Reduce Join和Map Join

Reduce Join工作原理

Map端的主要工作：对来自不同表或文件的key/value对，打上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中将哪些来源于不同记录分开，最后进行合并。

编程案例

创建商品和合并后的Bean类

package com.atguigu.mapreduce.table;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable;

public class TableBean implements Writable {

    private String order_id; // 订单id
    private String p_id;      // 产品id
    private int amount;       // 产品数量
    private String pname;     // 产品名称
    private String flag;      // 表的标记

    public TableBean() {
        super();
    }

    public TableBean(String order_id, String p_id, int amount, String pname, String flag) {

        super();

        this.order_id = order_id;
        this.p_id = p_id;
        this.amount = amount;
        this.pname = pname;
        this.flag = flag;
    }

    public String getFlag() {
        return flag;
    }

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

    public String getOrder_id() {
        return order_id;
    }

    public void setOrder_id(String order_id) {
        this.order_id = order_id;
    }

    public String getP_id() {
        return p_id;
    }

    public void setP_id(String p_id) {
        this.p_id = p_id;
    }

    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;
    }

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

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

    @Override
    public String toString() {
        return order_id + "\t" + pname + "\t" + amount + "\t" ;
    }
}

编写TableMapper类，获取输入文件名称，键k为连接值，比如两个表的共有属性，输出(k,bean)

package com.atguigu.mapreduce.table;
import java.io.IOException;
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 TableMapper extends Mapper<LongWritable, Text, Text, TableBean>{

String name;
    TableBean bean = new TableBean();
    Text k = new Text();
    
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {

        // 1 获取输入文件切片
        FileSplit split = (FileSplit) context.getInputSplit();

        // 2 获取输入文件名称
        name = split.getPath().getName();
    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        
        // 1 获取输入数据
        String line = value.toString();
        
        // 2 不同文件分别处理
        if (name.startsWith("order")) {// 订单表处理

            // 2.1 切割
            String[] fields = line.split("\t");
            
            // 2.2 封装bean对象
            bean.setOrder_id(fields[0]);
            bean.setP_id(fields[1]);
            bean.setAmount(Integer.parseInt(fields[2]));
            bean.setPname("");
            bean.setFlag("order");
            
            k.set(fields[1]);
        }else // 产品表处理

            // 2.3 切割
            String[] fields = line.split("\t");
            
            // 2.4 封装bean对象
            bean.setP_id(fields[0]);
            bean.setPname(fields[1]);
            bean.setFlag("pd");
            bean.setAmount(0);
            bean.setOrder_id("");
            
            k.set(fields[0]);
        }

        // 3 写出
        context.write(k, bean);
    }
}

编写TableReducer类，合并两个表的内容，输出（bean,nullWritable）

package com.atguigu.mapreduce.table;
import java.io.IOException;
import java.util.ArrayList;
import org.apache.commons.beanutils.BeanUtils;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class TableReducer extends Reducer<Text, TableBean, TableBean, NullWritable> {

    @Override
    protected void reduce(Text key, Iterable<TableBean> values, Context context)    throws IOException, InterruptedException {

        // 1准备存储订单的集合
        ArrayList<TableBean> orderBeans = new ArrayList<>();
        
// 2 准备bean对象
        TableBean pdBean = new TableBean();

        for (TableBean bean : values) {

            if ("order".equals(bean.getFlag())) {// 订单表

                // 拷贝传递过来的每条订单数据到集合中
                TableBean orderBean = new TableBean();

                try {
                    BeanUtils.copyProperties(orderBean, bean);
                } catch (Exception e) {
                    e.printStackTrace();
                }

                orderBeans.add(orderBean);
            } else {// 产品表

                try {
                    // 拷贝传递过来的产品表到内存中
                    BeanUtils.copyProperties(pdBean, bean);
                } catch (Exception e) {
                    e.printStackTrace();
                }
            }
        }

        // 3 表的拼接
        for(TableBean bean:orderBeans){

            bean.setPname (pdBean.getPname());
            
            // 4 数据写出去
            context.write(bean, NullWritable.get());
        }
    }
}

编写TableDriver类

package com.atguigu.mapreduce.table;
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;

public class TableDriver {

    public static void main(String[] args) throws Exception {
        
// 0 根据自己电脑路径重新配置
args = new String[]{"e:/input/inputtable","e:/output1"};

// 1 获取配置信息，或者job对象实例
        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

        // 2 指定本程序的jar包所在的本地路径
        job.setJarByClass(TableDriver.class);

        // 3 指定本业务job要使用的Mapper/Reducer业务类
        job.setMapperClass(TableMapper.class);
        job.setReducerClass(TableReducer.class);

        // 4 指定Mapper输出数据的kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(TableBean.class);

        // 5 指定最终输出的数据的kv类型
        job.setOutputKeyClass(TableBean.class);
        job.setOutputValueClass(NullWritable.class);

        // 6 指定job的输入原始文件所在目录
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

Reduce Join的缺点：合并操作在reduce阶段完成，Reduce端处理压力大，而Map端资源利用率不高，易产生数据倾斜。

解决方案：在Map端实现数据合并

Map Join

Map Join适用于一张表十分小，一张表十分大的场景。

用处：在Map端缓存多张表，提前处理业务逻辑，减少Reduce端的压力，减少数据倾斜。

方法：在Mapper的setup阶段，将小表缓存到集合中。而后在map阶段拼接。

编程案例

不需要Reduce阶段，设置ReduceTask数量为0.

在驱动模块中添加缓存文件

package test;
import java.net.URI;
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;

public class DistributedCacheDriver {

    public static void main(String[] args) throws Exception {
        
// 0 根据自己电脑路径重新配置
args = new String[]{"e:/input/inputtable2", "e:/output1"};

// 1 获取job信息
        Configuration configuration = new Configuration();
        Job job = Job.getInstance(configuration);

        // 2 设置加载jar包路径
        job.setJarByClass(DistributedCacheDriver.class);

        // 3 关联map
        job.setMapperClass(DistributedCacheMapper.class);
        
// 4 设置最终输出数据类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        // 5 设置输入输出路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 6 加载缓存数据
        job.addCacheFile(new URI("file:///e:/input/inputcache/pd.txt"));
        
        // 7 Map端Join的逻辑不需要Reduce阶段，设置reduceTask数量为0
        job.setNumReduceTasks(0);

        // 8 提交
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

读取缓存的文件数据

package test;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.HashMap;
import java.util.Map;
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;

public class DistributedCacheMapper extends Mapper<LongWritable, Text, Text, NullWritable>{

    Map<String, String> pdMap = new HashMap<>();
    
    @Override
    protected void setup(Mapper<LongWritable, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException {

        // 1 获取缓存的文件
        URI[] cacheFiles = context.getCacheFiles();
        String path = cacheFiles[0].getPath().toString();
        
        BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(path), "UTF-8"));
        
        String line;
        while(StringUtils.isNotEmpty(line = reader.readLine())){

            // 2 切割
            String[] fields = line.split("\t");
            
            // 3 缓存数据到集合
            pdMap.put(fields[0], fields[1]);
        }
        
        // 4 关流
        reader.close();
    }
    
    Text k = new Text();
    
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        // 1 获取一行
        String line = value.toString();
        
        // 2 截取
        String[] fields = line.split("\t");
        
        // 3 获取产品id
        String pId = fields[1];
        
        // 4 获取商品名称
        String pdName = pdMap.get(pId);
        
        // 5 拼接
        k.set(line + "\t"+ pdName);
        
        // 6 写出
        context.write(k, NullWritable.get());
    }
}

Reduce Join工作原理

编程案例

Map Join

编程案例

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