Spark Overview

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala and Python, and an optimized engine that supports general execution graphs. 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.


Get Spark from the downloads page of the project website. This documentation is for Spark version 1.0.2. The downloads page contains Spark packages for many popular HDFS versions. If you’d like to build Spark from scratch, visit building Spark with Maven.

Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It’s easy to run locally on one machine — all you need is to have java installed on your system PATH, or the JAVA_HOME environment variable pointing to a Java installation.

Spark runs on Java 6+ and Python 2.6+. For the Scala API, Spark 1.0.2 uses Scala 2.10. You will need to use a compatible Scala version (2.10.x).

Running the Examples and Shell

Spark comes with several sample programs. Scala, Java and Python examples are in the examples/src/main directory. To run one of the Java or Scala sample programs, use bin/run-example <class> [params] in the top-level Spark directory. (Behind the scenes, this invokes the more general spark-submit script for launching applications). For example,

./bin/run-example SparkPi 10

You can also run Spark interactively through a modified version of the Scala shell. This is a great way to learn the framework.

./bin/spark-shell --master local[2]

The --master option specifies the master URL for a distributed cluster, 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. For a full list of options, run Spark shell with the --help option.

Spark also provides a Python API. To run Spark interactively in a Python interpreter, use bin/pyspark:

./bin/pyspark --master local[2]

Example applications are also provided in Python. For example,

./bin/spark-submit examples/src/main/python/ 10

Launching on a Cluster

The Spark cluster mode overview 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:

Where to Go from Here

Programming Guides:

API Docs:

Deployment Guides:

Other Documents:

External Resources:


To get help using Spark or keep up with Spark development, sign up for the user mailing list.

If you’re in the San Francisco Bay Area, there’s a regular Spark meetup every few weeks. Come by to meet the developers and other users.

Finally, if you’d like to contribute code to Spark, read how to contribute.