Start by clarifying functional and non-functional requirements, then propose a high-level architecture that separates ingestion, storage, query, and alerting. Dive into storage strategy (e.g., time-series database, downsampling) and alert evaluation (e.g., stream processing vs. periodic queries), highlighting trade-offs in scalability, latency, and cost.
Pro tip: Emphasize the importance of cardinality control and downsampling to handle high-volume metrics efficiently, and discuss how you'd design for multi-tenancy and fault tolerance—key concerns at LinkedIn's scale.
Ask questions to understand scale (number of services, metrics per second, retention), query patterns (real-time vs. historical), alerting needs (latency, thresholds), and dashboard requirements.
Outline components: ingestion (collectors, message queue), storage (time-series DB), query service, alert evaluator, and dashboard. Explain data flow from services to storage and query.
Choose a time-series database (e.g., Prometheus, InfluxDB, or custom). Discuss data model (metric name, tags, timestamp, value), retention policies, downsampling, and partitioning for scalability.
Describe alert rule management, evaluation engine (streaming vs. batch), and notification. Discuss how to handle high cardinality and avoid alert storms.
Discuss scaling ingestion (horizontal partitioning, sharding), query (caching, pre-aggregation), and alerting (distributed evaluation). Trade-offs: consistency vs. availability, latency vs. cost, and complexity vs. maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.