Setup
npx convex login # the user approves in the browser
npx convex dev --once # creates the project + dev deployment, writes .env.local
npx convex env set KEY value # server secrets (dev); add --prod for production
npx convex deploy # production deploy
Keep two deployments: dev (break anything) and prod (real users). Never point local dev at prod data.
Write correct functions
- Every public
query,mutationandactionis callable by anyone who has the deployment URL. Check the user (ctx.author the Better Auth helper) and ownership inside each one; useinternalQuery/internalMutationfor anything only the server calls. - Validate every argument with
v.*validators. - Read with
.withIndex(...)on a selective index, never.filteron a large table. Define the index inschema.tsfor each access pattern (by_userId,by_courseId_order, ...). - Bound reads:
.take(n)or.paginate()before enriching rows; never.collect()a table that grows. - Return only the fields the client needs.
- Secrets live in Convex env vars, never in client code.
Optimize from live evidence
npx convex insights --prod --details
Extract function name, warning type, per-table docs/bytes, and whether it failed or only neared a limit. Prioritize high Documents Read with low Documents Returned.
Map each hot export to convex/** and its client callsites, then diagnose before patching:
.filter/.collectwithout a selective.withIndex, or an index that narrows only by tenant and still scans thousands of rows;- per-row related fetches (N+1); recount helpers on hot mutations;
- jobs or crons that scan every row on every run;
- concurrent patches on the same document (OCC conflicts).
Apply the smallest change: add the index used by the query; .take/paginate before enrichment; denormalize list previews and fetch full rows only on detail; incremental counters instead of full recounts; cursor + per-run limit for jobs; skip no-op patches and merge same-document writes; move high-churn state to a narrower table.
Verify with the project's typecheck, tests and build, plus npx convex codegen. Re-run insights after deploy and report: hot functions, root cause, change, expected read/byte/OCC impact, validation.