Start by framing the core challenge: ensuring exactly-once payment semantics in a distributed system with unreliable downstream providers. Then walk through a concrete design using idempotency keys, a state machine with reconciliation, and compensating actions for partial failures. Emphasize trade-offs between consistency and availability, and how you'd handle edge cases like timeouts and provider errors.
Pro tip: Mention that you'd treat the payment provider as untrusted and design for idempotency at every layer, including using a unique idempotency key per payment attempt that the provider honors. Also, highlight the importance of a reconciliation job that runs periodically to catch and resolve stuck payments, as this shows you think about long-term operational reliability.
Ask about the payment provider's capabilities (idempotency support, timeout behavior), the expected failure modes, and the business impact of double-paying or losing money. Confirm the need for exactly-once semantics and the acceptable latency for resolution.
Propose generating a unique idempotency key for each payment attempt and persisting a payment record with a state machine (e.g., INITIATED, PENDING, SUCCEEDED, FAILED, UNKNOWN). Ensure all state transitions are atomic and logged.
On timeout, do not assume failure; instead, mark the payment as UNKNOWN and trigger a reconciliation process. Use the idempotency key to query the provider for the actual status, and only update the state based on confirmed responses.
If a downstream provider partially fails (e.g., debits but doesn't credit), use compensating transactions (like refunds or credits) to restore consistency. Ensure these actions are also idempotent and tracked.
Run a periodic reconciliation job that compares internal payment records with provider reports, resolves discrepancies, and alerts on anomalies. Add monitoring for stuck payments and failure rates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.