Recognizing a Sliding Window Problem (Interview Concept)

Interview Concept: Recognizing a Sliding Window Problem

  • Keywords to watch for: "contiguous", "subarray", "substring", "consecutive", "at most k", "exactly k", "longest/shortest window".
  • Monotonic condition: sliding window works when growing the window can only make the tracked property move in one predictable direction (e.g. sum only increases as elements are added), so shrinking from the left is a safe, one-way operation.
  • Red flag it will NOT work directly: if the array contains negative numbers and the problem is sum-based, a naive shrink-on-violation window breaks, since removing an element does not guarantee the sum decreases. That usually calls for prefix sums with a monotonic deque or hash map instead (see Shortest Subarray With Sum At Least K).
  • A fast self-check before coding: "if I extend the window by one element, and then remove elements from the left one at a time, does the property change in a single consistent direction?" If yes, sliding window fits.

Complexity

Recognising the pattern correctly is what turns an O(n²) brute-force scan of every subarray into an O(n) sliding window solution, since each element is then added and removed from consideration at most once.

Written by Lorens Mishra

Software Engineer Notes Management System Administrator

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