HADOOP之MAPREDUCE程序应用二

本是古典何须时尚 2022-08-24 00:52 139阅读 0赞

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，要求输出的结果按照单词字母的顺序进行排序。

解决方案：

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，然后把所有相同的单词聚集在一起，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 执行程序

1）创建输入目录

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，把结果存放在当前目录的WordCount目录下。

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）运行WordCount程序，输入目录为wordcount\_input，输出目录为wordcount\_output。

hadoop jar wordcount.jar com.wangluqing.WordCount wordcount\_input wordcount\_output

6） 查看各个单词频数结果