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

 

數據準備

 

數據格式

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

 

說明

–CUME_DIST ：小於等於當前值的行數/分組內總行數

 

查詢語句

好比，統計小於等於當前薪水的人數，所佔總人數的比例

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;

 

查詢結果 

 

 

結果說明

rn1: 沒有partition,全部數據均爲1組，總行數爲5，
     第一行：小於等於1000的行數爲1，所以，1/5=0.2
     第三行：小於等於3000的行數爲3，所以，3/5=0.6
rn2: 按照部門分組，dpet=d1的行數爲3,
     第二行：小於等於2000的行數爲2，所以，2/3=0.6666666666666666

 

玩一玩PERCENT_RANK

 

說明

 –PERCENT_RANK ：分組內當前行的RANK值-1/分組內總行數-1

 

查詢語句

select 
  dept,
  userid,
  sal,
  percent_rank() over (order by sal) as rn1, --分組內
  rank() over (order by sal) as rn11, --分組內的rank值
  sum(1) over (partition by null) as rn12, --分組內總行數
  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;

 

 

查詢結果

 

結果說明

–PERCENT_RANK ：分組內當前行的RANK值-1/分組內總行數-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分組，
     dept=d1的總行數爲3
     第一行，(1-1)/(3-1)=0
     第三行，(3-1)/(3-1)=1