Case Review

Case 3: Duplicated Orders

A case review of how incorrect join assumptions duplicate records and distort revenue aggregation logic.

Author: Casey Smith
Published: 2026-06-28
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
Case 3 Duplicated Orders

Core Problem & SQL Analysis

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.

Why This Query Intent Fails

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.

Key Takeaway

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

ReviewerX

Verified Member
2026-06-27 • Query Reviewer

Joins can be tricky if assumptions are wrong.

SqlFan

SqlFan

SQL Specialist
2026-06-28 • DBA Analyst

Nice explanation of the hidden duplicates.

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