The round-robin part is what got me initially.
Model the allocation as a priority queue keyed by bid price, with each bid level containing a round-robin queue of customers ordered by arrival timestamp. Simulate the allocation item by item, tracking remaining inventory and each customer's allocated count, then return customers with zero allocations.
Pro tip: Clarify edge cases upfront: what if inventory runs out mid-round, or if a customer's request exceeds remaining inventory? Also discuss how to handle large-scale data efficiently, as Amazon values scalability.
Ask about inventory size, request volume, tie-breaking rules, and whether partial fulfillment is allowed. Confirm the exact round-robin behavior within a bid level.
Use a max-heap (priority queue) for bid levels, and for each bid level, maintain a queue of customers ordered by timestamp. Track remaining inventory and a map of customer allocations.
While inventory remains, pop the highest bid level, then cycle through its customer queue, allocating one item per customer per round. If a customer's request is fulfilled, remove them from the queue.
Maintain a set of all customer IDs and a set of those who received at least one item. After simulation, return the difference.
Discuss time complexity (e.g., O(N log M) where N is items and M is bid levels) and space complexity. Consider optimizations for large datasets, such as batch processing or distributed allocation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.