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<h1 class="title">Spark Overview</h1>
<p>Apache Spark is a unified analytics engine for large-scale data processing.
It provides high-level APIs in Java, Scala, Python and R,
and an optimized engine that supports general execution graphs.
It also supports a rich set of higher-level tools including <a href="sql-programming-guide.html">Spark SQL</a> for SQL and structured data processing, <a href="ml-guide.html">MLlib</a> for machine learning, <a href="graphx-programming-guide.html">GraphX</a> for graph processing, and <a href="structured-streaming-programming-guide.html">Structured Streaming</a> for incremental computation and stream processing.</p>
<h1 id="security">Security</h1>
<p>Security in Spark is OFF by default. This could mean you are vulnerable to attack by default.
Please see <a href="security.html">Spark Security</a> before downloading and running Spark.</p>
<h1 id="downloading">Downloading</h1>
<p>Get Spark from the <a href="https://spark.apache.org/downloads.html">downloads page</a> of the project website. This documentation is for Spark version 3.1.3. Spark uses Hadoop&#8217;s client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions.
Users can also download a &#8220;Hadoop free&#8221; binary and run Spark with any Hadoop version
<a href="hadoop-provided.html">by augmenting Spark&#8217;s classpath</a>.
Scala and Java users can include Spark in their projects using its Maven coordinates and Python users can install Spark from PyPI.</p>
<p>If you&#8217;d like to build Spark from
source, visit <a href="building-spark.html">Building Spark</a>.</p>
<p>Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS), and it should run on any platform that runs a supported version of Java. This should include JVMs on x86_64 and ARM64. It&#8217;s easy to run locally on one machine &#8212; all you need is to have <code class="language-plaintext highlighter-rouge">java</code> installed on your system <code class="language-plaintext highlighter-rouge">PATH</code>, or the <code class="language-plaintext highlighter-rouge">JAVA_HOME</code> environment variable pointing to a Java installation.</p>
<p>Spark runs on Java 8/11, Scala 2.12, Python 3.6+ and R 3.5+.
Java 8 prior to version 8u92 support is deprecated as of Spark 3.0.0.
For the Scala API, Spark 3.1.3
uses Scala 2.12. You will need to use a compatible Scala version
(2.12.x).</p>
<p>For Python 3.9, Arrow optimization and pandas UDFs might not work due to the supported Python versions in Apache Arrow. Please refer to the latest <a href="https://arrow.apache.org/docs/python/install.html#python-compatibility">Python Compatibility</a> page.
For Java 11, <code class="language-plaintext highlighter-rouge">-Dio.netty.tryReflectionSetAccessible=true</code> is required additionally for Apache Arrow library. This prevents <code class="language-plaintext highlighter-rouge">java.lang.UnsupportedOperationException: sun.misc.Unsafe or java.nio.DirectByteBuffer.(long, int) not available</code> when Apache Arrow uses Netty internally.</p>
<h1 id="running-the-examples-and-shell">Running the Examples and Shell</h1>
<p>Spark comes with several sample programs. Scala, Java, Python and R examples are in the
<code class="language-plaintext highlighter-rouge">examples/src/main</code> directory. To run one of the Java or Scala sample programs, use
<code class="language-plaintext highlighter-rouge">bin/run-example &lt;class&gt; [params]</code> in the top-level Spark directory. (Behind the scenes, this
invokes the more general
<a href="submitting-applications.html"><code class="language-plaintext highlighter-rouge">spark-submit</code> script</a> for
launching applications). For example,</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./bin/run-example SparkPi 10
</code></pre></div></div>
<p>You can also run Spark interactively through a modified version of the Scala shell. This is a
great way to learn the framework.</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./bin/spark-shell --master local[2]
</code></pre></div></div>
<p>The <code class="language-plaintext highlighter-rouge">--master</code> option specifies the
<a href="submitting-applications.html#master-urls">master URL for a distributed cluster</a>, or <code class="language-plaintext highlighter-rouge">local</code> to run
locally with one thread, or <code class="language-plaintext highlighter-rouge">local[N]</code> to run locally with N threads. You should start by using
<code class="language-plaintext highlighter-rouge">local</code> for testing. For a full list of options, run Spark shell with the <code class="language-plaintext highlighter-rouge">--help</code> option.</p>
<p>Spark also provides a Python API. To run Spark interactively in a Python interpreter, use
<code class="language-plaintext highlighter-rouge">bin/pyspark</code>:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./bin/pyspark --master local[2]
</code></pre></div></div>
<p>Example applications are also provided in Python. For example,</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./bin/spark-submit examples/src/main/python/pi.py 10
</code></pre></div></div>
<p>Spark also provides an <a href="sparkr.html">R API</a> since 1.4 (only DataFrames APIs included).
To run Spark interactively in an R interpreter, use <code class="language-plaintext highlighter-rouge">bin/sparkR</code>:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./bin/sparkR --master local[2]
</code></pre></div></div>
<p>Example applications are also provided in R. For example,</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./bin/spark-submit examples/src/main/r/dataframe.R
</code></pre></div></div>
<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="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>
<li><a href="running-on-kubernetes.html">Kubernetes</a></li>
</ul>
<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="rdd-programming-guide.html">RDD Programming Guide</a>: overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables</li>
<li><a href="sql-programming-guide.html">Spark SQL, Datasets, and DataFrames</a>: processing structured data with relational queries (newer API than RDDs)</li>
<li><a href="structured-streaming-programming-guide.html">Structured Streaming</a>: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)</li>
<li><a href="streaming-programming-guide.html">Spark Streaming</a>: processing data streams using DStreams (old API)</li>
<li><a href="ml-guide.html">MLlib</a>: applying machine learning algorithms</li>
<li><a href="graphx-programming-guide.html">GraphX</a>: processing graphs</li>
<li><a href="sparkr.html">SparkR</a>: processing data with Spark in R</li>
<li><a href="api/python/getting_started/index.html">PySpark</a>: processing data with Spark in Python</li>
</ul>
<p><strong>API Docs:</strong></p>
<ul>
<li><a href="api/scala/org/apache/spark/index.html">Spark Scala API (Scaladoc)</a></li>
<li><a href="api/java/index.html">Spark Java API (Javadoc)</a></li>
<li><a href="api/python/index.html">Spark Python API (Sphinx)</a></li>
<li><a href="api/R/index.html">Spark R API (Roxygen2)</a></li>
<li><a href="api/sql/index.html">Spark SQL, Built-in Functions (MkDocs)</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="submitting-applications.html">Submitting Applications</a>: packaging and deploying applications</li>
<li>Deployment modes:
<ul>
<li><a href="https://github.com/amplab/spark-ec2">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="https://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>
<li><a href="running-on-kubernetes.html">Kubernetes</a>: deploy Spark on top of Kubernetes</li>
</ul>
</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="monitoring.html">Monitoring</a>: track the behavior of your applications</li>
<li><a href="tuning.html">Tuning Guide</a>: best practices to optimize performance and memory use</li>
<li><a href="job-scheduling.html">Job Scheduling</a>: scheduling resources across and within Spark applications</li>
<li><a href="security.html">Security</a>: Spark security support</li>
<li><a href="hardware-provisioning.html">Hardware Provisioning</a>: recommendations for cluster hardware</li>
<li>Integration with other storage systems:
<ul>
<li><a href="cloud-integration.html">Cloud Infrastructures</a></li>
<li><a href="storage-openstack-swift.html">OpenStack Swift</a></li>
</ul>
</li>
<li><a href="migration-guide.html">Migration Guide</a>: Migration guides for Spark components</li>
<li><a href="building-spark.html">Building Spark</a>: build Spark using the Maven system</li>
<li><a href="https://spark.apache.org/contributing.html">Contributing to Spark</a></li>
<li><a href="https://spark.apache.org/third-party-projects.html">Third Party Projects</a>: related third party Spark projects</li>
</ul>
<p><strong>External Resources:</strong></p>
<ul>
<li><a href="https://spark.apache.org">Spark Homepage</a></li>
<li><a href="https://spark.apache.org/community.html">Spark Community</a> resources, including local meetups</li>
<li><a href="http://stackoverflow.com/questions/tagged/apache-spark">StackOverflow tag <code class="language-plaintext highlighter-rouge">apache-spark</code></a></li>
<li><a href="https://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, Spark Streaming, Mesos, and more. <a href="http://ampcamp.berkeley.edu/6/">Videos</a>,
<a href="http://ampcamp.berkeley.edu/6/">slides</a> and <a href="http://ampcamp.berkeley.edu/6/exercises/">exercises</a> are
available online for free.</li>
<li><a href="https://spark.apache.org/examples.html">Code Examples</a>: more are also available in the <code class="language-plaintext highlighter-rouge">examples</code> subfolder of Spark (<a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples">Scala</a>,
<a href="https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples">Java</a>,
<a href="https://github.com/apache/spark/tree/master/examples/src/main/python">Python</a>,
<a href="https://github.com/apache/spark/tree/master/examples/src/main/r">R</a>)</li>
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