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| <li> |
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| <a href="/docs/latest/distributed-computing/executor-service" |
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| class='' |
| >Executor Service</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/map-reduce" |
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| >MapReduce API</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/load-balancing" |
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| class='' |
| >Load Balancing</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/fault-tolerance" |
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| class='' |
| >Fault Tolerance</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/job-scheduling" |
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| >Job Scheduling</a> |
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| <a href="/docs/latest/collocated-computations" class='' >Colocating Computations with Data</a> |
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| <li><a href="/docs/latest//machine-learning/clustering/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/clustering/gaussian-mixture" class=''>Gaussian mixture (GMM)</a></li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/split-the-dataset-on-test-and-train-datasets" class=''>Split the dataset on test and train datasets</a></li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/hyper-parameter-tuning" class=''>Hyper-parameter tuning</a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/introduction" class=''></a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/baggin" class=''>Bagging</a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/gradient-boosting" class=''>Gradient Boosting</a></li> |
| |
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| <li> |
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| <a href="/docs/latest/machine-learning/recommendation-systems" |
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| >Recommendation Systems</a> |
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| <li><a href="/docs/latest//machine-learning/importing-model/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/importing-model/model-import-from-gxboost" class=''>Import Model from XGBoost</a></li> |
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| <li><a href="/docs/latest//machine-learning/importing-model/model-import-from-apache-spark" class=''>Import Model from Apache Spark</a></li> |
| |
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| </nav> |
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| <a href="/docs/latest/monitoring-metrics/intro" |
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| <li> |
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| <a href="/docs/latest/monitoring-metrics/cluster-id" |
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| <li><a href="/docs/latest//monitoring-metrics/configuring-metrics" class=''>Configuring Metrics</a></li> |
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| <li><a href="/docs/latest//monitoring-metrics/metrics" class=''>JMX Metrics</a></li> |
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| >Python Thin Client</a> |
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| >ODBC Driver</a> |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/connection-string-dsn" |
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| >Connection String and DSN</a> |
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| <a href="/docs/latest/SQL/ODBC/querying-modifying-data" |
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| <a href="/docs/latest/SQL/ODBC/specification" |
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| >Specification</a> |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/data-types" |
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| class='' |
| >Data Types</a> |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/error-codes" |
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| class='' |
| >Error Codes</a> |
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| |
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| <a href="/docs/latest/restapi" class='' >REST API</a> |
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| <a href="/docs/latest/control-script" class='' >Control Script</a> |
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| <a href="/docs/latest/plugins" class='' >Plugins</a> |
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| <a href="/docs/latest/sqlline" class='' >SQLLine</a> |
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| <button type='button' data-guide-url="" class='group-toggle collapsed '>Ignite for Spark<img class="state-indicator" src="/assets/images/left-nav-arrow.svg"></button> |
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| <article data-swiftype-index='true'> |
| <a class='edit-link' href="https://github.com/apache/ignite/tree/IGNITE-7595/docs/_docs/ignite-for-spark/overview.adoc" target="_blank">Edit</a> |
| |
| <h1>Ignite for Spark</h1> |
| |
| <div id="preamble"> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Apache Ignite is a distributed memory-centric database and caching platform that is used by Apache Spark users to:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p>Achieve true in-memory performance at scale and avoid data movement from a data source to Spark workers and applications.</p> |
| </li> |
| <li> |
| <p>Boost DataFrame and SQL performance.</p> |
| </li> |
| <li> |
| <p>More easily share state and data among Spark jobs.</p> |
| </li> |
| </ul> |
| </div> |
| <div class="imageblock"> |
| <div class="content"> |
| <img src="/docs/2.9.0/images/spark_integration.png" alt="Spark Integration"> |
| </div> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="ignite-rdds">Ignite RDDs</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <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> |
| </div> |
| <div class="paragraph"> |
| <p>The way an <a href="/docs/latest/ignite-for-spark/ignitecontext-and-rdd">IgniteRDD</a> 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> |
| </div> |
| <div class="paragraph"> |
| <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> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="ignite-dataframes">Ignite DataFrames</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <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> |
| </div> |
| <div class="paragraph"> |
| <p>Ignite expands <a href="/docs/latest/ignite-for-spark/ignite-dataframe">DataFrame</a>, simplifying development and improving data access times whenever Ignite is used as memory-centric storage for Spark. Benefits include:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p>Ability to share data and state across Spark jobs by writing and reading DataFrames to/from Ignite.</p> |
| </li> |
| <li> |
| <p>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.</p> |
| </li> |
| </ul> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="supported-spark-version">Supported Spark Version</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Apache Ignite comes with two modules that support different versions of Apache Spark:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p>ignite-spark — integration with Spark 2.3</p> |
| </li> |
| <li> |
| <p>ignite-spark-2.4 — integration with Spark 2.4</p> |
| </li> |
| </ul> |
| </div> |
| </div> |
| </div> |
| <div class="copyright"> |
| © 2020 The Apache Software Foundation.<br/> |
| Apache, Apache Ignite, the Apache feather and the Apache Ignite logo are either registered trademarks or trademarks of The Apache Software Foundation. |
| |
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| <li><a href="#supported-spark-version">Supported Spark Version</a></li> |
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