The 'design Sora' framing almost got me spending time on the ML pipeline stuff, which is a trap.
Start by clarifying requirements and scale, then design a high-level architecture with a job queue, scheduler, and worker pool. Focus on the preemption mechanism: when resources are exhausted, lower-priority tasks are preempted, re-queued with preserved state, and users are notified upon completion. Discuss trade-offs between fairness, latency, and resource utilization.
Pro tip: Emphasize idempotency and checkpointing to ensure preempted tasks can resume without duplicating work, and design notifications to be reliable and non-intrusive, possibly with progress updates.
Ask about expected job volume, video generation complexity, latency SLAs, and user priorities. Define what 'compute resources exhausted' means (e.g., GPU memory, worker count).
Propose a distributed system with a job queue (e.g., Kafka, RabbitMQ), a scheduler that assigns tasks to workers, and a metadata store for job states. Include a notification service.
Design a priority-based preemption: when resources are low, preempt lower-priority tasks. Save task state (checkpoints) to allow resumption. Re-queue preempted tasks with updated priority or backoff.
Ensure users are notified when their video is ready, even after preemption. Use a reliable message queue or webhook with retries. Consider progress updates and estimated completion times.
Discuss trade-offs: preemption overhead vs. resource utilization, fairness vs. priority. Handle failures: worker crashes, duplicate notifications, and state corruption.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.