HardSQL challenge #374WINDOW FUNCTIONJOINAGGREGATION

Monthly Profit Growth Products

Find products with highest profit growth month over month.

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

Solve This SQL Challenge

The task

Find products with highest profit growth month over month.

A correct answer returns 470 rows with 3 columns: product_id, month, growth.

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

`orders` 200 rows

id INTEGER (PK) · user_id INTEGER · total REAL · discount REAL · item_count INTEGER · status TEXT · order_date TEXT · shipped_date TEXT · shipping_city TEXT · shipping_country TEXT

First 3 rows of the orders table
id	user_id	total	discount	item_count	status	order_date	shipped_date	shipping_city	shipping_country
1	43	2638.11	0.25	3	cancelled	2024-04-24	NULL	Los Angeles	USA
2	8	2947	0.19	5	delivered	2024-03-15	2023-04-09	Chicago	USA
3	136	683.24	0.08	2	delivered	2023-02-15	2023-07-10	Los Angeles	USA

`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
product_id	month	growth
22	2024-01	5593.35
10	2022-10	4413.48
14	2023-09	4270.280000000001
89	2024-07	3719.3599999999997
65	2024-04	3608.79

First 5 of 470 rows. This is the output of the model solution on the practice database.

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
Working with dates in SQLite: strftime, julianday and DATE
Reading the previous row's value with LAG()
Restarting a window calculation for each group with PARTITION BY
Sorting the result with ORDER BY
Deriving a new column from an expression and naming it with an alias

Hint and approach

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

Show the hint

LAG monthly profit

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 order_items table.
2. Join orders on order_items.order_id = orders.id.
3. Join products on order_items.product_id = products.id.
4. Group the rows by order_items.product_id and month (the year-month of orders.order_date), so the result has one row for each combination.
5. For each group, compute SUM((products.price - products.cost) * order_items.quantity).
Fetch profit from the previous row with LAG(), ordered by month, restarting for each product_id. Where there is no such row the value is NULL.
Combine those values into growth: profit - LAG(profit) OVER (PARTITION BY product_id ORDER BY month).
Sort the result by growth descending.

Model solution

Checking your session…

What to watch for

LAG(). LAG() returns NULL for the first row of each partition, and any arithmetic with that NULL is NULL too. Supply a default as the third argument if you need a number.
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.