Source code for pyspark.sql.udf

#
# Licensed to the Apache Software Foundation (ASF) under one or more
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# 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
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"""
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()