Linear Vs Non Linear Data Structure: Difference between Linear & Non Linear Data Structure

What is Data Structure?

Being a newbie or an expert, the term data structure will be something that will be constantly heard by anyone who’s in computer programming. Understanding the data structures is always critical for becoming a good programmer. A lot of topics are associated with the data structures with a focus on which structures are actually the important ones. Therefore, for being a successful programmer, data structure knowledge is highly recommendable. 

Data structure refers to the process whereby the data can be stored and organized in a way that the user can access and utilize the data efficiently. Various algorithms are present to work with the data structures. Therefore, the data structure includes a group of data values, their relation to other elements, and also the operations that can be carried over the data values.

It may be simplified as:

Programs= algorithms + data structures

Data structures=related data + allowed operations on that data

Storage of data can be carried out in two ways. The data structures can be divided into:

  • Linear data structure
  • Non-linear data structure

Linear Data structure

These are the types of structures where the storage of data takes place sequentially or in a linear fashion. Here, every element stored in the structure is linked to its neighboring elements. The elements can be accessed in a single run as they are arranged linearly.  Also, being linearly stored in the memory, implementation is an easy process. The various types are:

1. Array

The array is a type of data structure that stores elements of the same type. These are the most basic and fundamental data structures. Data stored in each position of an array is given a positive value called the index of the element. The index helps in identifying the location of the elements in an array.

If supposedly we have to store some data i.e. the price of ten cars, then we can create a structure of an array and store all the integers together. This doesn’t need creating ten separate integer variables. Therefore, the lines in a code are reduced and memory is saved. The index value starts with 0 for the first element in the case of an array.

2. Stack

The data structure follows the rule of LIFO (Last In-First Out) where the data last added element is removed first. Push operation is used for adding an element of data on a stack and the pop operation is used for deleting the data from the stack. This can be explained by the example of books stacked together. In order to access the last book, all the books placed on top of the last book have to be safely removed.

Explore our Popular Data Science Online Courses

3. Queue

This structure is almost similar to the stack as the data is stored sequentially. The difference is that the queue data structure follows FIFO which is the rule of First In-First Out where the first added element is to exit the queue first. Front and rear are the two terms to be used in a queue.

Enqueue is the insertion operation and dequeue is the deletion operation. The former is performed at the end of the queue and the latter is performed at the start end. The data structure might be explained with the example of people queuing up to ride a bus. The first person in the line will get the chance to exit the queue while the last person will be the last to exit.

4. Linked List

Linked lists are the types where the data is stored in the form of nodes which consist of an element of data and a pointer. The use of the pointer is that it points or directs to the node which is next to the element in the sequence. The data stored in a linked list might be of any form, strings, numbers, or characters. Both sorted and unsorted data can be stored in a linked list along with unique or duplicate elements.

5. Hash Tables

These types can be implemented as linear or non-linear data structures. The data structures consist of key-value pairs.

Must Read: What is Linear Data Structure?

Non-linear Data Structure

These data structures don’t follow linearity. As the name suggests the data are arranged in a manner that doesn’t follow the contiguous manner. The elements don’t have a set path to connect to the other elements but have multiple paths. Traversing through the elements is not possible in one run as the data is non-linearly arranged.

As compared to the linear structure where an element is connected to both the neighboring elements, in this case, an element can be connected to other elements which don’t need to be only two. Implementation of non-linear data is not easy but computer memory is used efficiently using this type of structure.

The types of structures following non-linearity are Trees and Graphs.

1. Trees

A tree data structure consists of various nodes linked together. The structure of a tree is hierarchical that forms a relationship like that of the parent and a child. The structure of the tree is formed in a way that there is one connection for every parent-child node relationship. Only one path should exist between the root to a node in the tree. Various types of trees are present based on their structures like AVL tree, binary tree, binary search tree, etc.

2. Graph

Graphs are those types of non-linear data structures which consist of a definite quantity of vertices and edges. The vertices or the nodes are involved in storing data and the edges show the vertices relationship. The difference between a graph to a tree is that in a graph there are no specific rules for the connection of nodes. Real-life problems like social networks, telephone networks, etc. can be represented through the graphs. 

An adjacency matrix is used for the representation of the Graphs. 

Top Data Science Skills to Learn to upskill

Difference between Linear and Non-linear data structures

We have discussed the linear and  non-linear types of data structures. But what are the key points that define linear vs non-linear data structure? 

The difference between linear and non-linear data structure is tabulated below:

Linear Data structure  Non-linear data structure
1 The data elements are stored in a linear order in the case of linear data structure. Each and every element is connected to the first and the next element in the sequence. The data elements in the case of a non-linear data structure are arranged in a non-linear way and attached hierarchically. The data elements are attached to multiple elements.
2 The structure of the data consists of a single level. There is no hierarchy in the linear data structure. In this structure, there are multiple levels involved in the structure. Therefore the elements are arranged hierarchically.
3 The implementation of the linear structure of data is easy as the elements are stored in a linear way. The implementation of the structure is a complex process compared to the linear structure.
4 Traversal of the elements in a linear data structure can be carried out in a single execution because the data is present in a single level Traversal of the elements cannot be carried out in a single execution only. Multiple runs are required for traversing the data in a non-linear data structure.
5 There is no efficient utilization of memory in a linear data structure. There is efficient utilization of memory in a non-linear data structure.
6 Examples of linear data structures include array, stack, queues, and linked list. Examples of non-linear data include trees and graphs
7 The linear structure of data is applied mainly in software development. The non-linear structure of data is mostly applied in Artificial intelligence and image processing.
8 With the increase in the size of the input, the time complexity increases. Even if there is an increase in the size of the input, the time complexity remains the same.
9 Only one type of relationship might be present between the data elements A one-to-one or one-to-many type of relationship can exist between the elements in a non-linear type of data structure.

Importance of Data structure 

Any solid computer programs are built over the concept of structures of data. No program can be efficiently built up without the use of the right data structure. Since there is huge reliability of the computer programs over large volumes of data, efficient storage of the information is required for easy access of data. Application of a data structure allows storing data logically for easy modification and access.

Read our popular Data Science Articles

upGrad’s Exclusive Data Science Webinar for you –

ODE Thought Leadership Presentation



Data structures have become complex with the increase in the size of the data. The article gave a brief understanding of the types of data structure highlighting the key differences between a linear and a non-linear data structure. However, different data structures have different applications.

The use of the data structure like adding, deleting, accessing elements, modifying elements each have to be studied in-depth to gain an expert understanding of the data structures. However, the first important step towards a good programmer is having a basic understanding of the concept. Learning data structures allows the easy understanding of different programming languages. Be it python, C++, or Java, the concept remains the same. 

As it is the era of artificial intelligence, knowledge of machine learning languages is quite important for those who are aiming to work in AI. Storage of data in an efficient form has found applications in the machine learning models. Since, data structures forms the foundation of machine learning programs, understanding it should be the main focus. 

If you are mid-level professionals and dreaming to become a data analyst, you can check the course Master of Science in Data Science for Leaders provided by upGrad. The course will train you through industry experts until you become a master of the field.

It covers several topics related to machine learning and AI and with around 75+ case studies and projects. Irrespective of your gender and age, you can find yourself as a quality data scientist a few years passed by. If you want to check out more details, or have any queries, drop us a message. Our team will be helping you.

Mention some real-life applications where non-linear data structures have been used?

There are a number of popular real-life applications that rely primarily on non-linear data structures.
Graphs are extensively used in Artificial Intelligence algorithms and image processing. Facebook uses graphs for connecting and recommending new friend suggestions.
Graphs are also used by Google in ranking web pages and finding optimal paths in the Google maps application.
Trees are used in file structure applications, database lookups, pattern searching algorithms, and indexing in databases.
Trees are also used in data compression techniques such as Huffman Coding, where the heap implementation of trees is used to encode the data.
The tree data structure is also used to solve mathematical expressions. The expression is evaluated by inserting the operators at the internal nodes and the operands at the leaf nodes.

What is a heap data structure and what are its types?

A heap is a non-linear tree-based data structure where the tree is a complete binary tree. A tree is said to be a complete binary tree if all the levels of the tree are filled completely. The heap data structure is of 2 types- min-heap and max-heap.
Min-heap: When the element in the root node is the smallest among all the nodes, the heap is said to be the min-heap.
Max-heap: When the element in the root node is largest among all the nodes, the heap is said to be the max-heap.

What is a queue data structure? Give real-life examples?

A queue is a linear data structure where the operations are operated in the FIFO (First in First out) order. The queue data structure is of 3 types:
Circular Queue: The queue where there is no rear (i.e., the front is the rear itself), is called the circular queue.
Dequeue: The queue that allows the insertion and deletion from both ends is a deque.
Priority Queue: The queue where the element with higher priority is operated first is a priority queue. If two elements have the same priority, the one being higher in order in the queue will be served first.
Some of the real-life examples of the queue data structure are:
1. Queues at the ATM.
2. CPU task scheduling.
3. Website request processing.
4. Input stream management system.

Want to share this article?

Prepare for a Career of the Future

Leave a comment

Your email address will not be published. Required fields are marked *

Our Popular Data Science Course

Get Free Consultation

Leave a comment

Your email address will not be published. Required fields are marked *

Get Free career counselling from upGrad experts!
Book a session with an industry professional today!
No Thanks
Let's do it
Get Free career counselling from upGrad experts!
Book a Session with an industry professional today!
Let's do it
No Thanks