This content originally appeared on DEV Community and was authored by sneha grian
Sliding window is one of those techniques that once you get it, you’ll start spotting it everywhere. It’s a go-to tool for solving problems involving subarrays, substrings, or continuous sequences and can turn an O(n²) brute-force problem into a sleek O(n) solution. Let’s dive in!
What is Sliding Window?
A sliding window helps you iterate through a series of elements in segments. It takes in each element one by one to check a condition, and removes elements when the condition is no longer met.
How Does It Work?
- The window moves from left to right.
- Each element is taken into the window to check if it satisfies a specific condition.
- We use two pointers, usually named l (left) and r (right).
Step-by-Step Explanation:
1. Initialize the two pointers:
Start with both l and r at the beginning of the array or string.
2. Expand the window:
While the condition is not yet satisfied, move the r pointer forward to increase the window size.
3. Check the condition:
As you include new elements, check if the current window meets the required condition (e.g., sum ≤ target, valid substring, etc.).
4. Shrink the window if needed:
If the condition fails, move the l pointer forward to reduce the window size and try to regain a valid window.
Example:
Imagine a sliding window: l is the left blade, and r is the right blade.
If we keep one side of the window constant (say, l), and move the other side (r), the window expands; this is how the sliding window works:
When the condition is not satisfied, we change the position of l to the next element. This marks the starting of the new window, and we check through elements using both pointers to find a new valid window.
- Moving l helps reduce the window size.
- Moving r helps determine the window's right boundary.
Why Do We Use Sliding Window?
Sliding windows are usually used to return a group of elements when our required output is a maximum value, sum, or any condition based on grouping.
The sliding window method helps us to:
Find the output by performing operations on a group of elements stored in a window.
Segment the list into all possible groups and check for the required condition.
💡 Key Benefits:
- Makes the process easier by reducing time complexity.
- We don’t need to check the already-checked groups again.
- Efficient in problems where we need to slide over input and check conditions dynamically.
LeetCode Example:
Let’s see how sliding window logic can solve one of the most asked interview question.
Longest Repeating Character Replacement: (Leetcode no.3)
Given a string s and an integer k, find the length of the longest substring where you can replace at most k characters to make all characters the same.
Example
Input: s = "AABABB", k = 2
We want the longest substring where we can replace at most 2 characters to make all characters the same.
Step-by-Step Logic
We apply the rule:
window size - max frequency ≤ k
Where:
- window size = r - l + 1
- max frequency = most repeated character in current window
If true:
We can replace the remaining characters → Expand window
If false:
Too many replacements needed → Shrink window from the left
Walkthrough:
Best window = "AABAB"
Max freq = 3 (A)
Replacements needed = 5 - 3 = 2
✅ 2 ≤ k → valid window
Result = 5
This is a classic sliding window problem:
📌 Expand → Check Condition → Shrink if needed → Track max
This content originally appeared on DEV Community and was authored by sneha grian
sneha grian | Sciencx (2025-07-13T08:45:03+00:00) Sliding Window: A Simple Yet Powerful Technique. Retrieved from https://www.scien.cx/2025/07/13/sliding-window-a-simple-yet-powerful-technique/
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