To address the uncertainty and delayed event interference caused by master node failures when the state application is completed but the response has not yet returned in the Raft payment order service, this paper proposes a bounded consistent replay mechanism, Raft-BRR. This mechanism, based on order-level request identity, introduces external event version context, dual representation of historical results and current state, version fencing, and a two-level result lifecycle to maintain request semantics under limited storage conditions. Experiments were conducted on a three-node prototype, including post-application response loss, cross-master node retries, old version events, and window sensitivity. Results show that Raft-BRR achieves a median TRSR of 96.4% and 93.8% in F1 and F2 scenarios, respectively, a dual-state response integrity rate of 95.7%, and zero old version event error acceptances. Compared to a strong idempotent caching baseline, its throughput decreases by 8.2%, and P95 latency increases by 19.5%. These results demonstrate that Raft-BRR can improve the interpretability and version security of the payment order state machine layer at a controllable resource cost.

Jiayue Hu. Research on Fault Tolerant Mechanism of Payment Order State Machine Based on Raft Consensus Algorithm. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 94-106. https://doi.org/10.38007/ML.2026.060111.

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