MapReduce实战（七）GroupingComparator

需求：

Order_0000001,Pdt_01,222.8
Order_0000001,Pdt_05,25.8
Order_0000002,Pdt_05,325.8
Order_0000002,Pdt_03,522.8
Order_0000002,Pdt_04,122.4
Order_0000003,Pdt_01,222.8

按照订单的编号分组，计算出每组的商品价格最大值。

分析：

我们可以把订单编号当做key，然后按照在reduce端去找出每组的最大值。在这里，我想介绍另外一种方法，顺便介绍GroupingComparator。

我们可以自定义一个类型，然后通过GroupingComparator来让其被看成一组（到达reduce端），如果我们对类型进行从大到小的排序，根据MapReduce的规则，同一组的内容到达reduce端，是取第一个内容的key作为reduce的key的，我们不妨利用这个规则，写一个OrderBean的类型，只要让其orderid相同，就被分到同一组，这样一来，到达reduce时，相同id的所有bean已经被看成一组，且金额最大的那个一排在第一位，就是我们想要的结果。

代码：

OrderBean.java:

package com.darrenchan.mr.groupingcomparator;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;

public class OrderBean implements WritableComparable<OrderBean>{

    private Text itemid;
    private DoubleWritable amount;

    public OrderBean() {
    }

    public OrderBean(Text itemid, DoubleWritable amount) {
        set(itemid, amount);
    }

    public void set(Text itemid, DoubleWritable amount) {
        this.itemid = itemid;
        this.amount = amount;
    }

    public Text getItemid() {
        return itemid;
    }

    public DoubleWritable getAmount() {
        return amount;
    }

    @Override
    public int compareTo(OrderBean o) {
//        int cmp = this.itemid.compareTo(o.getItemid());
//        if (cmp == 0) {
        int    cmp = -this.amount.compareTo(o.getAmount());
//        }
        return cmp;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(itemid.toString());
        out.writeDouble(amount.get());
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        String readUTF = in.readUTF();
        double readDouble = in.readDouble();

        this.itemid = new Text(readUTF);
        this.amount= new DoubleWritable(readDouble);
    }

    @Override
    public String toString() {
        return itemid.toString() + "\t" + amount.get();
    }

}

ItemidGroupingComparator.java:

package com.darrenchan.mr.groupingcomparator;

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

/**
 * 利用reduce端的GroupingComparator来实现将一组bean看成相同的key
 *
 */
public class ItemidGroupingComparator extends WritableComparator {

    //传入作为key的bean的class类型，以及制定需要让框架做反射获取实例对象
    protected ItemidGroupingComparator() {
        super(OrderBean.class, true);
    }

    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        OrderBean abean = (OrderBean) a;
        OrderBean bbean = (OrderBean) b;

        //比较两个bean时，指定只比较bean中的orderid
        return abean.getItemid().compareTo(bbean.getItemid());
    }

}

ItemIdPartitioner.java:

package com.darrenchan.mr.groupingcomparator;

import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Partitioner;

public class ItemIdPartitioner extends Partitioner<OrderBean, NullWritable>{

    @Override
    public int getPartition(OrderBean bean, NullWritable value, int numReduceTasks) {
        //相同id的订单bean，会发往相同的partition
        //而且，产生的分区数，是会跟用户设置的reduce task数保持一致
        return (bean.getItemid().hashCode() & Integer.MAX_VALUE) % numReduceTasks;

    }

}

SecondarySort.java:

package com.darrenchan.mr.groupingcomparator;

import java.io.IOException;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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 com.sun.xml.bind.v2.schemagen.xmlschema.List;

/**
 *
 */
public class SecondarySort {

    static class SecondarySortMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{

        OrderBean bean = new OrderBean();

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String line = value.toString();
            String[] fields = StringUtils.split(line, ",");

            bean.set(new Text(fields[0]), new DoubleWritable(Double.parseDouble(fields[2])));

            context.write(bean, NullWritable.get());

        }

    }

    static class SecondarySortReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable>{

        //到达reduce时，相同id的所有bean已经被看成一组，且金额最大的那个一排在第一位
        @Override
        protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
            context.write(key, NullWritable.get());
        }
    }

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(SecondarySort.class);

        job.setMapperClass(SecondarySortMapper.class);
        job.setReducerClass(SecondarySortReducer.class);

        job.setOutputKeyClass(OrderBean.class);
        job.setOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path("/grouping/srcdata"));
        FileOutputFormat.setOutputPath(job, new Path("/grouping/output"));

        //在此设置自定义的Groupingcomparator类
        job.setGroupingComparatorClass(ItemidGroupingComparator.class);
        //在此设置自定义的partitioner类
        job.setPartitionerClass(ItemIdPartitioner.class);

        job.setNumReduceTasks(3);

        job.waitForCompletion(true);

    }

}

运行结果：