Watch 3 video solutions for Running Total for Different Genders, a medium level problem involving Database. This walkthrough by Everyday Data Science has 6,520 views views. Want to try solving it yourself? Practice on FleetCode or read the detailed text solution.
Table: Scores
+---------------+---------+ | Column Name | Type | +---------------+---------+ | player_name | varchar | | gender | varchar | | day | date | | score_points | int | +---------------+---------+ (gender, day) is the primary key (combination of columns with unique values) for this table. A competition is held between the female team and the male team. Each row of this table indicates that a player_name and with gender has scored score_point in someday. Gender is 'F' if the player is in the female team and 'M' if the player is in the male team.
Write a solution to find the total score for each gender on each day.
Return the result table ordered by gender and day in ascending order.
The result format is in the following example.
Example 1:
Input: Scores table: +-------------+--------+------------+--------------+ | player_name | gender | day | score_points | +-------------+--------+------------+--------------+ | Aron | F | 2020-01-01 | 17 | | Alice | F | 2020-01-07 | 23 | | Bajrang | M | 2020-01-07 | 7 | | Khali | M | 2019-12-25 | 11 | | Slaman | M | 2019-12-30 | 13 | | Joe | M | 2019-12-31 | 3 | | Jose | M | 2019-12-18 | 2 | | Priya | F | 2019-12-31 | 23 | | Priyanka | F | 2019-12-30 | 17 | +-------------+--------+------------+--------------+ Output: +--------+------------+-------+ | gender | day | total | +--------+------------+-------+ | F | 2019-12-30 | 17 | | F | 2019-12-31 | 40 | | F | 2020-01-01 | 57 | | F | 2020-01-07 | 80 | | M | 2019-12-18 | 2 | | M | 2019-12-25 | 13 | | M | 2019-12-30 | 26 | | M | 2019-12-31 | 29 | | M | 2020-01-07 | 36 | +--------+------------+-------+ Explanation: For the female team: The first day is 2019-12-30, Priyanka scored 17 points and the total score for the team is 17. The second day is 2019-12-31, Priya scored 23 points and the total score for the team is 40. The third day is 2020-01-01, Aron scored 17 points and the total score for the team is 57. The fourth day is 2020-01-07, Alice scored 23 points and the total score for the team is 80. For the male team: The first day is 2019-12-18, Jose scored 2 points and the total score for the team is 2. The second day is 2019-12-25, Khali scored 11 points and the total score for the team is 13. The third day is 2019-12-30, Slaman scored 13 points and the total score for the team is 26. The fourth day is 2019-12-31, Joe scored 3 points and the total score for the team is 29. The fifth day is 2020-01-07, Bajrang scored 7 points and the total score for the team is 36.
Problem Overview: The table stores daily score updates for players, including gender, day, and score_points. The task is to compute a running (cumulative) total of scores for each gender as days progress. Each row should show the total score accumulated by that gender up to that specific day.
Approach 1: Window Function with SUM() OVER (Optimal) (Time: O(n log n), Space: O(1) additional)
The cleanest solution uses a SQL window function. Partition the dataset by gender so each gender maintains its own running calculation. Within each partition, order rows by day, then apply SUM(score_points) OVER(PARTITION BY gender ORDER BY day). The database engine computes a cumulative sum as it scans the ordered rows. This avoids joins and keeps the query compact and efficient. Window functions are designed exactly for analytics problems like running totals and ranking. If you frequently solve SQL interview questions involving cumulative metrics, mastering window functions is essential. Related concepts appear often in SQL, database, and window functions practice.
Approach 2: Self Join for Cumulative Sum (Time: O(n^2), Space: O(1))
Before window functions were widely supported, cumulative totals were often computed using a self join. For each row s1, join all rows s2 with the same gender where s2.day <= s1.day. Summing s2.score_points produces the running total up to that day. While logically straightforward, this approach scales poorly because every row may join with many earlier rows. Large datasets quickly make this solution impractical.
Recommended for interviews: The window function approach is what interviewers expect today. It demonstrates familiarity with modern analytical SQL features and produces concise, readable queries. Mentioning the self‑join alternative shows you understand how cumulative aggregates work internally, but the window function solution shows stronger SQL fluency.
| Approach | Time | Space | When to Use |
|---|---|---|---|
| Window Function (SUM OVER PARTITION) | O(n log n) | O(1) | Preferred modern SQL approach for running totals grouped by a category |
| Self Join Cumulative Sum | O(n^2) | O(1) | Useful when window functions are unavailable in older SQL engines |
