1 files changed, 0 insertions, 50 deletions
diff --git a/pyspark/examples/transitive_closure.py b/pyspark/examples/transitive_closure.py
deleted file mode 100644
index 73f7f8fbaf..0000000000
--- a/pyspark/examples/transitive_closure.py
+++ /dev/null
@@ -1,50 +0,0 @@
-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__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, \
-            "Usage: PythonTC <master> [<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()