Longest Substring Without Repeating Characters
Given a string `s`, find the length of the longest substring without repeating characters. Use a Sliding Window approach.
Why Interviewers Ask This
Amazon asks this question to evaluate a candidate's mastery of the Sliding Window technique, a core competency for optimizing O(N^2) solutions to O(N). Interviewers specifically look for your ability to manage state efficiently using hash maps or arrays while handling edge cases like duplicate characters. It tests logical precision and code clarity under pressure, aligning with Amazon's Leadership Principle of 'Dive Deep' into algorithmic efficiency.
How to Answer This Question
1. Clarify Requirements: Immediately ask about input constraints (empty strings, non-ASCII characters) and expected time complexity, as Amazon values understanding scope before coding.
2. Define the Strategy: Explicitly state you will use a Hash Map to store the last seen index of each character, enabling O(1) lookups and immediate window adjustments.
3. Initialize Variables: Set up two pointers (left and right), a max length tracker, and the map data structure.
4. Execute the Loop: Iterate with the right pointer; if a repeat is found, move the left pointer to max(left, map[char] + 1) to ensure no duplicates remain in the current window.
5. Update and Track: Continuously update the character's position in the map and calculate the current window size to update the maximum length found so far.
6. Verify Edge Cases: Briefly mention how you handle empty strings or single-character inputs to demonstrate thoroughness.
Key Points to Cover
- Explicitly stating the O(N) time complexity goal demonstrates awareness of performance constraints.
- Using the Math.max function for the left pointer logic shows deep understanding of overlapping windows.
- Clarifying input constraints early reflects the 'Customer Obsession' principle by ensuring robustness.
- Explaining the hash map's role in storing indices highlights efficient space-time tradeoff reasoning.
- Walking through a concrete example validates the logic flow before writing final code.
Sample Answer
To solve the longest substring without repeating characters problem efficiently, I would implement a sliding window approach using a hash map to achieve O(N) time complexity. First, I'd clarify that the string might contain any ASCII character and could be empty. My strategy involves maintaining a window defined by two pointers, 'left' and 'right', both starting at zero. As I iterate through the string with the 'right' pointer, I check if the current character exists in my hash map. If it does and its last recorded index is within the current window, I must shrink the window from the left. Crucially, I set the new 'left' pointer to the maximum of its current value or one past the previous occurrence of the duplicate character. This ensures we skip over the repeated character immediately without unnecessary backtracking. After adjusting the window, I update the character's index in the map and calculate the current window size by subtracting 'left' from 'right'. I compare this against a running maximum to track the longest valid substring found. For example, with input 'abcabcbb', when the second 'a' is encountered, the window shifts from [0,1,2] to [3,3], effectively discarding the first three characters. Finally, I return the maximum length observed. This approach handles all edge cases cleanly and meets Amazon's standards for optimized, readable code.
Common Mistakes to Avoid
- Failing to use Math.max when moving the left pointer can cause the window to shrink incorrectly or miss valid substrings.
- Using nested loops results in O(N^2) complexity, which Amazon interviewers will likely reject as inefficient.
- Ignoring empty string inputs leads to runtime errors during the initial array or map access.
- Not updating the character's index in the map after every iteration causes stale data to trigger false positives.
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