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