Session state needs versions, ordering, and retry semantics across concurrent requests

A session record might contain recent item IDs, interaction summaries, exposure exclusions, and a revision. Decide whether it is session-local or shared across a user's devices; these are different consistency domains. Attach a timestamp and a defined ordering rule to events rather than assuming arrival order equals user action order. A request should record the state revision and compatible feature, model, and index artifacts it used. That makes a later replay explainable even if the current session has already advanced or the serving model has changed.

A revision orders accepted state transitions in a particular store; it is not a universal clock, a user action timestamp, or proof that all prior events arrived. A monotonic producer sequence can help order one producer's events but does not automatically order two devices. Choose whether late interactions are appended, merged, reordered, or rejected under a documented policy. A seen-item cache that expires can legitimately permit an old item to reappear, so distinguish a soft freshness preference from a durable every-session exclusion guarantee.

Two writers can read revision v and each construct a replacement. An unconditional last write can erase the first writer's update. An atomic compare-and-set or equivalent transaction rejects the second stale write, which can reload and reapply its intended event. HTTP strong validators with a correctly implemented If-Match precondition express a related lost-update protection at a resource boundary. Actual stores and transaction APIs differ, so verify their supported atomic scope rather than assuming that a separate read followed by a separate check is sufficient.

Conflicts are not the same as duplicate delivery. A client may lose the successful response and retry an event that already committed. Use a stable scoped event identity and atomically record its application with the state change when exactly-once logical application is required within that scope. Retention and expiry limit the deduplication promise. Updating session state and publishing a feedback event to another system can still have a failure gap unless a defined transactional or outbox protocol connects them. A successful local CAS does not automatically solve distributed side effects.

Bound recent-history length and storage while documenting what truncation changes. A fallback request with missing session state may produce a valid generic slate but should record that degraded context instead of pretending it used the full history. Measure conflict retries, stale reads, unavailable state, and duplicate suppression by cohort. Evaluate the effect on recommendation quality and latency together: strong coordination can add delay, while stale state can repeat items or misinterpret a recent action. The right tradeoff follows the declared user-visible semantics rather than a generic promise of perfect consistency.

The Markov lab shows version-independent mathematical transitions rather than a session store. Reset, set both p=0.25 for 0→1 and q=0.25 for 1→0, predict initial probability of state 1 zero and probability 0.25 after one Step, then inspect. A second Step gives 0.375. Step advances one transition because it adds 0.5 seconds. The model starts at state 0 and computes exact distribution probabilities, not sampled sessions. It has no event IDs, CAS, concurrency, expiry, or storage revisions; it illustrates a transition rule only and does not validate a session consistency design.