Start by clarifying requirements and scale, then design a data model that tracks task assignments and labeler history to enforce no-repeat and fairness constraints. Propose an incremental scheduling algorithm that processes only new tasks and updates assignments daily, using techniques like priority queues or constraint programming. Discuss trade-offs between fairness, throughput, and complexity, and how to handle failures and rebalancing.
Pro tip: Emphasize idempotency and auditability: ensure that daily updates can be safely retried and that assignment decisions are logged for debugging and fairness audits. This shows production maturity beyond just algorithmic correctness.
Ask about task volume, labeler pool size, fairness metrics (e.g., equal exposure, skill matching), and latency requirements. Confirm that assignments are daily and that no-repeat means a labeler shouldn't see the same task twice, but tasks can be assigned to multiple labelers for consensus.
Propose tables/collections for tasks, labelers, assignments, and history. Include fields like task_id, labeler_id, assignment_date, status, and a history of past assignments to enforce no-repeat. Consider indexing for efficient lookups.
Describe an algorithm that processes only new tasks each day. Use a priority queue or greedy matching with constraints, leveraging existing assignments and labeler availability. Avoid full recomputation by maintaining state and using incremental updates.
Explain how to incorporate fairness (e.g., round-robin, quota-based, or optimization with fairness constraints) and prevent repeat assignments by checking history. Discuss potential conflicts and how to resolve them (e.g., fallback to next best labeler).
Discuss trade-offs between fairness, throughput, and complexity. Address scalability: partitioning, sharding, and handling failures. Mention monitoring and rebalancing strategies for long-term fairness.
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