W Code - Pattern-Based DSA Learning Platform

Bhanu Bisht

Template

Sliding Window Template: Pattern, Code & Cheat Sheet

The Sliding Window pattern is one of the most frequently tested coding interview patterns. Maintain a dynamic window to find optimal subarrays or substrings. This template gives you a reusable code skeleton, pseudocode, and implementation in multiple languages so you can solve 15+ problems using this single mental model.

Difficulty: Medium | Time Complexity: O(n) | Space Complexity: O(k)

When to Use This Template

Use the Sliding Window template when you see these signals in a problem:

1.Max/min subarray — Problems asking to max/min subarray

2.String matching — Problems asking to string matching

3.Frequency counting — Problems asking to frequency counting

Prerequisites: Two Pointers, Hash Maps

Problem count on W Code: 15 problems across Easy, Medium, and Hard difficulty levels.

If the problem does not match these signals, consider alternative patterns.

Pseudocode Template

function slidingWindow(arr, k):
    windowStart = 0
    windowSum = 0
    maxResult = -infinity
    
    for windowEnd in range(length(arr)):
        windowSum += arr[windowEnd]
        
        if windowEnd >= k - 1:
            maxResult = max(maxResult, windowSum)
            windowSum -= arr[windowStart]
            windowStart += 1
    
    return maxResult

Python Implementation

python
def max_subarray_sum(arr: list, k: int) -> int:
    window_sum = sum(arr[:k])
    max_sum = window_sum
    
    for i in range(k, len(arr)):
        window_sum += arr[i] - arr[i - k]
        max_sum = max(max_sum, window_sum)
    
    return max_sum

Java Implementation

java
public Object solve(Object[] input) {
    // Sliding Window template
    // Implement core logic here
    return null;
}

C++ Implementation

cpp
auto solve(vector<int>& input) {
    // Sliding Window template
    // Implement core logic
    return result;
}

Variations & Adaptations

The Sliding Window pattern has several variations you should master:

Variation 1: Fixed-size Window

This variation is useful when the problem specifically requires fixed-size window. Adapt the main template by modifying the core loop/recursion logic accordingly.

Variation 2: Variable-size Window (shrinkable)

This variation is useful when the problem specifically requires variable-size window (shrinkable). Adapt the main template by modifying the core loop/recursion logic accordingly.

Variation 3: Window with Hash Map (frequency)

This variation is useful when the problem specifically requires window with hash map (frequency). Adapt the main template by modifying the core loop/recursion logic accordingly.

Variation 4: Window with Deque (monotonic)

This variation is useful when the problem specifically requires window with deque (monotonic). Adapt the main template by modifying the core loop/recursion logic accordingly.

Common Mistakes & Edge Cases

When implementing Sliding Window, watch out for:

1.Off-by-one errors — Carefully handle boundary conditions in loops

2.Missing base case — Ensure recursion terminates properly

3.Integer overflow — Use appropriate data types for large inputs

4.Empty input — Handle null/empty arrays, strings, or graphs

5.Single element — Test with minimal inputs

Edge cases to always test:

•Empty input → expected output

•Single element → correct result

•All same elements → no infinite loop

•Maximum constraints → within time limit

Step-by-Step Problem Solving Guide

1.Identify the pattern: Does the problem involve Max/min subarray, String matching, Frequency counting?

2.Choose the template: Select the variation from above that best fits

3.Define variables: Map problem-specific entities to template variables

4.Implement the core logic: Fill in the template skeleton

5.Handle edge cases: Add boundary checks

6.Verify with examples: Trace through sample inputs

7.Analyze complexity: Confirm O(n) time and O(k) space

Frequently Asked Questions

What problems can I solve with the Sliding Window template?

The Sliding Window template applies to 15+ problems including: Max/min subarray, String matching, Frequency counting. Look for problems that involve maintain a dynamic window to find optimal subarrays or substrings..

What is the time complexity of Sliding Window?

The typical time complexity is O(n) with space complexity of O(k). Some variations may differ.

What should I learn before Sliding Window?

Prerequisites include: Two Pointers, Hash Maps. Make sure you are comfortable with these concepts first.

How do I recognize a Sliding Window problem in an interview?

Key signals: the problem involves max/min subarray; the problem involves string matching; the problem involves frequency counting. Practice identifying these cues by solving at least 10 problems using this pattern.

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