convex-scale-optimization

What problem does it solve?

Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.

Core Features & Use Cases

• One-shot fetches for public pages to replace reactive subscriptions
• Digest tables to denormalize data for hot read paths
• Change detection to avoid unnecessary writes and thundering herd
• Compound indexes and query optimization to reduce data scanned
• Rate-controlled backfills and split mutations for scalable backfills
• Quick audit and safe deployment guidance with Convex Insights

Quick Start

Run an assessment with convex insights to identify top bandwidth consumers and implement a targeted optimization cycle.