<commit_before>---
layout: global
title: Spark Overview
---

Apache Spark is a fast and general-purpose cluster computing system.
It provides high-level APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html) that make parallel jobs easy to write, and an optimized engine that supports general computation graphs.
It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).

# Downloading

Get Spark by visiting the [downloads page](http://spark.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}.

Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is to have `java` to installed on your system `PATH`, or the `JAVA_HOME` environment variable pointing to a Java installation.

# Building

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

For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_BINARY_VERSION}}. If you write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X) -- 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 `./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
to connect to. This can be a [URL for a distributed cluster](scala-programming-guide.html#master-urls),
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 (`./bin/spark-shell`) or
Python interpreter (`./bin/pyspark`). These are a great way to learn the framework.

# Launching on a Cluster

The Spark [cluster mode overview](cluster-overview.html) explains the key concepts in running on a cluster.
Spark can run both by itself, or over several existing cluster managers. It currently provides several
options for deployment:

* [Amazon EC2](ec2-scripts.html): our EC2 scripts let you launch a cluster in about 5 minutes
* [Standalone Deploy Mode](spark-standalone.html): simplest way to deploy Spark on a private cluster
* [Apache Mesos](running-on-mesos.html)
* [Hadoop YARN](running-on-yarn.html)

# A Note About Hadoop Versions

Spark uses the Hadoop-client library to talk to HDFS and other Hadoop-supported
storage systems. Because the HDFS protocol has changed in different versions of
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:<commit_msg>[Spark-1261] add instructions for running python examples to doc overview page<commit_after>---
layout: global
title: Spark Overview
---

Apache Spark is a fast and general-purpose cluster computing system.
It provides high-level APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html) that make parallel jobs easy to write, and an optimized engine that supports general computation graphs.
It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).

# Downloading

Get Spark by visiting the [downloads page](http://spark.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}.

Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is to have `java` to installed on your system `PATH`, or the `JAVA_HOME` environment variable pointing to a Java installation.

# Building

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

For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_BINARY_VERSION}}. If you write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X) -- 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.  Scala and Java examples are in the `examples` directory, and Python examples are in `python/examples`.
To run one of the Java or Scala sample programs, 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`.
To run a Python sample program, use `./bin/pyspark <sample-program> <params>`.  For example, try `./bin/pyspark ./python/examples/pi.py 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
to connect to. This can be a [URL for a distributed cluster](scala-programming-guide.html#master-urls),
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 (`./bin/spark-shell`) or
Python interpreter (`./bin/pyspark`). These are a great way to learn the framework.

# Launching on a Cluster

The Spark [cluster mode overview](cluster-overview.html) explains the key concepts in running on a cluster.
Spark can run both by itself, or over several existing cluster managers. It currently provides several
options for deployment:

* [Amazon EC2](ec2-scripts.html): our EC2 scripts let you launch a cluster in about 5 minutes
* [Standalone Deploy Mode](spark-standalone.html): simplest way to deploy Spark on a private cluster
* [Apache Mesos](running-on-mesos.html)
* [Hadoop YARN](running-on-yarn.html)

# A Note About Hadoop Versions

Spark uses the Hadoop-client library to talk to HDFS and other Hadoop-supported
storage systems. Because the HDFS protocol has changed in different versions of
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:
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