Self-Balancing Binary Search Trees are height-balanced binary search trees that automatically keep the height as small as possible when insertion and deletion operations are performed on the tree.
The height is typically maintained in order of logN so that all operations take O(logN) time on average.
Examples: The most common examples of self-balancing binary search trees are
- AVL Tree
- Red Black Tree and
- Splay Tree
An AVL tree defined as a self-balancing Binary Search Tree (BST) where the difference between heights of left and right subtrees for any node cannot be more than one.
Example of AVL Tree
Basic operations on AVL Tree include:
To learn more about this, refer to the article on AVL Tree.
Red-Black tree is a self-balancing binary search tree in which every node is colored with either red or black. The root and leaf nodes (i.e., NULL nodes) are always marked as black.
Example of Red-Black Tree
Some Properties of Red-Black Tree:
- Root property: The root is black.
- External property: Every leaf (Leaf is a NULL child of a node) is black in Red-Black tree.
- Internal property: The children of a red node are black. Hence possible parent of red node is a black node.
- Depth property: All the leaves have the same black depth.
- Path property: Every simple path from the root to descendant leaf node contains the same number of black nodes
To learn more about this, refer to the article on “Red-Black Tree“.
Splay is a self-balancing binary search tree. The basic idea behind splay trees is to bring the most recently accessed or inserted element to the root of the tree by performing a sequence of tree rotations, called splaying.
Basic operations that are performed in a splay tree are:
- Rotation (There are two types of rotation in a Splay tree named zig rotation and zag rotation)
To learn more about this, refer to the article on “Splay Tree“.
Language Implementations of self-balancing BST:
- set and map in C++ STL.
- TreeSet and TreeMap in Java. Most of the library implementations use Red Black Tree.
- Python standard library does not support Self Balancing BST. In Python, we can use bisect module to keep a set of sorted data. We can also use PyPi modules like rbtree (implementation of red-black tree) and pyavl (implementation of AVL tree).
How does Self-Balancing Binary Search Tree maintain height?
A typical operation done by trees is rotation. Following are two basic operations that can be performed to re-balance a BST without violating the BST property (keys(left) < key(root) < keys(right)).
- Left Rotation
- Right Rotation
T1, T2 and T3 are subtrees of the tree rooted with y (on the left side) or x (on the right side)
/ \ Right Rotation / \
x T3 – – – – – – – – – > T1 y
/ \ <- – – – – – – – – / \
T1 T2 Left Rotation T2 T3
Keys in both of the above trees follow the following order
keys(T1) < key(x) < keys(T2) < key(y) < keys(T3)
So BST property is not violated anywhere.
Comparisons among Red-Black Tree, AVL Tree and Splay Tree:
In this article, we will compare the efficiency of these trees:
|Efficient Implementation requires
||Three pointers with color bit per node
||Two pointers with balance factor per
|Only two pointers with
no extra information
||As universal data structure
||When frequent lookups are required
||When same element is
retrieved again and again
|Real world Application
||Multiset, Multimap, Map, Set, etc.
||Cache implementation, Garbage collection Algorithms
AVL Tree is considered to be Strict as we have to maintain Balance Factor i.e. abs(Height of Left Sub-Tree – Height of Right Sub-Tree) ≤ 1, and In Red Black Tree, it is not considered to be as Strict as AVL Tree, due to which there are less number of rotations are required to make the Unbalanced BST -> Balanced as compared to AVL Tree. Also, In summary, if the application requires fast search times, an AVL Tree may be the better choice. If the application requires fast insertion and deletion times, a Red-Black Tree may be the better choice. It’s also important to consider other factors such as the size and complexity of the data being stored, as well as the memory and processing resources available.
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