共同好友案例
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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)
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?
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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
….
代码实现:
- 第一次Mapper
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));
}
}
}
第一次Reducer
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()));
}
}
第一次Driver
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[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 提交
boolean result = job.waitForCompletion(true);
System.exit(result?0:1);
}
}
第二次Mapper
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));
}
}
}
}
第二次Reducer
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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()));
}
}
第二次Driver
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[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 提交
boolean result = job.waitForCompletion(true);
System.exit(result?0:1);
}
}
一个Driver串联
public class AllShareFriendsReducer {
public static void main(String[] args) throws IOException {
args = new String[]{
"F:\\date\\A\\friends.txt","F:\\date\\A\\FA1","F:\\date\\A\\FA11"};
Configuration conf = new Configuration();
Job job1 = Job.getInstance(conf);
job1.setMapperClass(OneShareFriendsMapper.class);
job1.setReducerClass(OneShareFriendsReducer.class);
job1.setMapOutputKeyClass(Text.class);
job1.setMapOutputValueClass(Text.class);
job1.setOutputKeyClass(Text.class);
job1.setOutputValueClass(Text.class);
FileInputFormat.setInputPaths(job1, new Path(args[0]));
FileOutputFormat.setOutputPath(job1, new Path(args[1]));
Job job2 = Job.getInstance(conf);
job2.setMapperClass(TwoShareFriendsMapper.class);
job2.setReducerClass(TwoShareFriendsReducer.class);
job2.setMapOutputKeyClass(Text.class);
job2.setMapOutputValueClass(Text.class);
job2.setOutputKeyClass(Text.class);
job2.setOutputValueClass(Text.class);
FileInputFormat.setInputPaths(job2, new Path(args[1]));
FileOutputFormat.setOutputPath(job2, new Path(args[2]));
JobControl control = new JobControl("Andy");
ControlledJob ajob = new ControlledJob(job1.getConfiguration());
ControlledJob bjob = new ControlledJob(job2.getConfiguration());
bjob.addDependingJob(ajob);
control.addJob(ajob);
control.addJob(bjob);
Thread thread = new Thread(control);
thread.start();
}
}