This one looked manageable at first glance but the complexity stacks up fast.
Start by clarifying requirements and constraints, then design a modular system with a scheduler, evaluator, and notifier. Walk through the data flow, state management for alert states, and trade-offs around scheduling, concurrency, and failure handling.
Pro tip: Emphasize idempotency and state transitions to avoid duplicate notifications, and discuss how to handle missed evaluations due to downtime or delays.
Ask about scale, alert types, notification channels, and expected latency. Confirm whether alerts are per-client or global, and how configs are updated.
Outline components: a scheduler to trigger evaluations, a worker pool to execute metric queries, a state store to track alert status, and a notifier to send alerts. Describe how they interact.
Explain how to schedule each alert on its interval (e.g., using a priority queue or cron-like system). Discuss concurrency, load balancing, and handling of long-running queries.
Detail how to track alert state (OK, PENDING, FIRING, RESOLVED) and ensure correct transitions. Use a persistent store with atomic updates to avoid race conditions.
Describe how to send notifications via the client API, with retries and idempotency. Discuss deduplication, rate limiting, and handling failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.