The baseline system was handed to me, so the real challenge was figuring out the right data structures for the scheduling layer.
Start by clarifying requirements and constraints, then design a data model that stores scheduled payments with status and execution time. Discuss the execution mechanism (e.g., background worker or scheduler) and how to ensure atomicity and idempotency. Finally, analyze trade-offs and edge cases.
Pro tip: Emphasize idempotency and failure recovery: scheduled payments must not double-execute if the system crashes. Mention using a unique payment ID as an idempotency key and persisting state before execution.
Ask about expected scale, precision of execute_at, whether payments can be modified, and failure handling. Confirm that cancelPayment only works for pending payments.
Define a ScheduledPayment entity with fields: payment_id (unique), from_user, to_user, amount, execute_at, status (PENDING, EXECUTED, CANCELLED). Store in a durable database with an index on execute_at and status.
Use a background worker or scheduler that polls for due payments (execute_at <= now AND status = PENDING). For each, atomically update status to EXECUTED and perform the transfer, ensuring idempotency via payment_id.
cancelPayment(payment_id) should atomically check if the payment is PENDING and update status to CANCELLED. Return true if successful, false otherwise (e.g., already executed or cancelled).
Cover consistency (e.g., using transactions or two-phase commit), scalability (sharding by user or time), and failure recovery (retries, dead-letter queues). Address clock skew, time zones, and partial failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.