MapReduce之GroupingComparator分组（辅助排序、二次排序）

指对Reduce阶段的数据根据某一个或几个字段进行分组。

案例

需求
有如下订单数据

现在需要找出每一个订单中最贵的商品，如图

需求分析

• 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先按照订单id(升降序都可以)，再按照acount(降序)排序，发送到Reduce。

• 在Reduce端利用groupingComparator将订单id相同的kv聚合成组，然后取第一个成交金额即是最大值(若有多个成交金额并排第一，则都输出)。

• Mapper阶段主要做三件事：
  keyin-valuein
map()
keyout-valueout

• 期待shuffle之后的数据：
  10000001 Pdt_02 222.8
  10000001 Pdt_01 222.8
  10000001 Pdt_05 25.8

  10000002 Pdt_06 722.4
  10000002 Pdt_03 522.8
  10000002 Pdt_04 122.4

  10000003 Pdt_01 232.8
  10000003 Pdt_01 33.8

• Reducer阶段主要做三件事：
  keyin-valuein
reduce()
keyout-valueout

• 进入Reduce需要考虑的事

1. 获取分组比较器，如果没设置默认使用MapTask排序时key的比较器
2. 默认的比较器比较策略不符合要求，它会将orderId一样且acount一样的记录才认为是一组的
3. 自定义分组比较器，只按照orderId进行对比，只要OrderId一样，认为key相等，这样可以将orderId相同的分到一个组！
   在组内去第一个最大的即可

编写程序

利用“订单id和成交金额”作为key，所以把每一行记录封装为bean。由于需要比较ID，所以实现了WritableComparable接口
OrderBean.java

public class OrderBean implements WritableComparable<OrderBean>{
	
	private String orderId;
	private String pId;
	private Double acount;
	
	public String getOrderId() {
		return orderId;
	}
	public void setOrderId(String orderId) {
		this.orderId = orderId;
	}
	public String getpId() {
		return pId;
	}
	public void setpId(String pId) {
		this.pId = pId;
	}
	public Double getAcount() {
		return acount;
	}
	public void setAcount(Double acount) {
		this.acount = acount;
	}
	public OrderBean() {
		
	}
	
	@Override
	public String toString() {
		return orderId + "\t" + pId + "\t" + acount ;
	}
	
	@Override
	public void write(DataOutput out) throws IOException {
		out.writeUTF(orderId);
		out.writeUTF(pId);
		out.writeDouble(acount);
	}
	
	@Override
	public void readFields(DataInput in) throws IOException {
		orderId=in.readUTF();
		pId=in.readUTF();
		acount=in.readDouble();
	}
	
	// 二次排序，先按照orderid排序(升降序都可以)，再按照acount(降序)排序
	@Override
	public int compareTo(OrderBean o) {
		
		//先按照orderid排序升序排序
		int result=this.orderId.compareTo(o.getOrderId());
		
		if (result==0) {//订单ID相同，就比较成交金额的大小
			//再按照acount(降序)排序
			result=-this.acount.compareTo(o.getAcount());
			
		}

		return result;
	}
}

自定义比较器，可以通过两种方法：

• 继承WritableCompartor
• 实现RawComparator

MyGroupingComparator.java

//实现RawComparator
public class MyGroupingComparator implements RawComparator<OrderBean>{
	
	private OrderBean key1=new OrderBean();
	private OrderBean key2=new OrderBean();
	private  DataInputBuffer buffer=new DataInputBuffer();

	@Override
	public int compare(OrderBean o1, OrderBean o2) {
		return o1.getOrderId().compareTo(o2.getOrderId());
	}

	@Override
	public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
		
		try {
		      buffer.reset(b1, s1, l1);                   // parse key1
		      key1.readFields(buffer);
		      
		      buffer.reset(b2, s2, l2);                   // parse key2
		      key2.readFields(buffer);
		      
		      buffer.reset(null, 0, 0);                   // clean up reference
		    } catch (IOException e) {
		      throw new RuntimeException(e);
		    }
		
		return compare(key1, key2);
	}

}

MyGroupingComparator2.java

 //继承WritableCompartor
public class MyGroupingComparator2 extends WritableComparator{
	
	public MyGroupingComparator2() {
		super(OrderBean.class,null,true);
	}
	
	public int compare(WritableComparable a, WritableComparable b) {
		OrderBean o1=(OrderBean) a;
		OrderBean o2=(OrderBean) b;
	    return o1.getOrderId().compareTo(o2.getOrderId());
	  }
}

OrderMapper.java

public class OrderMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{
	
	private OrderBean out_key=new OrderBean();
	private NullWritable out_value=NullWritable.get();
	
	@Override
	protected void map(LongWritable key, Text value,
			Mapper<LongWritable, Text, OrderBean, NullWritable>.Context context)
			throws IOException, InterruptedException {
		
		String[] words = value.toString().split("\t");
		
		out_key.setOrderId(words[0]);
		out_key.setpId(words[1]);
		out_key.setAcount(Double.parseDouble(words[2]));
		
		context.write(out_key, out_value);
		
	}
}

OrderReducer.java

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

	/*
	 * OrderBean key-NullWritable nullWritable在reducer工作期间，
	 * 	只会实例化一个key-value的对象！
	 * 		每次调用迭代器迭代下个记录时，使用反序列化器从文件中或内存中读取下一个key-value数据的值，
	 * 		封装到之前OrderBean key-NullWritable nullWritable在reducer的属性中
	 */
	@Override
	protected void reduce(OrderBean key, Iterable<NullWritable> values,
			Reducer<OrderBean, NullWritable, OrderBean, NullWritable>.Context context)
			throws IOException, InterruptedException {
		
		Double maxAcount = key.getAcount();
		
		for (NullWritable nullWritable : values) {
			
			if (!key.getAcount().equals(maxAcount)) {
				break;
			}
			//复合条件的记录
			context.write(key, nullWritable);
			
		}
		
	}	
}

OrderBeanDriver.java

public class OrderBeanDriver {
	
	public static void main(String[] args) throws Exception {
		
		Path inputPath=new Path("E:\\mrinput\\groupcomparator");
		Path outputPath=new Path("e:/mroutput/groupcomparator");
		
		//作为整个Job的配置
		Configuration conf = new Configuration();
		
		//保证输出目录不存在
		FileSystem fs=FileSystem.get(conf);
		
		if (fs.exists(outputPath)) {
			
			fs.delete(outputPath, true);
			
		}
		
		// ①创建Job
		Job job = Job.getInstance(conf);
		
		// ②设置Job
		// 设置Job运行的Mapper，Reducer类型，Mapper,Reducer输出的key-value类型
		job.setMapperClass(OrderMapper.class);
		job.setReducerClass(OrderReducer.class);
		
		// Job需要根据Mapper和Reducer输出的Key-value类型准备序列化器，通过序列化器对输出的key-value进行序列化和反序列化
		// 如果Mapper和Reducer输出的Key-value类型一致，直接设置Job最终的输出类型

		job.setOutputKeyClass(OrderBean.class);
		job.setOutputValueClass(NullWritable.class);
		
		// 设置输入目录和输出目录
		FileInputFormat.setInputPaths(job, inputPath);
		FileOutputFormat.setOutputPath(job, outputPath);
		
		// 设置自定义的分组比较器
		job.setGroupingComparatorClass(MyGroupingComparator2.class);
		
		// ③运行Job
		job.waitForCompletion(true);
		
	}
}

输出结果