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| <section id="distributed-query-engines"> | |
| <span id="id1"></span><h1>Distributed query engines<a class="headerlink" href="#distributed-query-engines" title="Link to this heading">#</a></h1> | |
| <p>A distributed query engine takes the query you already wrote and runs | |
| it across several machines for you. You do not partition anything by | |
| hand and you do not change your queries — the engine installs itself | |
| on your session, rewrites the plan into stages, and executes those | |
| stages on its own workers.</p> | |
| <p>This is the counterpart to <a class="reference internal" href="expressions.html#distributed-expressions"><span class="std std-ref">Shipping expressions to workers</span></a>, where | |
| <em>you</em> decide the partitioning and ship one expression per slice of | |
| data. Choose an engine when you want a single query spread across a | |
| cluster; choose expressions when you already know how to split the | |
| work and only need parallelism.</p> | |
| <section id="how-an-engine-attaches-to-your-session"> | |
| <h2>How an engine attaches to your session<a class="headerlink" href="#how-an-engine-attaches-to-your-session" title="Link to this heading">#</a></h2> | |
| <p>An engine library ships an object you hand to | |
| <code class="xref py py-meth docutils literal notranslate"><span class="pre">with_extensions()</span></code>:</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">SessionContext</span> | |
| <span class="kn">import</span><span class="w"> </span><span class="nn">my_engine</span> | |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">SessionContext</span><span class="p">()</span><span class="o">.</span><span class="n">with_extensions</span><span class="p">(</span><span class="n">my_engine</span><span class="o">.</span><span class="n">Extension</span><span class="p">(</span><span class="s2">"scheduler:50050"</span><span class="p">))</span> | |
| <span class="n">ctx</span><span class="o">.</span><span class="n">register_table</span><span class="p">(</span><span class="s2">"events"</span><span class="p">,</span> <span class="n">my_engine</span><span class="o">.</span><span class="n">TableProvider</span><span class="p">(</span><span class="s2">"s3://bucket/events"</span><span class="p">))</span> | |
| <span class="n">ctx</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s2">"SELECT country, count(*) FROM events GROUP BY country"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> | |
| </pre></div> | |
| </div> | |
| <p>The <code class="docutils literal notranslate"><span class="pre">sql</span></code> call is unchanged from a single-process program. What | |
| changed is underneath it: installing the engine gave the session a | |
| query planner of the engine’s own, and that planner is what turns | |
| your plan into distributed stages.</p> | |
| <p>Two consequences worth knowing:</p> | |
| <ul class="simple"> | |
| <li><p><strong>The engine must be installed before you run the query</strong>, not | |
| before you register tables. Registration order does not matter; | |
| <code class="docutils literal notranslate"><span class="pre">with_extensions</span></code> is a session-level setup step.</p></li> | |
| <li><p><strong>The engine has to be able to reach your tables and functions.</strong> | |
| It ships the plan to its workers, so anything the plan references | |
| has to be reconstructible there. Table providers from the engine’s | |
| own library always are. A Python UDF may or may not be — the engine | |
| library documents what it supports, and | |
| <a class="reference internal" href="expressions.html#distributed-udf-portability"><span class="std std-ref">Portability requirements for inline Python UDFs</span></a> describes the constraints that | |
| apply to Python callables crossing a process boundary in general.</p></li> | |
| </ul> | |
| <p>If you install more than one library, pass them in one | |
| <code class="docutils literal notranslate"><span class="pre">with_extensions</span></code> call so they can see each other. See | |
| <a class="reference internal" href="../extensions.html#user-guide-extensions"><span class="std std-ref">Using extension libraries</span></a> for the details of installing extension | |
| libraries, and <a class="reference internal" href="../../extension-guide/index.html#ffi"><span class="std std-ref">Extension Guide</span></a> if you want to write an engine yourself.</p> | |
| </section> | |
| <section id="available-engines"> | |
| <h2>Available engines<a class="headerlink" href="#available-engines" title="Link to this heading">#</a></h2> | |
| <p>Query-level distribution is being built upstream. Neither project | |
| below is usable from datafusion-python yet; both sections will fill | |
| in as the integrations land.</p> | |
| <section id="datafusion-distributed"> | |
| <h3>datafusion-distributed<a class="headerlink" href="#datafusion-distributed" title="Link to this heading">#</a></h3> | |
| <p>🚧 <em>Work in progress upstream — not yet usable from datafusion-python.</em></p> | |
| <p><a class="reference external" href="https://github.com/apache/datafusion-distributed">datafusion-distributed</a> | |
| splits a single physical plan into stages and runs each stage on a | |
| different worker node. The driver writes a SQL or DataFrame query | |
| once; the runtime handles partitioning, shuffles, and reassembly.</p> | |
| <p>A datafusion-python integration is in development. In the meantime, | |
| <a class="reference internal" href="expressions.html#distributed-expressions"><span class="std std-ref">Shipping expressions to workers</span></a> covers most use cases that do not | |
| require automatic plan partitioning.</p> | |
| </section> | |
| <section id="apache-ballista"> | |
| <h3>Apache Ballista<a class="headerlink" href="#apache-ballista" title="Link to this heading">#</a></h3> | |
| <p>🚧 <em>Work in progress upstream — not yet usable from datafusion-python.</em></p> | |
| <p><a class="reference external" href="https://github.com/apache/datafusion-ballista">Apache Ballista</a> | |
| provides distributed query execution on top of DataFusion with a | |
| scheduler / executor model better suited to long-lived cluster | |
| deployments. A datafusion-python integration is on the roadmap.</p> | |
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