Big O Notation: The Language of Algorithm Efficiency (Definitive Guide)

11. Summary

1. Big O is the standard language for discussing Code Efficiency.
2. Scale Matters: It describes growth rates, not absolute seconds.
3. Hierarchy: $O(1) < O(\log N) < O(N) < O(N \log N) < O(N^2) < O(2^N)$.
4. Trade-offs: You can often improved Time Complexity by sacrificing Space Complexity (Memorization).
5. Reality: Watch out for hidden costs like Immutable Strings or Recursion Stack depths.

Mastering Big O separates "Coders" who just make it work from "Engineers" who make it scalable.