I have a log of customers going through a workflow. I want to do two things, and I am struggling with either of them.
First is: I wish to filter out customers who didn't start by entering the first state at the beginning of the workflow (enter state 0).
Second is: For remaining customers I want to know how much time they spent in each step of the workflow.
Each record has:
I tried to do a query that would allow me to get the timestamp of entry and exit grouped by customer and state like so:
SELECT
CUSTOMER_ID,
STATE,
MIN(UPDATE_DT) AS ENTRY_DATE,
MAX(UPDATE_DT) AS EXIT_DATE
FROM LOG_DATA
GROUP BY CUSTOMER_ID, STATE
ORDER BY CUSTOMER_ID, STATE;
But I immediately run into a few problems. The query will run just fine but:
I tried to focus on the first problem by introducing an additional attribute in my select thusly:
MIN(STATE) OVER(PARTITION BY CUSTOMER_ID) AS EARLIEST_STATE
But then ran into a few problems. I am unable to include EARLIEST_STATE as a condition of the WHERE or the GROUP BY HAVING because to the WHERE it does not exist, and the GROUP BY will not allow me to include EARLIEST_STATE.
As I thought this through it gets worse - MIN(STATE) can only prove, at best, customer has STATE = 0 but not that they have a record that says ACTION = "enter" and STATE = 0. So this approach fails not only because I can't get it to run but because it's also logically not correct.
I know I could do multiple SELECT with SELECTs but this feels clunky and I want to learn the right way to do this. It also doesn't help that I am dealing with 10 million rows of data so efficiency is important.
I am using Postgres 9.5, I have no control over either the DB technology or the schema of the data in this instance.
It would be slow but I could use something my Python to do this, but I would really like to know the correct way to do this using the DB.
If I understand correctly, you want at least one row with Action = 'Enter'
and state = 0
for any customer that is in the result set. That suggests a window function:
SELECT CUSTOMER_ID, STATE,
MIN(UPDATE_DT) AS ENTRY_DATE,
MAX(UPDATE_DT) AS EXIT_DATE,
FROM (SELECT l.*,
SUM(CASE WHEN ACTION = 'Enter' AND state = 0 THEN 1 ELSE 0 END) OVER (PARTITION BY CUSTOMER_ID) as num_validenter
FROM LOG_DATA l
) l
WHERE num_validenter > 0
GROUP BY CUSTOMER_ID, STATE
ORDER BY CUSTOMER_ID, STATE
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