第2节 mapreduce深入学习：15reduce端的join算法的实现

Posted 2022-01-13 mediocreworld

reduce端的join算法：

例子：

商品表数据 product： 
pid
p0001,小米5,1000,2000
p0002,锤子T1,1000,3000

订单表数据 order： 
　　　　　　     pid
1001,20150710,p0001,2
1002,20150710,p0002,3
1002,20150710,p0003,3

mapReduce可以实现sql语句的功能：select 。。。。。。from product p left join order o on p.pid = o.pid

 

思路：将关联的条件作为map输出的key。

 

缺点：这种方式中，join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。

替代解决方案： map端join实现方式。

 

代码：

ReduceJoinMain：

package cn.itcast.demo4.reduceJoin;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class ReduceJoinMain extends Configured implements Tool
    @Override
    public int run(String[] args) throws Exception 

        Job job = Job.getInstance(this.getConf(), ReduceJoinMain.class.getSimpleName());
//        job.setJarByClass(ReduceJoinMain.class);

        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.addInputPath(job,new Path("file:///D:\\Study\\BigData\\heima\\stage2\\4、大数据离线第四天\\map端join\\input"));

        job.setMapperClass(ReduceJoinMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        job.setReducerClass(ReduceJoinReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path("file:///D:\\Study\\BigData\\heima\\stage2\\4、大数据离线第四天\\map端join\\reduce_join_output"));

        boolean b = job.waitForCompletion(true);
        return b?0:1;
    

    public static void main(String[] args) throws Exception 
        int run = ToolRunner.run(new Configuration(), new ReduceJoinMain(), args);
        System.exit(run);
    

ReduceJoinMapper：

package cn.itcast.demo4.reduceJoin;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class ReduceJoinMapper extends Mapper<LongWritable,Text,Text,Text> 
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException 
        String line = value.toString();
        String[] split = line.split(",");
        if(line.startsWith("p"))
            context.write(new Text(split[0]),value);
        else
            context.write(new Text(split[2]),value);
        
    

ReduceJoinReducer：

package cn.itcast.demo4.reduceJoin;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class ReduceJoinReducer extends Reducer<Text,Text,Text,Text> 
    @Override
    protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException 
        String firP = "";
        String secP = "";

        for(Text text:values)
            String value = text.toString();
            if(value!=null && !"".equals(value)) 
                if(value.startsWith("p"))
                    secP += value;
                else
                    firP += value+"\t";
            
        

        context.write(key,new Text(firP+secP));