Explain worker state machine load balancer design
Company: Scale AI
Role: Software Engineer
Category: Software Engineering Fundamentals
Difficulty: medium
Interview Round: Technical Screen
You are designing a lightweight load balancer for a Python-based backend service that dispatches tasks to a pool of worker processes.
Describe how you would design the load balancer with the following requirements:
1. **Worker State Machine**
- Each worker can be in states such as: `IDLE`, `BUSY`, `FAILED`, `DRAINING`, etc.
- The load balancer must track each worker's state and only assign new tasks to eligible workers.
- State transitions should be well-defined (e.g., `IDLE -> BUSY -> IDLE`, `BUSY -> FAILED`, etc.).
2. **Task Dispatching with a Priority Queue**
- Incoming tasks have priorities (e.g., higher priority tasks should be processed first).
- Use a priority queue (or similar) so that the dispatcher always assigns the highest-priority available task to a suitable worker.
- Handle the case where tasks may expire or time out if not processed within a deadline.
3. **Dynamic Scaling (Scale Up / Scale Down)**
- The system should automatically scale out (add workers) when load increases and scale in (remove workers) when load decreases.
- Explain what metrics you would monitor (e.g., queue length, task latency, worker utilization) and how they drive scaling decisions.
- Describe how to safely drain and remove workers without losing or duplicating tasks.
4. **Timeouts and Reliability**
- If a worker does not complete a task within a configured timeout, the task should be retried or reassigned.
- Workers can fail or become unreachable; the load balancer must detect this and transition their state appropriately.
- Ensure at-least-once processing of tasks while minimizing duplicate processing.
5. **Implementation Considerations**
- Assume this system will be implemented in Python.
- Discuss the core components/classes you would define (e.g., `Worker`, `Task`, `Scheduler`, `PriorityQueue` abstraction).
- Explain the data structures to track workers, their states, and tasks in the queue.
- Clarify how concurrency is handled: threads vs processes vs async IO.
Explain your design end-to-end. Include how tasks enter the system, how they are scheduled and executed, how worker states are updated, and how the system remains consistent and resilient under failures and scaling events.
Quick Answer: This question evaluates understanding of designing resilient task dispatch systems, covering worker state machines, priority-based scheduling, dynamic scaling, timeouts, reliability, and concurrency considerations in a Python backend.
