convex-performance-audit

To diagnose slow reads and high mutation retries in Convex, gather health signals from insights or dashboards and map them to problem classes like hot-path reads or write contention. This guides targeted fixes for your specific bottlenecks.

Excessive reactive updates and subscription costs in Convex are caused by inefficient query designs that trigger unnecessary reactivity. Auditing subscription-related costs maps these signals to targeted optimizations using function budgets and subscription cost references.

Fix write contention and OCC conflicts in Convex by applying targeted optimizations from OCC conflict references. The audit maps contention signals to problem classes and guides you through implementing fixes while inspecting sibling functions for safety.

The best way to audit Convex performance bottlenecks is a signals-driven approach: trigger the audit from the strongest signal, map it to a problem class, implement fixes using reference guides, and validate one concrete user flow for regressions.

Yes, you can use this audit approach for Convex apps with hot-path read issues. The audit specifically diagnoses hot-path reads using reference-guided fixes and validates changes by inspecting sibling functions to ensure no regressions occur.

High mutation retries in Convex indicate write contention or OCC conflicts. Resolve them by mapping these signals to problem classes during an audit and applying targeted fixes from the OCC conflict references to reduce contention.