Real-World Analogy
Run-Length String Compression is turning `"aaabbc"` into `"a3b2c1"`—saving space when repeating characters appear sequentially.
Frequency Counting & Compression
Use an array of size 26 (`int[] freq = new int[26]`) to count ASCII character occurrences in $O(N)$ time.
Production Code Example:
public class AnagramCheck {
public boolean isAnagram(String s, String t) {
if (s.length() != t.length()) return false;
int[] count = new int[26];
for (int i = 0; i < s.length(); i++) {
count[s.charAt(i) - 'a']++;
count[t.charAt(i) - 'a']--;
}
for (int c : count) if (c != 0) return false;
return true;
}
}
Key Complexity & Algorithmic Takeaways:
When implementing Character Frequency & String Compression in coding interviews and production applications, keep these core guidelines in mind:
- Time Complexity Analysis: Always evaluate best-case, average-case, and worst-case time complexities ($O(1)$, $O(\log n)$, $O(n)$, $O(n \log n)$, $O(n^2)$).
- Space Complexity & Memory Bounds: Account for auxiliary memory usage, call stack frame recursion overhead, and heap allocations.
- Edge Cases & Validation: Test empty inputs, null pointers, single-element collections, duplicate values, and integer overflow bounds.
- Optimal vs Naive Solutions: Start with a clear brute-force solution, then optimize using techniques like Hashing, Two Pointers, Windowing, or Dynamic Programming.
Summary Takeaway:
Mastering Character Frequency & String Compression provides the foundational problem-solving skills needed to pass technical coding interviews at top tech companies and write ultra-performant software systems.
Unicode Extension
For extended Unicode characters, replace `int[26]` with a `HashMap