MapReduce实战（六）共同粉丝

需求：

利用mapReduce实现类似微博中查找共同粉丝的功能。如下：

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粉丝，及他俩的共同粉丝都是谁。
比如:
A,B　　[C,E]

分析：

在利用MapReduce程序解答之前，我们不妨用单机程序练习一下，思路很简单，可以利用两个for循环进行遍历，分别找之间的共同好友，如果有则存到list中，设一个map，key就是两个人的ID，value就是存的list，最后就能求得两个人之间的共同好友。程序如下：

package com.darrenchan.test;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

public class Test {
    public static void main(String[] args) throws Exception {
        FileInputStream fis = new FileInputStream(new File("data.txt"));
        InputStreamReader isr = new InputStreamReader(fis);
        BufferedReader br = new BufferedReader(isr);
        String line = null;

        // 将文件中的内容存到list中
        List<String> list = new ArrayList<String>();
        while ((line = br.readLine()) != null) {
            list.add(line);
        }

        Map<String, List<String>> map = new LinkedHashMap<>();
        // 对list进行处理
        for (int i = 0; i < list.size(); i++) {
            for (int j = i + 1; j < list.size(); j++) {
                //临时的list，用于拼接最后结果中的共同好友
                List<String> tempList = new ArrayList<>();
                //按照":"进行分割
                String keyi = list.get(i).split(":")[0];
                String keyj = list.get(j).split(":")[0];
                String contenti = list.get(i).split(":")[1];
                String contentj = list.get(j).split(":")[1];

                //让i层的每一个好友分别和j层的好友找共同好友
                String[] fields = contenti.split(",");
                for (int k = 0; k < fields.length; k++) {
                    if (contentj.contains(fields[k])) {
                        tempList.add(fields[k]);
                    }
                }

                // 如果tempList里面有内容说明就是有相同元素
                if (tempList.size() > 0) {
                    map.put(keyi + "," + keyj, tempList);
                }
            }
        }

        // 打印map
        for (String key : map.keySet()) {
            System.out.println(key + ":" + map.get(key));
        }
    }
}

求得结果：

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

接下来我们思考：如何用MapReduce的程序进行求解呢？

一般如果一个步骤解决不了的问题，我们通常会采用两个步骤来进行求解。在本题中，我们进行思考，让求任意两个人的共同粉丝，那么我们不妨先求得某一个人是哪些人的粉丝，比如：B是A,E,F,G的粉丝，这是第一步我们需要求的。第二步呢？我们就两两配对，AE共同粉丝有B，AF共同粉丝有B，AG共同粉丝有B......然后reduce合并一下即可。

ShareFriendsStepOne.java:

package com.darrenchan.sharefriends;

import java.io.IOException;

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;

public class ShareFriendsStepOne {

    public static class ShareFriendsStepOneMapper extends Mapper<LongWritable, Text, Text, Text>{
        Text keyText = new Text();
        Text valueText = new Text();
        /**
         * 拿到的数据格式是A:B,C,D,F,E,O
         */
        @Override
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            //按照":"进行分割
            String person = line.split(":")[0];
            String content = line.split(":")[1];
            //该person下的所有fans
            String[] fans = content.split(",");
            valueText.set(person);
            for (int i = 0; i < fans.length; i++) {
                keyText.set(fans[i]);
                context.write(keyText, valueText);
            }
        }
    }

    public static class ShareFriendsStepOneReducer extends Reducer<Text, Text, Text, Text>{
        /**
         * 拿到的数据格式是<B,A E F G>，即B是AEFG的粉丝
         */
        Text valueText = new Text();
        @Override
        protected void reduce(Text key, Iterable<Text> values, Context context)
                throws IOException, InterruptedException {
            StringBuffer sb = new StringBuffer();
            for (Text fan : values) {
                sb.append(fan).append(",");
            }
            //最后多了一个“,”，把它消掉
            String outFans = sb.substring(0, sb.length()-1);

            valueText.set(outFans);
            context.write(key, valueText);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(ShareFriendsStepOne.class);

        job.setMapperClass(ShareFriendsStepOneMapper.class);
        job.setReducerClass(ShareFriendsStepOneReducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }

}

ShareFriendsStepTwo.java:

package com.darrenchan.sharefriends;

import java.io.IOException;
import java.util.Arrays;

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;

public class ShareFriendsStepTwo {

    public static class ShareFriendsStepTwoMapper extends Mapper<LongWritable, Text, Text, Text> {
        Text keyText = new Text();
        Text valueText = new Text();

        /**
         * 拿到的数据格式是A I,K,C,B,G,F,H,O,D 即A是I,K,C,B,G,F,H,O,D的粉丝，然后将后面的两两配对
         */
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            String fan = line.split("\t")[0];
            String content = line.split("\t")[1];
            String[] persons = content.split(",");

            // 将persons进行排序
            Arrays.sort(persons);

            valueText.set(fan);
            for (int i = 0; i < persons.length; i++) {
                for (int j = i + 1; j < persons.length; j++) {
                    keyText.set(persons[i] + "," + persons[j]);
                    context.write(keyText, valueText);
                }
            }
        }
    }

    public static class ShareFriendsStepTwoReducer extends Reducer<Text, Text, Text, Text> {
        /**
         * 拿到的数据格式是<AB,C E>，即AB之间的共同粉丝有CE
         */
        Text valueText = new Text();

        @Override
        protected void reduce(Text key, Iterable<Text> values, Context context)
                throws IOException, InterruptedException {
            StringBuffer sb = new StringBuffer();
            sb.append("[");
            for (Text fan : values) {
                sb.append(fan).append(",");
            }
            sb.append("]");
            //去掉多余的“,”
            sb.deleteCharAt(sb.length()-2);

            valueText.set(sb.toString());
            context.write(key, valueText);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(ShareFriendsStepTwo.class);

        job.setMapperClass(ShareFriendsStepTwoMapper.class);
        job.setReducerClass(ShareFriendsStepTwoReducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }

}

求得结果同上：

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]