Self-Balancing Binary Search Trees are height-balanced binary search trees that automatically keeps height as small as possible when insertion and deletion operations are performed on tree. The height is typically maintained in order of Log n so that all operations take O(Log n) time on average.
Red Black Tree
How do Self-Balancing-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)). 1) Left Rotation 2) Right Rotation
T1, T2 and T3 are subtrees of the tree rooted with y (on the left side) or x (on the right side) y x / \ 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.
|Metric||RB Tree||AVL Tree||Splay Tree|
|Efficient Implementation requires||Three pointers with color bit per node||Two pointers with balance factor per
|Only two pointers with
no extra information
|Mostly used||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.||Database Transactions||Cache implementation, Garbage collection Algorithms|
- Comparisons involved in Modified Quicksort Using Merge Sort Tree
- Second minimum element using minimum comparisons
- Ackermann Function
- Count the number of words with given prefix using Trie
- Vertical and Horizontal retrieval (MRT) on Tapes
- Duplicates Removal in Array using BST
- Build a segment tree for N-ary rooted tree
- Minimum Cost Graph
- Count of different groups using Graph
- Count number of increasing sub-sequences : O(NlogN)
- Median of sliding window in an array
- Octree | Insertion and Searching
- Perform append, update, delete and range sum queries on the given array
- Longest subsequence having difference atmost K
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