HardSQL challenge #324WINDOW FUNCTIONDATEAGGREGATION

Calculate Week-over-Week Revenue Change

Return each week with the change in revenue compared with the previous week.

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.

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The task

Calculate weekly revenue growth.

A correct answer returns 108 rows with 2 columns: week, growth.

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
iduser_idtotaldiscountitem_countstatusorder_dateshipped_dateshipping_cityshipping_country
1432638.110.253cancelled2024-04-24NULLLos AngelesUSA
2829470.195delivered2024-03-152023-04-09ChicagoUSA
3136683.240.082delivered2023-02-152023-07-10Los AngelesUSA

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
weekgrowth
2022-01NULL
2022-026620.74
2022-03-149.4399999999987
2022-04-3619.5300000000007
2022-05-3708.8900000000003

First 5 of 108 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 rows with GROUP BY to get one result row per group
  • 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()
  • 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

strftime week + LAG

Show the step-by-step approach

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

  1. Build an inner query first (a derived table):
    1. Read from the orders table.
    2. Group the rows by week (the year-week of order_date), so the result has one row for each value.
    3. For each group, compute SUM(total).
  2. Fetch revenue from the previous row with LAG(), ordered by week. Where there is no such row the value is NULL.
  3. Combine those values into growth: revenue - LAG(revenue) OVER (ORDER BY week).

Model solution

Checking your session…

What to watch for

strftime('%Y-%W') labels each date with its year and week number, with weeks starting on Monday. Weeks without orders have no row, so the previous row is the last week that had sales.

  • 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.
  • Derived table. Columns computed in the inner query are only visible outside under their alias, so every calculated column there needs a name.
  • GROUP BY. Every selected column must either be in GROUP BY or sit inside an aggregate. SQLite accepts other columns and returns an arbitrary value for them; most databases raise an error.
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