12 Years of Stock Register Architecture: Why AI and Textbook SQL Fall Short

Calculating inventory balances using standard SQL methods like SUM() or window functions becomes a serious performance problem at scale, as these approaches require scanning millions of rows on every query. Real-world business scenarios such as backdated entries, negative sales, and on-the-fly manufacturing further compound the complexity, often causing data integrity issues. A software engineer with 12 years of experience in high-load systems argues that AI assistants and junior developers tend to 'clean up' low-level database optimizations without understanding their purpose, leading to corrupted accounting data. The article outlines four core pain points in stock register design and explains the query optimizer behavior of MySQL and MariaDB. The author presents a Snapshot Log architecture that limits scanned data by up to 95% using date-range conditions, and has packaged these engineering principles into a paid AI system prompt intended to guide code generation tools.