Alternative to looping - sql

Question

I have a table like the following and I am required to show the subtotal of the use_time_sec column grouping by event_datetime, event_name (only show login), user_id and system_id.
sample input table
with sample_input as (
select '12/01/2023 14:27:59' as event_datetime, 'login' as event_name,'1' as user_id, 'X' as system_id, '0' as use_time_sec
union all
select '12/01/2023 14:28:05', 'screen 1', '1', 'X', '2'
union all
select '12/01/2023 14:28:05', 'screen 2', '1', 'X', '5',
union all
select '12/01/2023 14:28:17', 'screen 1', '1', 'X', '3',
union all
select '12/01/2023 14:28:23', 'logout', '1', '', '0',
union all
select '12/01/2023 14:28:23', 'login', '2', 'Y', '0',
union all
select '12/01/2023 14:28:23', 'screen 1', '2', 'Y', '10',
union all
select '12/01/2023 14:28:24', 'screen 2', '2', 'Y', '100',
union all
select '12/01/2023 14:28:29', 'login', '1', 'X', '0',
union all
select '12/01/2023 14:28:29', 'screen 1', '1', 'X', '500',
union all
select '12/01/2023 14:28:29', 'logout', '1', '', '0',
)
select * from sample_input
sample output
I can loop through the table to get my desired output. But thats not the most efficient solution as there are few millions of record in the table and growing everyday.
Will appreciate if someone can provide a better solution than what I have.
Note: The data is in google BigQuery.
Thanks

Answer 1

This is known as the Gaps and Islands problem. We're trying to identify the islands of user sessions. We need to do a query which gives us some way to identify a session. This relies heavily on window functions.
One way is to count the number of logins seen per user.
select
*,
sum(1)
filter(where event_name = 'login')
over(partition by user_id order by event_time)
as session_num
from events
order by event_time
That will keep a tally per user_id. It will add to the tally every time it sees a user login.
event_time
event_type
user_id
use_time_sec
session_num
1000
login
1
0
1
1001
things
1
3
1
1001
login
2
10
1
1002
logout
1
7
1
1005
logout
2
20
1
1100
login
1
5
2
1101
logout
1
10
2
Now we have a way to identify each user's sessions. We can grouping by user_id and session_num. These are our islands.
with sessions as (
select
*,
sum(1)
filter(where event_name = 'login')
over(partition by user_id order by event_time)
as session_num
from events
order by event_time
)
select
min(event_time) as session_start,
user_id,
sum(use_time_sec) as total_use_time_sec
from sessions
group by user_id, session_num
order by session_start
session_start
user_id
total_use_time_sec
1000
1
10
1001
2
130
1100
1
15
Demonstration in PostgreSQL, but it should work fine on BigQuery.