Java tutorial
/* * 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 SparkExamples; import scala.Tuple2; import com.google.common.collect.Iterables; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; 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.storage.StorageLevel; import java.io.BufferedWriter; import java.io.FileWriter; import java.util.ArrayList; import java.util.List; import java.util.regex.Pattern; /** * 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 SparkPR { 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> { @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(); SparkConf sparkConf = new SparkConf().setAppName("SparkPR"); JavaSparkContext ctx = new JavaSparkContext(sparkConf); // Loads in input file. It should be in format of: // URL neighbor URL // URL neighbor URL // URL neighbor URL // ... long start = System.currentTimeMillis(); JavaRDD<String> lines = ctx.textFile(args[0], 1); int partition = Integer.parseInt(args[3]); // Loads all URLs from input file and initialize their neighbors. JavaPairRDD<String, Iterable<String>> links = lines.mapToPair(new PairFunction<String, String, String>() { @Override public Tuple2<String, String> call(String s) { String[] parts = SPACES.split(s); parts[0] = parts[0].toString() .replaceAll("AAAAAAAAAZAAAAAAAAAZAAAAAAAAAZAAAAAAAAAZAAAAAAAAAZAAAAAAAAAZAAAAAAAAAZ", ""); parts[1] = parts[1].toString() .replaceAll("AAAAAAAAAZAAAAAAAAAZAAAAAAAAAZAAAAAAAAAZAAAAAAAAAZAAAAAAAAAZAAAAAAAAAZ", ""); return new Tuple2<String, String>(parts[0], parts[1]); } }).groupByKey(partition).persist(StorageLevel.MEMORY_AND_DISK()); // 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>() { @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>() { @Override public Iterable<Tuple2<String, Double>> call(Tuple2<Iterable<String>, Double> s) { int urlCount = Iterables.size(s._1); List<Tuple2<String, Double>> results = new ArrayList<Tuple2<String, Double>>(); for (String n : s._1) { results.add(new Tuple2<String, Double>(n, s._2() / urlCount)); } return results; } }); // Re-calculates URL ranks based on neighbor contributions. ranks = contribs.reduceByKey(new Sum()).mapValues(new Function<Double, Double>() { @Override public Double call(Double sum) { return 0.15 + sum * 0.85; } }); } ranks.foreach(p -> System.out.println(p)); long end = System.currentTimeMillis(); System.out.println("running time " + (end - start) / 1000 + "s"); String results = "running time " + (new Double((end - start) / 1000)).toString() + "s" + "input file : " + args[0] + ", iteration : " + args[1]; System.out.println(results); String outputurl = ".//results.txt"; BufferedWriter writer = new BufferedWriter(new FileWriter(outputurl, true)); writer.write(results); writer.newLine(); writer.close(); ctx.stop(); } }