Seemed easy at first and I jumped straight into code without thinking about edge cases.
Start by clarifying requirements such as case sensitivity, handling of whitespace and special characters, and expected file size. Then outline a solution using a hash map (dictionary) to store character frequencies, iterating through the file line by line or in chunks to handle large files efficiently. Finally, discuss time and space complexity and potential edge cases.
Pro tip: Demonstrate awareness of real-world constraints by mentioning memory-efficient streaming for large files and the importance of defining what counts as a 'character' (e.g., Unicode vs ASCII). This shows you think beyond the basic algorithm.
Ask about case sensitivity, whether to include whitespace and punctuation, and the expected file size. This ensures your solution meets the interviewer's expectations.
Select a hash map (dictionary) to map each character to its count, as it provides O(1) average-time insertions and lookups.
Read the file line by line or in chunks to handle large files without loading everything into memory. For each character, increment its count in the hash map.
State that time complexity is O(n) where n is the number of characters, and space complexity is O(k) where k is the number of distinct characters (bounded by character set size).
Discuss empty files, non-existent files, and Unicode characters. Mention error handling for file I/O operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.