Optimizing DBs at scale: how we reduced writes by 1000x for one of our most accessed tables

Logan Gore's blog post discusses how Stytch optimized its database operations by reducing unnecessary writes to one of its most accessed tables, effectively decreasing writes by 1000x. The optimization targeted the frequent operation of adding custom claims to user sessions, which previously led to numerous unnecessary database upsert operations. By implementing a logic to detect when incoming claims matched existing ones, the team avoided no-op updates, significantly reducing database workload and freeing up connections for more impactful tasks. This change resulted in a dramatic reduction in custom claim writes, lowered database CPU usage, fewer open connections, and improved response times, highlighting the importance of balancing code simplicity with strategic optimization for system scalability. The experience also emphasized the need for better performance monitoring tools to preemptively identify and address potential bottlenecks as the system scales.

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