HardSQL challenge #272WINDOW FUNCTIONGROUP BYRANKING

Top 10% Users by Spend

Find users in top 10% by total 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 in top 10% by total spend.

A correct answer returns 12 rows with 3 columns: user_id, spend, bucket.

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_idspendbucket
11815384.261
7714593.171
12412059.321
9811837.521
5411780.5299999999991

First 5 of 12 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

  • Filtering rows with a WHERE condition
  • 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
  • Splitting rows into equal-sized buckets with NTILE()

Hint and approach

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

Show the hint

NTILE(10)

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, so the result has one row for each value.
    3. For each group, compute SUM(total).
    4. Deal the rows into 10 equal-sized buckets with NTILE(10), ordered by SUM(total) descending.
  2. Filter in WHERE: keep the rows where bucket equals 1. The filter sits in the outer query because a window function cannot be tested in the WHERE clause of the query that computes it.

Model solution

Checking your session…

What to watch for

  • NTILE(). NTILE() balances the number of rows per bucket, not the values. Rows with the same value can end up in different buckets.
  • 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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