HADOOP之MAPREDUCE程序应用二

摘要：MapReduce程序进行单词计数。

关键词：MapReduce程序  单词计数

数据源：人工构造英文文档file1.txt,file2.txt。

file1.txt 内容

Hello   Hadoop

I   am  studying   the   Hadoop  technology

file2.txt内容

Hello  world

The  world  is  very  beautiful

I   love    the   Hadoop    and    world

问题描写叙述：

统计人工构造的英文文档中单词的频数，要求输出的结果依照单词字母的顺序进行排序。

解决方式：

1  开发工具：VM10+ Ubuntu12.04+ Hadoop1.1.2

2  设计思路：把英文文档内容且分成单词，然后把全部同样的单词聚集在一起，最后计算各个单词的频数。

程序清单：

package com.wangluqing;

import java.io.IOException;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

public static class TokenizerMapper extends Mapper<Object,Text,Text,IntWritable> {

private final static IntWritable one = new IntWritable(1);

private Text word = new Text();

public void map(Object key, Text value, Context context) throws IOException,InterruptedException {

StringTokenizer its = new StringTokenizer(value.toString());

while (its.hasMoreTokens()) {

word.set(its.nextToken());

context.write(word,one);

}

}

}

public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> {

private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {

int sum = 0;

for(IntWritable val:values) {

sum += val.get();

}

result.set(sum);

context.write(key,result);

}

}

public static void main(String[] args) throws Exception {

Configuration conf = new Configuration();

String[] otherArgs = new GenericOptionsParser(conf,args).getRemainingArgs();

if(otherArgs.length !=2 ) {

System.err.println("Usage:wordcount<in><out>");

System.exit(2);

}

Job job = new Job(conf,"word count");

job.setJarByClass(WordCount.class);

job.setMapperClass(TokenizerMapper.class);

job.setCombinerClass(IntSumReducer.class);

job.setReducerClass(IntSumReducer.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

FileInputFormat.addInputPath(job,new Path(otherArgs[0]));

FileOutputFormat.setOutputPath(job,new Path(otherArgs[1]));

System.exit(job.waitForCompletion(true)?0:1);

}

}

3 运行程序

1）创建输入文件夹

hadoop  fs  -mkdir   wordcount_input

2）上传本地英文文档

hadoop  fs -put  /usr/local/datasource/article/*   wordcount_input

3）编译WordCount.java程序，把结果存放在当前文件夹的WordCount文件夹下。

root@hadoop:/usr/local/program/hadoop# javac -classpath hadoop-core-1.1.2.jar:lib/commons-cli-1.2.jar -d WordCount WordCount.java

4） 将编译结果打成Jar包

jar -cvf  wordcount.jar   WordCount/  .

5）执行WordCount程序，输入文件夹为wordcount_input，输出文件夹为wordcount_output。

hadoop jar wordcount.jar  com.wangluqing.WordCount  wordcount_input  wordcount_output

6） 查看各个单词频数结果

root@hadoop:/usr/local/program/hadoop# hadoop fs -cat wordcount_output/part-r-00000

Hadoop 3

Hello 2

I 2

The 1

am 1

and 1

beautiful 1

is 1

love 1

studying 1

technology 1

the 2

very 1

world 3

总结：

WordCount程序是最简单也是最具代表性的MapReduce程序，一定程度上MapReduce设计的初衷，即对日志文件的分析。

Resource：

1  http://www.wangluqing.com/2014/03/hadoop-mapreduce-programapp2/

2  《Hadoop实战 第二版》陆嘉恒著 第5章 MapReduce应用案例