core pattern

Sliding Window

Maintain a dynamic window over contiguous elements to efficiently compute aggregates like sum, max, or count. Expand the window by adding elements, shrink by removing.

Time

O(n)

Space

O(1)

🧠Mental Model

“A spotlight sliding across a stage — it illuminates a section at a time while moving forward.”

Verbal cue: Expand right edge, shrink left edge when condition breaks

🎯Recognition Triggers

When you see these patterns in a problem, consider this approach:

Subarray or substringContiguous elementsMaximum/minimum of size KLongest/shortest with conditionWindow with specific sumString permutation/anagram

📝Code Template

Sliding Window Template

1function slidingWindow(arr, k) {
2  let windowSum = 0;
3  let maxSum = -Infinity;
4  let left = 0;
5  
6  for (let right = 0; right < arr.length; right++) {
7    // Expand window
8    windowSum += arr[right];
9    
10    // Window reached size k
11    if (right >= k - 1) {
12      maxSum = Math.max(maxSum, windowSum);
13      
14      // Shrink window
15      windowSum -= arr[left];
16      left++;
17    }
18  }
19  
20  return maxSum;
21}

Complexity rationale: Each element enters and leaves the window exactly once

💡Interview Tips

1Clarify if the window size is fixed or dynamic
2For variable windows, define the shrinking condition clearly
3Use a hash map when tracking character frequencies
4Consider what state you need to maintain in the window

⚠️Common Mistakes

✕Not tracking when to shrink the window properly
✕Forgetting to update the answer before shrinking
✕Using wrong window size calculation (right - left + 1)
✕Not handling edge cases: empty array, k > array length