Spent the first few minutes just trying to scope it, which I think was the right call but I probably over-indexed on clarifying questions and burned time.
Start by clarifying requirements: scale (events per second), latency, retention, query needs, and whether it's for internal metrics or product analytics. Then propose a high-level architecture with ingestion, storage, and query layers, and dive into trade-offs like push vs pull, sampling, and storage choices. Finally, discuss operational concerns like reliability, cost, and scalability.
Pro tip: Emphasize that metrics and logging are different: metrics are aggregated time-series data optimized for real-time monitoring, while logs are discrete events for debugging. Show you understand the need for separate pipelines but also how they can share infrastructure.
Ask about scale (events/sec, data volume), latency requirements (real-time vs batch), retention period, query patterns, and whether it's for operational monitoring or product analytics.
Outline components: ingestion (agents, API), processing (stream processing, aggregation), storage (time-series DB for metrics, distributed log store for logs), and query/visualization layer.
Discuss push vs pull for metrics collection, sampling vs full ingestion for logs, storage options (e.g., Prometheus vs InfluxDB vs custom), and consistency vs availability.
Explain how to scale horizontally (sharding, partitioning), handle failures (replication, retries), and ensure data durability and availability.
Cover monitoring the system itself, cost optimization (tiered storage, compression), security (access control, encryption), and query performance (indexing, downsampling).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.