spark-instrumented-optimizer/pyspark/examples/tc.py

import sys
from random import Random
from pyspark.context 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__":
    if len(sys.argv) == 1:
        print >> sys.stderr, \
            "Usage: PythonTC <host> [<slices>]"
        exit(-1)
    sc = SparkContext(sys.argv[1], "PythonTC")
    slices = sys.argv[2] if len(sys.argv) > 2 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()