Given a string, find the length of the longest substring without repeating characters.

**Input:** A single string `s`

.

**Output:** An integer representing the length of the longest substring without repeating characters.

**Constraints:**

`0 ≤ s.length ≤ 5 * 10`

^{4}- The string
`s`

consists of English letters, digits, symbols, and spaces.

**Example:**

Input: "abcabcbb" Output: 3 Explanation: The answer is "abc", with the length of 3.

The core challenge of this problem is to identify the longest substring within a given string that does not contain any repeating characters. This problem is significant in various applications such as data validation, parsing, and text processing where unique sequences are required.

Potential pitfalls include misunderstanding the definition of a substring (which must be contiguous) and not efficiently handling the search for repeating characters, leading to suboptimal solutions.

To solve this problem, we can use a sliding window technique. The idea is to use two pointers to represent the current window of characters being considered and a set to track the characters within this window.

Here is a step-by-step approach:

- Initialize two pointers,
`left`

and`right`

, both set to the start of the string. - Use a set to keep track of characters in the current window.
- Expand the window by moving the
`right`

pointer and adding characters to the set until a duplicate character is found. - When a duplicate is found, move the
`left`

pointer to the right until the duplicate is removed from the set. - Keep track of the maximum length of the window during this process.

This approach ensures that each character is processed at most twice, resulting in a linear time complexity of `O(n)`

.

Here is a detailed breakdown of the algorithm:

- Initialize
`left`

and`right`

pointers to 0, and an empty set`charSet`

. - Iterate with the
`right`

pointer over the string. - If the character at
`right`

is not in`charSet`

, add it to the set and update the maximum length. - If the character is in the set, remove the character at
`left`

from the set and move`left`

to the right. - Repeat until the
`right`

pointer reaches the end of the string.

```
public class Solution {
public int lengthOfLongestSubstring(String s) {
// Initialize the set to store unique characters
Set<Character> charSet = new HashSet<>();
int left = 0, right = 0, maxLength = 0;
// Iterate over the string with the right pointer
while (right < s.length()) {
// If the character is not in the set, add it and update maxLength
if (!charSet.contains(s.charAt(right))) {
charSet.add(s.charAt(right));
maxLength = Math.max(maxLength, right - left + 1);
right++;
} else {
// If the character is in the set, remove the leftmost character and move left pointer
charSet.remove(s.charAt(left));
left++;
}
}
return maxLength;
}
}
```

The time complexity of this approach is `O(n)`

because each character is processed at most twice (once by the `right`

pointer and once by the `left`

pointer). The space complexity is `O(min(n, m))`

, where `n`

is the length of the string and `m`

is the size of the character set (which is constant for English letters, digits, symbols, and spaces).

Consider the following edge cases:

- Empty string: The output should be 0.
- String with all identical characters: The output should be 1.
- String with all unique characters: The output should be the length of the string.

These cases can be tested to ensure the robustness of the solution.

To test the solution comprehensively, consider the following test cases:

`""`

(Empty string)`"bbbbbb"`

(All identical characters)`"abcdef"`

(All unique characters)`"pwwkew"`

(Mixed characters)

JUnit or any other testing framework can be used to automate these tests.

When approaching such problems, consider the following tips:

- Break down the problem into smaller parts and understand each part.
- Think about different data structures that can help solve the problem efficiently.
- Practice similar problems to improve your problem-solving skills.

In this blog post, we discussed how to solve the problem of finding the longest substring without repeating characters using a sliding window technique. We covered the problem definition, approach, algorithm, code implementation, complexity analysis, edge cases, and testing. Understanding and solving such problems is crucial for improving your algorithmic thinking and coding skills.

For further reading and practice, consider the following resources: