Today, there’s a wide range of data structures out there to store and organize data elements. Linear data structures like arrays or linked lists are fundamental in computer science, yet they present some limitations, especially in time complexity for many operations.
In other words, such operations as finding or inserting data elements often require moving through the entire linear data structure. When the amount of data increases, these operations become extremely time-consuming. This is unacceptable for many applications like big data analytics, machine learning, or systems (e.g., online gaming servers) that need instant responses.
To solve this problem, we need to count on non-linear data structures, like trees or graphs. It’s because they provide more effective operations, especially on vast data volumes.
With the booming of the Internet and social media platforms, the amount of data we create, store, and consume everyday has become increasingly large, with 181 zettabytes estimated in 2025. Therefore, we’ll witness a wider adoption of non-linear data structures in the future. Here in this article, we’ll focus on one common data structure in this group – that’s Tree. So, what’s it, and how does it work? Let’s find it out!
In the first section, we’ll give you a quick, yet detailed overview of a tree data structure by answering the following questions:
Recommended reading: What Is Data Structure and Their Applications?
A tree is one data structure that helps represent hierarchical data. For example, let’s say you want to show employees in an organization and their positions in an organizational hierarchy. Here’s how you show it like this:
This particular logical structure in the image above is a Tree. If you look at this structure upside down and then it will resemble a real tree. The root here is at the top and you are branching out in a downward direction. The logical representation of the tree data structure is always like that. Root at the top and branch out in a downward direction.
A tree data structure is a collection of entities called nodes linked together to simulate a hierarchy. The topmost node in the tree is called the root of the tree. Furthermore, each node will contain some data and this can be data of any type. In other words, each node will contain some data and may contain a link or reference to some other nodes that can be called its children.
In the image above, data is the name of the employee and their designation. Accordingly, you may have an object with two string fields to store that info.
A tree works in accordance with the following basic operations:
With what we described above, you may seemingly envision when to use a Tree. Here are several applications of a Tree data structure in real life:
A Tree is an efficient way of storing and organizing data that is naturally hierarchical – or in other words, has layers. One typical example is a folder containing other folders and files to form a tree structure on a computer.
Similarly, the Domain Name System (DNS) also has a tree structure that organizes domain names in layers, with the broadest categories (“top-level domains”) like .com or .org serving as the “root” layer of the hierarchy. Each top-level domain, accordingly, will have second-level domains (which are often the names of websites like “designveloper” in designveloper.com).
Trees speed up searches. For example, in a binary search tree, looking for a data element is much faster than sifting through a linear data structure as you don’t need to check every element one by one. Instead, you can go directly to parts of the tree that contain that element. This is especially useful when you have to deal with massive datasets.
Trees assist in sorting data in a consistent, orderly way. For example, data elements are often automatically organized in a self-balancing binary search tree after they’re added. This allows you to look for the largest, smallest, and other specific values faster.
Trees can grow or shrink when you add or remove nodes. Therefore, they’re helpful in processing data that regularly changes, particularly in real-time applications such as chat apps, gaming apps, navigation & GPS systems, or stock trading platforms.
FURTHER READING: |
1. What Is Big O Notation? |
2. Stacks and Queues in Data Structures: An Overview in 2022 |
Various types of trees have been created throughout the past years, to suit certain apps and meet certain constraints or requirements. Some examples are binary search tree, B tree, red-black tree, splay tree, treap, AVL tree, and n-ary tree. Let’s take a closer look at some of them.
A binary search tree (BST), as the name indicates, is a binary tree where data is organized in a hierarchical structure. This data structure stores values in sorted order. A BinaryBinary Tree is a Tree in which each node can have at most 2 children.
Every node in a binary search tree comprises four attributes.
A binary search tree has a unique property that distinguishes it from other trees. This property is known as the binary-search-tree property.
Let’s assume that A is a node in a binary search tree.
A Heap is a special case of a binary tree where the parent nodes are compared to their children with their values and are arranged accordingly.
Let us see how we can represent heaps. Heaps can also be represented using trees as well as arrays. The following images show us how we can represent a binary heap using a binary tree and an array.
Heaps can be one of two types:
Our choice of data structure depends upon several factors:
This article gave you a detailed look at what a tree data structure is and how it works to benefit computer science. Choosing the right data structure will help you manage data and implement different programming tasks effectively. For more articles like this, just follow our Facebook, LinkedIn, and Twitter or subscribe to our blog!
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