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| <title>Fast Apache Spark SQL Queries - Apache Ignite</title> |
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| <section id="spark-sqlqueries" class="page-section"> |
| <h1 class="first">Accelerate Apache Spark SQL Queries</h1> |
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| <p> |
| Running SQL queries using Ignite shared RDDs or DataFrames is much faster than running Spark SQL |
| via native RDDs or Data Frame implementations. |
| </p> |
| <div class="page-heading">In-Memory Indexes</div> |
| <p> |
| Spark does not support SQL indexes, resulting in slow SQL queries due to full scans across |
| the whole data set. Such full-scan queries in spark can take minutes and introduce significant |
| wait times, especially when running many queries within the same Spark application. |
| <p> |
| Apache Ignite, on the other hand, supports SQL with <span style="white-space: nowrap;"><code>in-memory indexing</code>.</span> |
| Because of advanced in-memory indexing capabilities, IgniteRDD executes SQL |
| queries 100s of times faster than Spark native RDDs or Data Frames. |
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| <div class="page-heading">Off-Heap Memory</div> |
| <p> |
| Ignite stores data and indexes in an off-heap memory that allows holding petabytes of data in Ignite |
| and process them from Spark without worrying about JVM garbage collection overhead. |
| </p> |
| <p> |
| <a href="https://apacheignite-fs.readme.io/docs/ignitecontext-igniterdd#section-running-sql-queries-against-ignite-cache" target="docs"> |
| <b>Run SQL Queries against Ignite cluster <i class="fa fa-angle-double-right"></i></b> |
| </a> |
| </p> |
| <p> |
| <a href="https://apacheignite-fs.readme.io/docs/ignite-data-frame" target="docs"> |
| <b>Speeding up DataFrames access with Ignite <i class="fa fa-angle-double-right"></i></b> |
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