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<h2 id="what-is-apache-spark">What is Apache Spark?</h2>
<p>Apache Spark is an open source cluster computing system that aims to make data analytics <em>fast</em> — both fast to run and fast to write.
To run programs faster, Spark provides primitives for in-memory cluster computing: your job can load data into memory and query it repeatedly much more quickly than with disk-based systems like Hadoop MapReduce.
To make programming faster, Spark provides clean, concise APIs in <a href="http://www.scala-lang.org" onclick="javascript:_gaq.push(['_trackEvent','outbound-article','http://www.scala-lang.org']);">Scala</a>, <a href="/docs/latest/quick-start.html#a-standalone-job-in-java">Java</a> and <a href="/docs/latest/quick-start.html#a-standalone-job-in-python">Python</a>. You can also use Spark interactively from the Scala and Python shells to rapidly query big datasets.</p>
<h2 id="what-can-it-do">What can it do?</h2>
<p>Spark was initially developed for two applications where keeping data in memory helps: <em>iterative</em> algorithms, which are common in machine learning, and <em>interactive</em> data mining. In both cases, Spark can run up to <b>100x</b> faster than Hadoop MapReduce. However, you can use Spark for general data processing too. Check out our <a href="/examples.html">example jobs</a>.
Spark is also the engine behind <a href="http://shark.cs.berkeley.edu" onclick="javascript:_gaq.push(['_trackEvent','outbound-article','http://shark.cs.berkeley.edu']);">Shark</a>, a fully <a href="http://hive.apache.org" onclick="javascript:_gaq.push(['_trackEvent','outbound-article','http://hive.apache.org']);">Apache Hive</a>-compatible data warehousing system that can run 100x faster than Hive.
While Spark is a new engine, it can access any data source supported by Hadoop, making it easy to run over existing data.</p>
<h2 id="who-uses-it">Who uses it?</h2>
<p>Spark was developed in the <a href="https://amplab.cs.berkeley.edu" onclick="javascript:_gaq.push(['_trackEvent','outbound-article','http://amplab.cs.berkeley.edu']);">UC Berkeley AMPLab</a>. It’s used by several groups of researchers at Berkeley to run large-scale applications such as spam filtering and traffic prediction. It’s also used to accelerate data analytics at <a href="http://www.yahoo.com" onclick="javascript:_gaq.push(['_trackEvent','outbound-article','http://www.yahoo.com']);">Yahoo!</a>, <a href="http://www.conviva.com" onclick="javascript:_gaq.push(['_trackEvent','outbound-article','http://www.conviva.com']);">Conviva</a>, <a href="http://www.quantifind.com" onclick="javascript:_gaq.push(['_trackEvent','outbound-article','http://www.quantifind.com']);">Quantifind</a>, and other companies — in total, 17 companies have contributed to Spark! Spark is <a href="https://github.com/mesos/spark" onclick="javascript:_gaq.push(['_trackEvent','outbound-article','http://github.com']);">open source</a> under a BSD license, so <a href="/downloads.html">download</a> it to check it out.</p>
<h2 id="apache-incubator-notice">Apache Incubator notice</h2>
<p>Apache Spark is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.</p>
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<div><a href="/news/amp-camp-2013-registration-ope.html">Registration open for AMP Camp training camp in Berkeley</a> <span class="post-info">(July 23, 2013)</span></div>
<div><a href="/news/spark-mailing-lists-moving-to-apache.html">Spark mailing lists moving to Apache</a> <span class="post-info">(July 22, 2013)</span></div>
<div><a href="/news/spark-0-7-3-released.html">Spark 0.7.3 released</a> <span class="post-info">(July 16, 2013)</span></div>
<div><a href="/news/spark-featured-in-wired.html">Spark featured in Wired</a> <span class="post-info">(June 21, 2013)</span></div>
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<p><!-- Not porting the following to Pygments since it becomes a lot less colorful --></p>
<div class="code" style="margin-top: 20px;">
file = spark.textFile(<span class="string">"hdfs://..."</span>)<br />
<br />
file.<span class="sparkop">flatMap</span>(<span class="closure">line => line.split(" ")</span>)<br />
.<span class="sparkop">map</span>(<span class="closure">word => (word, 1)</span>)<br />
.<span class="sparkop">reduceByKey</span>(<span class="closure">_ + _</span>)
</div>
<div class="caption">Word Count implemented in Spark</div>
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<img src="/images/spark-lr.png" alt="Logistic regression performance in Spark vs Hadoop" />
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<div class="caption">Logistic regression in Spark vs Hadoop</div>
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