MultipleInputs实现reduce端连接

MultipleInputs允许定义多个数据源，并且为每一个数据源指定一个独立的输入格式和Mapper，因此可以对多个输入文件执行Reduce操作。

示例源代码：

package org.cy.pack;

import java.io.IOException;
import java.net.URISyntaxException;
import java.util.Date;

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.*;
import org.apache.hadoop.mapreduce.lib.output.*;

public class ReduceJoin {

	public static class SalesRecordMapper extends Mapper<Object,Text,Text,Text>{
		 public void map(Object key,Text value, Context context) throws InterruptedException{
			String record = value.toString();
			String[] parts = record.split("\t");
			
			try {
				context.write(new Text(parts[0]),new Text("Sales\t"+parts[1]));
			} catch (IOException e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
			}
		 }
	}
	
	public static class AccountRecordMapper extends Mapper<Object,Text,Text,Text>{
		 public void map(Object key,Text value, Context context) throws InterruptedException{
			String record = value.toString();
			String[] parts = record.split("\t");
			
			try {
				context.write(new Text(parts[0]),new Text("Accounts\t"+parts[1]));
			} catch (IOException e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
			}
		 }
	}
	
	public static class ReduceJoinReducer extends Reducer<Text,Text,Text,Text>{
		public void reduce(Text key,Iterable<Text> values,Context context) throws IOException, InterruptedException{
			String name = "";
			double total = 0.0;
			int count = 0;
			
			for(Text val:values){
				String[] parts = val.toString().split("\t");
				
				if(parts[0].equals("Sales")){
					count++;
					total += Float.parseFloat(parts[1]);
				}else if(parts[0].equals("Accounts")){
					name = parts[1];
				}
			}
			
			String str = String.format("%d\t%f", count,total);
			context.write(new Text(name), new Text(str));
			
		}
	}
		
	/**
	 * @param args
	 * @throws IOException 
	 * @throws InterruptedException 
	 * @throws ClassNotFoundException 
	 * @throws URISyntaxException 
	 */
	@SuppressWarnings("deprecation")
	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException, URISyntaxException {
		// TODO Auto-generated method stub
	    Date startTime = new Date();
	    System.out.println("Job started: " + startTime);
	    
		Configuration conf = new Configuration();
		Job job = new Job(conf,"ReduceJoin");
		job.setJarByClass(ReduceJoin.class);
		job.setReducerClass(ReduceJoinReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		
		Path in1 = new Path(args[0]);
		Path in2 = new Path(args[1]);
		Path out = new Path(args[2]);
		
		MultipleInputs.addInputPath(job, in1, TextInputFormat.class,SalesRecordMapper.class);
		MultipleInputs.addInputPath(job, in2, TextInputFormat.class, AccountRecordMapper.class);
		
		FileOutputFormat.setOutputPath(job, out);
		out.getFileSystem(conf).delete(out);
		job.waitForCompletion(true);
	    
	    int flag = job.waitForCompletion(true)?0:1;
	    
	    Date end_time = new Date();
	    System.out.println("Job ended: " + end_time);
	    System.out.println("The job took " +  (end_time.getTime() - startTime.getTime())  + "  ms.");
	    
	    System.exit(flag);

	}

}

输入文件的内容：

输入内容的各行的字段以tab输入作为分隔符。

sales.txt内容详情：账户ID，销售额，时间
sales.txt:
001 35.99   time
002 12.49   time
004 13.42   time
003 499.99  time
001 78.95   time
002 21.99   time
002 93.45   time
001 9.99    time
Accounts.txt内容详情：账户ID，姓名，时间
Accounts.txt:
001 J   time
002 AB  time
003 AP  time
004 NA  time

上传输入文件：

caiyong@caiyong:/opt/hadoop$ bin/hadoop fs -copyFromLocal /home/caiyong/桌面/sales.txt  /
caiyong@caiyong:/opt/hadoop$ bin/hadoop fs -copyFromLocal /home/caiyong/桌面/Accounts.txt  /

运行配置：

Arhuments:
hdfs://127.0.0.1:8020/sales.txt
hdfs://127.0.0.1:8020/Accounts.txt
hdfs://127.0.0.1:8020/ReduceJoinRes

运行结果：

caiyong@caiyong:/opt/hadoop$ bin/hadoop fs -cat /ReduceJoinRes/*
J   3   124.929998
AB  3   127.929996
AP  1   499.989990
NA  1   13.420000

参考资料：《Hadoop Beginner's Guide》   [英]Garry Tukington  著