What is the time complexity of Substrings of Size Three with Distinct Characters?

The optimal solution has a time complexity of O(n).

What data structures are used to solve Substrings of Size Three with Distinct Characters?

This problem uses Hash Table, String, Sliding Window, Counting. Understanding these concepts is essential for solving this Easy problem efficiently.

What are the key insights for solving Substrings of Size Three with Distinct Characters?

Insight 1: Sliding window technique can be effectively used to iterate through all substrings of length 3 in a single pass. Insight 2: Using a `Set` (Python) or checking character equality directly (other languages) efficiently determines if a substring contains distinct characters.

Which companies ask LeetCode 1876. Substrings of Size Three with Distinct Characters in interviews?

This problem is frequently asked at Accenture, Quora, Visa.

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Substrings of Size Three with Distinct Characters - LeetCode 1876 Solution

Substrings of Size Three with Distinct Characters - Complete Solution Guide

Substrings of Size Three with Distinct Characters is LeetCode problem 1876, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

A string is good if there are no repeated characters. Given a string s , return the number of good substrings of length three in s . Note that if there are multiple occurrences of the same substring, every occurrence should be counted. A substring is a contiguous sequence of characters in a string. Example 1: Input: s = "xyzzaz" Output: 1 Explanation: There are 4 substrings of size 3: "xyz", "yzz", "zza", and "zaz". The only good substring of length 3 is "xyz". Example 2: Input: s = "

Detailed Explanation

The problem asks you to count the number of substrings of length 3 within a given string that contain only distinct characters (no repeated characters). The input is a string `s` consisting of lowercase English letters. The output is an integer representing the count of these 'good' substrings. The problem emphasizes that overlapping substrings should be counted separately. For instance, in the string "abc", there's only one substring of length 3, which is "abc" itself and is a good substring. In the string "aababcabc", there are multiple substrings of length 3, such as "aab", "aba", "bab", "abc", "bca", "cab", and "abc". Only "abc", "bca", "cab" are good substrings (distinct characters), so the answer would be 4.

Solution Approach

The provided solutions employ a sliding window approach. They iterate through the string using a loop. In each iteration, a substring of length 3 is extracted. Then, it checks if the characters within this substring are distinct. If they are distinct (no repetitions), the counter `count` is incremented. This process continues until all possible substrings of length 3 have been examined. The final value of `count` is then returned.

Step-by-Step Algorithm

Step 1: Initialize a counter `count` to 0.
Step 2: Iterate through the string using a loop, starting from index 0 up to `len(s) - 2`. Each iteration represents a sliding window of size 3.
Step 3: Extract a substring of length 3 starting at the current index.
Step 4: Check if the substring contains only distinct characters. This can be done using a `Set` (Python) or by explicitly comparing the characters in the substring (Java, C++, C).
Step 5: If the characters are distinct, increment the `count`.
Step 6: Repeat steps 3-5 for each possible substring.
Step 7: Return the final value of `count`.

Key Insights

Insight 1: Sliding window technique can be effectively used to iterate through all substrings of length 3 in a single pass.
Insight 2: Using a `Set` (Python) or checking character equality directly (other languages) efficiently determines if a substring contains distinct characters.
Insight 3: The time complexity is inherently linear because each character in the input string is visited at most a constant number of times (3 times to be precise).

Complexity Analysis

Time Complexity: O(n)

Space Complexity: O(1)

Topics

This problem involves: Hash Table, String, Sliding Window, Counting.

Companies

Asked at: Accenture, Quora, Visa.

Substrings of Size Three with Distinct Characters | LeetCode 1876

Easy

Hash Table String Sliding Window Counting

Time: O(n)Space: O(1)

Solution Code

Available in:

class Solution {
    public int countGoodSubstrings(String s) {
        int count = 0;
        for (int i = 0; i <= s.length() - 3; i++) {
            String sub = s.substring(i, i + 3);
            if (sub.charAt(0) != sub.charAt(1) && sub.charAt(1) != sub.charAt(2) && sub.charAt(0) != sub.charAt(2)) {
                count++;
            }
        }
        return count;
    }
}

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Problem Info

Description

Problem 1876: Substrings of Size Three with Distinct Characters - Detailed explanation and solution approaches.

Topics

Hash Table String Sliding Window Counting

Companies

AccentureQuoraVisa

Stats

Difficulty:Easy

Languages:4

Companies:3

Problem #:1876

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

7 steps

1
Step 1: Initialize a counter `count` to 0.
2
Step 2: Iterate through the string using a loop, starting from index 0 up to `len(s) - 2`. Each iteration represents a sliding window of size 3.
3
Step 3: Extract a substring of length 3 starting at the current index.
4
Step 4: Check if the substring contains only distinct characters. This can be done using a `Set` (Python) or by explicitly comparing the characters in the substring (Java, C++, C).
5
Step 5: If the characters are distinct, increment the `count`.
6
Step 6: Repeat steps 3-5 for each possible substring.
7
Step 7: Return the final value of `count`.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n)

👍 Linear - Good! Visits each element once.

💾

Space Complexity

O(1)

💪 Constant space - Uses same memory regardless of input!

📝

Why?

The time complexity is O(n) because the loop iterates through the string once. The operations within the loop (substring extraction and character comparison) take constant time. The space complexity is O(1) because we only use a constant amount of extra space to store the counter and possibly a few temporary variables. The Set in Python has a constant space complexity because the size of the set is at most 3.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Using a bitmask

Represent the presence of characters in each substring using a bitmask. A bit is set to 1 if the character is present, 0 otherwise. If a bit is already set before, it means a repeated character exists.

Time: O(n)Space: O(1)

Trade-offs: This approach might be slightly faster for some systems because bit manipulation is often highly optimized. However, it's less readable than the provided solutions.

✨

Key Insights

1Insight 1: Sliding window technique can be effectively used to iterate through all substrings of length 3 in a single pass.
2Insight 2: Using a `Set` (Python) or checking character equality directly (other languages) efficiently determines if a substring contains distinct characters.
3Insight 3: The time complexity is inherently linear because each character in the input string is visited at most a constant number of times (3 times to be precise).

📚

Key Terms Explained

For Students

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

Sliding Window

A moving "window" over data to process subsets efficiently.

String

A sequence of characters. Like a word or sentence in programming!

Edge Cases

Edge case 1: If the input string `s` has a length less than 3, the function should correctly return 0, as there are no substrings of length 3. The code handles this because the loop condition `i <= s.length() - 3` prevents the loop from running.
Edge case 2: If the input string contains only repeating characters (e.g., "aaa"), the function should return 0. The code correctly handles this, because the distinct character check will fail for all substrings.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Forgetting to handle the case where the input string is shorter than 3 characters. This can lead to an index out of bounds error or an incorrect result.

✗Common mistake 2: Inefficiently checking for distinct characters. Using nested loops for comparison instead of a `Set` or direct comparison results in O(n^2) time complexity.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: DNA sequence analysis: Identifying short sequences with unique nucleotides.

🏦Real-world scenario 2: Network security: Detecting patterns in network traffic that might indicate malicious activity (though in real-world scenarios, longer sequences and more sophisticated pattern recognition would be used).

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

Hash tables trade space for time. Before using one, consider if the problem constraints allow the extra memory. In interviews, always mention this trade-off.

🎯 Interview Tip

Use this problem to demonstrate clean code practices. Variable naming and code organization matter.

⚠️ Watch Out

Don't forget to handle key collisions and null/undefined keys. Consider what happens with duplicate values.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: August 16, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 14

I was asked this exact problem at my Google interview. The interviewer wanted me to optimize space complexity.

@InterviewProJul 7

❓

Frequently Asked Questions

What are the trade-offs of using a hash table?

Hash tables provide O(1) average-case lookup, insertion, and deletion, making them ideal for frequency counting and duplicate detection. However, they use O(n) extra space and may degrade to O(n) time in worst-case scenarios with hash collisions. Always consider if the space trade-off is acceptable.

How do I handle string manipulation efficiently?

In languages with immutable strings (Java, Python, C#), use StringBuilder/list to avoid O(n) concatenation cost. Consider using character arrays for in-place modifications. For pattern matching, consider KMP or Rabin-Karp for O(n+m) complexity.

How should I approach easy-level algorithm problems?

Easy problems build foundational skills. Focus on: 1) Understanding the problem completely before coding, 2) Writing clean, readable code, 3) Handling all edge cases (empty input, single element, etc.), 4) Analyzing time and space complexity. Don't skip complexity analysis even for simple solutions.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Substrings of Size Three with Distinct Characters"?

For this easy-level Hash Table problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether string techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

🤖 Need help with this problem?