MapReduce快速入門系列(13) | MapReduce之reduce端join與map端join算法實現

本片博文博主爲大家講解MapReduce之Join的多種應用。

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一. Reduce Join

1.1 Reduce Join 工作原理

  Map端的主要工作：爲來自不同表或文件的key/value對，打標籤以區別不同來源的記錄。然後用連接字段作爲key，其餘部分和新加的標誌作爲val，最後進行輸出。

  Reduce端的主要工作：在Reduce端以連接字段作爲key的分組已經完成，我們只需要在每一個分組當中將那些來源於不同文件的記錄(在Mao階段已經打標誌)分開，最後進行合併就ok了。

1.2 Reduce Join 案例

1. 需求

將商品信息表中數據根據商品pid合併到訂單數據表中。
最終形式如下表：

id	pname	amount
1001	小米	1
1004	小米	4
1002	華爲	2
1005	華爲	5
1003	格力	3
1006	格力	6

2. 需求分析

  通過將關聯條件作爲Map輸出的key，將兩表滿足Join條件的數據並攜帶數據所來源的文件信息，發往同一個ReduceTask，在Reduce中進行數據的串聯，如下圖所示。

3. 完成代碼

• 1. 創建商品和訂合併後的OrderBean類

package com.buwenbuhuo.reducejoin;

import org.apache.hadoop.io.WritableComparable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/**
 * @author 卜溫不火
 * @create 2020-04-25 17:24
 * com.buwenbuhuo.reducejoin - the name of the target package where the new class or interface will be created.
 * mapreduce0422 - the name of the current project.
 */
public class OrderBean implements WritableComparable<OrderBean> {
    private String id;
    private String pid;
    private int amount;
    private String pname;

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

    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;
    }

    @Override
    public int compareTo(OrderBean o) {
        int compare = this.pid.compareTo(o.pid);

        if (compare == 0) {
            return o.pname.compareTo(this.pname);
        } else {
            return compare;
        }
    }

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

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

• 2. 編寫RJMapper類

package com.buwenbuhuo.reducejoin;

import com.buwenbuhuo.reducejoin.OrderBean;
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 org.apache.hadoop.mapreduce.lib.input.FileSplit;

import java.io.IOException;
/**
 * @author 卜溫不火
 * @create 2020-04-25 17:24
 * com.buwenbuhuo.reducejoin - the name of the target package where the new class or interface will be created.
 * mapreduce0422 - the name of the current project.
 */
public class RJMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable> {

    private OrderBean orderBean = new OrderBean();

    private String filename;

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        FileSplit fs = (FileSplit) context.getInputSplit();
        filename = fs.getPath().getName();
    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] fields = value.toString().split("\t");
        if (filename.equals("order.txt")) {
            orderBean.setId(fields[0]);
            orderBean.setPid(fields[1]);
            orderBean.setAmount(Integer.parseInt(fields[2]));
            orderBean.setPname("");
        } else {
            orderBean.setPid(fields[0]);
            orderBean.setPname(fields[1]);
            orderBean.setId("");
            orderBean.setAmount(0);
        }
        context.write(orderBean, NullWritable.get());
    }
}

• 3. 編寫RJReducer類

package com.buwenbuhuo.reducejoin;

import com.buwenbuhuo.reducejoin.OrderBean;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;
import java.util.Iterator;
/**
 * @author 卜溫不火
 * @create 2020-04-25 17:24
 * com.buwenbuhuo.reducejoin - the name of the target package where the new class or interface will be created.
 * mapreduce0422 - the name of the current project.
 */
public class RJReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {

    @Override
    protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
        //拿到迭代器
        Iterator<NullWritable> iterator = values.iterator();
        //數據指針下移，獲取第一個OrderBean
        iterator.next();
        //從第一個OrderBean中取出品牌名稱
        String pname = key.getPname();

        //遍歷剩下的OrderBean，設置品牌名稱並寫出
        while (iterator.hasNext()) {
            iterator.next();
            key.setPname(pname);
            context.write(key, NullWritable.get());
        }
    }
}

• 4. 編寫 RJComparator類(構造器)

package com.buwenbuhuo.reducejoin;

import com.buwenbuhuo.reducejoin.OrderBean;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
/**
 * @author 卜溫不火
 * @create 2020-04-25 17:24
 * com.buwenbuhuo.reducejoin - the name of the target package where the new class or interface will be created.
 * mapreduce0422 - the name of the current project.
 */
public class RJComparator extends WritableComparator {

    protected RJComparator() {
        super(OrderBean.class, true);
    }

    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        OrderBean oa = (OrderBean) a;
        OrderBean ob = (OrderBean) b;
        return oa.getPid().compareTo(ob.getPid());
    }
}

• 5. 編寫RJDriver類

package com.buwenbuhuo.reducejoin;

import com.buwenbuhuo.reducejoin.OrderBean;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
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;
/**
 * @author 卜溫不火
 * @create 2020-04-25 17:24
 * com.buwenbuhuo.reducejoin - the name of the target package where the new class or interface will be created.
 * mapreduce0422 - the name of the current project.
 */
public class RJDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(RJDriver.class);

        job.setMapperClass(RJMapper.class);
        job.setReducerClass(RJReducer.class);

        job.setMapOutputKeyClass(OrderBean.class);
        job.setMapOutputValueClass(NullWritable.class);

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

        job.setGroupingComparatorClass(RJComparator.class);

        FileInputFormat.setInputPaths(job, new Path("d:\\input"));
        FileOutputFormat.setOutputPath(job, new Path("d:\\output"));

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

4. 查看運行結果

• 1. 運行

• 2. 結果

結果正確,說明我們的reducer端的join算法算是成功實現了！！！

二. Map Join

2.1 使用場景

Map Join適用於一張表十分小、一張表很大的場景。

2.2 優點

思考：在Reduce端處理過多的表，非常容易產生數據傾斜。怎麼辦？
在Map端緩存多張表，提前處理業務邏輯，這樣增加Map端業務，減少Reduce端數據的壓力，儘可能的減少數據傾斜。

2.3 具體辦法：採用DistributedCache

• （1）在Mapper的setup階段，將文件讀取到緩存集合中。
• （2）在驅動函數中加載緩存。

// 緩存普通文件到Task運行節點。
job.addCacheFile(new URI("file://d:/cache/pd.txt"));

2.4 Map Join案例

1. 需求

將商品信息表中數據根據商品pid合併到訂單數據表中。

id	pname	amount
1001	小米	1
1004	小米	4
1002	華爲	2
1005	華爲	5
1003	格力	3
1006	格力	6

2. 需求分析

MapJoin適用於關聯表中有小表的情形。

3. 代碼實現

• 1. 創建MJMapper類

package com.buwenbuhuo.mapjoin;

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

import java.io.IOException;
import java.net.URI;
import java.util.HashMap;
import java.util.Map;
/**
 * @author 卜溫不火
 * @create 2020-04-25 17:54
 * com.buwenbuhuo.mapjoin - the name of the target package where the new class or interface will be created.
 * mapreduce0422 - the name of the current project.
 */
public class MJMapper extends Mapper<LongWritable, Text, Text, NullWritable> {

    private Map<String, String> pMap = new HashMap<>();

    private Text k = new Text();

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        URI[] cacheFiles = context.getCacheFiles();
        String path = cacheFiles[0].getPath().toString();
        FileSystem fileSystem = FileSystem.get(context.getConfiguration());
        FSDataInputStream bufferedReader = fileSystem.open(new Path(path));
        String line;
        while (StringUtils.isNotEmpty(line = bufferedReader.readLine())) {
            String[] fields = line.split("\t");
            pMap.put(fields[0], fields[1]);
        }
        IOUtils.closeStream(bufferedReader);
    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] fields = value.toString().split("\t");
        String pname = pMap.get(fields[1]);
        if (pname == null) {
            pname = "NULL";
        }
        k.set(fields[0] + "\t" + pname + "\t" + fields[2]);
        context.write(k, NullWritable.get());
    }
}

• 2. 創建MJDriver類

package com.buwenbuhuo.mapjoin;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;
/**
 * @author 卜溫不火
 * @create 2020-04-25 17:54
 * com.buwenbuhuo.mapjoin - the name of the target package where the new class or interface will be created.
 * mapreduce0422 - the name of the current project.
 */
public class MJDriver {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(MJDriver.class);

        job.setMapperClass(MJMapper.class);
        job.setNumReduceTasks(0);

        job.addCacheFile(URI.create("file:///d:/input/pd.txt"));

        FileInputFormat.setInputPaths(job, new Path("d:\\input\\order.txt"));
        FileOutputFormat.setOutputPath(job, new Path("d:\\output"));

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

4. 運行及查看結果

• 1. 運行

• 2. 查看結果
  
  結果正確,說明我們的map端的join算法算是成功實現了！！！

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