Running Total Revenue

SQL coding challenge · Difficulty: medium · Topic: Window Functions · +100 XP

Problem

For each salesperson, compute a running (cumulative) total of their revenue in order_date order.

The running total restarts from zero for each salesperson.

Tables

Table: orders

| Column | Type |
| --- | --- |
| order_id | INT |
| salesperson | VARCHAR |
| order_date | DATE |
| amount | INT |

Example Input

| order_id | salesperson | order_date | amount |
| --- | --- | --- | --- |
| 1 | Alice | 2024-01-05 | 2500 |
| 2 | Bob | 2024-01-08 | 1800 |
| 3 | Alice | 2024-01-12 | 900 |
| 4 | Carol | 2024-01-15 | 3200 |
| 5 | Bob | 2024-01-20 | 650 |
| 6 | Alice | 2024-01-25 | 1100 |
| 7 | Carol | 2024-01-28 | 1500 |

Expected Output

| salesperson | order_date | amount | running_total |
| --- | --- | --- | --- |
| Alice | 2024-01-05 | 2500 | 2500 |
| Alice | 2024-01-12 | 900 | 3400 |
| Alice | 2024-01-25 | 1100 | 4500 |
| Bob | 2024-01-08 | 1800 | 1800 |
| Bob | 2024-01-20 | 650 | 2450 |
| Carol | 2024-01-15 | 3200 | 3200 |
| Carol | 2024-01-28 | 1500 | 4700 |

Explanation

Alice accumulates 2500 -> 3400 -> 4500 across her three orders. Bob starts again at 1800, because the window is partitioned per salesperson.

Notes

• Return: salesperson, order_date, amount, running_total
• Sort by salesperson ASC, then order_date ASC
• Function to use: SUM(amount) OVER (PARTITION BY salesperson ORDER BY order_date)
• A plain GROUP BY collapses the rows — a window function keeps every input row and gives each its own total

What this SQL challenge teaches you

“Running Total Revenue” is a medium-level SQL challenge focused on Window Functions. Working through it gives you hands-on practice with SUM OVER, running total, ORDER BY — 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.

Concepts covered

• SUM OVER
• running total
• ORDER BY

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:

1. Your result should return: salesperson, sale_date, amount,.
2. Compare your output to the Expected Output — the columns, values and row order must match exactly.
3. SUM(amount) OVER ( PARTITION BY salesperson ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW )

Where this comes up

Variations of this problem have been reported in interviews at Swiggy, Zomato, Razorpay. 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
• Median Salary per Department
• Latest Order Per Customer
• 3-Day Rolling Sum of Sales
• 7-Day Rolling Purchase Amount by Customer
• Rolling Sum with Missing Dates

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.

Solve this challenge free on PySpark.in