LeetCode 3163 String Compression III Solution – C++ Implementation and Explanation

Why Solving LeetCode 3163: String Compression III in C++ is Important?

LeetCode 3163, String Compression III, is more than just a coding challenge—it's a practical exercise in C++ string manipulation and algorithmic thinking that mirrors real-world data compression and text processing tasks.

Key Skills Developed

• Efficient String Handling in C++: Learn to traverse and compress strings without exceeding memory limits, a common requirement in performance-sensitive applications.

• Edge Case Management: Handle scenarios where character sequences exceed the maximum allowed count, ensuring robustness in your LeetCode 3163 solution.
• Algorithm Optimization: Implement linear-time string compression algorithms that efficiently process large datasets.

Real-World Applications

• Data Compression: Understand the principles behind string compression algorithms in C++, applicable in file storage and transmission.

• Text Processing: Optimize text data for storage or transmission by reducing redundancy.
• Algorithm Design: Enhance your ability to design efficient string optimization algorithms.

Interview Relevance

Top tech companies often assess candidates with challenges like LeetCode String Compression III. Mastery of these C++ string problems demonstrates strong analytical skills and readiness for technical interviews.

Alternative Approaches and Optimizations

While the primary solution for LeetCode 3163: String Compression III in C++ uses a simple counting loop to group characters and limit each group to 9, there are other ways to implement this logic efficiently. Below are a few alternative approaches and small optimizations that can make your code faster and more memory-friendly.

1. Using a String Builder (ostringstream)

Instead of repeatedly concatenating strings, which can be inefficient, you can use std::ostringstream or a std::string buffer to append compressed parts more efficiently. This minimizes reallocation and improves runtime, especially for long strings.

class Solution {
public:
    string compressedString(string word) {
        ostringstream res;
        int sz = word.size();
        for (int i = 0; i < sz; ) {
            char current = word[i];
            int cnt = 0;

            while (i < sz && word[i] == current && cnt < 9) {
                i++;
                cnt++;
            }

            res << cnt << current;
        }

        return res.str();
    }
};

2. Preallocating the Result String

If the input length is known and large, you can reserve memory for the output string using result.reserve(s.size()). Although the compressed string may be shorter, this prevents multiple memory reallocations during appends.

3. Vector-Based Compression

For extremely large datasets, using a std::vector> to first store character–count pairs and then building the final string in one pass can improve performance and maintain code clarity.

4. Handling Continuous Groups Over 9

A small optimization is to directly calculate how many groups of 9 you’ll need when a character repeats many times. Instead of manually looping and resetting counters, you can divide the count and append results in fewer iterations.

5. Clean Code Improvements

• Use for loops instead of while where iteration is predictable.
• Extract the compression logic into a helper function for reusability.
• Leverage constexpr or inline helper methods to reduce runtime overhead.

These optimizations don’t drastically change the algorithm’s complexity (it remains O(n)), but they can enhance readability, maintainability, and real-world efficiency. Always benchmark before applying micro-optimizations.