ReduceJoin

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

案例

输入文件：

• order.txt
• pd.txt

需求：两个文件，通过pid关联，然后把数据合并输出

通过将关联条件作为Map输出的key，将两表满足Join条件的数据并携带数据所来源的文件信息，发往同一个ReduceTask，在Reduce中进行数据的串联。

Bean类

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; //产品id
    private int amount; //产品数量
    private String pname; //产品名称
    private String flag; //判断是order表还是pd表的标志字段

    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;
    }

    // 输出调用的是这个方法
    @Override
    public String toString() {
        return id + "\t" + pname + "\t" + amount;
    }

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

Mapper类

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;
    private Text outK = new Text();
    private TableBean outV = new TableBean();

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        //获取对应文件名称
        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 {

        //获取一行
        String line = value.toString();
        String[] split = line.split("\t");

        //判断是哪个文件,然后针对文件进行不同的操作
        if(filename.contains("order")){  //订单表的处理
            //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 {                             //商品表的处理
            //封装outK
            outK.set(split[0]);
            //封装outV
            outV.setId("");
            outV.setPid(split[0]);
            outV.setAmount(0);
            outV.setPname(split[1]);
            outV.setFlag("pd");
        }

        //写出KV
        context.write(outK,outV);
    }
}

Reducer类

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 {
        ArrayList<TableBean> orderBeans = new ArrayList<>();
        TableBean pdBean = new TableBean();

        for (TableBean value : values) {
            try {
                //判断数据来自哪个表
                if("order".equals(value.getFlag())){   //订单表
                    //创建一个临时TableBean对象接收value
                    TableBean tmpOrderBean = new TableBean();
                    BeanUtils.copyProperties(tmpOrderBean,value);
                    //将临时TableBean对象添加到集合orderBeans
                    orderBeans.add(tmpOrderBean);
                }else {                                    //商品表
                    BeanUtils.copyProperties(pdBean,value);
                }
            } catch (IllegalAccessException e) {
                e.printStackTrace();
            } catch (InvocationTargetException e) {
                e.printStackTrace();
            }
        }
        //遍历集合orderBeans,替换掉每个orderBean的pid为pname,然后写出
        for (TableBean orderBean : orderBeans) {
            orderBean.setPname(pdBean.getPname());
		   //写出修改后的orderBean对象
            context.write(orderBean,NullWritable.get());
        }
    }
}

Driver类

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 {
        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(TableDriver.class);
        job.setMapperClass(TableMapper.class);
        job.setReducerClass(TableReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(TableBean.class);

        job.setOutputKeyClass(TableBean.class);
        job.setOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path("C:\\Users\\MaHe666\\Desktop\\input"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\output"));

        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }
}

总结

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。（大量的数据全都堆在Reducer）