AVL trees vs Red-Black trees

In this post, we will learn the difference between AVL trees and Red-Black trees in Java. This is a frequently asked question in Java interviews for beginners. Let's dive into it.

Difference between AVL trees and Red-Black trees in Java

Feature AVL Tree Red-Black Tree
Balancing Criteria Strictly balanced (height-balanced) Semi-balanced (color-balanced)
Balancing Mechanism Rotations Rotations and Color Flips
Balance Factor -1, 0, +1 0, 1 (Represented by Colors)
Height Balance Perfectly balanced Approximately balanced
Lookup Time Complexity O(log n) O(log n)
Insertion Time Complexity O(log n) O(log n)
Deletion Time Complexity O(log n) O(log n)
Space Complexity O(n) O(n)
Use Case When searching is more frequent and the tree needs to be strictly balanced. When frequent insertions and deletions are required and the tree needs to be relatively balanced.
Notable Properties AVL trees tend to have slightly faster lookup compared to Red-Black trees but may require more rotations during insertion and deletion. Red-Black trees are more flexible in balancing and require fewer rotations during insertion and deletion, making them better suited for dynamic data structures.
Self-Balancing Mechanism AVL trees use height difference (balance factor) to trigger rotations for balancing. Red-Black trees use colors on nodes to trigger rotations and color flips for balancing.
Performance Trade-offs AVL trees may be more suitable for read-heavy applications with occasional writes. Red-Black trees may be more suitable for applications with frequent insertions and deletions.
Implementation Complexity AVL trees can be more complex to implement due to strict balancing requirements. Red-Black trees are relatively easier to implement due to color-based balancing.


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