-$ ./pyspark SimpleApp.py
+$ ./bin/pyspark SimpleApp.py
...
Lines with a: 46, Lines with b: 23
{% endhighlight python %}
@@ -301,14 +301,38 @@ assembly jar (or "uber" jar) containing your code and its dependencies. Both
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.
+add it to the SparkContext as shown here. It is also possible to add
+your dependent jars one-by-one using the `addJar` method of `SparkContext`.
+
+For Python, you can use the `pyFiles` argument of SparkContext
+or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed.
### Setting Configuration Options
-Spark includes several configuration options which influence the behavior
-of your application. 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.
+Spark includes several [configuration options](configuration.html#spark-properties)
+that influence the behavior of your application.
+These should be set by building a [SparkConf](api/core/index.html#org.apache.spark.SparkConf)
+object and passing it to the SparkContext constructor.
+For example, in Java and Scala, you can do:
+
+{% highlight scala %}
+import org.apache.spark.{SparkConf, SparkContext}
+val conf = new SparkConf()
+ .setMaster("local")
+ .setAppName("My application")
+ .set("spark.executor.memory", "1g")
+val sc = new SparkContext(conf)
+{% endhighlight %}
+
+Or in Python:
+
+{% highlight scala %}
+from pyspark import SparkConf, SparkContext
+conf = SparkConf()
+conf.setMaster("local")
+conf.setAppName("My application")
+conf.set("spark.executor.memory", "1g"))
+sc = SparkContext(conf = conf)
+{% endhighlight %}
### Accessing Hadoop Filesystems
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 322ff585f1..68259f0cb8 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -15,15 +15,16 @@ Spark can run on clusters managed by [Apache Mesos](http://mesos.apache.org/). F
* `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is usually `<prefix>/lib/libmesos.so` (where the prefix is `/usr/local` by default, see above). Also, on Mac OS X, the library is called `libmesos.dylib` instead of `libmesos.so`.
* `export SPARK_EXECUTOR_URI=<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`.
* `export MASTER=mesos://HOST:PORT` where HOST:PORT is the host and port (default: 5050) of your Mesos master (or `zk://...` if using Mesos with ZooKeeper).
-8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the first parameter. In addition, you'll need to set the `spark.executor.uri` property. For example:
+8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the master URL. In addition, you'll need to set the `spark.executor.uri` property. For example:
{% highlight scala %}
-System.setProperty("spark.executor.uri", "<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>")
-val sc = new SparkContext("mesos://HOST:5050", "App Name", ...)
+val conf = new SparkConf()
+ .setMaster("mesos://HOST:5050")
+ .setAppName("My app")
+ .set("spark.executor.uri", "<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>")
+val sc = new SparkContext(conf)
{% endhighlight %}
-If you want to run Spark on Amazon EC2, you can use the Spark [EC2 launch scripts](ec2-scripts.html), which provide an easy way to launch a cluster with Mesos, Spark, and HDFS pre-configured. This will get you a cluster in about five minutes without any configuration on your part.
-
# Mesos Run Modes
Spark can run over Mesos in two modes: "fine-grained" and "coarse-grained". In fine-grained mode, which is the default,
@@ -34,17 +35,15 @@ launch only *one* long-running Spark task on each Mesos machine, and dynamically
it. The benefit is much lower startup overhead, but at the cost of reserving the Mesos resources for the complete duration
of the application.
-To run in coarse-grained mode, set the `spark.mesos.coarse` system property to true *before* creating your SparkContext:
+To run in coarse-grained mode, set the `spark.mesos.coarse` property in your [SparkConf](configuration.html#spark-properties):
{% highlight scala %}
-System.setProperty("spark.mesos.coarse", "true")
-val sc = new SparkContext("mesos://HOST:5050", "App Name", ...)
+conf.set("spark.mesos.coarse", "true")
{% endhighlight %}
In addition, for coarse-grained mode, you can control the maximum number of resources Spark will acquire. By default,
it will acquire *all* cores in the cluster (that get offered by Mesos), which only makes sense if you run just one
-application at a time. You can cap the maximum number of cores using `System.setProperty("spark.cores.max", "10")` (for example).
-Again, this must be done *before* initializing a SparkContext.
+application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example).
# Running Alongside Hadoop
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index aa75ca4324..a35e003cdc 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -12,7 +12,7 @@ was added to Spark in version 0.6.0, and improved in 0.7.0 and 0.8.0.
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:
- SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly
+ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt assembly
The assembled JAR will be something like this:
`./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.
@@ -25,7 +25,7 @@ The build process now also supports new YARN versions (2.2.x). See below.
- The assembled jar can be installed into HDFS or used locally.
- 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 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.
+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 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
@@ -54,7 +54,7 @@ There are two scheduler mode that can be used to launch spark application on YAR
The command to launch the YARN Client is as follows:
- SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./spark-class org.apache.spark.deploy.yarn.Client \
+ SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./bin/spark-class org.apache.spark.deploy.yarn.Client \
--jar <YOUR_APP_JAR_FILE> \
--class <APP_MAIN_CLASS> \
--args <APP_MAIN_ARGUMENTS> \
@@ -72,14 +72,14 @@ The command to launch the YARN Client is as follows:
For example:
# Build the Spark assembly JAR and the Spark examples JAR
- $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly
+ $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt assembly
# Configure logging
$ cp conf/log4j.properties.template conf/log4j.properties
# Submit Spark's ApplicationMaster to YARN's ResourceManager, and instruct Spark to run the SparkPi example
$ SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
- ./spark-class org.apache.spark.deploy.yarn.Client \
+ ./bin/spark-class org.apache.spark.deploy.yarn.Client \
--jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
--class org.apache.spark.examples.SparkPi \
--args yarn-standalone \
@@ -107,17 +107,15 @@ For example:
SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
- ./run-example org.apache.spark.examples.SparkPi yarn-client
+ ./bin/run-example org.apache.spark.examples.SparkPi yarn-client
SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
- MASTER=yarn-client ./spark-shell
+ MASTER=yarn-client ./bin/spark-shell
# Building Spark for Hadoop/YARN 2.2.x
-Hadoop 2.2.x users must build Spark and publish it locally. The SBT build process handles Hadoop 2.2.x as a special case. This version of Hadoop has new YARN API changes and depends on a Protobuf version (2.5) that is not compatible with the Akka version (2.0.5) that Spark uses. Therefore, if the Hadoop version (e.g. set through ```SPARK_HADOOP_VERSION```) starts with 2.2.0 or higher then the build process will depend on Akka artifacts distributed by the Spark project compatible with Protobuf 2.5. Furthermore, the build process then uses the directory ```new-yarn``` (instead of ```yarn```), which supports the new YARN API. The build process should seamlessly work out of the box.
-
See [Building Spark with Maven](building-with-maven.html) for instructions on how to build Spark using the Maven process.
# Important Notes
@@ -126,4 +124,3 @@ See [Building Spark with Maven](building-with-maven.html) for instructions on ho
- The local directories used for spark will be the local directories configured for YARN (Hadoop Yarn config yarn.nodemanager.local-dirs). If the user specifies spark.local.dir, it will be ignored.
- The --files and --archives options support specifying file names with the # similar to Hadoop. For example you can specify: --files localtest.txt#appSees.txt and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name appSees.txt and your application should use the name as appSees.txt to reference it when running on YARN.
- The --addJars option allows the SparkContext.addJar function to work if you are using it with local files. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
-- YARN 2.2.x users cannot simply depend on the Spark packages without building Spark, as the published Spark artifacts are compiled to work with the pre 2.2 API. Those users must build Spark and publish it locally.
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index 56d2a3a4a0..3d0e8923d5 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -13,7 +13,7 @@ At a high level, every Spark application consists of a *driver program* that run
A second abstraction in Spark is *shared variables* that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: *broadcast variables*, which can be used to cache a value in memory on all nodes, and *accumulators*, which are variables that are only "added" to, such as counters and sums.
-This guide shows each of these features and walks through some samples. It assumes some familiarity with Scala, especially with the syntax for [closures](http://www.scala-lang.org/node/133). Note that you can also run Spark interactively using the `spark-shell` script. We highly recommend doing that to follow along!
+This guide shows each of these features and walks through some samples. It assumes some familiarity with Scala, especially with the syntax for [closures](http://www.scala-lang.org/node/133). Note that you can also run Spark interactively using the `bin/spark-shell` script. We highly recommend doing that to follow along!
# Linking with Spark
@@ -31,7 +31,7 @@ In addition, if you wish to access an HDFS cluster, you need to add a dependency
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).
+For other build systems, you can run `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).
Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
@@ -49,18 +49,21 @@ This is done through the following constructor:
new SparkContext(master, appName, [sparkHome], [jars])
{% endhighlight %}
+or through `new SparkContext(conf)`, which takes a [SparkConf](api/core/index.html#org.apache.spark.SparkConf)
+object for more advanced configuration.
+
The `master` parameter is a string specifying a [Spark or Mesos cluster URL](#master-urls) to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later.
-In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `spark-shell` on four cores, use
+In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `bin/spark-shell` on four cores, use
{% highlight bash %}
-$ MASTER=local[4] ./spark-shell
+$ MASTER=local[4] ./bin/spark-shell
{% endhighlight %}
Or, to also add `code.jar` to its classpath, use:
{% highlight bash %}
-$ MASTER=local[4] ADD_JARS=code.jar ./spark-shell
+$ MASTER=local[4] ADD_JARS=code.jar ./bin/spark-shell
{% endhighlight %}
### Master URLs
@@ -92,8 +95,7 @@ If you want to run your application on a cluster, you will need to specify the t
* `sparkHome`: The path at which Spark is installed on your worker machines (it should be the same on all of them).
* `jars`: A list of JAR files on the local machine containing your application's code and any dependencies, which Spark will deploy to all the worker nodes. You'll need to package your application into a set of JARs using your build system. For example, if you're using SBT, the [sbt-assembly](https://github.com/sbt/sbt-assembly) plugin is a good way to make a single JAR with your code and dependencies.
-If you run `spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it. This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed.
-
+If you run `bin/spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it. This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./bin/spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed.
# Resilient Distributed Datasets (RDDs)
@@ -364,9 +366,9 @@ res2: Int = 10
# Where to Go from Here
You can see some [example Spark programs](http://spark.incubator.apache.org/examples.html) on the Spark website.
-In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `run-example` script included in Spark; for example:
+In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `bin/run-example` script included in Spark; for example:
- ./run-example org.apache.spark.examples.SparkPi
+ ./bin/run-example org.apache.spark.examples.SparkPi
Each example program prints usage help when run without any arguments.
diff --git a/docs/spark-debugger.md b/docs/spark-debugger.md
index d6315d97f4..11c51d5cde 100644
--- a/docs/spark-debugger.md
+++ b/docs/spark-debugger.md
@@ -39,7 +39,7 @@ where `path/to/event-log` is where you want the event log to go relative to `$SP
### Loading the event log into the debugger
-1. Run a Spark shell with `MASTER=<i>host</i> ./spark-shell`.
+1. Run a Spark shell with `MASTER=<i>host</i> ./bin/spark-shell`.
2. Use `EventLogReader` to load the event log as follows:
{% highlight scala %}
spark> val r = new spark.EventLogReader(sc, Some("path/to/event-log"))
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 59adbce156..f426db0141 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -16,7 +16,7 @@ To install Spark Standlone mode, you simply place a compiled version of Spark on
You can start a standalone master server by executing:
- ./bin/start-master.sh
+ ./sbin/start-master.sh
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
@@ -24,7 +24,7 @@ the master's web UI, which is [http://localhost:8080](http://localhost:8080) by
Similarly, you can start one or more workers and connect them to the master via:
- ./spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT
+ ./bin/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).
@@ -66,12 +66,12 @@ To launch a Spark standalone cluster with the launch scripts, you need to create
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.
-- `bin/start-all.sh` - Starts both a master and a number of slaves as described above.
-- `bin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script.
-- `bin/stop-slaves.sh` - Stops the slave instances that were started via `bin/start-slaves.sh`.
-- `bin/stop-all.sh` - Stops both the master and the slaves as described above.
+- `sbin/start-master.sh` - Starts a master instance on the machine the script is executed on.
+- `sbin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file.
+- `sbin/start-all.sh` - Starts both a master and a number of slaves as described above.
+- `sbin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script.
+- `sbin/stop-slaves.sh` - Stops the slave instances that were started via `bin/start-slaves.sh`.
+- `sbin/stop-all.sh` - Stops both the master and the slaves as described above.
Note that these scripts must be executed on the machine you want to run the Spark master on, not your local machine.
@@ -139,9 +139,9 @@ constructor](scala-programming-guide.html#initializing-spark).
To run an interactive Spark shell against the cluster, run the following command:
- MASTER=spark://IP:PORT ./spark-shell
+ MASTER=spark://IP:PORT ./bin/spark-shell
-Note that if you are running spark-shell from one of the spark cluster machines, the `spark-shell` script will
+Note that if you are running spark-shell from one of the spark cluster machines, the `bin/spark-shell` script will
automatically set MASTER from the `SPARK_MASTER_IP` and `SPARK_MASTER_PORT` variables in `conf/spark-env.sh`.
You can also pass an option `-c <numCores>` to control the number of cores that spark-shell uses on the cluster.
@@ -182,11 +182,18 @@ the same client:
The standalone cluster mode currently only supports a simple FIFO scheduler across applications.
However, to allow multiple concurrent users, you can control the maximum number of resources each
-application will acquire.
+application will use.
By default, it will acquire *all* cores in the cluster, which only makes sense if you just run one
-application at a time. You can cap the number of cores using
-`System.setProperty("spark.cores.max", "10")` (for example).
-This value must be set *before* initializing your SparkContext.
+application at a time. You can cap the number of cores by setting `spark.cores.max` in your
+[SparkConf](configuration.html#spark-properties). For example:
+
+{% highlight scala %}
+val conf = new SparkConf()
+ .setMaster(...)
+ .setAppName(...)
+ .set("spark.cores.max", "10")
+val sc = new SparkContext(conf)
+{% endhighlight %}
# Monitoring and Logging
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 82f42e0b8d..1c9ece6270 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -245,7 +245,7 @@ $ nc -lk 9999
Then, in a different terminal, you can start NetworkWordCount by using
{% highlight bash %}
-$ ./run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999
+$ ./bin/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.
@@ -283,7 +283,7 @@ Time: 1357008430000 ms
</td>
</table>
-You can find more examples in `<Spark repo>/streaming/src/main/scala/org/apache/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.
+You can find more examples in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/`. They can be run in the similar manner using `./bin/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()`.
@@ -326,7 +326,7 @@ 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 [`PairDStreamFunctions`](api/streaming/index.html#org.apache.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 [config property](configuration.html#spark-properties) `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.
@@ -349,7 +349,7 @@ For a Spark Streaming application running on a cluster to be stable, the process
A good approach to figure out the right batch size for your application is to test it with a conservative batch size (say, 5-10 seconds) and a low data rate. To verify whether the system is able to keep up with data rate, you can check the value of the end-to-end delay experienced by each processed batch (in the Spark master logs, find the line having the phrase "Total delay"). If the delay is maintained to be less than the batch size, then system is stable. Otherwise, if the delay is continuously increasing, it means that the system is unable to keep up and it therefore unstable. Once you have an idea of a stable configuration, you can try increasing the data rate and/or reducing the batch size. Note that momentary increase in the delay due to temporary data rate increases maybe fine as long as the delay reduces back to a low value (i.e., less than batch size).
## 24/7 Operation
-By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.). But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic cleanup of it metadata. This can be enabled by setting the Java system property `spark.cleaner.ttl` to the number of seconds you want any metadata to persist. For example, setting `spark.cleaner.ttl` to 600 would cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes. Note, that this property needs to be set before the SparkContext is created.
+By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.). But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic cleanup of it metadata. This can be enabled by setting the [config property](configuration.html#spark-properties) `spark.cleaner.ttl` to the number of seconds you want any metadata to persist. For example, setting `spark.cleaner.ttl` to 600 would cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes. Note, that this property needs to be set before the SparkContext is created.
This value is closely tied with any window operation that is being used. Any window operation would require the input data to be persisted in memory for at least the duration of the window. Hence it is necessary to set the delay to at least the value of the largest window operation used in the Spark Streaming application. If this delay is set too low, the application will throw an exception saying so.
diff --git a/docs/tuning.md b/docs/tuning.md
index a4be188169..bbb870085c 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -38,14 +38,15 @@ in your operations) and performance. It provides two serialization libraries:
`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", "org.apache.spark.serializer.KryoSerializer")`
-*before* creating your SparkContext. This setting configures the serializer used for not only shuffling data between worker
+You can switch to using Kryo by initializing your job with a [SparkConf](configuration.html#spark-properties)
+and calling `conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")`.
+This setting configures the serializer used for not only shuffling data between worker
nodes but also when serializing RDDs to disk. The only reason Kryo 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
[`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:
+`spark.kryo.registrator` config property to point to it, as follows:
{% highlight scala %}
import com.esotericsoftware.kryo.Kryo
@@ -58,17 +59,17 @@ class MyRegistrator extends KryoRegistrator {
}
}
-// Make sure to set these properties *before* creating a SparkContext!
-System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-System.setProperty("spark.kryo.registrator", "mypackage.MyRegistrator")
-val sc = new SparkContext(...)
+val conf = new SparkConf().setMaster(...).setAppName(...)
+conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+conf.set("spark.kryo.registrator", "mypackage.MyRegistrator")
+val sc = new SparkContext(conf)
{% endhighlight %}
The [Kryo documentation](http://code.google.com/p/kryo/) describes more advanced
registration options, such as adding custom serialization code.
If your objects are large, you may also need to increase the `spark.kryoserializer.buffer.mb`
-system property. The default is 2, but this value needs to be large enough to hold the *largest*
+config property. The default is 2, but this value needs to be large enough to hold the *largest*
object you will serialize.
Finally, if you don't register your classes, Kryo will still work, but it will have to store the
@@ -165,7 +166,7 @@ cache RDDs. This means that 33% of memory is available for any objects created d
In case your tasks slow down and you find that your JVM is garbage-collecting frequently or running out of
memory, lowering this value will help reduce the memory consumption. To change this to say 50%, you can call
-`System.setProperty("spark.storage.memoryFraction", "0.5")`. Combined with the use of serialized caching,
+`conf.set("spark.storage.memoryFraction", "0.5")` on your SparkConf. Combined with the use of serialized caching,
using a smaller cache should be sufficient to mitigate most of the garbage collection problems.
In case you are interested in further tuning the Java GC, continue reading below.
@@ -219,7 +220,7 @@ enough. Spark automatically sets the number of "map" tasks to run on each file a
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#org.apache.spark.rdd.PairRDDFunctions) documentation),
-or set the system property `spark.default.parallelism` to change the default.
+or set the config property `spark.default.parallelism` to change the default.
In general, we recommend 2-3 tasks per CPU core in your cluster.
## Memory Usage of Reduce Tasks