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Scaling receivers vs implementing idempotency

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Audience observation: race conditions happen under load, which pushes receivers to scale out, which makes the atomic/central locking needed for idempotency harder to implement — so overloaded receivers are least likely to support idempotency. Is that the speaker's experience? Answer: yes, this is exactly CAP-theorem territory (consistency/availability/partitioning). The solution is sharding: use a deterministic function (e.g. a hash) so the same entity always lands on the same disjoint partition, which can then independently enforce atomicity. Not the only problem that will surface, but the canonical answer for this one.

answer_summary
Yes — it's the CAP theorem at work. Solve the partitioning dimension with sharding: route each entity to a deterministic partition via a hash function so disjoint partitions can enforce atomicity independently.
question Scaling receivers vs implementing idempotency
about
Sharding concept
Answer proposes sharding by hash function as the CAP-theorem response.
question Scaling receivers vs implementing idempotency
about
Idempotence concept
Question is about the tension between scaling and implementing idempotency.
question Scaling receivers vs implementing idempotency
asked_at
Third audience question in the Q&A.

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