MapReduce编程实例（三）

前提准备：

1.hadoop安装运行正常。Hadoop安装配置请参考：Ubuntu下 Hadoop 1.2.1 配置安装

2.集成开发环境正常。集成开发环境配置请参考 ：Ubuntu 搭建Hadoop源码阅读环境

MapReduce编程实例：

MapReduce编程实例（一）,详细介绍在集成环境中运行第一个MapReduce程序 WordCount及代码分析

MapReduce编程实例（二），计算学生平均成绩

MapReduce编程实例（三），数据去重

MapReduce编程实例（四），排序

MapReduce编程实例（五），MapReduce实现单表关联

MapReduce编程实例（六），MapReduce实现多表关联

输入：

2013-11-01 aa
2013-11-02 bb
2013-11-03 cc
2013-11-04 aa
2013-11-05 dd
2013-11-06 dd
2013-11-07 aa
2013-11-09 cc
2013-11-10 ee

2013-11-01 bb 
2013-11-02 33 
2013-11-03 cc
2013-11-04 bb
2013-11-05 23 
2013-11-06 dd
2013-11-07 99 
2013-11-09 99
2013-11-10 ee

.....

.....

.....

数据重复，map中每一行做为一个key，value值任意，经过shuffle之后输入到reduce中利用key的唯一性直接输出key

代码太简单，不解释，上代码：

package com.t.hadoop;

import java.io.IOException;
import java.util.HashSet;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;

/**
 * 数据去重
 * @author daT [email protected]
 *
 */
public class Dedup {

	public static class MyMapper extends Mapper<Object, Text, Text, Text>{

		@Override
		protected void map(Object key, Text value, Context context)
				throws IOException, InterruptedException {
				context.write(value, new Text(""));
		}
	}
	
	public static class MyReducer extends Reducer<Text, Text, Text, Text>{

		@Override
		protected void reduce(Text key, Iterable<Text> value,
				Context context)
				throws IOException, InterruptedException {
			context.write(key, new Text(""));
		}
	}
	
	
	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{
		Configuration conf = new Configuration();
		String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
		
		if(otherArgs.length<2){
			System.out.println("parameter errors!");
			System.exit(2);
		}
		
		Job job = new org.apache.hadoop.mapreduce.Job(conf, "Dedup");
		job.setJarByClass(Dedup.class);
		job.setMapperClass(MyMapper.class);
		job.setCombinerClass(MyReducer.class);
		job.setReducerClass(MyReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		
		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
		FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
		
		System.exit(job.waitForCompletion(true)?0:1);
		
	}
	
}

输出结果
2013-11-01 aa
2013-11-01 bb
2013-11-02 33
2013-11-02 bb
2013-11-03 cc
2013-11-03 cc
2013-11-04 98
2013-11-04 aa
2013-11-04 bb
2013-11-05 23
2013-11-05 93
2013-11-05 dd
2013-11-06 99
2013-11-06 dd
2013-11-07 92
2013-11-07 99
2013-11-07 aa
2013-11-09 99
2013-11-09 aa
2013-11-09 cc
2013-11-10 ee