Trees: Binary Trees, AVL Trees and Other Tree Structures

In computer science and computing, trees are common hierarchical data structures. They provide effective ways to arrange and depict the connections between various elements. We will look at many kinds of trees in this post, with a focus on binary trees and AVL trees. In this section, we’ll examine their implementation, use, and applications. You will be able to tackle complicated issues like finding, sorting, and maintaining hierarchical data if you understand these tree structures. This article will act as a thorough introduction to comprehending binary trees, AVL trees, and other tree structures, whether you are a novice learning about trees or an expert programmer trying to deepen your understanding.

Binary Trees

A binary tree is a sort of tree data structure in which each node has a maximum of two offspring, known as the left child and the right child. It is a hierarchical structure that arranges elements or nodes in a certain hierarchy.

Each node in a binary tree might have zero, one, or two offspring. The root node of the tree is the highest node, and it is from this node that the tree branches out into subtrees. The left and right child pointers connect the nodes in the subtrees to their parent nodes.

Operations of Binary Trees

Binary trees are compatible with several techniques that make tree manipulation and traversal efficient. Here are a few regular operations on binary trees:

  • Insertion:    In order to insert a new node into a binary tree, the correct position must be determined based on the node’s value, and the relevant connections must be made. The node is put in a way that preserves the tree’s ordering attribute if it is a binary search tree.
  • Deletion:    A binary tree’s deletion of a node entails removing the node from the tree while maintaining the structure of the binary tree. The deletion operation could include altering the tree structure, such as moving child nodes around or elevating successor/predecessor nodes, depending on the node’s position and the intended deletion approach.
  • Searching:     A binary tree can be searched for a certain value by going from root to leaf and comparing the target value to the values in each node. If the tree is a binary search tree, the search process can be made more efficient by comparing the target value to the values of the nodes and then traversing either the left or right subtree depending on the comparison outcome.
  • Traversals:    A binary tree can be traversed by going through each node in turn. There are various traversal techniques, such as: Visit the left subtree first, followed by the current node, and then the right subtree. For binary search trees, this traversal generates nodes in ascending order. Pre-order Visit the current node first, followed by the left subtree, and then the right subtree. This traversal is frequently used to evaluate expressions or duplicate the tree structure. Visit the left subtree first, then the right subtree, and finally the current node in the post-order traversal. When deleting nodes or carrying out other operations that call for processing child nodes before the parent node, this traversal is helpful. Visiting nodes at each level from left to right, beginning at the root, is known as level-order traversal. This traversal, which frequently makes use of a queue, examines the tree level by level.
  • Measurement of height:    The length of the longest path from a binary tree’s root to a leaf node determines the tree’s height. A tree’s overall structure and balance can be evaluated by computing its height. A tree with no nodes (the root) has a height of 1, while a tree with one node has a height of 0.
  • Size estimation:    The total number of nodes in a binary tree determines the size of the tree. By adding the sizes of the left and right subtrees together and adding one (for the root), it can be determined recursively.

Applications of Binary Trees

Due to their adaptability and effective operations, binary trees have a wide range of uses in computer science and programming. Following are a few typical uses for binary trees:

  1. Binary Search Trees (BST)
  2. Expression Trees
  3. Huffman Coding
  4. Decision Trees
  5. File System Indexing
  6. Game Trees
  7. Syntax Tree Parsing

These are just a few of the many uses that binary trees have. They are a fundamental data structure utilised in many different domains, including data structures, algorithms, artificial intelligence, and software development. This is due to their hierarchical structure, effective search capabilities, and adaptability.

Properties of Binary Trees

Multiple significant characteristics of binary trees define their structure and behaviour. Effective use of binary trees requires an understanding of these features. Here are a few crucial characteristics of binary trees:

  1. Node Structure
  2. Root Node
  3. Parent and child Nodes
  4. Leaf Nodes
  5. Internal Nodes
  6. Subtrees
  7. Balanced Vs Unbalanced
  8. Full Binary Trees
  9. Complete Binary Trees

AVL Trees

Binary search trees (BSTs) that self-balance and rotate automatically are known as AVL trees. It bears the names of its creators, Adelson-Velskii and Landis. Height-balanced AVL trees guarantee that every node’s left and right subtrees have height differences between them of no more than one.

Keeping search, insertion, and deletion operations effective is the basic objective of balancing an AVL tree. By maintaining the tree’s balance, the temporal complexity of these operations stays logarithmic, resulting in optimal performance.

Operations of AVL Trees

  • Insertion:    When adding a new node to an AVL tree, rotations may be necessary to keep the tree balanced. The rotations can be left, right, or double.
  • Deletion:    Like insertion, deletion can throw off the AVL tree’s equilibrium. To get back into balance and keep the AVL property, rotations are done.
  • Searching:    When conducting a search, the target value is compared to the values of each node in the AVL tree, and the left or right subtree is then traversed as necessary.

Applications of AVL Trees

  • Databases:    To facilitate effective data retrieval and maintenance, AVL trees are frequently employed in database indexing systems.
  • AVL trees are frequently used in compiler symbol tables for quick look up and storage of identifiers.
  • For effective route management in computer networks, AVL trees can be utilised in routeing algorithms.
  • Caches:    To store frequently accessed data and improve cache lookup, AVL trees are used in cache implementations.

Properties of AVL Trees

  • Balance Factor:    Each node in an AVL tree has a balancing factor, which is the difference between the heights of its left and right subtrees. The balance factor, which can range from -1 to 1, indicates whether the node is balanced, slightly left- or right-heavy, respectively.
  • Height:    The length of the longest path from a node to a leaf determines the node’s height. An empty tree is said to have a height of 0.
  • Balance Condition:    In an AVL tree, the balancing factor for each node must fall between [-1, 1].

Other Trees Structures

In addition to binary trees and AVL trees, there are several other tree designs that have unique advantages in various situations and are used for tasks. Here are some of these tree architectures that we can examine:


  • B-trees are balanced search trees that can effectively manage massive volumes of data.
  • In file systems and databases where rapid disc access is essential, they are frequently employed.
  • B-trees efficiently perform insertion, deletion, and searching operations and preserve balance by constantly modifying the tree structure.

Black-Red Trees:

  • Another sort of balanced search tree that ensures operations will take logarithmic amounts of time is red-black trees.
  • They are utilised in several programmes, such as memory allocators, data storage systems, and language compilers.
  • Red-black trees conduct balancing operations like colour flips and rotations to keep the system in balance using the colour characteristics supplied to each node.

The Trie Trees

  • Trie trees, commonly referred to as prefix trees, are specialised tree structures mostly used for string operations.
  • Applications with dictionary search, auto-complete recommendations, and spell checking frequently employ them.
  • Trie trees make it possible to quickly match prefixes and store strings with frequent prefixes, which lowers the temporal complexity of string-related operations.

Heap Trees

  •  Heap trees are complete binary trees that adhere to the heap property; they may be either a min-heap or a max-heap.
  • They are frequently employed in sorting algorithms like heap sort and priority queues.
  • Heap trees offer effective methods for adding and removing the minimum or maximum element.

Oct trees and quad trees

  • Applications that work with spatial data and partitions typically use quad trees and oct trees as their spatial tree structures.
  • In computer graphics, image processing, and geographic information systems (GIS), they are frequently employed.
  • The two-dimensional space is divided into quadrants by quad trees, while the three-dimensional space is divided into octants by oct trees.

The Splay Trees

  • Splay trees are binary search trees that self-adjust to optimise frequently visited components.
  • They facilitate subsequent searches for the same element by bringing the root node of the most recently accessed tree.
  • Applications that require data access patterns that indicate temporal locality use splay trees.

Each of these tree topologies has a unique set of benefits and uses that make it possible to organise and manipulate data effectively in particular situations. By comprehending these many tree topologies, developers can select the best data structure for their unique requirements, optimising performance and successfully resolving challenging issues.


In conclusion, trees are fundamental data structures in computer science and programming. This includes binary trees, AVL trees, and other tree forms. In a variety of applications, they offer effective data organisation, manipulation, and traversal. Binary trees have benefits including quick traversal, memory efficiency, hierarchical structure, fast searching, and sorted order.

Binary search trees are appropriate for symbol tables, databases, and search algorithms because they allow for quick searching and insertion while retaining a sorted order. As self-balancing binary search trees, AVL trees guarantee top performance by preserving balance through rotations and modifications.

Other tree forms with specific functions in other fields include B-trees, red-black trees, trie trees, heap trees, and spatial trees. They are employed for a variety of tasks, including efficient file system indexing, efficient string operations, priority queues, gaming trees, and more.

Working with these structures successfully requires an understanding of the operations and characteristics of binary trees, such as insertion, deletion, searching, traversals, and height computation. Effective data manipulation, analysis, and traversal are made possible by these procedures.

In general, trees offer a flexible and effective method for arranging and modifying data, enabling the effective use of algorithms, data storage systems, artificial intelligence techniques, and more. Programmers and developers can improve efficiency, optimise their solutions, and successfully handle complicated problems by utilising trees and their varied architectures.

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