Data Structures
Red-Black Tree
A widely-used self-balancing binary search tree that guarantees logarithmic time complexity for all major operations through a clever node-coloring system.
Red-Black Tree Conceptual Overview
Implementing a Red-Black Tree from scratch is a significant undertaking and rarely expected in a standard coding interview. The focus is usually on understanding its properties and performance characteristics.
# Red-Black Tree implementation is complex and typically not required in interviews.
# Below are the core properties that define a Red-Black Tree.
class RedBlackNode:
def __init__(self, key, color='RED'):
self.key = key
self.color = color
self.left = None
self.right = None
self.parent = None
# Key Properties:
# 1. Every node is either Red or Black.
# 2. The root is always Black.
# 3. There are no two adjacent Red nodes (A Red node cannot have a Red parent or Red child).
# 4. Every path from a node to any of its descendant NIL nodes has the same number of Black nodes.
# 5. All NIL leaves are Black.
AI Coach Hint: While AVL trees are more strictly balanced, Red-Black trees require fewer rotations on average for insertions and deletions. This makes them a better choice in write-heavy scenarios. This trade-off is why they are used in many standard library implementations, like `std::map` and `std::set` in C++.
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