Yep, it's the one everyone warns you about.
Start by clarifying the requirements: what type of collection, what defines uniqueness (e.g., equality, identity), and whether order matters. Then propose a solution using a hash set to track seen elements, iterating through the collection and collecting unique items. Discuss time and space complexity, and consider edge cases like empty input or unhashable elements.
Pro tip: Mention that for large datasets or streaming input, a hash set approach is optimal for O(n) time, but if memory is constrained, sorting first can reduce space to O(1) extra (though O(n log n) time). Also, clarify if the function should modify the input in-place or return a new collection.
Ask about the input type (list, array, stream), definition of uniqueness (value equality, custom comparator), and whether order preservation is required. Confirm if the function should return a new collection or modify in-place.
Select a hash set to track seen elements for O(1) average lookup, and a result list to maintain order if needed. If elements are unhashable, consider sorting or using a custom equality check.
Iterate through the collection, check if each element is in the seen set; if not, add it to the result and the set. This yields O(n) time and O(n) space.
State time and space complexity. Discuss alternatives like sorting (O(n log n) time, O(1) extra space) or using a boolean array if the range is small. Mention stability and order preservation.
Consider empty input, all duplicates, unhashable elements, and large datasets. 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.