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reduce端join与map端join算法实现

zmhc 2022-04-01 阅读 152
ReduceMapmap端join算法reduce端join算法hadoop大数据


        本篇博客小菌为大家带来的是MapReduce中reduce端join与map端join算法的实现。

reduce端join算法实现

        先让我们来看下需求,有下面两种表格:


订单数据表 ​t_order：

id

date

pid

amount

1001

20150710

P0001

3

1002

20150710

P0002

3

商品信息表 ​t_product:

id

pname

category_id

price

P0001

小米5

1000

2000

P002

锤子T1

1000

3000

        假如数据量巨大，两表的数据是以文件的形式存储在HDFS中，需要用mapreduce程序来实现一下。

用SQL查询运算的话,语句如下:

select  a.id,a.date,b.pname,b.category_id,b.price from t_order a join t_product b on a.pid = b.id

        但如果现在想用MapReduce实现类似的效果该如何实现呢?

        正确的思路是:​通过将关联的条件作为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reduce task，在reduce中进行数据的串联​

        我们先表格中的数据整理成文件。

​orders.txt​

1001,20150710,p0001,2
1002,20150710,p0001,3
1002,20150710,p0002,3

​product.txt​

p0001,小米5,1000,2000
p0002,锤子T1,1000,3000


        接下来我们就开始上手代码~~

第一步：定义OrderBean

package demo14_join算法_reducejoin;

import org.apache.hadoop.io.Writable;

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

/**
 * @Auther: 传智新星
 * @Date: 2019/11/18 10:42
 * @Description:
 */

public class JoinBean implements Writable {

    
    private String id;

    private String date;

    private String pid;
      
    private String amount;

    private String pname;

    private String category_id;

    private String price;

    @Override
    public String toString() {
        return "JoinBean{" +
                "id='" + id + '\'' +
                ", date='" + date + '\'' +
                ", pid='" + pid + '\'' +
                ", amount='" + amount + '\'' +
                ", pname='" + pname + '\'' +
                ", category_id='" + category_id + '\'' +
                ", price='" + price + '\'' +
                '}';
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public String getDate() {
        return date;
    }

    public void setDate(String date) {
        this.date = date;
    }

    public String getPid() {
        return pid;
    }

    public void setPid(String pid) {
        this.pid = pid;
    }

    public String getAmount() {
        return amount;
    }

    public void setAmount(String amount) {
        this.amount = amount;
    }

    public String getPname() {
        return pname;
    }

    public void setPname(String pname) {
        this.pname = pname;
    }

    public String getCategory_id() {
        return category_id;
    }

    public void setCategory_id(String category_id) {
        this.category_id = category_id;
    }

    public String getPrice() {
        return price;
    }

    public void setPrice(String price) {
        this.price = price;
    }

    public JoinBean() {
    }

    public JoinBean(String id, String date, String pid, String amount, String pname, String category_id, String price) {
        this.id = id;
        this.date = date;
        this.pid = pid;
        this.amount = amount;
        this.pname = pname;
        this.category_id = category_id;
        this.price = price;
    }


    //序列化
    @Override
    public void write(DataOutput out) throws IOException {


        out.writeUTF(id+"");
        out.writeUTF(date+"");
        out.writeUTF(pid+"");
        out.writeUTF(amount+"");
        out.writeUTF(pname+"");
        out.writeUTF(category_id+"");
        out.writeUTF(price+"");


    }


    //反序列化

    @Override
    public void readFields(DataInput in) throws IOException {

        this.id=in.readUTF();
        this.date=in.readUTF();
        this.pid=in.readUTF();
        this.amount=in.readUTF();
        this.pname=in.readUTF();
        this.category_id=in.readUTF();
        this.price=in.readUTF();


    }
}

第二步：定义map类

package demo14_join算法_reducejoin;

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;

/**
 * @Auther: 传智新星
 * @Date: 2019/11/18 10:48
 * @Description:
 */
public class JoinMap extends Mapper<LongWritable, Text,Text,JoinBean> {

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {


        //实例JoinBean
        JoinBean joinBean = new JoinBean();

        //通过context可以获取这行文本所属的文件名称
        FileSplit inputSplit = (FileSplit)context.getInputSplit();
        String name = inputSplit.getPath().getName();


        String [] split = value.toString().split(",");
        //对文件名进行判断

        //包含orders的就获取角标为2 的数据
        if (name.contains("orders")){

            joinBean.setId(split[0]);
            joinBean.setDate(split[1]);
            joinBean.setPid(split[2]);
            joinBean.setAmount(split[3]);

            context.write(new Text(split[2]),joinBean);


        }else{
            //不包含orders的就获取数据内角标为0的数据
            joinBean.setPname(split[1]);
            joinBean.setCategory_id(split[2]);
            joinBean.setPrice(split[3]);

            context.write(new Text(split[0]),joinBean);

        }

    }
}

第三步：自定义reduce类

package demo14_join算法_reducejoin;

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

import java.io.IOException;

/**
 * @Auther: 传智新星
 * @Date: 2019/11/18 11:00
 * @Description:
 */
public class JoinReduce extends Reducer<Text,JoinBean,JoinBean, NullWritable> {


    // 遍历values 进将多个一半的joinBean 拼接到一起
    @Override
    protected void reduce(Text key, Iterable<JoinBean> values, Context context) throws IOException, InterruptedException {

        //实例一个最终的bean
        JoinBean joinBean = new JoinBean();

        for (JoinBean value : values) {


            if (value.getId()!=null&&!value.getId().equals("null")){

                joinBean.setId(value.getId());
                joinBean.setDate(value.getDate());
                joinBean.setPid(value.getPid());
                joinBean.setAmount(value.getAmount());


            }else{

                joinBean.setPname(value.getPname());
                joinBean.setCategory_id(value.getCategory_id());
                joinBean.setPrice(value.getPrice());

            }

        }

        //将赋值完的对象赋值
        context.write(joinBean,NullWritable.get());



    }
}

第四步：开发main方法入口

package demo14_join算法_reducejoin;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * @Auther: 传智新星
 * @Date: 2019/11/18 11:08
 * @Description:
 */
public class JoinDrive extends Configured implements Tool {


    public static void main(String[] args) throws Exception {

        int run = ToolRunner.run(new JoinDrive(), args);

        System.out.println("运行的状态:"+run);

    }

    @Override
    public int run(String[] args) throws Exception {

        //1.实例化Configuration对象
        Configuration conf = new Configuration();

        //实例化Job对象
        Job job = Job.getInstance(conf, "MoreFile");

        job.setJarByClass(JoinDrive.class);

        //2.设置输入
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.addInputPath(job,new Path("E:\\2019大数据课程\\DeBug\\测试\\order\\素材\\4\\map端join\\input"));

        //3.设置map
        job.setMapperClass(JoinMap.class);

        //设置key,value的输出类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(JoinBean.class);


        //4.设置reduce
        job.setReducerClass(JoinReduce.class);

        //设置key,value的输出类型
        job.setOutputKeyClass(JoinBean.class);
        job.setOutputValueClass(NullWritable.class);

        //5.设置输出
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path("E:\\2019大数据课程\\DeBug\\测试结果\\join1"));

        //返回运行结果
        return job.waitForCompletion(true)?0:1;

    }
}

        让我们打开join1目录下生成的文件

reduce端join与map端join算法实现_reduce端join算法

        说明我们的程序运行成功!

        但我们这个程序也有一个很明显的​缺点​:join算法是在reduce阶段完成的,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!

        具体的解决方案是什么?这自然而然地引出了我们后面的"主角"——​map端的join算法!



map端join算法实现

        先让我们来看下map的join算法的原理阐述

  • 适用于关联表中有小表的情形
  • 可以将小表分发到所有的map节点。这样，map节点就可以在本地对自己所读到的大表数据进行join并输出最终结果，​可以大大提高join操作的并发度，加快处理速度​

        先让我们准备一下数据


​pdts.txt​(作为"小表"存在的文件必须位于Hadoop集群上)

reduce端join与map端join算法实现_ReduceMap_02

p0001,xiaomi,1000,2
p0002,appale,1000,3
p0003,samsung,1000,4

​orders.txt​(map_join_iput文件夹下)

1001,20150710,p0001,2
1002,20150710,p0002,3
1003,20150710,p0003,3

        终于可以开始上手代码了~

第一步：定义mapJoin

package demo14_join算法_reducejoin.mapjoin;

import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.net.URI;
import java.util.HashMap;

public class JoinMap extends Mapper<LongWritable, Text,Text,Text> {

    HashMap<String,String> b_tab = new HashMap<String, String>();
    String line = null;



     /*
    map端的初始化方法当中获取缓存文件，一次性加载到map当中来
     */
    @Override
    public void setup(Context context) throws IOException, InterruptedException {
        //这种方式获取所有的缓存文件
     //   URI[] cacheFiles1 = DistributedCache.getCacheFiles(context.getConfiguration());

        URI[] cacheFiles = DistributedCache.getCacheFiles(context.getConfiguration());

        //  获取map的缓存文件
        FileSystem fileSystem = FileSystem.get(cacheFiles[0], context.getConfiguration());
        //打开缓存文件
        FSDataInputStream open = fileSystem.open(new Path(cacheFiles[0]));

        //创建缓冲流对象进行读取
        BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(open));


        while ((line = bufferedReader.readLine())!=null){
            String[] split = line.split(",");
            b_tab.put(split[0],split[1]+"\t"+split[2]+"\t"+split[3]);

        }

        fileSystem.close();
        IOUtils.closeStream(bufferedReader);

    }

    @Override
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //这里读的是这个map task所负责的那一个切片数据（在hdfs上）
        String[] fields = value.toString().split(",");

        String orderId = fields[0];
        String date = fields[1];
        String pdId = fields[2];
        String amount = fields[3];

        //获取map当中的商品详细信息
        String productInfo = b_tab.get(pdId);
        context.write(new Text(orderId), new Text(date + "\t" + productInfo+"\t"+amount));


    }
}

第二步：定义程序运行main方法

package demo14_join算法_reducejoin.mapjoin;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.filecache.DistributedCache;
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;

import java.net.URI;

/**
 * @Auther: 传智新星
 * @Date: 2019/11/18 11:46
 * @Description:
 */
public class MapJoinDriver extends Configured implements Tool {


    @Override
    public int run(String[] args) throws Exception {


        Configuration conf = new Configuration();

        //设置缓存文件
        DistributedCache.addCacheFile(new URI("hdfs://192.168.100.100/tmp/pdts.txt"),conf);

        Job job = Job.getInstance(conf, "MapJoin");
        job.setInputFormatClass(TextInputFormat.class);

        TextInputFormat.addInputPath(job,new Path("E:\\2019大数据课程\\DeBug\\测试\\order\\素材\\4\\map端join\\map_join_iput"));

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

        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path("E:\\2019大数据课程\\DeBug\\测试结果\\mapjoin2"));

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

    }

    public static void main(String[] args) throws Exception {


        int run = ToolRunner.run(new MapJoinDriver(), args);

        System.out.println("运行状态:"+run);
    }
    }

        程序运行完后,我们进入写入的目录,打开文件

reduce端join与map端join算法实现_大数据_03

        同样结果正确,说明我们的map端的join算法算是成功实现了！！！

    那么本次的分享就到这里了，后续小菌还会为大家带来更多Hadoop的内容,喜欢的朋友们不要忘了关注小菌吖٩(๑>◡<๑)۶ 。



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