mapreduce项目中加入combiner

combiner相当于是一个本地的reduce，它的存在是为了减少网络的负担，在本地先进行一次计算再叫计算结果提交给reduce进行二次处理。

现在的流程为：

 

对于combiner我们有这些理解：

 

 

 

   Mapper代码展示：

package com.nenu.mprd.test;

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

public class MyMap extends Mapper<LongWritable, Text, Text, Text> {
    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
            throws IOException, InterruptedException {
        // TODO Auto-generated method stub
        //获取到单词
        String line=value.toString();
        String[] words=line.split(" ");
        //获取到文件名
        FileSplit filesplit = (FileSplit)context.getInputSplit();
        String fileName =  filesplit.getPath().getName().trim();//.substring(0,5).
        
        String outkey=null;
        for (String word : words) {
            //字母+:+文件名
            outkey=word.trim()+":"+fileName;
            System.out.println("map:"+outkey);
            
            context.write(new Text(outkey), new Text("1"));
        }
    }
}

  Combiner代码展示：

package com.nenu.mprd.test;

import java.io.IOException;

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

public class MyCombiner extends Reducer<Text, Text, Text, Text>{
    @Override
    protected void reduce(Text key, Iterable<Text> values,Context context) throws IOException, InterruptedException {
           Text n = null;//输出key
           int count=0;
           Text m=null;//输出value
           for(Text v :values){ //对同一个map输出的k,v对进行按k进行一次汇总。不同map的k,v汇总必须要用reduce方法
                 String[] words=key.toString().split(":");
                 n=new Text(words[0].trim());//字母--key
                 System.out.println("MyCombiner KEY:"+n);
                 
                 count+=Integer.parseInt(v.toString());
                 m=new Text("("+words[1].trim()+" "+count+")");
                 
           }
           System.out.println("MyCombiner value:"+m);
           context.write(n, m);
    }

}

　Reduce代码展示：

package com.nenu.mprd.test;

import java.io.IOException;

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

public class MyReduce extends Reducer<Text, Text, Text, Text> {
    @Override
    protected void reduce(Text key, Iterable<Text> values,
            Reducer<Text, Text, Text, Text>.Context context) throws IOException, InterruptedException {
        // TODO Auto-generated method stub
        System.out.println("reduce: key"+key);
        String out="";
        for (Text Text : values) {
            //sum+=intWritable.get();
            out+=Text.toString()+" ";
        }
        System.out.println("reduce value:"+out);
        context.write(key, new Text(out));
    }
}

　Job代码展示：

package com.nenu.mprd.test;

import java.net.URI;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
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;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class MyJob extends Configured implements Tool{
    
    public static void main(String[] args) throws Exception {
        MyJob myJob=new MyJob();
        ToolRunner.run(myJob, null);
    }
    @Override
    public int run(String[] args) throws Exception {
        // TODO Auto-generated method stub
        Configuration conf=new Configuration();
        conf.set("fs.defaultFS", "hdfs://192.168.64.141:9000");
        
        //添加自动删除hadoop下的文件
        //如果导成架包则需要改变一些参数作为手动输入
        FileSystem filesystem =FileSystem.get(new URI("hdfs://192.168.64.141:9000"), conf, "root");
        Path deletePath=new Path("/hadoop/wordcount/city/out");
        if(filesystem.exists(deletePath)){
            filesystem.delete(deletePath,true);//str:  b:
        }
        
        
        Job job=Job.getInstance(conf);
        job.setJarByClass(MyJob.class);
        job.setMapperClass(MyMap.class);
        
        //设置combiner 如果combiner和reduce一样则可以不用设置
        job.setCombinerClass(MyCombiner.class);
        
        job.setReducerClass(MyReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job, new Path("/hadoop/wordcount/city"));
        FileOutputFormat.setOutputPath(job, new Path("/hadoop/wordcount/city/out"));
        job.waitForCompletion(true);
        return 0;
    }

}