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| <b>Spark SQL</b> is Apache Spark's module for working with structured data. |
| </div> |
| |
| <div class="row row-padded"> |
| <div class="col-md-7 col-sm-7"> |
| <h2>Integrated</h2> |
| <p class="lead"> |
| Seamlessly mix SQL queries with Spark programs. |
| </p> |
| <p> |
| Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar <a href="/docs/latest/sql-programming-guide.html">DataFrame API</a>. Usable in Java, Scala, Python and R. |
| </p> |
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| <div style="margin-top: 15px; text-align: left; display: inline-block;"> |
| <div class="code"> |
| results = spark.<span class="sparkop">sql</span>(<br /> <span class="closure">"SELECT * FROM people"</span>)<br /> |
| names = results.<span class="sparkop">map</span>(<span class="closure">lambda p: p.name</span>)<br /> |
| </div> |
| <div class="caption">Apply functions to results of SQL queries.</div> |
| </div> |
| </div> |
| </div> |
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| <div class="row row-padded"> |
| <div class="col-md-7 col-sm-7"> |
| <h2>Uniform data access</h2> |
| <p class="lead"> |
| Connect to any data source the same way. |
| </p> |
| <p> |
| DataFrames and SQL provide a common way to access a variety of data sources, including Hive, Avro, Parquet, ORC, JSON, and JDBC. You can even join data across these sources. |
| </p> |
| </div> |
| <div class="col-md-5 col-sm-5 col-padded-top col-center"> |
| <div style="margin-top: 15px; text-align: left; display: inline-block;"> |
| <div class="code"> |
| spark.<span class="sparkop">read.json</span>(<span class="closure">"s3n://..."</span>)<br /> .registerTempTable("json")<br /> |
| results = spark.<span class="sparkop">sql</span>(<span class="closure"><br /> |
| """SELECT * <br /> |
| FROM people<br /> |
| JOIN json ..."""</span>)<br /> |
| </div> |
| <div class="caption">Query and join different data sources.</div> |
| </div> |
| </div> |
| </div> |
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| <div class="row row-padded"> |
| <div class="col-md-7 col-sm-7"> |
| <h2>Hive integration</h2> |
| <p class="lead"> |
| Run SQL or HiveQL queries on existing warehouses. |
| </p> |
| <p> |
| Spark SQL supports the HiveQL syntax as well as Hive SerDes and UDFs, allowing |
| you to access existing Hive warehouses. |
| </p> |
| </div> |
| <div class="col-md-5 col-sm-5 col-padded-top col-center"> |
| <div style="width: 100%; max-width: 323px; display: inline-block"> |
| <img src="/images/sql-hive-arch.png" style="width: 100%; max-width: 323px;" /> |
| <div class="caption">Spark SQL can use existing Hive metastores, SerDes, and UDFs.</div> |
| </div> |
| </div> |
| </div> |
| |
| <div class="row row-padded"> |
| <div class="col-md-7 col-sm-7"> |
| <h2>Standard connectivity</h2> |
| <p class="lead"> |
| Connect through JDBC or ODBC. |
| </p> |
| <p> |
| A server mode provides industry standard JDBC and ODBC connectivity for business intelligence tools. |
| </p> |
| </div> |
| <div class="col-md-5 col-sm-5 col-padded-top col-center"> |
| <div style="width: 100%; max-width: 323px; display: inline-block"> |
| <img src="/images/jdbc.png" style="width: 75%; max-width: 323px;" /> |
| <div class="caption">Use your existing BI tools to query big data.</div> |
| </div> |
| </div> |
| </div> |
| |
| <!-- |
| <div class="row row-padded"> |
| <div class="col-md-7 col-sm-7"> |
| <h2>Speed</h2> |
| <p class="lead"> |
| Optimized to execute on Spark. |
| </p> |
| <p> |
| Spark SQL was built using the Catalyst optimizer, which automatically rewrites your queries to execute more efficiently. |
| By leveraging advanced techniques like runtime code generation, Spark SQL makes it easier to write lightning-fast analytic applications. |
| </p> |
| </div> |
| <div class="col-md-5 col-sm-5 col-padded-top col-center"> |
| <div style="width: 100%; max-width: 272px; display: inline-block; text-align: center;"> |
| <img src="/images/sqlperf.png" style="width: 100%; max-width: 250px;"> |
| <div class="caption" style="min-width: 272px;">Performance comparison between Shark and Spark SQL</div> |
| </div> |
| </div> |
| </div> |
| --> |
| |
| <div class="row"> |
| <div class="col-md-4 col-padded"> |
| <h3>Performance & scalability</h3> |
| <p> |
| Spark SQL includes a cost-based optimizer, columnar storage and code generation to make queries fast. |
| At the same time, it scales to thousands of nodes and multi hour queries using the Spark engine, which provides full mid-query fault tolerance. |
| Don't worry about using a different engine for historical data. |
| </p> |
| </div> |
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| <div class="col-md-4 col-padded"> |
| <h3>Community</h3> |
| <p> |
| Spark SQL is developed as part of Apache Spark. It thus gets |
| tested and updated with each Spark release. |
| </p> |
| <p> |
| If you have questions about the system, ask on the |
| <a href="/community.html#mailing-lists">Spark mailing lists</a>. |
| </p> |
| <p> |
| The Spark SQL developers welcome contributions. If you'd like to help out, |
| read <a href="/contributing.html">how to |
| contribute to Spark</a>, and send us a patch! |
| </p> |
| </div> |
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| <h3>Getting started</h3> |
| <p> |
| To get started with Spark SQL: |
| </p> |
| <ul class="list-narrow"> |
| <li><a href="/downloads.html">Download Spark</a>. It includes Spark SQL as a module.</li> |
| <li>Read the <a href="/docs/latest/sql-programming-guide.html">Spark SQL and DataFrame guide</a> to learn the API.</li> |
| </ul> |
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| |
| <li><a href="/news/spark-3-5-1-released.html">Spark 3.5.1 released</a> |
| <span class="small">(Feb 23, 2024)</span></li> |
| |
| <li><a href="/news/spark-3-3-4-released.html">Spark 3.3.4 released</a> |
| <span class="small">(Dec 16, 2023)</span></li> |
| |
| <li><a href="/news/spark-3-4-2-released.html">Spark 3.4.2 released</a> |
| <span class="small">(Nov 30, 2023)</span></li> |
| |
| <li><a href="/news/spark-3-5-0-released.html">Spark 3.5.0 released</a> |
| <span class="small">(Sep 13, 2023)</span></li> |
| |
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