Month-to-Date (MTD) Rolling Sum
SQL coding challenge · Difficulty: medium · Topic: Window Functions · +200 XP
Problem
Show a Month-to-Date (MTD) running total of sales that resets at the start of every month.
Tables
Table: daily_sales
| sale_date | sales | | --- | --- | | 2024-01-30 | 100 | | 2024-01-31 | 200 | | 2024-02-01 | 50 | | 2024-02-02 | 150 | | 2024-02-03 | 300 | | 2024-03-01 | 400 |
Expected Output
| sale_date | sales | mtd_total | | --- | --- | --- | | 2024-01-30 | 100 | 100 | | 2024-01-31 | 200 | 300 | | 2024-02-01 | 50 | 50 | | 2024-02-02 | 150 | 200 | | 2024-02-03 | 300 | 500 | | 2024-03-01 | 400 | 400 |
- Return:
sale_date,sales,mtd_total - Sort by
sale_date ascending - Function to use:
SUM() OVER (PARTITION BY YEAR(sale_date), MONTH(sale_date) ORDER BY sale_date)
What this SQL challenge teaches you
“Month-to-Date (MTD) Rolling Sum” is a medium-level SQL challenge focused on Window Functions. Working through it gives you hands-on practice with Window Functions — the kind of transformation you are asked to write in real data engineering work and in technical interviews. You can solve it directly in the browser: the dataset is pre-loaded, so you write the query or DataFrame code, run it, and compare your output against the expected result immediately.
How to approach it
If you get stuck, work through these steps in order before looking at a full solution — each one narrows the problem down:
- Approach: use SUM() OVER (PARTITION BY YEAR(sale_date), MONTH(sale_date) ORDER BY sale_date).
- Your result should return: sale_date, sales, mtd_total.
- Order the output sale_date ascending.
- Compare your output to the Expected Output — the columns, values and row order must match exactly.
Where this comes up
Variations of this problem have been reported in interviews at Amazon. Interviewers use it to check whether you can express the logic cleanly and reason about correctness on edge cases such as ties, nulls and empty groups.
How to practise it on PySpark.in
Open the challenge, write your SQL query in the editor and press Run to execute it against the sample dataset. Submitting checks your output against every test case, including hidden ones, so you find out straight away whether your logic holds up. You can retry as often as you like, and each solved challenge adds to your XP.
Related SQL challenges
- Top 3 Products per Category
- Running Total Revenue
- Median Salary per Department
- Latest Order Per Customer
- 3-Day Rolling Sum of Sales
- 7-Day Rolling Purchase Amount by Customer
Frequently asked questions
Do I need to install Spark or a database to solve this?
No. The SQL environment runs in your browser with the sample data already loaded, so there is nothing to install or configure.
Is this challenge free?
Yes - the problem, the sample dataset, the hints and unlimited test runs are free.
What level is it?
It is rated medium and covers Window Functions.