I jumped straight to sorting without thinking through the comparator and had to backtrack.
First, clarify the problem and edge cases, then propose an efficient solution using a hash map to count frequencies and a custom sort with a comparator that orders by frequency ascending and value ascending for ties. Walk through a small example to demonstrate correctness, and analyze time and space complexity.
Pro tip: Mention that you can achieve O(n log n) time by sorting the unique elements with a custom comparator, and that this is optimal for comparison-based sorting; also note that if the range of error codes is small, counting sort could be used for O(n + k) time, showing awareness of trade-offs.
Ask about input size, range of error codes, and whether the array can be empty or contain negative values. Confirm that duplicates should be included in the output.
Use a hash map to count frequencies of each unique code. Then sort the unique codes using a custom comparator that orders by frequency ascending, and by value ascending for ties.
Write pseudocode or actual code, then trace through a small example (e.g., [4,5,6,5,4,3]) to verify the sorting logic and output.
State that time complexity is O(n + m log m) where m is the number of unique codes (worst-case O(n log n)), and space is O(m). Mention alternative approaches like bucket sort or counting sort if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.