1）需求：
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）
输入数据
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
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.atguigu.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[] fields = line.split(":");
// 3 获取person和好友 String person = fields[0]; String[] friends = fields[1].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.atguigu.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.atguigu.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);
FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 提交 boolean result = job.waitForCompletion(true);
System.exit(result?0:1); } } |
（4）第二次Mapper
| package com.atguigu.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[0]; String[] persons = friend_persons[1].split(",");
Arrays.sort(persons);
for (int i = 0; i < persons.length - 1; i++) {
for (int j = i + 1; 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.atguigu.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.atguigu.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);
FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 提交 boolean result = job.waitForCompletion(true); System.exit(result?0:1); } } |
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