Pretty straightforward once you see it's basically just iterate and accumulate.
Clarify that the problem is essentially a linear scan since there is no capacity constraint, so the load order is simply the input order. Then describe a single-pass algorithm that accumulates total weight and count while recording each item's id (or index) in order. Emphasize that O(n) is achieved by avoiding sorting or nested loops.
Pro tip: Mention that if the input is a stream or iterator, you can process it lazily and still produce the load order without storing all items, which is a common follow-up in interviews. Also, explicitly state that you would handle edge cases like empty list or missing ids by using a fallback (e.g., index) to keep the output consistent.
Confirm that there is no capacity limit, so all items are loaded in the given order. Ask whether the load order should include ids or just indices, and whether the input is a list or a stream.
Explain that you will iterate through the items once, appending each item's identifier to a result list, and simultaneously summing weights and counting items. This ensures O(n) time and O(n) space for the output.
Discuss how to handle missing ids (e.g., use the index as a fallback), empty input, and potential integer overflow for total weight (use a 64-bit integer if needed).
Present a clean implementation in a language of your choice, using a single loop and simple variables. Avoid unnecessary data structures or sorting.
State that the time complexity is O(n) and space is O(n) for the output (or O(1) extra space if only returning aggregates). Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.