大数据学习之路21-MR编程，join算法

数据集如下：

任务为：将两个文档通过用户id相同的进行合并

思路：将两个文档的内容合并为一个实体,实体中再加一个用户与订单区分的字段，在map阶段使用uid做为key,我们可以通过context拿到FileSplit,再通过FileSplit得到文件名，通过文件名区分用户数据与订单数据。经过map阶段uid相同的肯定会被分配到同一个区。到了reduce阶段，这里的数据都是用户id相同的用户数据与订单数据，我们首先要做的是将用户数据与订单数据分开。这里一定要注意，我们在遍历的时候是使用迭代器遍历的，迭代器中的value其实每次都是同一个对象，只是被worker重新赋值而已。之后我们要做的就是，遍历订单数据，将用户数据与订单数据进行合并。

以下贴出代码：

package com.test.user_order;

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

import org.apache.hadoop.io.Writable;

public class Entity implements Writable{
	private String oid;
	private String uid;
	private String name;
	private Integer age;
	private String love;
    private String table;
	public String getTable() {
		return table;
	}


	public void setTable(String table) {
		this.table = table;
	}
   

	@Override
	public String toString() {
		return oid+","+uid+","+name+","+age+","+love;
	}


	public void set(String oid, String uid, String name, Integer age, String love,String table) {
		
		this.oid = oid;
		this.uid = uid;
		this.name = name;
		this.age = age;
		this.love = love;
		this.table = table;
	}
	

	public String getOid() {
		return oid;
	}


	public void setOid(String oid) {
		this.oid = oid;
	}


	public String getUid() {
		return uid;
	}


	public void setUid(String uid) {
		this.uid = uid;
	}


	public String getName() {
		return name;
	}


	public void setName(String name) {
		this.name = name;
	}


	public Integer getAge() {
		return age;
	}


	public void setAge(Integer age) {
		this.age = age;
	}


	public String getLove() {
		return love;
	}


	public void setLove(String love) {
		this.love = love;
	}


	@Override
	public void readFields(DataInput in) throws IOException {
		// TODO Auto-generated method stub
		this.oid = in.readUTF();
		this.uid = in.readUTF();
		this.name = in.readUTF();
		this.age = in.readInt();
		this.love = in.readUTF();
		this.table = in.readUTF();
	}

	@Override
	public void write(DataOutput out) throws IOException {
		// TODO Auto-generated method stub
		out.writeUTF(this.oid); 
		out.writeUTF(this.uid);
		out.writeUTF(this.name);
		out.writeInt(this.age);
		 out.writeUTF(this.love);
		 out.writeUTF(this.table);
	}
        
}

package com.test.user_order;

import java.io.IOException;

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;

public class UMapper extends Mapper<LongWritable, Text, Text, Entity>{
	Text t = new Text();
	    @Override
	    protected void map(LongWritable key, Text value,Context context)
	    		throws IOException, InterruptedException {
	             FileSplit inputSplit = (FileSplit) context.getInputSplit();
	             String name = inputSplit.getPath().getName();
	             String line = value.toString();
	             String[] split = line.split(",");
	             Entity entity = new Entity();
	             if(name.startsWith("order")){
	            	  t.set(split[1]);
	            	 entity.set(split[0], split[1], "", -1, "","t_order");
	            	 context.write(t, entity);
	             }else{
	            	 t.set(split[0]);
	            	 entity.set("", split[0], split[1], Integer.parseInt(split[2]), split[3], "t_user");
	                 context.write(t, entity);
	             }
	             
	    }
       
}

package com.test.user_order;

import java.io.IOException;
import java.util.ArrayList;

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

public class UReducer extends Reducer<Text, Entity, Entity, NullWritable>{
	     
            @Override
            protected void reduce(Text key, Iterable<Entity> values, Context context)
            		throws IOException, InterruptedException {
            	 ArrayList<Entity> list =  new ArrayList<Entity>();
       	    
       	      Entity userBean = new Entity();
            	//这一步的目的是将用户与订单分开
            	for (Entity value : values) {
					if(value.getTable().equals("t_order")){
						/**
						 * 这里我们不能直接将value添加到list中，
						 * 因为这里的value始终是一个对象，这样添加进去的话
						 * 值会变的都一样
						 */
						Entity e = new Entity();
						e.set(value.getOid(),value.getUid(), "", -1, "", "t_order");
						list.add(e);
					}else{
						userBean.set("", value.getUid(), value.getName(), value.getAge(), value.getLove(), "t_user");
					}
				} 
            	//分开之后，就可以遍历订单，将订单与用户合并
            	  for (Entity entity : list) {
      				  userBean.setOid(entity.getOid());
      				  context.write(userBean, NullWritable.get());
      			  }
            }
}

package com.test.user_order;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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 Driver {
        public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
			Configuration conf = new Configuration();
			Job job = Job.getInstance(conf);
			job.setMapperClass(UMapper.class);
			job.setReducerClass(UReducer.class);
			job.setMapOutputKeyClass(Text.class);
			job.setMapOutputValueClass(Entity.class);
			job.setOutputKeyClass(Entity.class);
			job.setOutputValueClass(NullWritable.class);
			FileInputFormat.setInputPaths(job, new Path(args[0]));
			FileSystem fs = FileSystem.get(conf);
			Path p = new Path(args[1]);
			if(fs.exists(p)){
				fs.delete(p,true);
			}
			FileOutputFormat.setOutputPath(job,p);
			job.setNumReduceTasks(1);
			boolean res = job.waitForCompletion(true);
			System.out.println(res?"mr程序成功执行":"mr程序好像被外星人抓走了");
		}
}

运行成功截图：

大数据学习之路21-MR编程，join算法

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