（十一）Hive分析窗口函数(三) CUME_DIST和PERCENT_RANK

 

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

 

创建表

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

比如，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,所有数据均为1组，总行数为5，
     第一行：小于等于1000的行数为1，因此，1/5=0.2
     第三行：小于等于3000的行数为3，因此，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，因此，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