b2ce17b4c9
## What changes were proposed in this pull request? Add support for using pandas UDFs with groupby().agg(). This PR introduces a new type of pandas UDF - group aggregate pandas UDF. This type of UDF defines a transformation of multiple pandas Series -> a scalar value. Group aggregate pandas UDFs can be used with groupby().agg(). Note group aggregate pandas UDF doesn't support partial aggregation, i.e., a full shuffle is required. This PR doesn't support group aggregate pandas UDFs that return ArrayType, StructType or MapType. Support for these types is left for future PR. ## How was this patch tested? GroupbyAggPandasUDFTests Author: Li Jin <ice.xelloss@gmail.com> Closes #19872 from icexelloss/SPARK-22274-groupby-agg.
383 lines
16 KiB
Python
383 lines
16 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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from pyspark import SparkContext, since
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from pyspark.rdd import _prepare_for_python_RDD, PythonEvalType, ignore_unicode_prefix
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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, ArrayType, StructType, MapType, \
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_parse_datatype_string
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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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if evalType in (PythonEvalType.SQL_PANDAS_SCALAR_UDF,
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PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF,
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PythonEvalType.SQL_PANDAS_GROUP_AGG_UDF):
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import inspect
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from pyspark.sql.utils import require_minimum_pyarrow_version
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require_minimum_pyarrow_version()
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argspec = inspect.getargspec(f)
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if evalType == PythonEvalType.SQL_PANDAS_SCALAR_UDF and len(argspec.args) == 0 and \
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argspec.varargs is None:
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raise ValueError(
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"Invalid function: 0-arg pandas_udfs are not supported. "
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"Instead, create a 1-arg pandas_udf and ignore the arg in your function."
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)
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if evalType == PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF and len(argspec.args) != 1:
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raise ValueError(
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"Invalid function: pandas_udfs with function type GROUP_MAP "
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"must take a single arg that is a pandas DataFrame."
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)
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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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"""
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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 returnType: 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 evalType: 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_PANDAS_GROUP_MAP_UDF \
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and not isinstance(self._returnType_placeholder, StructType):
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raise ValueError("Invalid returnType: returnType must be a StructType for "
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"pandas_udf with function type GROUP_MAP")
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elif self.evalType == PythonEvalType.SQL_PANDAS_GROUP_AGG_UDF \
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and isinstance(self._returnType_placeholder, (StructType, ArrayType, MapType)):
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raise NotImplementedError(
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"ArrayType, StructType and MapType are not supported with PandasUDFType.GROUP_AGG")
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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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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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@ignore_unicode_prefix
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@since("1.3.1")
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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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:param name: name of the user-defined function in SQL statements.
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:param f: 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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:param returnType: 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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:return: a user-defined function.
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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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`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 below.
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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)=u'4')]
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>>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect()
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[Row(stringLengthString(text)=u'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:
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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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>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
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>>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP
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... def add_one(x):
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... return x + 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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.. note:: Registration for a user-defined function (case 2.) was added from
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Spark 2.3.0.
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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 returnType: 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_PANDAS_SCALAR_UDF]:
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raise ValueError(
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"Invalid f: f must be either SQL_BATCHED_UDF or SQL_PANDAS_SCALAR_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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@ignore_unicode_prefix
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@since(2.3)
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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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:param name: name of the user-defined function
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:param javaClassName: fully qualified name of java class
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:param returnType: 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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>>> 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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>>> spark.sql("SELECT javaStringLength('test')").collect()
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[Row(UDF: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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>>> spark.sql("SELECT javaStringLength2('test')").collect()
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[Row(UDF: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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>>> spark.sql("SELECT javaStringLength3('test')").collect()
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[Row(UDF: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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@ignore_unicode_prefix
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@since(2.3)
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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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:param name: name of the user-defined aggregate function
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:param javaClassName: fully qualified name of java class
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>>> spark.udf.registerJavaUDAF("javaUDAF", "test.org.apache.spark.sql.MyDoubleAvg")
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>>> df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "a")],["id", "name"])
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>>> df.registerTempTable("df")
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>>> spark.sql("SELECT name, javaUDAF(id) as avg from df group by name").collect()
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[Row(name=u'b', avg=102.0), Row(name=u'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()
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globs['spark'] = spark
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(failure_count, test_count) = doctest.testmod(
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pyspark.sql.udf, globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
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spark.stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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