How many concurrent connections can a single Apinator node handle?

This depends on your server resources and event volume. A single Go process can realistically handle 10,000-50,000+ idle WebSocket connections. With frequent event publishing, CPU and Redis throughput become the limits. Horizontal scaling (multiple data plane nodes) is the standard path beyond that.

Do I need sticky sessions with Apinator?

Yes, within a region. Each client maintains a persistent WebSocket connection to one node. Your load balancer must use sticky sessions (e.g., session affinity by IP or cookie) so reconnects land on the same node, or a node that can receive their connection state. Redis pub/sub ensures events reach all nodes regardless.

How does Apinator handle presence at scale?

Presence state (who is in a channel) is stored in Redis hashes, not in-process memory. This means presence is consistent across nodes — any node can answer "who is online?" by reading Redis. Heartbeats update a sorted set; stale members are culled periodically.

What is the latency impact of the Redis pub/sub hop?

For intra-region delivery (client and node in the same region), the Redis pub/sub hop adds less than 1ms in a co-located setup. The total end-to-end latency from server publish to client receipt is typically under 10ms within a region. Cross-region delivery depends on your network.

Can Apinator handle multi-region with data residency requirements?

Yes. Each regional data plane is independent — it only stores presence data in its own Redis and serves clients in that region. If your data must not leave the EU, your EU data plane never forwards payloads to other regions. Each region has its own pub/sub loop.

How WebSockets Scale at Production

The architecture behind distributed WebSocket infrastructure — from a single server to multi-region.

Scaling WebSockets is fundamentally different from scaling stateless HTTP. Persistent connections, state on the connection, and the need to broadcast to thousands of clients create specific architectural requirements. This is how Apinator solves them.

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The Sticky Session Problem

Each WebSocket client maintains a persistent connection to one server node. Standard round-robin load balancing breaks subscriptions. Sticky sessions (or a pub/sub layer) are required.

Redis Pub/Sub Fan-Out

When you publish an event, Apinator fans it out to all nodes via Redis pub/sub. Each node delivers to its locally-connected subscribers. No direct node-to-node communication needed.

Ref-Counted Subscriptions

Apinator subscribes to a Redis channel only when the first local client joins. It unsubscribes when the last client leaves. This minimizes Redis traffic at scale.

Zero-Database Data Plane

WebSocket nodes (data plane) have no PostgreSQL dependency. Tenant config is cached in Redis. Adding more nodes is instant — no schema migrations, no shared state.

Multi-Region Topology

Deploy independent data planes per region. Users connect to the nearest node. Publish once to any region — the event reaches all regions via their own Redis pub/sub.

Presence at Scale

Presence tracking uses Redis hashes (member state) and sorted sets (heartbeat timestamps). Stale members are evicted automatically, even across server restarts.

Publish Across All Regions

One trigger() call delivers the event to every connected client in every region — via Redis pub/sub fan-out on each data plane.

publish.js
import { ApinatorServer } from '@apinator/server'

// Control plane — single point of configuration
const apinator = new ApinatorServer({
  appId: 'your-app-id',
  key: 'api-key',
  secret: 'api-secret'
})

// Publish once → fans out to all connected clients across all nodes
await apinator.trigger('prices', 'tick', {
  symbol: 'AAPL',
  price: 182.45,
  timestamp: Date.now()
})

// Internally: Redis PUBLISH realtime:{app}:prices → all subscribed nodes
// Each node delivers to locally connected clients in-process
// No direct node-to-node traffic, no broadcast storms

Frequently Asked Questions

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