Skip to content
← Back to Blog

How to Identify Sliding Window Problems: 5 Key Signals

7 min read

Sliding Window is one of the most frequently tested patterns in coding interviews. It appears on LeetCode, in FAANG onsite rounds, and in online assessments. The hard part is not implementing it — the hard part is recognizing when to use it. This guide gives you 5 concrete signals to look for, a step-by-step framework for solving these problems, and the most common pitfalls to avoid.

What Is Sliding Window?

The Sliding Window pattern maintains a dynamic range (window) over an array or string using two pointers — usually called left and right. The right pointer expands the window to include new elements. When a constraint is violated, the left pointer shrinks the window. This lets you examine every possible contiguous subarray in O(n) time instead of the brute-force O(n²).

let left = 0;
for (let right = 0; right < n; right++) {
  // expand: add arr[right] to window state
  while (windowIsInvalid()) {
    // shrink: remove arr[left] from window state
    left++;
  }
  // update best answer
}

The 5 Signals

Signal 1: The problem asks for a contiguous subarray or substring

This is the most reliable signal. If the problem specifically says "subarray" (contiguous) or "substring" (not subsequence), Sliding Window is almost certainly in play. The word "contiguous" means elements must be adjacent — exactly what a window represents.

Examples: "Find the longest subarray...", "Find the minimum window substring..."

Signal 2: Words like "longest", "shortest", or "maximum/minimum"

Optimization keywords combined with subarrays are a strong signal. The window expands to explore longer ranges and shrinks to find the optimal one. "Longest substring without repeating characters" and "minimum window containing all characters" are textbook sliding window problems.

Examples: "Longest substring with at most K distinct characters", "Smallest subarray with sum ≥ S"

Signal 3: A constraint on the window contents

Look for constraints like "at most K distinct characters", "sum does not exceed S", or "no repeating characters." These constraints determine when to shrink the window. Without a constraint, the answer would trivially be the entire array.

Examples: "...with at most 2 distinct fruits", "...where the difference between max and min is ≤ K"

Signal 4: Fixed-size window (K consecutive elements)

When the problem specifies a fixed window size K (e.g., "maximum sum of K consecutive elements"), you can use a simpler variant: expand until the window reaches size K, then slide by adding one element on the right and removing one on the left.

Examples: "Maximum sum of any 3 consecutive elements", "Maximum average subarray of length K"

Signal 5: Frequency tracking within a range

If you need to track character or element frequencies within a moving range, that is a sliding window with a hash map. Problems about anagram matching, permutation matching, or character frequency constraints fall into this category.

Examples: "Find all anagrams of p in s", "Permutation in String"

When It Looks Like Sliding Window but Is Not

Not every problem with "subarray" is a sliding window problem. Here are the traps:

  • Subarray sum equals exactly K (with negatives): If the array can contain negative numbers, the window is not monotonic — shrinking does not guarantee reducing the sum. Use Prefix Sum with a hash map instead.
  • Maximum subarray sum (no constraint): This is Kadane's Algorithm, not sliding window. There is no fixed constraint to expand and shrink around.
  • Subsequence problems: "Longest increasing subsequence" is not a window problem because elements do not need to be contiguous. Use DP instead.
  • Two-sum-style pair problems: If you need to find a pair (not a range), use Two Pointers or Hash Map.

Step-by-Step Solving Framework

Once you have identified the problem as sliding window, follow this 5-step framework:

  1. Define the window state. Decide what data structure tracks the window contents. A running sum? A frequency hash map? A count of distinct characters?
  2. Expand the window. Move the right pointer and update the state.
  3. Check the constraint. Is the window still valid? If not, move to step 4.
  4. Shrink the window. Move the left pointer and update the state until the window is valid again.
  5. Update the answer. After each valid state, check if the current window improves the best answer.

Classic Sliding Window Problems

1. Maximum Sum Subarray of Size K

Given an array of integers and a number K, find the maximum sum of any contiguous subarray of size K. This is the simplest fixed-size sliding window problem.

function maxSumSubarray(arr: number[], k: number): number {
  let windowSum = 0;
  let maxSum = -Infinity;

  for (let i = 0; i < arr.length; i++) {
    windowSum += arr[i];
    if (i >= k - 1) {
      maxSum = Math.max(maxSum, windowSum);
      windowSum -= arr[i - k + 1];
    }
  }
  return maxSum;
}

2. Longest Substring Without Repeating Characters

The constraint is "no repeating characters." Use a hash map to track each character's last position. When a repeat is found, jump the left pointer past the previous occurrence.

Signal used: Contiguous substring + constraint (no repeats) + "longest"

3. Minimum Window Substring

Given strings s and t, find the smallest window in s that contains all characters of t. This is a variable-size window with a frequency-based constraint. Track how many characters still need to be matched. Shrink when all are matched.

Signal used: Substring + "minimum" + frequency tracking

4. Fruit Into Baskets

Collect fruit from a contiguous section of trees, but you can only carry 2 types of fruit. This is "longest subarray with at most 2 distinct values" — a textbook variable-size window with a distinct-count constraint.

Signal used: Contiguous subarray + constraint (at most K distinct) + "longest"

5. Permutation in String

Check if s2 contains a permutation of s1. This is a fixed-size window (size of s1) with a frequency-matching constraint. Slide a window of size |s1| across s2 and compare character counts.

Signal used: Fixed-size window + frequency tracking

Summary

When you see a problem asking for an optimal contiguous subarray or substring with some constraint, think Sliding Window. The 5 signals — contiguous range, optimization keywords, window constraints, fixed size, and frequency tracking — will guide you to the right pattern. For a hands-on walkthrough, try our interactive Sliding Window animation.

Related reads:

Not sure which pattern to use?

Our decision tree asks you 3-5 questions and tells you the exact pattern to apply.

Try the Decision TreeTake the Quiz