Hive的开窗函数
1.基本语法
Function (arg1,..., argn) OVER ([PARTITION BY <...>] [ORDER BY <....>]
[<window_expression>])Function (arg1,..., argn) 可以是下面的四类函数：
(1)Aggregate Functions: 聚合函数,比如：sum(...)、 max(...)、min(...)、avg(...)等
(2) Sort Functions: 数据排序函数, 比如 ：rank(...)、row_number(...)等
(3)Analytics Functions: 统计和比较函数, 比如： lead(...)、lag(...)、 first_value(...)等
2.数据准备
(1)样例数据
[职工姓名|部门编号|职工ID|工资|岗位类型|入职时间]
Michael|1000|100|5000|full|2014-01-29
Will|1000|101|4000|full|2013-10-02
Wendy|1000|101|4000|part|2014-10-02
Steven|1000|102|6400|part|2012-11-03
Lucy|1000|103|5500|full|2010-01-03
Lily|1001|104|5000|part|2014-11-29
Jess|1001|105|6000|part|2014-12-02
Mike|1001|106|6400|part|2013-11-03
Wei|1002|107|7000|part|2010-04-03
Yun|1002|108|5500|full|2014-01-29
Richard|1002|109|8000|full|2013-09-01
(2)建表语句：
CREATE TABLE IF NOT EXISTS employee (
name string,
dept_num int,
employee_id int,
salary int,
type string,
start_date date
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '|'
STORED as TEXTFILE;(3)加载数据
load data local inpath '/opt/datas/data/employee_contract.txt' into table employee;3.窗口聚合函数
(1)查询姓名、部门编号、工资以及部门人数
select
name,
dept_num as deptno ,
salary,
count(*) over (partition by dept_num) as cnt
from employee ;结果输出：
name deptno salary cnt
Lucy 1000 5500 5
Steven 1000 6400 5
Wendy 1000 4000 5
Will 1000 4000 5
Michael 1000 5000 5
Mike 1001 6400 3
Jess 1001 6000 3
Lily 1001 5000 3
Richard 1002 8000 3
Yun 1002 5500 3
Wei 1002 7000 3（2）查询姓名、部门编号、工资以及每个部门的总工资，部门总工资按照降序输出
select
name ,
dept_num as deptno,
salary,
sum(salary) over (partition by dept_num order by dept_num) as sum_dept_salary
from employee
order by sum_dept_salary desc;结果输出：
name deptno salary sum_dept_salary
Michael 1000 5000 24900
Will 1000 4000 24900
Wendy 1000 4000 24900
Steven 1000 6400 24900
Lucy 1000 5500 24900
Wei 1002 7000 20500
Yun 1002 5500 20500
Richard 1002 8000 20500
Lily 1001 5000 17400
Jess 1001 6000 17400
Mike 1001 6400 174004.窗口排序函数
(1)简介
窗口排序函数提供了数据的排序信息，比如行号和排名。在一个分组的内部将行号或者排名作为数据的一部分进行返回，最常用的排序函数主要包括：
row_number:根据具体的分组和排序，为每行数据生成一个起始值等于1的唯一序列数
rank：对组中的数据进行排名，如果名次相同，则排名也相同，但是下一个名次的排名序号会出现不连续。比如查找具体条件的topN行
dense_rank:dense_rank函数的功能与rank函数类似，dense_rank函数在生成序号时是连续的，而rank函数生成的序号有可能不连续。当出现名次相同时，则排名序号也相同。而下一个排名的序号与上一个排名序号是连续的。
percent_rank:排名计算公式为：(current rank - 1)/(total number of rows - 1)
ntile：将一个有序的数据集划分为多个桶(bucket)，并为每行分配一个适当的桶数。它可用于将数据划分为相等的小切片，为每一行分配该小切片的数字序号。
(2)查询姓名、部门编号、工资、排名编号(按工资的多少排名)
select
name ,
dept_num as dept_no ,
salary,
row_number() over (order by salary desc ) rnum
from employee;结果输出：
name dept_no salary rnum
Richard 1002 8000 1
Wei 1002 7000 2
Mike 1001 6400 3
Steven 1000 6400 4
Jess 1001 6000 5
Yun 1002 5500 6
Lucy 1000 5500 7
Lily 1001 5000 8
Michael 1000 5000 9
Wendy 1000 4000 10
Will 1000 4000 11(3)查询每个部门工资最高的两个人的信息(姓名、部门、薪水)
select
name,
dept_num,
salary
from
(
select name ,
dept_num ,
salary,
row_number() over (partition by dept_num order by salary desc ) rnum
from employee) t1
where rnum <= 2;结果输出：
name dept_num salary
Steven 1000 6400
Lucy 1000 5500
Mike 1001 6400
Jess 1001 6000
Richard 1002 8000
Wei 1002 7000(4)查询每个部门的员工工资排名信息
select
name ,
dept_num as dept_no ,
salary,row_number() over (partition by dept_num order by salary desc ) rnum
from employee;结果输出：
name dept_no salary rnum
Steven 1000 6400 1
Lucy 1000 5500 2
Michael 1000 5000 3
Wendy 1000 4000 4
Will 1000 4000 5
Mike 1001 6400 1
Jess 1001 6000 2
Lily 1001 5000 3
Richard 1002 8000 1
Wei 1002 7000 2
Yun 1002 5500 3(5)使用rank函数进行排名
select
name,
dept_num,
salary,
rank() over (order by salary desc) rank
from employee;结果输出：
name dept_num salary rank
Richard 1002 8000 1
Wei 1002 7000 2
Mike 1001 6400 3
Steven 1000 6400 3
Jess 1001 6000 5
Yun 1002 5500 6
Lucy 1000 5500 6
Lily 1001 5000 8
Michael 1000 5000 8
Wendy 1000 4000 10
Will 1000 4000 10（6）使用dense_rank进行排名
select
name,
dept_num,
salary,
dense_rank() over (order by salary desc) rank
from employee;结果输出：
name dept_num salary rank
Richard 1002 8000 1
Wei 1002 7000 2
Mike 1001 6400 3
Steven 1000 6400 3
Jess 1001 6000 4
Yun 1002 5500 5
Lucy 1000 5500 5
Lily 1001 5000 6
Michael 1000 5000 6
Wendy 1000 4000 7
Will 1000 4000 7(7)使用percent_rank()进行排名
select
name,
dept_num,
salary,
percent_rank() over (order by salary desc) rank
from employee;结果输出：
name dept_num salary rank
Richard 1002 8000 0.0
Wei 1002 7000 0.1
Mike 1001 6400 0.2
Steven 1000 6400 0.2
Jess 1001 6000 0.4
Yun 1002 5500 0.5
Lucy 1000 5500 0.5
Lily 1001 5000 0.7
Michael 1000 5000 0.7
Wendy 1000 4000 0.9
Will 1000 4000 0.9（8）使用ntile进行数据分片排名
SELECT
name,
dept_num as deptno,
salary,
ntile(4) OVER(ORDER BY salary desc) as ntile
FROM employee;结果输出：
name deptno salary ntile
Richard 1002 8000 1
Wei 1002 7000 1
Mike 1001 6400 1
Steven 1000 6400 2
Jess 1001 6000 2
Yun 1002 5500 2
Lucy 1000 5500 3
Lily 1001 5000 3
Michael 1000 5000 3
Wendy 1000 4000 4
Will 1000 4000 4从 Hive v2.1.0开始, 支持在OVER语句里使用聚集函数，比如
SELECT
dept_num,
row_number() OVER (PARTITION BY dept_num ORDER BY sum(salary)) as rk
FROM employee
GROUP BY dept_num;结果输出：
dept_num rk
1000 1
1001 1
1002 15.窗口分析函数
常用的分析函数主要包括：
cume_dist:如果按升序排列，则统计：小于等于当前值的行数/总行数(number of rows ≤ current row)/(total number of rows）。如果是降序排列，则统计：大于等于当前值的行数/总行数。比如，统计小于等于当前工资的人数占总人数的比例 ，用于累计统计。
lead(value_expr[,offset[,default]])：用于统计窗口内往下第n行值。第一个参数为列名，第二个参数为往下第n行（可选，默认为1），第三个参数为默认值（当往下第n行为NULL时候，取默认值，如不指定，则为NULL
lag(value_expr[,offset[,default]]): 与lead相反，用于统计窗口内往上第n行值。第一个参数为列名，第二个参数为往上第n行（可选，默认为1），第三个参数为默认值（当往上第n行为NULL时候，取默认值，如不指定，则为NULL）
first_value: 取分组内排序后，截止到当前行，第一个值
last_value: 取分组内排序后，截止到当前行，最后一个值
(1)统计小于等于当前工资的人数占总人数的比例
SELECT
name,
dept_num as deptno,
salary,
cume_dist() OVER (ORDER BY salary) as cume
FROM employee;结果输出：
name deptno salary cume
Wendy 1000 4000 0.18181818181818182
Will 1000 4000 0.18181818181818182
Lily 1001 5000 0.36363636363636365
Michael 1000 5000 0.36363636363636365
Yun 1002 5500 0.5454545454545454
Lucy 1000 5500 0.5454545454545454
Jess 1001 6000 0.6363636363636364
Mike 1001 6400 0.8181818181818182
Steven 1000 6400 0.8181818181818182
Wei 1002 7000 0.9090909090909091
Richard 1002 8000 1.0（2）统计大于等于当前工资的人数占总人数的比例
SELECT
name,
dept_num as deptno,
salary,
cume_dist() OVER (ORDER BY salary desc) as cume
FROM employee;
结果输出：
name deptno salary cume
Richard 1002 8000 0.09090909090909091
Wei 1002 7000 0.18181818181818182
Mike 1001 6400 0.36363636363636365
Steven 1000 6400 0.36363636363636365
Jess 1001 6000 0.45454545454545453
Yun 1002 5500 0.6363636363636364
Lucy 1000 5500 0.6363636363636364
Lily 1001 5000 0.8181818181818182
Michael 1000 5000 0.8181818181818182
Wendy 1000 4000 1.0
Will 1000 4000 1.0（3）按照部门统计小于等于当前工资的人数占部门总人数的比例
SELECT
name,
dept_num as deptno,
salary,
cume_dist() OVER (PARTITION BY dept_num ORDER BY salary) as cume
FROM employee;
结果输出：
name deptno salary cume
Wendy 1000 4000 0.4
Will 1000 4000 0.4
Michael 1000 5000 0.6
Lucy 1000 5500 0.8
Steven 1000 6400 1.0
Lily 1001 5000 0.3333333333333333
Jess 1001 6000 0.6666666666666666
Mike 1001 6400 1.0
Yun 1002 5500 0.3333333333333333
Wei 1002 7000 0.6666666666666666
Richard 1002 8000 1.0（4）按部门分组，统计每个部门员工的工资以及大于等于该员工工资的下一个员工的工资
SELECT
name,
dept_num as deptno,
salary,
lead(salary,1) OVER (PARTITION BY dept_num ORDER BY salary) as lead
FROM employee;结果输出：
name deptno salary lead
Wendy 1000 4000 4000
Will 1000 4000 5000
Michael 1000 5000 5500
Lucy 1000 5500 6400
Steven 1000 6400 NULL
Lily 1001 5000 6000
Jess 1001 6000 6400
Mike 1001 6400 NULL
Yun 1002 5500 7000
Wei 1002 7000 8000
Richard 1002 8000 NULL（5）按部门分组，统计每个部门员工的工资以及小于等于该员工工资的上一个员工的工资
SELECT
name,
dept_num as deptno,
salary,
lag(salary,1) OVER (PARTITION BY dept_num ORDER BY salary) as lead
FROM employee;
结果输出：
name deptno salary lead
Wendy 1000 4000 NULL
Will 1000 4000 4000
Michael 1000 5000 4000
Lucy 1000 5500 5000
Steven 1000 6400 5500
Lily 1001 5000 NULL
Jess 1001 6000 5000
Mike 1001 6400 6000
Yun 1002 5500 NULL
Wei 1002 7000 5500
Richard 1002 8000 7000（6）按部门分组，统计每个部门员工工资以及该部门最低的员工工资
SELECT
name,
dept_num as deptno,
salary,
first_value(salary) OVER (PARTITION BY dept_num ORDER BY salary) as fval
FROM employee;结果输出：
name deptno salary fval
Wendy 1000 4000 4000
Will 1000 4000 4000
Michael 1000 5000 4000
Lucy 1000 5500 4000
Steven 1000 6400 4000
Lily 1001 5000 5000
Jess 1001 6000 5000
Mike 1001 6400 5000
Yun 1002 5500 5500
Wei 1002 7000 5500
Richard 1002 8000 5500（7）按部门分组，统计每个部门员工工资以及该部门最高的员工工资
SELECT
name,
dept_num as deptno,
salary,
last_value(salary) OVER (PARTITION BY dept_num ORDER BY salary RANGE
BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) as lval
FROM employee;结果输出：
name deptno salary lval
Wendy 1000 4000 6400
Will 1000 4000 6400
Michael 1000 5000 6400
Lucy 1000 5500 6400
Steven 1000 6400 6400
Lily 1001 5000 6400
Jess 1001 6000 6400
Mike 1001 6400 6400
Yun 1002 5500 8000
Wei 1002 7000 8000
Richard 1002 8000 8000注意：last_value默认的窗口是RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW，表示当前行永远是最后一个值，需改成RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING。
• RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW，为默认值，即当指定了ORDER BY从句，而省略了window从句 ，表示从开始到当前行。
• RANGE BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING，表示从当前行到最后一行
• RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING，表示所有行
•n PRECEDING m FOLLOWING: 表示窗口的范围是：[（当前行的行数）- n, （当前行的行数）+m]
row.
