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 [e-mail] * */public class Dedup {public static class MyMapper extends Mapper<Object, Text, Text, Text>{@Overrideprotected 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>{@Overrideprotected 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