I'm trying to put together a query that will retrieve the statistics of a user (profit/loss) as a cumulative result, over a period of time.
Here's the query I have so far:
SELECT p.name, e.date,
sum(sp.payout) OVER (ORDER BY e.date)
- sum(s.buyin) OVER (ORDER BY e.date) AS "Profit/Loss"
FROM result r
JOIN game g ON r.game_id = g.game_id
JOIN event e ON g.event_id = e.event_id
JOIN structure s ON g.structure_id = s.structure_id
JOIN structure_payout sp ON g.structure_id = sp.structure_id
AND r.position = sp.position
JOIN player p ON r.player_id = p.player_id
WHERE p.player_id = 17
GROUP BY p.name, e.date, e.event_id, sp.payout, s.buyin
ORDER BY p.name, e.date ASC
The query will run. However, the result is slightly incorrect. The reason is that an event
can have multiple games (with different sp.payouts
). Therefore, the above comes out with multiple rows if a user has 2 results in an event with different payouts (i.e. there are 4 games per event, and a user gets £20 from one, and £40 from another).
The obvious solution would be to amend the GROUP BY
to:
GROUP BY p.name, e.date, e.event_id
However, Postgres complains at this as it doesn't appear to be recognizing that sp.payout
and s.buyin
are inside an aggregate function. I get the error:
column "sp.payout" must appear in the GROUP BY clause or be used in an aggregate function
I'm running 9.1 on Ubuntu Linux server.
Am I missing something, or could this be a genuine defect in Postgres?
You are not, in fact, using aggregate functions. You are using window functions. That's why PostgreSQL demands sp.payout
and s.buyin
to be included in the GROUP BY
clause.
By appending an OVER
clause, the aggregate function sum()
is turned into a window function, which aggregates values per partition while keeping all rows.
You can combine window functions and aggregate functions. Aggregations are applied first. I did not understand from your description how you want to handle multiple payouts / buyins per event. As a guess, I calculate a sum of them per event. Now I can remove sp.payout
and s.buyin
from the GROUP BY
clause and get one row per player
and event
:
SELECT p.name
, e.event_id
, e.date
, sum(sum(sp.payout)) OVER w
- sum(sum(s.buyin )) OVER w AS "Profit/Loss"
FROM player p
JOIN result r ON r.player_id = p.player_id
JOIN game g ON g.game_id = r.game_id
JOIN event e ON e.event_id = g.event_id
JOIN structure s ON s.structure_id = g.structure_id
JOIN structure_payout sp ON sp.structure_id = g.structure_id
AND sp.position = r.position
WHERE p.player_id = 17
GROUP BY e.event_id
WINDOW w AS (ORDER BY e.date, e.event_id)
ORDER BY e.date, e.event_id;
In this expression: sum(sum(sp.payout)) OVER w
, the outer sum()
is a window function, the inner sum()
is an aggregate function.
Assuming p.player_id
and e.event_id
are PRIMARY KEY
in their respective tables.
I added e.event_id
to the ORDER BY
of the WINDOW
clause to arrive at a deterministic sort order. (There could be multiple events on the same date.) Also included event_id
in the result to distinguish multiple events per day.
While the query restricts to a single player (WHERE p.player_id = 17
), we don't need to add p.name
or p.player_id
to GROUP BY
and ORDER BY
. If one of the joins would multiply rows unduly, the resulting sum would be incorrect (partly or completely multiplied). Grouping by p.name
could not repair the query then.
I also removed e.date
from the GROUP BY
clause. The primary key e.event_id
covers all columns of the input row since PostgreSQL 9.1.
If you change the query to return multiple players at once, adapt:
...
WHERE p.player_id < 17 -- example - multiple players
GROUP BY p.name, p.player_id, e.date, e.event_id -- e.date and p.name redundant
WINDOW w AS (ORDER BY p.name, p.player_id, e.date, e.event_id)
ORDER BY p.name, p.player_id, e.date, e.event_id;
Unless p.name
is defined unique (?), group and order by player_id
additionally to get correct results in a deterministic sort order.
I only kept e.date
and p.name
in GROUP BY
to have identical sort order in all clauses, hoping for a performance benefit. Else, you can remove the columns there. (Similar for just e.date
in the first query.)