spark-instrumented-optimizer/python/pyspark/join.py
Allan Douglas R. de Oliveira 6a224c31e8 SPARK-1868: Users should be allowed to cogroup at least 4 RDDs
Adds cogroup for 4 RDDs.

Author: Allan Douglas R. de Oliveira <allandouglas@gmail.com>

Closes #813 from douglaz/more_cogroups and squashes the following commits:

f8d6273 [Allan Douglas R. de Oliveira] Test python groupWith for one more case
0e9009c [Allan Douglas R. de Oliveira] Added scala tests
c3ffcdd [Allan Douglas R. de Oliveira] Added java tests
517a67f [Allan Douglas R. de Oliveira] Added tests for python groupWith
2f402d5 [Allan Douglas R. de Oliveira] Removed TODO
17474f4 [Allan Douglas R. de Oliveira] Use new cogroup function
7877a2a [Allan Douglas R. de Oliveira] Fixed code
ba02414 [Allan Douglas R. de Oliveira] Added varargs cogroup to pyspark
c4a8a51 [Allan Douglas R. de Oliveira] Added java cogroup 4
e94963c [Allan Douglas R. de Oliveira] Fixed spacing
f1ee57b [Allan Douglas R. de Oliveira] Fixed scala style issues
d7196f1 [Allan Douglas R. de Oliveira] Allow the cogroup of 4 RDDs
2014-06-20 11:03:03 -07:00

94 lines
3.4 KiB
Python

"""
Copyright (c) 2011, Douban Inc. <http://www.douban.com/>
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following disclaimer
in the documentation and/or other materials provided with the
distribution.
* Neither the name of the Douban Inc. nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
from pyspark.resultiterable import ResultIterable
def _do_python_join(rdd, other, numPartitions, dispatch):
vs = rdd.map(lambda (k, v): (k, (1, v)))
ws = other.map(lambda (k, v): (k, (2, v)))
return vs.union(ws).groupByKey(numPartitions).flatMapValues(lambda x : dispatch(x.__iter__()))
def python_join(rdd, other, numPartitions):
def dispatch(seq):
vbuf, wbuf = [], []
for (n, v) in seq:
if n == 1:
vbuf.append(v)
elif n == 2:
wbuf.append(v)
return [(v, w) for v in vbuf for w in wbuf]
return _do_python_join(rdd, other, numPartitions, dispatch)
def python_right_outer_join(rdd, other, numPartitions):
def dispatch(seq):
vbuf, wbuf = [], []
for (n, v) in seq:
if n == 1:
vbuf.append(v)
elif n == 2:
wbuf.append(v)
if not vbuf:
vbuf.append(None)
return [(v, w) for v in vbuf for w in wbuf]
return _do_python_join(rdd, other, numPartitions, dispatch)
def python_left_outer_join(rdd, other, numPartitions):
def dispatch(seq):
vbuf, wbuf = [], []
for (n, v) in seq:
if n == 1:
vbuf.append(v)
elif n == 2:
wbuf.append(v)
if not wbuf:
wbuf.append(None)
return [(v, w) for v in vbuf for w in wbuf]
return _do_python_join(rdd, other, numPartitions, dispatch)
def python_cogroup(rdds, numPartitions):
def make_mapper(i):
return lambda (k, v): (k, (i, v))
vrdds = [rdd.map(make_mapper(i)) for i, rdd in enumerate(rdds)]
union_vrdds = reduce(lambda acc, other: acc.union(other), vrdds)
rdd_len = len(vrdds)
def dispatch(seq):
bufs = [[] for i in range(rdd_len)]
for (n, v) in seq:
bufs[n].append(v)
return tuple(map(ResultIterable, bufs))
return union_vrdds.groupByKey(numPartitions).mapValues(dispatch)