spark-2.2.0-bin-hadoop2.6和spark-1.6.1-bin-hadoop2.6发行包自带案例全面详解(java、python、r和scala)之Basic包下的JavaPageRank.java(图文详解)-阿里云开发者社区

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spark-2.2.0-bin-hadoop2.6和spark-1.6.1-bin-hadoop2.6发行包自带案例全面详解(java、python、r和scala)之Basic包下的JavaPageRank.java(图文详解)

简介:

spark-1.6.1-bin-hadoop2.6里Basic包下的JavaPageRank.java

复制代码
/*
 * 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.spark.examples;
package zhouls.bigdata.Basic;



import scala.Tuple2;//scala里的元组
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 java.util.ArrayList;
import java.util.List;
import java.util.Iterator;
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 JavaPageRank {
  private static final Pattern SPACES = Pattern.compile("\\s+");

  /*
   * 显示警告函数
   */
  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;
    }
  }

  
  /*
   * 主函数
   */
  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("JavaPageRank").setMaster("local");
    JavaSparkContext ctx = new JavaSparkContext(sparkConf);

    // Loads in input file. It should be in format of:
    //     URL         neighbor URL
    //     URL         neighbor URL
    //     URL         neighbor URL
    //     ...
//  JavaRDD<String> lines = ctx.textFile(args[0], 1);//这是官网发行包里写的
    JavaRDD<String> lines = ctx.textFile("data/input/mllib/pagerank_data.txt", 1);
    
    
    // Loads all URLs from input file and initialize their neighbors.
    //根据边关系数据生成 邻接表 如:(1,(2,3,4,5)) (2,(1,5))...  
    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);
        return new Tuple2<String, String>(parts[0], parts[1]);
      }
    }).distinct().groupByKey().cache();

    //初始化 ranks, 每一个url初始分值为1
    // 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;
      }
    });

    
    /* 
     * 迭代iters次; 每次迭代中做如下处理, links(urlKey, neighborUrls) join (urlKey, rank(分值));
     * 对neighborUrls以及初始 rank,每一个neighborUrl  , neighborUrlKey, 初始rank/size(新的rank贡献值);
     * 然后再进行reduceByKey相加 并对分值 做调整 0.15 + 0.85 * _ 
     */
    // 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;
        }
      });
    }

    
    //输出排名
    // 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() + ".");
    }

    ctx.stop();
  }
}
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spark-2.2.0-bin-hadoop2.6里Basic包下的JavaPageRank.java

复制代码
/*
 * 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.spark.examples;
package zhouls.bigdata.Basic;

import java.util.ArrayList;
import java.util.List;
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.Function2;
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
 *
 * Example Usage:
 * <pre>
 * bin/run-example JavaPageRank data/mllib/pagerank_data.txt 10
 * </pre>
 */
public final class JavaPageRank {
  private static final Pattern SPACES = Pattern.compile("\\s+");

  /*
   * 显示警告函数
   */
  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;
    }
  }

  /*
   * 主函数
   */
  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()
      .master("local")
      .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();
    JavaRDD<String> lines = spark.read().textFile("data/input/mllib/pagerank_data.txt").javaRDD();
    
    
    
    
    
    // Loads all URLs from input file and initialize their neighbors.
    //根据边关系数据生成 邻接表 如:(1,(2,3,4,5)) (2,(1,5))...  
    JavaPairRDD<String, Iterable<String>> links = lines.mapToPair(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.
    //初始化 ranks, 每一个url初始分值为1
    JavaPairRDD<String, Double> ranks = links.mapValues(rs -> 1.0);

    
    /* 
     * 迭代iters次; 每次迭代中做如下处理, links(urlKey, neighborUrls) join (urlKey, rank(分值));
     * 对neighborUrls以及初始 rank,每一个neighborUrl  , neighborUrlKey, 初始rank/size(新的rank贡献值);
     * 然后再进行reduceByKey相加 并对分值 做调整 0.15 + 0.85 * _ 
     */
    // 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(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(sum -> 0.15 + sum * 0.85);
    }

    
    //输出排名
    // 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();
  }
}


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