MapReduce:输入是两个文件，file1代表工厂表，包含工厂名列和地址编号列；file2代表地址表，包含地址名列和地址编号列。要求从输入数据中找出工厂名和地址名的对应关系，输出"工厂名----地址名"表

文件如下：

file1：

Beijing Red Star
Shenzhen Thunder
Guangzhou Honda
Beijing Rising
Guangzhou Development Bank
Tencent
Back of Beijing    

file2：

    Beijing
    Guangzhou
    Shenzhen
    Xian

代码如下（由于水平有限，不保证完全正确，如果发现错误欢迎指正）：

package com;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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;

public class Test {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration config = new  Configuration();
        config.set("fs.defaultFS", "hdfs://192.168.0.100:9000");
        config.set("yarn.resourcemanager.hostname", "192.168.0.100");

        FileSystem fs = FileSystem.get(config);

        Job job = Job.getInstance(config);

        job.setJarByClass(Test.class);

        //设置所用到的map类
        job.setMapperClass(myMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        //设置用到的reducer类
        job.setReducerClass(myReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        //设置s输入输出地址
        FileInputFormat.addInputPath(job, new Path("/FactoryName/"));

        Path path = new Path("/output2/");

        if(fs.exists(path)){
            fs.delete(path, true);
        }

        //指定文件的输出地址
        FileOutputFormat.setOutputPath(job, path);

        //启动处理任务job
        boolean completion = job.waitForCompletion(true);
        if(completion){
            System.out.println("Job Success!");
        }
    }

    public static class myMapper extends Mapper<Object, Text, Text, Text> {

        // 实现map函数
        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String temp=new String();// 左右表标识
            String values=value.toString();
            String words[]=values.split("\t");

            String mapkey = new String();
            String mapvalue = new String(); 

            //右表：1  Beijing
            if (words[].charAt() >= '' && words[].charAt() <= '') {
                mapkey = words[];
                mapvalue =words[];
                temp = "";

            }else{
                //左表：Beijing Red Star  1
                mapkey = words[];
                mapvalue =words[];
                temp = "";
            }

            // 输出左右表
            //左表：(1,1+Beijing Red Star)
            //右表：(1,2+Beijing)
            context.write(new Text(mapkey), new Text(temp + "+"+ mapvalue));
        }
    }

    //reduce解析map输出，将value中数据按照左右表分别保存
    public static class myReducer extends Reducer<Text, Text, Text, Text> {
        // 实现reduce函数
        public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {

            List<String> factory = new ArrayList<String>();
            List<String> address = new ArrayList<String>();

            for (Text value : values) {
                // 取得左右表标识
                char temp=(char) value.charAt();
                String words[]=value.toString().split("[+]");//1,Beijing Red Star

                if(temp==''){
                    factory.add(words[]);// 左表
                }

                if(temp==''){
                    address.add(words[]);// 右表
                }
            }

            //求出笛卡尔积，并输出
            for (String f : factory) {
                for (String a : address) {
                    context.write(new Text(f), new Text(a));
                }
            }
        }
    }
}

输出结果如下：

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