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#
# 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.
#
"""
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()
def countDistinct(col, *cols):
""" Return a new 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)
def approxCountDistinct(col, rsd=None):
""" Return a new 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())
judf = sc._jvm.UserDefinedPythonFunction(f.__name__, 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)
def udf(f, returnType=StringType()):
"""Create 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['sqlCtx'] = 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()
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