hive复杂UDAF使用方法

周海汉 /文 2013.3.27 前文《hive mapreduce script用法示例》 示例了mapreduce脚本。本文采用较复杂的方式自定义hive聚合函数。 package com.abloz.hive;/** * @author zhouhh * @date 2013-3-27 * note: for count value =1 */import org.apache.hadoop.hi

周海汉 /文
2013.3.27

前文《hive mapreduce script用法示例》
示例了mapreduce脚本。本文采用较复杂的方式自定义hive聚合函数。

package com.abloz.hive;
/**
 * @author zhouhh
 * @date 2013-3-27
 * note: for count value >=1
 */
import org.apache.hadoop.hive.ql.exec.UDAF;
import org.apache.hadoop.hive.ql.exec.UDAFEvaluator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.log4j.Logger;
public class JJPokerStat extends UDAF {
	public static class JJCountUDAFEvaluator implements UDAFEvaluator {
	public static final Logger log = Logger.getLogger(JJPokerStat.class);
	public static class PartialResult {
	int total=0;
	int win=0;
	int fold=0;
	int allin=0;
	}
	private PartialResult result;
	public void init() {
	result = new PartialResult();
	}
	public boolean calwin(int rbet,String chipwon)
	{
	if(chipwon.equalsIgnoreCase("NULL"))
	{
	return false;
	}
	String[] cw = chipwon.split("\\|");
	int chipwons=0;
	//log.info("calwin:"+chipwon);
	for(String v:cw)
	{
	String[] c = v.split(":");
	//log.info("calwin:v "+v+",c.length:"+c.length);
	if(c.length>1)
	{
	chipwons += Integer.parseInt(c[1]);
	}
	}
	//log.info("calwin:chipwons:"+chipwons+",rbet:"+rbet);
	if(chipwons>rbet)
	{
	return true;
	}
	return false;
	}
	public boolean iterate(IntWritable rbet,Text chipwon,IntWritable f,IntWritable a) {
	if ( rbet == null || chipwon == null || f == null || a == null) {
	return true;
	}
	boolean win = calwin(rbet.get(),chipwon.toString());
	if(result == null)
	{
	result = new PartialResult();
	}
	result.total++;
	if(win)
	{
	result.win++;
	}
	int v = f.get();
	if (v>=1)
	{
	result.fold++;
	}
	v = a.get();
	if (v>=1)
	{
	result.allin++;
	}
	return true;
	}
	public PartialResult terminatePartial() {
	return result;
	}
	public boolean merge(PartialResult other) {
	if(other == null)
	{
	return true;
	}
	result.total+=other.total;
	result.win += other.win;
	result.fold += other.fold;
	result.allin += other.allin;
	return true;
	}
	public Text terminate() {
	if(result == null) {
	return new Text("0\t0\t0\t0");
	}
	String s=""+result.total+"\t"+result.win+"\t"+result.fold+"\t"+result.allin;
	return new Text(s);
	}
	}
}

相关博文：

1. hive mapreduce script用法示例
2. 手工移除.META.表的错误信息
3. 编程方式将hbase数据复制到mysql

原文地址：hive 复杂 UDAF 使用方法, 感谢原作者分享。