c3d2b90bde
Currently, PySpark does not support narrow dependency during cogroup/join when the two RDDs have the partitioner, another unnecessary shuffle stage will come in. The Python implementation of cogroup/join is different than Scala one, it depends on union() and partitionBy(). This patch will try to use PartitionerAwareUnionRDD() in union(), when all the RDDs have the same partitioner. It also fix `reservePartitioner` in all the map() or mapPartitions(), then partitionBy() can skip the unnecessary shuffle stage. Author: Davies Liu <davies@databricks.com> Closes #4629 from davies/narrow and squashes the following commits: dffe34e [Davies Liu] improve test, check number of stages for join/cogroup 1ed3ba2 [Davies Liu] Merge branch 'master' of github.com:apache/spark into narrow 4d29932 [Davies Liu] address comment cc28d97 [Davies Liu] add unit tests 940245e [Davies Liu] address comments ff5a0a6 [Davies Liu] skip the partitionBy() on Python side eb26c62 [Davies Liu] narrow dependency in PySpark
112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
"""
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Copyright (c) 2011, Douban Inc. <http://www.douban.com/>
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are
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met:
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* Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above
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copyright notice, this list of conditions and the following disclaimer
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in the documentation and/or other materials provided with the
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distribution.
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* Neither the name of the Douban Inc. nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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"""
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from pyspark.resultiterable import ResultIterable
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def _do_python_join(rdd, other, numPartitions, dispatch):
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vs = rdd.mapValues(lambda v: (1, v))
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ws = other.mapValues(lambda v: (2, v))
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return vs.union(ws).groupByKey(numPartitions).flatMapValues(lambda x: dispatch(x.__iter__()))
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def python_join(rdd, other, numPartitions):
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def dispatch(seq):
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vbuf, wbuf = [], []
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for (n, v) in seq:
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if n == 1:
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vbuf.append(v)
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elif n == 2:
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wbuf.append(v)
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return [(v, w) for v in vbuf for w in wbuf]
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return _do_python_join(rdd, other, numPartitions, dispatch)
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def python_right_outer_join(rdd, other, numPartitions):
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def dispatch(seq):
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vbuf, wbuf = [], []
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for (n, v) in seq:
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if n == 1:
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vbuf.append(v)
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elif n == 2:
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wbuf.append(v)
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if not vbuf:
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vbuf.append(None)
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return [(v, w) for v in vbuf for w in wbuf]
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return _do_python_join(rdd, other, numPartitions, dispatch)
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def python_left_outer_join(rdd, other, numPartitions):
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def dispatch(seq):
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vbuf, wbuf = [], []
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for (n, v) in seq:
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if n == 1:
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vbuf.append(v)
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elif n == 2:
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wbuf.append(v)
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if not wbuf:
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wbuf.append(None)
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return [(v, w) for v in vbuf for w in wbuf]
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return _do_python_join(rdd, other, numPartitions, dispatch)
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def python_full_outer_join(rdd, other, numPartitions):
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def dispatch(seq):
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vbuf, wbuf = [], []
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for (n, v) in seq:
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if n == 1:
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vbuf.append(v)
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elif n == 2:
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wbuf.append(v)
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if not vbuf:
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vbuf.append(None)
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if not wbuf:
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wbuf.append(None)
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return [(v, w) for v in vbuf for w in wbuf]
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return _do_python_join(rdd, other, numPartitions, dispatch)
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def python_cogroup(rdds, numPartitions):
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def make_mapper(i):
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return lambda v: (i, v)
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vrdds = [rdd.mapValues(make_mapper(i)) for i, rdd in enumerate(rdds)]
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union_vrdds = reduce(lambda acc, other: acc.union(other), vrdds)
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rdd_len = len(vrdds)
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def dispatch(seq):
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bufs = [[] for i in range(rdd_len)]
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for (n, v) in seq:
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bufs[n].append(v)
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return tuple(map(ResultIterable, bufs))
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return union_vrdds.groupByKey(numPartitions).mapValues(dispatch)
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