AVL Trees Explained: Rotations, Insertion & Full Code Implementation

Описание: Master the AVL Tree, the foundation of self-balancing search trees. In this deep-dive tutorial, we strip away the confusion surrounding tree rotations and balance factors to show you exactly how this data structure maintains an O(logn) time complexity for all operations.
🚀 What You’ll Learn:

The "Why": Why standard Binary Search Trees (BSTs) fail in the worst-case scenario and how AVL trees fix "skewing."

The Math: Calculating the Balance Factor (BF) and identifying critical nodes.

The Rotations: Visualizing the four rotation cases: Left-Left (LL), Right-Right (RR), Left-Right (LR), and Right-Left (RL).

The Implementation: A complete walkthrough of the insertion logic and height-update code.

💡 Key Concepts Covered:

Self-Balancing Property: Maintaining the height difference between subtrees to no more than 1.

Height Analysis: Understanding why the height of an AVL tree is strictly logarithmic.

Big O Notation: Comparative analysis of Search, Insertion, and Deletion vs. a standard BST.

🛠️ Developer Resources:

Source Code: [Insert Link to GitHub/Gist]

Documentation: [Link to official language docs or blog post]

Join the Discussion: If you're stuck on a specific rotation logic, drop a comment below—I reply to every technical question!

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