這兩個序列分析函數不是很常用，這裏也練習一下。

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

創建表

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

說明

–CUME_DIST ：小於等於當前值的行數/分組內總行數

查詢語句

比如，統計小於等於當前薪水的人數，所占總人數的比例

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,所有數據均為1組，總行數為5，
     第一行：小於等於1000的行數為1，因此，1/5=0.2
     第三行：小於等於3000的行數為3，因此，3/5=0.6
rn2: 按照部門分組，dpet=d1的行數為3,
     第二行：小於等於2000的行數為2，因此，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的總行數為3
     第一行，(1-1)/(3-1)=0
     第三行，(3-1)/(3-1)=1

Hive學習之路 （十五）Hive分析窗口函數(三) CUME_DIST和PERCENT_RANK