The tree data structure is a non-linear data structure that hierarchically organizes data, unlike arrays and linked lists, which linearly store data. Trees are non-linear data structures that represent a multilevel data structure. Another example of non-linear data structure is Graphs. The nonlinearity feature of Trees allows easier and quicker access to the data. For instance, linear data structures such as arrays, linked lists, stack, and queue store or organize data sequentially whose accessibility becomes complex with increased data size. But time complexity is unacceptable in the present computational world. However, the tree data structure is computationally efficient as it provides flexible data access and efficient insertion and searching operations. In this article, the core aim is to provide a comprehensive understanding of tree data structure with technical terminologies and applications.

Let’s understand Tree with a real-life example!

Imagine a “family tree” that depicts relationships such as grandparents, parents, children, siblings, etc. Figure 1 demonstrates the analogy of a family tree with a tree data structure.

Figure 1: Analogy of a family tree with the tree data structure

In technical terms, the Tree is a collection of nodes connected by edges, and each node contains data. Here, the grandparents’ blocks represent the root node. Further, when other nodes (mother and father) are connected to the root node, the root becomes the parent node, and the connected node is called the child node. Children are the leaf nodes as these are without children nodes.

Tree Terminology

Root Node: It is the foremost node of the tree hierarchy.

Edge: It connects two nodes.

Child Node: A node consists of a parent node.

Parent Node: A node that has a child node and has an edge towards the parent.

Leaf Node: The bottom-most node of the tree hierarchy that doesn’t have any child nodes.

Height of a Node: It shows the longest path between the node and the leaf node or the number of edges from the node to the deepest node.

Depth of a Node: It represents the number of edges between the root node and the node.

Height of a Tree: It measures the depth of the deepest node or the root node's height.

Types of Tree Data Structure

In programming, the most known tree data structure is as follows:

  • General Tree
  • Binary search tree
  • Binary Tree
  • Red-black Tree
  • Splay tree
  • AVL tree
  • Treap
  • B-tree

All these tree data structures, as mentioned above, have different properties. And these are used for specific applications based on properties. For instance, the general tree data structure has no limitation on the hierarchical structure, i.e., a node is free to have any number of children nodes. Due to this feature, the general tree data structure is used in folder structures to store hierarchical data.

Applications of Tree Data Structure

The main applications of the tree data structure are summarized below:

  • Compliers use the Tree to build a syntax tree.
  • To store router-tables in routers.
  • Sorting algorithms.
  • Frequent insertion of data.
  • They are used in the memory management system.
  • To implement caches and used for garbage collection and data compression.
  • To implement directories in file systems.
  • Database indexing

In a nutshell, the Tree data structure is an inevitable part of programming that hierarchically organizes data. It is computationally efficient as compared to other linear data structures due to its multilevel non-linearity feature. Hence, Tree data structures are used for various purposes in programming.