The stale data handling part is where I think I undersold myself.
Start by clarifying the requirements and constraints, then propose a caching layer with a TTL of 10 minutes that serves the most recent data, even if it's older than 10 minutes, while implementing a background refresh to fetch new data as soon as it's available. Discuss trade-offs between freshness and availability, and how to handle stale data gracefully with appropriate headers and monitoring.
Pro tip: Mention that you would include a 'data age' indicator in the API response and frontend, so users and downstream services are aware of the data's staleness, and set up alerts for when data exceeds a threshold like 2 hours to detect upstream issues.
Confirm the 10-minute freshness requirement, understand the upstream update schedule (hourly zip), and identify acceptable staleness thresholds and failure modes.
Implement a cache with a 10-minute TTL that stores the latest processed data. Use a background job to periodically check for new upstream data (e.g., every 5 minutes) and update the cache when new data is available.
When the cache is older than 10 minutes, still serve the stale data but include metadata (e.g., timestamp, age) and log/monitor the event. Optionally, return a warning header or status code to indicate staleness.
Add retries and exponential backoff for upstream fetches, set up alerts for prolonged staleness, and provide fallback mechanisms (e.g., last known good data) to maintain service availability.
Acknowledge the trade-off between strict freshness and availability, and consider alternatives like pushing updates via websockets or using a CDN with stale-while-revalidate, but justify your chosen approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.