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以连续3天为例,使用工具:MySQL。 1.创建SQL表: create table if not exists orde(id varchar(10),date datetime,orders varchar(10)); insert into orde values('1' , '2019/1/1',10 ); insert into orde values('1' , '2019/1/2',109 ); insert into orde values('1' , '2019/1/3',150 ); insert into orde values('1' , '2019/1/4',99); insert into orde values('1' , '2019/1/5',145); insert into orde values('1' , '2019/1/6',1455); insert into orde values('1' , '2019/1/7',199); insert into orde values('1' , '2019/1/8',188 ); insert into orde values('4' , '2019/1/1',10 ); insert into orde values('2' , '2019/1/2',109 ); insert into orde values('3' , '2019/1/3',150 ); insert into orde values('4' , '2019/1/4',99); insert into orde values('5' , '2019/1/5',145); insert into orde values('6' , '2019/1/6',1455); insert into orde values('7' , '2019/1/7',199); insert into orde values('8' , '2019/1/8',188 ); insert into orde values('9' , '2019/1/1',10 ); insert into orde values('9' , '2019/1/2',109 ); insert into orde values('9' , '2019/1/3',150 ); insert into orde values('9' , '2019/1/4',99); insert into orde values('9' , '2019/1/6',145); insert into orde values('9' , '2019/1/9',1455); insert into orde values('9' , '2019/1/10',199); insert into orde values('9' , '2019/1/13',188 ); 查看数据表: 2.使用row_number() over() 排序函数计算每个id的排名,SQL如下: select *,row_number() over(partition by id order by date ) 'rank' from orde where orders is not NULL; 查看数据表: 3.将date日期字段减去rank排名字段,SQL如下: select *,DATE_SUB(a.date,interval a.rank day) 'date_sub' from( select *,row_number() over(partition by id order by date ) 'rank' from orde where orders is not NULL ) a; 查看数据:
4.根据id和date分组并计算分组后的数量(count)、计算最早登录和最晚登录的时间,SQL如下: select b.id,min(date) 'start_time',max(date) 'end_time',count(*) 'date_count' from( select *,DATE_SUB(a.date,interval a.rank day) 'date_sub' from( select *,row_number() over(partition by id order by date ) 'rank' from orde where orders is not NULL ) a ) b group by b.date_sub,id having count(*) >= 3 ; 查看数据: 参考资料: SQL查询连续七天以上下单的用户 https://blog.csdn.net/qq_43807789/article/details/99091753?spm=1001.2101.3001.6661.1&utm_medium=distribute.pc_relevant_t0.none-task-blog-2~default~CTRLIST~default-1.highlightwordscore&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-2~default~CTRLIST~default-1.highlightwordscore 到此这篇关于SQL 查询连续n天登录的用户情况的文章就介绍到这了,更多相关SQL 查询用户连续登录情况内容请搜索极客世界以前的文章或继续浏览下面的相关文章希望大家以后多多支持极客世界! |
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