Start by clarifying requirements: what metrics, scale, latency, and use cases (e.g., real-time dashboards, alerting, billing). Then propose a high-level architecture covering ingestion, storage, querying, and visualization, and dive into trade-offs for key components like time-series databases and aggregation strategies.
Pro tip: Emphasize how you would handle Stripe-scale data volumes and the need for both real-time and historical analytics, and discuss how to ensure data accuracy and consistency for financial metrics.
Ask questions to understand the scope: what metrics (e.g., API latency, error rates, business KPIs), expected data volume, query patterns, latency requirements, and retention policies.
Outline the main components: data collection (agents, SDKs), ingestion pipeline (message queue), storage (time-series DB), query service, and visualization/alerting.
Discuss trade-offs for ingestion (push vs pull, batching), storage (TSDB vs. data lake, partitioning, retention), and querying (pre-aggregation, downsampling, caching).
Explain how to scale horizontally, handle failures (replication, sharding), and ensure data durability and consistency.
Describe how to monitor the metrics service itself and how to evolve the system as needs change (e.g., adding new metrics, supporting new query types).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.