← Anthropic Interview Insights
First instinct was a simple cron job sweeping for expired tasks, which works but they pushed back pretty fast on scale.
Start by clarifying requirements such as scale, TTL precision, and deletion semantics (hard vs. soft delete). Then propose a design that combines a time-ordered data structure (e.g., min-heap or time-wheel) with a persistent store, and discuss trade-offs between active expiration and lazy deletion. Finally, address scalability, fault tolerance, and monitoring.
Pro tip: Emphasize that TTL enforcement is a trade-off between accuracy, latency, and resource usage; propose a hybrid approach (e.g., lazy deletion on read plus a background sweeper) to balance these concerns. Also, mention that you would make TTL configurable per task and consider clock skew in distributed systems.
Ask about expected scale (tasks per second, total tasks), required TTL precision (seconds vs. milliseconds), and deletion semantics (hard delete vs. mark as expired). Also clarify if tasks can be updated or if TTL is fixed at creation.
Propose a system with a task store (e.g., database) and a TTL manager. The TTL manager tracks expiration times and triggers deletion. Consider using a min-heap or time-wheel for efficient expiration tracking.
Design the task store schema (include TTL field and status). Design the TTL manager: how it schedules checks, handles failures, and scales (e.g., sharding by task ID or time). Discuss active vs. lazy expiration.
Compare active expiration (background sweeper) vs. lazy expiration (check on read). Discuss precision vs. overhead, and how to handle large volumes (e.g., batch deletions). Mention using Redis TTL or database TTL features if applicable.
Explain how to scale the TTL manager (e.g., partitioning, distributed locks). Ensure fault tolerance (e.g., persistent queue, retries). Suggest monitoring expiration lag and deletion success rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.