Hive学习之路 （十五）Hive分析窗口函数(三) CUME_DIST和PERCENT_RANK

 这两个序列分析函数不是很经常使用，这里也练习一下。

数据准备

数据格式

cookie3.txt

d1,user1,1000 d1,user2,2000 d1,user3,3000 d2,user4,4000 d2,user5,5000

建立表

use cookie; drop table if exists cookie3; create table cookie3(dept string, userid string, sal int) row format delimited fields terminated by ','; load data local inpath "/home/hadoop/cookie3.txt" into table cookie3; select * from cookie3;

玩一玩CUME_DIST

说明

–CUME_DIST ：小于等于当前值的行数/分组内总行数

查询语句

好比，统计小于等于当前薪水的人数，所占总人数的比例

select dept, userid, sal, cume_dist() over (order by sal) as rn1, cume_dist() over (partition by dept order by sal) as rn2 from cookie.cookie3;

查询结果 

 

结果说明

rn1: 没有partition,全部数据均为1组，总行数为5， 第一行：小于等于1000的行数为1，所以，1/5=0.2 第三行：小于等于3000的行数为3，所以，3/5=0.6 rn2: 按照部门分组，dpet=d1的行数为3, 第二行：小于等于2000的行数为2，所以，2/3=0.6666666666666666

 

玩一玩PERCENT_RANK

说明

 –PERCENT_RANK ：分组内当前行的RANK值-1/分组内总行数-1

查询语句

select dept, userid, sal, percent_rank() over (order by sal) as rn1, --分组内
  rank() over (order by sal) as rn11, --分组内的rank值
  sum(1) over (partition by null) as rn12, --分组内总行数
  percent_rank() over (partition by dept order by sal) as rn2, rank() over (partition by dept order by sal) as rn21, sum(1) over (partition by dept) as rn22 from cookie.cookie3;

 

查询结果

结果说明

–PERCENT_RANK ：分组内当前行的RANK值-1/分组内总行数-1

rn1 ==  (rn11-1) / (rn12-1)

rn2 ==  (rn21-1) / (rn22-1)

rn1: rn1 = (rn11-1) / (rn12-1) 第一行,(1-1)/(5-1)=0/4=0 第二行,(2-1)/(5-1)=1/4=0.25 第四行,(4-1)/(5-1)=3/4=0.75 rn2: 按照dept分组， dept=d1的总行数为3 第一行，(1-1)/(3-1)=0 第三行，(3-1)/(3-1)=1