Sliding Window Looks Easy Until You Face These Interview Problems

While preparing for coding interviews, I noticed that Sliding Window is one of those DSA topics where understanding the pattern matters much more than memorising individual solutions.

At first, many problems look different. Some ask for the longest substring, some ask for the smallest subarray, and others involve finding the maximum sum. But once I started grouping them by approach, I realised that a lot of these questions follow a few common patterns.

I thought I'd share the patterns I'm revising and the problems I'm practising so that I can recognise them more easily during interviews.

Fixed-Size Sliding Window

This is the simplest pattern to start with. The window size remains constant throughout the problem.

Instead of calculating the result for every possible subarray from scratch, I calculate the first window and then update it by removing the element that goes out and adding the new element that comes in.

Problems I'm practising:

• Maximum Sum Subarray of Size K

• First Negative Number in Every Window of Size K

• Maximum of All Subarrays of Size K

• Count Occurrences of Anagrams

Variable-Size Sliding Window

In this pattern, the window size changes depending on whether the current window satisfies a given condition.

I usually maintain two pointers, left and right. I expand the window by moving right and shrink it by moving left whenever the condition needs to be restored.

This pattern is especially useful for questions asking for the longest or shortest subarray or substring that satisfies a condition.

Problems I'm practising:

• Longest Substring Without Repeating Characters

• Longest Repeating Character Replacement

• Minimum Size Subarray Sum

• Max Consecutive Ones III

Sliding Window With Frequency Counting

Some problems require tracking how frequently characters or numbers appear inside a window.

For these, I maintain a frequency map or an array. Whenever the window expands or shrinks, I update the frequency accordingly.

This avoids repeatedly checking every element in the current window.

Problems I'm practising:

• Permutation in String

• Find All Anagrams in a String

• Minimum Window Substring

• Longest Substring With At Most K Distinct Characters

Sliding Window With Multiple Conditions

In some interview problems, maintaining a valid window requires tracking more than one condition.

For example, a question might ask for the longest substring containing at most K distinct characters, or the longest subarray with a limited number of zeros.

The important part is to understand when the current window becomes invalid and which elements need to be removed before continuing.

Problems I'm practising:

• Fruit Into Baskets

• Longest Substring With At Most Two Distinct Characters

• Subarrays With K Different Integers

• Binary Subarrays With Sum

Monotonic Deque With Sliding Window

This is a slightly different pattern. Sometimes, we need to maintain the maximum or minimum element inside a moving window.

A deque helps maintain useful candidates in sorted order, allowing us to find the maximum or minimum efficiently without scanning the entire window every time.

Problems I'm practising:

• Sliding Window Maximum

• Sliding Window Minimum

• Shortest Subarray With Sum at Least K

How I'm Practising These Patterns

Rather than jumping between random questions, I'm trying to solve problems pattern by pattern.

For every question, I first identify whether the window size is fixed or variable. Then I understand what makes the window valid, what information I need to maintain, and when I should move the left pointer.

I'm also trying to solve the same problem again after a few days without looking at my previous code. This helps me check whether I've actually understood the approach.

If you're revising Sliding Window too, which problem do you think is the best one to test your understanding of this topic?