This is a big compound question and I underestimated how much they'd push on the failure handling piece.
Start by clarifying the core use case: an internal service to compute a dasher's pay for a given time range, likely called by other services or a batch job. Then design a RESTful API with clear resource modeling, focusing on idempotency and failure handling for reliability. Finally, discuss trade-offs around consistency, caching, and observability to show depth.
Pro tip: Emphasize idempotency keys and deterministic pay computation—since pay is financial, the same request must always yield the same result, and retries must not double-pay. Also, mention that you'd version the API to allow pay logic changes without breaking clients.
Ask questions to understand who calls the API, expected volume, latency requirements, and whether pay computation is synchronous or can be async. Confirm that the existing logic is deterministic and can be exposed as a pure function.
Propose a RESTful endpoint like GET /dashers/{dasher_id}/pay?start_date=...&end_date=... for retrieval, and possibly POST /pay-computations for complex queries. Define request/response schemas with clear fields (e.g., total_pay, breakdown, currency) and use standard HTTP status codes.
For POST endpoints, require an idempotency key in the header to ensure retries don't create duplicate computations. Discuss failure modes: timeouts, partial failures, and how to handle them with retries, circuit breakers, and fallback responses.
Validate inputs (e.g., date range, dasher ID format) and return 400 errors with details. Add logging, metrics (latency, error rates), and tracing to monitor the service, plus alerts for anomalies.
Talk about caching pay results for immutable past periods, consistency vs. availability, and how to scale horizontally. Mention versioning the API to evolve pay logic safely.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.