Spark Overview

Spark is a MapReduce-like cluster computing framework designed for low-latency iterative jobs and interactive use from an interpreter. It provides clean, language-integrated APIs in Scala and Java, with a rich array of parallel operators. Spark can run on top of the Apache Mesos cluster manager, Hadoop YARN, Amazon EC2, or without an independent resource manager (“standalone mode”).


Get Spark by visiting the downloads page of the Spark website. This documentation is for Spark version 0.6.0.


Spark requires Scala 2.9.2. You will need to have Scala’s bin directory in your PATH, or you will need to set the SCALA_HOME environment variable to point to where you’ve installed Scala. Scala must also be accessible through one of these methods on slave nodes on your cluster.

Spark uses Simple Build Tool, which is bundled with it. To compile the code, go into the top-level Spark directory and run

sbt/sbt package

Testing the Build

Spark comes with a number of sample programs in the examples directory. To run one of the samples, use ./run <class> <params> in the top-level Spark directory (the run script sets up the appropriate paths and launches that program). For example, ./run spark.examples.SparkPi will run a sample program that estimates Pi. Each of the examples prints usage help if no params are given.

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, 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, Spark can be used interactively from a modified version of the Scala interpreter that you can start through ./spark-shell. This is a great way to learn Spark.

A Note About Hadoop Versions

Spark uses the Hadoop core 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 runs. You can change the version by setting the HADOOP_VERSION variable at the top of project/SparkBuild.scala, then rebuilding Spark (sbt/sbt clean compile).

Where to Go from Here

Programming guides:

Deployment guides:

Other documents:

External resources:


To get help using Spark or keep up with Spark development, sign up for the spark-users 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.