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| <section id="configuration"> |
| <h1>Configuration<a class="headerlink" href="#configuration" title="Link to this heading">¶</a></h1> |
| <p>Let’s look at how we can configure DataFusion. When creating a <a class="reference internal" href="../autoapi/datafusion/context/index.html#datafusion.context.SessionContext" title="datafusion.context.SessionContext"><code class="xref py py-class docutils literal notranslate"><span class="pre">SessionContext</span></code></a>, you can pass in |
| a <a class="reference internal" href="../autoapi/datafusion/context/index.html#datafusion.context.SessionConfig" title="datafusion.context.SessionConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">SessionConfig</span></code></a> and <a class="reference internal" href="../autoapi/datafusion/context/index.html#datafusion.context.RuntimeEnvBuilder" title="datafusion.context.RuntimeEnvBuilder"><code class="xref py py-class docutils literal notranslate"><span class="pre">RuntimeEnvBuilder</span></code></a> object. These two cover a wide range of options.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">datafusion</span><span class="w"> </span><span class="kn">import</span> <span class="n">RuntimeEnvBuilder</span><span class="p">,</span> <span class="n">SessionConfig</span><span class="p">,</span> <span class="n">SessionContext</span> |
| |
| <span class="c1"># create a session context with default settings</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">SessionContext</span><span class="p">()</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="c1"># create a session context with explicit runtime and config settings</span> |
| <span class="n">runtime</span> <span class="o">=</span> <span class="n">RuntimeEnvBuilder</span><span class="p">()</span><span class="o">.</span><span class="n">with_disk_manager_os</span><span class="p">()</span><span class="o">.</span><span class="n">with_fair_spill_pool</span><span class="p">(</span><span class="mi">10000000</span><span class="p">)</span> |
| <span class="n">config</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="n">SessionConfig</span><span class="p">()</span> |
| <span class="o">.</span><span class="n">with_create_default_catalog_and_schema</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_default_catalog_and_schema</span><span class="p">(</span><span class="s2">"foo"</span><span class="p">,</span> <span class="s2">"bar"</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_target_partitions</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_information_schema</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_repartition_joins</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_repartition_aggregations</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_repartition_windows</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_parquet_pruning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"datafusion.execution.parquet.pushdown_filters"</span><span class="p">,</span> <span class="s2">"true"</span><span class="p">)</span> |
| <span class="p">)</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">SessionContext</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">runtime</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <section id="maximizing-cpu-usage"> |
| <h2>Maximizing CPU Usage<a class="headerlink" href="#maximizing-cpu-usage" title="Link to this heading">¶</a></h2> |
| <p>DataFusion uses partitions to parallelize work. For small queries the |
| default configuration (number of CPU cores) is often sufficient, but to |
| fully utilize available hardware you can tune how many partitions are |
| created and when DataFusion will repartition data automatically.</p> |
| <p>Configure a <code class="docutils literal notranslate"><span class="pre">SessionContext</span></code> with a higher partition count:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">datafusion</span><span class="w"> </span><span class="kn">import</span> <span class="n">SessionConfig</span><span class="p">,</span> <span class="n">SessionContext</span> |
| |
| <span class="c1"># allow up to 16 concurrent partitions</span> |
| <span class="n">config</span> <span class="o">=</span> <span class="n">SessionConfig</span><span class="p">()</span><span class="o">.</span><span class="n">with_target_partitions</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">SessionContext</span><span class="p">(</span><span class="n">config</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Automatic repartitioning for joins, aggregations, window functions and |
| other operations can be enabled to increase parallelism:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="n">SessionConfig</span><span class="p">()</span> |
| <span class="o">.</span><span class="n">with_target_partitions</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_repartition_joins</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_repartition_aggregations</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">with_repartition_windows</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Manual repartitioning is available on DataFrames when you need precise |
| control:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">datafusion</span><span class="w"> </span><span class="kn">import</span> <span class="n">col</span> |
| |
| <span class="n">df</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">"data.parquet"</span><span class="p">)</span> |
| |
| <span class="c1"># Evenly divide into 16 partitions</span> |
| <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">repartition</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span> |
| |
| <span class="c1"># Or partition by the hash of a column</span> |
| <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">repartition_by_hash</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"a"</span><span class="p">),</span> <span class="n">num</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span> |
| |
| <span class="n">result</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <section id="benchmark-example"> |
| <h3>Benchmark Example<a class="headerlink" href="#benchmark-example" title="Link to this heading">¶</a></h3> |
| <p>The repository includes a benchmark script that demonstrates how to maximize CPU usage |
| with DataFusion. The <code class="code docutils literal notranslate"><span class="pre">benchmarks/max_cpu_usage.py</span></code> script shows a practical example |
| of configuring DataFusion for optimal parallelism.</p> |
| <p>You can run the benchmark script to see the impact of different configuration settings:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Run with default settings (uses all CPU cores)</span> |
| python<span class="w"> </span>benchmarks/max_cpu_usage.py |
| |
| <span class="c1"># Run with specific number of rows and partitions</span> |
| python<span class="w"> </span>benchmarks/max_cpu_usage.py<span class="w"> </span>--rows<span class="w"> </span><span class="m">5000000</span><span class="w"> </span>--partitions<span class="w"> </span><span class="m">16</span> |
| |
| <span class="c1"># See all available options</span> |
| python<span class="w"> </span>benchmarks/max_cpu_usage.py<span class="w"> </span>--help |
| </pre></div> |
| </div> |
| <p>Here’s an example showing the performance difference between single and multiple partitions:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Single partition - slower processing</span> |
| $<span class="w"> </span>python<span class="w"> </span>benchmarks/max_cpu_usage.py<span class="w"> </span>--rows<span class="o">=</span><span class="m">10000000</span><span class="w"> </span>--partitions<span class="w"> </span><span class="m">1</span> |
| Processed<span class="w"> </span><span class="m">10000000</span><span class="w"> </span>rows<span class="w"> </span>using<span class="w"> </span><span class="m">1</span><span class="w"> </span>partitions<span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">0</span>.107s |
| |
| <span class="c1"># Multiple partitions - faster processing</span> |
| $<span class="w"> </span>python<span class="w"> </span>benchmarks/max_cpu_usage.py<span class="w"> </span>--rows<span class="o">=</span><span class="m">10000000</span><span class="w"> </span>--partitions<span class="w"> </span><span class="m">10</span> |
| Processed<span class="w"> </span><span class="m">10000000</span><span class="w"> </span>rows<span class="w"> </span>using<span class="w"> </span><span class="m">10</span><span class="w"> </span>partitions<span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">0</span>.038s |
| </pre></div> |
| </div> |
| <p>This example demonstrates nearly 3x performance improvement (0.107s vs 0.038s) when using |
| 10 partitions instead of 1, showcasing how proper partitioning can significantly improve |
| CPU utilization and query performance.</p> |
| <p>The script demonstrates several key optimization techniques:</p> |
| <ol class="arabic simple"> |
| <li><p><strong>Higher target partition count</strong>: Uses <code class="code docutils literal notranslate"><span class="pre">with_target_partitions()</span></code> to set the number of concurrent partitions</p></li> |
| <li><p><strong>Automatic repartitioning</strong>: Enables repartitioning for joins, aggregations, and window functions</p></li> |
| <li><p><strong>Manual repartitioning</strong>: Uses <code class="code docutils literal notranslate"><span class="pre">repartition()</span></code> to ensure all partitions are utilized</p></li> |
| <li><p><strong>CPU-intensive operations</strong>: Performs aggregations that can benefit from parallelization</p></li> |
| </ol> |
| <p>The benchmark creates synthetic data and measures the time taken to perform a sum aggregation |
| across the specified number of partitions. This helps you understand how partition configuration |
| affects performance on your specific hardware.</p> |
| <section id="important-considerations"> |
| <h4>Important Considerations<a class="headerlink" href="#important-considerations" title="Link to this heading">¶</a></h4> |
| <p>The provided benchmark script demonstrates partitioning concepts using synthetic in-memory data |
| and simple aggregation operations. While useful for understanding basic configuration principles, |
| actual performance in production environments may vary significantly based on numerous factors:</p> |
| <p><strong>Data Sources and I/O Characteristics:</strong></p> |
| <ul class="simple"> |
| <li><p><strong>Table providers</strong>: Performance differs greatly between Parquet files, CSV files, databases, and cloud storage</p></li> |
| <li><p><strong>Storage type</strong>: Local SSD, network-attached storage, and cloud storage have vastly different characteristics</p></li> |
| <li><p><strong>Network latency</strong>: Remote data sources introduce additional latency considerations</p></li> |
| <li><p><strong>File sizes and distribution</strong>: Large files may benefit differently from partitioning than many small files</p></li> |
| </ul> |
| <p><strong>Query and Workload Characteristics:</strong></p> |
| <ul class="simple"> |
| <li><p><strong>Operation complexity</strong>: Simple aggregations versus complex joins, window functions, or nested queries</p></li> |
| <li><p><strong>Data distribution</strong>: Skewed data may not partition evenly, affecting parallel efficiency</p></li> |
| <li><p><strong>Memory usage</strong>: Large datasets may require different memory management strategies</p></li> |
| <li><p><strong>Concurrent workloads</strong>: Multiple queries running simultaneously affect resource allocation</p></li> |
| </ul> |
| <p><strong>Hardware and Environment Factors:</strong></p> |
| <ul class="simple"> |
| <li><p><strong>CPU architecture</strong>: Different processors have varying parallel processing capabilities</p></li> |
| <li><p><strong>Available memory</strong>: Limited RAM may require different optimization strategies</p></li> |
| <li><p><strong>System load</strong>: Other applications competing for resources affect DataFusion performance</p></li> |
| </ul> |
| <p><strong>Recommendations for Production Use:</strong></p> |
| <p>To optimize DataFusion for your specific use case, it is strongly recommended to:</p> |
| <ol class="arabic simple"> |
| <li><p><strong>Create custom benchmarks</strong> using your actual data sources, formats, and query patterns</p></li> |
| <li><p><strong>Test with representative data volumes</strong> that match your production workloads</p></li> |
| <li><p><strong>Measure end-to-end performance</strong> including data loading, processing, and result handling</p></li> |
| <li><p><strong>Evaluate different configuration combinations</strong> for your specific hardware and workload</p></li> |
| <li><p><strong>Monitor resource utilization</strong> (CPU, memory, I/O) to identify bottlenecks in your environment</p></li> |
| </ol> |
| <p>This approach will provide more accurate insights into how DataFusion configuration options |
| will impact your particular applications and infrastructure.</p> |
| <p>For more information about available <a class="reference internal" href="../autoapi/datafusion/context/index.html#datafusion.context.SessionConfig" title="datafusion.context.SessionConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">SessionConfig</span></code></a> options, see the <a class="reference external" href="https://arrow.apache.org/datafusion/user-guide/configs.html">rust DataFusion Configuration guide</a>, |
| and about <code class="code docutils literal notranslate"><span class="pre">RuntimeEnvBuilder</span></code> options in the rust <a class="reference external" href="https://docs.rs/datafusion/latest/datafusion/execution/runtime_env/struct.RuntimeEnvBuilder.html">online API documentation</a>.</p> |
| </section> |
| </section> |
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