Sliding window with a hash set is the move here.
Use a sliding window with two pointers and a hash map to track the last seen index of each character. Expand the right pointer, and when a duplicate is found, move the left pointer to the maximum of its current position and the duplicate's last index plus one. Track the maximum window length throughout.
Pro tip: Clarify assumptions upfront (e.g., ASCII vs. Unicode, empty string) and mention that the optimal solution runs in O(n) time and O(min(n, alphabet size)) space. Also, briefly discuss how this technique applies to ML feature engineering, such as finding unique user behavior sequences.
Ask about character set (ASCII/Unicode), empty string, and whether the substring must be contiguous. Confirm that the goal is to return the length, not the substring itself.
Mention that a naive approach checks all substrings for uniqueness in O(n^3) or O(n^2) time, but this is inefficient for large inputs.
Use two pointers (left and right) and a hash map to store the last index of each character. Expand right, and if a duplicate is found, update left to max(left, last_index[char] + 1).
Explain that each character is visited at most twice, giving O(n) time. Space is O(min(n, m)) where m is the alphabet size. Optionally, use an array for ASCII to reduce overhead.
Walk through examples like 'abcabcbb' and 'pwwkew'. Discuss how to adapt for at most K repeats or for streaming data, which is relevant for ML pipelines.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.