Start by clarifying requirements and scale, then design a config service with versioned, immutable configs and a staged rollout pipeline that includes health checks and automatic rollback. Emphasize client-side caching, push/pull mechanisms, and observability, and discuss mitigation strategies for bad configs already in production.
Pro tip: Netflix values resilience and rapid iteration; highlight how your design minimizes blast radius and enables quick rollback, and mention real-world examples like feature flags and canary deployments.
Ask about expected config size, update frequency, number of clients, and latency requirements. Establish consistency, availability, and partition tolerance needs.
Propose a versioned, immutable config store (e.g., Git-backed or database) with metadata like region, environment, and rollout status. Ensure atomic updates and audit logs.
Define a pipeline that deploys configs in stages (e.g., canary, regional, global) with health checks (error rates, latency). Automatically rollback on regression using predefined thresholds.
Design clients to fetch configs via a resilient API (e.g., long-polling, push via WebSocket) with local caching and fallback to last-known-good config. Include versioning and TTL.
Instrument metrics, logging, and tracing for config changes and client behavior. For bad configs in production, discuss kill switches, dynamic overrides, and client-side validation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.