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# The ASF licenses this file to You under the Apache License, Version 2.0
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
A collections of builtin functions
"""
import sys
if sys.version < "3":
from itertools import imap as map
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, _to_seq
__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 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)
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
jc = sc._jvm.functions.countDistinct(_to_java_column(col), _to_seq(sc, cols, _to_java_column))
return Column(jc)
def monotonicallyIncreasingId():
"""A column that generates monotonically increasing 64-bit integers.
The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive.
The current implementation puts the partition ID in the upper 31 bits, and the record number
within each partition in the lower 33 bits. The assumption is that the data frame has
less than 1 billion partitions, and each partition has less than 8 billion records.
As an example, consider a [[DataFrame]] with two partitions, each with 3 records.
This expression would return the following IDs:
0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594.
>>> df0 = sc.parallelize(range(2), 2).mapPartitions(lambda x: [(1,), (2,), (3,)]).toDF(['col1'])
>>> df0.select(monotonicallyIncreasingId().alias('id')).collect()
[Row(id=0), Row(id=1), Row(id=2), Row(id=8589934592), Row(id=8589934593), Row(id=8589934594)]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.monotonicallyIncreasingId())
def sparkPartitionId():
"""A column for partition ID of the Spark task.
Note that this is indeterministic because it depends on data partitioning and task scheduling.
>>> df.repartition(1).select(sparkPartitionId().alias("pid")).collect()
[Row(pid=0), Row(pid=0)]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.sparkPartitionId())
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: map(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
jc = self._judf.apply(_to_seq(sc, cols, _to_java_column))
return Column(jc)
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()