Convex Real-Time Backend: The Complete Developer Guide

I've built real-time features the hard way. PostgreSQL triggers piped into a WebSocket server. Redis pub/sub with custom serialization. Firebase with its quirky security rules and data modeling limitations. Each one worked. Each one was months of infrastructure work before we could build actual features.

Convex makes all of that disappear.

Not by hiding complexity behind a simple API that breaks when you scale. By actually solving the underlying problems in a fundamentally different way. Your data changes. Every client subscribed to that data gets the update. Automatically. No WebSockets to manage. No cache invalidation to debug. No eventual consistency headaches at 3 AM.

Schema Design That Doesn't Fight You

Convex uses a document-oriented data model. If you've used MongoDB, the shape is familiar. If you've used SQL databases, the mental shift is smaller than you think. Documents are JSON-like objects stored in tables. References between documents use IDs instead of JOINs.

But here's where Convex differs from MongoDB and every other document database. Your schema is defined in TypeScript. With real types. And Convex enforces those types at write time.

You define your tables with TypeScript types. Users table with name as string, email as string, role as a union of "admin" or "member." Messages table with typed references to users and channels, indexed by channel and timestamp. Every field is typed. Every index is declared.

Try to write a number to the name field. Convex rejects it. Try to set role to "superadmin" when your schema only allows "admin" or "member." Rejected. These aren't runtime surprises in production. They're immediate errors during development.

The TypeScript schema also powers IDE autocomplete throughout your entire codebase. Query results are typed. Mutation arguments are typed. Your frontend components know exactly what shape the data will be. No guessing. No runtime type assertions. No "any" scattered across your codebase because you lost track of what your API returns.

Queries That Stay Alive

This is Convex's defining feature. Every query is a subscription.

In a traditional backend, you fetch data once. If the data changes, your UI is stale until the user refreshes or you implement some polling mechanism. Adding real-time updates means wiring up WebSockets, maintaining connection state, handling reconnection, and figuring out which clients need which updates.

In Convex, you use a query. The client gets the result. When any data that query touches changes, the client automatically gets the updated result. That's it. No WebSocket code. No subscription management. No "hey server, I'm interested in these changes."

A chat application illustrates this perfectly. Your query fetches messages for a channel. When someone posts a new message, every client viewing that channel sees it instantly. Not because you wrote real-time code. Because all Convex queries are real-time by default.

The reactivity is granular. If a user's name changes, only queries that include that user's name re-run. If a message is edited, only queries that include that message re-run. The system doesn't blast every client with every change. It calculates the minimum set of updates needed and delivers exactly those.

For complex dashboards with dozens of data points, this granularity matters enormously. Each widget on the dashboard subscribes to its own query. A change in sales data doesn't trigger a re-render of the inventory widget. Everything stays independent and efficient.

Mutations With Guarantees

Data writes in most backends are optimistic prayers. You send the request. You hope it works. You update the UI assuming it did. If it failed, you roll back the UI and show an error. Maybe. If you remembered to handle errors.

Convex mutations are transactional. Every write operation runs inside a transaction. If any part of the mutation fails, everything rolls back. No partial writes. No orphaned records. No data corruption from concurrent updates.

Optimistic updates happen automatically on the client. When a user sends a message, the UI shows it immediately. The mutation runs on the server. If it succeeds, the subscription updates with the confirmed data. If it fails, the optimistic update gets removed and the UI reflects reality.

You don't build any of this. Convex handles the optimistic rendering, the conflict resolution, and the rollback logic. Your mutation function just describes what data should change. The platform handles how to make that change safely.

Retries are automatic too. If a mutation fails due to a transient error, Convex retries it. If two mutations conflict because they're modifying the same document simultaneously, Convex serializes them. One runs first, the other retries with the updated state. No lost writes. No silent failures.

Scaling Without Thinking About Scaling

The question every developer asks about new backend platforms is "will it scale?" For Convex, the answer is architectural.

Queries are pure functions. They read data and return results. No side effects. This means Convex can cache query results aggressively and invalidate precisely when underlying data changes. Your query doesn't re-run on every request. It re-runs only when its data changes.

Mutations are serialized per document. Two users editing different documents run in parallel. Two users editing the same document run sequentially with automatic retry. This provides strong consistency without global locking.

The practical result is a backend that handles thousands of concurrent users with real-time updates without you configuring a single scaling parameter. No replica sets. No sharding configuration. No read replicas for query performance.

Will you eventually need to think about performance? Maybe. But "eventually" is much further away than with traditional architectures. And when you get there, the optimizations are about writing better queries, not managing infrastructure.

For most applications, Convex's default behavior just works. And "just works" is the most underrated feature in all of software engineering.