问题：

有如下数据文件 city.txt （id， city， value）

cat city.txt 
1 wh 500
2 bj 600
3 wh 100
4 sh 400
5 wh 200
6 bj 100
7 sh 200
8 bj 300
9 sh 900
需要按 city 分组聚合，然后从每组数据中取出前两条value最大的记录。

1、这是实际业务中经常会遇到的 group TopK 问题，下面来看看 pig 如何解决：

a = load '/data/city.txt'  using PigStorage(' ') as (id:chararray, city:chararray, value:int);
b = group a by city;
c = foreach b {c1=order a by value desc; c2=limit c1 2; generate group,c2.value;};
d = stream c through `sed 's/[(){}]//g'`;
dump d;

(bj,600,300)
(sh,900,400)
(wh,500,200)

a = load '/data/city.txt'  using PigStorage(' ') as (id:chararray, city:chararray, value:int);
b = group a by city;
c = foreach b {c1=order a by value desc;  generate group,c1.value;};
d = stream c through `sed 's/[(){}]//g'`;
dump d;

(bj,600,300,100)
(sh,900,400,200)
(wh,500,200,100)

2、下面我们再来看看hive如何处理group topk的问题：

本质上HSQL和sql有很多相同的地方，但HSQL目前功能还有很多缺失，至少不如原生态的SQL功能强大，

比起PIG也有些差距，如果SQL中这类分组topk的问题如何解决呢？

select * from city a where 
2>(select count(1) from city where cname=a.cname and value>a.value) 
distribute by a.cname sort by a.cname,a.value desc;

但是这种写法在HQL中直接报语法错误了，下面我们只能用hive udf的思路来解决了：

排序city和value，然后对city计数，最后where过滤掉city列计数器大于k的行即可。

好了，上代码：

（1）定义UDF：

package com.example.hive.udf;
import org.apache.hadoop.hive.ql.exec.UDF;
	 
public final class Rank extends UDF{
	private int  counter;
	private String last_key;
	public int evaluate(final String key){
	  if ( !key.equalsIgnoreCase(this.last_key) ) {
	     this.counter = 0;
	     this.last_key = key;
	  }
	  return this.counter++;
	}
}

add jar Rank.jar;
create temporary function rank as 'com.example.hive.udf.Rank';
create table city(id int,cname string,value int) row format delimited fields terminated by ' ';
LOAD DATA LOCAL INPATH 'city.txt' OVERWRITE INTO TABLE city;
select cname, value from (
	select cname,rank(cname) csum,value from (
		select id, cname, value from city distribute by cname sort by cname,value desc
	)a
)b where csum < 2;

（3）结果：

bj	600
bj	300
sh	900
sh	400
wh	500
wh	200

REF：hive中分组取前N个值的实现

3、最后我们来看一下原生态的MR：

import java.io.IOException;
import java.util.TreeSet;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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 org.apache.hadoop.util.GenericOptionsParser;

public class GroupTopK {
	// 这个 MR 将会取得每组年龄中 id 最大的前 3 个
	// 测试数据由脚本生成：http://my.oschina.net/leejun2005/blog/76631
	public static class GroupTopKMapper extends
			Mapper<LongWritable, Text, IntWritable, LongWritable> {
		IntWritable outKey = new IntWritable();
		LongWritable outValue = new LongWritable();
		String[] valArr = null;

		public void map(LongWritable key, Text value, Context context)
				throws IOException, InterruptedException {
			valArr = value.toString().split("\t");
			outKey.set(Integer.parseInt(valArr[2]));// age int
			outValue.set(Long.parseLong(valArr[0]));// id long
			context.write(outKey, outValue);
		}
	}

	public static class GroupTopKReducer extends
			Reducer<IntWritable, LongWritable, IntWritable, LongWritable> {

		LongWritable outValue = new LongWritable();

		public void reduce(IntWritable key, Iterable<LongWritable> values,
				Context context) throws IOException, InterruptedException {
			TreeSet<Long> idTreeSet = new TreeSet<Long>();
			for (LongWritable val : values) {
				idTreeSet.add(val.get());
				if (idTreeSet.size() > 3) {
					idTreeSet.remove(idTreeSet.first());
				}
			}
			for (Long id : idTreeSet) {
				outValue.set(id);
				context.write(key, outValue);
			}
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();

		System.out.println(otherArgs.length);
		System.out.println(otherArgs[0]);
		System.out.println(otherArgs[1]);

		if (otherArgs.length != 3) {
			System.err.println("Usage: GroupTopK <in> <out>");
			System.exit(2);
		}
		Job job = new Job(conf, "GroupTopK");
		job.setJarByClass(GroupTopK.class);
		job.setMapperClass(GroupTopKMapper.class);
		job.setReducerClass(GroupTopKReducer.class);
		job.setNumReduceTasks(1);
		job.setOutputKeyClass(IntWritable.class);
		job.setOutputValueClass(LongWritable.class);
		FileInputFormat.addInputPath(job, new Path(otherArgs[1]));
		FileOutputFormat.setOutputPath(job, new Path(otherArgs[2]));
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
}

hadoop jar GroupTopK.jar GroupTopK /tmp/decli/record_new.txt /tmp/1

hadoop fs -cat /tmp/1/part-r-00000
0       12869695
0       12869971
0       12869976
1       12869813
1       12869870
1       12869951

......

数据验证：

awk '$3==0{print $1}' record_new.txt|sort -nr|head -3
12869976
12869971
12869695

可以看到结果没有问题。 

注：测试数据由以下脚本生成：

http://my.oschina.net/leejun2005/blog/76631
 

PS：

如果说hive类似sql的话，那pig就类似plsql存储过程了：程序编写更自由，逻辑能处理的更强大了。

pig中还能直接通过反射调用java的静态类中的方法，这块内容请参考之前的相关pig博文。

附几个HIVE UDAF链接，有兴趣的同学自己看下：

Hive UDAF和UDTF实现group by后获取top值 http://blog.csdn.net/liuzhoulong/article/details/7789183
hive中自定义函数(UDAF)实现多行字符串拼接为一行 http://blog.sina.com.cn/s/blog_6ff05a2c0100tjw4.html
编写Hive UDAF http://www.fuzhijie.me/?p=118
Hive UDAF开发 http://richiehu.blog.51cto.com/2093113/386113