The sliding window technique is one of the most powerful optimization patterns you’ll encounter in coding interviews. It transforms brute-force O(n²) solutions into elegant O(n) algorithms by maintaining a “window” that moves across your data structure, processing elements as it expands and contracts.
Why Sliding Window Matters
This pattern appears in 20+ LeetCode problems and is frequently tested at FAANG companies. The core insight is simple: instead of recalculating results for every possible subarray from scratch, we maintain state as we slide through the data, adding new elements and removing old ones incrementally.
Fixed vs Variable Window: The Two Flavors
Fixed-Length Windows
Fixed-length sliding windows maintain a constant size k throughout execution. These problems explicitly state the window size and ask you to find optimal properties within all k-sized subarrays.
When to use: Problems mentioning “k consecutive elements,” “subarray of size k,” or “fixed window of length k”
Template structure:
def fixed_window(arr, k):
window_sum = 0
max_sum = float('-inf')
# Build initial window
for i in range(k):
window_sum += arr[i]
max_sum = window_sum
# Slide the window
for i in range(k, len(arr)):
window_sum += arr[i] # Add new element
window_sum -= arr[i - k] # Remove old element
max_sum = max(max_sum, window_sum)
return max_sum