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

O(1) vs O(N^2) decides your server's fate. Deep dive into Amortized Analysis, Master Theorem, P vs NP, and Space-Time Tradeoffs.

April 8, 2025

Codemapo

Computer Science·March 11, 2025

Memory Management: Contiguous vs Non-Contiguous Allocation

Why does my server crash? OS's desperate struggle to manage limited memory. War against Fragmentation.

#CS#OS#Memory

Memory Management: Contiguous vs Non-Contiguous Allocation

·May 25, 2025

Clean Architecture: The Dependency Rule and Separation of Concerns

A deep dive into Robert C. Martin's Clean Architecture. Learn how to decouple your business logic from frameworks, databases, and UI using Entities, Use Cases, and the Dependency Rule. Includes Screaming Architecture and Testing strategies.

#Software Architecture#Clean Code#Design Patterns#Backend

Clean Architecture: The Dependency Rule and Separation of Concerns

Computer Science·April 24, 2025

BFS vs DFS: Graph Traversal

Two ways to escape a maze. Spread out wide (BFS) or dig deep (DFS)? Who finds the shortest path?

#CS#Algorithm#Graph#Search

Computer Science·April 21, 2025

Quick Sort: Divide and Conquer

Fast by name. Partitioning around a Pivot. Why is it the standard library choice despite O(N²) worst case?

#CS#Algorithm#Sorting

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11. Summary

Big O is the standard language for discussing Code Efficiency.
Scale Matters: It describes growth rates, not absolute seconds.
Hierarchy: $O(1) < O(\log N) < O(N) < O(N \log N) < O(N^2) < O(2^N)$.
Trade-offs: You can often improved Time Complexity by sacrificing Space Complexity (Memorization).
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.