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<section id="spark-compatible-functions">
<span id="spark-functions"></span><h1>Spark-Compatible Functions<a class="headerlink" href="#spark-compatible-functions" title="Link to this heading">#</a></h1>
<p>DataFusion ships Spark-compatible versions of a wide set of functions
(string, math, datetime, hash, array, aggregate) through the upstream
<code class="docutils literal notranslate"><span class="pre">datafusion-spark</span></code> crate. <code class="docutils literal notranslate"><span class="pre">datafusion-python</span></code> exposes these under
<code class="docutils literal notranslate"><span class="pre">datafusion.functions.spark</span></code> for use from the DataFrame API, and via
<code class="xref py py-meth docutils literal notranslate"><span class="pre">enable_spark_functions()</span></code> for use from
SQL.</p>
<section id="why-a-separate-namespace">
<h2>Why a Separate Namespace?<a class="headerlink" href="#why-a-separate-namespace" title="Link to this heading">#</a></h2>
<p>Several Spark functions share names with DataFusion built-ins but differ in
semantics. The most common divergences:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">concat</span></code> propagates NULL. <code class="docutils literal notranslate"><span class="pre">concat('a',</span> <span class="pre">NULL,</span> <span class="pre">'b')</span></code> returns NULL under
Spark semantics, whereas the DataFusion default returns <code class="docutils literal notranslate"><span class="pre">'ab'</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">substring</span></code> is 1-indexed and supports negative positions counting from
the end of the string.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">round</span></code> uses HALF_UP rounding mode (<code class="docutils literal notranslate"><span class="pre">round(2.5,</span> <span class="pre">0)</span> <span class="pre">==</span> <span class="pre">3</span></code>).</p></li>
<li><p>Numeric functions (<code class="docutils literal notranslate"><span class="pre">floor</span></code>, <code class="docutils literal notranslate"><span class="pre">ceil</span></code>, <code class="docutils literal notranslate"><span class="pre">mod</span></code>) follow Spark’s edge-case
handling for negative values and decimals.</p></li>
</ul>
<p>Enabling Spark functions does not affect the DataFrame API: you choose which
implementation to call by which module you import from.</p>
</section>
<section id="dataframe-api">
<h2>DataFrame API<a class="headerlink" href="#dataframe-api" title="Link to this heading">#</a></h2>
<p>Import <code class="docutils literal notranslate"><span class="pre">spark</span></code> and use it like any other functions module. The Spark
functions can go anywhere you’d put a DataFusion expression — inside
<code class="docutils literal notranslate"><span class="pre">select</span></code>, <code class="docutils literal notranslate"><span class="pre">filter</span></code>, <code class="docutils literal notranslate"><span class="pre">with_column</span></code>, <code class="docutils literal notranslate"><span class="pre">aggregate</span></code>, and so on.</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="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">lit</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">datafusion.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">spark</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">&quot;s&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;hello&quot;</span><span class="p">,</span> <span class="s2">&quot;world&quot;</span><span class="p">]})</span>
<span class="c1"># SHA-256 hash with Spark semantics</span>
<span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">spark</span><span class="o">.</span><span class="n">sha2</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">&quot;s&quot;</span><span class="p">),</span> <span class="n">lit</span><span class="p">(</span><span class="mi">256</span><span class="p">))</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">&quot;h&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 1-indexed substring</span>
<span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">spark</span><span class="o">.</span><span class="n">substring</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">&quot;s&quot;</span><span class="p">),</span> <span class="n">lit</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">lit</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">&quot;p&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</section>
<section id="sql">
<h2>SQL<a class="headerlink" href="#sql" title="Link to this heading">#</a></h2>
<p>To use Spark functions in SQL queries, call
<code class="xref py py-meth docutils literal notranslate"><span class="pre">enable_spark_functions()</span></code> on the context.
This registers every Spark UDF/UDAF/UDWF, overriding any DataFusion built-in
of the same name.</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="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">enable_spark_functions</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">&quot;SELECT sha2(&#39;hello&#39;, 256)&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</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">&quot;SELECT concat(&#39;a&#39;, NULL, &#39;b&#39;)&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> <span class="c1"># -&gt; NULL, not &#39;ab&#39;</span>
</pre></div>
</div>
<p>The override applies for the lifetime of the session. To call DataFusion’s
built-in versions afterwards, create a fresh <code class="docutils literal notranslate"><span class="pre">SessionContext</span></code>.</p>
</section>
<section id="function-reference">
<h2>Function Reference<a class="headerlink" href="#function-reference" title="Link to this heading">#</a></h2>
<p>The full, up-to-date list of available Spark functions — with signatures
and per-function docstrings — lives in the
<a class="reference internal" href="../../autoapi/datafusion/functions/spark/index.html#module-datafusion.functions.spark" title="datafusion.functions.spark"><code class="xref py py-mod docutils literal notranslate"><span class="pre">datafusion.functions.spark</span></code></a> API reference.</p>
</section>
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