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N019-M2 Tier 2 · Core SQL · medium ecommerce · Brightlane

Return the category name for every category that has no products currently assigned to it

Part of FULL OUTER JOIN in SQL

The problem

Brightlane's catalogue team is auditing in the opposite direction now — looking at the categories side rather than the products side.

Write a query to return the category name for every category that has no products currently assigned to it.

Assumptions:

  • The products table contains every product in the catalogue.
  • The categories table contains every defined category.
  • A category is empty if no row in products has its id as category_id.

Output:

  • One row per empty category, with a single column category_name.
Schema · ecommerce 5 tables
categories
id integer
name text
parent_id? integer
products
id integer
name text
category_id integer
price numeric
stock_qty integer
attributes? jsonb
order_items
id integer
order_id integer
product_id integer
quantity integer
unit_price numeric
customers
id integer
name text
email text
city? text
country text
created_at timestamptz
is_active boolean
orders
id integer
customer_id integer
ordered_at timestamptz
status text
total_amount numeric
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Solution query
SELECT
  cat.name AS category_name
FROM
  categories cat
  FULL OUTER JOIN products p ON cat.id = p.category_id
WHERE
  p.id IS NULL

The shape

Flipping the audit direction means flipping which side's key the IS NULL filter targets. The outer join produces matched rows, orphan categories, and orphan products. WHERE p.id IS NULL keeps only the orphan-category rows — categories that no product carries as its category_id. For Brightlane that's Clothing and Home & Garden.

Clause by clause

  • SELECT cat.name AS category_name returns just the category name. The rest of the join's output is scaffolding for the filter.
  • FROM categories cat FULL OUTER JOIN products p ON cat.id = p.category_id is the reconciliation, written with categories on the left. The table position doesn't change what FULL OUTER JOIN does — both tables are preserved either way — but reading the query left to right with categories first keeps the analyst frame aligned with the question.
  • WHERE p.id IS NULL is the anti-join filter, this time targeting the products side. After the join, any category with no products carries NULL in every p.* column, including p.id. Matched rows have a real p.id and drop out. Orphan products (the kind that have no category) also have a real p.id and drop out — leaving only the empty categories.

Why filter on p.id and not on p.category_id

A learner might reach for WHERE p.category_id IS NULL instead. That filter looks similar but it's testing a different column. Some orphan products have a category_id that's explicitly NULL, and those rows would match the filter even though they're products, not categories. Filtering on p.id is the safe choice because id is the table's primary key — it's NULL only on rows the outer join padded in, never on rows that came from the products table. The rule generalises: the anti-join's IS NULL filter belongs on a column that can only be NULL because the join didn't match, never on a column that can be NULL in the source data.

You practiced isolating the other side's orphans from a FULL OUTER JOIN (or equivalent LEFT JOIN anchored on categories). The recurring rule: which side appears as NULL after the join depends on which side had no match, not on table position — the filter goes on whichever side's key should be missing.

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