Mapreduce实例——二次排序

在电商网站中，用户进入页面浏览商品时会产生访问日志，记录用户对商品的访问情况，现有goods_visit2表，包含（goods_id,click_num）两个字段，数据内容如下：

goods_id    click_num
1010037    100
1010102    100
1010152    97
1010178    96
1010280    104
1010320    103
1010510    104
1010603    96
1010637    97

编写MapReduce代码，功能为根据商品的点击次数(click_num)进行降序排序，再根据goods_id升序排序，并输出所有商品：

package mapreduce8;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

//07.Mapreduce实例——二次排序
public class SecondarySort {
    public static class IntPair implements WritableComparable<IntPair> {
        int first;
        int second;

        public void set(int left, int right) {
            first = left;
            second = right;
        }
        public int getFirst() {
            return first;
        }
        public int getSecond() {
            return second;
        }
        @Override

        public void readFields(DataInput in) throws IOException {
            // TODO Auto-generated method stub
            first = in.readInt();
            second = in.readInt();
        }
        @Override

        public void write(DataOutput out) throws IOException {
            // TODO Auto-generated method stub
            out.writeInt(first);
            out.writeInt(second);
        }
        @Override

        public int compareTo(IntPair o) {
            // TODO Auto-generated method stub
            if (first != o.first) {
                return first < o.first ? 1 : -1;
            }
            else if (second != o.second) {
                return second < o.second ? -1 : 1;
            }
            else {
                return 0;
            }
        }
        @Override
        public int hashCode() {
            return first * 157 + second;
        }
        @Override
        public boolean equals(Object right) {
            if (right == null)
                return false;
            if (this == right)
                return true;
            if (right instanceof IntPair) {
                IntPair r = (IntPair) right;
                return r.first == first && r.second == second;
            }
            else {
                return false;
            }
        }
    }

    public static class FirstPartitioner extends Partitioner<IntPair, IntWritable> {
        @Override
        public int getPartition(IntPair key, IntWritable value,int numPartitions) {
            return Math.abs(key.getFirst() * 127) % numPartitions;
        }
    }
    public static class GroupingComparator extends WritableComparator {
        protected GroupingComparator() {
            super(IntPair.class, true);
        }
        @Override
        //Compare two WritableComparables.
        public int compare(WritableComparable w1, WritableComparable w2) {
            IntPair ip1 = (IntPair) w1;
            IntPair ip2 = (IntPair) w2;
            int l = ip1.getFirst();
            int r = ip2.getFirst();
            return l == r ? 0 : (l < r ? -1 : 1);
        }
    }
    public static class Map extends Mapper<LongWritable, Text, IntPair, IntWritable> {
        private final IntPair intkey = new IntPair();
        private final IntWritable intvalue = new IntWritable();
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            StringTokenizer tokenizer = new StringTokenizer(line);
            int left = 0;
            int right = 0;
            if (tokenizer.hasMoreTokens()) {
                left = Integer.parseInt(tokenizer.nextToken());
                if (tokenizer.hasMoreTokens())
                    right = Integer.parseInt(tokenizer.nextToken());
                intkey.set(right, left);
                intvalue.set(left);
                context.write(intkey, intvalue);
            }
        }
    }

    public static class Reduce extends Reducer<IntPair, IntWritable, Text, IntWritable> {
        private final Text left = new Text();
        private static final Text SEPARATOR = new Text("------------------------------------------------");

        public void reduce(IntPair key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
            context.write(SEPARATOR, null);
            left.set(Integer.toString(key.getFirst()));
            System.out.println(left);
            for (IntWritable val : values) {
                context.write(left, val);
                //System.out.println(val);
            }
        }
    }
    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

        Configuration conf = new Configuration();
        Job job = new Job(conf, "secondarysort");
        job.setJarByClass(SecondarySort.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setPartitionerClass(FirstPartitioner.class);

        job.setGroupingComparatorClass(GroupingComparator.class);
        job.setMapOutputKeyClass(IntPair.class);

        job.setMapOutputValueClass(IntWritable.class);

        job.setOutputKeyClass(Text.class);

        job.setOutputValueClass(IntWritable.class);

        job.setInputFormatClass(TextInputFormat.class);

        job.setOutputFormatClass(TextOutputFormat.class);
        String[] otherArgs=new String[2];
        otherArgs[0]="hdfs://192.168.51.100:8020/mymapreduce8/in/goods_visit2";
        otherArgs[1]="hdfs://192.168.51.100:8020/mymapreduce8/out";

        FileInputFormat.setInputPaths(job, new Path(otherArgs[0]));

        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

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

结果：

原理：

在Map阶段，使用job.setInputFormatClass定义的InputFormat将输入的数据集分割成小数据块splites，同时InputFormat提供一个RecordReder的实现。本实验中使用的是TextInputFormat，他提供的RecordReder会将文本的字节偏移量作为key，这一行的文本作为value。这就是自定义Map的输入是<LongWritable, Text>的原因。然后调用自定义Map的map方法，将一个个<LongWritable, Text>键值对输入给Map的map方法。注意输出应该符合自定义Map中定义的输出<IntPair, IntWritable>。最终是生成一个List<IntPair, IntWritable>。在map阶段的最后，会先调用job.setPartitionerClass对这个List进行分区，每个分区映射到一个reducer。每个分区内又调用job.setSortComparatorClass设置的key比较函数类排序。可以看到，这本身就是一个二次排序。 如果没有通过job.setSortComparatorClass设置key比较函数类，则可以使用key实现的compareTo方法进行排序。 在本实验中，就使用了IntPair实现的compareTo方法。

在Reduce阶段，reducer接收到所有映射到这个reducer的map输出后，也是会调用job.setSortComparatorClass设置的key比较函数类对所有数据对排序。然后开始构造一个key对应的value迭代器。这时就要用到分组，使用job.setGroupingComparatorClass设置的分组函数类。只要这个比较器比较的两个key相同，他们就属于同一个组，它们的value放在一个value迭代器，而这个迭代器的key使用属于同一个组的所有key的第一个key。最后就是进入Reducer的reduce方法，reduce方法的输入是所有的（key和它的value迭代器）。同样注意输入与输出的类型必须与自定义的Reducer中声明的一致。