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, Java, and Python, with a rich array of parallel operators. Spark can run on 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 0.7.0.
Spark requires Scala 2.9.2 (Scala 2.10 is not yet supported). You will need to have Scala’s
bin directory in your
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
Testing the Build
Spark comes with a number of sample programs in the
To run one of the samples, use
./run <class> <params> in the top-level Spark directory
run script sets up the appropriate paths and launches that program).
./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,
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
project/SparkBuild.scala, then rebuilding Spark (
sbt/sbt clean compile).
Where to Go from Here
- Quick Start: a quick introduction to the Spark API; start here!
- Spark Programming Guide: an overview of Spark concepts, and details on the Scala API
- Java Programming Guide: using Spark from Java
- Python Programming Guide: using Spark from Python
- Spark Streaming Guide: using the alpha release of Spark Streaming
- Running Spark on Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
- Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager
- Running Spark on Mesos: deploy a private cluster using Apache Mesos
- Running Spark on YARN: deploy Spark on top of Hadoop NextGen (YARN)
- Configuration: customize Spark via its configuration system
- Tuning Guide: best practices to optimize performance and memory use
- Bagel: an implementation of Google’s Pregel on Spark
- Contributing to Spark
- Spark Homepage
- Mailing List: ask questions about Spark here
- AMP Camp: a two-day training camp at UC Berkeley that featured talks and exercises about Spark, Shark, Mesos, and more. Videos, slides and exercises are available online for free.
- Code Examples: more are also available in the examples subfolder of Spark
- Paper Describing Spark
- Paper Describing Spark Streaming
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.