I went straight to load balancers and horizontal scaling, which felt right, but I skipped over database bottlenecks entirely until they nudged me.
Start by clarifying the workload characteristics (read/write ratio, request size, latency SLA) and then walk through a scalable architecture that distributes load across multiple layers. Emphasize horizontal scaling, caching, asynchronous processing, and database strategies, while discussing trade-offs and how you would validate the design with load testing.
Pro tip: Don't just list technologies; explain how each component scales and what metrics you'd monitor to ensure the system meets SLAs. Show that you consider cost and operational complexity, not just performance.
Ask about the nature of the 1,000 concurrent users: are they read-heavy or write-heavy? What are the latency and throughput requirements? What is the expected data size and growth?
Propose a multi-tier architecture with load balancers, stateless application servers, caching layers, and a scalable database. Mention CDN for static content and message queues for asynchronous tasks.
Explain how each layer scales horizontally: auto-scaling groups for app servers, read replicas and sharding for databases, distributed caches like Redis, and partitioning for queues.
Discuss potential bottlenecks (e.g., database writes, network latency) and trade-offs between consistency and availability, cost vs. performance, and complexity vs. maintainability.
Describe how you would load test the system (e.g., using JMeter or Locust) and what metrics you'd monitor (CPU, memory, latency, error rates) to ensure it handles the load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.