A case review of how incorrect join assumptions duplicate records and distort revenue aggregation logic.
SELECT user_id, SUM(amount) AS total_spent
FROM orders
INNER JOIN users ON orders.user_id = users.id
WHERE status = 'Active'
GROUP BY user_id;
-> Hash Aggregate (user_id)
-> Hash Join (orders.user_id = users.id)
-> Filter (users.status = 'Active')
-> Seq Scan on users
-> Seq Scan on orders
A case where duplicated orders inflated the final aggregation due to a bad join. When joining orders to shipment events without grouping, rows multiplied, causing the SUM(amount) to reflect double or triple the actual transaction totals.
Often in production data environments, developers default to simple joins without reviewing the row multiplicity. When duplicate keys exist or table joins do not represent direct dependencies, metrics like revenue and active user count become inflated.
Always verify the primary keys of joined datasets and execute aggregate checks before using nested layouts. Improperly scoped aggregation levels will distort downstream BI dashboard reports.
Discussion & Reviews
ReviewerX
Verified MemberJoins can be tricky if assumptions are wrong.
SqlFan
SQL SpecialistNice explanation of the hidden duplicates.
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