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A shared correlation key carried from realtime ingress into queued work gives your presence handler a name for the update history it must protect. To survive reconnects safely, pair that key with a connection generation and a monotonic sequence, then reject stale events when applying state. The key does not provide global ordering, exactly-once delivery, or accurate presence by itself.
What one key buys during a reconnect
A key such as roomID:playerID:sessionID scopes a history: it tells the state writer which updates may supersede one another. The session component should remain stable through transport recovery only while the application considers the reconnect part of the same logical lifetime. If the application declares a replacement connection, mint a new generation.
This is more precise than ordering every event in a room. A per-session scope lets independent player sessions proceed concurrently, while giving the writer a boundary for deciding whether a delayed heartbeat or disconnect can still affect that session.
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Keep the identifier bounded and opaque. Region, server address, and online status are mutable facts, so represent them as event fields rather than embedding them in the key. Using only a player ID can also accidentally place multiple tabs or superseded connections in the same ordering domain. Long, high-cardinality identifiers add cost to queue records, logs, indexes, and metrics.
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Use generation and sequence to reject stale updates
The key names the history; it does not tell you which event is newest. Include a generation to identify a replacement connection and a sequence to order events within that generation. A newer generation supersedes an older one, allowing a late disconnect from the old connection to be rejected. Within the current generation, accept only events whose sequence is greater than the stored sequence.
An event envelope can contain a correlation key, room ID, player ID, generation, sequence, event kind, and observation time. The fields answer different questions: the key says which history, generation says which replacement, and sequence says which update is newer inside that generation. Observation time can help measure latency or apply expiry rules, but clocks and delayed delivery mean it cannot replace generation or sequence.
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Apply the comparison with the state write
- Read or conditionally target the stored generation and sequence for the presence state being updated.
- Reject the event if its generation is older than the stored generation.
- If the generations match, reject the event if its sequence is less than or equal to the stored sequence.
- Apply the event and record its generation and sequence as one serialized or conditional state change.
Sequence allocation needs its own rule. If producers can allocate concurrently, use one allocator or another safe allocation strategy. If consumers can process the same key concurrently, serialize compare-and-apply or use conditional writes; otherwise two workers can pass comparisons against stale state and commit in the wrong order.
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| Ordering scope | What it protects | Trade-off |
|---|---|---|
| Per connection lifetime or session key | One player-session history | Independent sessions can proceed concurrently; correctness depends on generation lifecycle and sequence enforcement. |
| Room-wide key | Activity grouped at room scope | Can serialize more work than necessary and still requires a rule for ordering updates from different players. Combined room snapshots may need a separate room-revision rule. |
| Global key | A total order across unrelated sessions | Creates a broad serialization point and reduces parallelism; use only when the business rule truly requires that total order. |
For ordinary player presence, order the history that can overwrite the same player. Do not infer a room-wide order merely because a room count or room snapshot is visible to several players.
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What queue and Pub/Sub delivery do—and do not—guarantee
Redis’s Go job-queue guide describes at-least-once processing, worker recovery, and retry controls. A handler may therefore see the same job more than once. Make state application idempotent, for example by rejecting an already-applied generation-and-sequence pair. Recovery helps stuck work run again; it does not establish exactly-once effects.
Redis Pub/Sub is a different mechanism. The official Go Pub/Sub guide states: “At-most-once delivery: subscribers that are offline when a message is published miss it, so durable state should live in keys or a Stream, not in the pub/sub channel itself.” Pub/Sub can notify online subscribers about a presence change, but it should not be the durable record from which presence is reconstructed.
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For heavier handlers, the guide recommends handing work to a worker pool, buffered job channel, or Redis Streams consumer group. Avoid unbounded subscriber buffers: if downstream work falls behind, queued in-memory messages can exhaust process memory. The guide also recommends clear queue prefixes and separate queue names for independent work.
Keep connection state, presence, and matchmaking distinct
A transport reconnect or disconnect is an input to the application’s presence state machine, not proof that a player is present or absent. The application decides when a connection transition changes presence and when an expiry or replacement should take effect. A room-count mismatch during a reconnect storm can occur when an older disconnect or heartbeat is applied after a newer reconnect, or when stale heartbeat work keeps an expired generation alive. The count shows a symptom, not which event caused it.
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Useful operational signals include the rate of rejected stale updates by room and operation, and the age of the oldest unapplied presence event. These help distinguish stale-work problems from delays in processing; they are diagnostic suggestions, not performance benchmarks.
Presence ordering is not matchmaking queue correctness. Redis’s matchmaking tutorial, dated March 25, 2026, demonstrates sorted sets for ordered queues, hashes for player metadata, and JSON room-state keys with TTL. Its join flow uses WATCH/MULTI optimistic locking to avoid double-matching players under concurrent joins. Those structures address distinct responsibilities; a correlation key for presence does not replace them. The tutorial’s 30-minute room TTL is an example default, not a universal production setting.
Likewise, Redis data structures should be chosen by role: a sorted set can represent ordering, a hash can hold player metadata, an expiring JSON string can represent room state, and Pub/Sub can carry transient notifications. None alone constitutes the complete presence and matchmaking model.
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