The sliding window technique keeps a moving range over an array or string and updates a running result as the window grows or shrinks, turning many O(n squared) brute-force scans into O(n) solutions.
The sliding window technique - Interview Concepts - ExtraBrain is part of ExtraBrain's local-first Mac workflow for live interviews, meetings, transcription, provider control, and responsible AI use.
The sliding window technique keeps a moving range over an array or string and updates a running result as the window grows or shrinks, turning many O(n squared) brute-force scans into O(n) solutions.
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A fixed window keeps a constant size; a dynamic window expands and contracts based on a condition such as a sum or character count.
Reach for it on contiguous-subarray or substring problems asking for a maximum, minimum, or count under a constraint.
Watch off-by-one bounds, when to shrink the window, and correctly updating state as elements leave.
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Use it for contiguous subarray or substring problems where you can update a result incrementally as the range moves.
Most sliding window solutions run in O(n) because each element enters and leaves the window at most once.
