Cumulative Sales per Product (PySpark)

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

Problem

Each product needs its own running total. The total must restart at every new product rather than carrying over from the previous one.

Schema — `product_sales`

| Column |
| --- |
| order_date |
| product |
| sales |

Example Input — `product_sales`

| order_date | product | sales |
| --- | --- | --- |
| 2024-06-01 | A | 100 |
| 2024-06-02 | A | 150 |
| 2024-06-03 | A | 200 |
| 2024-06-04 | B | 120 |
| 2024-06-05 | B | 180 |

Expected Output

| order_date | product | sales | cumulative_sales |
| --- | --- | --- | --- |
| 2024-06-01 | A | 100 | 100 |
| 2024-06-02 | A | 150 | 250 |
| 2024-06-03 | A | 200 | 450 |
| 2024-06-04 | B | 120 | 120 |
| 2024-06-05 | B | 180 | 300 |

Explanation

The running total restarts for each product rather than carrying across.

Product A accumulates 100, then 250, then 450. Product B's first row starts again from its own 120 — it does not continue from A's 450 — and then reaches 300.

That restart is what partitionBy(\"product\") does. Without it you would get one continuous total running through both products.

Notes

• The DataFrame is created for you — do not recreate it
• Build a DataFrame called df_result and finish with df_result.show()
• Same problem in SQL: [Cumulative Sales per Product](/challenges/cumulative-sales-per-product)

What this PYSPARK challenge teaches you

“Cumulative Sales per Product (PySpark)” is a medium-level PYSPARK challenge focused on Window Functions. Working through it gives you hands-on practice with Window, partitionBy, sum, Running Total — 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

• Window
• partitionBy
• sum
• Running Total

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. Window.partitionBy('product') restarts the running total per product.
2. Add .orderBy('order_date').rowsBetween(Window.unboundedPreceding, 0).
3. Sort the final result by product then order_date.

How to practise it on PySpark.in

Open the challenge, write your PySpark code 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 PYSPARK 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 PYSPARK 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