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package spark.examples
import spark._
import SparkContext._
import scala.util.Random
import scala.collection.mutable
/**
* Transitive closure on a graph.
*/
object SparkTC {
val numEdges = 200
val numVertices = 100
val rand = new Random(42)
def generateGraph = {
val edges: mutable.Set[(Int, Int)] = mutable.Set.empty
while (edges.size < numEdges) {
val from = rand.nextInt(numVertices)
val to = rand.nextInt(numVertices)
if (from != to) edges.+=((from, to))
}
edges.toSeq
}
def main(args: Array[String]) {
if (args.length == 0) {
System.err.println("Usage: SparkTC <master> [<slices>]")
System.exit(1)
}
val spark = new SparkContext(args(0), "SparkTC",
System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
val slices = if (args.length > 1) args(1).toInt else 2
var tc = spark.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.
val edges = tc.map(x => (x._2, x._1))
// This join is iterated until a fixed point is reached.
var oldCount = 0L
var nextCount = tc.count()
do {
oldCount = nextCount
// Perform the join, obtaining an RDD of (y, (z, x)) pairs,
// then project the result to obtain the new (x, z) paths.
tc = tc.union(tc.join(edges).map(x => (x._2._2, x._2._1))).distinct().cache();
nextCount = tc.count()
} while (nextCount != oldCount)
println("TC has " + tc.count() + " edges.")
System.exit(0)
}
}
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