HardSQL challenge #347WINDOW FUNCTIONPARTITIONRANKING

Cumulative Rank per User

Assign rank to each order cumulatively per user.

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

Solve This SQL Challenge

The task

Assign rank to each order cumulatively per user.

A correct answer returns 200 rows with 11 columns: id, user_id, total, discount, item_count, status, order_date, shipped_date, shipping_city, shipping_country, rnk.

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

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
id	user_id	total	discount	item_count	status	order_date	shipped_date	shipping_city	shipping_country	rnk
109	1	4064.75	0.04	3	delivered	2022-09-26	2023-09-19	Austin	USA	1
13	2	1618.29	0.23	3	delivered	2023-04-02	2024-04-02	Seattle	USA	1
87	4	2501.23	0	6	delivered	2022-08-22	2024-09-16	San Francisco	USA	1
181	4	843.7	0	5	shipped	2024-04-20	2023-01-18	Charlotte	USA	2
11	4	1518.36	0.09	1	processing	2024-11-15	NULL	Denver	USA	3

First 5 of 200 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

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

RANK partition

Show the step-by-step approach

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

Read from the orders table.
Rank the rows with RANK(), ordered by order_date, restarting for each user_id. 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.