Sliding Window: Cheat Sheet for 2026 Interviews

Sliding Window Quick Reference

Key Concepts at a Glance

• •Core technique: Apply the specific pattern that Sliding Window defines
• •Time complexity: Know the optimal complexity and why it is achievable
• •Space complexity: Understand the tradeoff between time and space
• •Key data structures: Know which structures enable the technique
• •Common variations: Be ready for at least three variations of the base problem
• •Edge cases: Empty input, single element, duplicates, negative values

Python Template

def max_sum_subarray(nums, k):
    window_sum = sum(nums[:k])
    max_sum = window_sum
    for i in range(k, len(nums)):
        window_sum += nums[i] - nums[i - k]
        max_sum = max(max_sum, window_sum)
    return max_sum

TypeScript Template

function maxSumSubarray(nums: number[], k: number): number {
  let windowSum = nums.slice(0, k).reduce((a, b) => a + b, 0);
  let maxSum = windowSum;
  for (let i = k; i < nums.length; i++) {
    windowSum += nums[i] - nums[i - k];
    maxSum = Math.max(maxSum, windowSum);
  }
  return maxSum;
}

Pattern Signals Cheat Sheet

• •Signal 1: The input has a specific structure that the technique exploits
• •Signal 2: The brute force solution has a known inefficiency that this pattern addresses
• •Signal 3: The problem asks for optimization that matches this pattern's strengths
• •Signal 4: The constraints suggest a time complexity achievable with this technique
• •Signal 5: Similar problems in the same category use this pattern

Complexity Reference Table

• •Brute force: Usually O(n squared) or O(n cubed) for Sliding Window problems
• •Optimized with technique: Typically O(n) or O(n log n)
• •Space for iterative: O(1) extra space if done in-place
• •Space for recursive: O(n) for the call stack in worst case
• •Space for hash-based: O(n) for the auxiliary data structure

Interview Day Checklist

• •Can you explain the technique in one sentence?
• •Can you write the solution from memory in under ten minutes?
• •Can you trace through the algorithm with a small example?
• •Do you know the time and space complexity?
• •Can you name three variations of the base problem?
• •Have you practiced explaining your approach out loud?
• •Are you prepared for follow-up questions about optimization?
• •Do you have a clean code template ready to adapt?

Last-Minute Reminders