用eclipce编写 MR程序 MapReduce

 

package com.bw.mr;

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

//  yarn  mr--->Mapper  map    Reducer reduce
// Mapper:四个泛型 
//keyin :Map端输入的K值   keyin :偏移量
// hello word hello tom hello jim 
//hello word    9 (hello word)    String
// hello tom     17( hello tom)
//  hello jim    .....
//valuein:  word   
//  hadoop 的api   writeable
//   keyout  valueout ---->    k(单词)   
public class WCMapper  extends Mapper<LongWritable, Text, Text, IntWritable>{
          	Text t=new Text();
          	IntWritable i  =new IntWritable(1);
           @Override
//           map端 分别和1 组装
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
        		throws IOException, InterruptedException {
//        	      hadoop  Api      " hello word hello tom" --->"hello"" word" hello tom   
        	           String splits[]= value.toString().split(" ");
//        	            java hadoop
        	              for(String word:splits) {
//        	            	    word  --->text
        	            	    t.set(word);
//        	        上下文信息：   map 端信息发出去   context  发出去
        	            	    context.write(t, i);    
        	              }
        }
}

　　

package com.bw.mr;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

//    Mr :input map  reduce output
//   reducer  reduce hello(1,1,1,1,1)-->hello(1+1+1+...)
//     map(LongWriteable,text) --->(text,IntWriteable)\
//      reduce (text,IntWriteable) ---->(text,IntWriteable)
//     hello(1,1,1,1,1)-->
public class WCReducer extends Reducer<Text, IntWritable, Text, IntWritable>  {
//    重写 reduce 方法
	  @Override
//	         text  :word      Iterable (111111111111111)
	protected void reduce(Text arg0, Iterable<IntWritable> arg1,
			Reducer<Text, IntWritable, Text, IntWritable>.Context arg2) throws IOException, InterruptedException {
//		reduce --->归并  ---》 word(1,1,1,1,...)---->word(count)
		      int count =0;
//		      循环   。。。for
		     for(IntWritable i:arg1) {
		    	 count++;
		     }
//		        输出最后 的结果
		     arg2.write(arg0,new IntWritable(count));
	}
}

　　

package com.bw.mr;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCount {//  触发    启动类
	public static void main(String[] args) throws Exception {
		// 配置信息
		Configuration conf = new Configuration();
		// mr 程序 job
		Job job = Job.getInstance(conf);
		// job 运行 class
		job.setJarByClass(WordCount.class);
		//
		job.setMapperClass(WCMapper.class);
		// job：有关于 mr的全部 ----》jar包 （包含所有的四要素，所有的类）
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);
		job.setReducerClass(WCReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		// job WC ：mr:job 数据
		FileInputFormat.addInputPath(job, new Path("hdfs://linux04:9000/aa.txt"));
		// 是经过 mapreduce 之后的输出结果
		FileOutputFormat.setOutputPath(job, new Path("hdfs://linux04:9000/aajiegou.txt"));
		// job 要提交到集群上去的
		job.waitForCompletion(true);
		// jar ---->集群上传 -————》
		// hadoop jar wordcountjar cn.beiwang.mr.Wordcount
		// 1.8    hadoop jar hadoop jar jar     hadoop jar wordcountjar 具体路径
	}
}