MapReduce案例十：二次排序与辅助排序

目录

• 一、数据样例
• 二、需求
• 三、分析
• 四、代码实现
• 五、数据结果

一、数据样例

• 文件GroupingComparator.txt 内容如下：(订单id,商品id,成交金额)

0000001	Pdt_01	222.8
0000001	Pdt_05	25.8
0000002	Pdt_03	522.8
0000002	Pdt_04	122.4
0000002	Pdt_05	722.4
0000003	Pdt_01	222.8
0000003	Pdt_02	33.8

二、需求

• 求出每一个订单中最贵的商品。

三、分析

• 利用“订单id和成交金额”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。

• 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。

四、代码实现

• 1、定义订单信息OrderBean，创建OrderBean 类：

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;

public class OrderBean implements WritableComparable<OrderBean> {

    private int order_id; // 订单id号
    private double price; // 价格

    public OrderBean() {
        super();
    }

    public OrderBean(int order_id, double price) {
        super();
        this.order_id = order_id;
        this.price = price;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeInt(order_id);
        out.writeDouble(price);
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        order_id = in.readInt();
        price = in.readDouble();
    }

    @Override
    public String toString() {
        return order_id + "\t" + price;
    }

    public int getOrder_id() {
        return order_id;
    }

    public void setOrder_id(int order_id) {
        this.order_id = order_id;
    }

    public double getPrice() {
        return price;
    }

    public void setPrice(double price) {
        this.price = price;
    }

    // 二次排序
    @Override
    public int compareTo(OrderBean o) {
	
        int result = order_id > o.getOrder_id() ? 1 : -1;

        if (order_id > o.getOrder_id()) {
            result = 1;
        } else if (order_id < o.getOrder_id()) {
            result = -1;
        } else {
            // 价格倒序排序
            result = price > o.getPrice() ? -1 : 1;
        }

        return result;
    }
}

• 2、编写OrderSortMapper处理流程，创建OrderMapper 类：

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class OrderMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable> {
    OrderBean k = new OrderBean();
    
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {      
        // 1 获取一行
        String line = value.toString();
        // 2 截取
        String[] fields = line.split("\t");        
        // 3 封装对象
        k.setOrder_id(Integer.parseInt(fields[0]));
        k.setPrice(Double.parseDouble(fields[2]));        
        // 4 写出
        context.write(k, NullWritable.get());
    }
}

• 3、编写OrderSortReducer处理流程，创建OrderReducer 类：

package com.xyg.mapreduce.order;

import java.io.IOException;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Reducer;

public class OrderReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {

    @Override
    protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {   
        context.write(key, NullWritable.get());
    }
}

• 4、编写OrderSortPartitioner处理流程，创建OrderPartitioner 类：

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

public class OrderPartitioner extends Partitioner<OrderBean, NullWritable> {

    @Override
    public int getPartition(OrderBean key, NullWritable value, int numReduceTasks) {       
        return (key.getOrder_id() & Integer.MAX_VALUE) % numReduceTasks;
    }
}

• 5、编写OrderSortGroupingComparator处理流程，创建OrderGroupingComparator 类：

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

public class OrderGroupingComparator extends WritableComparator {

    protected OrderGroupingComparator() {
        super(OrderBean.class, true);
    }

    @SuppressWarnings("rawtypes")
    @Override
    public int compare(WritableComparable a, WritableComparable b) {

        OrderBean aBean = (OrderBean) a;
        OrderBean bBean = (OrderBean) b;

        int result;
        if (aBean.getOrder_id() > bBean.getOrder_id()) {
            result = 1;
        } else if (aBean.getOrder_id() < bBean.getOrder_id()) {
            result = -1;
        } else {
            result = 0;
        }

        return result;
    }
}

• 6、编写OrderSortDriver处理流程，创建OrderDriver 类：

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class OrderDriver {

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

        args = new String[]{"D:\\大数据API\\GroupingComparator.txt","D:\\大数据API\\dataOut"};

        // 1 获取配置信息
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        // 2 设置jar包加载路径
        job.setJarByClass(OrderDriver.class);

        // 3 加载map/reduce类
        job.setMapperClass(OrderMapper.class);
        job.setReducerClass(OrderReducer.class);

        // 4 设置map输出数据key和value类型
        job.setMapOutputKeyClass(OrderBean.class);
        job.setMapOutputValueClass(NullWritable.class);

        // 5 设置最终输出数据的key和value类型
        job.setOutputKeyClass(OrderBean.class);
        job.setOutputValueClass(NullWritable.class);

        // 6 设置输入数据和输出数据路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 10 设置reduce端的分组
        job.setGroupingComparatorClass(OrderGroupingComparator.class);

        // 7 设置分区
        job.setPartitionerClass(OrderPartitioner.class);

        // 8 设置reduce个数
        job.setNumReduceTasks(3);

        // 9 提交
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

五、数据结果

  按照上文代码运行，输出文件为3个。

• part-r-00000：

3	222.8

• part-r-00001：

1	222.8

• part-r-00002：

2	722.4