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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.
*/
package org.apache.flink.spargel.java.examples;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.spargel.java.MessageIterator;
import org.apache.flink.spargel.java.MessagingFunction;
import org.apache.flink.spargel.java.OutgoingEdge;
import org.apache.flink.spargel.java.VertexCentricIteration;
import org.apache.flink.spargel.java.VertexUpdateFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
/**
* An implementation of the basic PageRank algorithm in the vertex-centric API (spargel).
* In this implementation, the edges carry a weight (the transition probability).
*/
@SuppressWarnings("serial")
public class SpargelPageRank {
private static final double BETA = 0.85;
public static void main(String[] args) throws Exception {
final int numVertices = 100;
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
// enumerate some sample edges and assign an initial uniform probability (rank)
DataSet<Tuple2<Long, Double>> intialRanks = env.generateSequence(1, numVertices)
.map(new MapFunction<Long, Tuple2<Long, Double>>() {
public Tuple2<Long, Double> map(Long value) {
return new Tuple2<Long, Double>(value, 1.0/numVertices);
}
});
// generate some random edges. the transition probability on each edge is 1/num-out-edges of the source vertex
DataSet<Tuple3<Long, Long, Double>> edgesWithProbability = env.generateSequence(1, numVertices)
.flatMap(new FlatMapFunction<Long, Tuple3<Long, Long, Double>>() {
public void flatMap(Long value, Collector<Tuple3<Long, Long, Double>> out) {
int numOutEdges = (int) (Math.random() * (numVertices / 2));
for (int i = 0; i < numOutEdges; i++) {
long target = (long) (Math.random() * numVertices) + 1;
out.collect(new Tuple3<Long, Long, Double>(value, target, 1.0/numOutEdges));
}
}
});
DataSet<Tuple2<Long, Double>> result = intialRanks.runOperation(
VertexCentricIteration.withValuedEdges(edgesWithProbability,
new VertexRankUpdater(numVertices, BETA), new RankMessenger(), 20));
result.print();
env.execute("Spargel PageRank");
}
/**
* Function that updates the rank of a vertex by summing up the partial ranks from all incoming messages
* and then applying the dampening formula.
*/
public static final class VertexRankUpdater extends VertexUpdateFunction<Long, Double, Double> {
private final long numVertices;
private final double beta;
public VertexRankUpdater(long numVertices, double beta) {
this.numVertices = numVertices;
this.beta = beta;
}
@Override
public void updateVertex(Long vertexKey, Double vertexValue, MessageIterator<Double> inMessages) {
double rankSum = 0.0;
for (double msg : inMessages) {
rankSum += msg;
}
// apply the dampening factor / random jump
double newRank = (beta * rankSum) + (1-BETA)/numVertices;
setNewVertexValue(newRank);
}
}
/**
* Distributes the rank of a vertex among all target vertices according to the transition probability,
* which is associated with an edge as the edge value.
*/
public static final class RankMessenger extends MessagingFunction<Long, Double, Double, Double> {
@Override
public void sendMessages(Long vertexId, Double newRank) {
for (OutgoingEdge<Long, Double> edge : getOutgoingEdges()) {
sendMessageTo(edge.target(), newRank * edge.edgeValue());
}
}
}
}