Given these sample data:
ID | STATUS | STATUS_TIME | STATUS_BY |
---|---|---|---|
1 | ONE | 2016-09-28-19.47.52.501398 | USER_1 |
3 | ONE | 2016-09-28-19.47.52.501511 | USER_2 |
1 | THREE | 2016-09-28-19.47.52.501517 | USER_3 |
3 | TWO | 2016-09-28-19.47.52.501521 | USER_2 |
3 | THREE | 2016-09-28-19.47.52.501524 | USER_4 |
Items identified by ID
values must move from STATUS
'ONE' to 'TWO' to 'THREE' in sequence, without skipping statuses. The problem is to find users (STATUS_BY
) values who violate the rule and move from 'ONE' immediately to 'THREE'.
The LAG()
analytical function helps to solve the problem by returning for each row the value in the preceding row:
SELECT * FROM (
SELECT
t.*,
LAG(status) OVER (PARTITION BY id ORDER BY status_time) AS prev_status
FROM test t
) t1 WHERE status = 'THREE' AND prev_status != 'TWO'
In case your database doesn't have LAG() you can use this to produce the same result:
SELECT A.id, A.status, B.status as prev_status, A.status_time, B.status_time as prev_status_time
FROM Data A, Data B
WHERE A.id = B.id
AND B.status_time = (SELECT MAX(status_time) FROM Data where status_time < A.status_time and id = A.id)
AND A.status = 'THREE' AND NOT B.status = 'TWO'