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| <h1 class="title">Performance Tuning</h1> |
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| <ul id="markdown-toc"> |
| <li><a href="#caching-data-in-memory" id="markdown-toc-caching-data-in-memory">Caching Data In Memory</a></li> |
| <li><a href="#other-configuration-options" id="markdown-toc-other-configuration-options">Other Configuration Options</a></li> |
| <li><a href="#broadcast-hint-for-sql-queries" id="markdown-toc-broadcast-hint-for-sql-queries">Broadcast Hint for SQL Queries</a></li> |
| </ul> |
| |
| <p>For some workloads, it is possible to improve performance by either caching data in memory, or by |
| turning on some experimental options.</p> |
| |
| <h2 id="caching-data-in-memory">Caching Data In Memory</h2> |
| |
| <p>Spark SQL can cache tables using an in-memory columnar format by calling <code>spark.catalog.cacheTable("tableName")</code> or <code>dataFrame.cache()</code>. |
| Then Spark SQL will scan only required columns and will automatically tune compression to minimize |
| memory usage and GC pressure. You can call <code>spark.catalog.uncacheTable("tableName")</code> to remove the table from memory.</p> |
| |
| <p>Configuration of in-memory caching can be done using the <code>setConf</code> method on <code>SparkSession</code> or by running |
| <code>SET key=value</code> commands using SQL.</p> |
| |
| <table class="table"> |
| <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr> |
| <tr> |
| <td><code>spark.sql.inMemoryColumnarStorage.compressed</code></td> |
| <td>true</td> |
| <td> |
| When set to true Spark SQL will automatically select a compression codec for each column based |
| on statistics of the data. |
| </td> |
| </tr> |
| <tr> |
| <td><code>spark.sql.inMemoryColumnarStorage.batchSize</code></td> |
| <td>10000</td> |
| <td> |
| Controls the size of batches for columnar caching. Larger batch sizes can improve memory utilization |
| and compression, but risk OOMs when caching data. |
| </td> |
| </tr> |
| |
| </table> |
| |
| <h2 id="other-configuration-options">Other Configuration Options</h2> |
| |
| <p>The following options can also be used to tune the performance of query execution. It is possible |
| that these options will be deprecated in future release as more optimizations are performed automatically.</p> |
| |
| <table class="table"> |
| <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr> |
| <tr> |
| <td><code>spark.sql.files.maxPartitionBytes</code></td> |
| <td>134217728 (128 MB)</td> |
| <td> |
| The maximum number of bytes to pack into a single partition when reading files. |
| </td> |
| </tr> |
| <tr> |
| <td><code>spark.sql.files.openCostInBytes</code></td> |
| <td>4194304 (4 MB)</td> |
| <td> |
| The estimated cost to open a file, measured by the number of bytes could be scanned in the same |
| time. This is used when putting multiple files into a partition. It is better to over-estimated, |
| then the partitions with small files will be faster than partitions with bigger files (which is |
| scheduled first). |
| </td> |
| </tr> |
| <tr> |
| <td><code>spark.sql.broadcastTimeout</code></td> |
| <td>300</td> |
| <td> |
| <p> |
| Timeout in seconds for the broadcast wait time in broadcast joins |
| </p> |
| </td> |
| </tr> |
| <tr> |
| <td><code>spark.sql.autoBroadcastJoinThreshold</code></td> |
| <td>10485760 (10 MB)</td> |
| <td> |
| Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when |
| performing a join. By setting this value to -1 broadcasting can be disabled. Note that currently |
| statistics are only supported for Hive Metastore tables where the command |
| <code>ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan</code> has been run. |
| </td> |
| </tr> |
| <tr> |
| <td><code>spark.sql.shuffle.partitions</code></td> |
| <td>200</td> |
| <td> |
| Configures the number of partitions to use when shuffling data for joins or aggregations. |
| </td> |
| </tr> |
| </table> |
| |
| <h2 id="broadcast-hint-for-sql-queries">Broadcast Hint for SQL Queries</h2> |
| |
| <p>The <code>BROADCAST</code> hint guides Spark to broadcast each specified table when joining them with another table or view. |
| When Spark deciding the join methods, the broadcast hash join (i.e., BHJ) is preferred, |
| even if the statistics is above the configuration <code>spark.sql.autoBroadcastJoinThreshold</code>. |
| When both sides of a join are specified, Spark broadcasts the one having the lower statistics. |
| Note Spark does not guarantee BHJ is always chosen, since not all cases (e.g. full outer join) |
| support BHJ. When the broadcast nested loop join is selected, we still respect the hint.</p> |
| |
| <div class="codetabs"> |
| |
| <div data-lang="scala"> |
| |
| <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.sql.functions.broadcast</span> |
| <span class="n">broadcast</span><span class="o">(</span><span class="n">spark</span><span class="o">.</span><span class="n">table</span><span class="o">(</span><span class="s">"src"</span><span class="o">)).</span><span class="n">join</span><span class="o">(</span><span class="n">spark</span><span class="o">.</span><span class="n">table</span><span class="o">(</span><span class="s">"records"</span><span class="o">),</span> <span class="s">"key"</span><span class="o">).</span><span class="n">show</span><span class="o">()</span></code></pre></figure> |
| |
| </div> |
| |
| <div data-lang="java"> |
| |
| <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import static</span> <span class="nn">org.apache.spark.sql.functions.broadcast</span><span class="o">;</span> |
| <span class="n">broadcast</span><span class="o">(</span><span class="n">spark</span><span class="o">.</span><span class="na">table</span><span class="o">(</span><span class="s">"src"</span><span class="o">)).</span><span class="na">join</span><span class="o">(</span><span class="n">spark</span><span class="o">.</span><span class="na">table</span><span class="o">(</span><span class="s">"records"</span><span class="o">),</span> <span class="s">"key"</span><span class="o">).</span><span class="na">show</span><span class="o">();</span></code></pre></figure> |
| |
| </div> |
| |
| <div data-lang="python"> |
| |
| <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">broadcast</span> |
| <span class="n">broadcast</span><span class="p">(</span><span class="n">spark</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="s2">"src"</span><span class="p">))</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">spark</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="s2">"records"</span><span class="p">),</span> <span class="s2">"key"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></code></pre></figure> |
| |
| </div> |
| |
| <div data-lang="r"> |
| |
| <figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>src <span class="o"><-</span> sql<span class="p">(</span><span class="s">"SELECT * FROM src"</span><span class="p">)</span> |
| records <span class="o"><-</span> sql<span class="p">(</span><span class="s">"SELECT * FROM records"</span><span class="p">)</span> |
| <span class="kp">head</span><span class="p">(</span>join<span class="p">(</span>broadcast<span class="p">(</span>src<span class="p">),</span> records<span class="p">,</span> src<span class="o">$</span>key <span class="o">==</span> records<span class="o">$</span>key<span class="p">))</span></code></pre></figure> |
| |
| </div> |
| |
| <div data-lang="sql"> |
| |
| <figure class="highlight"><pre><code class="language-sql" data-lang="sql"><span></span><span class="c1">-- We accept BROADCAST, BROADCASTJOIN and MAPJOIN for broadcast hint</span> |
| <span class="k">SELECT</span> <span class="cm">/*+ BROADCAST(r) */</span> <span class="o">*</span> <span class="k">FROM</span> <span class="n">records</span> <span class="n">r</span> <span class="k">JOIN</span> <span class="n">src</span> <span class="n">s</span> <span class="k">ON</span> <span class="n">r</span><span class="p">.</span><span class="k">key</span> <span class="o">=</span> <span class="n">s</span><span class="p">.</span><span class="k">key</span></code></pre></figure> |
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