Choose a system you know deeply and walk through it top-down, starting with requirements and high-level architecture before diving into each component. For each component, explain its role, how you optimized it for performance, expected QPS, and how Kafka guarantees delivery. Emphasize trade-offs and lessons learned to show depth.
Pro tip: Quantify everything: QPS, latency, error rates, and cost. Interviewers at TikTok value data-driven decisions, so back your optimizations with metrics and explain how you measured them.
Briefly state the system's purpose, scale, and key non-functional requirements (e.g., latency, availability, consistency). This sets context and shows you think before designing.
Draw or describe the main components (e.g., clients, API gateway, services, databases, caches, message queues) and how data flows between them. Keep it concise but complete.
For each key component, explain its responsibility, technology choices, and how you optimized it for performance (e.g., caching, sharding, batching). Include expected QPS and how you scaled to handle it.
Detail how Kafka is used for asynchronous communication, decoupling, and buffering. Describe producer/consumer configurations (acks, replication, idempotence) that ensure at-least-once or exactly-once delivery, and how you handle failures.
Highlight key decisions (e.g., SQL vs NoSQL, sync vs async), their trade-offs, and what you would improve. This demonstrates maturity and reflection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.