MySQL에서 윈도우 기능을 사용하는 방법

# 开窗函数语法 
func_name(<parameter>) 
OVER([PARTITION BY <part_by_condition>] 
[ORDER BY <order_by_list> ASC|DESC])

# 首先创建虚拟的业务员销售数据 
CREATE TABLE Sales
( 
idate date, 
iname char(2), 
sales int
); 
# 向表中插入数据 
INSERT INTO Sales VALUES 
('2021/1/1', '丁一', 200), 
('2021/2/1', '丁一', 180), 
('2021/2/1', '李四', 100), 
('2021/3/1', '李四', 150), 
('2021/2/1', '刘猛', 180), 
('2021/3/1', '刘猛', 150), 
('2021/1/1', '王二', 200), 
('2021/2/1', '王二', 180), 
('2021/3/1', '王二', 300), 
('2021/1/1', '张三', 300), 
('2021/2/1', '张三', 280), 
('2021/3/1', '张三', 280); 
# 数据查询 
SELECT * FROM Sales; 
# 查询各月中销售业绩最差的业务员
SELECT month(idate),iname,sales, 
	ROW_NUMBER() 
	OVER(PARTITION BY month(idate) 
			 ORDER BY sales) as sales_order 
FROM Sales;

SELECT * FROM 
(SELECT month(idate),iname,sales, 
	 ROW_NUMBER() 
	 OVER(PARTITION BY month(idate) 
   ORDER BY sales) as sales_order FROM Sales) as t
WHERE sales_order=1;

# ROW_NUMBER()、RANK()、DENSE_RANK()的区别 
SELECT * FROM 
(SELECT month(idate) as imonth,iname,sales, 
ROW_NUMBER() 
OVER(PARTITION BY month(idate) ORDER BY sales) as row_order,
RANK() 
OVER(PARTITION BY month(idate) ORDER BY sales) as rank_order, 
DENSE_RANK() 
OVER(PARTITION BY month(idate) ORDER BY sales) as dense_order 
FROM Sales) as t;

# 首先创建虚拟的用户登录表，并插入数据 
create table user_login
( 
user_id varchar(100), 
login_time datetime
); 

insert into user_login values 
(1,'2020-11-25 13:21:12'), 
(1,'2020-11-24 13:15:22'), 
(1,'2020-11-24 10:30:15'), 
(1,'2020-11-24 09:18:27'), 
(1,'2020-11-23 07:43:54'), 
(1,'2020-11-10 09:48:36'), 
(1,'2020-11-09 03:30:22'), 
(1,'2020-11-01 15:28:29'), 
(1,'2020-10-31 09:37:45'), 
(2,'2020-11-25 13:54:40'), 
(2,'2020-11-24 13:22:32'), 
(2,'2020-11-23 10:55:52'), 
(2,'2020-11-22 06:30:09'), 
(2,'2020-11-21 08:33:15'), 
(2,'2020-11-20 05:38:18'), 
(2,'2020-11-19 09:21:42'), 
(2,'2020-11-02 00:19:38'), 
(2,'2020-11-01 09:03:11'), 
(2,'2020-10-31 07:44:55'), 
(2,'2020-10-30 08:56:33'), 
(2,'2020-10-29 09:30:28'); 
# 查看数据 
SELECT * FROM user_login;

# 数据预处理：由于统计的窗口期是天数，所以可以对登录时间字段进行格式转换，将其变成日期格式然后再去重（去掉用户同一天内多次登录的情况） 
# 为方便后续代码查看，将处理结果放置新表中，一步一步操作 
create table user_login_date(
select distinct user_id, date(login_time) login_date from user_login);
# 处理后的数据如下： 
select * from user_login_date;

# 第一种情况：查看每位用户连续登陆的情况 
# 对用户登录数据进行排序 
create table user_login_date_1( 
select *,
rank() over(partition by user_id order by login_date) irank 
from user_login_date); 
#查看结果 
select * from user_login_date_1;
 
# 增加辅助列，帮助判断用户是否连续登录 
create table user_login_date_2( 
select *,
date_sub(login_date, interval irank DAY) idate  #data_sub从指定的日期减去指定的时间间隔
from user_login_date_1); 
# 查看结果 
select * from user_login_date_2; 

# 计算每位用户连续登录天数 
select user_id, 
min(login_date) as start_date, 
max(login_date) as end_date, 
count(login_date) as days 
from user_login_date_2 
group by user_id,idate;

# ===============【整合代码，解决用户连续登录问题】=================== 
select user_id, 
       min(login_date) start_date, 
       max(login_date) end_date, 
       count(login_date) days 
from (select *,date_sub(login_date, interval irank day) idate 
from (select *,rank() over(partition by user_id order by login_date) irank 
from (select distinct user_id, date(login_time) login_date from user_login) as a) as b) as c 
group by user_id,idate;

# 计算每个用户最大连续登录天数 
select user_id,max(days) from 
(select user_id, 
			 min(login_date) start_date, 
			 max(login_date) end_date, 
			 count(login_date) days 
from (select *,date_sub(login_date, interval irank day) idate 
from (select *,rank() over(partition by user_id order by login_date) irank 
from (select distinct user_id, date(login_time) login_date from user_login) as a) as b) as c 
group by user_id,idate) as d 
group by user_id;

# 查看在这段时间内连续登录天数≥5天的用户 
select distinct user_id from 
(select user_id, 
		min(login_date) start_date, 
		max(login_date) end_date, 
		count(login_date) days 
from (select *,date_sub(login_date, interval irank day) idate 
from (select *,rank() over(partition by user_id order by login_date) irank 
from (select distinct user_id, date(login_time) login_date from user_login) as a) as b) as c 
group by user_id,idate 
having days>=5
) as d;

select *, 
lead(login_date,4) over(partition by user_id order by login_date) as idate5 
from user_login_date;

# 计算第5次登录日期与当天的差值 
select *,datediff(idate5,login_date)+1 days 
from (select *,lead(login_date,4) over(partition by user_id order by login_date) idate5
from user_login_date) as a; 
# 找出相差天数为5的记录 
select distinct user_id 
from (select *,datediff(idate5,login_date)+1 as days 
from (select *,lead(login_date,4) over(partition by user_id order by login_date) idate5 
from user_logrin_date) as a)as b 
where days = 5;