1. Self-Balancing Search Trees Chapter 11

2. Chapter 11: Self-Balancing Search Trees2 Chapter Objectives To understand the impact that balance has on the performance of binary search trees To learn about the AVL tree for storing and maintaining a binary search tree in balance To learn about the Red-Black tree for storing and maintaining a binary search tree in balance To learn about 2-3 trees, 2-3-4 trees, and B-trees and how they achieve balance To understand the process of search and insertion in each of these trees and to be introduced to removal

3. Chapter 11: Self-Balancing Search Trees3 Why Balance is Important Searches into an unbalanced search tree could be O(n) at worst case

4. Chapter 11: Self-Balancing Search Trees4 Rotation To achieve self-adjusting capability, we need an operation on a binary tree that will change the relative heights of left and right subtrees but preserve the binary search tree property Algorithm for rotation Remember value of root.left (temp = root.left) Set root.left to value of temp.right Set temp.right to root Set root to temp

5. Chapter 11: Self-Balancing Search Trees5 Rotation (continued)

6. Chapter 11: Self-Balancing Search Trees6 Rotation (continued)

7. Chapter 11: Self-Balancing Search Trees7 Rotation (continued)

8. Chapter 11: Self-Balancing Search Trees8 Implementing Rotation

9. Chapter 11: Self-Balancing Search Trees9 AVL Tree As items are added to or removed from the tree, the balance or each subtree from the insertion or removal point up to the root is updated Rotation is used to bring a tree back into balance The height of a tree is the number of nodes in the longest path from the root to a leaf node

10. Chapter 11: Self-Balancing Search Trees10 Balancing a Left-Left Tree The heights of the left and right subtrees are unimportant; only the relative difference matters when balancing A left-left tree is a tree in which the root and the left subtree of the root are both left-heavy Right rotations are required

11. Chapter 11: Self-Balancing Search Trees11 Balancing a Left-Right Tree Root is left-heavy but the left subtree of the root is right- heavy A simple right rotation cannot fix this Need both left and right rotations

12. Chapter 11: Self-Balancing Search Trees12 Four Kinds of Critically Unbalanced Trees Left-Left (parent balance is -2, left child balance is -1) Rotate right around parent Left-Right (parent balance -2, left child balance +1) Rotate left around child Rotate right around parent Right-Right (parent balance +2, right child balance +1) Rotate left around parent Right-Left (parent balance +2, right child balance -1) Rotate right around child Rotate left around parent

13. Chapter 11: Self-Balancing Search Trees13 Implementing an AVL Tree

14. Chapter 11: Self-Balancing Search Trees14 Red-Black Trees Rudolf Bayer developed the red-black tree as a special case of his B-tree A node is either red or black The root is always black A red node always has black children The number of black nodes in any path from the root to a leaf is the same

15. Chapter 11: Self-Balancing Search Trees15 Insertion into a Red-Black Tree Follows same recursive search process used for all binary search trees to reach the insertion point When a leaf is found, the new item is inserted and initially given the color red It the parent is black we are done otherwise there is some rearranging to do

16. Chapter 11: Self-Balancing Search Trees16 Insertion into a Red-Black Tree (continued)

17. Chapter 11: Self-Balancing Search Trees17 Implementation of a Red-Black Tree Class

18. Chapter 11: Self-Balancing Search Trees18 Algorithm for Red-Black Tree Insertion

19. Chapter 11: Self-Balancing Search Trees19 2-3 Trees 2-3 tree named for the number of possible children from each node Made up of nodes designated as either 2-nodes or 3- nodes A 2-node is the same as a binary search tree node A 3-node contains two data fields, ordered so that first is less than the second, and references to three children One child contains values less than the first data field One child contains values between the two data fields Once child contains values greater than the second data field 2-3 tree has property that all of the leaves are at the lowest level

20. Chapter 11: Self-Balancing Search Trees20 Searching a 2-3 Tree

21. Chapter 11: Self-Balancing Search Trees21 Searching a 2-3 Tree (continued)

22. Chapter 11: Self-Balancing Search Trees22 Inserting into a 2-3 Tree

23. Chapter 11: Self-Balancing Search Trees23 Algorithm for Insertion into a 2-3 Tree

24. Chapter 11: Self-Balancing Search Trees24 Removal from a 2-3 Tree Removing an item from a 2-3 tree is the reverse of the insertion process If the item to be removes is in a leaf, simply delete it If not in a leaf, remove it by swapping it with its inorder predecessor in a leaf node and deleting it from the leaf node

25. Chapter 11: Self-Balancing Search Trees25 Removal from a 2-3 Tree (continued)

26. Chapter 11: Self-Balancing Search Trees26 2-3-4 and B-Trees 2-3 tree was the inspiration for the more general B-tree which allows up to n children per node B-tree designed for building indexes to very large databases stored on a hard disk 2-3-4 tree is a specialization of the B-tree because it is basically a B-tree with n equal to 4 A Red-Black tree can be considered a 2-3-4 tree in a binary-tree format

27. Chapter 11: Self-Balancing Search Trees27 2-3-4 Trees Expand on the idea of 2-3 trees by adding the 4-node Addition of this third item simplifies the insertion logic

28. Chapter 11: Self-Balancing Search Trees28 Algorithm for Insertion into a 2-3-4 Tree

29. Chapter 11: Self-Balancing Search Trees29 Relating 2-3-4 Trees to Red-Black Trees A Red-Black tree is a binary-tree equivalent of a 2-3-4 tree A 2-node is a black node A 4-node is a black node with two red children A 3-node can be represented as either a black node with a left red child or a black node with a right red child

30. Chapter 11: Self-Balancing Search Trees30 Relating 2-3-4 Trees to Red-Black Trees (continued)

31. Chapter 11: Self-Balancing Search Trees31 Relating 2-3-4 Trees to Red-Black Trees (continued)

32. Chapter 11: Self-Balancing Search Trees32 B-Trees A B-tree extends the idea behind the 2-3 and 2-3-4 trees by allowing a maximum of CAP data items in each node The order of a B-tree is defined as the maximum number of children for a node B-trees were developed to store indexes to databases on disk storage

33. Chapter 11: Self-Balancing Search Trees33 Chapter Review Tree balancing is necessary to ensure that a search tree has O(log n) behavior An AVL tree is a balanced binary tree in which each node has a balance value that is equal to the difference between the heights of its right and left subtrees For an AVL tree, there are four kinds of imbalance and a different remedy for each A Red-Black tree is a balanced tree with red and black nodes To maintain tree balance in a Red-Black tree, it may be necessary to recolor a node and also to rotate around a node

34. Chapter 11: Self-Balancing Search Trees34 Chapter Review (continued) Trees whose nodes have more than two children are an alternative to balanced binary search trees A 2-3-4 tree can be balanced on the way down the insertion path by splitting a 4-node into two 2-nodes before inserting a new item A B-tree is a tree whose nodes can store up to CAP items and is a generalization of a 2-3-4 tree