HardSQL challenge #296WINDOW FUNCTIONPARTITIONPERCENTAGE

Calculate Each Order Line's Share of Daily Sales

Return every order line with the percentage of that day's sales it represents.

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

Solve This SQL Challenge

The task

Find each product’s contribution to daily sales.

A correct answer returns 496 rows with 3 columns: order_date, product_id, pct.

Tables used in this challenge

`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
order_date	product_id	pct
2022-01-06	10	69.55579219593824
2022-01-06	83	30.44420780406176
2022-01-10	48	100
2022-01-15	95	30.424274086283976
2022-01-15	24	37.70577938708292

First 5 of 496 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

Combining rows from two tables on a matching key with an inner join
Attaching a total or average to every row with an aggregate OVER ()
Restarting a window calculation for each group with PARTITION 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

SUM OVER partition by date

Show the step-by-step approach

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

Read from the orders table.
Join order_items on orders.id = order_items.order_id.
Compute SUM(order_items.subtotal) as a window function over each orders.order_date, so the group's value is repeated on every one of its rows.
Combine those values into pct: order_items.subtotal * 100.0 / SUM(order_items.subtotal) OVER (PARTITION BY orders.order_date).

Model solution

Checking your session…

What to watch for

SUM(...) OVER (PARTITION BY order_date) is the day's total repeated on each line. A product sold on two lines the same day appears twice with two shares; group by product first if you need one row per product.

Window aggregates. A window aggregate keeps every row, while a plain aggregate collapses them. Mixing the two in one SELECT without GROUP BY is an error.
PARTITION BY. PARTITION BY is not GROUP BY: it keeps every row and only decides which rows a window function looks at.
INNER JOIN. An inner join drops rows that have no partner on the other side, and it repeats a row once for every match, which inflates SUM and COUNT.