Jumped straight to sorting each string and using that as a hash map key, which works fine.
Clarify the problem constraints and then propose a hash map solution where the key is a canonical representation of each string (e.g., sorted string or character count). Discuss trade-offs between sorting each string (O(n * k log k)) and counting characters (O(n * k)), and mention how this scales for large inputs. Finally, walk through an example and analyze time/space complexity.
Pro tip: Mention that using a sorted string as key is simple but can be costly for long strings; using a character count key (e.g., a tuple of 26 counts) is more efficient for large alphabets or long strings. Also, note that in production, you might consider memory usage and whether to use a streaming approach if the input is huge.
Ask about input size, character set (e.g., lowercase letters only?), and whether the output order matters. Confirm that anagrams are case-sensitive and that empty strings are possible.
Decide on a canonical key for each string: either sort the string or use a character frequency count. Discuss the trade-offs: sorting is O(k log k) per string, counting is O(k) but requires a fixed alphabet.
Iterate through the array, compute the key for each string, and append the string to the list in the hash map corresponding to that key. Return the hash map's values as the grouped lists.
State time complexity: O(n * k) for counting or O(n * k log k) for sorting, where n is number of strings and k is max length. Space complexity: O(n * k) to store the groups.
Walk through an example, test with empty strings, single-character strings, and strings with repeated characters. Mention potential optimizations like using a prime number product as key (though note overflow risks).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.