What is the Sliding Window pattern?

A technique using two pointers to maintain a window over a linear data structure, processing elements efficiently in O(n).

When should I use Sliding Window?

When the problem asks about contiguous subarrays or substrings, longest/shortest sequences, or when brute force is O(n²) and can be optimized.

Is Sliding Window important for interviews?

Yes — it appears in 20-30% of coding interview questions at FAANG companies, especially for array and string problems.

What is the time complexity?

O(n) time, since each element is processed at most twice. Space is O(1) for fixed windows or O(k) for variable windows with a hashmap.

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pattern / EASY

Prefix Sum.

Precompute cumulative sums for range queries

complexity

time

O(n) preprocessing, O(1) per query

space

O(n)

practice questions

browse and solve all 5 pattern questions →

02' — visual

interactive

Sliding window — k = 3

Maximum sum subarray

Window

[0, 2]

Sum

Step

0 / 3

03'

mental
model.

Build an array prefix[] where prefix[i] = sum of all elements from index 0 to i. Then any range sum [l, r] = prefix[r] - prefix[l-1].

Prefix Sum technique precomputes cumulative sums to answer range sum queries in O(1) time. Often combined with a HashMap for subarray sum problems.

when to use ✓

01Range sum queries on static array
02Subarray sum equals K
03Number of subarrays with sum divisible by K
042D range sum queries

when not ✗

01Array has frequent updates (use BIT or segment tree)
02When element-wise operations are needed

04'

recognition signals

Multiple range sum queries

Find subarray with given sum

Subarray sum equals K

Range updates or queries

1D prefix sum2D prefix sum (matrix)Prefix XORPrefix product

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