Cracking the Sliding Window: Longest Substring Without Repeating Characters in Python

String manipulation problems are a staple in technical interviews at companies like Google, Amazon, and Meta. They look simple on the surface but test multiple core skills at once: data structures, algorithmic thinking, and the ability to optimize inefficient solutions.

One pattern appears again and again in these problems: the Sliding Window.

If you truly understand the sliding window pattern, you unlock an entire class of problems involving substrings, subarrays, and contiguous sequences. In this guide, we will break down one of the most well-known examples and build an intuition you can reuse in many other scenarios.

Problem Breakdown: What Are We Really Solving?

We are given a string s, and our goal is to:

Find the length of the longest contiguous substring that contains no repeating characters.

Key constraints:

• The substring must be contiguous
• All characters must be unique
• We only return the length, not the substring itself

Example

s = "abcabcbb"

Valid substrings with unique characters include:

• "abc" with length 3
• "bca" with length 3
• "cab" with length 3

The correct output is:

3

The Naive Approach (O(n²)): Why It Does Not Scale

A straightforward approach is to:

1. Start at every index in the string
2. Build substrings character by character
3. Stop when a duplicate appears
4. Track the maximum length

This approach uses nested loops and checks each possible substring.

Why this fails in practice:

• There are O(n²) possible substrings
• Performance degrades quickly as the input grows
• This solution will not pass interview constraints for large inputs

We need a more efficient strategy.

The Sliding Window Solution (O(n))

This problem is a perfect fit for the Sliding Window pattern.

Core Idea

Instead of restarting from scratch, we maintain a window that always satisfies our condition:

All characters inside the window are unique.

We control the window using two pointers:

• Left pointer (left): start of the window
• Right pointer (right): end of the window

The window expands and contracts as we move through the string.

Data Structure Choice

We use a Hash Map (dictionary) to store:

• Key: character
• Value: the last index where that character appeared

This allows us to:

• Detect duplicates instantly
• Move the left pointer efficiently

The Key Optimization

When we encounter a duplicate character:

• We do not move the left pointer step by step
• We jump it directly to the index after the previous occurrence

This avoids unnecessary iterations and ensures linear time complexity.

Python Code Implementation

Below is a clean, PEP-8 compliant solution:

def length_of_longest_substring(s: str) -> int:
    char_index = {}  # Stores last seen index of each character
    left = 0
    max_length = 0

    for right, char in enumerate(s):
        if char in char_index and char_index[char] >= left:
            left = char_index[char] + 1

        char_index[char] = right
        max_length = max(max_length, right - left + 1)

    return max_length

Step-by-Step Walkthrough: "pwwkew"

Let us trace how the algorithm works.

s = "pwwkew"

Final result:

3

Complexity Analysis

Time Complexity: O(n)

• Each character is processed once
• Both pointers only move forward
• Dictionary operations are constant time on average

Space Complexity: O(min(m, n))

• n is the string length
• m is the size of the character set
• In the worst case, every character is stored once

Closing Challenge

The current solution returns only the length of the longest substring.

Challenge: How would you modify this code to return the actual substring instead?

Hint:

• Track the starting index when max_length is updated
• Use string slicing at the end

If you can implement this cleanly, you have fully internalized the sliding window pattern.