将间隔小于60s的数据分到同一组
一、题目
有一份用户访问记录表，记录用户id和访问时间，如果用户访问时间间隔小于60s则认为时一次浏览。
样例数据
+----------+--------------+
| user_id | access_time |
+----------+--------------+
| 1 | 1736337600 |
| 1 | 1736337660 |
| 2 | 1736337670 |
| 1 | 1736337710 |
| 3 | 1736337715 |
| 2 | 1736337750 |
| 1 | 1736337760 |
| 3 | 1736337820 |
| 2 | 1736337850 |
| 1 | 1736337910 |
+----------+--------------+二、分析
1.首先对每个用户的访问时间排序，计算出时间差，考察的是开窗函数lag();
2.对时间差进行判断，确认是否需要新建一个组；
3.然后使用sum()的开窗函数，累加小计，赋予组ID；
维度 | 评分 |
|---|---|
题目难度 | ⭐️⭐️⭐️⭐️⭐️ |
题目清晰度 | ⭐️⭐️⭐️⭐️⭐️ |
业务常见度 | ⭐️⭐️⭐️⭐️ |
三、SQL
1.分用户计算出每次点击时间差；
select
user_id,
access_time,
last_access_time,
access_time-last_access_time as time_diff
from
(select
user_id,
access_time,
lag(access_time)over(partition by user_id order by access_time) as last_access_time
from user_access_log) t
查询结果
2.确认是否需要新建一个组；
select
user_id,
access_time,
last_access_time,
if(access_time-last_access_time>=60,1,0) as is_new_group
from
(select
user_id,
access_time,
lag(access_time)over(partition by user_id order by access_time) as last_access_time
from user_access_log) t查询结果
3.使用sum()over(partition by ** order by **)累加计算，给出组ID。聚合函数开窗使用order by 计算结果是从分组开始计算到当前行的结果，这里的技巧：需要新建组的时候就给标签赋值1，否则0，然后累加计算结果在新建组的时候值就会变化
with t_group as
(select
user_id,
access_time,
last_access_time,
if(access_time-last_access_time>=60,1,0) as is_new_group
from
(select
user_id,
access_time,
lag(access_time)over(partition by user_id order by access_time) as last_access_time
from user_access_log) t
)
select
user_id,
access_time,
last_access_time,
is_new_group,
sum(is_new_group)over(partition by user_id order by access_time asc) as group_id
from t_group
查询结果
四、建表语句和数据插入
--建表语句
CREATE TABLE user_access_log (
user_id INT,
access_time BIGINT
) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
--插入数据
insert into user_access_log (user_id,access_time)
values
(1,1736337600),
(1,1736337660),
(2,1736337670),
(1,1736337710),
(3,1736337715),
(2,1736337750),
(1,1736337760),
(3,1736337820),
(2,1736337850),
(1,1736337910);本文参与 腾讯云自媒体分享计划，分享自微信公众号。
原始发表：2023-06-09，如有侵权请联系 cloudcommunity@tencent.com 删除
评论
登录后参与评论
推荐阅读
目录