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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.
#
import sys
from random import Random
from pyspark import SparkContext
numEdges = 200
numVertices = 100
rand = Random(42)
def generateGraph():
edges = set()
while len(edges) < numEdges:
src = rand.randrange(0, numEdges)
dst = rand.randrange(0, numEdges)
if src != dst:
edges.add((src, dst))
return edges
if __name__ == "__main__":
"""
Usage: transitive_closure [slices]
"""
sc = SparkContext(appName="PythonTransitiveClosure")
slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
tc = sc.parallelize(generateGraph(), slices).cache()
# Linear transitive closure: each round grows paths by one edge,
# by joining the graph's edges with the already-discovered paths.
# e.g. join the path (y, z) from the TC with the edge (x, y) from
# the graph to obtain the path (x, z).
# Because join() joins on keys, the edges are stored in reversed order.
edges = tc.map(lambda (x, y): (y, x))
oldCount = 0L
nextCount = tc.count()
while True:
oldCount = nextCount
# Perform the join, obtaining an RDD of (y, (z, x)) pairs,
# then project the result to obtain the new (x, z) paths.
new_edges = tc.join(edges).map(lambda (_, (a, b)): (b, a))
tc = tc.union(new_edges).distinct().cache()
nextCount = tc.count()
if nextCount == oldCount:
break
print "TC has %i edges" % tc.count()
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