MediumSQL challenge #188SUBQUERY

Highest Items per Day

Find order with highest item_count each day.

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 order with highest item_count each day.

A correct answer returns 185 rows with 10 columns: id, user_id, total, discount, item_count, status, order_date, shipped_date, shipping_city, shipping_country.

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
iduser_idtotaldiscountitem_countstatusorder_dateshipped_dateshipping_cityshipping_country
2829470.195delivered2024-03-152023-04-09ChicagoUSA
41153920.440.084processing2022-01-10NULLSan JoseUSA
51401120.990.134delivered2023-06-162023-12-21DenverUSA
6501327.010.085shipped2024-02-162024-12-13SeattleUSA
7691755.540.22refunded2023-03-22NULLPhiladelphiaUSA

First 5 of 185 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

  • Returning every column of a table with SELECT *
  • Filtering rows with a WHERE condition
  • 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

Hint and approach

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

Show the hint

Correlated subquery

Show the step-by-step approach

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

  1. Read from the orders table.
  2. Filter in WHERE: keep the rows where item_count equals the value returned by a subquery.
  3. The subquery is correlated: it refers to the current outer row, so it is evaluated for each row. It works like this:
    1. Read from the orders table.
    2. Filter in WHERE: keep the rows where order_date equals the outer row's order_date.
    3. Aggregate over all the rows that are left: compute MAX(item_count). With no GROUP BY, the result is a single row.
  4. Return every column with SELECT *.

Model solution

Checking your session…

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.
  • Aggregate functions. Aggregates skip NULLs. AVG divides by the number of non-NULL values and COUNT(column) can be smaller than COUNT(*).
  • WHERE. WHERE is evaluated before the SELECT list, so it cannot use a column alias defined there or the result of an aggregate.
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