This came as a follow-up after I'd already presented my initial solution, and I wasn't fully ready for it.
Start by clarifying the current solution's bottlenecks and the expected access patterns, then propose a layered scaling strategy that addresses storage, compute, and data access. Emphasize trade-offs between consistency, latency, and cost, and tie your choices to Uber's scale and real-time needs.
Pro tip: Anchor your answer in concrete numbers (e.g., QPS, data size, latency SLOs) and mention how you'd validate the design with load testing and incremental rollout. Showing awareness of operational complexity and cost will set you apart.
Ask about data volume growth rate, read/write patterns, latency and consistency requirements, and budget. This ensures your scaling plan is grounded in real needs.
Analyze where the system breaks at scale: single database, monolithic compute, lack of caching, or inefficient queries. Prioritize the most critical bottleneck.
Suggest partitioning/sharding, horizontal scaling, caching layers, asynchronous processing, and read replicas. Choose technologies based on access patterns (e.g., NoSQL for high write throughput, distributed cache for hot data).
Discuss consistency vs. availability, cost implications, operational complexity, and how to handle hotspots, rebalancing, and failures. Show you understand the CAP theorem and practical mitigations.
Describe how you'd test the scaled solution (load testing, canary deployments), monitor performance, and iterate. Mention metrics like p99 latency, throughput, and error rates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.