Start by clarifying requirements and constraints (e.g., task types, worker pool size, latency goals), then propose a modular design with a central dispatcher, worker state machines, and a priority queue. Walk through each component—state transitions, priority scheduling, dynamic scaling, and timeout handling—while discussing trade-offs and concurrency primitives.
Pro tip: Emphasize simplicity and avoid over-engineering; for a lightweight solution, consider using Python's multiprocessing and asyncio with a single dispatcher process to minimize coordination overhead. Discuss how you'd handle backpressure and ensure graceful degradation under load.
Ask about expected task volume, priority levels, worker pool size limits, timeout policies, and whether tasks are CPU-bound or I/O-bound. This shapes the choice of concurrency model (threads vs. processes vs. async).
Outline states: IDLE, BUSY, DRAINING, and DEAD. Describe transitions (e.g., IDLE -> BUSY on task assignment, BUSY -> IDLE on completion, any -> DEAD on crash) and how the dispatcher monitors and reacts to state changes.
Use a priority queue (e.g., heapq) to order tasks by priority and arrival time. Explain how the dispatcher picks the highest-priority task and assigns it to an available worker, ensuring fairness and avoiding starvation.
Describe scaling policies: scale up when queue length exceeds a threshold, scale down when workers are idle. For timeouts, use per-task deadlines and a monitor that kills or restarts workers exceeding them, with retries or fallbacks.
Propose classes: Task, Worker, Dispatcher, and LoadBalancer. Use multiprocessing for CPU-bound tasks, asyncio for I/O-bound, or a hybrid. Discuss synchronization primitives (locks, queues) and inter-process communication (pipes, shared memory).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.