<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>
Research | Apache Spark
</title>
<!-- Bootstrap core CSS -->
<link href="/css/cerulean.min.css" rel="stylesheet">
<link href="/css/custom.css" rel="stylesheet">
<script type="text/javascript">
<!-- Google Analytics initialization -->
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-32518208-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
<!-- Adds slight delay to links to allow async reporting -->
function trackOutboundLink(link, category, action) {
try {
_gaq.push(['_trackEvent', category , action]);
} catch(err){}
setTimeout(function() {
document.location.href = link.href;
}, 100);
}
</script>
<!-- HTML5 shim and Respond.js IE8 support of HTML5 elements and media queries -->
<!--[if lt IE 9]>
<script src="https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js"></script>
<script src="https://oss.maxcdn.com/libs/respond.js/1.3.0/respond.min.js"></script>
<![endif]-->
</head>
<body>
<script src="https://code.jquery.com/jquery.js"></script>
<script src="//netdna.bootstrapcdn.com/bootstrap/3.0.3/js/bootstrap.min.js"></script>
<script src="/js/lang-tabs.js"></script>
<script src="/js/downloads.js"></script>
<div class="container" style="max-width: 1200px;">
<div class="masthead">
<p class="lead">
<a href="/">
<img src="/images/spark-logo.png"
style="height:100px; width:auto; vertical-align: bottom; margin-top: 20px;"></a><span class="tagline">
Lightning-fast cluster computing
</span>
</p>
</div>
<nav class="navbar navbar-default" role="navigation">
<!-- Brand and toggle get grouped for better mobile display -->
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse"
data-target="#navbar-collapse-1">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
</div>
<!-- Collect the nav links, forms, and other content for toggling -->
<div class="collapse navbar-collapse" id="navbar-collapse-1">
<ul class="nav navbar-nav">
<li><a href="/downloads.html">Download</a></li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">
Libraries <b class="caret"></b>
</a>
<ul class="dropdown-menu">
<li><a href="/sql/">SQL and DataFrames</a></li>
<li><a href="/streaming/">Spark Streaming</a></li>
<li><a href="/mllib/">MLlib (machine learning)</a></li>
<li><a href="/graphx/">GraphX (graph)</a></li>
<li class="divider"></li>
<li><a href="http://spark-packages.org">Third-Party Packages</a></li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">
Documentation <b class="caret"></b>
</a>
<ul class="dropdown-menu">
<li><a href="/docs/latest/">Latest Release (Spark 1.4.1)</a></li>
<li><a href="/documentation.html">Other Resources</a></li>
</ul>
</li>
<li><a href="/examples.html">Examples</a></li>
<li class="dropdown">
<a href="/community.html" class="dropdown-toggle" data-toggle="dropdown">
Community <b class="caret"></b>
</a>
<ul class="dropdown-menu">
<li><a href="/community.html">Mailing Lists</a></li>
<li><a href="/community.html#events">Events and Meetups</a></li>
<li><a href="/community.html#history">Project History</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Powered+By+Spark">Powered By</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Committers">Project Committers</a></li>
<li><a href="https://issues.apache.org/jira/browse/SPARK">Issue Tracker</a></li>
</ul>
</li>
<li><a href="/faq.html">FAQ</a></li>
</ul>
</div>
<!-- /.navbar-collapse -->
</nav>
<div class="row">
<div class="col-md-3 col-md-push-9">
<div class="news" style="margin-bottom: 20px;">
<h5>Latest News</h5>
<ul class="list-unstyled">
<li><a href="/news/spark-1-4-1-released.html">Spark 1.4.1 released</a>
<span class="small">(Jul 15, 2015)</span></li>
<li><a href="/news/spark-summit-2015-videos-posted.html">Spark Summit 2015 Videos Posted</a>
<span class="small">(Jun 29, 2015)</span></li>
<li><a href="/news/spark-1-4-0-released.html">Spark 1.4.0 released</a>
<span class="small">(Jun 11, 2015)</span></li>
<li><a href="/news/one-month-to-spark-summit-2015.html">One month to Spark Summit 2015 in San Francisco</a>
<span class="small">(May 16, 2015)</span></li>
</ul>
<p class="small" style="text-align: right;"><a href="/news/index.html">Archive</a></p>
</div>
<div class="hidden-xs hidden-sm">
<a href="/downloads.html" class="btn btn-success btn-lg btn-block" style="margin-bottom: 30px;">
Download Spark
</a>
<p style="font-size: 16px; font-weight: 500; color: #555;">
Built-in Libraries:
</p>
<ul class="list-none">
<li><a href="/sql/">SQL and DataFrames</a></li>
<li><a href="/streaming/">Spark Streaming</a></li>
<li><a href="/mllib/">MLlib (machine learning)</a></li>
<li><a href="/graphx/">GraphX (graph)</a></li>
</ul>
<a href="http://spark-packages.org">Third-Party Packages</a>
</div>
</div>
<div class="col-md-9 col-md-pull-3">
<h2>Spark Research</h2>
<p>
Apache Spark started as a research project at UC Berkeley in the <a href="https://amplab.cs.berkeley.edu">AMPLab</a>, which focuses on big data analytics.
</p>
<p class="noskip">
Our goal was to design a programming model that supports a much wider class of applications than MapReduce, while maintaining its automatic fault tolerance. In particular, MapReduce is inefficient for <em>multi-pass</em> applications that require low-latency data sharing across multiple parallel operations. These applications are quite common in analytics, and include:
</p>
<ul>
<li><em>Iterative algorithms</em>, including many machine learning algorithms and graph algorithms like PageRank.</li>
<li><em>Interactive data mining</em>, where a user would like to load data into RAM across a cluster and query it repeatedly.</li>
<li><em>Streaming applications</em> that maintain aggregate state over time.</li>
</ul>
<p>
Traditional MapReduce and DAG engines are suboptimal for these applications because they are based on acyclic data flow: an application has to run as a series of distinct jobs, each of which reads data from stable storage (e.g. a distributed file system) and writes it back to stable storage. They incur significant cost loading the data on each step and writing it back to replicated storage.
</p>
<p>
Spark offers an abstraction called <a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf"><em>resilient distributed datasets (RDDs)</em></a> to support these applications efficiently. RDDs can be stored in memory between queries <em>without</em> requiring replication. Instead, they rebuild lost data on failure using <em>lineage</em>: each RDD remembers how it was built from other datasets (by transformations like <code>map</code>, <code>join</code> or <code>groupBy</code>) to rebuild itself. RDDs allow Spark to outperform existing models by up to 100x in multi-pass analytics. We showed that RDDs can support a wide variety of iterative algorithms, as well as interactive data mining and a highly efficient SQL engine (<a href="http://shark.cs.berkeley.edu">Shark</a>).
</p>
<p class="noskip">You can find more about the research behind Spark in the following papers:</p>
<ul>
<li>
<a href="http://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf">Spark SQL: Relational Data Processing in Spark</a>. Michael Armbrust, Reynold S. Xin, Cheng Lian, Yin Huai, Davies Liu, Joseph K. Bradley, Xiangrui Meng, Tomer Kaftan, Michael J. Franklin, Ali Ghodsi, Matei Zaharia. <em>SIGMOD 2015</em>. June 2015.
</li>
<li>
<a href="https://amplab.cs.berkeley.edu/wp-content/uploads/2014/02/graphx.pdf">GraphX: Unifying Data-Parallel and Graph-Parallel Analytics</a>. Reynold S. Xin, Daniel Crankshaw, Ankur Dave, Joseph E. Gonzalez, Michael J. Franklin, Ion Stoica. <em>OSDI 2014</em>. October 2014.
<li>
<a href="http://people.csail.mit.edu/matei/papers/2013/sosp_spark_streaming.pdf">Discretized Streams: Fault-Tolerant Streaming Computation at Scale</a>. Matei Zaharia, Tathagata Das, Haoyuan Li, Timothy Hunter, Scott Shenker, Ion Stoica. <em>SOSP 2013</em>. November 2013.
</li>
<li>
<a href="http://people.csail.mit.edu/matei/papers/2013/sigmod_shark.pdf">Shark: SQL and Rich Analytics at Scale</a>. Reynold S. Xin, Joshua Rosen, Matei Zaharia, Michael J. Franklin, Scott Shenker, Ion Stoica. <em>SIGMOD 2013</em>. June 2013.
</li>
<li>
<a href="http://people.csail.mit.edu/matei/papers/2012/hotcloud_spark_streaming.pdf">Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters</a>. Matei Zaharia, Tathagata Das, Haoyuan Li, Scott Shenker, Ion Stoica. <em>HotCloud 2012</em>. June 2012.
</li>
<li>
<a href="http://people.csail.mit.edu/matei/papers/2012/sigmod_shark_demo.pdf">Shark: Fast Data Analysis Using Coarse-grained Distributed Memory</a> (demo). Cliff Engle, Antonio Lupher, Reynold S. Xin, Matei Zaharia, Haoyuan Li, Scott Shenker, Ion Stoica. <em>SIGMOD 2012</em>. May 2012. <b>Best Demo Award</b>.
</li>
<li>
<a href="http://people.csail.mit.edu/matei/papers/2012/nsdi_spark.pdf">Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing</a>. Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, Ion Stoica. <em>NSDI 2012</em>. April 2012. <b>Best Paper Award</b>.
</li>
<li>
<a href="http://people.csail.mit.edu/matei/papers/2010/hotcloud_spark.pdf">Spark: Cluster Computing with Working Sets</a>. Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, Ion Stoica. <em>HotCloud 2010</em>. June 2010.
</li>
</li></ul>
</div>
</div>
<footer class="small">
<hr>
Apache Spark, Spark, Apache, and the Spark logo are trademarks of
<a href="http://www.apache.org">The Apache Software Foundation</a>.
</footer>
</div>
</body>
</html>