1. 需求概述

在实际工作中可能会遇到这样的需求，将多个关联的表格整合到一张表中。

2. 解决思路

Map 端的主要工作：为来自不同表或文件的 key/value 对，打标签以区别不同来源的记录。然后用连接字段作为 key，其余部分和新加的标志作为 value，最后进行输出。

Reduce 端的主要工作：在 Reduce 端以连接字段作为 key 的分组已经完成，我们只需要在每一个分组当中将那些来源于不同文件的记录（在 Map 阶段已经打标志）分开，最后进行合并就 ok 了。

3.代码实现

3.1编写TableBean类

package com.yangmin.mapreduce.reduceJoin;

import org.apache.hadoop.io.Writable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

public class TableBean implements Writable {

    private String id;  //订单id
    private String pid; //商品pid
    private int amount; //商品数量
    private String pname; //商品名称
    private String flag; //标记来自哪个表格

    public TableBean() {
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public String getPid() {
        return pid;
    }

    public void setPid(String pid) {
        this.pid = pid;
    }

    public int getAmount() {
        return amount;
    }

    public void setAmount(int amount) {
        this.amount = amount;
    }

    public String getPname() {
        return pname;
    }

    public void setPname(String pname) {
        this.pname = pname;
    }

    public String getFlag() {
        return flag;
    }

    public void setFlag(String flag) {
        this.flag = flag;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(this.id);
        out.writeUTF(this.pid);
        out.writeInt(amount);
        out.writeUTF(this.pname);
        out.writeUTF(this.flag);
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        this.id = in.readUTF();
        this.pid = in.readUTF();
        this.amount = in.readInt();
        this.pname = in.readUTF();
        this.flag = in.readUTF();
    }

    @Override
    public String toString() {
        return id + '\t' + amount + "\t" + pname + "\t";
    }
}

3.2 编写mapper类

package com.yangmin.mapreduce.reduceJoin;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;

import java.io.IOException;


public class TableMapper extends Mapper<LongWritable, Text,Text,TableBean> { 
    private  String filename;  //文件名
    private Text outK = new Text(); //输出的key
    private TableBean outV = new TableBean(); //输出的value

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        //确定当前切片来自哪个文件，并获取文件名
        InputSplit split = context.getInputSplit();
        FileSplit fileSplit = (FileSplit) split; 
        filename = fileSplit.getPath().getName();
    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
       //取出出一行
        String s = value.toString();

        //判断是哪个文件,然后针对文件进行不同的操作
        if (filename.contains("order")){ //处理订单表
            //切割
            String[] split = s.split("\t");
            //封装outk
            outK.set(split[1]);
            //封装outV
            outV.setId(split[0]);
            outV.setPid(split[1]);
            outV.setAmount(Integer.parseInt(split[2]));
            outV.setPname("");
            outV.setFlag("order");

        }else {  //处理商品表
            //切割
            String[] split = s.split("\t");
            //封装outK
            outK.set(split[0]);
            //封装outV
            outV.setId("");
            outV.setPid(split[0]);
            outV.setPname(split[1]);
            outV.setAmount(0);
            outV.setFlag("pd");
        }
        //写出
        context.write(outK, outV);
    }
}

3.3 编写reducer类

package com.yangmin.mapreduce.reduceJoin;

import org.apache.commons.beanutils.BeanUtils;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;
import java.lang.reflect.InvocationTargetException;
import java.util.ArrayList;

public class TableReducer extends Reducer<Text,TableBean,TableBean,NullWritable> {
    @Override
    protected void reduce(Text key, Iterable<TableBean> values, Context context) throws IOException, InterruptedException {
        //准备初始化
        ArrayList<TableBean> orderBean = new ArrayList<>();
        TableBean pdBean = new TableBean();

        // 循环遍历
        for (TableBean value : values) {
            if ("order".equals(value.getFlag())){
                TableBean tmpBean = new TableBean(); //创建新的对象 
                try {
                    BeanUtils.copyProperties(tmpBean, value); //将对象属性赋予新的对象
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }
                orderBean.add(tmpBean);
            }else {
                try {
                    BeanUtils.copyProperties(pdBean, value);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }
            }
        }
        
        //遍历orderBean，并setPaname
        for (TableBean bean : orderBean) {
            bean.setPname(pdBean.getPname());
            context.write(bean,NullWritable.get() );
        }

    }
}

3.4 编写Driver类

package com.yangmin.mapreduce.reduceJoin;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
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 java.io.IOException;

public class TableDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //获取Job对象
        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

        //关联map和reduce
        job.setMapperClass(TableMapper.class);
        job.setReducerClass(TableReducer.class);

        //设置map端输出KV
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(TableBean.class);

        //设置最终输出KV
        job.setOutputKeyClass(NullWritable.class);
        job.setOutputValueClass(TableBean.class);

        //设置程序的输入输出路径
        FileInputFormat.setInputPaths(job, new Path("C:\\ZProject\\bigdata\\input\\inputtable"));
        FileOutputFormat.setOutputPath(job,new Path("C:\\ZProject\\bigdata\\output\\Join_Table"));
        
        //提交Job
        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }
}

4.总结

缺点：这种方式中，合并的操作是在 Reduce 阶段完成，Reduce 端的处理压力太大，Map 节点的运算负载则很低，资源利用率不高，且在 Reduce 阶段极易产生数据倾斜。

下一篇博文将会解决这个问题，期待你的关注！！

Hadoop案例：Mapreduce解决多个关联表整合问题（Redue Join）