十二、MapReduce--mapjoin和reducejoin

一、map join

1、适用场景：
一张表很大，一张表很小

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2、解决方案：
在map端缓存多张表，提前处理业务逻辑，这样增加map端业务，减少reduce端的数据压力，尽可能减少数据倾斜。

3、具体方法：采用分布式缓存
（1）在mapper的setup阶段，将文件读取到缓存集合中
（2）在driver中加载缓存，job.addCacheFile(new URI("file:/e:/mapjoincache/pd.txt"));// 缓存普通文件到task运行节点。

4、实例

//order.txt
订单id  商品id  商品数量
1001    01  1
1002    02  2
1003    03  3
1004    01  4
1005    02  5
1006    03  6

//pd.txt
商品id 商品名
01  小米
02  华为
03  格力

要将order中的商品id替换为商品名称，缓存 pd.txt 这个小表

mapper：

package MapJoin;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.*;
import java.util.HashMap;
import java.util.Map;

public class MapJoinMapper extends Mapper {
    Map productMap = new HashMap();
    Text k = new Text();

    /**
     *
     * 将 pd.txt加载到hashmap中，只加载一次
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        BufferedReader productReader = new BufferedReader(new InputStreamReader(new FileInputStream(new File("G:\\test\\A\\mapjoin\\pd.txt"))));

        String line;
        while (StringUtils.isNotEmpty(line = productReader.readLine())) {
            String[] fields = line.split("\t");
            productMap.put(fields[0], fields[1]);
        }

        productReader.close();

    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        String[] fields = line.split("\t");

        String productName = productMap.get(fields[1]);

        k.set(fields[0] + "\t" + productName + "\t" + fields[2]);

        context.write(k, NullWritable.get());

    }

    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        super.cleanup(context);
    }
}

driver:

package MapJoin;

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;
import java.net.URI;
import java.net.URISyntaxException;

public class MapJoinDriver {
    public static void main(String[] args) throws IOException, URISyntaxException, ClassNotFoundException, InterruptedException {
        args = new String[]{"G:\\test\\A\\mapjoin\\order.txt", "G:\\test\\A\\mapjoin\\join2\\"};

        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf);

        job.setJarByClass(MapJoinDriver.class);
        job.setMapperClass(MapJoinMapper.class);

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

        //将重复使用的小文件加载到缓存中
        job.addCacheFile(new URI("file:///G:/test/A/mapjoin/pd.txt"));

        job.setNumReduceTasks(0);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.waitForCompletion(true);

    }
}

二、reduce join

1、分析思路
通过将关联条件作为map的输出的key，也就是使用商品ID来作为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reduce task，在reduce中进行数据的串联

输入的数据和上面的map join一样，输出的结果也和上面的类似

bean：

package ReduceJoin;

import lombok.AllArgsConstructor;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.Setter;
import org.apache.hadoop.io.Writable;

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

@Getter
@Setter
@NoArgsConstructor
@AllArgsConstructor
public class OrderBean implements Writable {
    private String orderID;
    private String productID;
    private int amount;
    private String productName;
    private String flag;

    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeUTF(this.orderID);
        dataOutput.writeUTF(this.productID);
        dataOutput.writeInt(this.amount);
        dataOutput.writeUTF(this.productName);
        dataOutput.writeUTF(this.flag);

    }

    @Override
    public void readFields(DataInput dataInput) throws IOException {
        this.orderID = dataInput.readUTF();
        this.productID = dataInput.readUTF();
        this.amount = dataInput.readInt();
        this.productName = dataInput.readUTF();
        this.flag = dataInput.readUTF();
    }

    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append(this.orderID);
        sb.append("\t");
        sb.append(this.productName);
        sb.append("\t");
        sb.append(this.amount);
        sb.append("\t");
        sb.append(this.flag);
        return sb.toString();
    }
}

map：

package ReduceJoin;

import org.apache.commons.beanutils.BeanUtils;
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 OrderMapper extends Mapper {
    Text k = new Text();
    OrderBean v = new OrderBean();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        String[] fields = line.split("\t");
        FileSplit inputSplit = (FileSplit)context.getInputSplit();
        String fileName = inputSplit.getPath().getName();

        //将商品id作为map输出的key
        if (fileName.startsWith("order")) {
            k.set(fields[1]);
            v.setOrderID(fields[0]);
            v.setProductID(fields[1]);
            v.setAmount(Integer.parseInt(fields[2]));
            v.setFlag("0");
            v.setProductName("");
        } else {
            k.set(fields[0]);
            v.setOrderID("");
            v.setAmount(0);
            v.setProductID(fields[0]);
            v.setProductName(fields[1]);
            v.setFlag("1");
        }

        context.write(k, v);

    }
}

reduce：

package 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 OrderReducer extends Reducer {
    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        //key是productID，如果订单表和商品名称表的productID相同，则key相同，会merge在一起
        //
        //reduce输出是将每个订单列表输出的

        ArrayList orderBeans = new ArrayList<>();
        OrderBean pdBean = new OrderBean();
        OrderBean tmp = new OrderBean();

        for(OrderBean bean : values) {
            if ("0".equals(bean.getFlag())) {
                try {
                    BeanUtils.copyProperties(tmp, bean);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }
                orderBeans.add(tmp);
                //orderBeans.add(bean);

            } else {
                //取出商品名称的KV
                try {
                    BeanUtils.copyProperties(pdBean, bean);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }

            }

        }

        //获取当前的KV的productName，并输出
        for (OrderBean o : orderBeans) {
            o.setProductName(pdBean.getProductName());
            context.write(o, NullWritable.get());

        }
    }
}

driver：

package 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 OrderDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        args = new String[]{"G:\\test\\A\\mapjoin\\", "G:\\test\\A\\reducejoin12\\"};

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

        job.setJarByClass(OrderDriver.class);
        job.setMapperClass(OrderMapper.class);
        job.setReducerClass(OrderReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(OrderBean.class);
        job.setOutputKeyClass(OrderBean.class);
        job.setOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.waitForCompletion(true);
    }
}