This is basically a full distributed systems design in one question.
Start by clarifying requirements and scale (e.g., number of hosts, metrics per second, retention, query latency) to frame the design. Then walk through the end-to-end architecture: agent-based collection, ingestion pipeline, time-series storage with compression/downsampling, query layer, and multi-tenant isolation. Emphasize how ingest and query can scale independently and discuss trade-offs at each layer.
Pro tip: Anchor your design around the data model and access patterns: metrics are time-series with high write throughput and range-scan reads, so choose storage and indexing accordingly. Also, explicitly call out how you'd handle cardinality explosion and tenant isolation, as these are common failure points in real systems.
Ask questions to understand scale (hosts, metrics/sec, retention), query patterns (dashboards vs ad-hoc), and multi-tenancy needs. This ensures the design is grounded in realistic constraints.
Describe agent-based collection (e.g., pull vs push), buffering, and an ingestion pipeline that validates, enriches, and routes data. Discuss partitioning and backpressure to handle high throughput.
Propose a time-series storage engine with compression (e.g., delta-of-delta, Gorilla) and downsampling/retention policies. Explain how data is partitioned (time, series) and indexed for efficient range queries.
Outline a query service that supports dashboards and ad-hoc queries, with caching, query planning, and pushdown of filters. Discuss how to isolate query workloads from ingest.
Explain tenant isolation (e.g., separate namespaces, resource quotas) and how ingest and query components scale independently (e.g., separate clusters, autoscaling).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.