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| <div class="section" id="python-user-defined-table-functions-udtfs"> |
| <h1>Python User-defined Table Functions (UDTFs)<a class="headerlink" href="#python-user-defined-table-functions-udtfs" title="Permalink to this headline">¶</a></h1> |
| <p>Spark 3.5 introduces the Python user-defined table function (UDTF), a new type of user-defined function. |
| Unlike scalar functions that return a single result value from each call, each UDTF is invoked in |
| the <code class="docutils literal notranslate"><span class="pre">FROM</span></code> clause of a query and returns an entire table as output. |
| Each UDTF call can accept zero or more arguments. |
| These arguments can either be scalar expressions or table arguments that represent entire input tables.</p> |
| <div class="section" id="implementing-a-python-udtf"> |
| <h2>Implementing a Python UDTF<a class="headerlink" href="#implementing-a-python-udtf" title="Permalink to this headline">¶</a></h2> |
| <p>To implement a Python UDTF, you first need to define a class implementing the methods:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">PythonUDTF</span><span class="p">:</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Initializes the user-defined table function (UDTF). This is optional.</span> |
| |
| <span class="sd"> This method serves as the default constructor and is called once when the</span> |
| <span class="sd"> UDTF is instantiated on the executor side.</span> |
| |
| <span class="sd"> Any class fields assigned in this method will be available for subsequent</span> |
| <span class="sd"> calls to the `eval` and `terminate` methods. This class instance will remain</span> |
| <span class="sd"> alive until all rows in the current partition have been consumed by the `eval`</span> |
| <span class="sd"> method.</span> |
| |
| <span class="sd"> Notes</span> |
| <span class="sd"> -----</span> |
| <span class="sd"> - This method does not accept any extra arguments. Only the default</span> |
| <span class="sd"> constructor is supported.</span> |
| <span class="sd"> - You cannot create or reference the Spark session within the UDTF. Any</span> |
| <span class="sd"> attempt to do so will result in a serialization error.</span> |
| <span class="sd"> """</span> |
| <span class="o">...</span> |
| |
| <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Iterator</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Evaluates the function using the given input arguments.</span> |
| |
| <span class="sd"> This method is required and must be implemented.</span> |
| |
| <span class="sd"> Argument Mapping:</span> |
| <span class="sd"> - Each provided scalar expression maps to exactly one value in the</span> |
| <span class="sd"> `*args` list.</span> |
| <span class="sd"> - Each provided table argument maps to a pyspark.sql.Row object containing</span> |
| <span class="sd"> the columns in the order they appear in the provided input table,</span> |
| <span class="sd"> and with the names computed by the query analyzer.</span> |
| |
| <span class="sd"> This method is called on every input row, and can produce zero or more</span> |
| <span class="sd"> output rows. Each element in the output tuple corresponds to one column</span> |
| <span class="sd"> specified in the return type of the UDTF.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> *args : Any</span> |
| <span class="sd"> Arbitrary positional arguments representing the input to the UDTF.</span> |
| |
| <span class="sd"> Yields</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> tuple</span> |
| <span class="sd"> A tuple representing a single row in the UDTF result table.</span> |
| <span class="sd"> Yield as many times as needed to produce multiple rows.</span> |
| |
| <span class="sd"> Notes</span> |
| <span class="sd"> -----</span> |
| <span class="sd"> - The result of the function must be a tuple representing a single row</span> |
| <span class="sd"> in the UDTF result table.</span> |
| <span class="sd"> - UDTFs currently do not accept keyword arguments during the function call.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> eval that returns one row and one column for each input.</span> |
| |
| <span class="sd"> >>> def eval(self, x: int):</span> |
| <span class="sd"> ... yield (x, )</span> |
| |
| <span class="sd"> eval that returns two rows and two columns for each input.</span> |
| |
| <span class="sd"> >>> def eval(self, x: int, y: int):</span> |
| <span class="sd"> ... yield (x + y, x - y)</span> |
| <span class="sd"> ... yield (y + x, y - x)</span> |
| <span class="sd"> """</span> |
| <span class="o">...</span> |
| |
| <span class="k">def</span> <span class="nf">terminate</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Iterator</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Called when the UDTF has processed all input rows.</span> |
| |
| <span class="sd"> This method is optional to implement and is useful for performing any</span> |
| <span class="sd"> cleanup or finalization operations after the UDTF has finished processing</span> |
| <span class="sd"> all rows. It can also be used to yield additional rows if needed.</span> |
| <span class="sd"> Table functions that consume all rows in the entire input partition</span> |
| <span class="sd"> and then compute and return the entire output table can do so from</span> |
| <span class="sd"> this method as well (please be mindful of memory usage when doing</span> |
| <span class="sd"> this).</span> |
| |
| <span class="sd"> Yields</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> tuple</span> |
| <span class="sd"> A tuple representing a single row in the UDTF result table.</span> |
| <span class="sd"> Yield this if you want to return additional rows during termination.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> def terminate(self) -> Iterator[Any]:</span> |
| <span class="sd"> >>> yield "done", None</span> |
| <span class="sd"> """</span> |
| <span class="o">...</span> |
| </pre></div> |
| </div> |
| <p>The return type of the UDTF defines the schema of the table it outputs. |
| It must be either a <code class="docutils literal notranslate"><span class="pre">StructType</span></code>, for example <code class="docutils literal notranslate"><span class="pre">StructType().add("c1",</span> <span class="pre">StringType())</span></code> |
| or a DDL string representing a struct type, for example <code class="docutils literal notranslate"><span class="pre">c1:</span> <span class="pre">string</span></code>.</p> |
| <p><strong>Example of UDTF Class Implementation</strong></p> |
| <p>Here is a simple example of a UDTF class implementation:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the UDTF class and implement the required `eval` method.</span> |
| <span class="k">class</span> <span class="nc">SquareNumbers</span><span class="p">:</span> |
| <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">end</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">num</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">num</span> <span class="o">*</span> <span class="n">num</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p><strong>Instantiating a UDTF with the ``udtf`` Decorator</strong></p> |
| <p>To make use of the UDTF, you’ll first need to instantiate it using the <code class="docutils literal notranslate"><span class="pre">@udtf</span></code> decorator:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">lit</span><span class="p">,</span> <span class="n">udtf</span> |
| |
| <span class="c1"># Create a UDTF using the class definition and the `udtf` function.</span> |
| <span class="n">square_num</span> <span class="o">=</span> <span class="n">udtf</span><span class="p">(</span><span class="n">SquareNumbers</span><span class="p">,</span> <span class="n">returnType</span><span class="o">=</span><span class="s2">"num: int, squared: int"</span><span class="p">)</span> |
| |
| <span class="c1"># Invoke the UDTF in PySpark.</span> |
| <span class="n">square_num</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">show</span><span class="p">()</span> |
| <span class="c1"># +---+-------+</span> |
| <span class="c1"># |num|squared|</span> |
| <span class="c1"># +---+-------+</span> |
| <span class="c1"># | 1| 1|</span> |
| <span class="c1"># | 2| 4|</span> |
| <span class="c1"># | 3| 9|</span> |
| <span class="c1"># +---+-------+</span> |
| </pre></div> |
| </div> |
| <p><strong>Instantiating a UDTF with the ``udtf`` Function</strong></p> |
| <p>An alternative way to create a UDTF is to use the <a class="reference internal" href="../../reference/pyspark.sql/api/pyspark.sql.functions.udtf.html#pyspark.sql.functions.udtf" title="pyspark.sql.functions.udtf"><code class="xref py py-func docutils literal notranslate"><span class="pre">udtf()</span></code></a> function:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">lit</span><span class="p">,</span> <span class="n">udtf</span> |
| |
| <span class="c1"># Define a UDTF using the `udtf` decorator directly on the class.</span> |
| <span class="nd">@udtf</span><span class="p">(</span><span class="n">returnType</span><span class="o">=</span><span class="s2">"num: int, squared: int"</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">SquareNumbers</span><span class="p">:</span> |
| <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">end</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">num</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">num</span> <span class="o">*</span> <span class="n">num</span><span class="p">)</span> |
| |
| <span class="c1"># Invoke the UDTF in PySpark using the SquareNumbers class directly.</span> |
| <span class="n">SquareNumbers</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">show</span><span class="p">()</span> |
| <span class="c1"># +---+-------+</span> |
| <span class="c1"># |num|squared|</span> |
| <span class="c1"># +---+-------+</span> |
| <span class="c1"># | 1| 1|</span> |
| <span class="c1"># | 2| 4|</span> |
| <span class="c1"># | 3| 9|</span> |
| <span class="c1"># +---+-------+</span> |
| </pre></div> |
| </div> |
| <p>For more detailed usage, please see <a class="reference internal" href="../../reference/pyspark.sql/api/pyspark.sql.functions.udtf.html#pyspark.sql.functions.udtf" title="pyspark.sql.functions.udtf"><code class="xref py py-func docutils literal notranslate"><span class="pre">udtf()</span></code></a>.</p> |
| </div> |
| <div class="section" id="registering-and-using-python-udtfs-in-sql"> |
| <h2>Registering and Using Python UDTFs in SQL<a class="headerlink" href="#registering-and-using-python-udtfs-in-sql" title="Permalink to this headline">¶</a></h2> |
| <p>Python UDTFs can also be registered and used in SQL queries.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">udtf</span> |
| |
| <span class="nd">@udtf</span><span class="p">(</span><span class="n">returnType</span><span class="o">=</span><span class="s2">"word: string"</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">WordSplitter</span><span class="p">:</span> |
| <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">" "</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="p">(</span><span class="n">word</span><span class="o">.</span><span class="n">strip</span><span class="p">(),)</span> |
| |
| <span class="c1"># Register the UDTF for use in Spark SQL.</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">udtf</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">"split_words"</span><span class="p">,</span> <span class="n">WordSplitter</span><span class="p">)</span> |
| |
| <span class="c1"># Example: Using the UDTF in SQL.</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s2">"SELECT * FROM split_words('hello world')"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="c1"># +-----+</span> |
| <span class="c1"># | word|</span> |
| <span class="c1"># +-----+</span> |
| <span class="c1"># |hello|</span> |
| <span class="c1"># |world|</span> |
| <span class="c1"># +-----+</span> |
| |
| <span class="c1"># Example: Using the UDTF with a lateral join in SQL.</span> |
| <span class="c1"># The lateral join allows us to reference the columns and aliases</span> |
| <span class="c1"># in the previous FROM clause items as inputs to the UDTF.</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span> |
| <span class="s2">"SELECT * FROM VALUES ('Hello World'), ('Apache Spark') t(text), "</span> |
| <span class="s2">"LATERAL split_words(text)"</span> |
| <span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="c1"># +------------+------+</span> |
| <span class="c1"># | text| word|</span> |
| <span class="c1"># +------------+------+</span> |
| <span class="c1"># | Hello World| Hello|</span> |
| <span class="c1"># | Hello World| World|</span> |
| <span class="c1"># |Apache Spark|Apache|</span> |
| <span class="c1"># |Apache Spark| Spark|</span> |
| <span class="c1"># +------------+------+</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="arrow-optimization"> |
| <h2>Arrow Optimization<a class="headerlink" href="#arrow-optimization" title="Permalink to this headline">¶</a></h2> |
| <p>Apache Arrow is an in-memory columnar data format used in Spark to efficiently transfer |
| data between Java and Python processes. Apache Arrow is disabled by default for Python UDTFs.</p> |
| <p>Arrow can improve performance when each input row generates a large result table from the UDTF.</p> |
| <p>To enable Arrow optimization, set the <code class="docutils literal notranslate"><span class="pre">spark.sql.execution.pythonUDTF.arrow.enabled</span></code> |
| configuration to <code class="docutils literal notranslate"><span class="pre">true</span></code>. You can also enable it by specifying the <code class="docutils literal notranslate"><span class="pre">useArrow</span></code> parameter |
| when declaring the UDTF.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">udtf</span> |
| |
| <span class="nd">@udtf</span><span class="p">(</span><span class="n">returnType</span><span class="o">=</span><span class="s2">"c1: int, c2: int"</span><span class="p">,</span> <span class="n">useArrow</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">PlusOne</span><span class="p">:</span> |
| <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="n">x</span><span class="p">,</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</span> |
| </pre></div> |
| </div> |
| <p>For more details, please see <a class="reference internal" href="../arrow_pandas.html"><span class="doc">Apache Arrow in PySpark</span></a>.</p> |
| <div class="section" id="table-input-argument"> |
| <h3>TABLE input argument<a class="headerlink" href="#table-input-argument" title="Permalink to this headline">¶</a></h3> |
| <p>Python UDTFs can also take a TABLE as input argument, and it can be used in conjunction |
| with scalar input arguments. |
| By default, you are allowed to have only one TABLE argument as input, primarily for |
| performance reasons. If you need to have more than one TABLE input argument, |
| you can enable this by setting the <code class="docutils literal notranslate"><span class="pre">spark.sql.tvf.allowMultipleTableArguments.enabled</span></code> |
| configuration to <code class="docutils literal notranslate"><span class="pre">true</span></code>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">udtf</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="n">Row</span> |
| |
| <span class="nd">@udtf</span><span class="p">(</span><span class="n">returnType</span><span class="o">=</span><span class="s2">"id: int"</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">FilterUDTF</span><span class="p">:</span> |
| <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">:</span> <span class="n">Row</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s2">"id"</span><span class="p">]</span> <span class="o">></span> <span class="mi">5</span><span class="p">:</span> |
| <span class="k">yield</span> <span class="n">row</span><span class="p">[</span><span class="s2">"id"</span><span class="p">],</span> |
| |
| <span class="n">spark</span><span class="o">.</span><span class="n">udtf</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">"filter_udtf"</span><span class="p">,</span> <span class="n">FilterUDTF</span><span class="p">)</span> |
| |
| <span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s2">"SELECT * FROM filter_udtf(TABLE(SELECT * FROM range(10)))"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="c1"># +---+</span> |
| <span class="c1"># | id|</span> |
| <span class="c1"># +---+</span> |
| <span class="c1"># | 6|</span> |
| <span class="c1"># | 7|</span> |
| <span class="c1"># | 8|</span> |
| <span class="c1"># | 9|</span> |
| <span class="c1"># +---+</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="more-examples"> |
| <h2>More Examples<a class="headerlink" href="#more-examples" title="Permalink to this headline">¶</a></h2> |
| <p>A Python UDTF that expands date ranges into individual dates:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">lit</span><span class="p">,</span> <span class="n">udtf</span> |
| |
| <span class="nd">@udtf</span><span class="p">(</span><span class="n">returnType</span><span class="o">=</span><span class="s2">"date: string"</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">DateExpander</span><span class="p">:</span> |
| <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_date</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">end_date</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="n">current</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="s1">'%Y-%m-</span><span class="si">%d</span><span class="s1">'</span><span class="p">)</span> |
| <span class="n">end</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">end_date</span><span class="p">,</span> <span class="s1">'%Y-%m-</span><span class="si">%d</span><span class="s1">'</span><span class="p">)</span> |
| <span class="k">while</span> <span class="n">current</span> <span class="o"><=</span> <span class="n">end</span><span class="p">:</span> |
| <span class="k">yield</span> <span class="p">(</span><span class="n">current</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">'%Y-%m-</span><span class="si">%d</span><span class="s1">'</span><span class="p">),)</span> |
| <span class="n">current</span> <span class="o">+=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| |
| <span class="n">DateExpander</span><span class="p">(</span><span class="n">lit</span><span class="p">(</span><span class="s2">"2023-02-25"</span><span class="p">),</span> <span class="n">lit</span><span class="p">(</span><span class="s2">"2023-03-01"</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="c1"># +----------+</span> |
| <span class="c1"># | date|</span> |
| <span class="c1"># +----------+</span> |
| <span class="c1"># |2023-02-25|</span> |
| <span class="c1"># |2023-02-26|</span> |
| <span class="c1"># |2023-02-27|</span> |
| <span class="c1"># |2023-02-28|</span> |
| <span class="c1"># |2023-03-01|</span> |
| <span class="c1"># +----------+</span> |
| </pre></div> |
| </div> |
| <p>A Python UDTF with <code class="docutils literal notranslate"><span class="pre">__init__</span></code> and <code class="docutils literal notranslate"><span class="pre">terminate</span></code>:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">udtf</span> |
| |
| <span class="nd">@udtf</span><span class="p">(</span><span class="n">returnType</span><span class="o">=</span><span class="s2">"cnt: int"</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">CountUDTF</span><span class="p">:</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># Initialize the counter to 0 when an instance of the class is created.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="c1"># Increment the counter by 1 for each input value received.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span> |
| |
| <span class="k">def</span> <span class="nf">terminate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># Yield the final count when the UDTF is done processing.</span> |
| <span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> |
| |
| <span class="n">spark</span><span class="o">.</span><span class="n">udtf</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">"count_udtf"</span><span class="p">,</span> <span class="n">CountUDTF</span><span class="p">)</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s2">"SELECT * FROM range(0, 10, 1, 1), LATERAL count_udtf(id)"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="c1"># +---+---+</span> |
| <span class="c1"># | id|cnt|</span> |
| <span class="c1"># +---+---+</span> |
| <span class="c1"># | 9| 10|</span> |
| <span class="c1"># +---+---+</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s2">"SELECT * FROM range(0, 10, 1, 2), LATERAL count_udtf(id)"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="c1"># +---+---+</span> |
| <span class="c1"># | id|cnt|</span> |
| <span class="c1"># +---+---+</span> |
| <span class="c1"># | 4| 5|</span> |
| <span class="c1"># | 9| 5|</span> |
| <span class="c1"># +---+---+</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
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