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| <h1 class="title">Spark Overview</h1> |
| |
| <p>Apache Spark is a fast and general-purpose cluster computing system. |
| It provides high-level APIs in <a href="scala-programming-guide.html">Scala</a>, <a href="java-programming-guide.html">Java</a>, and <a href="python-programming-guide.html">Python</a> 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 <a href="http://shark.cs.berkeley.edu">Shark</a> (Hive on Spark), <a href="mllib-guide.html">MLlib</a> for machine learning, <a href="graphx-programming-guide.html">GraphX</a> for graph processing, and <a href="streaming-programming-guide.html">Spark Streaming</a>.</p> |
| |
| <h1 id="downloading">Downloading</h1> |
| |
| <p>Get Spark by visiting the <a href="http://spark.apache.org/downloads.html">downloads page</a> of the Apache Spark site. This documentation is for Spark version 0.9.0-incubating.</p> |
| |
| <p>Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is to have <code>java</code> to installed on your system <code>PATH</code>, or the <code>JAVA_HOME</code> environment variable pointing to a Java installation.</p> |
| |
| <h1 id="building">Building</h1> |
| |
| <p>Spark uses <a href="http://www.scala-sbt.org">Simple Build Tool</a>, which is bundled with it. To compile the code, go into the top-level Spark directory and run</p> |
| |
| <pre><code>sbt/sbt assembly |
| </code></pre> |
| |
| <p>For its Scala API, Spark 0.9.0-incubating depends on Scala 2.10. If you write applications in Scala, you will need to use a compatible Scala version (e.g. 2.10.X) – newer major versions may not work. You can get the right version of Scala from <a href="http://www.scala-lang.org/download/">scala-lang.org</a>.</p> |
| |
| <h1 id="running-the-examples-and-shell">Running the Examples and Shell</h1> |
| |
| <p>Spark comes with several sample programs in the <code>examples</code> directory. |
| To run one of the samples, use <code>./bin/run-example <class> <params></code> in the top-level Spark directory |
| (the <code>bin/run-example</code> script sets up the appropriate paths and launches that program). |
| For example, try <code>./bin/run-example org.apache.spark.examples.SparkPi local</code>. |
| Each example prints usage help when run with no parameters.</p> |
| |
| <p>Note that all of the sample programs take a <code><master></code> parameter specifying the cluster URL |
| to connect to. This can be a <a href="scala-programming-guide.html#master-urls">URL for a distributed cluster</a>, |
| or <code>local</code> to run locally with one thread, or <code>local[N]</code> to run locally with N threads. You should start by using |
| <code>local</code> for testing.</p> |
| |
| <p>Finally, you can run Spark interactively through modified versions of the Scala shell (<code>./bin/spark-shell</code>) or |
| Python interpreter (<code>./bin/pyspark</code>). These are a great way to learn the framework.</p> |
| |
| <h1 id="launching-on-a-cluster">Launching on a Cluster</h1> |
| |
| <p>The Spark <a href="cluster-overview.html">cluster mode overview</a> 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:</p> |
| |
| <ul> |
| <li><a href="ec2-scripts.html">Amazon EC2</a>: our EC2 scripts let you launch a cluster in about 5 minutes</li> |
| <li><a href="spark-standalone.html">Standalone Deploy Mode</a>: simplest way to deploy Spark on a private cluster</li> |
| <li><a href="running-on-mesos.html">Apache Mesos</a></li> |
| <li><a href="running-on-yarn.html">Hadoop YARN</a></li> |
| </ul> |
| |
| <h1 id="a-note-about-hadoop-versions">A Note About Hadoop Versions</h1> |
| |
| <p>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 |
| <code>SPARK_HADOOP_VERSION</code> variable when compiling:</p> |
| |
| <pre><code>SPARK_HADOOP_VERSION=2.2.0 sbt/sbt assembly |
| </code></pre> |
| |
| <p>In addition, if you wish to run Spark on <a href="running-on-yarn.html">YARN</a>, set |
| <code>SPARK_YARN</code> to <code>true</code>:</p> |
| |
| <pre><code>SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly |
| </code></pre> |
| |
| <p>Note that on Windows, you need to set the environment variables on separate lines, e.g., <code>set SPARK_HADOOP_VERSION=1.2.1</code>.</p> |
| |
| <p>For this version of Spark (0.8.1) Hadoop 2.2.x (or newer) users will have to build Spark and publish it locally. See <a href="running-on-yarn.html">Launching Spark on YARN</a>. This is needed because Hadoop 2.2 has non backwards compatible API changes.</p> |
| |
| <h1 id="where-to-go-from-here">Where to Go from Here</h1> |
| |
| <p><strong>Programming guides:</strong></p> |
| |
| <ul> |
| <li><a href="quick-start.html">Quick Start</a>: a quick introduction to the Spark API; start here!</li> |
| <li><a href="scala-programming-guide.html">Spark Programming Guide</a>: an overview of Spark concepts, and details on the Scala API |
| <ul> |
| <li><a href="java-programming-guide.html">Java Programming Guide</a>: using Spark from Java</li> |
| <li><a href="python-programming-guide.html">Python Programming Guide</a>: using Spark from Python</li> |
| </ul> |
| </li> |
| <li><a href="streaming-programming-guide.html">Spark Streaming</a>: Spark’s API for processing data streams</li> |
| <li><a href="mllib-guide.html">MLlib (Machine Learning)</a>: Spark’s built-in machine learning library</li> |
| <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a>: simple graph processing model</li> |
| <li><a href="graphx-programming-guide.html">GraphX (Graphs on Spark)</a>: Spark’s new API for graphs</li> |
| </ul> |
| |
| <p><strong>API Docs:</strong></p> |
| |
| <ul> |
| <li><a href="api/core/index.html">Spark for Java/Scala (Scaladoc)</a></li> |
| <li><a href="api/pyspark/index.html">Spark for Python (Epydoc)</a></li> |
| <li><a href="api/streaming/index.html">Spark Streaming for Java/Scala (Scaladoc)</a></li> |
| <li><a href="api/mllib/index.html">MLlib (Machine Learning) for Java/Scala (Scaladoc)</a></li> |
| <li><a href="api/bagel/index.html">Bagel (Pregel on Spark) for Scala (Scaladoc)</a></li> |
| <li><a href="api/graphx/index.html">GraphX (Graphs on Spark) for Scala (Scaladoc)</a></li> |
| </ul> |
| |
| <p><strong>Deployment guides:</strong></p> |
| |
| <ul> |
| <li><a href="cluster-overview.html">Cluster Overview</a>: overview of concepts and components when running on a cluster</li> |
| <li><a href="ec2-scripts.html">Amazon EC2</a>: scripts that let you launch a cluster on EC2 in about 5 minutes</li> |
| <li><a href="spark-standalone.html">Standalone Deploy Mode</a>: launch a standalone cluster quickly without a third-party cluster manager</li> |
| <li><a href="running-on-mesos.html">Mesos</a>: deploy a private cluster using |
| <a href="http://mesos.apache.org">Apache Mesos</a></li> |
| <li><a href="running-on-yarn.html">YARN</a>: deploy Spark on top of Hadoop NextGen (YARN)</li> |
| </ul> |
| |
| <p><strong>Other documents:</strong></p> |
| |
| <ul> |
| <li><a href="configuration.html">Configuration</a>: customize Spark via its configuration system</li> |
| <li><a href="tuning.html">Tuning Guide</a>: best practices to optimize performance and memory use</li> |
| <li><a href="hardware-provisioning.html">Hardware Provisioning</a>: recommendations for cluster hardware</li> |
| <li><a href="job-scheduling.html">Job Scheduling</a>: scheduling resources across and within Spark applications</li> |
| <li><a href="building-with-maven.html">Building Spark with Maven</a>: build Spark using the Maven system</li> |
| <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li> |
| </ul> |
| |
| <p><strong>External resources:</strong></p> |
| |
| <ul> |
| <li><a href="http://spark.apache.org">Spark Homepage</a></li> |
| <li><a href="http://shark.cs.berkeley.edu">Shark</a>: Apache Hive over Spark</li> |
| <li><a href="http://spark.apache.org/mailing-lists.html">Mailing Lists</a>: ask questions about Spark here</li> |
| <li><a href="http://ampcamp.berkeley.edu/">AMP Camps</a>: a series of training camps at UC Berkeley that featured talks and |
| exercises about Spark, Shark, Mesos, and more. <a href="http://ampcamp.berkeley.edu/agenda-2012">Videos</a>, |
| <a href="http://ampcamp.berkeley.edu/agenda-2012">slides</a> and <a href="http://ampcamp.berkeley.edu/exercises-2012">exercises</a> are |
| available online for free.</li> |
| <li><a href="http://spark.apache.org/examples.html">Code Examples</a>: more are also available in the <a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/">examples subfolder</a> of Spark</li> |
| <li><a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf">Paper Describing Spark</a></li> |
| <li><a href="http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf">Paper Describing Spark Streaming</a></li> |
| </ul> |
| |
| <h1 id="community">Community</h1> |
| |
| <p>To get help using Spark or keep up with Spark development, sign up for the <a href="http://spark.apache.org/mailing-lists.html">user mailing list</a>.</p> |
| |
| <p>If you’re in the San Francisco Bay Area, there’s a regular <a href="http://www.meetup.com/spark-users/">Spark meetup</a> every few weeks. Come by to meet the developers and other users.</p> |
| |
| <p>Finally, if you’d like to contribute code to Spark, read <a href="contributing-to-spark.html">how to contribute</a>.</p> |
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