convex-performance-patterns

Convex performance optimization improves read traffic throughput by applying denormalization strategies. This technique pre-computes and stores related data together, reducing expensive join operations during peak load.

Fixing N+1 query patterns in Convex involves restructuring database calls to batch nested reads. This Skill identifies inefficient loops and provides guidelines for consolidating them into single optimized queries.

Designing Convex indexes to avoid filter scans requires placing indexed fields before filtered fields in the schema. This approach prevents full table scans and ensures queries efficiently locate relevant records.

Handling optimistic concurrency control (OCC) conflicts in Convex requires strategies for managing hot spots during high-contention writes. This Skill suggests solutions to retry or structure mutations that minimize collision rates.

Denormalization in a Convex database should be used when read performance is critical and outweighs write complexity. It enhances read performance by duplicating data to eliminate runtime joins and complex aggregations.

Troubleshooting Convex bottlenecks with this Skill requires no external dependencies. It operates as a standalone script component, guiding developers and DBAs in diagnosing and resolving performance issues directly.