How to Solve Longest Substring Without Repeating Characters on LeetCode
The Longest Substring Without Repeating Characters is one of the most popular LeetCode interview problems. It tests your ability to use hash maps, string indexing, and the sliding window technique—crucial concepts in coding interviews.
Problem Statement
Given a string
s, find the length of the longest substring without repeating characters.
A substring is a contiguous sequence of characters within a string.
Example:
Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.
Why This Problem Matters for Interviews
This problem:
- Tests your understanding of hash maps and window-based algorithms
- Pushes you to optimize for time and space
- Shows how you handle edge cases and input constraints
It's a classic test of whether you know how to avoid brute force and reason about optimal subarrays.
Two Key Approaches to Solve Longest Substring Without Repeating Characters
1. Brute Force (Inefficient)
Check every possible substring and test for repeats—O(n³) time, not practical for interviews.
def lengthOfLongestSubstring(s: str) -> int:
n = len(s)
max_len = 0
for i in range(n):
for j in range(i, n):
substring = s[i:j+1]
if len(set(substring)) == len(substring):
max_len = max(max_len, len(substring))
return max_len
Tip: Always mention why you wouldn’t use this in production.
2. Sliding Window with Hash Map (Optimal)
Use a hash map to store the index of characters and a left/right window to track the current substring.
Time Complexity: O(n)
Space Complexity: O(min(n, m)), where m is the size of the charset
def lengthOfLongestSubstring(s: str) -> int:
char_index = {}
left = max_len = 0
for right, char in enumerate(s):
if char in char_index and char_index[char] >= left:
left = char_index[char] + 1
char_index[char] = right
max_len = max(max_len, right - left + 1)
return max_len
Key Interview Talking Points
- Sliding window technique: why it works and when to use it.
- Hash map for character positions.
- Time and space complexity.
- Handling edge cases: empty string, all identical characters, unicode.
Big O Complexity Recap
| Approach | Time Complexity | Space Complexity |
|---|---|---|
| Brute Force | O(n³) | O(n) |
| Sliding Window + Hash Map | O(n) | O(min(n, m)) |
Pro Interview Tips
- Walk through the sliding window algorithm on sample input.
- Mention alternative approaches and their drawbacks.
- Practice with test cases