16. 窗口函数 (Window Function) 的使用
从SQL Server 2005起,SQL Server开始支持窗口函数 (Window Function),以及到SQL Server 2012,窗口函数功能增强,目前为止支持以下几种窗口函数:
1. 排序函数 (Ranking Function) ;
2. 聚合函数 (Aggregate Function) ;
3. 分析函数 (Analytic Function) ;
4. NEXT VALUE FOR Function, 这是给sequence专用的一个函数;
一. 排序函数(Ranking Function)
帮助文档里的代码示例很全。
排序函数中,ROW_NUMBER()较为常用,可用于去重、分页、分组中选择数据,生成数字辅助表等等;
排序函数在语法上要求OVER子句里必须含ORDER BY,否则语法不通过,对于不想排序的场景可以这样变通;
drop table if exists test_ranking create table test_ranking
(
id int not null,
name varchar(20) not null,
value int not null
) insert test_ranking
select 1,'name1',1 union all
select 1,'name2',2 union all
select 2,'name3',2 union all
select 3,'name4',2 select id , name, ROW_NUMBER() over (PARTITION by id ORDER BY name) as num
from test_ranking select id , name, ROW_NUMBER() over (PARTITION by id) as num
from test_ranking
/*
Msg 4112, Level 15, State 1, Line 1
The function 'ROW_NUMBER' must have an OVER clause with ORDER BY.
*/ --ORDERY BY后面给一个和原表无关的派生列
select id , name, ROW_NUMBER() over (PARTITION by id ORDER BY GETDATE()) as num
from test_ranking select id , name, ROW_NUMBER() over (PARTITION by id ORDER BY (select 0)) as num
from test_ranking
二. 聚合函数 (Aggregate Function)
SQL Server 2005中,窗口聚合函数仅支持PARTITION BY,也就是说仅能对分组的数据整体做聚合运算;
SQL Server 2012开始,窗口聚合函数支持ORDER BY,以及ROWS/RAGNE选项,原本需要子查询来实现的需求,如: 移动平均 (moving averages), 总计聚合 (cumulative aggregates), 累计求和 (running totals) 等,变得更加方便;
代码示例1:总计/小计/累计求和
drop table if exists test_aggregate; create table test_aggregate
(
event_id varchar(100),
rk int,
price int
) insert into test_aggregate
values
('a',1,10),
('a',2,10),
('a',3,50),
('b',1,10),
('b',2,20),
('b',3,30) --1. 没有窗口函数时,用子查询
select a.event_id,
a.rk, --build ranking column if needed
a.price,
(select sum(price) from test_aggregate b where b.event_id = a.event_id and b.rk <= a.rk) as totalprice
from test_aggregate a --2. 从SQL Server 2012起,用窗口函数
--2.1
--没有PARTITION BY, 没有ORDER BY,为全部总计;
--只有PARTITION BY, 没有ORDER BY,为分组小计;
--只有ORDER BY,没有PARTITION BY,为全部累计求和(RANGE选项,见2.2)
select *,
sum(price) over() as TotalPrice,
sum(price) over(partition by event_id) as SubTotalPrice,
sum(price) over(order by rk) as RunningTotalPrice
from test_aggregate a --2.2 注意ORDER BY列的选择,可能会带来不同结果
select *,
sum(price) over(partition by event_id order by rk) as totalprice
from test_aggregate a
/*
event_id rk price totalprice
a 1 10 10
a 2 10 20
a 3 50 70
b 1 10 10
b 2 20 30
b 3 30 60
*/ select *,
sum(price) over(partition by event_id order by price) as totalprice
from test_aggregate a
/*
event_id rk price totalprice
a 1 10 20
a 2 10 20
a 3 50 70
b 1 10 10
b 2 20 30
b 3 30 60
*/ --因为ORDER BY还有个子选项ROWS/RANGE,不指定的情况下默认为RANGE UNBOUNDED PRECEDING AND CURRENT ROW
--RANGE按照ORDER BY中的列值,将相同的值的行均视为当前同一行
select *,sum(price) over(partition by event_id order by price) as totalprice from test_aggregate a
select *,sum(price) over(partition by event_id order by price range between unbounded preceding and current row) as totalprice from test_aggregate a --如果ORDER BY中的列值有重复值,手动改用ROWS选项即可实现逐行累计求和
select *,sum(price) over(partition by event_id order by price rows between unbounded preceding and current row) as totalprice from test_aggregate a
代码示例2:移动平均
--移动平均,举个例子,就是求前N天的平均值,和股票市场的均线类似
drop table if exists test_moving_avg create table test_moving_avg
(
ID int,
Value int,
DT datetime
) insert into test_moving_avg
values
(1,10,GETDATE()-10),
(2,110,GETDATE()-9),
(3,100,GETDATE()-8),
(4,80,GETDATE()-7),
(5,60,GETDATE()-6),
(6,40,GETDATE()-5),
(7,30,GETDATE()-4),
(8,50,GETDATE()-3),
(9,20,GETDATE()-2),
(10,10,GETDATE()-1) --1. 没有窗口函数时,用子查询
select *,
(select AVG(Value) from test_moving_avg a where a.DT >= DATEADD(DAY, -5, b.DT) AND a.DT < b.DT) AS avg_value_5days
from test_moving_avg b --2. 从SQL Server 2012起,用窗口函数
--三个内置常量,第一行,最后一行,当前行:UNBOUNDED PRECEDING, UNBOUNDED FOLLOWING, CURRENT ROW
--在行间移动,用BETWEEN m preceding AND n following (m, n > 0)
SELECT *,
sum(value) over (ORDER BY DT ROWS BETWEEN 5 preceding AND CURRENT ROW) moving_sum,
avg(value) over (ORDER BY DT ROWS BETWEEN 4 preceding AND CURRENT ROW) moving_avg1,
avg(value) over (ORDER BY DT ROWS BETWEEN 5 preceding AND 1 preceding) moving_avg2,
avg(value) over (ORDER BY DT ROWS BETWEEN 3 preceding AND 1 following) moving_avg3
FROM test_moving_avg
ORDER BY DT
三. 分析函数 (Analytic Function)
代码示例1:取当前行某列的前一个/下一个值
drop table if exists test_analytic create table test_analytic
(
SalesYear varchar(10),
Revenue int,
Offset int
) insert into test_analytic
values
(2013,1001,1),
(2014,1002,1),
(2015,1003,1),
(2016,1004,1),
(2017,1005,1),
(2018,1006,1) --当年及去年的销售额
select *,lag(Revenue,1,null) over(order by SalesYear asc) as PreviousYearRevenue from test_analytic
select *,lag(Revenue,Offset,null) over(order by SalesYear asc) as PreviousYearRevenue from test_analytic
select *,lead(Revenue,1,null) over(order by SalesYear desc) as PreviousYearRevenue from test_analytic --当年及下一年的销售额
select *,lead(Revenue,1,null) over(order by SalesYear asc) as NextYearRevenue from test_analytic
select *,lead(Revenue,Offset,null) over(order by SalesYear asc) as NextYearRevenue from test_analytic
select *,lag(Revenue,1,null) over(order by SalesYear desc) as NextYearRevenue from test_analytic --可以根据offset调整跨度
代码示例2:分组中某列最大/最小值,对应的其他列值
假设有个门禁系统,在员工每次进门时写入一条记录,记录了“身份号码”,“进门时间”,“衣服颜色",查询每个员工最后一次进门时的“衣服颜色”。
drop table if exists test_first_last create table test_first_last
(
EmployeeID int,
EnterTime datetime,
ColorOfClothes varchar(20)
) insert into test_first_last
values
(1001, GETDATE()-9, 'GREEN'),
(1001, GETDATE()-8, 'RED'),
(1001, GETDATE()-7, 'YELLOW'),
(1001, GETDATE()-6, 'BLUE'),
(1002, GETDATE()-5, 'BLACK'),
(1002, GETDATE()-4, 'WHITE') --1. 用子查询
--LastColorOfColthes
select * from test_first_last a
where not exists(select 1 from test_first_last b where a.EmployeeID = b.EmployeeID and a.EnterTime < b.EnterTime) --LastColorOfColthes
select *
from
(select *, ROW_NUMBER() over(partition by EmployeeID order by EnterTime DESC) num
from test_first_last ) t
where t.num =1 --2. 用窗口函数
--用LAST_VALUE时,必须加上ROWS/RANGE BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING,否则结果不正确
select *,
FIRST_VALUE(ColorOfClothes) OVER (PARTITION BY EmployeeID ORDER BY EnterTime DESC) as LastColorOfClothes,
FIRST_VALUE(ColorOfClothes) OVER (PARTITION BY EmployeeID ORDER BY EnterTime ASC) as FirstColorOfClothes,
LAST_VALUE(ColorOfClothes) OVER (PARTITION BY EmployeeID ORDER BY EnterTime ASC ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) as LastColorOfClothes,
LAST_VALUE(ColorOfClothes) OVER (PARTITION BY EmployeeID ORDER BY EnterTime DESC ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) as FirstColorOfClothes
from test_first_last --对于显示表中所有行,并追加Last/First字段时用窗口函数方便些
--对于挑选表中某一行/多行时,用子查询更方便
四. NEXT VALUE FOR Function
drop sequence if exists test_seq create sequence test_seq
start with 1
increment by 1; GO drop table if exists test_next_value create table test_next_value
(
ID int,
Name varchar(10)
) insert into test_next_value(Name)
values
('AAA'),
('AAA'),
('BBB'),
('CCC') --对于多行数据获取sequence的next value,是否使用窗口函数都会逐行计数
--窗口函数中ORDER BY用于控制不同列值的计数顺序
select *, NEXT VALUE FOR test_seq from test_next_value
select *, NEXT VALUE FOR test_seq OVER(ORDER BY Name DESC) from test_next_value
参考:
SELECT - OVER Clause (Transact-SQL)
SQL Server Windowing Functions: ROWS vs. RANGE
https://www.sqlpassion.at/archive/2015/01/22/sql-server-windowing-functions-rows-vs-range/
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