找博客共同好友案例
1）数据准备
以下是博客的好友列表数据，冒号前是一个用户，冒号后是该用户的所有好友（数据中的好友关系是单向的）
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 多对多的关系
数据库：学生 课程 成绩表
学生表和课程表的自然连接 A
A A : B
A : C
B : C A I,K,C,B,G,F,H,O,D,
B A,F,J,E,
C A,B
D A,B A-B C,D
friends.txt
求出哪些人两两之间有共同好友，及他俩的共同好友都有谁？
2）需求分析
先求出A、B、C、….等是谁的好友
第一次输出结果
A I,K,C,B,G,F,H,O,D,
B A,F,J,E,
C A,E,B,H,F,G,K,
D G,C,K,A,L,F,E,H,
E G,M,L,H,A,F,B,D,
F L,M,D,C,G,A,
G M,
H O,
I O,C,
J O,
K B,
L D,E,
M E,F,
O A,H,I,J,F,
第二次输出结果
A-B E C
A-C D F
A-D E F
A-E D B C
A-F O B C D E
A-G F E C D
A-H E C D O
A-I O
A-J O B
A-K D C
A-L F E D
A-M E F
B-C A
B-D A E
B-E C
B-F E A C
B-G C E A
B-H A E C
B-I A
B-K C A
B-L E
B-M E
B-O A
C-D A F
C-E D
C-F D A
C-G D F A
C-H D A
C-I A
C-K A D
C-L D F
C-M F
C-O I A
D-E L
D-F A E
D-G E A F
D-H A E
D-I A
D-K A
D-L E F
D-M F E
D-O A
E-F D M C B
E-G C D
E-H C D
E-J B
E-K C D
E-L D
F-G D C A E
F-H A D O E C
F-I O A
F-J B O
F-K D C A
F-L E D
F-M E
F-O A
G-H D C E A
G-I A
G-K D A C
G-L D F E
G-M E F
G-O A
H-I O A
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
3）代码实现
（1）第一次Mapper
package com.xyg.mapreduce.friends;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper; public class OneShareFriendsMapper extends Mapper<LongWritable, Text, Text, Text>{ @Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
throws IOException, InterruptedException {
// 1 获取一行 A:B,C,D,F,E,O
String line = value.toString(); // 2 切割
String[] fileds = line.split(":"); // 3 获取person和好友
String person = fileds[];
String[] friends = fileds[].split(","); // 4写出去
for(String friend: friends){
// 输出 <好友，人>
context.write(new Text(friend), new Text(person));
}
}
}
（2）第一次Reducer
package com.xyg.mapreduce.friends;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer; public class OneShareFriendsReducer extends Reducer<Text, Text, Text, Text>{ @Override
protected void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException { StringBuffer sb = new StringBuffer();
//1 拼接
for(Text person: values){
sb.append(person).append(",");
} //2 写出
context.write(key, new Text(sb.toString()));
}
}
（3）第一次Driver
package com.xyg.mapreduce.friends;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 OneShareFriendsDriver { public static void main(String[] args) throws Exception {
// 1 获取job对象
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration); // 2 指定jar包运行的路径
job.setJarByClass(OneShareFriendsDriver.class); // 3 指定map/reduce使用的类
job.setMapperClass(OneShareFriendsMapper.class);
job.setReducerClass(OneShareFriendsReducer.class); // 4 指定map输出的数据类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class); // 5 指定最终输出的数据类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class); // 6 指定job的输入原始所在目录
FileInputFormat.setInputPaths(job, new Path(args[]));
FileOutputFormat.setOutputPath(job, new Path(args[])); // 7 提交
boolean result = job.waitForCompletion(true); System.exit(result?:);
}
}
（4）第二次Mapper
package com.xyg.mapreduce.friends;
import java.io.IOException;
import java.util.Arrays;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper; public class TwoShareFriendsMapper extends Mapper<LongWritable, Text, Text, Text>{ @Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// A I,K,C,B,G,F,H,O,D,
// 友 人，人，人
String line = value.toString();
String[] friend_persons = line.split("\t"); String friend = friend_persons[];
String[] persons = friend_persons[].split(","); Arrays.sort(persons); for (int i = ; i < persons.length - ; i++) { for (int j = i + ; j < persons.length; j++) {
// 发出 <人-人，好友> ，这样，相同的“人-人”对的所有好友就会到同1个reduce中去
context.write(new Text(persons[i] + "-" + persons[j]), new Text(friend));
}
}
}
}
（5）第二次Reducer
package com.xyg.mapreduce.friends;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer; public class TwoShareFriendsReducer extends Reducer<Text, Text, Text, Text>{ @Override
protected void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException { StringBuffer sb = new StringBuffer(); for (Text friend : values) {
sb.append(friend).append(" ");
} context.write(key, new Text(sb.toString()));
}
}
（6）第二次Driver
package com.xyg.mapreduce.friends; import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 TwoShareFriendsDriver { public static void main(String[] args) throws Exception {
// 1 获取job对象
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration); // 2 指定jar包运行的路径
job.setJarByClass(TwoShareFriendsDriver.class); // 3 指定map/reduce使用的类
job.setMapperClass(TwoShareFriendsMapper.class);
job.setReducerClass(TwoShareFriendsReducer.class); // 4 指定map输出的数据类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class); // 5 指定最终输出的数据类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class); // 6 指定job的输入原始所在目录
FileInputFormat.setInputPaths(job, new Path(args[]));
FileOutputFormat.setOutputPath(job, new Path(args[])); // 7 提交
boolean result = job.waitForCompletion(true); System.exit(result?:);
}
}