# [DSA] Sliding Window Algorithm

Page: https://stenobird.com/podcast/software-engineer-interview-prep-podcast-7756520/dsa-sliding-window-algorithm
Text version: https://stenobird.com/podcast/software-engineer-interview-prep-podcast-7756520/dsa-sliding-window-algorithm.md
Podcast: [Software Engineer Interview Prep Podcast](https://stenobird.com/podcast/software-engineer-interview-prep-podcast-7756520)
Published: 2026-03-13T11:26:20+00:00
Episode link: https://podcasters.spotify.com/pod/show/prabuddha-ganegoda/episodes/DSA-Sliding-Window-Algorithm-e3gcps1
Audio file: https://anchor.fm/s/10f2c0f8c/podcast/play/116860225/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2026-2-13%2F419929944-44100-2-e2cecaa05f701.mp3
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JSON: https://stenobird.com/v1/public/podcasts/software-engineer-interview-prep-podcast-7756520/episodes/dsa-sliding-window-algorithm
Duration seconds: 371

## Resource

The Sliding Window Algorithm is a powerful technique used to reduce the time complexity of problems involving arrays or strings—specifically those that require finding a sub-segment that meets certain criteria. Instead of using nested loops O(n^2), the sliding window maintains a dynamic range that "slides" across the data, usually bringing the complexity down to O(n). Problem:Find the maximum sum of a contiguous subarray of size `k`. public class SlidingWindow { public static int findMaxSum(int[] arr, int k) { int n = arr.length; if (n < k) return -1; int windowSum = 0; // 1. Compute sum of the first window for (int i = 0; i < k; i++) { windowSum += arr[i]; } int maxSum = windowSum; // 2. Slide the window from index k to n-1 for (int i = k; i < n; i++) { // Add the next element, remove the first element of the previous window windowSum += arr[i] - arr[i - k]; maxSum = Math.max(maxSum, windowSum); } return maxSum; } }

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## Transcript

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