What is the Sliding Window pattern?

The Sliding Window pattern is a systematic approach to solving a class of coding interview problems. By recognizing the Sliding Window structure, you can solve problems in optimal time complexity using proven techniques.

When should I use the Sliding Window pattern?

Use the Sliding Window pattern when the problem structure matches its recognition signals — typically when brute force would be O(n²) or worse, and the pattern reduces complexity to O(n).

What is the time complexity of Sliding Window?

The Sliding Window pattern typically runs in O(n) time and uses O(1) or O(k) for the hashmap space. The exact complexity depends on the specific sub-variant being applied.

Is Sliding Window important for coding interviews?

Yes — the Sliding Window pattern is frequently tested at FAANG companies and competitive engineering interviews. Mastering this pattern gives you a reliable framework to solve many problem variations quickly.

How do I practice the Sliding Window pattern?

Start by studying the pattern's mental model and recognition signals on DSAPatterns, then practice the curated LeetCode problems below in order from Easy to Hard. Aim for pattern recognition before memorizing solutions.

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pattern / MEDIUM

Sliding Window.

Process subarrays with a moving window

complexity

time

O(n)

space

O(1) or O(k) for the hashmap

practice questions

browse and solve all 0 pattern questions →

02' — visual

interactive

Sliding window — k = 3

Maximum sum subarray

Window

[0, 2]

Sum

Step

0 / 3

03'

mental
model.

Imagine a window (defined by left and right pointers) that slides over the array. Expand the right pointer to add elements, shrink the left pointer to remove elements when a condition is violated.

Sliding Window is a technique used to perform operations on a contiguous subarray or substring of a given size within an array/string. The window slides over the data structure to find the optimal solution.

when to use ✓

01Maximum sum subarray of size K
02Longest substring with K distinct characters
03Find all anagrams in a string
04Minimum size subarray sum

when not ✗

01Non-contiguous subarrays
02When order of elements matters globally
03Static array queries (use prefix sum instead)

04'

recognition signals

Problem involves contiguous subarray or substring

Asks for longest/shortest/maximum/minimum of something in a range

Brute force is O(n²) or O(n³)

Keywords: 'subarray', 'substring', 'window', 'contiguous'

Fixed-size windowVariable-size window with constraintWindow with at most K distinctWindow with exactly K distinctMinimum window substring

05'

templates

python

1# Beginner: Fixed-size sliding window
2def max_sum_k(nums, k):
3   n = len(nums)
4   if n < k:
5      return 0
6   window_sum = sum(nums[:k])
7   max_sum = window_sum
8   for i in range(k, n):
9      window_sum += nums[i] - nums[i - k]
10      max_sum = max(max_sum, window_sum)
11   return max_sum

common mistakes

01Not handling the edge case when window is empty
02Confusing fixed-size and variable-size windows
03Off-by-one errors with the right pointer
04Forgetting to update the answer when window shrinks

interview tips

01Always clarify: fixed or variable window size?
02Draw the window on paper before coding
03Think about what data structure to track the window state
04Consider both 'at most K' and 'exactly K' variants

06'

practice this pattern

0 curated problems from LeetCode

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No questions found for this pattern yet.

07'

faq

1# Beginner: Fixed-size sliding window

2def max_sum_k(nums, k):

3 n = len(nums)

4 if n < k:

5 return 0

6 window_sum = sum(nums[:k])

7 max_sum = window_sum

8 for i in range(k, n):

9 window_sum += nums[i] - nums[i - k]

10 max_sum = max(max_sum, window_sum)

11 return max_sum