HardSQL challenge #348GROUP BYSTATISTICSANALYSIS

Measure Monthly Revenue Volatility per Product

Return each product with the variance of its monthly revenue, most volatile first.

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.

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The task

Find products with highest variance in monthly revenue.

A correct answer returns 119 rows with 2 columns: product_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

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
idorder_idproduct_idquantityunit_pricesubtotal
11765192.21961.05
21823107.1321.3
316515.395.39

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
product_idvariance
831058473.4246888892
7829500.1563039998
35816678.9905555558
17816105.1953888888
38814291.5662246913

First 5 of 119 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
  • Combining rows from two tables on a matching key with an inner join
  • 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 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 order_items table.
    2. Join orders on order_items.order_id = orders.id.
    3. Group the rows by order_items.product_id and month (the year-month of orders.order_date), so the result has one row for each combination.
    4. For each group, compute SUM(order_items.subtotal).
  2. Group the rows by product_id, so the result has one row for each value.
  3. For each group, compute AVG(revenue*revenue) and AVG(revenue).
  4. Calculate variance from those aggregates: AVG(revenue*revenue) - AVG(revenue)*AVG(revenue).
  5. Sort the result by variance descending.

Model solution

Checking your session…

What to watch for

Variance has to be taken over monthly totals, not over single order lines, so the monthly sums are built in a derived table first. Months with no sales are missing rather than zero, which understates volatility for products that sell rarely.

  • Derived table. Columns computed in the inner query are only visible outside under their alias, so every calculated column there needs a name.
  • 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.
  • 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.
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Same technique, other tables

More practice on this pattern with different columns:

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