# Self-Balancing-Binary-Search-Trees (Comparisons)

**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.

**Examples : **

Red Black Tree

AVL 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.

We have already discussed AVL tree, Red Black Tree and Splay Tree. In this acrticle, we will compare the efficiency of these trees:

Metric | RB Tree | AVL Tree | Splay Tree |
---|---|---|---|

Insertion inworst case |
O(1) |
O(logn) |
Amortized O(logn) |

Maximum heightof tree |
2*log(n) |
1.44*log(n) |
O(n) |

Search inworst case |
O(logn),Moderate |
O(logn),Faster |
Amortized O(logn),Slower |

Efficient Implementation requires |
Three pointers with color bit per node |
Two pointers with balance factor pernode |
Only two pointers withno extra information |

Deletion inworst case |
O(logn) |
O(logn) |
Amortized O(logn) |

Mostly used |
As universal data structure |
When frequent lookups are required |
When same element isretrieved again and again |

Real world Application |
Multiset, Multimap, Map, Set, etc. |
Database Transactions |
Cache implementation, Garbage collection Algorithms |

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