This is the kind of question where you think you know the answer and then the follow-ups expose every gap.
Start by framing the core problem: exactly-once semantics over an at-least-once network, achieved through idempotency keys and server-side deduplication. Walk through the end-to-end flow: client generates a unique key per logical charge, server stores it with the charge result, and retries with the same key return the cached result. Then address edge cases: concurrent requests, timeouts, and reconciliation, and close with UX and availability trade-offs.
Pro tip: Emphasize that idempotency keys must be generated by the client, not the server, and that the server should store the key with a unique constraint to handle races atomically. Also mention that you'd return the same response for duplicate requests, including the original charge ID, to make retries safe and transparent.
Explain that the client generates a unique idempotency key (e.g., UUID) for each logical charge and includes it in the request header. The server uses this key to deduplicate requests, ensuring that multiple identical requests result in only one charge.
Describe how the server maintains a deduplication table (or uses a unique index) mapping idempotency keys to charge results. On receiving a request, it attempts to insert the key; if it already exists, it returns the stored result instead of reprocessing.
Discuss using database transactions or locks to handle concurrent requests with the same key, ensuring only one charge is processed. For timeouts, implement a reconciliation process that checks the payment provider's status and updates the dedup table accordingly.
Explain that clients should retry with the same idempotency key on network failures. The server should have a background job to reconcile pending charges by querying the payment provider and finalizing the state, ensuring consistency even if the initial response was lost.
Highlight that the user should see a consistent state (e.g., 'processing' or 'completed') and not be charged twice. Discuss trade-offs: strict consistency might reduce availability, so use techniques like eventual consistency with reconciliation to keep the system available.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.