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Usually, live cursor positions do not need a durable message history; notifications whose loss could leave an application wrong or a user unaware of an important event do. Choose the delivery model per event: use ephemeral fan-out for replaceable state, and persistence plus acknowledgement and replay—or an authoritative way to resynchronize—for events that must survive disconnects. A realtime connection alone does not guarantee that missed messages will be delivered later.
What durability means for a collaborative app
A cursor coordinate describes a moment: where a user’s pointer or selection is now. If a client misses one coordinate, a newer position can often replace it. A durable notification is different when it records a consequential event—such as a change the user must see or an action another component must process. Losing it may leave the application or its users out of sync.
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This distinction is an architectural inference, not a universal rule for every collaboration product. Decide whether an event is safely replaceable by newer state, must be processed once for its effect, or must remain available for a disconnected client. For some products, rebuilding the current state from an authoritative snapshot is sufficient; others need a replayable event history.
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Ask what a reconnecting client must recover
- If only the latest cursor position matters, sending a fresh position or rebuilding current presence can be more useful than replaying every old coordinate.
- If a missed event can cause a user-visible or application-level error, define how the client obtains that event or detects and repairs the resulting state gap.
- If every historical transition matters, retain an event history and establish how consumers resume from it.
Live channels and durable logs are different tools
“Realtime” describes how quickly a message can reach connected clients; it does not, by itself, describe whether messages are stored, acknowledged, or replayed. A live channel may provide fast best-effort fan-out while a subscriber is connected, but leave no history for a subscriber that was offline. A persisted stream can retain entries for later consumption, but recovery still depends on retention, consumer position, and acknowledgement behavior.
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Redis documentation describes Redis Pub/Sub as at-most-once delivery: a subscriber that cannot receive a message loses it. Core NATS likewise delivers to currently connected subscribers and does not replay messages missed while a subscriber is offline. These models can suit transient updates, but neither is a durable event log on its own. See the official Redis Pub/sub documentation and NATS documentation, accessed 2026-10-07.
How the main options differ
| Option | Persistence and replay | Delivery or recovery behavior | Reasonable fit | Important limitation |
|---|---|---|---|---|
| Redis Pub/Sub | No message history. | At-most-once; subscribers that cannot receive a message miss it. | Presence and transient cursor updates where a fresh state can replace missed updates. | Alone, it cannot recover events missed during a disconnect. Source: Redis, Redis Pub/sub, accessed 2026-10-07. |
| Socket.IO Redis adapter | The adapter does not store packets in Redis; it forwards through Pub/Sub. | When Redis disconnects, packets can still reach clients connected to the current server, but cross-node forwarding is lost. | Socket.IO deployments where cross-node fan-out is useful and missing transient packets is acceptable. | Sticky sessions are required; the cited adapter documentation does not support connection-state recovery. Source: Socket.IO Redis adapter documentation, accessed 2026-10-07. |
| Redis Streams | Stores ordered entries that can be read later; consumer groups track consumer progress. | Acknowledgements and pending-entry recovery support at-least-once processing patterns. | Retained event history and consumers that need to work independently. | Retention and trimming must not remove entries that consumers still need to recover. Source: Redis Streams documentation, accessed 2026-10-07. |
| Socket.IO Redis Streams adapter | Uses Redis Streams for inter-server forwarding; the stream is bounded by configuration. | Can resume after a temporary Redis disconnection. | Socket.IO deployments that need recovery after temporary disconnects and connection-state recovery. | The cited documentation gives a default maxLen of 10,000; recovery depends on the missed packets remaining in the stream. Verify the deployed package version and configuration. Source: Socket.IO Redis Streams adapter documentation, accessed 2026-10-07. |
| Core NATS | No persistence or replay in Core NATS. | At-most-once delivery to active subscribers; offline subscribers miss messages. | Fast transient messaging and request paths where disconnected subscribers do not need a later copy. | Missed messages are not replayed. Source: NATS documentation, accessed 2026-10-07. |
| NATS JetStream | Configurable memory or file storage, retention, replication, and replay. | Acknowledgements and consumer state support at-least-once delivery patterns. | Durable streams and producer/consumer lifetimes that should be decoupled. | Storage, replication, retention, and acknowledgement choices determine what survives a given failure. Source: NATS JetStream documentation, accessed 2026-10-07. |
Choose a delivery model for each event
Use ephemeral delivery for replaceable cursor state
If the product only needs a collaborator’s current location, old coordinates usually have less value than the newest one. A best-effort channel can be appropriate when reconnecting clients can receive a fresh position or rebuild presence state, and when the product can tolerate a temporary stale or missing cursor during a disruption. This is a design choice to validate against the experience the product promises, not a prescribed architecture from Redis or NATS.
Persist notifications that must outlive a connection
For a notification whose loss matters, persist the event before treating publication as successful. Decide how the consumer acknowledges work and where a reconnecting consumer resumes. A persisted stream can provide replay; an authoritative state snapshot can instead repair a gap when historical replay is not necessary. Redis Streams documents consumer groups, pending entries, and acknowledgements; JetStream documents publish acknowledgements and consumer replay. The right recovery path depends on whether the application needs the event itself or only the state that results from it.
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Plan for duplicate delivery and retention limits
Make retries safe
At-least-once delivery is not a promise that a handler runs only once. A consumer may complete work but fail to acknowledge it, so the message can be delivered again. Give consequential events stable IDs and make handlers idempotent or deduplicate processing where repeating an effect would be harmful. This is especially important when a retry could repeat a user-visible action.
Set a recovery window deliberately
A stream’s existence does not guarantee that every old entry remains available. Choose limits for message age, size, or count according to the longest outage or reconnect period the product promises to cover. Understand whether trimming can remove entries before a consumer recovers them, and what the system does when its configured limit is reached. In JetStream, storage, replication, and retention settings affect durability; in Redis Streams, trimming policy affects replay availability. These are configuration-dependent behaviors, so “durable” should always be qualified by the failure the system is expected to withstand.
Socket.IO: check which Redis adapter is in use
The standard Socket.IO Redis adapter and the Redis Streams adapter do not have the same recovery behavior. The standard adapter uses Pub/Sub, stores no packets in Redis, requires sticky sessions, and does not support connection-state recovery in the cited documentation. The Redis Streams adapter can resume after a temporary Redis disconnection and its documentation supports connection-state recovery, but only while the missed packets remain retained. Check the actual adapter package, version, and stream settings in the deployed application rather than inferring behavior from the fact that Redis is present.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test the failures the product promises to handle
Write down the expected outcome for each event type before testing. Then exercise the relevant failure boundaries in a staging environment:
- Disconnect a subscriber. Reconnect it and confirm whether the expected behavior is a fresh cursor snapshot, replay of missed events, or an explicit resynchronization.
- Interrupt the broker connection. Check what reaches clients on the same application server and what must cross to clients on other servers.
- Restart a broker node. Confirm which stored messages and consumer positions remain available under the configured persistence and replication settings.
- Exceed the retention limit. Verify what entries are removed and whether a consumer that was offline longer than the recovery window can still catch up.
- Resume from both a saved cursor and a fresh snapshot. Check for gaps, duplicates, and inconsistent state, including the case where a handler finished but its acknowledgement did not.
These are validation scenarios, not results of a product test. Delivery behavior, adapter support, and defaults can change by release and configuration; official Redis, NATS, and Socket.IO documentation referenced here was accessed 2026-10-07.
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