hadoop reduce端联结

　　此例子摘自hadoop基础教程。

　　其中sales.txt内容如下

客户编号 客户消费额度 消费时间
001	35.99	2012-03-15
002	12.29	2004-07-02
004	13.42	2005-12-20
003	499.99	2010-12-20
001	78.95	2012-04-02
002	21.99	2006-11-30
002	93.45	2008-09-10
001	9.99	2012-05-17

　　accounts.txt内容如下:

　　

客户编号 姓名　　　　　　　　　　　　 注册时间
001	John AllenStandard	2012-03-15
002	Abigail SmithPremium	2004-07-13
003	April StevensStandard	2010-12-20
004	Nasser HafezPremium	2001-04-23

　　　我们的目标是通过reduce端联结求出每个客户姓名 消费的次数 消费额

　　代码如下:

import java.io.*;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.*;
import org.apache.hadoop.mapreduce.lib.output.*;

public class ReduceJoin {
	//sales.txt的处理 客户ID 消费额度 消费时间
	public static class SalesRecordMapper extends Mapper<Object, Text, Text, Text> {
		public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
			String record = value.toString();
			String[] parts = record.split("\t");
			context.write(new Text(parts[0]), new Text("sales\t"+parts[1]));
		}
	}
	//accounts.txt的处理 客户id 客户姓名 办卡时间
	public static class AccountRecordMapper extends Mapper<Object, Text, Text, Text> {
		public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
			String record = value.toString();
			String[] parts = record.split("\t");
			context.write(new Text(parts[0]), new Text("accounts\t"+parts[1]));
		}
	}
	
	//reduce
	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 t:values) {
				String[] parts = t.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));
		}
	}
	
	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		Job job = new Job(conf, "Reduce端join");
		job.setJarByClass(ReduceJoin.class);
		job.setReducerClass(ReduceJoinReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		System.out.println(args[0]);
		MultipleInputs.addInputPath(job, new Path(args[0]), TextInputFormat.class, SalesRecordMapper.class);
		MultipleInputs.addInputPath(job, new Path(args[1]), TextInputFormat.class, AccountRecordMapper.class);
		Path outputPath = new Path(args[2]);
		FileOutputFormat.setOutputPath(job, outputPath);
		outputPath.getFileSystem(conf).delete(outputPath);
		System.exit(job.waitForCompletion(true)?0:1);
		
	}
}

　　结果截图