Warm-up question, but the clock pressure is real on phone screens.
Clarify the problem constraints and edge cases, then propose a hash map approach where the key is the sorted string (or character count) and the value is a list of anagrams. Discuss time/space complexity and potential optimizations, such as using a character count tuple as key to avoid sorting overhead.
Pro tip: Mention that for Unicode strings, sorting by code points may not be correct for all languages; using a character frequency count is more robust. Also, consider memory usage and whether the input can be processed in a streaming fashion.
Ask about input size, character set (ASCII vs Unicode), case sensitivity, and whether the output order matters. This shows attention to detail and helps tailor the solution.
Decide between sorting each string (O(n log n)) or using a character count array/tuple (O(n)). Explain the trade-offs: sorting is simpler but slower for long strings; counting is faster but requires a fixed alphabet or a hashable representation.
Iterate through the list, compute the key for each string, and append the string to the corresponding list in the hash map. Finally, return the values of the map as the groups.
State time complexity: O(N * K log K) with sorting or O(N * K) with counting, where N is number of strings and K is max length. Space complexity: O(N * K). Discuss edge cases like empty strings, single string, and strings with repeated characters.
Mention alternative approaches like using a prime number product as key (with caution for overflow) or sorting the list of strings first to group anagrams together. Also, consider if the input is too large for memory and how to handle it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.