Hadoop_26_MapReduce_Reduce端使用GroupingComparator求同一订单中最大金额的订单

1. 自定义GroupingComparator

1.1.需求：有如下订单

现在需要求出每一个订单中成交金额最大的一笔交易

1.2.分析：

　　1、利用“订单id和成交金额”Bean作为key，可以将map阶段读取到的所有订单数据按照id分区，按照金额排序，

发送到reduce

　　2、在reduce端利用GroupingComparator将订单id相同的kv聚合成组，然后取第一个即是最大值

定义订单信息bean，实现CompareTo()方法用于排序

package cn.bigdata.hdfs.secondarySort;

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;

/**
 * 订单信息bean，实现hadoop的序列化机制
 */
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;
    }
    //1.模型必须实现Comparable<T>接口
    /*2.Collections.sort(list);会自动调用compareTo，如果没有这句，list是不会排序的，也不会调用compareTo方法
      3.如果是数组则用的是Arrays.sort(a)方法*/
    //implements WritableComparable必须要实现的方法,用于比较排序
    @Override
    public int compareTo(OrderBean o) {
        //根據ID排序
        int cmp = this.itemid.compareTo(o.getItemid());
        //id相同根据金额排序
        if (cmp == 0) {
            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();
    }
}

自定义Partitioner用于分区

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;
    }
}

自定义GroupingComparator，在调用Reduce时对数据分组

package cn.bigdata.hdfs.secondarySort;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

/**
 * 用于控制shuffle过程中reduce端对kv对的聚合逻辑
 * 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;
        //将item_id相同的bean都视为相同，从而聚合为一组
        //比较两个bean时，指定只比较bean中的orderid
        return abean.getItemid().compareTo(bbean.getItemid());
    }
}

编写mapreduce处理流程

/**
 *    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
 */
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，且金额最大的那个排在第一位
        //在设置了groupingcomparator以后，这里收到的kv数据就是：  <1001 87.6>,null  <1001 76.5>,null  ....
        //此时，reduce方法中的参数key就是上述kv组中的第一个kv的key：<1001 87.6>
        //要输出同一个item的所有订单中最大金额的那一个，就只要输出这个key
        @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("F:/secondary"));
        FileOutputFormat.setOutputPath(job, new Path("F:/secondaryOut"));

        //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);
        //设置Reduce的数量
        job.setNumReduceTasks(2);
        job.waitForCompletion(true);
    }
}

文件：

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