不多说，直接上干货！
spark-1.6.1-bin-hadoop2.6里Basic包下的JavaPageRank.java
/*
* 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 < ) {
System.err.println("Usage: JavaPageRank <file> <number_of_iterations>");
System.exit();
} 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", ); // 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[], parts[]);
}
}).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 = ; current < Integer.parseInt(args[]); 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();
}
}
没结果，暂时
spark-2.2.0-bin-hadoop2.6里Basic包下的JavaPageRank.java
/*
* 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 < ) {
System.err.println("Usage: JavaPageRank <file> <number_of_iterations>");
System.exit();
} 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[], parts[]);
}).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 = ; current < Integer.parseInt(args[]); 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();
}
}
没结果，暂时