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| <b>GraphX</b> is Apache Spark's API for graphs and graph-parallel computation. |
| </div> |
| |
| <div class="row row-padded"> |
| <div class="col-md-7 col-sm-7"> |
| <h2>Flexibility</h2> |
| <p class="lead"> |
| Seamlessly work with both graphs and collections. |
| </p> |
| <p> |
| 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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| <div class="code"> |
| 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 /> |
| } |
| </div> |
| <div class="caption">Using GraphX in Scala</div> |
| </div> |
| </div> |
| </div> |
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| <div class="col-md-7 col-sm-7"> |
| <h2>Speed</h2> |
| <p class="lead"> |
| Comparable performance to the fastest specialized graph processing systems. |
| </p> |
| <p> |
| GraphX competes on performance with the fastest graph systems while retaining Spark's flexibility, fault tolerance, and ease of use. |
| </p> |
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| <div style="width: 100%; max-width: 272px; display: inline-block; text-align: center; padding:0;"> |
| <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> |
| </div> |
| </div> |
| </div> |
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| <div class="col-md-7 col-sm-7"> |
| <h2>Algorithms</h2> |
| <p class="lead"> |
| Choose from a growing library of graph algorithms. |
| </p> |
| <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> |
| </div> |
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| <ul class="list-narrow"> |
| <li>PageRank</li> |
| <li>Connected components</li> |
| <li>Label propagation</li> |
| <li>SVD++</li> |
| <li>Strongly connected components</li> |
| <li>Triangle count</li> |
| </ul> |
| </div> |
| </div> |
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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. |
| </p> |
| <p> |
| If you have questions about the library, ask on the |
| <a href="/community.html#mailing-lists">Spark mailing lists</a>. |
| </p> |
| <p> |
| GraphX is in the alpha stage and welcomes contributions. If you'd like to submit a change to GraphX, |
| read <a href="/contributing.html">how to |
| contribute to Spark</a> and send us a patch! |
| </p> |
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| <h3>Getting started</h3> |
| <p> |
| To get started with GraphX: |
| </p> |
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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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| <span class="small">(Apr 18, 2024)</span></li> |
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