Transitive closure on a graph via apache spark - Java Big Data

Java examples for Big Data:apache spark

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Description

Transitive closure on a graph via apache spark

Demo Code

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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
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 * Unless required by applicable law or agreed to in writing, software
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package org.apache.spark.examples;

import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Random;
import java.util.Set;

import scala.Tuple2;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.PairFunction;

/**
 * Transitive closure on a graph, implemented in Java.
 * Usage: JavaTC [slices]
 */
public final class JavaTC {

    private static final int numEdges = 200;
    private static final int numVertices = 100;
    private static final Random rand = new Random(42);

    static List<Tuple2<Integer, Integer>> generateGraph() {
        Set<Tuple2<Integer, Integer>> edges = new HashSet<Tuple2<Integer, Integer>>(
                numEdges);
        while (edges.size() < numEdges) {
            int from = rand.nextInt(numVertices);
            int to = rand.nextInt(numVertices);
            Tuple2<Integer, Integer> e = new Tuple2<Integer, Integer>(from,
                    to);
            if (from != to) {
                edges.add(e);
            }
        }
        return new ArrayList<Tuple2<Integer, Integer>>(edges);
    }

    static class ProjectFn
            implements
            PairFunction<Tuple2<Integer, Tuple2<Integer, Integer>>, Integer, Integer> {
        static final ProjectFn INSTANCE = new ProjectFn();

        @Override
        public Tuple2<Integer, Integer> call(
                Tuple2<Integer, Tuple2<Integer, Integer>> triple) {
            return new Tuple2<Integer, Integer>(triple._2()._2(), triple
                    ._2()._1());
        }
    }

    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setAppName("JavaHdfsLR");
        JavaSparkContext sc = new JavaSparkContext(sparkConf);
        Integer slices = (args.length > 0) ? Integer.parseInt(args[0]) : 2;
        JavaPairRDD<Integer, Integer> tc = sc.parallelizePairs(
                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.
        JavaPairRDD<Integer, Integer> edges = tc
                .mapToPair(new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() {
                    @Override
                    public Tuple2<Integer, Integer> call(
                            Tuple2<Integer, Integer> e) {
                        return new Tuple2<Integer, Integer>(e._2(), e._1());
                    }
                });

        long oldCount;
        long 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).mapToPair(ProjectFn.INSTANCE))
                    .distinct().cache();
            nextCount = tc.count();
        } while (nextCount != oldCount);

        System.out.println("TC has " + tc.count() + " edges.");
        sc.stop();
    }
}

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