基于腾讯大数据QQ共同好友推荐系统，我们基于mapreduce来实现下

测试数据：前面代表QQ用户,：后面代表用户QQ好友

A:B,C,D,F,E,O
B:A,C,E,K
C:F,A,D,I
D:A,E,F,L
E:B,C,D,M,L
F:A,B,C,D,E,O,M
G:A,C,D,E,F
H:A,C,D,E,O
I:A,O
J:B,O
K:A,C,D
L:D,E,F
M:E,F,G
O:A,H,I,J

可以分两个步骤来实现

第一步map和reduce阶段

1.先根据用户QQ好友作为key,用户作为value来输出：

比如第一行：B->A  C->A  D->A  F->A  E->A  O->A

第二行：A->B  C->B  E->B  K->B

然后根据相同的key使用reduce来聚合

比如输出：B->A  B->C  B->D....

reduce会根据相同的key分发给相应的reduce task来执行

结果输出为：B->A C D...

再写代码组合输出：

A-C B

A-D B

C-D B

.....

代码实现

package com.xuyu.friends;

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.ArrayList;
import java.util.Collections;

public class CommonFriendsOne {

public static class CommonFriendsMapper extends Mapper<LongWritable,Text,Text,Text>{

Text k=new Text();
Text v=new Text();
//A:B,C,D,F,E,O
//输出：B->A C->A D-A...
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

String[] userAndFriends = value.toString().split(":");
String user=userAndFriends[0];
String[] friends= userAndFriends[1].split(",");
v.set(user);
for (String f:friends){
k.set(f);
//friend user
context.write(k,v);
}
}
}

public static class CommonFriendsOneReduce extends Reducer<Text,Text,Text,Text>{

//一组数据：B->A E F J...
//一组数据：C->B F E J...
//所以需要排序
@Override
protected void reduce(Text friend, Iterable<Text> users, Context context) throws IOException, InterruptedException {
ArrayList<String> userList=new ArrayList<String>();
for (Text user:users){
userList.add(user.toString());
}
//排序
Collections.sort(userList);
//组合输出
for(int i=0;i<userList.size()-1;i++){
for(int j=i+1;j<userList.size();j++){
context.write(new Text(userList.get(i)+"-"+userList.get(j)),friend);
}
}
}
}

public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
//动态获取jar包在哪里
job.setJarByClass(CommonFriendsOne.class);
//2.封装参数：本次job所要调用的mapper实现类
job.setMapperClass(CommonFriendsMapper.class);
job.setReducerClass(CommonFriendsOneReduce.class);
//3.封装参数：本次job的Mapper实现类产生的数据key,value的类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
//4.封装参数：本次Reduce返回的key,value数据类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);

FileInputFormat.setInputPaths(job,new Path("F:\\mrdata\\friends\\input"));
FileOutputFormat.setOutputPath(job,new Path("F:\\mrdata\\friends\\output"));
boolean res = job.waitForCompletion(true);
System.exit(res ? 0:-1);
}
}

结果输出

part-r-000000文件

B-C	A
B-D	A
B-F	A
B-G	A
B-H	A
B-I	A
B-K	A
B-O	A
C-D	A
C-F	A
C-G	A
C-H	A
C-I	A
C-K	A
C-O	A
D-F	A
D-G	A
D-H	A
D-I	A
D-K	A
D-O	A
F-G	A
F-H	A
F-I	A
F-K	A
F-O	A
G-H	A
G-I	A
G-K	A
G-O	A
H-I	A
H-K	A
H-O	A
I-K	A
I-O	A
K-O	A
A-E	B
A-F	B
A-J	B
E-F	B
E-J	B
F-J	B
A-B	C
A-E	C
A-F	C
A-G	C
A-H	C
A-K	C
B-E	C
B-F	C
B-G	C
B-H	C
B-K	C
E-F	C
E-G	C
E-H	C
E-K	C
F-G	C
F-H	C
F-K	C
G-H	C
G-K	C
H-K	C
A-C	D
A-E	D
A-F	D
A-G	D
A-H	D
A-K	D
A-L	D
C-E	D
C-F	D
C-G	D
C-H	D
C-K	D
C-L	D
E-F	D
E-G	D
E-H	D
E-K	D
E-L	D
F-G	D
F-H	D
F-K	D
F-L	D
G-H	D
G-K	D
G-L	D
H-K	D
H-L	D
K-L	D
A-B	E
A-D	E
A-F	E
A-G	E
A-H	E
A-L	E
A-M	E
B-D	E
B-F	E
B-G	E
B-H	E
B-L	E
B-M	E
D-F	E
D-G	E
D-H	E
D-L	E
D-M	E
F-G	E
F-H	E
F-L	E
F-M	E
G-H	E
G-L	E
G-M	E
H-L	E
H-M	E
L-M	E
A-C	F
A-D	F
A-G	F
A-L	F
A-M	F
C-D	F
C-G	F
C-L	F
C-M	F
D-G	F
D-L	F
D-M	F
G-L	F
G-M	F
L-M	F
C-O	I
D-E	L
E-F	M
A-F	O
A-H	O
A-I	O
A-J	O
F-H	O
F-I	O
F-J	O
H-I	O
H-J	O
I-J	O

第二步：将第一步输出的结果文件再进行一次map reduce

输出结果应该为这种：第一行表示：A和B用户都有C和E这两个共同好友

A-B	[C, E]
A-C	[D, F]
A-D	[E, F]
A-E	[B, C, D]
A-F	[B, C, D, E, O]
A-G	[C, D, E, F]

代码实现

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.ArrayList;
import java.util.Collections;

public class CommonFriendsOne2 {

public static class CommonFriendsMapper extends Mapper<LongWritable,Text,Text,Text>{

@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] friendsAndUser = value.toString().split("\t");
context.write(new Text(friendsAndUser[0]),new Text(friendsAndUser[1]));
}
}

public static class CommonFriendsOneReduce extends Reducer<Text,Text,Text,Text>{

@Override
protected void reduce(Text friends, Iterable<Text> users, Context context) throws IOException, InterruptedException {
ArrayList<String> userList=new ArrayList<String>();
for (Text user:users){
userList.add(user.toString());
}
//排序
Collections.sort(userList);
context.write(friends,new Text(userList.toString()));
}
}

public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
//动态获取jar包在哪里
job.setJarByClass(CommonFriendsOne2.class);
//2.封装参数：本次job所要调用的mapper实现类
job.setMapperClass(CommonFriendsMapper.class);
job.setReducerClass(CommonFriendsOneReduce.class);
//3.封装参数：本次job的Mapper实现类产生的数据key,value的类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
//4.封装参数：本次Reduce返回的key,value数据类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);

FileInputFormat.setInputPaths(job,new Path("F:\\mrdata\\friends\\output"));
FileOutputFormat.setOutputPath(job,new Path("F:\\mrdata\\friends\\output2"));
boolean res = job.waitForCompletion(true);
System.exit(res ? 0:-1);
}
}

统计输出结果为

A-B	[C, E]
A-C	[D, F]
A-D	[E, F]
A-E	[B, C, D]
A-F	[B, C, D, E, O]
A-G	[C, D, E, F]A-H	[C, D, E, O]
A-I	[O]
A-J	[B, O]
A-K	[C, D]
A-L	[D, E, F]
A-M	[E, F]
B-C	[A]
B-D	[A, E]
B-E	[C]
B-F	[A, C, E]
B-G	[A, C, E]
B-H	[A, C, E]
B-I	[A]
B-K	[A, C]
B-L	[E]
B-M	[E]
B-O	[A]
C-D	[A, F]
C-E	[D]
C-F	[A, D]
C-G	[A, D, F]
C-H	[A, D]
C-I	[A]
C-K	[A, D]
C-L	[D, F]
C-M	[F]
C-O	[A, I]
D-E	[L]
D-F	[A, E]
D-G	[A, E, F]
D-H	[A, E]
D-I	[A]
D-K	[A]
D-L	[E, F]
D-M	[E, F]
D-O	[A]
E-F	[B, C, D, M]
E-G	[C, D]
E-H	[C, D]
E-J	[B]
E-K	[C, D]
E-L	[D]
F-G	[A, C, D, E]
F-H	[A, C, D, E, O]
F-I	[A, O]
F-J	[B, O]
F-K	[A, C, D]
F-L	[D, E]
F-M	[E]
F-O	[A]
G-H	[A, C, D, E]
G-I	[A]
G-K	[A, C, D]
G-L	[D, E, F]
G-M	[E, F]
G-O	[A]
H-I	[A, O]
H-J	[O]
H-K	[A, C, D]
H-L	[D, E]
H-M	[E]
H-O	[A]
I-J	[O]
I-K	[A]
I-O	[A]
K-L	[D]
K-O	[A]
L-M	[E, F]