MapReduce:共同好友详解

首先我们要使用的数据如下

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

要想实现共同好友，首先，我们先求出哪些用户是他的好友，如B是A的好友，C是A的好友，D是A的好友…

那么我们以好友为key,用户为value向第一个Reduce中传输数据，传输的数据就会是：

key:B value:A
key:C value:A
key:D value:A
…

这样，我们在Reduce合并数据后，就变成了

key:B value:A,…
key:C value:A,…
key:D value:A,…

MapReduce01

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;

public class FirendMapReduce01 {

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

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //读取一行数据
            String line = value.toString();
            //将数据按照“：”拆分 splits[0]为用户，splits[1]为该用户好友
            String[] splits = line.split(":");
            //将好友按照“，”拆分
            String[] firends = splits[1].split(",");
            //循环写出信息格式为： key:好友,value:用户
            for (String firend : firends) {
                context.write(new Text(firend), new Text(splits[0]));
            }
        }
    }

    public static class ReduceTask extends Reducer<Text, Text, Text, Text> {
        @Override
        protected void reduce(Text firend, Iterable<Text> users, Context context) throws IOException,
                InterruptedException {
            StringBuffer sb = new StringBuffer();
            //循环传输过来的values
            for (Text user : users) {
                //将循环的数据按照“，”组合
                sb.append(user).append(",");
            }
            //写出，格式为：A	I,K,C,B,G,F,H,O,D
            context.write(firend, new Text(sb.substring(0, sb.length() - 1)));
        }
    }


    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = new Job(conf);

        job.setMapperClass(MapTask.class);
        job.setReducerClass(ReduceTask.class);
        job.setJarByClass(FirendMapReduce01.class);


        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

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


        FileInputFormat.addInputPath(job, new Path("F:\\hadoop\\friend.txt"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\hadoop\\test"));
        boolean completion = job.waitForCompletion(true);
    }


}

结果如下：

在得到了如下的数据后,我们就知道了如：I,K,C,B…有共同好友A
那么，我们把他们拆分重新组装成固定合适就OK了

MapReduce2

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.Arrays;

public class FirendMapReduce02 {

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

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //读取一行数据
            String lines = value.toString();
            //将数据按照“\t”拆分 splits[0]为好友，splits[1]为有该好友的用户
            String[] split = lines.split("\t");
            //将好友按照“，”拆分
            String[] users = split[1].split(",");
            //排序，以防止数据重复
            Arrays.sort(users);
            //循环写出数据 格式为：key:A-B,value:共同拥有的好友
            for (int i = 0; i < users.length; i++) {
                for (int j = i + 1; j < users.length; j++) {
                    context.write(new Text(users[i] + "-" + users[j]), new Text(split[0]));
                }
            }


        }
    }

    public static class ReduceTask extends Reducer<Text, Text, Text, Text> {
        @Override
        protected void reduce(Text key, Iterable<Text> firends, Context context) throws IOException,
                InterruptedException {

            StringBuffer sb = new StringBuffer();
            //循环整合数据，按“ ”拼接
            for (Text firend : firends) {
                sb.append(firend).append(" ");
            }
            //写出数据，格式为A-B	E C
            context.write(key, new Text(sb.toString()));
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = new Job(conf);

        job.setMapperClass(FirendMapReduce02.MapTask.class);
        job.setReducerClass(FirendMapReduce02.ReduceTask.class);
        job.setJarByClass(FirendMapReduce02.class);


        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

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


        FileInputFormat.addInputPath(job, new Path("F:\\hadoop\\test\\part-r-00000"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\hadoop\\test1"));


        boolean completion = job.waitForCompletion(true);
    }

}

结果如下（仅参考）：