3959f0d987
### What changes were proposed in this pull request? This PR proposes to migrate to [NumPy documentation style](https://numpydoc.readthedocs.io/en/latest/format.html), see also SPARK-33243. While I am migrating, I also fixed some Python type hints accordingly. ### Why are the changes needed? For better documentation as text itself, and generated HTMLs ### Does this PR introduce _any_ user-facing change? Yes, they will see a better format of HTMLs, and better text format. See SPARK-33243. ### How was this patch tested? Manually tested via running `./dev/lint-python`. Closes #30181 from HyukjinKwon/SPARK-33250. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
455 lines
20 KiB
Python
455 lines
20 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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User-defined function related classes and functions
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"""
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import functools
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import sys
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from pyspark import SparkContext
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from pyspark.rdd import _prepare_for_python_RDD, PythonEvalType
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from pyspark.sql.column import Column, _to_java_column, _to_seq
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from pyspark.sql.types import StringType, DataType, StructType, _parse_datatype_string
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from pyspark.sql.pandas.types import to_arrow_type
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__all__ = ["UDFRegistration"]
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def _wrap_function(sc, func, returnType):
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command = (func, returnType)
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pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command)
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return sc._jvm.PythonFunction(bytearray(pickled_command), env, includes, sc.pythonExec,
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sc.pythonVer, broadcast_vars, sc._javaAccumulator)
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def _create_udf(f, returnType, evalType):
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# Set the name of the UserDefinedFunction object to be the name of function f
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udf_obj = UserDefinedFunction(
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f, returnType=returnType, name=None, evalType=evalType, deterministic=True)
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return udf_obj._wrapped()
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class UserDefinedFunction(object):
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"""
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User defined function in Python
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.. versionadded:: 1.3
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Notes
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-----
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The constructor of this class is not supposed to be directly called.
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Use :meth:`pyspark.sql.functions.udf` or :meth:`pyspark.sql.functions.pandas_udf`
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to create this instance.
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"""
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def __init__(self, func,
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returnType=StringType(),
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name=None,
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evalType=PythonEvalType.SQL_BATCHED_UDF,
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deterministic=True):
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if not callable(func):
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raise TypeError(
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"Invalid function: not a function or callable (__call__ is not defined): "
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"{0}".format(type(func)))
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if not isinstance(returnType, (DataType, str)):
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raise TypeError(
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"Invalid return type: returnType should be DataType or str "
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"but is {}".format(returnType))
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if not isinstance(evalType, int):
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raise TypeError(
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"Invalid evaluation type: evalType should be an int but is {}".format(evalType))
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self.func = func
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self._returnType = returnType
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# Stores UserDefinedPythonFunctions jobj, once initialized
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self._returnType_placeholder = None
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self._judf_placeholder = None
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self._name = name or (
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func.__name__ if hasattr(func, '__name__')
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else func.__class__.__name__)
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self.evalType = evalType
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self.deterministic = deterministic
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@property
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def returnType(self):
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# This makes sure this is called after SparkContext is initialized.
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# ``_parse_datatype_string`` accesses to JVM for parsing a DDL formatted string.
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if self._returnType_placeholder is None:
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if isinstance(self._returnType, DataType):
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self._returnType_placeholder = self._returnType
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else:
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self._returnType_placeholder = _parse_datatype_string(self._returnType)
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if self.evalType == PythonEvalType.SQL_SCALAR_PANDAS_UDF or \
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self.evalType == PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF:
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try:
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to_arrow_type(self._returnType_placeholder)
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except TypeError:
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raise NotImplementedError(
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"Invalid return type with scalar Pandas UDFs: %s is "
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"not supported" % str(self._returnType_placeholder))
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elif self.evalType == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF:
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if isinstance(self._returnType_placeholder, StructType):
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try:
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to_arrow_type(self._returnType_placeholder)
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except TypeError:
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raise NotImplementedError(
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"Invalid return type with grouped map Pandas UDFs or "
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"at groupby.applyInPandas: %s is not supported" % str(
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self._returnType_placeholder))
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else:
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raise TypeError("Invalid return type for grouped map Pandas "
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"UDFs or at groupby.applyInPandas: return type must be a "
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"StructType.")
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elif self.evalType == PythonEvalType.SQL_MAP_PANDAS_ITER_UDF:
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if isinstance(self._returnType_placeholder, StructType):
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try:
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to_arrow_type(self._returnType_placeholder)
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except TypeError:
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raise NotImplementedError(
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"Invalid return type in mapInPandas: "
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"%s is not supported" % str(self._returnType_placeholder))
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else:
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raise TypeError("Invalid return type in mapInPandas: "
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"return type must be a StructType.")
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elif self.evalType == PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF:
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if isinstance(self._returnType_placeholder, StructType):
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try:
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to_arrow_type(self._returnType_placeholder)
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except TypeError:
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raise NotImplementedError(
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"Invalid return type in cogroup.applyInPandas: "
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"%s is not supported" % str(self._returnType_placeholder))
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else:
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raise TypeError("Invalid return type in cogroup.applyInPandas: "
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"return type must be a StructType.")
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elif self.evalType == PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF:
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try:
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# StructType is not yet allowed as a return type, explicitly check here to fail fast
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if isinstance(self._returnType_placeholder, StructType):
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raise TypeError
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to_arrow_type(self._returnType_placeholder)
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except TypeError:
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raise NotImplementedError(
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"Invalid return type with grouped aggregate Pandas UDFs: "
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"%s is not supported" % str(self._returnType_placeholder))
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return self._returnType_placeholder
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@property
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def _judf(self):
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# It is possible that concurrent access, to newly created UDF,
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# will initialize multiple UserDefinedPythonFunctions.
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# This is unlikely, doesn't affect correctness,
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# and should have a minimal performance impact.
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if self._judf_placeholder is None:
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self._judf_placeholder = self._create_judf()
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return self._judf_placeholder
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def _create_judf(self):
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from pyspark.sql import SparkSession
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spark = SparkSession.builder.getOrCreate()
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sc = spark.sparkContext
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wrapped_func = _wrap_function(sc, self.func, self.returnType)
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jdt = spark._jsparkSession.parseDataType(self.returnType.json())
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judf = sc._jvm.org.apache.spark.sql.execution.python.UserDefinedPythonFunction(
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self._name, wrapped_func, jdt, self.evalType, self.deterministic)
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return judf
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def __call__(self, *cols):
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judf = self._judf
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sc = SparkContext._active_spark_context
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return Column(judf.apply(_to_seq(sc, cols, _to_java_column)))
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# This function is for improving the online help system in the interactive interpreter.
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# For example, the built-in help / pydoc.help. It wraps the UDF with the docstring and
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# argument annotation. (See: SPARK-19161)
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def _wrapped(self):
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"""
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Wrap this udf with a function and attach docstring from func
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"""
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# It is possible for a callable instance without __name__ attribute or/and
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# __module__ attribute to be wrapped here. For example, functools.partial. In this case,
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# we should avoid wrapping the attributes from the wrapped function to the wrapper
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# function. So, we take out these attribute names from the default names to set and
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# then manually assign it after being wrapped.
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assignments = tuple(
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a for a in functools.WRAPPER_ASSIGNMENTS if a != '__name__' and a != '__module__')
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@functools.wraps(self.func, assigned=assignments)
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def wrapper(*args):
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return self(*args)
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wrapper.__name__ = self._name
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wrapper.__module__ = (self.func.__module__ if hasattr(self.func, '__module__')
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else self.func.__class__.__module__)
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wrapper.func = self.func
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wrapper.returnType = self.returnType
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wrapper.evalType = self.evalType
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wrapper.deterministic = self.deterministic
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wrapper.asNondeterministic = functools.wraps(
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self.asNondeterministic)(lambda: self.asNondeterministic()._wrapped())
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return wrapper
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def asNondeterministic(self):
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"""
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Updates UserDefinedFunction to nondeterministic.
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.. versionadded:: 2.3
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"""
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# Here, we explicitly clean the cache to create a JVM UDF instance
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# with 'deterministic' updated. See SPARK-23233.
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self._judf_placeholder = None
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self.deterministic = False
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return self
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class UDFRegistration(object):
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"""
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Wrapper for user-defined function registration. This instance can be accessed by
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:attr:`spark.udf` or :attr:`sqlContext.udf`.
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.. versionadded:: 1.3.1
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"""
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def __init__(self, sparkSession):
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self.sparkSession = sparkSession
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def register(self, name, f, returnType=None):
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"""Register a Python function (including lambda function) or a user-defined function
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as a SQL function.
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.. versionadded:: 1.3.1
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Parameters
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----------
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name : str,
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name of the user-defined function in SQL statements.
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f : function, :meth:`pyspark.sql.functions.udf` or :meth:`pyspark.sql.functions.pandas_udf`
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a Python function, or a user-defined function. The user-defined function can
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be either row-at-a-time or vectorized. See :meth:`pyspark.sql.functions.udf` and
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:meth:`pyspark.sql.functions.pandas_udf`.
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returnType : :class:`pyspark.sql.types.DataType` or str, optional
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the return type of the registered user-defined function. The value can
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be either a :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.
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`returnType` can be optionally specified when `f` is a Python function but not
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when `f` is a user-defined function. Please see the examples below.
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Returns
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-------
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function
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a user-defined function
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Notes
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-----
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To register a nondeterministic Python function, users need to first build
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a nondeterministic user-defined function for the Python function and then register it
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as a SQL function.
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Examples
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--------
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1. When `f` is a Python function:
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`returnType` defaults to string type and can be optionally specified. The produced
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object must match the specified type. In this case, this API works as if
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`register(name, f, returnType=StringType())`.
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>>> strlen = spark.udf.register("stringLengthString", lambda x: len(x))
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>>> spark.sql("SELECT stringLengthString('test')").collect()
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[Row(stringLengthString(test)='4')]
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>>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect()
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[Row(stringLengthString(text)='3')]
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>>> from pyspark.sql.types import IntegerType
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>>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType())
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>>> spark.sql("SELECT stringLengthInt('test')").collect()
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[Row(stringLengthInt(test)=4)]
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>>> from pyspark.sql.types import IntegerType
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>>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType())
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>>> spark.sql("SELECT stringLengthInt('test')").collect()
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[Row(stringLengthInt(test)=4)]
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2. When `f` is a user-defined function (from Spark 2.3.0):
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Spark uses the return type of the given user-defined function as the return type of
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the registered user-defined function. `returnType` should not be specified.
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In this case, this API works as if `register(name, f)`.
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>>> from pyspark.sql.types import IntegerType
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>>> from pyspark.sql.functions import udf
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>>> slen = udf(lambda s: len(s), IntegerType())
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>>> _ = spark.udf.register("slen", slen)
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>>> spark.sql("SELECT slen('test')").collect()
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[Row(slen(test)=4)]
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>>> import random
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>>> from pyspark.sql.functions import udf
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>>> from pyspark.sql.types import IntegerType
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>>> random_udf = udf(lambda: random.randint(0, 100), IntegerType()).asNondeterministic()
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>>> new_random_udf = spark.udf.register("random_udf", random_udf)
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>>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP
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[Row(random_udf()=82)]
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>>> import pandas as pd # doctest: +SKIP
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>>> from pyspark.sql.functions import pandas_udf
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>>> @pandas_udf("integer") # doctest: +SKIP
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... def add_one(s: pd.Series) -> pd.Series:
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... return s + 1
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...
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>>> _ = spark.udf.register("add_one", add_one) # doctest: +SKIP
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>>> spark.sql("SELECT add_one(id) FROM range(3)").collect() # doctest: +SKIP
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[Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)]
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>>> @pandas_udf("integer") # doctest: +SKIP
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... def sum_udf(v: pd.Series) -> int:
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... return v.sum()
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...
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>>> _ = spark.udf.register("sum_udf", sum_udf) # doctest: +SKIP
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>>> q = "SELECT sum_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2"
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>>> spark.sql(q).collect() # doctest: +SKIP
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[Row(sum_udf(v1)=1), Row(sum_udf(v1)=5)]
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"""
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# This is to check whether the input function is from a user-defined function or
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# Python function.
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if hasattr(f, 'asNondeterministic'):
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if returnType is not None:
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raise TypeError(
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"Invalid return type: data type can not be specified when f is"
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"a user-defined function, but got %s." % returnType)
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if f.evalType not in [PythonEvalType.SQL_BATCHED_UDF,
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PythonEvalType.SQL_SCALAR_PANDAS_UDF,
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PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
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PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
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PythonEvalType.SQL_MAP_PANDAS_ITER_UDF]:
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raise ValueError(
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"Invalid f: f must be SQL_BATCHED_UDF, SQL_SCALAR_PANDAS_UDF, "
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"SQL_SCALAR_PANDAS_ITER_UDF, SQL_GROUPED_AGG_PANDAS_UDF or "
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"SQL_MAP_PANDAS_ITER_UDF.")
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register_udf = UserDefinedFunction(f.func, returnType=f.returnType, name=name,
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evalType=f.evalType,
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deterministic=f.deterministic)
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return_udf = f
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else:
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if returnType is None:
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returnType = StringType()
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register_udf = UserDefinedFunction(f, returnType=returnType, name=name,
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evalType=PythonEvalType.SQL_BATCHED_UDF)
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return_udf = register_udf._wrapped()
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self.sparkSession._jsparkSession.udf().registerPython(name, register_udf._judf)
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return return_udf
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def registerJavaFunction(self, name, javaClassName, returnType=None):
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"""Register a Java user-defined function as a SQL function.
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In addition to a name and the function itself, the return type can be optionally specified.
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When the return type is not specified we would infer it via reflection.
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.. versionadded:: 2.3.0
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Parameters
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----------
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name : str
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name of the user-defined function
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javaClassName : str
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fully qualified name of java class
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returnType : :class:`pyspark.sql.types.DataType` or str, optional
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the return type of the registered Java function. The value can be either
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a :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.
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Examples
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--------
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>>> from pyspark.sql.types import IntegerType
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>>> spark.udf.registerJavaFunction(
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... "javaStringLength", "test.org.apache.spark.sql.JavaStringLength", IntegerType())
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... # doctest: +SKIP
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>>> spark.sql("SELECT javaStringLength('test')").collect() # doctest: +SKIP
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[Row(javaStringLength(test)=4)]
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>>> spark.udf.registerJavaFunction(
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... "javaStringLength2", "test.org.apache.spark.sql.JavaStringLength")
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... # doctest: +SKIP
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>>> spark.sql("SELECT javaStringLength2('test')").collect() # doctest: +SKIP
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[Row(javaStringLength2(test)=4)]
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>>> spark.udf.registerJavaFunction(
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... "javaStringLength3", "test.org.apache.spark.sql.JavaStringLength", "integer")
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... # doctest: +SKIP
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>>> spark.sql("SELECT javaStringLength3('test')").collect() # doctest: +SKIP
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[Row(javaStringLength3(test)=4)]
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"""
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jdt = None
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if returnType is not None:
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if not isinstance(returnType, DataType):
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returnType = _parse_datatype_string(returnType)
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jdt = self.sparkSession._jsparkSession.parseDataType(returnType.json())
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self.sparkSession._jsparkSession.udf().registerJava(name, javaClassName, jdt)
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def registerJavaUDAF(self, name, javaClassName):
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"""Register a Java user-defined aggregate function as a SQL function.
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.. versionadded:: 2.3.0
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name : str
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name of the user-defined aggregate function
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javaClassName : str
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fully qualified name of java class
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Examples
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--------
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>>> spark.udf.registerJavaUDAF("javaUDAF", "test.org.apache.spark.sql.MyDoubleAvg")
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... # doctest: +SKIP
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>>> df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "a")],["id", "name"])
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>>> df.createOrReplaceTempView("df")
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>>> q = "SELECT name, javaUDAF(id) as avg from df group by name order by name desc"
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>>> spark.sql(q).collect() # doctest: +SKIP
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[Row(name='b', avg=102.0), Row(name='a', avg=102.0)]
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"""
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self.sparkSession._jsparkSession.udf().registerJavaUDAF(name, javaClassName)
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def _test():
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import doctest
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from pyspark.sql import SparkSession
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import pyspark.sql.udf
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globs = pyspark.sql.udf.__dict__.copy()
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spark = SparkSession.builder\
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.master("local[4]")\
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.appName("sql.udf tests")\
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.getOrCreate()
|
|
globs['spark'] = spark
|
|
(failure_count, test_count) = doctest.testmod(
|
|
pyspark.sql.udf, globs=globs,
|
|
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
|
|
spark.stop()
|
|
if failure_count:
|
|
sys.exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|