HIVE UDF函数和Transform

1、编写UDF函数，来将原来创建的buck_ip_test表中的英文国籍转换成中文

iptest.txt文件内容：

1	张三	192.168.1.1	china
2	李四	192.168.1.2	china
3	王五	192.168.1.3	china
4	makjon	192.168.1.4	china
1	aa	192.168.1.1	japan
2	bb	192.168.1.2	japan
3	cc	192.168.1.3	japan
4	makjon	192.168.1.4	japan

表数据截图：

UdfTest.java代码如下：

import java.util.HashMap;

import org.apache.hadoop.hive.ql.exec.UDF;

public class UdfTest extends UDF{
	
	private static HashMap<String,String> countryMap = new HashMap();
	
	static {
		countryMap.put("china", "中国");
		countryMap.put("japan", "日本");
		
	}
	
	//此段代码进行国家的转换
	public  String evaluate(String str){
		String country  = countryMap.get(str);
		if(country ==null){
			return "其他";
		}else{
			return country;
		}
	}
    
	//在函数中可以定义多个evaluate方法，进行重载
	//此段代码进行国家和IP的拼接，测试重载用
	public  String evaluate(String country,String ip){
		
			return country+"_"+ip;
	}
	
	/*
	 *
	 *此段代码用于测试上面编写的方法是否正确
	public static void main(String[] args) {
		UdfTest ut = new UdfTest();
		// TODO Auto-generated method stub
		String aa = ut.evaluate("AAAAAA");
        System.out.println(aa);
	}
	*/

}

在eclipse测试无问题后，导出成utftest.jar并上传到服务器的/opt目录

进入hive，执行：
add jar /opt/udftest.jar;
将jar包导入到hive中
再执行create temporary function convert as  'UdfTest';
创建convert方法
执行结果如下图：

然后在Hive中进行查询：

 select country,convert(country,ip),convert(country) from buck_ip_test;

执行结果如下图：

这样一个简单的udf就开发完成啦

2、Hive中使用udf对JSON进行处理

 数据文件movie.txt内容如下：

{"movie":"2797","rate":"4","timeStamp":"978302039","uid":"1"}
{"movie":"2321","rate":"3","timeStamp":"978302205","uid":"1"}
{"movie":"720","rate":"3","timeStamp":"978300760","uid":"1"}
{"movie":"1270","rate":"5","timeStamp":"978300055","uid":"1"}
{"movie":"527","rate":"5","timeStamp":"978824195","uid":"1"}
{"movie":"2340","rate":"3","timeStamp":"978300103","uid":"1"}
{"movie":"48","rate":"5","timeStamp":"978824351","uid":"1"}
{"movie":"1097","rate":"4","timeStamp":"978301953","uid":"1"}
{"movie":"1721","rate":"4","timeStamp":"978300055","uid":"1"}
{"movie":"1545","rate":"4","timeStamp":"978824139","uid":"1"}

将数据导入到hive中的rating表中：

create table rating(rate string);
load data local inpath '/opt/movie.txt' overwrite into table rating;
select * from rating;

结果如下图：

在本例中我们使用ObjectMapper来处理json的数据，

首先创建MovierateBean.java，代码如下：

import java.sql.Timestamp;

public class MovierateBean {
	private String movie;
	private String rate;
	private Timestamp timeStamp;
	private String uid;
	
	public String getMovie() {
		return movie;
	}

	public void setMovie(String movie) {
		this.movie = movie;
	}

	public String getRate() {
		return rate;
	}

	public void setRate(String rate) {
		this.rate = rate;
	}
	
	public Timestamp getTimeStamp() {
		return timeStamp;
	}

	public void setTimeStamp(Timestamp timeStamp) {
		this.timeStamp = timeStamp;
	}

	public String getUid() {
		return uid;
	}

	public void setUid(String uid) {
		this.uid = uid;
	}

	@Override
	public String toString() {
		// TODO Auto-generated method stub
		return movie+"	"+rate+"	"+timeStamp+"	"+uid;
	}
	
	
}

　　

然后创建MovieJsonTest.java，代码如下：

import org.apache.hadoop.hive.ql.exec.UDF;
import org.codehaus.jackson.map.ObjectMapper;

public class MovieJsonTest extends UDF {
	
	
	public String evaluate(String jsonline){
		ObjectMapper om = new ObjectMapper();
		try{
			MovierateBean  bean = om.readValue(jsonline,MovierateBean.class);
			return bean.toString();
		}catch(Exception e){
			return(jsonline);
		}	
		
	}
	
	/*
	public static void main(String[] args){
		MovieJsonTest mt = new MovieJsonTest();
		String jsonline="{"movie":"527","rate":"5","timeStamp":"978824195","uid":"1"}";
		System.out.println(mt.evaluate(jsonline));
	}
	*/


}

将上述文件打包成movie.jar，并上传到服务器的/opt目录下，并执行如下代码：

add jar /opt/movie.jar;
create temporary function movie_convert as 'MovieJsonTest';
select movie_convert(rate) from rating;

执行结果如下：

可以看到原来的json格式以及被解析成对应的字段了

3、Hive Transform简单介绍

Hive的UDF、UDAF需要通过java语言编写。Hive提供了另一种方式，达到自定义UDF和UDAF的目的，但使用方法更简单。这就是TRANSFORM。TRANSFORM语言支持通过多种语言，实现类似于UDF的功能。

Hive还提供了MAP和REDUCE这两个关键字。但MAP和REDUCE一般可理解为只是TRANSFORM的别名。并不代表一般是在map阶段或者是在reduce阶段调用。详见官网说明。

我们可以使用如下的python脚本代替上面的UDF函数：

服务器端/opt/movie_trans.py脚本内容如下:

import sys
import datetime
import json

for line in sys.stdin:
    #line='{"movie":"2797","rate":"4","timeStamp":"978302039","uid":"1"}'
    line = line.strip()
    hjson = json.loads(line)
    movie = hjson['movie']
    rate = hjson['rate']
    timeStamp = hjson['timeStamp']
    uid = hjson['uid']
    timeStamp = datetime.datetime.fromtimestamp(float(timeStamp))
    print '	'.join([movie, rate, str(timeStamp),uid])

在hive中执行如下脚本：

ADD FILE /opt/movie_trans.py;

SELECT
  TRANSFORM (rate)
  USING 'python movie_trans.py'
  AS (movie,rate, timeStamp, uid)
FROM rating;

执行结果如下图：

可以看到我们使用transform实现了上述UDF实现的功能