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| <h1>Source code for pyspark.sql.udf</h1><div class="highlight"><pre> |
| <span></span><span class="c1">#</span> |
| <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span> |
| <span class="c1"># contributor license agreements. See the NOTICE file distributed with</span> |
| <span class="c1"># this work for additional information regarding copyright ownership.</span> |
| <span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span> |
| <span class="c1"># (the "License"); you may not use this file except in compliance with</span> |
| <span class="c1"># the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing, software</span> |
| <span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span> |
| <span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span> |
| <span class="c1"># See the License for the specific language governing permissions and</span> |
| <span class="c1"># limitations under the License.</span> |
| <span class="c1">#</span> |
| <span class="sd">"""</span> |
| <span class="sd">User-defined function related classes and functions</span> |
| <span class="sd">"""</span> |
| <span class="kn">from</span> <span class="nn">inspect</span> <span class="kn">import</span> <span class="n">getfullargspec</span> |
| |
| <span class="kn">import</span> <span class="nn">functools</span> |
| <span class="kn">import</span> <span class="nn">inspect</span> |
| <span class="kn">import</span> <span class="nn">sys</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Any</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">cast</span><span class="p">,</span> <span class="n">Union</span> |
| |
| <span class="kn">from</span> <span class="nn">py4j.java_gateway</span> <span class="kn">import</span> <span class="n">JavaObject</span> |
| |
| <span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkContext</span> |
| <span class="kn">from</span> <span class="nn">pyspark.profiler</span> <span class="kn">import</span> <span class="n">Profiler</span> |
| <span class="kn">from</span> <span class="nn">pyspark.rdd</span> <span class="kn">import</span> <span class="n">_prepare_for_python_RDD</span><span class="p">,</span> <span class="n">PythonEvalType</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.column</span> <span class="kn">import</span> <span class="n">Column</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">,</span> <span class="n">_to_seq</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="p">(</span> |
| <span class="n">ArrayType</span><span class="p">,</span> |
| <span class="n">BinaryType</span><span class="p">,</span> |
| <span class="n">DataType</span><span class="p">,</span> |
| <span class="n">MapType</span><span class="p">,</span> |
| <span class="n">StringType</span><span class="p">,</span> |
| <span class="n">StructType</span><span class="p">,</span> |
| <span class="n">_parse_datatype_string</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.utils</span> <span class="kn">import</span> <span class="n">get_active_spark_context</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.pandas.types</span> <span class="kn">import</span> <span class="n">to_arrow_type</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.pandas.utils</span> <span class="kn">import</span> <span class="n">require_minimum_pandas_version</span><span class="p">,</span> <span class="n">require_minimum_pyarrow_version</span> |
| |
| <span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql._typing</span> <span class="kn">import</span> <span class="n">DataTypeOrString</span><span class="p">,</span> <span class="n">ColumnOrName</span><span class="p">,</span> <span class="n">UserDefinedFunctionLike</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.session</span> <span class="kn">import</span> <span class="n">SparkSession</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"UDFRegistration"</span><span class="p">]</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_wrap_function</span><span class="p">(</span> |
| <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">returnType</span><span class="p">:</span> <span class="s2">"DataTypeOrString"</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">JavaObject</span><span class="p">:</span> |
| <span class="n">command</span> <span class="o">=</span> <span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">returnType</span><span class="p">)</span> |
| <span class="n">pickled_command</span><span class="p">,</span> <span class="n">broadcast_vars</span><span class="p">,</span> <span class="n">env</span><span class="p">,</span> <span class="n">includes</span> <span class="o">=</span> <span class="n">_prepare_for_python_RDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">command</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="k">return</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">SimplePythonFunction</span><span class="p">(</span> |
| <span class="nb">bytearray</span><span class="p">(</span><span class="n">pickled_command</span><span class="p">),</span> |
| <span class="n">env</span><span class="p">,</span> |
| <span class="n">includes</span><span class="p">,</span> |
| <span class="n">sc</span><span class="o">.</span><span class="n">pythonExec</span><span class="p">,</span> |
| <span class="n">sc</span><span class="o">.</span><span class="n">pythonVer</span><span class="p">,</span> |
| <span class="n">broadcast_vars</span><span class="p">,</span> |
| <span class="n">sc</span><span class="o">.</span><span class="n">_javaAccumulator</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_create_udf</span><span class="p">(</span> |
| <span class="n">f</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> |
| <span class="n">returnType</span><span class="p">:</span> <span class="s2">"DataTypeOrString"</span><span class="p">,</span> |
| <span class="n">evalType</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> |
| <span class="n">name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">deterministic</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="s2">"UserDefinedFunctionLike"</span><span class="p">:</span> |
| <span class="c1"># Set the name of the UserDefinedFunction object to be the name of function f</span> |
| <span class="n">udf_obj</span> <span class="o">=</span> <span class="n">UserDefinedFunction</span><span class="p">(</span> |
| <span class="n">f</span><span class="p">,</span> <span class="n">returnType</span><span class="o">=</span><span class="n">returnType</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">evalType</span><span class="o">=</span><span class="n">evalType</span><span class="p">,</span> <span class="n">deterministic</span><span class="o">=</span><span class="n">deterministic</span> |
| <span class="p">)</span> |
| <span class="k">return</span> <span class="n">udf_obj</span><span class="o">.</span><span class="n">_wrapped</span><span class="p">()</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_create_py_udf</span><span class="p">(</span> |
| <span class="n">f</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> |
| <span class="n">returnType</span><span class="p">:</span> <span class="s2">"DataTypeOrString"</span><span class="p">,</span> |
| <span class="n">evalType</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="s2">"UserDefinedFunctionLike"</span><span class="p">:</span> |
| <span class="c1"># The following table shows the results when the type coercion in Arrow is needed, that is,</span> |
| <span class="c1"># when the user-specified return type(SQL Type) of the UDF and the actual instance(Python</span> |
| <span class="c1"># Value(Type)) that the UDF returns are different.</span> |
| <span class="c1"># Arrow and Pickle have different type coercion rules, so a UDF might have a different result</span> |
| <span class="c1"># with/without Arrow optimization. That's the main reason the Arrow optimization for Python</span> |
| <span class="c1"># UDFs is disabled by default.</span> |
| <span class="c1"># +-----------------------------+--------------+----------+------+---------------+--------------------+-----------------------------+----------+----------------------+---------+--------------------+----------------------------+------------+--------------+ # noqa</span> |
| <span class="c1"># |SQL Type \ Python Value(Type)|None(NoneType)|True(bool)|1(int)| a(str)| 1970-01-01(date)|1970-01-01 00:00:00(datetime)|1.0(float)|array('i', [1])(array)|[1](list)| (1,)(tuple)|bytearray(b'ABC')(bytearray)| 1(Decimal)|{'a': 1}(dict)| # noqa</span> |
| <span class="c1"># +-----------------------------+--------------+----------+------+---------------+--------------------+-----------------------------+----------+----------------------+---------+--------------------+----------------------------+------------+--------------+ # noqa</span> |
| <span class="c1"># | boolean| None| True| None| None| None| None| None| None| None| None| None| None| None| # noqa</span> |
| <span class="c1"># | tinyint| None| None| 1| None| None| None| None| None| None| None| None| None| None| # noqa</span> |
| <span class="c1"># | smallint| None| None| 1| None| None| None| None| None| None| None| None| None| None| # noqa</span> |
| <span class="c1"># | int| None| None| 1| None| None| None| None| None| None| None| None| None| None| # noqa</span> |
| <span class="c1"># | bigint| None| None| 1| None| None| None| None| None| None| None| None| None| None| # noqa</span> |
| <span class="c1"># | string| None| 'true'| '1'| 'a'|'java.util.Gregor...| 'java.util.Gregor...| '1.0'| '[I@120d813a'| '[1]'|'[Ljava.lang.Obje...| '[B@48571878'| '1'| '{a=1}'| # noqa</span> |
| <span class="c1"># | date| None| X| X| X|datetime.date(197...| datetime.date(197...| X| X| X| X| X| X| X| # noqa</span> |
| <span class="c1"># | timestamp| None| X| X| X| X| datetime.datetime...| X| X| X| X| X| X| X| # noqa</span> |
| <span class="c1"># | float| None| None| None| None| None| None| 1.0| None| None| None| None| None| None| # noqa</span> |
| <span class="c1"># | double| None| None| None| None| None| None| 1.0| None| None| None| None| None| None| # noqa</span> |
| <span class="c1"># | binary| None| None| None|bytearray(b'a')| None| None| None| None| None| None| bytearray(b'ABC')| None| None| # noqa</span> |
| <span class="c1"># | decimal(10,0)| None| None| None| None| None| None| None| None| None| None| None|Decimal('1')| None| # noqa</span> |
| <span class="c1"># +-----------------------------+--------------+----------+------+---------------+--------------------+-----------------------------+----------+----------------------+---------+--------------------+----------------------------+------------+--------------+ # noqa</span> |
| <span class="c1"># Note: Python 3.9.15, Pandas 1.5.2 and PyArrow 10.0.1 are used.</span> |
| <span class="c1"># Note: The values of 'SQL Type' are DDL formatted strings, which can be used as `returnType`s.</span> |
| <span class="c1"># Note: The values inside the table are generated by `repr`. X' means it throws an exception</span> |
| <span class="c1"># during the conversion.</span> |
| |
| <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span> |
| |
| <span class="n">session</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">_instantiatedSession</span> |
| <span class="n">is_arrow_enabled</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="n">session</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="ow">and</span> <span class="n">session</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"spark.sql.execution.pythonUDF.arrow.enabled"</span><span class="p">)</span> <span class="o">==</span> <span class="s2">"true"</span> |
| <span class="p">)</span> |
| |
| <span class="n">regular_udf</span> <span class="o">=</span> <span class="n">_create_udf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">returnType</span><span class="p">,</span> <span class="n">evalType</span><span class="p">)</span> |
| <span class="n">return_type</span> <span class="o">=</span> <span class="n">regular_udf</span><span class="o">.</span><span class="n">returnType</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">is_func_with_args</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">getfullargspec</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">args</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> |
| <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span> |
| <span class="n">is_func_with_args</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">is_output_atomic_type</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">return_type</span><span class="p">,</span> <span class="n">StructType</span><span class="p">)</span> |
| <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">return_type</span><span class="p">,</span> <span class="n">MapType</span><span class="p">)</span> |
| <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">return_type</span><span class="p">,</span> <span class="n">ArrayType</span><span class="p">)</span> |
| <span class="p">)</span> |
| <span class="k">if</span> <span class="n">is_arrow_enabled</span> <span class="ow">and</span> <span class="n">is_output_atomic_type</span> <span class="ow">and</span> <span class="n">is_func_with_args</span><span class="p">:</span> |
| <span class="n">require_minimum_pandas_version</span><span class="p">()</span> |
| <span class="n">require_minimum_pyarrow_version</span><span class="p">()</span> |
| |
| <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.pandas.functions</span> <span class="kn">import</span> <span class="n">_create_pandas_udf</span> <span class="c1"># type: ignore[attr-defined]</span> |
| |
| <span class="c1"># "result_func" ensures the result of a Python UDF to be consistent with/without Arrow</span> |
| <span class="c1"># optimization.</span> |
| <span class="c1"># Otherwise, an Arrow-optimized Python UDF raises "pyarrow.lib.ArrowTypeError: Expected a</span> |
| <span class="c1"># string or bytes dtype, got ..." whereas a non-Arrow-optimized Python UDF returns</span> |
| <span class="c1"># successfully.</span> |
| <span class="n">result_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">pdf</span><span class="p">:</span> <span class="n">pdf</span> <span class="c1"># noqa: E731</span> |
| <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span> <span class="o">==</span> <span class="n">StringType</span><span class="p">:</span> |
| <span class="n">result_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="n">r</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">r</span> <span class="c1"># noqa: E731</span> |
| <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span> <span class="o">==</span> <span class="n">BinaryType</span><span class="p">:</span> |
| <span class="n">result_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="nb">bytes</span><span class="p">(</span><span class="n">r</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">r</span> <span class="c1"># noqa: E731</span> |
| |
| <span class="k">def</span> <span class="nf">vectorized_udf</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="o">-></span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">arg</span><span class="p">:</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">),</span> <span class="n">args</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> |
| <span class="s2">"Struct input type are not supported with Arrow optimization "</span> |
| <span class="s2">"enabled in Python UDFs. Disable "</span> |
| <span class="s2">"'spark.sql.execution.pythonUDF.arrow.enabled' to workaround."</span> |
| <span class="p">)</span> |
| <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">result_func</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="o">*</span><span class="n">a</span><span class="p">))</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">))</span> |
| |
| <span class="c1"># Regular UDFs can take callable instances too.</span> |
| <span class="n">vectorized_udf</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">"__name__"</span><span class="p">)</span> <span class="k">else</span> <span class="n">f</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> |
| <span class="n">vectorized_udf</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="n">f</span><span class="o">.</span><span class="vm">__module__</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">"__module__"</span><span class="p">)</span> <span class="k">else</span> <span class="n">f</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__module__</span> |
| <span class="p">)</span> |
| <span class="n">vectorized_udf</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="vm">__doc__</span> |
| <span class="n">pudf</span> <span class="o">=</span> <span class="n">_create_pandas_udf</span><span class="p">(</span><span class="n">vectorized_udf</span><span class="p">,</span> <span class="n">returnType</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="c1"># Keep the attributes as if this is a regular Python UDF.</span> |
| <span class="n">pudf</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">f</span> |
| <span class="n">pudf</span><span class="o">.</span><span class="n">returnType</span> <span class="o">=</span> <span class="n">return_type</span> |
| <span class="n">pudf</span><span class="o">.</span><span class="n">evalType</span> <span class="o">=</span> <span class="n">regular_udf</span><span class="o">.</span><span class="n">evalType</span> |
| <span class="k">return</span> <span class="n">pudf</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">regular_udf</span> |
| |
| |
| <div class="viewcode-block" id="UserDefinedFunction"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.udf.UserDefinedFunction.html#pyspark.sql.UserDefinedFunction">[docs]</a><span class="k">class</span> <span class="nc">UserDefinedFunction</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> User defined function in Python</span> |
| |
| <span class="sd"> .. versionadded:: 1.3</span> |
| |
| <span class="sd"> Notes</span> |
| <span class="sd"> -----</span> |
| <span class="sd"> The constructor of this class is not supposed to be directly called.</span> |
| <span class="sd"> Use :meth:`pyspark.sql.functions.udf` or :meth:`pyspark.sql.functions.pandas_udf`</span> |
| <span class="sd"> to create this instance.</span> |
| <span class="sd"> """</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="n">func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> |
| <span class="n">returnType</span><span class="p">:</span> <span class="s2">"DataTypeOrString"</span> <span class="o">=</span> <span class="n">StringType</span><span class="p">(),</span> |
| <span class="n">name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">evalType</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_BATCHED_UDF</span><span class="p">,</span> |
| <span class="n">deterministic</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">callable</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s2">"Invalid function: not a function or callable (__call__ is not defined): "</span> |
| <span class="s2">"</span><span class="si">{0}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">func</span><span class="p">))</span> |
| <span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">returnType</span><span class="p">,</span> <span class="p">(</span><span class="n">DataType</span><span class="p">,</span> <span class="nb">str</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type: returnType should be DataType or str "</span> |
| <span class="s2">"but is </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">returnType</span><span class="p">)</span> |
| <span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">evalType</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s2">"Invalid evaluation type: evalType should be an int but is </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">evalType</span><span class="p">)</span> |
| <span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">func</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_returnType</span> <span class="o">=</span> <span class="n">returnType</span> |
| <span class="c1"># Stores UserDefinedPythonFunctions jobj, once initialized</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DataType</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_judf_placeholder</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span> <span class="ow">or</span> <span class="p">(</span> |
| <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="s2">"__name__"</span><span class="p">)</span> <span class="k">else</span> <span class="n">func</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> |
| <span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">=</span> <span class="n">evalType</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">deterministic</span> <span class="o">=</span> <span class="n">deterministic</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">returnType</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">DataType</span><span class="p">:</span> |
| <span class="c1"># This makes sure this is called after SparkContext is initialized.</span> |
| <span class="c1"># ``_parse_datatype_string`` accesses to JVM for parsing a DDL formatted string.</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType</span><span class="p">,</span> <span class="n">DataType</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_returnType</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span> <span class="o">=</span> <span class="n">_parse_datatype_string</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">==</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_SCALAR_PANDAS_UDF</span> |
| <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">==</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_SCALAR_PANDAS_ITER_UDF</span> |
| <span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">to_arrow_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type with scalar Pandas UDFs: </span><span class="si">%s</span><span class="s2"> is "</span> |
| <span class="s2">"not supported"</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">==</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_GROUPED_MAP_PANDAS_UDF</span> |
| <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">==</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE</span> |
| <span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">,</span> <span class="n">StructType</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">to_arrow_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type with grouped map Pandas UDFs or "</span> |
| <span class="s2">"at groupby.applyInPandas(WithState): </span><span class="si">%s</span><span class="s2"> is not supported"</span> |
| <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type for grouped map Pandas "</span> |
| <span class="s2">"UDFs or at groupby.applyInPandas(WithState): return type must be a "</span> |
| <span class="s2">"StructType."</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">==</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_MAP_PANDAS_ITER_UDF</span> |
| <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">==</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_MAP_ARROW_ITER_UDF</span> |
| <span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">,</span> <span class="n">StructType</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">to_arrow_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type in mapInPandas: "</span> |
| <span class="s2">"</span><span class="si">%s</span><span class="s2"> is not supported"</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type in mapInPandas/mapInArrow: "</span> |
| <span class="s2">"return type must be a StructType."</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">==</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_COGROUPED_MAP_PANDAS_UDF</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">,</span> <span class="n">StructType</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">to_arrow_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type in cogroup.applyInPandas: "</span> |
| <span class="s2">"</span><span class="si">%s</span><span class="s2"> is not supported"</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type in cogroup.applyInPandas: "</span> |
| <span class="s2">"return type must be a StructType."</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="o">==</span> <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_GROUPED_AGG_PANDAS_UDF</span><span class="p">:</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="c1"># StructType is not yet allowed as a return type, explicitly check here to fail fast</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">,</span> <span class="n">StructType</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span> |
| <span class="n">to_arrow_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type with grouped aggregate Pandas UDFs: "</span> |
| <span class="s2">"</span><span class="si">%s</span><span class="s2"> is not supported"</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span><span class="p">)</span> |
| <span class="p">)</span> |
| |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_returnType_placeholder</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">_judf</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">JavaObject</span><span class="p">:</span> |
| <span class="c1"># It is possible that concurrent access, to newly created UDF,</span> |
| <span class="c1"># will initialize multiple UserDefinedPythonFunctions.</span> |
| <span class="c1"># This is unlikely, doesn't affect correctness,</span> |
| <span class="c1"># and should have a minimal performance impact.</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_judf_placeholder</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_judf_placeholder</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_judf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_judf_placeholder</span> |
| |
| <span class="k">def</span> <span class="nf">_create_judf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="n">JavaObject</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span> |
| |
| <span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">_getActiveSessionOrCreate</span><span class="p">()</span> |
| <span class="n">sc</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sparkContext</span> |
| |
| <span class="n">wrapped_func</span> <span class="o">=</span> <span class="n">_wrap_function</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">returnType</span><span class="p">)</span> |
| <span class="n">jdt</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">_jsparkSession</span><span class="o">.</span><span class="n">parseDataType</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">returnType</span><span class="o">.</span><span class="n">json</span><span class="p">())</span> |
| <span class="k">assert</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="n">judf</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">execution</span><span class="o">.</span><span class="n">python</span><span class="o">.</span><span class="n">UserDefinedPythonFunction</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_name</span><span class="p">,</span> <span class="n">wrapped_func</span><span class="p">,</span> <span class="n">jdt</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">deterministic</span> |
| <span class="p">)</span> |
| <span class="k">return</span> <span class="n">judf</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="s2">"ColumnOrName"</span><span class="p">)</span> <span class="o">-></span> <span class="n">Column</span><span class="p">:</span> |
| <span class="n">sc</span> <span class="o">=</span> <span class="n">get_active_spark_context</span><span class="p">()</span> |
| <span class="n">profiler</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Profiler</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">memory_profiler</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Profiler</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">sc</span><span class="o">.</span><span class="n">profiler_collector</span><span class="p">:</span> |
| <span class="n">profiler_enabled</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"spark.python.profile"</span><span class="p">,</span> <span class="s2">"false"</span><span class="p">)</span> <span class="o">==</span> <span class="s2">"true"</span> |
| <span class="n">memory_profiler_enabled</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"spark.python.profile.memory"</span><span class="p">,</span> <span class="s2">"false"</span><span class="p">)</span> <span class="o">==</span> <span class="s2">"true"</span> |
| |
| <span class="c1"># Disable profiling Pandas UDFs with iterators as input/output.</span> |
| <span class="k">if</span> <span class="n">profiler_enabled</span> <span class="ow">or</span> <span class="n">memory_profiler_enabled</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="ow">in</span> <span class="p">[</span> |
| <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_SCALAR_PANDAS_ITER_UDF</span><span class="p">,</span> |
| <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_MAP_PANDAS_ITER_UDF</span><span class="p">,</span> |
| <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_MAP_ARROW_ITER_UDF</span><span class="p">,</span> |
| <span class="p">]:</span> |
| <span class="n">profiler_enabled</span> <span class="o">=</span> <span class="n">memory_profiler_enabled</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span> |
| <span class="s2">"Profiling UDFs with iterators input/output is not supported."</span><span class="p">,</span> |
| <span class="ne">UserWarning</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="c1"># Disallow enabling two profilers at the same time.</span> |
| <span class="k">if</span> <span class="n">profiler_enabled</span> <span class="ow">and</span> <span class="n">memory_profiler_enabled</span><span class="p">:</span> |
| <span class="c1"># When both profilers are enabled, they interfere with each other,</span> |
| <span class="c1"># that makes the result profile misleading.</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> |
| <span class="s2">"'spark.python.profile' and 'spark.python.profile.memory' configuration"</span> |
| <span class="s2">" cannot be enabled together."</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="n">profiler_enabled</span><span class="p">:</span> |
| <span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">func</span> |
| <span class="n">profiler</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">profiler_collector</span><span class="o">.</span><span class="n">new_udf_profiler</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span> |
| |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">func</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">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">profiler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="k">return</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="n">func</span><span class="o">.</span><span class="n">__signature__</span> <span class="o">=</span> <span class="n">inspect</span><span class="o">.</span><span class="n">signature</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="n">judf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_judf</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="n">jPythonUDF</span> <span class="o">=</span> <span class="n">judf</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">))</span> |
| <span class="nb">id</span> <span class="o">=</span> <span class="n">jPythonUDF</span><span class="o">.</span><span class="n">expr</span><span class="p">()</span><span class="o">.</span><span class="n">resultId</span><span class="p">()</span><span class="o">.</span><span class="n">id</span><span class="p">()</span> |
| <span class="n">sc</span><span class="o">.</span><span class="n">profiler_collector</span><span class="o">.</span><span class="n">add_profiler</span><span class="p">(</span><span class="nb">id</span><span class="p">,</span> <span class="n">profiler</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> <span class="c1"># memory_profiler_enabled</span> |
| <span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">func</span> |
| <span class="n">memory_profiler</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">profiler_collector</span><span class="o">.</span><span class="n">new_memory_profiler</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span> |
| <span class="p">(</span><span class="n">sub_lines</span><span class="p">,</span> <span class="n">start_line</span><span class="p">)</span> <span class="o">=</span> <span class="n">inspect</span><span class="o">.</span><span class="n">getsourcelines</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="vm">__code__</span><span class="p">)</span> |
| |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">func</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">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">memory_profiler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="k">return</span> <span class="n">memory_profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span> |
| <span class="n">sub_lines</span><span class="p">,</span> <span class="n">start_line</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="c1"># type: ignore[arg-type]</span> |
| <span class="p">)</span> |
| |
| <span class="n">func</span><span class="o">.</span><span class="n">__signature__</span> <span class="o">=</span> <span class="n">inspect</span><span class="o">.</span><span class="n">signature</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="n">judf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_judf</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="n">jPythonUDF</span> <span class="o">=</span> <span class="n">judf</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">))</span> |
| <span class="nb">id</span> <span class="o">=</span> <span class="n">jPythonUDF</span><span class="o">.</span><span class="n">expr</span><span class="p">()</span><span class="o">.</span><span class="n">resultId</span><span class="p">()</span><span class="o">.</span><span class="n">id</span><span class="p">()</span> |
| <span class="n">sc</span><span class="o">.</span><span class="n">profiler_collector</span><span class="o">.</span><span class="n">add_profiler</span><span class="p">(</span><span class="nb">id</span><span class="p">,</span> <span class="n">memory_profiler</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">judf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_judf</span> |
| <span class="n">jPythonUDF</span> <span class="o">=</span> <span class="n">judf</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jPythonUDF</span><span class="p">)</span> |
| |
| <span class="c1"># This function is for improving the online help system in the interactive interpreter.</span> |
| <span class="c1"># For example, the built-in help / pydoc.help. It wraps the UDF with the docstring and</span> |
| <span class="c1"># argument annotation. (See: SPARK-19161)</span> |
| <span class="k">def</span> <span class="nf">_wrapped</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="s2">"UserDefinedFunctionLike"</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Wrap this udf with a function and attach docstring from func</span> |
| <span class="sd"> """</span> |
| |
| <span class="c1"># It is possible for a callable instance without __name__ attribute or/and</span> |
| <span class="c1"># __module__ attribute to be wrapped here. For example, functools.partial. In this case,</span> |
| <span class="c1"># we should avoid wrapping the attributes from the wrapped function to the wrapper</span> |
| <span class="c1"># function. So, we take out these attribute names from the default names to set and</span> |
| <span class="c1"># then manually assign it after being wrapped.</span> |
| <span class="n">assignments</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span> |
| <span class="n">a</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">functools</span><span class="o">.</span><span class="n">WRAPPER_ASSIGNMENTS</span> <span class="k">if</span> <span class="n">a</span> <span class="o">!=</span> <span class="s2">"__name__"</span> <span class="ow">and</span> <span class="n">a</span> <span class="o">!=</span> <span class="s2">"__module__"</span> |
| <span class="p">)</span> |
| |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">,</span> <span class="n">assigned</span><span class="o">=</span><span class="n">assignments</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">wrapper</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="s2">"ColumnOrName"</span><span class="p">)</span> <span class="o">-></span> <span class="n">Column</span><span class="p">:</span> |
| <span class="k">return</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">wrapper</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> |
| <span class="n">wrapper</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="vm">__module__</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">,</span> <span class="s2">"__module__"</span><span class="p">)</span> |
| <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__module__</span> |
| <span class="p">)</span> |
| |
| <span class="n">wrapper</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="n">wrapper</span><span class="o">.</span><span class="n">returnType</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">returnType</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="n">wrapper</span><span class="o">.</span><span class="n">evalType</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evalType</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="n">wrapper</span><span class="o">.</span><span class="n">deterministic</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">deterministic</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="n">wrapper</span><span class="o">.</span><span class="n">asNondeterministic</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">asNondeterministic</span> |
| <span class="p">)(</span><span class="k">lambda</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">asNondeterministic</span><span class="p">()</span><span class="o">.</span><span class="n">_wrapped</span><span class="p">())</span> |
| <span class="n">wrapper</span><span class="o">.</span><span class="n">_unwrapped</span> <span class="o">=</span> <span class="bp">self</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="k">return</span> <span class="n">wrapper</span> <span class="c1"># type: ignore[return-value]</span> |
| |
| <div class="viewcode-block" id="UserDefinedFunction.asNondeterministic"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.udf.UserDefinedFunction.asNondeterministic.html#pyspark.sql.UserDefinedFunction.asNondeterministic">[docs]</a> <span class="k">def</span> <span class="nf">asNondeterministic</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="s2">"UserDefinedFunction"</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Updates UserDefinedFunction to nondeterministic.</span> |
| |
| <span class="sd"> .. versionadded:: 2.3</span> |
| <span class="sd"> """</span> |
| <span class="c1"># Here, we explicitly clean the cache to create a JVM UDF instance</span> |
| <span class="c1"># with 'deterministic' updated. See SPARK-23233.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_judf_placeholder</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">deterministic</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">return</span> <span class="bp">self</span></div></div> |
| |
| |
| <div class="viewcode-block" id="UDFRegistration"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.UDFRegistration.html#pyspark.sql.UDFRegistration">[docs]</a><span class="k">class</span> <span class="nc">UDFRegistration</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Wrapper for user-defined function registration. This instance can be accessed by</span> |
| <span class="sd"> :attr:`spark.udf` or :attr:`sqlContext.udf`.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.1</span> |
| <span class="sd"> """</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="n">sparkSession</span><span class="p">:</span> <span class="s2">"SparkSession"</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sparkSession</span> <span class="o">=</span> <span class="n">sparkSession</span> |
| |
| <div class="viewcode-block" id="UDFRegistration.register"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.UDFRegistration.register.html#pyspark.sql.UDFRegistration.register">[docs]</a> <span class="k">def</span> <span class="nf">register</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">f</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="s2">"UserDefinedFunctionLike"</span><span class="p">],</span> |
| <span class="n">returnType</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="s2">"DataTypeOrString"</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="s2">"UserDefinedFunctionLike"</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Register a Python function (including lambda function) or a user-defined function</span> |
| <span class="sd"> as a SQL function.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.1</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str,</span> |
| <span class="sd"> name of the user-defined function in SQL statements.</span> |
| <span class="sd"> f : function, :meth:`pyspark.sql.functions.udf` or :meth:`pyspark.sql.functions.pandas_udf`</span> |
| <span class="sd"> a Python function, or a user-defined function. The user-defined function can</span> |
| <span class="sd"> be either row-at-a-time or vectorized. See :meth:`pyspark.sql.functions.udf` and</span> |
| <span class="sd"> :meth:`pyspark.sql.functions.pandas_udf`.</span> |
| <span class="sd"> returnType : :class:`pyspark.sql.types.DataType` or str, optional</span> |
| <span class="sd"> the return type of the registered user-defined function. The value can</span> |
| <span class="sd"> be either a :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.</span> |
| <span class="sd"> `returnType` can be optionally specified when `f` is a Python function but not</span> |
| <span class="sd"> when `f` is a user-defined function. Please see the examples below.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> function</span> |
| <span class="sd"> a user-defined function</span> |
| |
| <span class="sd"> Notes</span> |
| <span class="sd"> -----</span> |
| <span class="sd"> To register a nondeterministic Python function, users need to first build</span> |
| <span class="sd"> a nondeterministic user-defined function for the Python function and then register it</span> |
| <span class="sd"> as a SQL function.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> 1. When `f` is a Python function:</span> |
| |
| <span class="sd"> `returnType` defaults to string type and can be optionally specified. The produced</span> |
| <span class="sd"> object must match the specified type. In this case, this API works as if</span> |
| <span class="sd"> `register(name, f, returnType=StringType())`.</span> |
| |
| <span class="sd"> >>> strlen = spark.udf.register("stringLengthString", lambda x: len(x))</span> |
| <span class="sd"> >>> spark.sql("SELECT stringLengthString('test')").collect()</span> |
| <span class="sd"> [Row(stringLengthString(test)='4')]</span> |
| |
| <span class="sd"> >>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect()</span> |
| <span class="sd"> [Row(stringLengthString(text)='3')]</span> |
| |
| <span class="sd"> >>> from pyspark.sql.types import IntegerType</span> |
| <span class="sd"> >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType())</span> |
| <span class="sd"> >>> spark.sql("SELECT stringLengthInt('test')").collect()</span> |
| <span class="sd"> [Row(stringLengthInt(test)=4)]</span> |
| |
| <span class="sd"> >>> from pyspark.sql.types import IntegerType</span> |
| <span class="sd"> >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType())</span> |
| <span class="sd"> >>> spark.sql("SELECT stringLengthInt('test')").collect()</span> |
| <span class="sd"> [Row(stringLengthInt(test)=4)]</span> |
| |
| <span class="sd"> 2. When `f` is a user-defined function (from Spark 2.3.0):</span> |
| |
| <span class="sd"> Spark uses the return type of the given user-defined function as the return type of</span> |
| <span class="sd"> the registered user-defined function. `returnType` should not be specified.</span> |
| <span class="sd"> In this case, this API works as if `register(name, f)`.</span> |
| |
| <span class="sd"> >>> from pyspark.sql.types import IntegerType</span> |
| <span class="sd"> >>> from pyspark.sql.functions import udf</span> |
| <span class="sd"> >>> slen = udf(lambda s: len(s), IntegerType())</span> |
| <span class="sd"> >>> _ = spark.udf.register("slen", slen)</span> |
| <span class="sd"> >>> spark.sql("SELECT slen('test')").collect()</span> |
| <span class="sd"> [Row(slen(test)=4)]</span> |
| |
| <span class="sd"> >>> import random</span> |
| <span class="sd"> >>> from pyspark.sql.functions import udf</span> |
| <span class="sd"> >>> from pyspark.sql.types import IntegerType</span> |
| <span class="sd"> >>> random_udf = udf(lambda: random.randint(0, 100), IntegerType()).asNondeterministic()</span> |
| <span class="sd"> >>> new_random_udf = spark.udf.register("random_udf", random_udf)</span> |
| <span class="sd"> >>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP</span> |
| <span class="sd"> [Row(random_udf()=82)]</span> |
| |
| <span class="sd"> >>> import pandas as pd # doctest: +SKIP</span> |
| <span class="sd"> >>> from pyspark.sql.functions import pandas_udf</span> |
| <span class="sd"> >>> @pandas_udf("integer") # doctest: +SKIP</span> |
| <span class="sd"> ... def add_one(s: pd.Series) -> pd.Series:</span> |
| <span class="sd"> ... return s + 1</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> _ = spark.udf.register("add_one", add_one) # doctest: +SKIP</span> |
| <span class="sd"> >>> spark.sql("SELECT add_one(id) FROM range(3)").collect() # doctest: +SKIP</span> |
| <span class="sd"> [Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)]</span> |
| |
| <span class="sd"> >>> @pandas_udf("integer") # doctest: +SKIP</span> |
| <span class="sd"> ... def sum_udf(v: pd.Series) -> int:</span> |
| <span class="sd"> ... return v.sum()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> _ = spark.udf.register("sum_udf", sum_udf) # doctest: +SKIP</span> |
| <span class="sd"> >>> q = "SELECT sum_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2"</span> |
| <span class="sd"> >>> spark.sql(q).collect() # doctest: +SKIP</span> |
| <span class="sd"> [Row(sum_udf(v1)=1), Row(sum_udf(v1)=5)]</span> |
| |
| <span class="sd"> """</span> |
| |
| <span class="c1"># This is to check whether the input function is from a user-defined function or</span> |
| <span class="c1"># Python function.</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">"asNondeterministic"</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">returnType</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s2">"Invalid return type: data type can not be specified when f is"</span> |
| <span class="s2">"a user-defined function, but got </span><span class="si">%s</span><span class="s2">."</span> <span class="o">%</span> <span class="n">returnType</span> |
| <span class="p">)</span> |
| <span class="n">f</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="s2">"UserDefinedFunctionLike"</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">f</span><span class="o">.</span><span class="n">evalType</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span> |
| <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_BATCHED_UDF</span><span class="p">,</span> |
| <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_SCALAR_PANDAS_UDF</span><span class="p">,</span> |
| <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_SCALAR_PANDAS_ITER_UDF</span><span class="p">,</span> |
| <span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_GROUPED_AGG_PANDAS_UDF</span><span class="p">,</span> |
| <span class="p">]:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"Invalid f: f must be SQL_BATCHED_UDF, SQL_SCALAR_PANDAS_UDF, "</span> |
| <span class="s2">"SQL_SCALAR_PANDAS_ITER_UDF or SQL_GROUPED_AGG_PANDAS_UDF."</span> |
| <span class="p">)</span> |
| <span class="n">register_udf</span> <span class="o">=</span> <span class="n">_create_udf</span><span class="p">(</span> |
| <span class="n">f</span><span class="o">.</span><span class="n">func</span><span class="p">,</span> |
| <span class="n">returnType</span><span class="o">=</span><span class="n">f</span><span class="o">.</span><span class="n">returnType</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> |
| <span class="n">evalType</span><span class="o">=</span><span class="n">f</span><span class="o">.</span><span class="n">evalType</span><span class="p">,</span> |
| <span class="n">deterministic</span><span class="o">=</span><span class="n">f</span><span class="o">.</span><span class="n">deterministic</span><span class="p">,</span> |
| <span class="p">)</span><span class="o">.</span><span class="n">_unwrapped</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="n">return_udf</span> <span class="o">=</span> <span class="n">f</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">returnType</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">returnType</span> <span class="o">=</span> <span class="n">StringType</span><span class="p">()</span> |
| <span class="n">return_udf</span> <span class="o">=</span> <span class="n">_create_udf</span><span class="p">(</span> |
| <span class="n">f</span><span class="p">,</span> <span class="n">returnType</span><span class="o">=</span><span class="n">returnType</span><span class="p">,</span> <span class="n">evalType</span><span class="o">=</span><span class="n">PythonEvalType</span><span class="o">.</span><span class="n">SQL_BATCHED_UDF</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span> |
| <span class="p">)</span> |
| <span class="n">register_udf</span> <span class="o">=</span> <span class="n">return_udf</span><span class="o">.</span><span class="n">_unwrapped</span> <span class="c1"># type: ignore[attr-defined]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sparkSession</span><span class="o">.</span><span class="n">_jsparkSession</span><span class="o">.</span><span class="n">udf</span><span class="p">()</span><span class="o">.</span><span class="n">registerPython</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">register_udf</span><span class="o">.</span><span class="n">_judf</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">return_udf</span></div> |
| |
| <div class="viewcode-block" id="UDFRegistration.registerJavaFunction"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.UDFRegistration.registerJavaFunction.html#pyspark.sql.UDFRegistration.registerJavaFunction">[docs]</a> <span class="k">def</span> <span class="nf">registerJavaFunction</span><span class="p">(</span> |
| <span class="bp">self</span><span class="p">,</span> |
| <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">javaClassName</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">returnType</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="s2">"DataTypeOrString"</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</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">"""Register a Java user-defined function as a SQL function.</span> |
| |
| <span class="sd"> In addition to a name and the function itself, the return type can be optionally specified.</span> |
| <span class="sd"> When the return type is not specified we would infer it via reflection.</span> |
| |
| <span class="sd"> .. versionadded:: 2.3.0</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> name of the user-defined function</span> |
| <span class="sd"> javaClassName : str</span> |
| <span class="sd"> fully qualified name of java class</span> |
| <span class="sd"> returnType : :class:`pyspark.sql.types.DataType` or str, optional</span> |
| <span class="sd"> the return type of the registered Java function. The value can be either</span> |
| <span class="sd"> a :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> from pyspark.sql.types import IntegerType</span> |
| <span class="sd"> >>> spark.udf.registerJavaFunction(</span> |
| <span class="sd"> ... "javaStringLength", "test.org.apache.spark.sql.JavaStringLength", IntegerType())</span> |
| <span class="sd"> ... # doctest: +SKIP</span> |
| <span class="sd"> >>> spark.sql("SELECT javaStringLength('test')").collect() # doctest: +SKIP</span> |
| <span class="sd"> [Row(javaStringLength(test)=4)]</span> |
| |
| <span class="sd"> >>> spark.udf.registerJavaFunction(</span> |
| <span class="sd"> ... "javaStringLength2", "test.org.apache.spark.sql.JavaStringLength")</span> |
| <span class="sd"> ... # doctest: +SKIP</span> |
| <span class="sd"> >>> spark.sql("SELECT javaStringLength2('test')").collect() # doctest: +SKIP</span> |
| <span class="sd"> [Row(javaStringLength2(test)=4)]</span> |
| |
| <span class="sd"> >>> spark.udf.registerJavaFunction(</span> |
| <span class="sd"> ... "javaStringLength3", "test.org.apache.spark.sql.JavaStringLength", "integer")</span> |
| <span class="sd"> ... # doctest: +SKIP</span> |
| <span class="sd"> >>> spark.sql("SELECT javaStringLength3('test')").collect() # doctest: +SKIP</span> |
| <span class="sd"> [Row(javaStringLength3(test)=4)]</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">jdt</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">returnType</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">returnType</span><span class="p">,</span> <span class="n">DataType</span><span class="p">):</span> |
| <span class="n">returnType</span> <span class="o">=</span> <span class="n">_parse_datatype_string</span><span class="p">(</span><span class="n">returnType</span><span class="p">)</span> |
| <span class="n">jdt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparkSession</span><span class="o">.</span><span class="n">_jsparkSession</span><span class="o">.</span><span class="n">parseDataType</span><span class="p">(</span><span class="n">returnType</span><span class="o">.</span><span class="n">json</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sparkSession</span><span class="o">.</span><span class="n">_jsparkSession</span><span class="o">.</span><span class="n">udf</span><span class="p">()</span><span class="o">.</span><span class="n">registerJava</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">javaClassName</span><span class="p">,</span> <span class="n">jdt</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="UDFRegistration.registerJavaUDAF"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.UDFRegistration.registerJavaUDAF.html#pyspark.sql.UDFRegistration.registerJavaUDAF">[docs]</a> <span class="k">def</span> <span class="nf">registerJavaUDAF</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">javaClassName</span><span class="p">:</span> <span class="nb">str</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">"""Register a Java user-defined aggregate function as a SQL function.</span> |
| |
| <span class="sd"> .. versionadded:: 2.3.0</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</span> |
| |
| <span class="sd"> name : str</span> |
| <span class="sd"> name of the user-defined aggregate function</span> |
| <span class="sd"> javaClassName : str</span> |
| <span class="sd"> fully qualified name of java class</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> spark.udf.registerJavaUDAF("javaUDAF", "test.org.apache.spark.sql.MyDoubleAvg")</span> |
| <span class="sd"> ... # doctest: +SKIP</span> |
| <span class="sd"> >>> df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "a")],["id", "name"])</span> |
| <span class="sd"> >>> df.createOrReplaceTempView("df")</span> |
| <span class="sd"> >>> q = "SELECT name, javaUDAF(id) as avg from df group by name order by name desc"</span> |
| <span class="sd"> >>> spark.sql(q).collect() # doctest: +SKIP</span> |
| <span class="sd"> [Row(name='b', avg=102.0), Row(name='a', avg=102.0)]</span> |
| <span class="sd"> """</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">sparkSession</span><span class="o">.</span><span class="n">_jsparkSession</span><span class="o">.</span><span class="n">udf</span><span class="p">()</span><span class="o">.</span><span class="n">registerJavaUDAF</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">javaClassName</span><span class="p">)</span></div></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_test</span><span class="p">()</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">doctest</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span> |
| <span class="kn">import</span> <span class="nn">pyspark.sql.udf</span> |
| |
| <span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">udf</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">"local[4]"</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">"sql.udf tests"</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span> |
| <span class="n">globs</span><span class="p">[</span><span class="s2">"spark"</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span> |
| <span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span> |
| <span class="n">pyspark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">udf</span><span class="p">,</span> <span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span> <span class="o">|</span> <span class="n">doctest</span><span class="o">.</span><span class="n">NORMALIZE_WHITESPACE</span> |
| <span class="p">)</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span> |
| <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| |
| |
| <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span> |
| <span class="n">_test</span><span class="p">()</span> |
| </pre></div> |
| |
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