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大数据Hadoop之MR GroupingComparator辅助排序案例实操

程序员文章站 2022-04-28 16:29:58
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1.需求

有如下订单数据
大数据Hadoop之MR GroupingComparator辅助排序案例实操

现在需要求出每一个订单中最贵的商品。

(1)输入数据

0000001	Pdt_01	222.8
0000002	Pdt_05	722.4
0000001	Pdt_02	33.8
0000003	Pdt_06	232.8
0000003	Pdt_02	33.8
0000002	Pdt_03	522.8
0000002	Pdt_04	122.4

(2)期望输出数据

1	222.8
2	722.4
3	232.8

2.需求分析

(1)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升序排序,如果id相同再按照金额降序排序,发送到Reduce。

(2)在Reduce端利用groupingComparator将订单id相同的kv聚合成组,然后取第一个即是该订单中最贵商品,如图所示。

大数据Hadoop之MR GroupingComparator辅助排序案例实操

3.代码实现

OrderBean(封装订单id和价格,比较时还运用到了二次排序):

package com.mapreduce.groupcomparator;

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 orderId; // 订单id
	private double price; // 商品价格
	
	public OrderBean() {
		
	}
	
	public OrderBean(int orderId, double price) {
		this.orderId = orderId;
		this.price = price;
	}

	// 反序列化
	@Override
	public void readFields(DataInput in) throws IOException {
		orderId = in.readInt();
		price = in.readDouble();
	}
	
	// 序列化
	@Override
	public void write(DataOutput out) throws IOException {
		out.writeInt(orderId);
		out.writeDouble(price);
	}
	
	// 比较器,二次排序
	@Override
	public int compareTo(OrderBean o) {
		if(this.orderId > o.getOrderId()) {
			return 1;
		} else if (this.orderId < o.getOrderId()) {
			return -1;
		} else {
			return (this.price > o.getPrice()) ? -1 : 1;
		}
	}

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

	public int getOrderId() {
		return orderId;
	}

	public void setOrderId(int orderId) {
		this.orderId = orderId;
	}

	public double getPrice() {
		return price;
	}

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

Mapper:

package com.mapreduce.groupcomparator;

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. 读取数据
		String line = value.toString();
		
		// 2. 分割数据
		String[] fields = line.split("\t");
		
		// 3. 封装对象
		k.setOrderId(Integer.parseInt(fields[0]));
		k.setPrice(Double.parseDouble(fields[2]));
		
		// 4. 写出
		context.write(k, NullWritable.get());
	}
}

Reduce:

package com.mapreduce.groupcomparator;

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 k, Iterable<NullWritable> values, Context context)
			throws IOException, InterruptedException {
		 
		for(NullWritable n : values) {
			context.write(k, NullWritable.get());
		}
	}
}

OrderSortGroupingComparator(分组规则):

package com.mapreduce.groupcomparator;

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

public class OrderSortGroupingComparator extends WritableComparator {

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

	public int compare(WritableComparable a, WritableComparable b) {
		OrderBean aBean = (OrderBean) a;
		OrderBean bBean = (OrderBean) b;
		
		int result;
		
		if(aBean.getOrderId() > bBean.getOrderId()) {
			result = 1;
		} else if(aBean.getOrderId() < bBean.getOrderId()) {
			result = -1;
		} else {
			result = 0;
		}
		return result;
	}
}

Driver(注意第8步):

package com.mapreduce.groupcomparator;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
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 IllegalArgumentException, IOException, ClassNotFoundException, Exception {
		// 初始化args
		args = new String[] { "D:\\hadoop-2.7.1\\winMR\\OrderGroupComparator\\input",
				"D:\\hadoop-2.7.1\\winMR\\OrderGroupComparator\\output2" };

		// 1. 获取job实例
		Configuration conf = new Configuration();
		Job job = Job.getInstance(conf);

		// 2. 设置jar
		job.setJarByClass(OrderDriver.class);

		// 3. 关联map和reduce
		job.setMapperClass(OrderMapper.class);
		job.setReducerClass(OrderReducer.class);

		// 4. 设置map输出的kv类型
		job.setMapOutputKeyClass(OrderBean.class);
		job.setMapOutputValueClass(NullWritable.class);

		// 5. 设置最终输出的kv类型
		job.setOutputKeyClass(OrderBean.class);
		job.setOutputValueClass(NullWritable.class);

		// 8. 设置reduce端的分组
		job.setGroupingComparatorClass(OrderSortGroupingComparator.class);
		
		// 6. 设置输入输出路径
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 7. 提交job
		job.waitForCompletion(true);
	}
}

4. 运行结果:

大数据Hadoop之MR GroupingComparator辅助排序案例实操