AVL Tree in Data Structures with Examples

06 Jun 2024

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AVL Tree in Data Structures: An Overview

We know that the Binary Search Tree cannot guarantee logarithmic complexity. If the elements are inserted in a sorted increasing order, the tree becomes skewed, so the worst-case time complexity for insert/search operations becomes O(n). To overcome this limitation of binary search trees, AVL Trees came into existence. In this DSA tutorial, we will discuss AVL trees, balance factors, rotations of AVL trees, operations, etc.

To further enhance your understanding and application of AVL Trees, consider enrolling in the Best Data Structures And Algorithms Course to gain comprehensive insights into effective data structure utilization for improved problem-solving and time management.

What is an AVL Tree in Data Structures?

AVL tree in data structures is a popular self-balancing binary search tree where the difference between the heights of left and right subtrees for any node does not exceed unity. It was introduced by Georgy Adelson-Velsky and Evgenii Landis in 1962, hence the name AVL. It automatically adjusts its structure to maintain the minimum possible height after any operation with the help of a balance factor for each node.

Balance Factor in AVL Tree in Data Structures

The balance factor of a node in an AVL Tree is a numerical value that represents the difference in height between the left and right subtrees of that node. It is the extra information used to determine the tree's balance. The balance factor is calculated as follows:

Balance Factor = height(left subtree) - height(right subtree)
or 
bf = hl − hr, 
where bf = balance factor of a given node, hl = height of the left subtree, and hr = height of the right subtree.

The value of the balance factor always lies between -1 and 1.

∣bf∣ = ∣hl − hr∣ ≤ 1

If the balance factor of any node = 1, the left sub-tree is one level higher than the right sub-tree i.e. the given node is left-heavy.
If the balance factor of any node = 0, the left sub-tree and right sub-tree are of equal height.

For leaf nodes, the balance factor is 0 as they do not contain any subtrees.

If the balance factor of any node = -1, the left sub-tree is one level lower than the right sub-tree i.e. the given node is right-heavy.

Hence, in this way, the self-balancing property of an AVL tree is maintained by the balance factor. Thus, we can find an unbalanced node in the tree and locate where the height-affecting operation was performed that caused the imbalance of the tree.

AVL tree

AVL Tree Rotations

AVL tree rotation is a fundamental operation used in self-balancing binary search trees, specifically in AVL trees. Due to any operations like insertion or deletion, if any node of an AVL tree becomes unbalanced, specific tree rotations are performed to restore the balance.

The tree rotations involve rearranging the tree structure without changing the order of elements. The positions of the nodes of a subtree are interchanged. There are four types of AVL rotations:

AVL Tree Rotations

Left Rotation (LL Rotation)

In a left rotation, a node's right child becomes the new root, while the original node becomes the left child of the new root. The new root's left child becomes the original node's right child.

Left rotation of AVL tree

Right Rotation (RR Rotation)

In a right rotation, a node's left child becomes the new root, while the original node becomes the right child of the new root. The new root's right child becomes the original node's left child.

Right rotation of AVL tree

Left-Right Rotation (LR Rotation)

An LR rotation is a combination of a left rotation followed by a right rotation. It is performed when the left subtree of a node is unbalanced to the right, and the right subtree of the left child of that node is unbalanced to the left.

Left-Right Rotation of AVL tree

Right-Left Rotation (RL Rotation)

An RL rotation is a combination of a right rotation followed by a left rotation. It is performed when the right subtree of a node is unbalanced to the left, and the left subtree of the right child of that node is unbalanced to the right.

Right Left Rotation of AVL tree

Standard Operations on AVL Trees in Data Structures

Insertion: A newNode is always inserted as a leaf node with a balance factor equal to 0. After each insertion, the ancestors of the newly inserted node are examined because the insertion only affects their heights, potentially inducing an imbalance. This process of traversing the ancestors to find the unbalanced node is called retracing.
Algorithm for Insertion in an AVL Tree
```
Step 1: START
Step 2: Insert the node using BST insertion logic.
Step 3: Calculate and check the balance factor of each node.
Step 4: If the balance factor follows the AVL criterion, go to step 6.
Step 5: Else, perform tree rotations according to the insertion done. Once the tree is balanced go to step 6.
Step 6: END
```
Let's understand with an example
1. Let the initial tree be:
  Let the node to be inserted be:
2. Go to the appropriate leaf node to insert a newNode using the following recursive steps. Compare newKey with rootKey of the current tree.
  1. If newKey < rootKey, call the insertion algorithm on the left subtree of the current node until the leaf node is reached.
  2. Else if newKey > rootKey, call the insertion algorithm on the right subtree of the current node until the leaf node is reached.
  3. Else, return leafNode.
3. Compare leafKey obtained from the above steps with newKey:
  1. If newKey < leafKey, make newNode as the leftChild of leafNode.
  2. Else, make newNode as rightChild of leafNode.
4. Update balanceFactor of the nodes.
5. If the nodes are unbalanced, then rebalance the node.
  1. If balanceFactor > 1, which means the height of the left subtree is greater than that of the right subtree. So, do a right rotation or left-right rotation
    1. If newNodeKey < leftChildKey do the right rotation.
    2. Else, do left-right rotation.
  2. If balanceFactor < -1, it means the height of the right subtree is greater than that of the left subtree. So, do a right rotation or right-left rotation
    1. If newNodeKey > rightChildKey do a left rotation.
    2. Else, do right-left rotation
6. The final tree is:
Deletion:A node is always deleted as a leaf node. After deleting a node, the balance factors of the nodes get changed. To rebalance the balance factor, suitable rotations are performed.
Algorithm for Deletion in an AVL Tree
```
Step 1: START
Step 2: Find the node in the tree. If the element is not found, go to step 7.
Step 3: Delete the node using BST deletion logic.
Step 4: Calculate and check the balance factor of each node.
Step 5: If the balance factor follows the AVL criterion, go to step 7.
Step 6: Else, perform tree rotations to balance the unbalanced nodes. Once the tree is balanced go to step 7.
Step 7: END
```
Let's understand with an example
1. Locate nodeToBeDeleted (recursion is used to find nodeToBeDeleted in the code used below).
2. There are three cases for deleting a node:
  1. If nodeToBeDeleted is the leaf node (ie. does not have any child), then remove nodeToBeDeleted.
  2. If nodeToBeDeleted has one child, then substitute the contents of nodeToBeDeleted with that of the child. Remove the child.
  3. If nodeToBeDeleted have two children, find the in-order successor w of nodeToBeDeleted (ie. node with a minimum value of key in the right subtree).
  - Substitute the contents of nodeToBeDeleted with that of w.
  - Remove the leaf node w.
3. Update balanceFactor of the nodes.
4. Rebalance the tree if the balance factor of any of the nodes is not equal to -1, 0, or 1.
  1. If balanceFactor of currentNode > 1,
    1. If balanceFactor of leftChild >= 0, do right rotation.
    2. Else do left-right rotation.
  2. If balanceFactor of currentNode < -1,
    1. If balanceFactor of rightChild <= 0, do left rotation.
    2. Else do right-left rotation.
5. The final tree is:

Implementation of AVL Tree in Different Programming Languages


import sys

# Create a tree node
class TreeNode(object):
    def __init__(self, key):
        self.key = key
        self.left = None
        self.right = None
        self.height = 1


class AVLTree(object):

    # Function to insert a node
    def insert_node(self, root, key):

        # Find the correct location and insert the node
        if not root:
            return TreeNode(key)
        elif key < root.key:
            root.left = self.insert_node(root.left, key)
        else:
            root.right = self.insert_node(root.right, key)

        root.height = 1 + max(self.getHeight(root.left),
                              self.getHeight(root.right))

        # Update the balance factor and balance the tree
        balanceFactor = self.getBalance(root)
        if balanceFactor > 1:
            if key < root.left.key:
                return self.rightRotate(root)
            else:
                root.left = self.leftRotate(root.left)
                return self.rightRotate(root)

        if balanceFactor < -1:
            if key > root.right.key:
                return self.leftRotate(root)
            else:
                root.right = self.rightRotate(root.right)
                return self.leftRotate(root)

        return root

    # Function to delete a node
    def delete_node(self, root, key):

        # Find the node to be deleted and remove it
        if not root:
            return root
        elif key < root.key:
            root.left = self.delete_node(root.left, key)
        elif key > root.key:
            root.right = self.delete_node(root.right, key)
        else:
            if root.left is None:
                temp = root.right
                root = None
                return temp
            elif root.right is None:
                temp = root.left
                root = None
                return temp
            temp = self.getMinValueNode(root.right)
            root.key = temp.key
            root.right = self.delete_node(root.right,
                                          temp.key)
        if root is None:
            return root

        # Update the balance factor of nodes
        root.height = 1 + max(self.getHeight(root.left),
                              self.getHeight(root.right))

        balanceFactor = self.getBalance(root)

        # Balance the tree
        if balanceFactor > 1:
            if self.getBalance(root.left) >= 0:
                return self.rightRotate(root)
            else:
                root.left = self.leftRotate(root.left)
                return self.rightRotate(root)
        if balanceFactor < -1:
            if self.getBalance(root.right) <= 0:
                return self.leftRotate(root)
            else:
                root.right = self.rightRotate(root.right)
                return self.leftRotate(root)
        return root

    # Function to perform left rotation
    def leftRotate(self, z):
        y = z.right
        T2 = y.left
        y.left = z
        z.right = T2
        z.height = 1 + max(self.getHeight(z.left),
                           self.getHeight(z.right))
        y.height = 1 + max(self.getHeight(y.left),
                           self.getHeight(y.right))
        return y

    # Function to perform right rotation
    def rightRotate(self, z):
        y = z.left
        T3 = y.right
        y.right = z
        z.left = T3
        z.height = 1 + max(self.getHeight(z.left),
                           self.getHeight(z.right))
        y.height = 1 + max(self.getHeight(y.left),
                           self.getHeight(y.right))
        return y

    # Get the height of the node
    def getHeight(self, root):
        if not root:
            return 0
        return root.height

    # Get balance factore of the node
    def getBalance(self, root):
        if not root:
            return 0
        return self.getHeight(root.left) - self.getHeight(root.right)

    def getMinValueNode(self, root):
        if root is None or root.left is None:
            return root
        return self.getMinValueNode(root.left)

    def preOrder(self, root):
        if not root:
            return
        print("{0} ".format(root.key), end="")
        self.preOrder(root.left)
        self.preOrder(root.right)

    # Print the tree
    def printHelper(self, currPtr, indent, last):
        if currPtr != None:
            sys.stdout.write(indent)
            if last:
                sys.stdout.write("R----")
                indent += "     "
            else:
                sys.stdout.write("L----")
                indent += "|    "
            print(currPtr.key)
            self.printHelper(currPtr.left, indent, False)
            self.printHelper(currPtr.right, indent, True)


myTree = AVLTree()
root = None
nums = [22, 14, 72, 44, 25, 63, 98]
for num in nums:
    root = myTree.insert_node(root, num)
myTree.printHelper(root, "", True)

key = 25
root = myTree.delete_node(root, key)
print("After Deletion: ")
myTree.printHelper(root, "", True)



// Create node
class Node {
    int item, height;
    Node left, right;

    Node(int d) {
        item = d;
        height = 1;
    }
}

// Tree class
class AVLTree {
    Node root;

    int height(Node N) {
        if (N == null)
            return 0;
        return N.height;
    }

    int max(int a, int b) {
        return (a > b) ? a : b;
    }

    Node rightRotate(Node y) {
        Node x = y.left;
        Node T2 = x.right;
        x.right = y;
        y.left = T2;
        y.height = max(height(y.left), height(y.right)) + 1;
        x.height = max(height(x.left), height(x.right)) + 1;
        return x;
    }

    Node leftRotate(Node x) {
        Node y = x.right;
        Node T2 = y.left;
        y.left = x;
        x.right = T2;
        x.height = max(height(x.left), height(x.right)) + 1;
        y.height = max(height(y.left), height(y.right)) + 1;
        return y;
    }

    // Get balance factor of a node
    int getBalanceFactor(Node N) {
        if (N == null)
            return 0;
        return height(N.left) - height(N.right);
    }

    // Insert a node
    Node insertNode(Node node, int item) {

        // Find the position and insert the node
        if (node == null)
            return (new Node(item));
        if (item < node.item)
            node.left = insertNode(node.left, item);
        else if (item > node.item)
            node.right = insertNode(node.right, item);
        else
            return node;

        // Update the balance factor of each node
        // And, balance the tree
        node.height = 1 + max(height(node.left), height(node.right));
        int balanceFactor = getBalanceFactor(node);
        if (balanceFactor > 1) {
            if (item < node.left.item) {
                return rightRotate(node);
            } else if (item > node.left.item) {
                node.left = leftRotate(node.left);
                return rightRotate(node);
            }
        }
        if (balanceFactor < -1) {
            if (item > node.right.item) {
                return leftRotate(node);
            } else if (item < node.right.item) {
                node.right = rightRotate(node.right);
                return leftRotate(node);
            }
        }
        return node;
    }

    Node nodeWithMinimumValue(Node node) {
        Node current = node;
        while (current.left != null)
            current = current.left;
        return current;
    }

    // Delete a node
    Node deleteNode(Node root, int item) {

        // Find the node to be deleted and remove it
        if (root == null)
            return root;
        if (item < root.item)
            root.left = deleteNode(root.left, item);
        else if (item > root.item)
            root.right = deleteNode(root.right, item);
        else {
            if ((root.left == null) || (root.right == null)) {
                Node temp = null;
                if (temp == root.left)
                    temp = root.right;
                else
                    temp = root.left;
                if (temp == null) {
                    temp = root;
                    root = null;
                } else
                    root = temp;
            } else {
                Node temp = nodeWithMinimumValue(root.right);
                root.item = temp.item;
                root.right = deleteNode(root.right, temp.item);
            }
        }
        if (root == null)
            return root;

        // Update the balance factor of each node and balance the tree
        root.height = max(height(root.left), height(root.right)) + 1;
        int balanceFactor = getBalanceFactor(root);
        if (balanceFactor > 1) {
            if (getBalanceFactor(root.left) >= 0) {
                return rightRotate(root);
            } else {
                root.left = leftRotate(root.left);
                return rightRotate(root);
            }
        }
        if (balanceFactor < -1) {
            if (getBalanceFactor(root.right) <= 0) {
                return leftRotate(root);
            } else {
                root.right = rightRotate(root.right);
                return leftRotate(root);
            }
        }
        return root;
    }

    void preOrder(Node node) {
        if (node != null) {
            System.out.print(node.item + " ");
            preOrder(node.left);
            preOrder(node.right);
        }
    }

    // Print the tree
    private void printTree(Node currPtr, String indent, boolean last) {
        if (currPtr != null) {
            System.out.print(indent);
            if (last) {
                System.out.print("R----");
                indent += "   ";
            } else {
                System.out.print("L----");
                indent += "|  ";
            }
            System.out.println(currPtr.item);
            printTree(currPtr.left, indent, false);
            printTree(currPtr.right, indent, true);
        }
    }

    public static void main(String[] args) {
        AVLTree tree = new AVLTree();
        tree.root = tree.insertNode(tree.root, 22);
        tree.root = tree.insertNode(tree.root, 14);
        tree.root = tree.insertNode(tree.root, 72);
        tree.root = tree.insertNode(tree.root, 44);
        tree.root = tree.insertNode(tree.root, 25);
        tree.root = tree.insertNode(tree.root, 63);
        tree.root = tree.insertNode(tree.root, 98);

        System.out.println("Before Deletion: ");
        tree.printTree(tree.root, "", true);
        
        tree.root = tree.deleteNode(tree.root, 25);
        System.out.println("After Deletion: ");
        tree.printTree(tree.root, "", true);
    }
}


#include <iostream>
using namespace std;

class Node {
   public:
  int key;
  Node *left;
  Node *right;
  int height;
};

int max(int a, int b);

// Calculate height
int height(Node *N) {
  if (N == NULL)
    return 0;
  return N->height;
}

int max(int a, int b) {
  return (a > b) ? a : b;
}

// New node creation
Node *newNode(int key) {
  Node *node = new Node();
  node->key = key;
  node->left = NULL;
  node->right = NULL;
  node->height = 1;
  return (node);
}

// Rotate right
Node *rightRotate(Node *y) {
  Node *x = y->left;
  Node *T2 = x->right;
  x->right = y;
  y->left = T2;
  y->height = max(height(y->left),
          height(y->right)) +
        1;
  x->height = max(height(x->left),
          height(x->right)) +
        1;
  return x;
}

// Rotate left
Node *leftRotate(Node *x) {
  Node *y = x->right;
  Node *T2 = y->left;
  y->left = x;
  x->right = T2;
  x->height = max(height(x->left),
          height(x->right)) +
        1;
  y->height = max(height(y->left),
          height(y->right)) +
        1;
  return y;
}

// Get the balance factor of each node
int getBalanceFactor(Node *N) {
  if (N == NULL)
    return 0;
  return height(N->left) -
       height(N->right);
}

// Insert a node
Node *insertNode(Node *node, int key) {
  // Find the correct postion and insert the node
  if (node == NULL)
    return (newNode(key));
  if (key < node->key)
    node->left = insertNode(node->left, key);
  else if (key > node->key)
    node->right = insertNode(node->right, key);
  else
    return node;

  // Update the balance factor of each node and
  // balance the tree
  node->height = 1 + max(height(node->left),
               height(node->right));
  int balanceFactor = getBalanceFactor(node);
  if (balanceFactor > 1) {
    if (key < node->left->key) {
      return rightRotate(node);
    } else if (key > node->left->key) {
      node->left = leftRotate(node->left);
      return rightRotate(node);
    }
  }
  if (balanceFactor < -1) {
    if (key > node->right->key) {
      return leftRotate(node);
    } else if (key < node->right->key) {
      node->right = rightRotate(node->right);
      return leftRotate(node);
    }
  }
  return node;
}

// Node with minimum value
Node *nodeWithMimumValue(Node *node) {
  Node *current = node;
  while (current->left != NULL)
    current = current->left;
  return current;
}

// Delete a node
Node *deleteNode(Node *root, int key) {
  // Find the node and delete it
  if (root == NULL)
    return root;
  if (key < root->key)
    root->left = deleteNode(root->left, key);
  else if (key > root->key)
    root->right = deleteNode(root->right, key);
  else {
    if ((root->left == NULL) ||
      (root->right == NULL)) {
      Node *temp = root->left ? root->left : root->right;
      if (temp == NULL) {
        temp = root;
        root = NULL;
      } else
        *root = *temp;
      free(temp);
    } else {
      Node *temp = nodeWithMimumValue(root->right);
      root->key = temp->key;
      root->right = deleteNode(root->right,
                   temp->key);
    }
  }

  if (root == NULL)
    return root;

  // Update the balance factor of each node and
  // balance the tree
  root->height = 1 + max(height(root->left),
               height(root->right));
  int balanceFactor = getBalanceFactor(root);
  if (balanceFactor > 1) {
    if (getBalanceFactor(root->left) >= 0) {
      return rightRotate(root);
    } else {
      root->left = leftRotate(root->left);
      return rightRotate(root);
    }
  }
  if (balanceFactor < -1) {
    if (getBalanceFactor(root->right) <= 0) {
      return leftRotate(root);
    } else {
      root->right = rightRotate(root->right);
      return leftRotate(root);
    }
  }
  return root;
}

// Print the tree
void printTree(Node *root, string indent, bool last) {
  if (root != nullptr) {
    cout << indent;
    if (last) {
      cout << "R----";
      indent += "   ";
    } else {
      cout << "L----";
      indent += "|  ";
    }
    cout << root->key << endl;
    printTree(root->left, indent, false);
    printTree(root->right, indent, true);
  }
}

int main() {
  Node *root = NULL;
  root = insertNode(root, 22);
  root = insertNode(root, 14);
  root = insertNode(root, 72);
  root = insertNode(root, 44);
  root = insertNode(root, 25);
  root = insertNode(root, 63);
  root = insertNode(root, 98);
  printTree(root, "", true);
  
  root = deleteNode(root, 25);
  cout << "After deleting " << endl;
  printTree(root, "", true);
}

Output

R----44
   L----22
   |  L----14
   |  R----25
   R----72
      L----63
      R----98
After deleting 
R----44
   L----22
   |  L----14
   R----72
      L----63
      R----98

Complexity Analysis of AVL Tree Operations

Time Complexity:
Operations Best Case Average Case Worst Case
Insertion
O (log n)
O (log n)
O (log n)
Deletion
O (log n)
O (log n)
O (log n)
Traversal
O (n)
O (n)
O (n)
Search
O (log n)
O (log n)
O (log n)
Space Complexity: Space complexity is the same as that of Binary Search Trees i.e., O(n) as AVL Trees don't modify the data itself.

Applications of AVL Trees

It is used to index huge records in a database and also search in that efficiently.
For all types of in-memory collections, including sets and dictionaries, AVL Trees are used.
Database applications, where insertions and deletions are less common but frequent data lookups are necessary.
Software that needs optimized search.
It is applied in corporate areas and storyline games.

Advantages of AVL Trees

AVL trees are capable of self-balancing.
It cannot be skewed.
Compared to Red-Black Trees, it offers faster lookups.
Superior searching efficiency compared to other trees, such as the binary tree.
Height is limited to log(n), where n is the number of nodes in the tree as a whole.

Disadvantages of AVL Trees

Implementing it is challenging.
Certain procedures have high constant factors.
Compared to Red-Black trees, AVL trees are less common.
As additional rotations are conducted, AVL trees offer complex insertion and removal procedures because of their rather rigid balance.
Requires to process more for balancing.

Summary

AVL Trees are a great tool to help structure and organize data. By keeping track of the difference in heights between two nodes, AVL trees can save you from calculating these values manually. Furthermore, as it is self-balancing, you need not worry about the tree becoming unbalanced as it grows. While traversing may take longer due to all the re-balancing, this method gives you greater control over your data sets than many regular BSTs. To implement your theoretical knowledge about AVL Trees, consider our Data Structures Certification.

FAQs

The balance factor of a node in an AVL Tree is a numerical value that represents the difference in height between the left and right subtrees of that node.

There are four types of AVL tree rotations.

Space complexity is the same as that of Binary Search Trees i.e., O(n) as AVL Trees don't modify the data itself.

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AVL Tree in Data Structures with Examples

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AVL Tree in Data Structures: An Overview

What is an AVL Tree in Data Structures?

Balance Factor in AVL Tree in Data Structures

AVL Tree Rotations

Left Rotation (LL Rotation)

Right Rotation (RR Rotation)

Left-Right Rotation (LR Rotation)

Right-Left Rotation (RL Rotation)

Standard Operations on AVL Trees in Data Structures

Algorithm for Insertion in an AVL Tree

Algorithm for Deletion in an AVL Tree

Implementation of AVL Tree in Different Programming Languages

Output

Complexity Analysis of AVL Tree Operations

Applications of AVL Trees

Advantages of AVL Trees

Disadvantages of AVL Trees

Summary

FAQs

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Operations	Best Case	Average Case	Worst Case
Insertion	`O (log n)`	`O (log n)`	`O (log n)`
Deletion	`O (log n)`	`O (log n)`	`O (log n)`
Traversal	`O (n)`	`O (n)`	`O (n)`
Search	`O (log n)`	`O (log n)`	`O (log n)`

01 Career Opportunities

02 Beginner

03 Intermediate

04 Advanced

05 Questions

06 Training Programs

AVL Tree in Data Structures with Examples

Free DSA Course with Certificate

AVL Tree in Data Structures: An Overview

What is an AVL Tree in Data Structures?

Balance Factor in AVL Tree in Data Structures

AVL Tree Rotations

Left Rotation (LL Rotation)

Right Rotation (RR Rotation)

Left-Right Rotation (LR Rotation)

Right-Left Rotation (RL Rotation)

Standard Operations on AVL Trees in Data Structures

Algorithm for Insertion in an AVL Tree

Algorithm for Deletion in an AVL Tree

Implementation of AVL Tree in Different Programming Languages

Output

Complexity Analysis of AVL Tree Operations

Applications of AVL Trees

Advantages of AVL Trees

Disadvantages of AVL Trees

Summary

FAQs

Q1. What is balance factor?

Q2. How many types of AVL Tree rotations are there?

Q3. What is the Space Complexity of an AVL Tree?

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