Find products that have never been ordered. Return their names.
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Find products that have never been ordered. Return their names.
This is a canonical anti-join pattern. In production reporting, you often need to identify customers with no purchases, features with no usage, or catalog items with no transactions. The SQL shape is the same: keep the master set and remove anything that already has a matching fact row.
idnameorder_idproduct_idSELECT
p.name
FROM products p
LEFT JOIN order_items oi
ON p.id = oi.product_id
WHERE oi.product_id IS NULLThis is the most explicit anti-join form. The LEFT JOIN preserves every product, and the NULL check keeps only those products that found no matching order rows.
SELECT
name
FROM products
WHERE id NOT IN (
SELECT product_id
FROM order_items
)The subquery version is short and readable for small clean datasets. It works well when the child table column is guaranteed not to contain NULLs and you want a compact answer shape.
The main trap is using NOT IN against a subquery that can return NULL. In that case the comparison can turn unknown and eliminate every row. The LEFT JOIN / IS NULL pattern is generally safer and easier to debug.
NOT IN can behave unexpectedly when the subquery includes NULLs. LEFT JOIN with an IS NULL filter expresses the anti-join directly and avoids the NULL-comparison trap.
No. Duplicates do not matter for the anti-join logic because the question only asks whether at least one matching order exists, not how many.
Use these guides when you want the fuller explanation behind the SQL pattern this challenge is testing.
Unlock the power of nested queries. Learn when to use subqueries in SELECT, WHERE, and FROM clauses with practical, runnable examples.
Understand the difference between EXISTS and IN in SQL. Learn which performs better and avoid the NOT IN NULL trap.
Stop guessing what JOINs do. Learn Inner, Left, and Cross Joins with live, interactive examples and animations running right in your browser.
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