Start by clarifying requirements and scale (e.g., millions of metrics per second, retention periods, alert latency). Then propose a hybrid architecture: a scalable time-series database for recent data and a separate store for long-term analytics, with a stream processing layer for real-time alerting. Finally, discuss trade-offs and how you'd handle failure and scaling.
Pro tip: Emphasize the separation of concerns: use a push-based ingestion pipeline for high throughput, and decouple alert evaluation from query serving to avoid contention. Mention that you'd start with a simple design and iterate based on observed bottlenecks.
Ask about write volume, query patterns, retention, alert latency, and consistency needs. Establish assumptions to guide design decisions.
Choose a time-series data model (e.g., metric name, tags, timestamp, value) and select storage: a scalable TSDB for recent data and a columnar store for long-term. Discuss partitioning and indexing.
Propose a distributed ingestion layer (e.g., Kafka) to buffer writes, with consumers that batch and write to storage. Ensure scalability and fault tolerance.
Design a query service that handles near real-time and historical queries, with caching and pre-aggregation for dashboards. Discuss query optimization.
Create a rule engine that evaluates user-defined conditions on streams or recent data, with state management and notification delivery. Ensure low latency and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.