I started with metrics collection and worked toward alerting, but I kept second-guessing the scope.
Start by clarifying requirements and scope with the interviewer, focusing on what aspects of performance monitoring are most critical (e.g., latency, error rates, throughput). Then outline a high-level architecture covering data collection, processing, storage, and visualization, and dive into key components and trade-offs as time allows.
Pro tip: Emphasize the importance of monitoring the monitoring system itself—ensure it's reliable, scalable, and doesn't become a bottleneck. Also, discuss how you would handle high cardinality metrics and sampling to manage costs.
Ask questions to understand the scope: what metrics to monitor (application-level, system-level), scale (number of hosts, requests per second), retention, alerting needs, and integration with existing tools.
Sketch the main components: data collection agents, ingestion pipeline, time-series database, query/alerting engine, and dashboards. Explain how data flows from applications to visualization.
Discuss trade-offs for each component: push vs pull for data collection, choice of time-series DB (e.g., Prometheus, InfluxDB), stream processing for aggregation, and storage optimizations.
Address how to scale the system horizontally, handle failures, and ensure data durability. Mention partitioning, replication, and backpressure mechanisms.
Explain how alerts are defined, evaluated, and routed. Discuss avoiding alert fatigue, and monitoring the monitoring system itself.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.