2015-02-14 02:03:22 -05:00
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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A collections of builtin functions
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"""
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from itertools import imap
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from py4j.java_collections import ListConverter
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from pyspark import SparkContext
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from pyspark.rdd import _prepare_for_python_RDD
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from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
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from pyspark.sql.types import StringType
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from pyspark.sql.dataframe import Column, _to_java_column
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__all__ = ['countDistinct', 'approxCountDistinct', 'udf']
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def _create_function(name, doc=""):
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""" Create a function for aggregator by name"""
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def _(col):
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sc = SparkContext._active_spark_context
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jc = getattr(sc._jvm.functions, name)(col._jc if isinstance(col, Column) else col)
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return Column(jc)
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_.__name__ = name
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_.__doc__ = doc
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return _
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_functions = {
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'lit': 'Creates a :class:`Column` of literal value.',
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'col': 'Returns a :class:`Column` based on the given column name.',
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'column': 'Returns a :class:`Column` based on the given column name.',
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'asc': 'Returns a sort expression based on the ascending order of the given column name.',
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'desc': 'Returns a sort expression based on the descending order of the given column name.',
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'upper': 'Converts a string expression to upper case.',
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'lower': 'Converts a string expression to upper case.',
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'sqrt': 'Computes the square root of the specified float value.',
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'abs': 'Computes the absolutle value.',
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'max': 'Aggregate function: returns the maximum value of the expression in a group.',
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'min': 'Aggregate function: returns the minimum value of the expression in a group.',
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'first': 'Aggregate function: returns the first value in a group.',
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'last': 'Aggregate function: returns the last value in a group.',
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'count': 'Aggregate function: returns the number of items in a group.',
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'sum': 'Aggregate function: returns the sum of all values in the expression.',
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'avg': 'Aggregate function: returns the average of the values in a group.',
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'mean': 'Aggregate function: returns the average of the values in a group.',
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'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.',
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}
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for _name, _doc in _functions.items():
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globals()[_name] = _create_function(_name, _doc)
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del _name, _doc
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__all__ += _functions.keys()
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__all__.sort()
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def countDistinct(col, *cols):
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""" Return a new Column for distinct count of `col` or `cols`
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>>> df.agg(countDistinct(df.age, df.name).alias('c')).collect()
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[Row(c=2)]
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>>> df.agg(countDistinct("age", "name").alias('c')).collect()
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[Row(c=2)]
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"""
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sc = SparkContext._active_spark_context
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jcols = ListConverter().convert([_to_java_column(c) for c in cols], sc._gateway._gateway_client)
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jc = sc._jvm.functions.countDistinct(_to_java_column(col), sc._jvm.PythonUtils.toSeq(jcols))
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return Column(jc)
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def approxCountDistinct(col, rsd=None):
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""" Return a new Column for approximate distinct count of `col`
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>>> df.agg(approxCountDistinct(df.age).alias('c')).collect()
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[Row(c=2)]
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"""
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sc = SparkContext._active_spark_context
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if rsd is None:
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jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col))
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else:
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jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col), rsd)
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return Column(jc)
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class UserDefinedFunction(object):
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"""
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User defined function in Python
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"""
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def __init__(self, func, returnType):
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self.func = func
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self.returnType = returnType
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self._broadcast = None
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self._judf = self._create_judf()
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def _create_judf(self):
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f = self.func # put it in closure `func`
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func = lambda _, it: imap(lambda x: f(*x), it)
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ser = AutoBatchedSerializer(PickleSerializer())
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command = (func, None, ser, ser)
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sc = SparkContext._active_spark_context
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pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self)
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ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc())
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jdt = ssql_ctx.parseDataType(self.returnType.json())
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judf = sc._jvm.UserDefinedPythonFunction(f.__name__, bytearray(pickled_command), env,
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includes, sc.pythonExec, broadcast_vars,
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sc._javaAccumulator, jdt)
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return judf
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def __del__(self):
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if self._broadcast is not None:
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self._broadcast.unpersist()
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self._broadcast = None
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def __call__(self, *cols):
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sc = SparkContext._active_spark_context
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jcols = ListConverter().convert([_to_java_column(c) for c in cols],
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sc._gateway._gateway_client)
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jc = self._judf.apply(sc._jvm.PythonUtils.toSeq(jcols))
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return Column(jc)
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def udf(f, returnType=StringType()):
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"""Create a user defined function (UDF)
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>>> from pyspark.sql.types import IntegerType
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>>> slen = udf(lambda s: len(s), IntegerType())
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>>> df.select(slen(df.name).alias('slen')).collect()
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[Row(slen=5), Row(slen=3)]
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"""
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return UserDefinedFunction(f, returnType)
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def _test():
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import doctest
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from pyspark.context import SparkContext
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from pyspark.sql import Row, SQLContext
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import pyspark.sql.functions
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globs = pyspark.sql.functions.__dict__.copy()
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sc = SparkContext('local[4]', 'PythonTest')
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globs['sc'] = sc
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globs['sqlCtx'] = SQLContext(sc)
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globs['df'] = sc.parallelize([Row(name='Alice', age=2), Row(name='Bob', age=5)]).toDF()
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(failure_count, test_count) = doctest.testmod(
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pyspark.sql.functions, globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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