HardSQL challenge #370GROUP BYSTATISTICSANALYSIS

Monthly Spend Variance Users

Find users with highest variance in monthly spend.

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 users with highest variance in monthly spend.

A correct answer returns 114 rows with 2 columns: user_id, variance.

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
user_idvariance
304870319.3344
284319247.758400001
1224273171.137225
1244166789.388288887
163677841.7729

First 5 of 114 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 rows with GROUP BY to get one result row per group
  • Grouping by more than one column to get a row per combination
  • 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
  • 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

aggregate monthly then variance

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 user_id and month (the year-month of order_date), so the result has one row for each combination.
    3. For each group, compute SUM(total).
  2. Group the rows by user_id, so the result has one row for each value.
  3. For each group, compute AVG(total*total) and AVG(total).
  4. Calculate variance from those aggregates: AVG(total*total) - AVG(total)*AVG(total).
  5. Sort the result by variance descending.

Model solution

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What to watch for

  • 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 on several columns. Each extra GROUP BY column splits the groups further. Grouping by a column that is unique per row gives back one group per row.
  • 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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