Reduce Join

时间:2020-08-01 21:35:36   收藏:0   阅读:37

一、概念

  Reduce Join工作原理

    1、Map端的主要工作是: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。

    2、Reduce端的主要工作是: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中将那些来源于不同文件的记录(在Map阶段已经打标签)分开,最后进行合并就好了

二、项目介绍

  1、待处理文本     

      order.txt  订单信息表里记录着订单ID,商品ID,订单销量

      pd.txt  商品信息表里记录着商品ID,商品名称   

      技术图片
1001    01    1
1002    02    2
1003    03    3
1004    01    4
1005    02    5
1006    03    6
order.txt
      技术图片
01    小米
02    华为
03    格力
pd.txt

 

  2、需求   

    将商品信息表中数据根据商品pid合并到订单数据表中

   3、OrderBean.java   

      技术图片
package com.jh.readuceJoin;

import org.apache.hadoop.io.Writable;

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

public class OrderBean implements Writable {
    private String orderID; //订单ID
    private String shopID; //商品ID
    private Integer orderNum; //订单商品数量
    private String shopName; //商品名称
    private String flag; // 做一个标记,标记数据来自哪个文件或数据库

    public OrderBean() {
        super();
    }

    @Override
    public String toString() {
        return orderID + "\t" + shopName + "\t" + orderNum;
    }

    public String getOrderID() {
        return orderID;
    }

    public void setOrderID(String orderID) {
        this.orderID = orderID;
    }

    public String getShopID() {
        return shopID;
    }

    public void setShopID(String shopID) {
        this.shopID = shopID;
    }

    public Integer getOrderNum() {
        return orderNum;
    }

    public void setOrderNum(Integer orderNum) {
        this.orderNum = orderNum;
    }

    public String getShopName() {
        return shopName;
    }

    public void setShopName(String shopName) {
        this.shopName = shopName;
    }

    public String getFlag() {
        return flag;
    }

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

    //序列化
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(orderID);
        out.writeUTF(shopID);
        out.writeInt(orderNum);
        out.writeUTF(shopName);
        out.writeUTF(flag);
    }

    //反序列化
    @Override
    public void readFields(DataInput in) throws IOException {
        orderID = in.readUTF();
        shopID = in.readUTF();
        orderNum = in.readInt();
        shopName = in.readUTF();
        flag = in.readUTF();
    }
}
OrderBean

 

 

 

  4、OrderMapper.java    

      技术图片
package com.jh.readuceJoin;

import org.apache.hadoop.fs.Path;
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;

import java.io.IOException;

public class OrderMapper extends Mapper<LongWritable,Text,Text,OrderBean> {
    private OrderBean orderBean = new OrderBean(); // 创建bean对象,赋值,为输出的值
    private Text outKey = new Text();   // 赋值商品ID作为输出的键
    private String fileName; // 待处理的文件名

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        //获取当前maptask处理的切片数据
        FileSplit fileSplit = (FileSplit)context.getInputSplit();
        // 获取待处理文件路径
        Path path = fileSplit.getPath();
        // 获取待处理文件名
        fileName = path.getName();
    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 获取每一行
        String line = value.toString();
        // 将每一行切割
        String[] split = line.split("\t");

        /*
            待处理文本有两个,为order.txt和pd.txt
            order.txt : 1001    01    1
                        1002    02    2
                        1003    03    3
                        1004    01    4
                        1005    02    5
                        1006    03    6

            pd.txt  :   01    小米
                        02    华为
                        03    格力
         */
        if (fileName.contains("order")){
            // 数据来自order.txt
            // 赋值键和值
            orderBean.setOrderID(split[0]);
            orderBean.setShopID(split[1]);
            orderBean.setOrderNum(Integer.parseInt(split[2]));
            orderBean.setShopName("");
            orderBean.setFlag("order");
            outKey.set(split[1]);
        }else {
            // 数据来自pd.txt
            // 赋值键和值
            orderBean.setShopID(split[0]);
            orderBean.setShopName(split[1]);
            orderBean.setOrderID("");
            orderBean.setOrderNum(0);
            orderBean.setFlag("pd");
            outKey.set(split[0]);
        }
        context.write(outKey,orderBean);
    }
}
OrderMapper

 

 

 

  5、OrderReduce.java 

      技术图片
package com.jh.readuceJoin;

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 OrderReduce extends Reducer<Text,OrderBean,OrderBean,NullWritable> {
    private OrderBean shopBean = new OrderBean(); // 存放商品的数据
    private ArrayList<OrderBean> orderList = new ArrayList<OrderBean>(); // 存放订单的数据

    @Override
    protected void reduce(Text key, Iterable<OrderBean> values, Context context) throws IOException, InterruptedException {
        /*
            此时reduce拿到的一组数据中既包括订单数据,也会包括商品数据
            需要筛选数据
         */
        orderList.clear();

        for (OrderBean value : values) {
            if (value.getFlag().contains("order")){
                // 当前的这组数据是订单数据
                OrderBean bean = new OrderBean();
                try {
                    // 工具类:将这组数据复制到bean
                    BeanUtils.copyProperties(bean,value);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }
                orderList.add(bean);
            }else {
                // 当前的这组数据是商品数据
                try {
                    BeanUtils.copyProperties(shopBean,value);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }
            }
        }

        // 遍历存放订单的数据
        for (OrderBean orderBean : orderList) {
            // 将对应的商品名称赋值进去
            orderBean.setShopName(shopBean.getShopName());
            context.write(orderBean,NullWritable.get());
        }
    }
}
OrderReduce

 

 

 

  6、OrderDriver1.java    

      技术图片
package com.jh.readuceJoin;

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 OrderDriver1 {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //1.获取job对象和配置文件对象
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //2.添加jar的路径
        job.setJarByClass(OrderDriver1.class);

        //3.设置mapper类和reducer类
        job.setMapperClass(OrderMapper.class);
        job.setReducerClass(OrderReduce.class);

        //4.设置mapper类输出的数据类型Text,OrderBean
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(OrderBean.class);

        //5.设置reducer类输出的数据类型OrderBean,NullWritable
        job.setOutputKeyClass(OrderBean.class);
        job.setOutputValueClass(NullWritable.class);

        //设置文件的输入出路径
        FileInputFormat.setInputPaths(job,new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));

        //提交任务
        boolean result = job.waitForCompletion(true);
        //成功返回0,失败返回1
        System.exit(result ? 0:1);
    }
}
OrderDriver1

 

 

 

  7、输出结果为

    技术图片

 

 

 

 

 

 

   

 

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