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| <title>Apache Spark Shared RDDs - Apache Ignite</title> |
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| <section id="igniterdd" class="page-section"> |
| <h1 class="first">Shared Apache Spark RDDs</h1> |
| <div class="col-sm-12 col-md-12 col-xs-12" style="padding:0 0 10px 0;"> |
| <div class="col-sm-6 col-md-6 col-xs-12" style="padding-left:0; padding-right:0"> |
| |
| <p> |
| Apache Ignite provides an implementation of Spark RDD abstraction which allows to easily |
| share state in memory across multiple Spark jobs, either within the same application |
| or between different Spark applications. |
| </p> |
| <p> |
| <code>IgniteRDD</code> is implemented is as a view over a distributed Ignite cache, |
| which may be deployed either within the Spark job executing process, or on a Spark worker, |
| or in its own cluster. |
| </p> |
| <p> |
| Depending on the pre-configured deployment mode, the shared state may either exist only |
| during the lifespan of a Spark application (<code>embedded mode</code>), or it may out-survive |
| the Spark application (<code>standalone mode</code>), in which case the state can be shared across |
| multiple Spark applications. |
| </p> |
| </div> |
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| <img class="img-responsive" src="/images/spark_integration.png" width="440px" style="float:right;"/> |
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| </div> |
| <div class="code-examples"><br/> |
| <div class="page-heading">Code Examples:</div> |
| <!-- Nav tabs --> |
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| <pre class="brush:java"> |
| val sharedRdd = igniteContext.fromCache("partitioned") |
| |
| // Store pairs of integers from 1 to 10000 into in-memory cache |
| // named "partitioned" using 10 parallel store operations. |
| sharedRdd.savePairs(sparkContext.parallelize(1 to 10000, 10).map(i => (i, i))) |
| </pre> |
| </div> |
| <div class="tab-pane" id="rdd-sql"> |
| <pre class="brush:java"> |
| val sharedRdd = igniteContext.fromCache("partitioned") |
| |
| val result = sharedRdd.sql( |
| "select _val from Integer where val > ? and val < ?", 10, 100) |
| </pre> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="key-features" class="page-section"> |
| <h2>IgniteRDD Features</h2> |
| <table class="formatted" name="IgniteRDD Features"> |
| <thead> |
| <tr> |
| <th width="35%" class="left">Feature</th> |
| <th>Description</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td class="left">Shared Spark RDDs</td> |
| <td> |
| <p> |
| <code>IgniteRDD</code> is an implementation of native Spark RDD |
| and DataFrame APIs which, in addition to all the standard RDD |
| functionality, also shares the state of the RDD across other |
| Spark jobs, applications and workers. |
| </p> |
| <div class="page-links"> |
| <a href="http://apacheignite-fs.readme.io/docs/ignite-for-spark" target="docs">Docs for this Feature <i class="fa fa-angle-double-right"></i></a> |
| </div> |
| </td> |
| </tr> |
| <tr> |
| <td class="left">Faster SQL</td> |
| <td> |
| <p> |
| Spark does not support SQL indexes, while Ignite does. Because of advanced in-memory |
| indexing capabilities, IgniteRDD allows to execute SQL queries 100s of times faster |
| than Spark native RDDs or Data Frames. |
| </p> |
| <div class="page-links"> |
| <a href="http://apacheignite-fs.readme.io/docs/ignitecontext-igniterdd#section-running-sql-queries-against-ignite-cache" target="docs">Docs for this Feature <i class="fa fa-angle-double-right"></i></a> |
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