ad7f0452ab
Adds links to new instructions in: * The main Spark project README.md * The docs nav menu called "More" * The docs Overview page under the "Building" and "Where to Go from Here" sections
102 lines
5.6 KiB
Markdown
102 lines
5.6 KiB
Markdown
---
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layout: global
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title: Spark Overview
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---
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Spark is a MapReduce-like cluster computing framework designed for low-latency iterative jobs and interactive use from an interpreter.
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It provides clean, language-integrated APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html), with a rich array of parallel operators.
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Spark can run on the Apache Mesos cluster manager, Hadoop YARN, Amazon EC2, or without an independent resource manager ("standalone mode").
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# Downloading
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Get Spark by visiting the [downloads page](http://spark-project.org/downloads.html) of the Spark website. This documentation is for Spark version {{site.SPARK_VERSION}}.
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# Building
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Spark requires [Scala {{site.SCALA_VERSION}}](http://www.scala-lang.org/). You will need to have Scala's `bin` directory in your `PATH`,
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or you will need to set the `SCALA_HOME` environment variable to point
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to where you've installed Scala. Scala must also be accessible through one
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of these methods on slave nodes on your cluster.
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Spark uses [Simple Build Tool](https://github.com/harrah/xsbt/wiki), which is bundled with it. To compile the code, go into the top-level Spark directory and run
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sbt/sbt package
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Spark also supports building using Maven. If you would like to build using Maven, see the [instructions for building Spark with Maven](building-with-maven.html).
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# Testing the Build
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Spark comes with a number of sample programs in the `examples` directory.
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To run one of the samples, use `./run <class> <params>` in the top-level Spark directory
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(the `run` script sets up the appropriate paths and launches that program).
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For example, `./run spark.examples.SparkPi` will run a sample program that estimates Pi. Each of the
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examples prints usage help if no params are given.
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Note that all of the sample programs take a `<master>` parameter specifying the cluster URL
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to connect to. This can be a [URL for a distributed cluster](scala-programming-guide.html#master-urls),
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or `local` to run locally with one thread, or `local[N]` to run locally with N threads. You should start by using
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`local` for testing.
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Finally, Spark can be used interactively from a modified version of the Scala interpreter that you can start through
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`./spark-shell`. This is a great way to learn Spark.
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# A Note About Hadoop Versions
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Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
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storage systems. Because the HDFS protocol has changed in different versions of
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Hadoop, you must build Spark against the same version that your cluster runs.
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You can change the version by setting the `HADOOP_VERSION` variable at the top
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of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
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# Where to Go from Here
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**Programming guides:**
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* [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
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* [Spark Programming Guide](scala-programming-guide.html): an overview of Spark concepts, and details on the Scala API
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* [Java Programming Guide](java-programming-guide.html): using Spark from Java
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* [Python Programming Guide](python-programming-guide.html): using Spark from Python
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* [Spark Streaming Guide](streaming-programming-guide.html): using the alpha release of Spark Streaming
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**API Docs:**
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* [Spark Java/Scala (Scaladoc)](api/core/index.html)
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* [Spark Python (Epydoc)](api/pyspark/index.html)
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* [Spark Streaming Java/Scala (Scaladoc)](api/streaming/index.html)
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**Deployment guides:**
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* [Running Spark on Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
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* [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
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* [Running Spark on Mesos](running-on-mesos.html): deploy a private cluster using
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[Apache Mesos](http://incubator.apache.org/mesos)
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* [Running Spark on YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
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**Other documents:**
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* [Building Spark With Maven](building-with-maven.html): Build Spark using the Maven build tool
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* [Configuration](configuration.html): customize Spark via its configuration system
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* [Tuning Guide](tuning.html): best practices to optimize performance and memory use
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* [Bagel](bagel-programming-guide.html): an implementation of Google's Pregel on Spark
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* [Contributing to Spark](contributing-to-spark.html)
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**External resources:**
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* [Spark Homepage](http://www.spark-project.org)
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* [Mailing List](http://groups.google.com/group/spark-users): ask questions about Spark here
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* [AMP Camp](http://ampcamp.berkeley.edu/): a two-day training camp at UC Berkeley that featured talks and exercises
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about Spark, Shark, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/agenda-2012),
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[slides](http://ampcamp.berkeley.edu/agenda-2012) and [exercises](http://ampcamp.berkeley.edu/exercises-2012) are
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available online for free.
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* [Code Examples](http://spark-project.org/examples.html): more are also available in the [examples subfolder](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/examples) of Spark
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* [Paper Describing Spark](http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf)
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* [Paper Describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)
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# Community
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To get help using Spark or keep up with Spark development, sign up for the [spark-users mailing list](http://groups.google.com/group/spark-users).
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If you're in the San Francisco Bay Area, there's a regular [Spark meetup](http://www.meetup.com/spark-users/) every few weeks. Come by to meet the developers and other users.
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Finally, if you'd like to contribute code to Spark, read [how to contribute](contributing-to-spark.html).
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