Spent the first few minutes going way too broad, talking about priority queues and cron-style scheduling before they nudged me toward something more concrete.
Start by clarifying requirements: what tasks, dependencies, priorities, and scale? Then outline a design using a priority queue (heap) for scheduling, discuss concurrency and fault tolerance, and finally dive into implementation details like data structures and algorithms. Emphasize trade-offs between simplicity and scalability, and relate to ML workflows (e.g., training jobs, data pipelines).
Pro tip: Demonstrate awareness of Google's scale by discussing distributed scheduling (e.g., Borg-like) and how you'd handle millions of tasks with dependencies. Also, mention monitoring and dynamic re-prioritization, which are critical in production ML systems.
Ask about task types (batch, streaming), dependencies, priorities, SLA, scale (tasks/sec), and failure handling. Confirm if it's for ML pipelines (e.g., training, inference) to tailor the design.
Propose a scheduler with a priority queue (heap) for ready tasks, a dependency graph (DAG) to track prerequisites, and worker pools for execution. Discuss components: task queue, scheduler, executor, and monitoring.
Detail the heap for O(log n) insertion/extraction, topological sort for dependency resolution, and possibly a timing wheel for delayed tasks. Explain how to handle priority and fairness.
Discuss threading vs. asyncio vs. multiprocessing, and how to scale horizontally with distributed queues (e.g., Redis, RabbitMQ). Address fault tolerance, retries, and idempotency.
Compare simple in-memory scheduler vs. distributed system. Highlight ML-specific needs: GPU allocation, data locality, and experiment tracking. Mention monitoring and dynamic adjustments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.