This one sprawled in a way I wasn't ready for.
Start by clarifying the read/write ratio, latency requirements, and consistency needs, then propose a layered approach: precompute the org chart into a denormalized structure, cache it aggressively at multiple levels, and handle updates with a write-through or event-driven invalidation strategy. Finally, discuss horizontal scaling of the cache and service tiers, including sharding and replication, to handle massive read traffic.
Pro tip: Emphasize that caching is not just about Redis—consider client-side caching, CDN for static parts, and in-process caches to reduce network hops. Also, discuss cache stampede protection and graceful degradation to show production maturity.
Ask about read/write ratio, acceptable staleness, latency SLOs, and data size to tailor the scaling strategy. This ensures you're solving the right problem.
Transform the org chart into a read-optimized format, such as a materialized view or a flat table, to avoid complex joins and recursive queries at read time.
Use a combination of in-memory caches (e.g., local LRU), distributed caches (e.g., Redis), and CDN for static assets, with appropriate TTLs and invalidation strategies.
Choose a strategy like write-through, write-behind, or event-driven invalidation (e.g., using Kafka) to keep caches fresh. Discuss trade-offs between consistency and latency.
Shard the cache and service instances, use read replicas for the database, and employ consistent hashing for cache distribution. Ensure the system can scale out linearly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.