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| <section id="distributing-work"> |
| <h1>Distributing work<a class="headerlink" href="#distributing-work" title="Link to this heading">#</a></h1> |
| <p>DataFusion supports splitting work across processes by shipping |
| serialized expressions to workers: the driver builds an |
| <a class="reference internal" href="../autoapi/datafusion/index.html#datafusion.Expr" title="datafusion.Expr"><code class="xref py py-class docutils literal notranslate"><span class="pre">Expr</span></code></a>, each worker evaluates it against its |
| own slice of data. This pattern suits embarrassingly-parallel |
| workloads where the driver decides partitioning up front.</p> |
| <p>Query-level distribution — where the runtime partitions a single |
| logical or physical plan across worker nodes — is in progress |
| upstream via <a class="reference external" href="https://github.com/apache/datafusion-distributed">datafusion-distributed</a> and <a class="reference external" href="https://github.com/apache/datafusion-ballista">Apache |
| Ballista</a>. Both |
| have short sections at the end of this page; integration details |
| will land as those projects become usable from datafusion-python.</p> |
| <section id="expression-level-distribution"> |
| <h2>Expression-level distribution<a class="headerlink" href="#expression-level-distribution" title="Link to this heading">#</a></h2> |
| <p>DataFusion expressions support distribution directly: pass one to a |
| worker process and Python’s standard |
| <a class="reference external" href="https://docs.python.org/3/library/pickle.html">pickle</a> machinery |
| serializes it transparently — the same machinery |
| <code class="xref py py-meth docutils literal notranslate"><span class="pre">multiprocessing.pool.Pool.map()</span></code>, Ray’s <code class="docutils literal notranslate"><span class="pre">@ray.remote</span></code>, and |
| similar libraries already use to ship function arguments. Python UDFs |
| — scalar, aggregate, and window — travel inside the serialized |
| expression; the receiver does not need to pre-register them.</p> |
| <section id="basic-worker-pool-example"> |
| <h3>Basic worker-pool example<a class="headerlink" href="#basic-worker-pool-example" title="Link to this heading">#</a></h3> |
| <p>Define a worker function that takes the expression plus a batch and |
| returns the evaluated result:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</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="k">def</span><span class="w"> </span><span class="nf">evaluate</span><span class="p">(</span><span class="n">expr</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span> |
| <span class="c1"># `expr` arrived here via the pool's automatic pickling —</span> |
| <span class="c1"># no manual serialization needed in user code.</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">SessionContext</span><span class="p">()</span> |
| <span class="n">df</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="n">batch</span><span class="p">})</span> |
| <span class="k">return</span> <span class="n">df</span><span class="o">.</span><span class="n">with_column</span><span class="p">(</span><span class="s2">"result"</span><span class="p">,</span> <span class="n">expr</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">"result"</span><span class="p">)</span><span class="o">.</span><span class="n">to_pydict</span><span class="p">()[</span><span class="s2">"result"</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <p>Then build the expression in the driver and fan it out:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mp</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="p">,</span> <span class="n">udf</span> |
| |
| <span class="n">double</span> <span class="o">=</span> <span class="n">udf</span><span class="p">(</span> |
| <span class="k">lambda</span> <span class="n">arr</span><span class="p">:</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="n">v</span><span class="o">.</span><span class="n">as_py</span><span class="p">()</span> <span class="ow">or</span> <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">arr</span><span class="p">]),</span> |
| <span class="p">[</span><span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()],</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">(),</span> <span class="n">volatility</span><span class="o">=</span><span class="s2">"immutable"</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"double"</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">expr</span> <span class="o">=</span> <span class="n">double</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">mp_ctx</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">get_context</span><span class="p">(</span><span class="s2">"forkserver"</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">mp_ctx</span><span class="o">.</span><span class="n">Pool</span><span class="p">(</span><span class="n">processes</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> <span class="k">as</span> <span class="n">pool</span><span class="p">:</span> |
| <span class="n">results</span> <span class="o">=</span> <span class="n">pool</span><span class="o">.</span><span class="n">starmap</span><span class="p">(</span> |
| <span class="n">evaluate</span><span class="p">,</span> |
| <span class="p">[(</span><span class="n">expr</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="p">(</span><span class="n">expr</span><span class="p">,</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])],</span> |
| <span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span> <span class="c1"># [[2, 4, 6], [20, 40, 60]]</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When saved to a <code class="docutils literal notranslate"><span class="pre">.py</span></code> file and executed with the <code class="docutils literal notranslate"><span class="pre">spawn</span></code> or |
| <code class="docutils literal notranslate"><span class="pre">forkserver</span></code> start method, wrap the driver block in |
| <code class="docutils literal notranslate"><span class="pre">if</span> <span class="pre">__name__</span> <span class="pre">==</span> <span class="pre">"__main__":</span></code> so worker processes can re-import |
| the module without re-running it. This is a standard Python |
| <code class="xref py py-mod docutils literal notranslate"><span class="pre">multiprocessing</span></code> requirement, not DataFusion-specific — |
| see <a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods">Safe importing of main module</a> |
| in the Python docs.</p> |
| </div> |
| </section> |
| <section id="what-travels-with-the-expression"> |
| <h3>What travels with the expression<a class="headerlink" href="#what-travels-with-the-expression" title="Link to this heading">#</a></h3> |
| <ul class="simple"> |
| <li><p><strong>Built-in functions</strong> (<code class="docutils literal notranslate"><span class="pre">abs</span></code>, <code class="docutils literal notranslate"><span class="pre">length</span></code>, arithmetic, comparisons, |
| etc.) — fully portable. Worker needs nothing pre-registered.</p></li> |
| <li><p><strong>Python UDFs</strong> — travel inline (subject to the two portability |
| requirements below). The callable, its signature, and any state |
| captured in closures travel inside the serialized expression and are |
| reconstructed on the worker automatically. Applies equally to:</p> |
| <ul> |
| <li><p><strong>scalar UDFs</strong> (<a class="reference internal" href="../autoapi/datafusion/index.html#datafusion.udf" title="datafusion.udf"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.udf()</span></code></a>)</p></li> |
| <li><p><strong>aggregate UDFs</strong> (<a class="reference internal" href="../autoapi/datafusion/index.html#datafusion.udaf" title="datafusion.udaf"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.udaf()</span></code></a>)</p></li> |
| <li><p><strong>window UDFs</strong> (<a class="reference internal" href="../autoapi/datafusion/index.html#datafusion.udwf" title="datafusion.udwf"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.udwf()</span></code></a>)</p></li> |
| </ul> |
| </li> |
| <li><p><strong>UDFs imported via the FFI capsule protocol</strong> — travel <strong>by name |
| only</strong>. The worker must already have a matching registration on its |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">SessionContext</span></code>. Without that registration, evaluation |
| raises an error.</p></li> |
| </ul> |
| </section> |
| <section id="portability-requirements-for-inline-python-udfs"> |
| <span id="distributed-udf-portability"></span><h3>Portability requirements for inline Python UDFs<a class="headerlink" href="#portability-requirements-for-inline-python-udfs" title="Link to this heading">#</a></h3> |
| <p>Inline Python UDFs ride on <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a>, which imposes two |
| requirements on the worker environment:</p> |
| <ul class="simple"> |
| <li><p><strong>Matching Python minor version.</strong> cloudpickle serializes Python |
| bytecode, which is not stable across minor versions. A UDF pickled |
| on 3.12 cannot be reconstructed on 3.11 or 3.13. The wire format |
| stamps the sender’s <code class="docutils literal notranslate"><span class="pre">(major,</span> <span class="pre">minor)</span></code>; mismatches raise a clear |
| error naming both versions. Align the Python version on driver and |
| workers.</p></li> |
| <li><p><strong>Imported modules must be importable on the worker.</strong> cloudpickle |
| captures the callable <em>by value</em> (bytecode and closure cells travel |
| whole), but names resolved through <code class="docutils literal notranslate"><span class="pre">import</span></code> are captured <em>by |
| reference</em> — module path only. A UDF doing |
| <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">mylib</span> <span class="pre">import</span> <span class="pre">transform</span></code> requires <code class="docutils literal notranslate"><span class="pre">mylib</span></code> installed on the |
| worker. Same applies to bound methods of imported classes. |
| Self-contained UDFs (no imports beyond what the worker already has, |
| e.g. <code class="docutils literal notranslate"><span class="pre">pyarrow</span></code>) avoid this entirely.</p></li> |
| </ul> |
| </section> |
| <section id="registering-shared-udfs-on-workers"> |
| <h3>Registering shared UDFs on workers<a class="headerlink" href="#registering-shared-udfs-on-workers" title="Link to this heading">#</a></h3> |
| <p>When an expression references an FFI capsule UDF (or any UDF the |
| worker must resolve from its registered functions), set up the |
| worker’s <code class="xref py py-class docutils literal notranslate"><span class="pre">SessionContext</span></code> once per process and install it |
| as the <em>worker context</em>:</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">from</span><span class="w"> </span><span class="nn">datafusion.ipc</span><span class="w"> </span><span class="kn">import</span> <span class="n">set_worker_ctx</span> |
| |
| |
| <span class="k">def</span><span class="w"> </span><span class="nf">init_worker</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">ctx</span><span class="o">.</span><span class="n">register_udaf</span><span class="p">(</span><span class="n">my_ffi_aggregate</span><span class="p">)</span> |
| <span class="n">set_worker_ctx</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| |
| |
| <span class="k">with</span> <span class="n">mp</span><span class="o">.</span><span class="n">get_context</span><span class="p">(</span><span class="s2">"forkserver"</span><span class="p">)</span><span class="o">.</span><span class="n">Pool</span><span class="p">(</span> |
| <span class="n">processes</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">initializer</span><span class="o">=</span><span class="n">init_worker</span> |
| <span class="p">)</span> <span class="k">as</span> <span class="n">pool</span><span class="p">:</span> |
| <span class="o">...</span> |
| </pre></div> |
| </div> |
| <p>Inside a worker, expressions arriving from the driver resolve their |
| by-name references against the installed worker context. If no worker |
| context is installed, the global <code class="xref py py-class docutils literal notranslate"><span class="pre">SessionContext</span></code> is used — |
| fine for expressions that only reference built-ins and Python UDFs, |
| but FFI-capsule-backed registrations must be installed on the global |
| context to resolve.</p> |
| </section> |
| <section id="python-3-14-default-change"> |
| <h3>Python 3.14 default change<a class="headerlink" href="#python-3-14-default-change" title="Link to this heading">#</a></h3> |
| <p>Python 3.14 changed the Linux default start method for |
| <code class="xref py py-mod docutils literal notranslate"><span class="pre">multiprocessing</span></code> from <code class="docutils literal notranslate"><span class="pre">fork</span></code> to <code class="docutils literal notranslate"><span class="pre">forkserver</span></code> (macOS has |
| defaulted to <code class="docutils literal notranslate"><span class="pre">spawn</span></code> since Python 3.8; Windows has always used |
| <code class="docutils literal notranslate"><span class="pre">spawn</span></code>). With <code class="docutils literal notranslate"><span class="pre">fork</span></code>, any state set in the parent was visible in |
| workers via copy-on-write; with <code class="docutils literal notranslate"><span class="pre">forkserver</span></code> and <code class="docutils literal notranslate"><span class="pre">spawn</span></code> it is |
| not. The <a class="reference internal" href="../autoapi/datafusion/ipc/index.html#datafusion.ipc.set_worker_ctx" title="datafusion.ipc.set_worker_ctx"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_worker_ctx()</span></code></a> pattern works on |
| every start method — prefer it over relying on inherited state.</p> |
| </section> |
| <section id="practical-considerations"> |
| <h3>Practical considerations<a class="headerlink" href="#practical-considerations" title="Link to this heading">#</a></h3> |
| <ul class="simple"> |
| <li><p><strong>Serialized size scales with what travels inline.</strong> A serialized |
| expression of just built-ins is small (tens of bytes). An |
| expression carrying a Python UDF is hundreds of bytes (the callable |
| and its signature). When the same UDF is shipped many times, |
| registering an equivalent FFI-capsule UDF on each worker via |
| <a class="reference internal" href="../autoapi/datafusion/ipc/index.html#datafusion.ipc.set_worker_ctx" title="datafusion.ipc.set_worker_ctx"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_worker_ctx()</span></code></a> and referring to it by |
| name cuts the per-trip overhead.</p></li> |
| <li><p><strong>Closure capture.</strong> When a Python UDF closes over surrounding |
| state — local variables, module-level objects, file paths — that |
| state is captured at serialization time. Surprises are possible if |
| the captured state is large, mutable, or not portable to the |
| worker’s environment. See <a class="reference internal" href="#distributed-udf-portability"><span class="std std-ref">Portability requirements for inline Python UDFs</span></a> for the Python-version and imported-module rules.</p></li> |
| </ul> |
| </section> |
| <section id="disabling-python-udf-inlining"> |
| <h3>Disabling Python UDF inlining<a class="headerlink" href="#disabling-python-udf-inlining" title="Link to this heading">#</a></h3> |
| <p>For a stricter wire format, call |
| <code class="xref py py-meth docutils literal notranslate"><span class="pre">SessionContext.with_python_udf_inlining(enabled=False)</span></code> on the session |
| producing or consuming the bytes. With inlining disabled, Python |
| UDFs travel by name only — the same way FFI-capsule UDFs do — and |
| the receiver must have a matching registration.</p> |
| <p>Two use cases:</p> |
| <ul class="simple"> |
| <li><p><strong>Cross-language portability.</strong> A non-Python decoder cannot |
| reconstruct a cloudpickled payload. Senders aimed at Java, C++, |
| or another Rust binary disable inlining and rely on the receiver |
| having compatible UDF registrations.</p></li> |
| <li><p><strong>Untrusted-source decode.</strong> With inlining disabled, |
| <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.from_bytes()</span></code> never calls <code class="docutils literal notranslate"><span class="pre">cloudpickle.loads</span></code> on |
| the incoming bytes — an inline payload from a misbehaving sender |
| raises a clear error instead of executing arbitrary Python code.</p></li> |
| </ul> |
| <p>Mismatched configurations raise a descriptive error: an inline blob |
| fed to a strict receiver fails fast rather than silently dropping |
| into <code class="docutils literal notranslate"><span class="pre">cloudpickle.loads</span></code>.</p> |
| <p>To make the toggle apply through <code class="xref py py-func docutils literal notranslate"><span class="pre">pickle.dumps()</span></code> (which |
| calls <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.to_bytes()</span></code> with no context), install the strict |
| session as the driver’s <em>sender context</em>:</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">from</span><span class="w"> </span><span class="nn">datafusion.ipc</span><span class="w"> </span><span class="kn">import</span> <span class="n">set_sender_ctx</span> |
| |
| <span class="n">set_sender_ctx</span><span class="p">(</span><span class="n">SessionContext</span><span class="p">()</span><span class="o">.</span><span class="n">with_python_udf_inlining</span><span class="p">(</span><span class="n">enabled</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> |
| <span class="c1"># Every subsequent pickle.dumps(expr) on this thread encodes</span> |
| <span class="c1"># without inlining the Python callable.</span> |
| </pre></div> |
| </div> |
| <p>Pair with a matching strict worker context |
| (<a class="reference internal" href="../autoapi/datafusion/ipc/index.html#datafusion.ipc.set_worker_ctx" title="datafusion.ipc.set_worker_ctx"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_worker_ctx()</span></code></a>) so the <code class="docutils literal notranslate"><span class="pre">pickle.loads</span></code> |
| side also refuses inline payloads. Explicit |
| <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.to_bytes(ctx)</span></code> and |
| <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.from_bytes(blob,</span> <span class="pre">ctx=ctx)</span></code> calls |
| honor the supplied <code class="docutils literal notranslate"><span class="pre">ctx</span></code> directly and ignore the sender / worker |
| contexts.</p> |
| <p>The toggle only narrows the <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.from_bytes()</span></code> surface; |
| <code class="xref py py-func docutils literal notranslate"><span class="pre">pickle.loads()</span></code> on untrusted bytes remains unsafe regardless |
| of this setting. See the <a class="reference internal" href="#distributed-expr-security"><span class="std std-ref">Security</span></a> section below for the full |
| threat model.</p> |
| </section> |
| <section id="security"> |
| <span id="distributed-expr-security"></span><h3>Security<a class="headerlink" href="#security" title="Link to this heading">#</a></h3> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>Reconstructing an expression containing a Python UDF executes |
| arbitrary Python code on the receiver — pickle is doing the work |
| under the hood and pickle is unsafe on untrusted input (see the |
| <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle">pickle module security warning</a> |
| in the Python standard library docs). Only accept expressions |
| from trusted sources. For untrusted-source workflows, disable |
| Python UDF inlining (see above), restrict senders to built-in |
| functions and pre-registered Rust-side UDFs, and avoid |
| <code class="xref py py-func docutils literal notranslate"><span class="pre">pickle.loads()</span></code> on externally supplied bytes entirely.</p> |
| </div> |
| </section> |
| <section id="reference-session-context-slots"> |
| <h3>Reference: session context slots<a class="headerlink" href="#reference-session-context-slots" title="Link to this heading">#</a></h3> |
| <p>There is only one type — <code class="xref py py-class docutils literal notranslate"><span class="pre">SessionContext</span></code>. It can occupy |
| up to four <em>slots</em> in a running program:</p> |
| <div class="pst-scrollable-table-container"><table class="table"> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Slot</p></th> |
| <th class="head"><p>Lifetime</p></th> |
| <th class="head"><p>Purpose</p></th> |
| <th class="head"><p>Set how</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p>User-held</p></td> |
| <td><p>Local variable / attribute</p></td> |
| <td><p>Build and run queries</p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">ctx</span> <span class="pre">=</span> <span class="pre">SessionContext(...)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>Global</p></td> |
| <td><p>Process singleton (lazy-init)</p></td> |
| <td><p>Backs module-level <a class="reference internal" href="../autoapi/datafusion/io/index.html#datafusion.io.read_parquet" title="datafusion.io.read_parquet"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_parquet()</span></code></a>, <a class="reference internal" href="../autoapi/datafusion/io/index.html#datafusion.io.read_csv" title="datafusion.io.read_csv"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_csv()</span></code></a>, <a class="reference internal" href="../autoapi/datafusion/io/index.html#datafusion.io.read_json" title="datafusion.io.read_json"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_json()</span></code></a>, <a class="reference internal" href="../autoapi/datafusion/io/index.html#datafusion.io.read_avro" title="datafusion.io.read_avro"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_avro()</span></code></a>; final fallback for <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.from_bytes()</span></code></p></td> |
| <td><p>Implicit; access via <code class="xref py py-meth docutils literal notranslate"><span class="pre">SessionContext.global_ctx()</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p>Sender</p></td> |
| <td><p>Thread-local on the driver</p></td> |
| <td><p>Codec settings for outbound <code class="xref py py-func docutils literal notranslate"><span class="pre">pickle.dumps()</span></code> / <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.to_bytes()</span></code> without <code class="docutils literal notranslate"><span class="pre">ctx</span></code></p></td> |
| <td><p><a class="reference internal" href="../autoapi/datafusion/ipc/index.html#datafusion.ipc.set_sender_ctx" title="datafusion.ipc.set_sender_ctx"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_sender_ctx()</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>Worker</p></td> |
| <td><p>Thread-local on the worker</p></td> |
| <td><p>Function registry for inbound <code class="xref py py-func docutils literal notranslate"><span class="pre">pickle.loads()</span></code> / <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.from_bytes()</span></code> without <code class="docutils literal notranslate"><span class="pre">ctx</span></code></p></td> |
| <td><p><a class="reference internal" href="../autoapi/datafusion/ipc/index.html#datafusion.ipc.set_worker_ctx" title="datafusion.ipc.set_worker_ctx"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_worker_ctx()</span></code></a></p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <p>The same <code class="xref py py-class docutils literal notranslate"><span class="pre">SessionContext</span></code> object may occupy more than one |
| slot simultaneously — installing it into a slot is a reference, not |
| a copy. A non-distributed program only ever uses the user-held slot; |
| the global slot is invisible unless you call top-level <code class="docutils literal notranslate"><span class="pre">read_*</span></code> |
| helpers.</p> |
| <p>Resolution order on the worker side is <em>explicit argument → |
| worker context → global context.</em> Explicit <code class="docutils literal notranslate"><span class="pre">ctx=</span></code> on |
| <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expr.from_bytes()</span></code> always wins; the sender slot is ignored |
| on decode and the worker slot is ignored on encode.</p> |
| <p>Sharp edges:</p> |
| <ul class="simple"> |
| <li><p>Sender and worker slots are <strong>thread-local</strong>. Background threads |
| on either side see <code class="docutils literal notranslate"><span class="pre">None</span></code> until they install their own.</p></li> |
| <li><p>Under the <code class="docutils literal notranslate"><span class="pre">fork</span></code> start method, the parent’s <code class="docutils literal notranslate"><span class="pre">threading.local()</span></code> |
| values are copied into the child by copy-on-write — a forked |
| worker initially observes whatever sender / worker slot the parent |
| had set, until the worker writes its own value (or calls the |
| matching <code class="docutils literal notranslate"><span class="pre">clear_*_ctx</span></code>). <code class="docutils literal notranslate"><span class="pre">spawn</span></code> and <code class="docutils literal notranslate"><span class="pre">forkserver</span></code> workers |
| start with empty thread-local slots. Treat the slot as |
| uninitialized on worker entry and install (or clear) it explicitly |
| in the worker initializer; do not rely on inherited state.</p></li> |
| <li><p>The global slot persists across <code class="docutils literal notranslate"><span class="pre">fork</span></code> workers (copy-on-write |
| memory inherit) but not across <code class="docutils literal notranslate"><span class="pre">spawn</span></code> / <code class="docutils literal notranslate"><span class="pre">forkserver</span></code> workers |
| (fresh process — register or install a worker context on |
| start-up).</p></li> |
| <li><p>The inlining toggle is per-context state, not a global switch. |
| Two contexts with different toggles can coexist in one process.</p></li> |
| </ul> |
| </section> |
| </section> |
| <section id="query-level-distribution-via-datafusion-distributed"> |
| <h2>Query-level distribution via datafusion-distributed<a class="headerlink" href="#query-level-distribution-via-datafusion-distributed" title="Link to this heading">#</a></h2> |
| <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. This section will |
| document the integration once it lands. In the meantime, the |
| expression-level approach above covers most use cases that do not |
| require automatic plan partitioning.</p> |
| </section> |
| <section id="query-level-distribution-via-apache-ballista"> |
| <h2>Query-level distribution via Apache Ballista<a class="headerlink" href="#query-level-distribution-via-apache-ballista" title="Link to this heading">#</a></h2> |
| <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; this |
| section will fill in once the integration is usable.</p> |
| </section> |
| <section id="see-also"> |
| <h2>See also<a class="headerlink" href="#see-also" title="Link to this heading">#</a></h2> |
| <ul class="simple"> |
| <li><p><a class="reference internal" href="../autoapi/datafusion/ipc/index.html#module-datafusion.ipc" title="datafusion.ipc"><code class="xref py py-mod docutils literal notranslate"><span class="pre">datafusion.ipc</span></code></a> — worker context API.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">examples/multiprocessing_pickle_expr.py</span></code> — runnable |
| <code class="docutils literal notranslate"><span class="pre">multiprocessing.Pool</span></code> example that ships a different parametric |
| expression to each worker and collects results back.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">examples/ray_pickle_expr.py</span></code> — runnable Ray actor example.</p></li> |
| </ul> |
| </section> |
| </section> |
| |
| |
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| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#basic-worker-pool-example">Basic worker-pool example</a></li> |
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