The Sliding Window Technique: Explained with 5 Interview Problems

Sun, 05 Jul 2026 11:00:00 +0500

“Longest substring without repeating characters.” “Maximum sum subarray of size k.” “Smallest window containing all characters of t.” These problems appear in FAANG interview loops constantly, and they share a shape: find the best contiguous chunk of an array or string.

The naive approach enumerates every subarray (O(n²) of them) and evaluates each one, often for O(n³) total. The sliding window technique solves the same problems in O(n) with one idea: when the window moves, don’t recompute its contents from scratch: update them incrementally. A window sliding one step right adds one element and (maybe) drops one element. Everything else it contains is unchanged, so recomputing it is pure waste.

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The Sliding Window Technique: Explained with 5 Interview Problems