HardSQL challenge #258WINDOW FUNCTIONGROUP BYPARTITION

Revenue Rank per Category

Rank products by revenue within category.

The challenge runs on the SQLabHub practice database (SQLite) and uses the products and order_items tables. You can solve it in the browser, with nothing to install.

Solve This SQL Challenge

The task

Rank products by revenue within category.

A correct answer returns 119 rows with 4 columns: category, id, revenue, rnk.

Tables used in this challenge

`products` 120 rows

id INTEGER (PK) · name TEXT · category TEXT · price REAL · cost REAL · stock INTEGER · sku TEXT · status TEXT · rating REAL · review_count INTEGER

First 3 rows of the products table
id	name	category	price	cost	stock	sku	status	rating	review_count
1	Play Kitchen Deluxe	Toys	1786.38	589.51	257	SKU-000001	active	4	57
2	Micro-Cut Paper Shredder 8-Sheet	Office Supplies	907.93	281.46	131	SKU-000002	active	2.5	447
3	4K Dual Dash Camera	Automotive	568.89	204.8	176	SKU-000003	active	4.1	29

`order_items` 496 rows

id INTEGER (PK) · order_id INTEGER · product_id INTEGER · quantity INTEGER · unit_price REAL · subtotal REAL

First 3 rows of the order_items table
id	order_id	product_id	quantity	unit_price	subtotal
1	1	76	5	192.21	961.05
2	1	82	3	107.1	321.3
3	1	65	1	5.39	5.39

The first rows of each table are shown. See how all the tables relate on the database schema page.

Expected output

Expected output of the challenge
category	id	revenue	rnk
Automotive	60	7519.96	1
Automotive	72	6460.59	2
Automotive	3	5765.639999999999	3
Automotive	10	5699.05	4
Automotive	102	4756.650000000001	5

First 5 of 119 rows. This is the output of the model solution on the practice database. The task does not fix a sort order, so your rows may come back in a different order.

What you practise

Summarising many rows into one value with COUNT, SUM, AVG, MIN or MAX
Grouping by more than one column to get a row per combination
Combining rows from two tables on a matching key with an inner join
Querying the result of another query with a subquery in FROM
Ranking rows with RANK() or DENSE_RANK() and handling ties
Restarting a window calculation for each group with PARTITION BY

Hint and approach

Try the challenge first. Open these only when you need them.

Show the hint

SUM + RANK

Show the step-by-step approach

How the model solution is built, in the order the database evaluates it:

Build an inner query first (a derived table):
1. Read from the products table.
2. Join order_items on products.id = order_items.product_id.
3. Group the rows by products.category and products.id, so the result has one row for each combination.
4. For each group, compute SUM(order_items.subtotal).
Rank the rows with RANK(), ordered by revenue descending, restarting for each category. Tied rows share a rank and the next rank is skipped.

Model solution

Checking your session…

What to watch for

RANK() and DENSE_RANK(). RANK() skips numbers after a tie (1, 1, 3) and DENSE_RANK() does not (1, 1, 2). Filtering on rank <= N returns more than N rows when there are ties.
PARTITION BY. PARTITION BY is not GROUP BY: it keeps every row and only decides which rows a window function looks at.
Derived table. Columns computed in the inner query are only visible outside under their alias, so every calculated column there needs a name.