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| <title>Apache Spark Shared Memory Layer - Apache Ignite</title> |
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| <section id="shared-memory-layer" class="page-section"> |
| <h1 class="first">Shared Memory Layer for Apache Spark</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 is a distributed memory-centric database and caching platform that is |
| used by Apache Spark users to: |
| <ul class="page-list" style="margin-bottom: 20px;"> |
| <li> |
| Achieve true in-memory performance at scale and avoid data movement from a data source |
| to Spark workers and applications. |
| </li> |
| <li> |
| Boost DataFrame and SQL performance. |
| </li> |
| <li> |
| More easily share state and data among Spark jobs. |
| </li> |
| </ul> |
| </p> |
| </div> |
| |
| <div class="col-sm-6 col-md-6 col-xs-12" style="padding-right:0"> |
| <img class="img-responsive" src="/images/spark_integration.png" width="440px" style="float:right;"/> |
| </div> |
| </div> |
| |
| <div class="page-heading">Ignite Shared RDDs</div> |
| <p> |
| Apache Ignite provides an implementation of the Spark RDD which allows any data and state to be shared |
| in memory as RDDs across Spark jobs. The Ignite RDD provides a shared, mutable view of the same data |
| in-memory in Ignite across different Spark jobs, workers, or applications. Native Spark RDDs cannot be |
| shared across Spark jobs or applications. |
| </p> |
| |
| <p> |
| The way an IgniteRDD is implemented is as a view over a distributed Ignite table (aka. cache). |
| It can be deployed with an Ignite node either within the Spark job executing process, on a Spark worker, |
| or in a separate Ignite cluster. It means that depending on the chosen deployment mode the shared |
| state may either exist only during the lifespan of a Spark application (embedded mode), or it may |
| out-survive the Spark application (standalone mode). |
| </p> |
| <p> |
| While Apache SparkSQL supports a fairly rich SQL syntax, it doesn't implement any indexing. As a result, |
| Spark queries may take minutes even on moderately small data sets because they have to do full data |
| scans. With Ignite, Spark users can configure primary and secondary indexes that can bring up to 1000x |
| performance gains. |
| </p> |
| |
| <p> |
| <a href="https://apacheignite-fs.readme.io/docs/ignitecontext-igniterdd" target="docs"> |
| <b>Ignite RDDs in Details <i class="fa fa-angle-double-right"></i></b> |
| </a> |
| </p> |
| |
| <div class="page-heading">Ignite DataFrames</div> |
| <p> |
| The Apache Spark DataFrame API introduced the concept of a schema to describe the data, |
| allowing Spark to manage the schema and organize the data into a tabular format. To put it simply, |
| a DataFrame is a distributed collection of data organized into named columns. It is conceptually |
| equivalent to a table in a relational database and allows Spark to leverage the Catalyst query |
| optimizer to produce much more efficient query execution plans in comparison to RDDs, which are |
| just collections of elements partitioned across the nodes of the cluster. |
| </p> |
| <p> |
| Ignite expands DataFrame, simplifying development and improving data access times whenever |
| Ignite is used as memory-centric storage for Spark. Benefits include: |
| <ul class="page-list" style="margin-bottom: 20px;"> |
| <li> |
| Ability to share data and state across Spark jobs by writing and reading DataFrames to/from Ignite. |
| </li> |
| <li> |
| Faster SparkSQL queries by optimizing Spark query execution plans with Ignite SQL engine which |
| include advanced indexing and avoid data movement across the network from Ignite to Spark. |
| </li> |
| </ul> |
| </p> |
| <p> |
| <a href="https://apacheignite-fs.readme.io/docs/ignite-data-frame" target="docs"> |
| <b>Ignite DataFrames in Details <i class="fa fa-angle-double-right"></i></b> |
| </a> |
| </p> |
| </section> |
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