Start by clarifying requirements and constraints, then break the system into core components like job scheduling, execution, and artifact management. Focus on scalability, reliability, and trade-offs, and discuss how to handle the massive scale with distributed systems and efficient resource utilization.
Pro tip: Emphasize the importance of observability and failure recovery at scale—showing you understand that at this volume, failures are inevitable and must be handled gracefully. Also, mention cost optimization as a key consideration for a platform serving tens of thousands of developers.
Ask questions to understand the specific needs: supported languages, build environments, security/compliance, and integration points. Clarify SLAs, expected growth, and budget constraints.
Outline the main components: a scalable job queue, distributed workers, artifact storage, and a control plane for orchestration. Discuss how these components interact and scale horizontally.
Detail the design of the job scheduler (e.g., using a distributed queue like Kafka), worker management (e.g., Kubernetes), and caching strategies for dependencies. Address how to handle peak loads and ensure fairness.
Explain how to achieve horizontal scaling, fault tolerance, and data consistency. Discuss partitioning, replication, and auto-scaling. Mention monitoring, logging, and alerting for observability.
Discuss trade-offs between consistency and availability, cost vs. performance, and build vs. buy. Highlight optimizations like incremental builds, caching, and spot instances to reduce cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.