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MapReduce实现多表链接

时间:2015-11-22 23:15:10      阅读:289      评论:0      收藏:0      [点我收藏+]

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多表链接

输入是两个文件,一个代表工厂表,包含工厂名列和地址编号列;另一个代表地址表,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地址名对应关系,输出"工厂名——地址名"表。

factory:

factoryname                    addressed
Beijing Red Star                    1
Shenzhen Thunder                3
Guangzhou Honda                2
Beijing Rising                       1
Guangzhou Development Bank      2
Tencent                        3
Back of Beijing                     1

address:

addressID    addressname
1            Beijing
2            Guangzhou
3            Shenzhen
4            Xian

设计思路

取出两个表中共同列作为map中的key,同时需要标识每个列所在的表,供在reduce中拆分。

代码实现

Mapper类

import java.io.IOException;
import java.util.StringTokenizer;

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 MyMapper extends Mapper<LongWritable,Text,Text,Text> {

    private static Text k = new Text();
    private static Text v= new Text();
    @Override
    protected void map(LongWritable key, Text value,Context context)
            throws IOException, InterruptedException {
        String path = ((FileSplit)context.getInputSplit()).getPath().getName();//获取文件名
        String line = value.toString();
        StringTokenizer st = new StringTokenizer(value.toString());
        String[] tmp = line.split("    +");
        if(tmp.length ==2){
            String first = tmp[0];
            String second = tmp[1];
            if(path.equals("factory")){
                if(first.equals("factoryname")) return;
                k.set(second);
                v.set(first+"1");
            }else if(path.equals("address")){
                if(second.equals("addressname")) return;
                k.set(first);
                v.set(second+"2");
            }
            context.write(k,v);
        }
    }
}

Reducer类

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

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

public class MyReducer extends Reducer<Text, Text, Text, Text>{
    
    private Text k = new Text();
    private Text v = new Text();
    
    @Override
    protected void setup(Reducer<Text, Text, Text, Text>.Context context)
            throws IOException, InterruptedException {
        context.write(new Text("factoryname"), new Text("addressname"));
    }

    @Override
    protected void reduce(Text key, Iterable<Text> value,Context context)
            throws IOException, InterruptedException {
            List<String> factory = new ArrayList<String>();
            List<String> address = new ArrayList<String>();
            for(Text val : value){
                String str = val.toString();
                String stf = str.substring(str.length()-1);
                String con = str.substring(0,str.length()-1);
                int flag = Integer.parseInt(stf);
                if(flag == 1){
                    factory.add(con);
                }else if(flag ==2){
                    address.add(con);
                }
            }
            for(int i=0;i<factory.size();i++){
                k.set(factory.get(i));
                for(int j=0;j<address.size();j++){
                    v.set(address.get(j));
                    context.write(k, v);
                }
            }
    }
}

Job驱动类

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 MTJoin {

    public static void main(String[] args) throws Exception {
        
        Configuration conf = new Configuration();
        Job job = new Job(conf,"multi table join");
        job.setJarByClass(MTJoin.class);
        job.setMapperClass(MyMapper.class);
        job.setReducerClass(MyReducer.class);    
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job, new Path("hdfs://127.0.0.1:9000/usr/qqx/mtinput"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://127.0.0.1:9000/usr/qqx/mtoutput"));
        System.exit(job.waitForCompletion(true)?0:1);
    }
}

 

MapReduce实现多表链接

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原文地址:http://www.cnblogs.com/qiaoqianxiong/p/4986940.html

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