HardSQL challenge #277SUBQUERYDATEROLLING WINDOW

7-Day Rolling Orders Count

Find the rolling 7-day order count for each order date, using a correlated subquery.

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

Find the rolling 7-day order count for each order date, using a correlated subquery.

A correct answer returns 182 rows with 2 columns: order_date, orders_7d.

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
order_dateorders_7d
2022-01-061
2022-01-102
2022-01-152
2022-01-163
2022-01-173

First 5 of 182 rows. This is the output of the model solution on the practice database.

What you practise

  • Filtering rows with a WHERE condition
  • Filtering a range of values with BETWEEN
  • Removing duplicate rows with DISTINCT
  • Summarising many rows into one value with COUNT, SUM, AVG, MIN or MAX
  • Writing a subquery that refers to the current row of the outer query
  • Querying the result of another query with a subquery in FROM
  • Working with dates in SQLite: strftime, julianday and DATE
  • Sorting the result with ORDER BY

Hint and approach

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

Show the hint

For each date, count the orders whose date falls in the 7 days ending on it

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. Return only the column order_date.
    3. DISTINCT removes duplicate rows, so each value appears once.
  2. A subquery in the SELECT list is evaluated for each output row, using values from that row:
    1. Read from the orders table.
    2. Filter in WHERE: keep the rows where order_date is between DATE(order_date, '-6 day') and the outer row's order_date, both ends included.
    3. Aggregate over all the rows that are left: compute COUNT(*). With no GROUP BY, the result is a single row.
  3. Sort the result by order_date.

Model solution

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What to watch for

  • Correlated subquery. A correlated subquery is evaluated per outer row, which gets slow on large tables. A join to a grouped subquery or a window function usually does the same work in one pass.
  • Derived table. Columns computed in the inner query are only visible outside under their alias, so every calculated column there needs a name.
  • Date functions. These date functions are SQLite syntax. MySQL uses DATE_FORMAT and DATEDIFF, PostgreSQL uses TO_CHAR, EXTRACT and date subtraction.
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