首先我们要使用的数据如下

A:B,C,D,F,E,OB:A,C,E,KC:F,A,D,ID:A,E,F,LE:B,C,D,M,LF:A,B,C,D,E,O,MG:A,C,D,E,FH:A,C,D,E,OI:A,OJ:B,OK:A,C,DL:D,E,FM:E,F,GO:A,H,I,J

要想实现共同好友，首先，我们先求出哪些用户是他的好友，如B是A的好友，C是A的好友，D是A的好友…

那么我们以好友为key,用户为value向第一个Reduce中传输数据，传输的数据就会是：

key:B value:A

key:C value:A

key:D value:A

…

这样，我们在Reduce合并数据后，就变成了

key:B value:A,…

key:C value:A,…

key:D value:A,…

MapReduce01

import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import java.io.IOException;public class FirendMapReduce01 {
        public static class MapTask extends Mapper
    
      {
            @Override        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
                //读取一行数据            String line = value.toString();            //将数据按照“：”拆分 splits[0]为用户，splits[1]为该用户好友            String[] splits = line.split(":");            //将好友按照“，”拆分            String[] firends = splits[1].split(",");            //循环写出信息格式为： key:好友,value:用户            for (String firend : firends) {
                    context.write(new Text(firend), new Text(splits[0]));            }        }    }    public static class ReduceTask extends Reducer
     
       {
            @Override        protected void reduce(Text firend, Iterable
      
        users, Context context) throws IOException,                InterruptedException {
                StringBuffer sb = new StringBuffer();            //循环传输过来的values            for (Text user : users) {
                    //将循环的数据按照“，”组合                sb.append(user).append(",");            }            //写出，格式为：A	I,K,C,B,G,F,H,O,D            context.write(firend, new Text(sb.substring(0, sb.length() - 1)));        }    }    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
            Configuration conf = new Configuration();        Job job = new Job(conf);        job.setMapperClass(MapTask.class);        job.setReducerClass(ReduceTask.class);        job.setJarByClass(FirendMapReduce01.class);        job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(Text.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(Text.class);        FileInputFormat.addInputPath(job, new Path("F:\\hadoop\\friend.txt"));        FileOutputFormat.setOutputPath(job, new Path("F:\\hadoop\\test"));        boolean completion = job.waitForCompletion(true);    }}
      
     
    

结果如下：

在得到了如下的数据后,我们就知道了如：I,K,C,B…有共同好友A

那么，我们把他们拆分重新组装成固定合适就OK了

MapReduce2

import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import java.io.IOException;import java.util.Arrays;public class FirendMapReduce02 {
        public static class MapTask extends Mapper
    
      {
            @Override        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
                //读取一行数据            String lines = value.toString();            //将数据按照“\t”拆分 splits[0]为好友，splits[1]为有该好友的用户            String[] split = lines.split("\t");            //将好友按照“，”拆分            String[] users = split[1].split(",");            //排序，以防止数据重复            Arrays.sort(users);            //循环写出数据 格式为：key:A-B,value:共同拥有的好友            for (int i = 0; i < users.length; i++) {
                    for (int j = i + 1; j < users.length; j++) {
                        context.write(new Text(users[i] + "-" + users[j]), new Text(split[0]));                }            }        }    }    public static class ReduceTask extends Reducer
     
       {
            @Override        protected void reduce(Text key, Iterable
      
        firends, Context context) throws IOException,                InterruptedException {
                StringBuffer sb = new StringBuffer();            //循环整合数据，按“ ”拼接            for (Text firend : firends) {
                    sb.append(firend).append(" ");            }            //写出数据，格式为A-B	E C            context.write(key, new Text(sb.toString()));        }    }    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
            Configuration conf = new Configuration();        Job job = new Job(conf);        job.setMapperClass(FirendMapReduce02.MapTask.class);        job.setReducerClass(FirendMapReduce02.ReduceTask.class);        job.setJarByClass(FirendMapReduce02.class);        job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(Text.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(Text.class);        FileInputFormat.addInputPath(job, new Path("F:\\hadoop\\test\\part-r-00000"));        FileOutputFormat.setOutputPath(job, new Path("F:\\hadoop\\test1"));        boolean completion = job.waitForCompletion(true);    }}
      
     
    

结果如下（仅参考）：