cn.com.bsfit.frms.spark.PageRank.java Source code

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Here is the source code for cn.com.bsfit.frms.spark.PageRank.java

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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 cn.com.bsfit.frms.spark;

import java.util.ArrayList;
import java.util.List;
import java.util.Iterator;
import java.util.regex.Pattern;

import scala.Tuple2;

import com.google.common.collect.Iterables;

import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFlatMapFunction;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.sql.SparkSession;

/**
 * Computes the PageRank of URLs from an input file. Input file should be in
 * format of: URL neighbor URL URL neighbor URL URL neighbor URL ... where URL
 * and their neighbors are separated by space(s).
 *
 * This is an example implementation for learning how to use Spark. For more
 * conventional use, please refer to org.apache.spark.graphx.lib.PageRank
 */
public final class PageRank {
    private static final Pattern SPACES = Pattern.compile("\\s+");

    static void showWarning() {
        String warning = "WARN: This is a naive implementation of PageRank " + "and is given as an example! \n"
                + "Please use the PageRank implementation found in "
                + "org.apache.spark.graphx.lib.PageRank for more conventional use.";
        System.err.println(warning);
    }

    private static class Sum implements Function2<Double, Double, Double> {
        private static final long serialVersionUID = 1L;

        @Override
        public Double call(Double a, Double b) {
            return a + b;
        }
    }

    public static void main(String[] args) throws Exception {
        if (args.length < 2) {
            System.err.println("Usage: JavaPageRank <file> <number_of_iterations>");
            System.exit(1);
        }

        showWarning();

        SparkSession spark = SparkSession.builder().appName("JavaPageRank").getOrCreate();

        // Loads in input file. It should be in format of:
        // URL neighbor URL
        // URL neighbor URL
        // URL neighbor URL
        // ...
        JavaRDD<String> lines = spark.read().textFile(args[0]).javaRDD();

        // Loads all URLs from input file and initialize their neighbors.
        JavaPairRDD<String, Iterable<String>> links = lines.mapToPair(new PairFunction<String, String, String>() {
            private static final long serialVersionUID = 1L;

            @Override
            public Tuple2<String, String> call(String s) {
                String[] parts = SPACES.split(s);
                return new Tuple2<>(parts[0], parts[1]);
            }
        }).distinct().groupByKey().cache();

        // Loads all URLs with other URL(s) link to from input file and
        // initialize ranks of them to one.
        JavaPairRDD<String, Double> ranks = links.mapValues(new Function<Iterable<String>, Double>() {
            private static final long serialVersionUID = 1L;

            @Override
            public Double call(Iterable<String> rs) {
                return 1.0;
            }
        });

        // Calculates and updates URL ranks continuously using PageRank
        // algorithm.
        for (int current = 0; current < Integer.parseInt(args[1]); current++) {
            // Calculates URL contributions to the rank of other URLs.
            JavaPairRDD<String, Double> contribs = links.join(ranks).values()
                    .flatMapToPair(new PairFlatMapFunction<Tuple2<Iterable<String>, Double>, String, Double>() {
                        private static final long serialVersionUID = 1L;

                        @Override
                        public Iterator<Tuple2<String, Double>> call(Tuple2<Iterable<String>, Double> s) {
                            int urlCount = Iterables.size(s._1);
                            List<Tuple2<String, Double>> results = new ArrayList<>();
                            for (String n : s._1) {
                                results.add(new Tuple2<>(n, s._2() / urlCount));
                            }
                            return results.iterator();
                        }
                    });

            // Re-calculates URL ranks based on neighbor contributions.
            ranks = contribs.reduceByKey(new Sum()).mapValues(new Function<Double, Double>() {
                private static final long serialVersionUID = 1L;

                @Override
                public Double call(Double sum) {
                    return 0.15 + sum * 0.85;
                }
            });
        }

        // Collects all URL ranks and dump them to console.
        List<Tuple2<String, Double>> output = ranks.collect();
        for (Tuple2<?, ?> tuple : output) {
            System.out.println(tuple._1() + " has rank: " + tuple._2() + ".");
        }

        spark.stop();
    }
}