This question sprawled in a way I didn't anticipate.
Start by clarifying requirements and constraints, then design the core data structures and algorithms for validation, cycle detection, and scheduling. Discuss concurrency control and scalability for a continuous stream, and finally address trade-offs and potential optimizations.
Pro tip: Emphasize the importance of idempotency and atomicity in task addition and consumption to handle retries and concurrent clients gracefully. Also, consider using a topological sort with a priority queue to efficiently find the next runnable task.
Ask about expected scale (tasks per second, number of tasks), latency requirements, consistency guarantees, and whether tasks can be updated or deleted. Clarify if the scheduler is distributed or single-node.
Define task structure (id, deadline, prerequisites). Outline validation rules: unique IDs, non-empty prerequisites, no self-dependencies, and deadline in the future. Discuss how to handle invalid tasks (reject with error).
Use topological sort or DFS to detect cycles when adding tasks. Maintain a priority queue of runnable tasks (those with all prerequisites completed) ordered by deadline. When a task is consumed, update dependents and add newly runnable tasks to the queue.
Use locks or optimistic concurrency to ensure atomicity of add and consume operations. For a continuous stream, consider partitioning tasks by ID or using a distributed queue. Discuss backpressure and how to handle tasks that become runnable after consumption.
Compare in-memory vs. persistent storage, centralized vs. distributed scheduling, and different concurrency models. Mention potential optimizations like caching, batching, and lazy cycle detection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.