
Many string problems look complicated because they involve:
- substrings
- repeated characters
- dynamic conditions
Beginners often use nested loops…
But sliding window reduces many of these problems from:
- O(n²) to:
- O(n)
1. Longest Substring Without Repeating Characters
Problem
Find the length of the longest substring with unique characters.
Efficient Sliding Window Solution
public static int lengthOfLongestSubstring(String s) {
Set<Character> set = new HashSet<>();
int left = 0, maxLen = 0;
for (int right = 0; right < s.length(); right++) {
while (set.contains(s.charAt(right))) {
set.remove(s.charAt(left));
left++;
}
set.add(s.charAt(right));
maxLen = Math.max(maxLen,
right - left + 1);
}
return maxLen;
}
Core Idea
Maintain a window:
- expand right pointer
- shrink left when duplicate appears
This keeps the substring valid.
*2. Minimum Window Substring
*Problem
Find the smallest substring containing all characters of a pattern.
Example:
Text = "ADOBECODEBANC"
Pattern = "ABC"
Answer:
"BANC"
Key Idea
Use:
- character frequency counts
- dynamic window resizing
Expand until valid,
then shrink to minimize window.
3. Longest Repeating Character Replacement
Problem
Given:
- a string
- k replacements
Find the longest substring that can become all same characters.
Smart Observation
Track:
- most frequent character inside window.
If:
windowSize - maxFreq > k
then shrink window.
Sliding window works because:
- we reuse previous computations instead of recalculating substrings.
That’s the optimization.
- Sliding window avoids nested loops
- Expand → valid state
- Use two pointers
- Track characters using set/map
- Expand right pointer
- Shrink left when condition breaks
- Complexity → O(n)
Sliding window is one of the most important interview patterns.
Once you master it,
- many “hard” string problems become manageable
Explore More: https://www.quipoin.com/tutorial/data-structure-with-java/sliding-window-on-strings
Top comments (0)