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assembly Preparing Spark release v1.3.1-rc3 Apr 11, 2015
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bin MAPR-17422: Ensure only Spark is picked up. Apr 22, 2015
build SPARK-5856: In Maven build script, launch Zinc with more memory Feb 17, 2015
core Preparing Spark release v1.3.1-rc3 Apr 11, 2015
docs [SPARK-6343] Doc driver-worker network reqs Apr 9, 2015
ec2 [SPARK-6636] Use public DNS hostname everywhere in Apr 7, 2015
examples Preparing Spark release v1.3.1-rc3 Apr 11, 2015
external Preparing Spark release v1.3.1-rc3 Apr 11, 2015
graphx Preparing Spark release v1.3.1-rc3 Apr 11, 2015
network Preparing Spark release v1.3.1-rc3 Apr 11, 2015
project [SPARK-6592][SQL] fix filter for scaladoc to generate API doc for Row… Mar 30, 2015
python [SPARK-5969][PySpark] Fix descending pyspark.rdd.sortByKey. Apr 10, 2015
repl Preparing Spark release v1.3.1-rc3 Apr 11, 2015
sbt Adde LICENSE Header to build/mvn, build/sbt and sbt/sbt Dec 29, 2014
streaming Preparing Spark release v1.3.1-rc3 Apr 11, 2015
tools Preparing Spark release v1.3.1-rc3 Apr 11, 2015
.gitattributes [SPARK-3870] EOL character enforcement Oct 31, 2014
.gitignore [SPARK-4501][Core] - Create build/mvn to automatically download maven… Dec 27, 2014
.rat-excludes [SPARK-5778] throw if nonexistent metrics config file provided Feb 17, 2015
CHANGES.txt Version info and CHANGES.txt for 1.3.1 Apr 4, 2015 [Docs] minor grammar fix Sep 17, 2014
LICENSE SPARK-5984: Fix TimSort bug causes ArrayOutOfBoundsException Mar 1, 2015
NOTICE [Docs] Fix Building Spark link text Feb 2, 2015
pom.xml Bug 18912: Make curator jar as compile time dependency. Jun 1, 2015
scalastyle-config.xml [Core] Upgrading ScalaStyle version to 0.5 and removing SparkSpaceAft… Oct 16, 2014
tox.ini [SPARK-3073] [PySpark] use external sort in sortBy() and sortByKey() Aug 26, 2014

Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:


Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:


And run the following command, which should also return 1000:

>>> sc.parallelize(range(1000)).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn-cluster" or "yarn-client" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:


Please see the guidance on how to run all automated tests.

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.


Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.

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