Pig、Hive、Map Reduce 解决分组 Top K 问题

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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;

结果：

(bj,600,300)

(sh,900,400)

(wh,500,200)

这几行代码其实也实现了mysql中的 group_concat 函数的功能：

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;

结果：

(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;

http://my.oschina.net/leejun2005/blog/78904

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++;

}

(2）注册jar、建表、导数据，查询：

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

)b where csum < 2;

（3）结果：

bj 600

bj 300

sh 900

sh 400

wh 500

wh 200

可以看到，hive相比pig来说，处理起来稍微复杂了点，但随着hive的日渐完善，以后比pig更简洁也说不定。

REF：hive中分组取前N个值的实现

http://baiyunl.iteye.com/blog/1466343

3、最后我们来看一下原生态的MR：

packagehunanuniversity.guanxiangqing.hadoopInputformat;

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;

importorg.apache.hadoop.mapreduce.lib.input.FileInputFormat;

importorg.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)

throwsIOException, InterruptedException {

valArr =value.toString().split("\t");

outKey.set(Integer.parseInt(valArr[2]));// ageint

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

结果：

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

参考文献：http://my.oschina.net/leejun2005/blog/85187

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