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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: 以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持极客世界。 |
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