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GraphX | Apache Spark
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GraphX
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<b>GraphX</b> is Apache Spark's API for graphs and graph-parallel computation.
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<h2>Flexibility</h2>
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Seamlessly work with both graphs and collections.
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GraphX unifies ETL, exploratory analysis, and iterative graph computation within a single system. You can <a href="/docs/latest/graphx-programming-guide.html#the-property-graph">view</a> the same data as both graphs and collections, <a href="/docs/latest/graphx-programming-guide.html#property-operators">transform</a> and <a href="/docs/latest/graphx-programming-guide.html#join-operators">join</a> graphs with RDDs efficiently, and write custom iterative graph algorithms using the <a href="/docs/latest/graphx-programming-guide.html#pregel-api">Pregel API</a>.
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graph = <span class="sparkop">Graph</span>(vertices, edges)<br />
messages = spark.textFile(<span class="string">"hdfs://..."</span>)<br />
graph2 = graph.<span class="sparkop">joinVertices</span>(messages) {<br />
<span class="closure">(id, vertex, msg) => ...</span><br />
}
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<div class="caption">Using GraphX in Scala</div>
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<h2>Speed</h2>
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Comparable performance to the fastest specialized graph processing systems.
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GraphX competes on performance with the fastest graph systems while retaining Spark's flexibility, fault tolerance, and ease of use.
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<img src="/images/graphx-perf-comparison.png" style="width: 60%; max-width: 250px;" />
<div class="caption" style="min-width: 272px;">End-to-end PageRank performance (20 iterations, 3.7B edges)</div>
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<h2>Algorithms</h2>
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Choose from a growing library of graph algorithms.
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<p>In addition to a <a href="/docs/latest/graphx-programming-guide.html#graph-operators">highly flexible API</a>, GraphX comes with a variety of graph algorithms, many of which were contributed by our users.</p>
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<li>PageRank</li>
<li>Connected components</li>
<li>Label propagation</li>
<li>SVD++</li>
<li>Strongly connected components</li>
<li>Triangle count</li>
</ul>
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<h3>Community</h3>
<p>
GraphX is developed as part of the Apache Spark project. It thus gets
tested and updated with each Spark release.
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<p>
If you have questions about the library, ask on the
<a href="/community.html#mailing-lists">Spark mailing lists</a>.
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GraphX is in the alpha stage and welcomes contributions. If you'd like to submit a change to GraphX,
read <a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">how to
contribute to Spark</a> and send us a patch!
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<h3>Getting Started</h3>
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To get started with GraphX:
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<li><a href="/downloads.html">Download Spark</a>. GraphX is included as a module.</li>
<li>Read the <a href="/docs/latest/graphx-programming-guide.html">GraphX guide</a>, which includes
usage examples.</li>
<li>Learn how to <a href="/docs/latest/#launching-on-a-cluster">deploy</a> Spark on a cluster
if you'd like to run in distributed mode. You can also run locally on a multicore machine
without any setup.
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