Tree Data Structure

Tree Data Structure


A Tree is a nonlinear data structure. Unlike other linear data structures such as an array, stack, queue, and linked list, a tree depicts a hierarchical structure. The ordering information of a tree is not important.

The Tree data structure is widely used in computer science and software engineering.

Tree Data Structure

Here are some common Tree Terminologies
  • Node: A node is an entity that contains a key or value and pointers to its child nodes.

  • Edge: The connection between nodes is called an edge.

  • Parent Node: The node which is a predecessor of a node is called the parent node of that node.

  • Root Node: The topmost node of a tree without any parent nodes is known as the root. Every tree has a single root node.

  • Leaf Node: The leaf node is a node with no children. It is the tree's last node. A tree may have several leaf nodes.

  • Child Node: The node that immediately succeeds another node is referred to as the child node of that node.

  • Sibling Nodes: Child node of the same parent node are called sibling nodes.

  • Level of Node: A level is the number of parents that make up a specific node in the tree.

  • Degree of Node: The degree of a node is the number of children it has. A node with no children is called a leaf node, and its degree is 0. A node with only one child is called a "unary" node, and its degree is 1. A node with two children is called a "binary" node, and its degree is 2.

  • Depth of Node: The depth of a node in a tree is the number of edges on the path from the root node to that node. The depth of the root node is 0.

  • Height of Node: The height of a node in a tree is the number of edges on the longest path from that node to a leaf node. If a node has no children, its height is 0. The height of a tree is the height of its root node.

  • Height of Tree: The height of a tree is the height of its root node. It is the maximum depth of any node in the tree.

Uses of Tree Data Structure

  • Representing Hierarchical Data: The Tree data structure is an excellent way to represent hierarchical data, such as file systems, XML documents, and JSON objects.

  • Searching and Sorting: Binary Search Trees (BST) are commonly used for searching and sorting data. A BST is a Tree where each node has two children at most, and the left child is smaller than the parent, while the right child is larger.

  • Routing and Network Topology: The Tree data structure is used in networking to represent network topologies and routing tables.

  • AI and Machine Learning: Decision Trees are used in AI and machine learning to model and predict outcomes based on input variables. Decision Trees are constructed by recursively splitting data based on the most significant variables.

Benefits of Tree Data Structure

  • Efficient Searching and Sorting: Binary Search Trees (BST) are an efficient data structure for searching and sorting data. The search and insert operations in a BST have a time complexity of O(log n), which is much faster than linear search algorithms in other data structures such as arrays and linked lists.

  • Easy to Modify and Update: The Tree data structure is easy to modify and update. Adding or deleting a node in a Tree only requires modifying the links between the nodes, which can be done in constant time. In contrast, modifying an array or linked list requires shifting elements, which can be expensive.

  • Hierarchical Representation: The Tree data structure is an excellent way to represent hierarchical data. The Tree structure provides a clear representation of parent-child relationships, which can be used to traverse and manipulate the data efficiently.

  • Natural Recursion: Trees have a natural recursive structure that makes them well-suited for recursive algorithms. Many Tree algorithms use recursion to traverse the Tree and perform operations on the nodes.

Limitations of Tree Data Structure

  • Memory Overhead : Trees can have higher memory overhead compared to simpler data structures like arrays or linked lists.

  • Complexity : Some tree types, especially self-balancing trees, introduce complexity in terms of implementation and maintenance.

  • Dependence on Ordering : The efficiency of operations in certain tree types, like BSTs, depends on the ordering of the elements.

Common Operations on Trees

  • Traversal : Tree traversal involves visiting each node in a specific order to perform operations or retrieve data. Common traversal methods include in-order, pre-order, and post-order traversals.

  • Insertion : Inserting a node into a tree involves finding the appropriate location based on the ordering property (e.g., for a BST) and adding the new node.

  • Deletion : Deleting a node from a tree requires maintaining the tree's structural integrity. The replacement strategy varies based on the type of tree.

  • Searching : Searching for a value in a tree involves traversing the tree based on the ordering property until the desired node is found or determining that the value does not exist.

Tree Traversal

Tree traversal refers to the process of visiting all the nodes in a tree in a specific order. There are three common traversal methods: InOrder, PreOrder, and PostOrder.

InOrder Traversal

In InOrder traversal, we visit the left subtree, then the current node, and then the right subtree. The algorithm is as follows:

  1. Traverse the left subtree in InOrder.
  2. Visit the root.
  3. Traverse the right subtree in InOrder.
Here's an example of InOrder traversal
   /   \
  2     3
 / \   / \
4   5 6   7
InOrder traversal : 4 2 5 1 6 3 7

PreOrder Traversal

In PreOrder traversal, we visit the current node, then the left subtree, and then the right subtree. The algorithm is as follows:

  1. Visit the root.
  2. Traverse the left subtree in PreOrder.
  3. Traverse the right subtree in PreOrder.
Here's an example of PreOrder traversal
   /   \
  2     3
 / \   / \
4   5 6   7
PreOrder traversal : 1 2 4 5 3 6 7

Post Order Traversal

In Post Order traversal, we visit the left subtree, then the right subtree, and then the current node. The algorithm is as follows:

  1. Traverse the left subtree in Post Order.
  2. Traverse the right subtree in Post Order.
  3. Visit the root.
Here's an example of Post Order traversal
   /   \
  2     3
 / \   / \
4   5 6   7
PostOrder traversal: 4 5 2 6 7 3 1

Types of Trees

  • Binary Tree : A binary tree is a tree in which each node has at most two children, referred to as the left child and the right child. This structure allows for efficient searching, insertion, and deletion operations.

  • Binary Search Tree (BST) : A binary search tree is a type of binary tree with the additional property that the left child of a node contains values less than the node, and the right child contains values greater than the node. This ordering property simplifies search operations.

  • Balanced Trees : Balanced trees, such as AVL trees and Red-Black trees, maintain balance during operations to ensure relatively uniform tree height. This balance helps optimize search, insertion, and deletion operations.

  • B-Trees : B-trees are balanced search trees designed for external storage, such as disk storage. They have a variable number of children per node, making them suitable for large datasets.

  • Trie : A trie is a tree-like data structure used to store a dynamic set of strings. Each node represents a character, and paths from the root to the leaves form words.

  • AVL Tree : An AVL tree is a self-balancing binary search tree. It maintains balance by performing rotations during insertions and deletions, ensuring the tree remains relatively balanced.

Best Practices of Tree Data Structure

  • Maintain Balance : Maintaining balance is crucial for trees like AVL and Red-Black trees. Balanced trees ensure efficient operations by preventing skewed structures that lead to worst-case scenarios.

  • Use Appropriate Tree Type : Choosing the appropriate type of tree based on the application's requirements is essential. For example, use AVL trees for search-intensive applications and B-trees for external storage.

  • Avoid Deep Trees : Deep trees with excessive height can result in inefficient operations. Ensuring the tree remains shallow helps maintain optimal performance.


Trees are versatile and fundamental data structures that play a crucial role in various applications. Understanding the basic concepts, types of trees, and operations allows developers to choose the most suitable tree structure for a given problem. By following best practices and considering the advantages and limitations associated with tree data structures, developers can design efficient and robust solutions. Whether representing hierarchical relationships, optimizing search operations, or facilitating storage on external devices, trees continue to be a cornerstone in computer science and data management.