Start by clarifying requirements and scale (e.g., number of services, metrics per second, query patterns), then propose a high-level architecture with separate ingestion, storage, alerting, and query/visualization layers. Dive into key components like time-series database choice, stream processing for alerts, and trade-offs around consistency, latency, and cost.
Pro tip: Emphasize the separation of concerns between the write path (ingestion) and read path (querying) to allow independent scaling, and discuss how you'd handle high cardinality metrics without exploding storage costs.
Ask questions to understand the number of services, metrics volume, retention period, query patterns, alerting latency, and dashboard refresh rates. This sets the stage for informed design decisions.
Outline the main components: ingestion pipeline (collectors, message queue), storage (time-series database), alerting engine, query service, and visualization layer. Explain data flow from services to dashboards.
Discuss the ingestion pipeline (e.g., Kafka for buffering, stream processing for aggregation), time-series storage (e.g., Prometheus, InfluxDB, or custom), and alerting (rule evaluation, notification). Address scalability and fault tolerance.
Explain how to model time-series data (tags, fields, timestamps) and optimize for common queries (downsampling, rollups, indexing). Discuss handling high cardinality and retention policies.
Summarize trade-offs (e.g., push vs pull, consistency vs availability, cost vs performance) and suggest potential extensions like anomaly detection or multi-region deployment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.