Source code for pyspark.sql.udf

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User-defined function related classes and functions
import functools
import sys

from pyspark import SparkContext, since
from pyspark.rdd import _prepare_for_python_RDD, PythonEvalType, ignore_unicode_prefix
from pyspark.sql.column import Column, _to_java_column, _to_seq
from pyspark.sql.types import StringType, DataType, StructType, _parse_datatype_string,\
    to_arrow_type, to_arrow_schema
from pyspark.util import _get_argspec

__all__ = ["UDFRegistration"]

def _wrap_function(sc, func, returnType):
    command = (func, returnType)
    pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command)
    return sc._jvm.PythonFunction(bytearray(pickled_command), env, includes, sc.pythonExec,
                                  sc.pythonVer, broadcast_vars, sc._javaAccumulator)

def _create_udf(f, returnType, evalType):

    if evalType in (PythonEvalType.SQL_SCALAR_PANDAS_UDF,

        from pyspark.sql.utils import require_minimum_pyarrow_version

        argspec = _get_argspec(f)

        if evalType == PythonEvalType.SQL_SCALAR_PANDAS_UDF and len(argspec.args) == 0 and \
                argspec.varargs is None:
            raise ValueError(
                "Invalid function: 0-arg pandas_udfs are not supported. "
                "Instead, create a 1-arg pandas_udf and ignore the arg in your function."

        if evalType == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF \
                and len(argspec.args) not in (1, 2):
            raise ValueError(
                "Invalid function: pandas_udfs with function type GROUPED_MAP "
                "must take either one argument (data) or two arguments (key, data).")

    # Set the name of the UserDefinedFunction object to be the name of function f
    udf_obj = UserDefinedFunction(
        f, returnType=returnType, name=None, evalType=evalType, deterministic=True)
    return udf_obj._wrapped()

class UserDefinedFunction(object):
    User defined function in Python

    .. versionadded:: 1.3
    def __init__(self, func,
        if not callable(func):
            raise TypeError(
                "Invalid function: not a function or callable (__call__ is not defined): "

        if not isinstance(returnType, (DataType, str)):
            raise TypeError(
                "Invalid returnType: returnType should be DataType or str "
                "but is {}".format(returnType))

        if not isinstance(evalType, int):
            raise TypeError(
                "Invalid evalType: evalType should be an int but is {}".format(evalType))

        self.func = func
        self._returnType = returnType
        # Stores UserDefinedPythonFunctions jobj, once initialized
        self._returnType_placeholder = 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

    def returnType(self):
        # This makes sure this is called after SparkContext is initialized.
        # ``_parse_datatype_string`` accesses to JVM for parsing a DDL formatted string.
        if self._returnType_placeholder is None:
            if isinstance(self._returnType, DataType):
                self._returnType_placeholder = self._returnType
                self._returnType_placeholder = _parse_datatype_string(self._returnType)

        if self.evalType == PythonEvalType.SQL_SCALAR_PANDAS_UDF:
            except TypeError:
                raise NotImplementedError(
                    "Invalid returnType with scalar Pandas UDFs: %s is "
                    "not supported" % str(self._returnType_placeholder))
        elif self.evalType == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF:
            if isinstance(self._returnType_placeholder, StructType):
                except TypeError:
                    raise NotImplementedError(
                        "Invalid returnType with grouped map Pandas UDFs: "
                        "%s is not supported" % str(self._returnType_placeholder))
                raise TypeError("Invalid returnType for grouped map Pandas "
                                "UDFs: returnType must be a StructType.")
        elif self.evalType == PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF:
            except TypeError:
                raise NotImplementedError(
                    "Invalid returnType with grouped aggregate Pandas UDFs: "
                    "%s is not supported" % str(self._returnType_placeholder))

        return self._returnType_placeholder

    def _judf(self):
        # 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()
        return self._judf_placeholder

    def _create_judf(self):
        from pyspark.sql import SparkSession

        spark = SparkSession.builder.getOrCreate()
        sc = spark.sparkContext

        wrapped_func = _wrap_function(sc, self.func, self.returnType)
        jdt = spark._jsparkSession.parseDataType(self.returnType.json())
        judf =
            self._name, wrapped_func, jdt, self.evalType, self.deterministic)
        return judf

    def __call__(self, *cols):
        judf = self._judf
        sc = SparkContext._active_spark_context
        return Column(judf.apply(_to_seq(sc, cols, _to_java_column)))

    # This function is for improving the online help system in the interactive interpreter.
    # For example, the built-in help / It wraps the UDF with the docstring and
    # argument annotation. (See: SPARK-19161)
    def _wrapped(self):
        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):
            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
        wrapper.returnType = self.returnType
        wrapper.evalType = self.evalType
        wrapper.deterministic = self.deterministic
        wrapper.asNondeterministic = functools.wraps(
            self.asNondeterministic)(lambda: self.asNondeterministic()._wrapped())
        return wrapper

    def asNondeterministic(self):
        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(object): """ 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): self.sparkSession = sparkSession
[docs] @ignore_unicode_prefix @since("1.3.1") def register(self, name, f, returnType=None): """Register a Python function (including lambda function) or a user-defined function as a SQL function. :param name: name of the user-defined function in SQL statements. :param f: 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`. :param returnType: 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. :return: a user-defined function. 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. `returnType` can be optionally specified when `f` is a Python function but not when `f` is a user-defined function. Please see below. 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)=u'4')] >>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect() [Row(stringLengthString(text)=u'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: 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)] >>> from pyspark.sql.functions import pandas_udf, PandasUDFType >>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP ... def add_one(x): ... return x + 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", PandasUDFType.GROUPED_AGG) # doctest: +SKIP ... def sum_udf(v): ... 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)] .. note:: Registration for a user-defined function (case 2.) was added from Spark 2.3.0. """ # 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 TypeError( "Invalid returnType: data type can not be specified when f is" "a user-defined function, but got %s." % returnType) if f.evalType not in [PythonEvalType.SQL_BATCHED_UDF, PythonEvalType.SQL_SCALAR_PANDAS_UDF, PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF]: raise ValueError( "Invalid f: f must be SQL_BATCHED_UDF, SQL_SCALAR_PANDAS_UDF or " "SQL_GROUPED_AGG_PANDAS_UDF") register_udf = UserDefinedFunction(f.func, returnType=f.returnType, name=name, evalType=f.evalType, deterministic=f.deterministic) return_udf = f else: if returnType is None: returnType = StringType() register_udf = UserDefinedFunction(f, returnType=returnType, name=name, evalType=PythonEvalType.SQL_BATCHED_UDF) return_udf = register_udf._wrapped() self.sparkSession._jsparkSession.udf().registerPython(name, register_udf._judf) return return_udf
[docs] @ignore_unicode_prefix @since(2.3) def registerJavaFunction(self, name, javaClassName, returnType=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. :param name: name of the user-defined function :param javaClassName: fully qualified name of java class :param returnType: 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. >>> from pyspark.sql.types import IntegerType >>> spark.udf.registerJavaFunction( ... "javaStringLength", "", IntegerType()) >>> spark.sql("SELECT javaStringLength('test')").collect() [Row(UDF:javaStringLength(test)=4)] >>> spark.udf.registerJavaFunction( ... "javaStringLength2", "") >>> spark.sql("SELECT javaStringLength2('test')").collect() [Row(UDF:javaStringLength2(test)=4)] >>> spark.udf.registerJavaFunction( ... "javaStringLength3", "", "integer") >>> spark.sql("SELECT javaStringLength3('test')").collect() [Row(UDF: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] @ignore_unicode_prefix @since(2.3) def registerJavaUDAF(self, name, javaClassName): """Register a Java user-defined aggregate function as a SQL function. :param name: name of the user-defined aggregate function :param javaClassName: fully qualified name of java class >>> spark.udf.registerJavaUDAF("javaUDAF", "") >>> df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "a")],["id", "name"]) >>> df.createOrReplaceTempView("df") >>> spark.sql("SELECT name, javaUDAF(id) as avg from df group by name").collect() [Row(name=u'b', avg=102.0), Row(name=u'a', avg=102.0)] """ self.sparkSession._jsparkSession.udf().registerJavaUDAF(name, javaClassName)
def _test(): 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()