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To run Socket.IO across multiple server processes, you need two separate pieces: session affinity so HTTP long-polling requests return to the process that owns the session, and a compatible adapter so broadcasts can reach clients connected to other processes. Redis Pub/Sub can provide the second piece; it does not provide the first, store application data, or guarantee that every event arrives.
Why a single-process Socket.IO setup stops working when you add nodes
Each Socket.IO process knows about its own connected clients and local adapter state. If a client on node A triggers a broadcast, node A can deliver it locally, but without inter-server communication it cannot reach matching clients connected to node B.
An adapter provides that communication path. With the Redis adapter, a server publishes broadcast packets to Redis Pub/Sub, and the other Socket.IO servers receive those packets and deliver them to their own matching local clients. The adapter page says it stores no Redis keys, so this is packet forwarding—not persistence for application state or event history. Socket.IO Redis adapter documentation
What a multi-node deployment needs
1. Affinity for HTTP long-polling
Long-polling uses multiple HTTP requests for a Socket.IO session. Those requests must reach the process that created the session. Configure the load balancer or proxy for session affinity when long-polling is enabled. The Redis adapter does not make session state interchangeable between processes: Socket.IO’s current adapter documentation warns that a request routed to a server unaware of the session can receive an HTTP 400 response. Socket.IO Redis adapter documentation
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Socket.IO’s multi-node guide gives the same routing requirement for long-polling. Socket.IO multi-node guide
2. An adapter for cross-node broadcasts
Affinity keeps a client’s requests with its owning process; it does not distribute broadcasts. Configure an adapter supported by your Socket.IO and Redis client versions if broadcasts must reach clients on other nodes. These solve different problems, so enabling one does not replace the other.
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3. A deliberate transport configuration
Socket.IO can use WebTransport, WebSocket, or HTTP long-polling; Engine.IO manages the transports and upgrade mechanism. Check the transports enabled by your server and clients rather than assuming every deployment has the same routing needs. In particular, do not infer that long-polling is absent simply because WebSocket is available. How Socket.IO works
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The Redis adapter uses Pub/Sub to relay broadcast packets among Socket.IO servers. Its documentation says it stores no Redis keys. If Redis connectivity is lost, packets are delivered only to clients connected to the current server; cross-node propagation stops. Treat Redis availability as part of the broadcast path and decide how your application should behave during an outage. Socket.IO Redis adapter documentation
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Socket.IO guarantees event ordering across its low-level transports, including during an upgrade from long-polling to WebSocket. Its default delivery guarantee is at most once. Correct ordering does not mean an event is persisted, retried, or guaranteed to arrive. If an application needs durable delivery or replay after disconnection, design that separately rather than treating the adapter as a message store. Socket.IO delivery guarantees
Choose an adapter against compatibility and recovery needs
For new development with Redis 7.0 sharded Pub/Sub, Socket.IO recommends the sharded adapter. The documented minimums are Redis 7.0 with redis@4.6.0 for the node-redis example, or Redis 7.0 with ioredis@5.9.0 for the ioredis example. The compatibility information lists Redis adapter 7.x and later with Socket.IO 4.3.1 and later. Verify the current compatibility table against the exact versions you deploy before rollout. Socket.IO Redis adapter documentation
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The same documentation says the Redis adapter does not support connection-state recovery. If clients must recover state after a temporary disconnection, confirm that the adapter you select supports the recovery behavior you need; do not assume the Redis adapter supplies it.
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The Redis adapter assumes Redis is trusted infrastructure. Its Pub/Sub messages are not signed, encrypted, or authenticated by the adapter. A party able to publish to adapter channels could inject packets or forged control messages; a party able to observe relevant traffic could inspect payloads. Keep Redis off untrusted networks and apply network isolation, access controls, authentication, TLS, firewall rules, and least-privilege credentials. Socket.IO Redis adapter documentation
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Plan capacity without assuming a universal per-process limit
Socket.IO identifies connected-client count and messages received and sent per second as major drivers of resource use, and says memory should scale linearly with connected clients. Its published measurements depend on the underlying WebSocket server implementation. The illustrated test context was Ubuntu 22.04 LTS, Node.js v20.3.0, socket.io@4.7.2, eiows@6.7.2, uWebSockets.js@20.33.0, and ws@8.11.0; those results are not a universal capacity promise for a different stack. Socket.IO memory usage
For a design review or incident diagnosis, check these dimensions independently:
- Transport and routing: Which transports are enabled, and does long-polling traffic have affinity?
- Broadcast path: Does the configured adapter relay events to every node that has relevant clients?
- Compatibility: Do the Socket.IO, adapter, Redis, and Redis client versions meet the documented requirements?
- Failure behavior: What should users see when Redis is unreachable and cross-node broadcasts stop?
- Delivery and recovery: Does the application need persistence, retries, replay, or connection-state recovery beyond at-most-once delivery?
- Security and operations: Is Redis isolated and access-controlled, and can the team monitor and maintain the added dependency?
The official materials cited here do not establish a universal client limit or a quantitative cost or latency comparison across adapters. Benchmark the intended deployment and workload rather than extrapolating a maximum from an implementation-specific memory chart.
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