


How to Correctly Use Window Functions with GROUP BY in Postgres to Calculate Cumulative User Statistics?
Postgres Window Function and Group By Exception
Problem
A query is seeking to retrieve cumulative user statistics over time, but it encounters inaccuracies. When multiple games exist within an event, the query produces multiple rows for different payouts. The obvious solution of grouping by event ID fails, prompting the error: "column "sp.payout" must appear in the GROUP BY clause or be used in an aggregate function."
Solution
The error arises because the query uses window functions, which aggregate values per partition while preserving all rows, rather than aggregate functions. Window functions require their arguments to be included in the GROUP BY clause.
To resolve the issue, one can combine window and aggregate functions as follows:
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;
Explanation
In this query, the outer sum() function is a window function that calculates the sum of the inner sum() function. The inner function aggregates the payouts and buyins for each player within each event. The result is one row per player and event, displaying the cumulative profit or loss.
Additional Considerations
- To include multiple players, replace the WHERE clause with: WHERE p.player_id < 17.
- If p.name is not unique, group and order by player_id additionally for correct results.
- Grouping by e.date and p.name may yield performance benefits.
The above is the detailed content of How to Correctly Use Window Functions with GROUP BY in Postgres to Calculate Cumulative User Statistics?. For more information, please follow other related articles on the PHP Chinese website!

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