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convex-scale-optimization

Optimize read-heavy Convex apps with digest tables, one-shot fetches, and indexing.

9|3|Updated Nov 28, 2025
Search Tags:#performance#denormalization#scale#bandwidth#read-heavy#digest-tables#query-costs
One-click install
npx skills add https://github.com/get-convex/components-submissions-directory --skill convex-scale-optimization-get-convex
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: convex-scale-optimization
Source: https://github.com/get-convex/components-submissions-directory/tree/main/.cursor/skills/convex-scale-optimization
Command: npx skills add https://github.com/get-convex/components-submissions-directory --skill convex-scale-optimization-get-convex

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Patterns and practices for scaling read-heavy Convex apps to millions of users, reducing bandwidth, lowering query costs, and decreasing latency while preserving data correctness.

Core Features & Use Cases

  • One-shot fetches for public pages to avoid thundering herd and unnecessary real-time updates.
  • Digest tables to denormalize hot reads and cut read volume.
  • Change-detection and split mutations to prevent unnecessary writes and cascading invalidations.
  • Compound indexes to push filtering into the database and minimize post-query JS filtering.
  • Rate-controlled backfills and backpressure strategies to spread load safely.

Quick Start

Run npx convex insights --prod to identify top bandwidth consumers, then apply the appropriate optimization pattern.

Frequently Asked Questions about convex-scale-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize Convex query bandwidth for read-heavy apps?▼

Reduce Convex query bandwidth by implementing digest tables for denormalized hot reads, applying one-shot fetches for public pages, and using compound indexes to eliminate post-query JS filtering.

What's the best way to scale a Convex app to millions of users?▼

Scale Convex apps by applying denormalization through digest tables, splitting mutations for change-detection, and enforcing rate-controlled backfills with backpressure to manage millions of users safely.

How do I prevent unnecessary writes and cascading invalidations in Convex?▼

Prevent unnecessary Convex writes and cascading invalidations by implementing change-detection logic and split mutations, ensuring queries only re-run when underlying data actually changes.

How do I identify top bandwidth consumers in my Convex app?▼

Identify top Convex bandwidth consumers by running `npx convex insights --prod`, which highlights the most expensive queries to target with digest tables and one-shot fetches.

Does this Convex optimization approach work for analytics dashboards?▼

Yes, these Convex optimization patterns work for analytics dashboards by using digest tables and compound indexes to lower query costs and decrease latency under high read loads.

When should I use one-shot fetches instead of real-time queries in Convex?▼

Use Convex one-shot fetches for public pages to avoid thundering herd issues and unnecessary real-time updates, significantly reducing bandwidth when live synchronization is not required.

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