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Head of Career Skills Development & Coaching

*Based on past data of successful IK students

Given a string, find the length of its longest substring with no repeating characters.

```
{
"s": abcdab
}
```

Output:

```
4
```

The given string has three substrings of length 4 with no repeating characters. They are the substrings: "abcd", "bcda" and "cdab". It also does not have any substring of a greater length consisting of unique characters.

```
{
"s": aaaaa
}
```

Output:

```
1
```

Constraints:

- 1 <= length of the string <= 10
^{5} - The string may contain the following ASCII characters only: [a - z], [A - Z], [0 - 9].

We provided three solutions.

We will first start with a brute force idea and will then try improving upon it using some general observations.

Throughout this editorial, we are going to refer the input string as `s`

and its length as `n`

.

For all the solutions, we will need a container to track whether a given character has occurred in the substring or not. We will need this to make sure that no character is repeated. For this, we can use a boolean array to store the occurrences of each character in the substring (other alternatives could be `unordered_map`

/`unordered_set`

) as it will facilitate the O(1) look-up and update for each character.

We can one-by-one consider all the substrings of the given string. Let us keep track of the length of the longest substring with no repetitions and call it `max_len`

.

If the length of the input string is `n`

, then there will be `n - start`

number of substrings starting from each index `start`

.

Then, for every substring starting at an index `start`

and ending at an index `end`

(`start <= end`

), we can check if it has all the characters unique. If the substring has all unique characters, we will update the value of `max_len`

if `end - start + 1`

is greater than the current value of `max_len`

. Finally, we will return the updated value of `max_len`

after checking all the substrings.

For example, suppose we have been given a string `s = "abcdab"`

. Then, we will try generating all of its substrings "a", "ab", abc", "abcd", "abcda" and so on.

Then, for each substring we will check if all the characters are unique in the current substring and if they are, we will check and update the value of `max_len`

.

O(n^{3}).

As you might have noted, there are O(n^{2}) numbers of substrings for any given string of length `n`

and traversing through each substring and checking for the unique characters using a boolean array takes O(n) time. Hence, the overall time complexity of this approach is O(n^{3}).

O(1).

Although we used a boolean array to store the occurrences of characters in the string, the size of that array is restricted to 256 irrespective of the size of the input string.

O(n).

Space used for input: O(n).

Auxiliary space used: O(1).

Space used for output: O(1).

So, total space complexity: O(n).

```
/*
Asymptotic complexity in terms of the length of input string \`s\` ( = \`n\`):
* Time: O(n ^ 3).
* Auxiliary space: O(1).
* Total space: O(n).
*/
int length_of_longest_substring(string &s) {
int n = s.length();
int max_len = 0;
vector <bool> character_set(256);
bool valid_substring;
for (int start = 0; start < n; ++start) {
for (int end = start; end < n; ++end) {
// Checking the substring from index \`start\` to index \`end\`
// Keeps track of unique characters in the current substring
// clearing the \`character_set\` for the current substring.
fill(character_set.begin(), character_set.end(), false);
valid_substring = true;
for (int ind = start; ind <= end; ++ind) {
// If the current character was found earlier in the same substring,
// then the current substring is not valid.
if (character_set[(int) s[ind]]) {
valid_substring = false;
break;
}
character_set[(int) s[ind]] = true;
}
if (valid_substring) {
max_len = max(max_len, end - start + 1);
}
}
}
return max_len;
}
```

We can improve this O(n^{3}) solution to O(n) by observing the fact that the answer cannot be greater than 256. Reason being, the number of unique characters in any substring cannot be greater than 256. The `end`

index of any substring will now be less than or equal to `min(n - 1, start + 256 - 1)`

. So, starting from any index `start`

, we will just have to iterate 256 characters in the worst case. Note that we are not using the index `end`

here. Thus, from any index `start`

, we will do 256 iterations at most. Here we can again use a boolean array to store the occurrences of each character while iterating through any substring.

O(n).

Starting at any index `start`

, we will iterate through 256 characters in the worst case. Therefore, the time complexity of this approach is O(n * 256) = O(n).

O(1).

Space used by the Boolean array: O(1).

O(n).

Space used for input: O(n).

Auxiliary space used: O(1).

Space used for output: O(1).

So, total space complexity: O(n).

```
/*
Asymptotic complexity in terms of the length of input string \`s\` ( = \`n\`):
* Time: O(n).
* Auxiliary space: O(1).
* Total space: O(n).
*/
int length_of_longest_substring(string &s) {
int n = s.length();
int max_len = 0;
vector <bool> character_set(256);
for (int start = 0; start < n; ++start) {
// Keeps track of unique characters in the current substring.
// Clearing the \`character_set\` for the current substring.
fill(character_set.begin(), character_set.end(), false);
int end = start;
// This inner loop will not get executed more than 256 times.
while (end < n) {
// If the current character has already been seen in the current substring, we break out.
if (character_set[(int) s[end]]) {
break;
}
character_set[(int) s[end]] = true; // Including the current character in the \`character_set\`.
max_len = max(max_len, end - start + 1); // Updating the result if possible.
end++;
}
}
return max_len;
}
```

We can also use the sliding window approach to solve this problem in O(n) time. The previous solution had a constant factor of around 256 in the time complexity. We can reduce this constant factor considerably using the sliding window technique.

We maintain two endpoints `start`

and `end`

of the substring under consideration (`start = end = 0`

initially).

We move the index `end`

to its right while there is no repetition (we will check for the repetition through the boolean array that we have been maintaining similar to the first two solutions).

Once there is a repetition, we will move the `start`

index until that repetition is removed. Basically, here, `s[end]`

will point to a character that is found elsewhere in the current substring. Therefore, to move this repetition out of our substring, we will squeeze the substring from the left until we remove the first occurrence of `s[end]`

from our substring. Reason being, without removing this repetition, we cannot move ahead as we consider only the substring with all unique characters.

Let us now see a simulation of this idea for `s = "abcdab"`

.

For the sake of simplicity, we will not show the complete boolean array of size 256. Let us call that array `character_set`

. Here, we will only denote the characters for which the `character_set`

holds a true value.

**Step 0:** Initially, we have `start = 0`

and `end = 0`

. Also, the character set is initially empty as it has all the values as false. Hence, `character_set = {}`

. Also, the result `max_len = 0`

initially.

**Step 1:** We check that `s[end]`

is not present in the character set, hence we include it in the character set and widen up our window from the right. So, we increment `end`

by 1.

Currently, we have:

```
[ab]cdab
character_set = {'a'}
max_len = 1
```

Here, we represent the sliding window with a set of matching brackets. The starting bracket is placed before the index `start`

and ending bracket is placed after index `end`

.

**Step 2:**

```
[abc]dab
character_set = {'a', 'b'}
max_len = 2
```

Again, `s[end]`

is not present in the `character_set`

, hence we include it in the `character_set`

and widen up our window from the right.

**Step 3:**

```
[abcd]ab
character_set = {'a', 'b', 'c'}
max_len = 3
```

Again, `s[end]`

is not present in the `character_set`

, hence we include it in the `character_set`

and widen up our window from the right.

**Step 4:**

```
[abcda]b
character_set = {'a', 'b', 'c', 'd'}
max_len = 4
```

Again, `s[end]`

is not present in the `character_set`

, hence we include it in the `character_set`

and widen up our window from the right.

**Step 5:**

```
a[bcdab]
character_set = {'a', 'b', 'c', 'd'}
max_len = 4
```

Here, `s[end] = 'a'`

was already present in the `character_set`

. So, we squeeze our window from the left and its first occurrence is removed from the string.

After that, we again widen up our window from the right to check for the next character.

**Step 6:**

```
ab[cdab]
character_set = {'a', 'b', 'c', 'd'}
max_len = 4
```

Here, `s[end] = 'b'`

was already present in the `character_set`

. So, we squeeze our window from the left and its first occurrence is removed from the string.

Now, `end = 6`

and thus, we have considered the complete string as we are no longer left with a different ending index of the substring. Therefore, we stop and report the final result as `max_len = 4`

.

O(n).

As we will iterate through any character at most twice. Once, via `start`

and once via `end`

.

O(1).

We used only a constant amount of extra space.

O(n).

Space used for input: O(n).

Auxiliary space used: O(1).

Space used for output: O(1).

So, total space complexity: O(n).

```
/*
Asymptotic complexity in terms of the length of input string \`s\` ( = \`n\`):
* Time: O(n).
* Auxiliary space: O(1).
* Total space: O(n).
*/
int length_of_longest_substring(string &s) {
int n = s.length();
// Keeps track of characters inside the current substring.
vector <bool> character_set(256, false);
// Starting and ending index of the substring under consideration.
int start = 0, end = 0;
int max_len = 0;
while (start < n and end < n) {
// If the current character isn't present in the current substring
// then, include it.
if (character_set[(int) s[end]] == false) {
character_set[(int) s[end]] = true;
end++;
max_len = max(max_len, end - start);
}
// Else, we remove the characters from the start of the substring
// until we remove the current repeating character.
else {
while (start < n and s[start] != s[end]) {
character_set[(int) s[start]] = false;
start++;
}
// Finally, start will point to the first occurrence of repeated character
// and end will point to its second occurrence.
// Hence, we update both.
start++;
end++;
}
}
return max_len;
}
```

We hope that these solutions to longest substring without repeating characters problem have helped you level up your coding skills. You can expect problems like these at top tech companies like Amazon and Google.

If you are preparing for a tech interview at FAANG or any other Tier-1 tech company, register for Interview Kickstart's FREE webinar to understand the best way to prepare.

Interview Kickstart offers interview preparation courses taught by FAANG+ tech leads and seasoned hiring managers. Our programs include a comprehensive curriculum, unmatched teaching methods, and career coaching to help you nail your next tech interview.

We offer 18 interview preparation courses, each tailored to a specific engineering domain or role, including the most in-demand and highest-paying domains and roles, such as:

- Back-end Engineering Interview Course
- Front-end Engineering Interview Course
- Full Stack Developer Interview Course

To learn more, register for the FREE webinar.

Note: Input and Output will already be taken care of.

Given a string, find the length of its longest substring with no repeating characters.

```
{
"s": abcdab
}
```

Output:

```
4
```

The given string has three substrings of length 4 with no repeating characters. They are the substrings: "abcd", "bcda" and "cdab". It also does not have any substring of a greater length consisting of unique characters.

```
{
"s": aaaaa
}
```

Output:

```
1
```

Constraints:

- 1 <= length of the string <= 10
^{5} - The string may contain the following ASCII characters only: [a - z], [A - Z], [0 - 9].

We provided three solutions.

We will first start with a brute force idea and will then try improving upon it using some general observations.

Throughout this editorial, we are going to refer the input string as `s`

and its length as `n`

.

For all the solutions, we will need a container to track whether a given character has occurred in the substring or not. We will need this to make sure that no character is repeated. For this, we can use a boolean array to store the occurrences of each character in the substring (other alternatives could be `unordered_map`

/`unordered_set`

) as it will facilitate the O(1) look-up and update for each character.

We can one-by-one consider all the substrings of the given string. Let us keep track of the length of the longest substring with no repetitions and call it `max_len`

.

If the length of the input string is `n`

, then there will be `n - start`

number of substrings starting from each index `start`

.

Then, for every substring starting at an index `start`

and ending at an index `end`

(`start <= end`

), we can check if it has all the characters unique. If the substring has all unique characters, we will update the value of `max_len`

if `end - start + 1`

is greater than the current value of `max_len`

. Finally, we will return the updated value of `max_len`

after checking all the substrings.

For example, suppose we have been given a string `s = "abcdab"`

. Then, we will try generating all of its substrings "a", "ab", abc", "abcd", "abcda" and so on.

Then, for each substring we will check if all the characters are unique in the current substring and if they are, we will check and update the value of `max_len`

.

O(n^{3}).

As you might have noted, there are O(n^{2}) numbers of substrings for any given string of length `n`

and traversing through each substring and checking for the unique characters using a boolean array takes O(n) time. Hence, the overall time complexity of this approach is O(n^{3}).

O(1).

Although we used a boolean array to store the occurrences of characters in the string, the size of that array is restricted to 256 irrespective of the size of the input string.

O(n).

Space used for input: O(n).

Auxiliary space used: O(1).

Space used for output: O(1).

So, total space complexity: O(n).

```
/*
Asymptotic complexity in terms of the length of input string \`s\` ( = \`n\`):
* Time: O(n ^ 3).
* Auxiliary space: O(1).
* Total space: O(n).
*/
int length_of_longest_substring(string &s) {
int n = s.length();
int max_len = 0;
vector <bool> character_set(256);
bool valid_substring;
for (int start = 0; start < n; ++start) {
for (int end = start; end < n; ++end) {
// Checking the substring from index \`start\` to index \`end\`
// Keeps track of unique characters in the current substring
// clearing the \`character_set\` for the current substring.
fill(character_set.begin(), character_set.end(), false);
valid_substring = true;
for (int ind = start; ind <= end; ++ind) {
// If the current character was found earlier in the same substring,
// then the current substring is not valid.
if (character_set[(int) s[ind]]) {
valid_substring = false;
break;
}
character_set[(int) s[ind]] = true;
}
if (valid_substring) {
max_len = max(max_len, end - start + 1);
}
}
}
return max_len;
}
```

We can improve this O(n^{3}) solution to O(n) by observing the fact that the answer cannot be greater than 256. Reason being, the number of unique characters in any substring cannot be greater than 256. The `end`

index of any substring will now be less than or equal to `min(n - 1, start + 256 - 1)`

. So, starting from any index `start`

, we will just have to iterate 256 characters in the worst case. Note that we are not using the index `end`

here. Thus, from any index `start`

, we will do 256 iterations at most. Here we can again use a boolean array to store the occurrences of each character while iterating through any substring.

O(n).

Starting at any index `start`

, we will iterate through 256 characters in the worst case. Therefore, the time complexity of this approach is O(n * 256) = O(n).

O(1).

Space used by the Boolean array: O(1).

O(n).

Space used for input: O(n).

Auxiliary space used: O(1).

Space used for output: O(1).

So, total space complexity: O(n).

```
/*
Asymptotic complexity in terms of the length of input string \`s\` ( = \`n\`):
* Time: O(n).
* Auxiliary space: O(1).
* Total space: O(n).
*/
int length_of_longest_substring(string &s) {
int n = s.length();
int max_len = 0;
vector <bool> character_set(256);
for (int start = 0; start < n; ++start) {
// Keeps track of unique characters in the current substring.
// Clearing the \`character_set\` for the current substring.
fill(character_set.begin(), character_set.end(), false);
int end = start;
// This inner loop will not get executed more than 256 times.
while (end < n) {
// If the current character has already been seen in the current substring, we break out.
if (character_set[(int) s[end]]) {
break;
}
character_set[(int) s[end]] = true; // Including the current character in the \`character_set\`.
max_len = max(max_len, end - start + 1); // Updating the result if possible.
end++;
}
}
return max_len;
}
```

We can also use the sliding window approach to solve this problem in O(n) time. The previous solution had a constant factor of around 256 in the time complexity. We can reduce this constant factor considerably using the sliding window technique.

We maintain two endpoints `start`

and `end`

of the substring under consideration (`start = end = 0`

initially).

We move the index `end`

to its right while there is no repetition (we will check for the repetition through the boolean array that we have been maintaining similar to the first two solutions).

Once there is a repetition, we will move the `start`

index until that repetition is removed. Basically, here, `s[end]`

will point to a character that is found elsewhere in the current substring. Therefore, to move this repetition out of our substring, we will squeeze the substring from the left until we remove the first occurrence of `s[end]`

from our substring. Reason being, without removing this repetition, we cannot move ahead as we consider only the substring with all unique characters.

Let us now see a simulation of this idea for `s = "abcdab"`

.

For the sake of simplicity, we will not show the complete boolean array of size 256. Let us call that array `character_set`

. Here, we will only denote the characters for which the `character_set`

holds a true value.

**Step 0:** Initially, we have `start = 0`

and `end = 0`

. Also, the character set is initially empty as it has all the values as false. Hence, `character_set = {}`

. Also, the result `max_len = 0`

initially.

**Step 1:** We check that `s[end]`

is not present in the character set, hence we include it in the character set and widen up our window from the right. So, we increment `end`

by 1.

Currently, we have:

```
[ab]cdab
character_set = {'a'}
max_len = 1
```

Here, we represent the sliding window with a set of matching brackets. The starting bracket is placed before the index `start`

and ending bracket is placed after index `end`

.

**Step 2:**

```
[abc]dab
character_set = {'a', 'b'}
max_len = 2
```

`s[end]`

is not present in the `character_set`

, hence we include it in the `character_set`

and widen up our window from the right.

**Step 3:**

```
[abcd]ab
character_set = {'a', 'b', 'c'}
max_len = 3
```

`s[end]`

is not present in the `character_set`

, hence we include it in the `character_set`

and widen up our window from the right.

**Step 4:**

```
[abcda]b
character_set = {'a', 'b', 'c', 'd'}
max_len = 4
```

`s[end]`

is not present in the `character_set`

, hence we include it in the `character_set`

and widen up our window from the right.

**Step 5:**

```
a[bcdab]
character_set = {'a', 'b', 'c', 'd'}
max_len = 4
```

Here, `s[end] = 'a'`

was already present in the `character_set`

. So, we squeeze our window from the left and its first occurrence is removed from the string.

After that, we again widen up our window from the right to check for the next character.

**Step 6:**

```
ab[cdab]
character_set = {'a', 'b', 'c', 'd'}
max_len = 4
```

Here, `s[end] = 'b'`

was already present in the `character_set`

. So, we squeeze our window from the left and its first occurrence is removed from the string.

Now, `end = 6`

and thus, we have considered the complete string as we are no longer left with a different ending index of the substring. Therefore, we stop and report the final result as `max_len = 4`

.

O(n).

As we will iterate through any character at most twice. Once, via `start`

and once via `end`

.

O(1).

We used only a constant amount of extra space.

O(n).

Space used for input: O(n).

Auxiliary space used: O(1).

Space used for output: O(1).

So, total space complexity: O(n).

```
/*
Asymptotic complexity in terms of the length of input string \`s\` ( = \`n\`):
* Time: O(n).
* Auxiliary space: O(1).
* Total space: O(n).
*/
int length_of_longest_substring(string &s) {
int n = s.length();
// Keeps track of characters inside the current substring.
vector <bool> character_set(256, false);
// Starting and ending index of the substring under consideration.
int start = 0, end = 0;
int max_len = 0;
while (start < n and end < n) {
// If the current character isn't present in the current substring
// then, include it.
if (character_set[(int) s[end]] == false) {
character_set[(int) s[end]] = true;
end++;
max_len = max(max_len, end - start);
}
// Else, we remove the characters from the start of the substring
// until we remove the current repeating character.
else {
while (start < n and s[start] != s[end]) {
character_set[(int) s[start]] = false;
start++;
}
// Finally, start will point to the first occurrence of repeated character
// and end will point to its second occurrence.
// Hence, we update both.
start++;
end++;
}
}
return max_len;
}
```

We hope that these solutions to longest substring without repeating characters problem have helped you level up your coding skills. You can expect problems like these at top tech companies like Amazon and Google.

If you are preparing for a tech interview at FAANG or any other Tier-1 tech company, register for Interview Kickstart's FREE webinar to understand the best way to prepare.

Interview Kickstart offers interview preparation courses taught by FAANG+ tech leads and seasoned hiring managers. Our programs include a comprehensive curriculum, unmatched teaching methods, and career coaching to help you nail your next tech interview.

We offer 18 interview preparation courses, each tailored to a specific engineering domain or role, including the most in-demand and highest-paying domains and roles, such as:

- Back-end Engineering Interview Course
- Front-end Engineering Interview Course
- Full Stack Developer Interview Course

To learn more, register for the FREE webinar.

**Attend our free webinar to amp up your career and get the salary you deserve.**

Hosted By

Ryan Valles

Founder, Interview Kickstart

- Designed by 500 FAANG+ experts
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