Start by clarifying requirements and scope, then design a data model that captures payment states, scheduling, cancellation, and outcomes. Outline the system components and APIs, and discuss how to generate the required report efficiently. Finally, address edge cases, scalability, and trade-offs.
Pro tip: Emphasize idempotency and auditability in payment operations, as these are critical in financial systems. Also, proactively discuss how you would handle failures and retries to demonstrate reliability thinking.
Ask questions to understand constraints: expected volume, latency, consistency needs, and regulatory requirements. Define the core entities: payments, schedules, cancellations, and reports.
Model payments with states (scheduled, processing, succeeded, failed, canceled) and transitions. Include fields for scheduling, cancellation, and outcome reporting. Consider using an event-sourced or append-only ledger for auditability.
Specify endpoints for scheduling, canceling, and querying payments. Outline services: scheduler, payment processor, cancellation handler, and reporting service. Discuss how they interact and handle failures.
Design a query to aggregate outgoing transactions by total loss amount (e.g., sum of failed/canceled payments) and sort descending. Discuss indexing, caching, or pre-aggregation for performance.
Cover idempotency, concurrency, retries, and data consistency. Discuss partitioning, sharding, and how to handle large volumes. Mention monitoring and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.