Hive中使用自定义函数(UDF)实现分析函数row_number的功能

之前部门实现row_number是使用的transform，我觉得用UDF实现后，平时的使用会更方便，免去了transform相对繁琐的语法。

之前部门实现row_number是使用的transform，，我觉得用UDF实现后，平时的使用会更方便，免去了transform相对繁琐的语法。

用到的测试表为：

hive> desc row_number_test;
OK
id1 int
id2 string
age int
score double
name string

hive> select * from row_number_test;
OK
2 t04 25 60.0 youlia
1 t01 20 85.0 liujiannan
1 t02 24 70.0 zengqiu
2 t03 30 88.0 hongqu
2 t03 27 70.0 yongqi
1 t02 19 75.0 wangdong
1 t02 24 70.0 zengqiu

使用时要先在子查询中进行分区与排序，比如Oracle中这样一句SQL：

select row_number() over (partition by id1 order by age desc) from row_number_test;

转换为hive语句应该是：

select row_number(id1) from --partition by的字段传到row_number函数中去

(select * from row_number_test distribute by id1 sort by id1,age desc) a;

如果partition by 两个字段：

select row_number() over (partition by id1,id2 order by score) from row_number_test;

转换为hive语句应该是：

select row_number(id1,id2) --partition by的字段传到row_number函数中去

from (select * from row_number_test distribute by id1,id2 sort by id1,id2,score) a;

展示一下查询结果：

1.

select id1,id2,age,score,name,row_number(id1) rn from (select * from row_number_test distribute by id1 sort by id1,age desc) a;

OK
2 t03 30 88.0 hongqu 1
2 t03 27 70.0 yongqi 2
2 t04 25 60.0 youlia 3
1 t02 24 70.0 zengqiu 1
1 t02 24 70.0 zengqiu 2
1 t01 20 85.0 liujiannan 3
1 t02 19 75.0 wangdong 4

2.

select id1,id2,age,score,name,row_number(id1,id2) rn from (select * from row_number_test distribute by id1,id2 sort by id1,id2,score) a;

OK
2 t04 25 60.0 youlia 1
1 t02 24 70.0 zengqiu 1
2 t03 27 70.0 yongqi 1
1 t02 24 70.0 zengqiu 2
1 t02 19 75.0 wangdong 3
1 t01 20 85.0 liujiannan 1
2 t03 30 88.0 hongqu 2

下面是代码，只实现了接收1个参数和2个参数的evaluator方法，参数再多的照搬代码就可以了，代码仅供参考：

package com.Hadoopbook.hive;

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

import org.apache.hadoop.hive.ql.udf.UDFType;

@UDFType(deterministic = false)

public class Row_number extends UDF {

private static int MAX_VALUE = 50;

private static String comparedColumn[] = new String[MAX_VALUE];

private static int rowNum = 1;

public int evaluate (Object ...args){

String columnValue[] = new String[args.length];

for(int i=0;i

columnValue[i] = args[i].toString();

if (rowNum == 1)

{

for(int i=0;i

comparedColumn[i] = columnValue[i];

}

for(int i=0;i

{

if ( !comparedColumn[i].equals(columnValue[i]) )

{

for (int j=0;j

{

comparedColumn[j] = columnValue[j];

}

rowNum = 1;

return rowNum++;

}

}

return rowNum++;

}

public static void main(String args[])

{

Row_number t = new Row_number();

System.out.println(t.evaluate(123));

System.out.println(t.evaluate(123));

System.out.println(t.evaluate(123));

System.out.println(t.evaluate(1234));

System.out.println(t.evaluate(1234));

System.out.println(t.evaluate(1234));

System.out.println(t.evaluate(1235));

}

}

Hive 的详细介绍：请点这里
Hive 的下载地址：请点这里

相关阅读：

基于Hadoop集群的Hive安装

Hive内表和外表的区别

Hadoop + Hive + Map +reduce 集群安装部署

Hive本地模式安装

Hive学习之WordCount单词统计