MapReduce实现单表关联（微博关注推荐）

利用MapReduce实现单表关联比多表关联稍微复杂一些。

例如有如下一个文件，两列数据：

<pre name="code" class="java">followed following
1	2
2	3
5	7
7	6

followed是被关注者，following是关注者，如果将其看做是一个简单的微博关注关系，我们可以从中看出，3关注了2，2关注了1，那么我们可以向3推荐1，因为1是3的偶像的偶像；6关注了7，7关注了5，则5是6的偶像的偶像，我们可以向6推荐5于是可以形成如下的推荐列表：

followed following
1           3
5           6

意即向6推荐5，向3推荐1.

实现思路如下：首先在map中将数据读入后生成如下两个表：一个左表一个右表：

这个是右表，在following列加“a+”标志：

followed following
1	a+2
2	a+3
5	a+7
7	a+6

这个是左表,在followed列加“b+”标志：

following <span style="font-family: Arial, Helvetica, sans-serif;">followed</span>
2         b+1
3         b+2
7         b+5
6         b+7

很显然，有一部分following其自身也是followed，将这两列数据和在一起的时候，利用map reduce中的shuffle和sort会去掉重复的，即找出既是following，又是followed的人，在这里是7和2，这两个人就可以被当做“中间人”

而reduce接收到的数据是这样的：

<span style="background-color: rgb(255, 102, 102);">然后，将类似于这样的数据在reduce里面解析，以a+开头的是原来的following,以b+开头的是原来的followed，如果在这里出现多组a+和b+,则应该对两个集合做笛卡尔积求出结果，在这里由于只有一对，一眼可以看出结果，就可以用不作直接出结果了，这样，我们就可以知道应该给3推荐1，给6推荐5.</span>

<span style="background-color: rgb(255, 102, 102);">具体实现代码如下：</span>

import java.io.IOException;
import java.net.URI;
import java.util.ArrayList;
import java.util.Iterator;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class STjoin {
	
	public static int time = 0;
	public static class Map extends Mapper<Object, Text, Text, Text> {
		public void map(Object key, Text value, Context context) throws IOException, InterruptedException{
			String followedName = new String();
			String followingName = new String();
			String leftOrRightISbORa = new String();
			String line = value.toString();
			int i = 0;
			while(line.charAt(i) != ' '){
				i++;
			}
			String[] values = {line.substring(0,i),line.substring(i+1)};  
			followedName = values[0];  
			followingName = values[1];  
			leftOrRightISbORa = "a";   //a represents the right table
			context.write(new Text(values[0]), new Text(leftOrRightISbORa+"+"+followingName+"+"+followedName));  //right table
			leftOrRightISbORa = "b";   //b represents the left table
			context.write(new Text(values[1]), new Text(leftOrRightISbORa+"+"+followingName+"+"+followedName));	//left table	
	}
}
		
	public static class Reduce extends Reducer<Text, Text, Text, Text> {
		public void reduce(Text key, Iterable<Text> values, Context context) throws InterruptedException,IOException {
			if (time == 0) {
				context.write(new Text("following"), new Text("followed"));   //generate the table head
				time++;
			}
			int followingNum = 0;
			ArrayList<String> followings = new ArrayList<>();
			//String followings[] = new String[100000];
			int followedNum = 0;
			ArrayList<String> followeds = new ArrayList<>();
			//String followeds[] = new String[100000];
			Iterator iterator = values.iterator();
			while(iterator.hasNext()){
				String record = iterator.next().toString();
				int len = record.length();
				int i = 2;
				if (len == 0) continue;
				char leftORright = record.charAt(0);
				String followingName =  new String();
				String followedName = new String();
				while (record.charAt(i)!='+') {
					followingName = followingName + record.charAt(i);
					i++;
				}
				i = i+1;   //jump over the sigh of '+'
				while(i<len){
					followedName = followedName + record.charAt(i);
					i++;
				}
				
				if (leftORright == 'a') {
					followings.add(followingName);
					//followings[followedNum]=followingName;
					followingNum++;
				}else {
					followeds.add(followedName);
//					followeds[followedNum]=followedName;
					followedNum++;
				}
				if (followedNum !=0 && followingNum!=0) {
					for(int m = 0;m<followingNum;m++)
						for(int n = 0; n<followedNum; n++){
							context.write(new Text(followings.get(m)), new Text (followeds.get(n)));
						}
							
				}
			}
			
		}
	}

	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
		Configuration configuration = new Configuration();
		String[] otherArgs = new GenericOptionsParser(configuration,args).getRemainingArgs();
		
		if (otherArgs.length!=2) {
			System.err.println("111111111111111111111");
			System.exit(2);
		}                   //判断参数是否是两个

<pre name="code" class="java">                FileSystem fileSystem = FileSystem.get(URI.create(otherArgs[1]),configuration);
		Path path = new Path(otherArgs[1]);
		if (fileSystem.exists(path)) {
			fileSystem.delete(path,true);
		}                  //如果输出地址已经存在，则删除这个地址

Job job = new Job(configuration,"single table join");job.setJarByClass(STjoin.class);job.setMapperClass(Map.class);job.setReducerClass(Reduce.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);FileInputFormat.addInputPath(job, new Path(otherArgs[0]));FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));System.exit((job.waitForCompletion(true) ? 0:1));}}