Source code for pyspark.sql.functions

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"""
A collections of builtin functions
"""

from itertools import imap

from py4j.java_collections import ListConverter

from pyspark import SparkContext
from pyspark.rdd import _prepare_for_python_RDD
from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
from pyspark.sql.types import StringType
from pyspark.sql.dataframe import Column, _to_java_column


__all__ = ['countDistinct', 'approxCountDistinct', 'udf']


def _create_function(name, doc=""):
    """ Create a function for aggregator by name"""
    def _(col):
        sc = SparkContext._active_spark_context
        jc = getattr(sc._jvm.functions, name)(col._jc if isinstance(col, Column) else col)
        return Column(jc)
    _.__name__ = name
    _.__doc__ = doc
    return _


_functions = {
    'lit': 'Creates a :class:`Column` of literal value.',
    'col': 'Returns a :class:`Column` based on the given column name.',
    'column': 'Returns a :class:`Column` based on the given column name.',
    'asc': 'Returns a sort expression based on the ascending order of the given column name.',
    'desc': 'Returns a sort expression based on the descending order of the given column name.',

    'upper': 'Converts a string expression to upper case.',
    'lower': 'Converts a string expression to upper case.',
    'sqrt': 'Computes the square root of the specified float value.',
    'abs': 'Computes the absolutle value.',

    'max': 'Aggregate function: returns the maximum value of the expression in a group.',
    'min': 'Aggregate function: returns the minimum value of the expression in a group.',
    'first': 'Aggregate function: returns the first value in a group.',
    'last': 'Aggregate function: returns the last value in a group.',
    'count': 'Aggregate function: returns the number of items in a group.',
    'sum': 'Aggregate function: returns the sum of all values in the expression.',
    'avg': 'Aggregate function: returns the average of the values in a group.',
    'mean': 'Aggregate function: returns the average of the values in a group.',
    'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.',
}


for _name, _doc in _functions.items():
    globals()[_name] = _create_function(_name, _doc)
del _name, _doc
__all__ += _functions.keys()
__all__.sort()


[docs]def countDistinct(col, *cols): """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``. >>> df.agg(countDistinct(df.age, df.name).alias('c')).collect() [Row(c=2)] >>> df.agg(countDistinct("age", "name").alias('c')).collect() [Row(c=2)] """ sc = SparkContext._active_spark_context jcols = ListConverter().convert([_to_java_column(c) for c in cols], sc._gateway._gateway_client) jc = sc._jvm.functions.countDistinct(_to_java_column(col), sc._jvm.PythonUtils.toSeq(jcols)) return Column(jc)
[docs]def approxCountDistinct(col, rsd=None): """Returns a new :class:`Column` for approximate distinct count of ``col``. >>> df.agg(approxCountDistinct(df.age).alias('c')).collect() [Row(c=2)] """ sc = SparkContext._active_spark_context if rsd is None: jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col)) else: jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col), rsd) return Column(jc)
class UserDefinedFunction(object): """ User defined function in Python """ def __init__(self, func, returnType): self.func = func self.returnType = returnType self._broadcast = None self._judf = self._create_judf() def _create_judf(self): f = self.func # put it in closure `func` func = lambda _, it: imap(lambda x: f(*x), it) ser = AutoBatchedSerializer(PickleSerializer()) command = (func, None, ser, ser) sc = SparkContext._active_spark_context pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self) ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) jdt = ssql_ctx.parseDataType(self.returnType.json()) fname = f.__name__ if hasattr(f, '__name__') else f.__class__.__name__ judf = sc._jvm.UserDefinedPythonFunction(fname, bytearray(pickled_command), env, includes, sc.pythonExec, broadcast_vars, sc._javaAccumulator, jdt) return judf def __del__(self): if self._broadcast is not None: self._broadcast.unpersist() self._broadcast = None def __call__(self, *cols): sc = SparkContext._active_spark_context jcols = ListConverter().convert([_to_java_column(c) for c in cols], sc._gateway._gateway_client) jc = self._judf.apply(sc._jvm.PythonUtils.toSeq(jcols)) return Column(jc)
[docs]def udf(f, returnType=StringType()): """Creates a :class:`Column` expression representing a user defined function (UDF). >>> from pyspark.sql.types import IntegerType >>> slen = udf(lambda s: len(s), IntegerType()) >>> df.select(slen(df.name).alias('slen')).collect() [Row(slen=5), Row(slen=3)] """ return UserDefinedFunction(f, returnType)
def _test(): import doctest from pyspark.context import SparkContext from pyspark.sql import Row, SQLContext import pyspark.sql.functions globs = pyspark.sql.functions.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc globs['sqlContext'] = SQLContext(sc) globs['df'] = sc.parallelize([Row(name='Alice', age=2), Row(name='Bob', age=5)]).toDF() (failure_count, test_count) = doctest.testmod( pyspark.sql.functions, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()