What is Linear Data Structure and its Types? Explore Differences With Nonlinear Structures
By Rohit Sharma
Updated on Jun 17, 2025 | 40 min read | 58.97K+ views
Share:
For working professionals
For fresh graduates
More
By Rohit Sharma
Updated on Jun 17, 2025 | 40 min read | 58.97K+ views
Share:
Table of Contents
Did You Know? As per Statista, the Indian data center market is expected to generate a revenue of USD 9.20 billion by 2025. |
Data remains a foundational aspect of computing, and linear data structures offer a direct way to arrange and retrieve information sequentially. They help keep elements organized and manageable, which will become even more critical as data volumes continue to grow in 2025 and beyond.
There are four principal linear data structures commonly encountered:
In this article, you will see how each one operates, compare linear with non-linear data structures, and learn how to pick the right structure for your projects.
Master data structures and elevate your tech skills! Enroll in our Online Data Science Course and build a strong foundation for a data-driven career.
Linear data structures describe a direct path in which elements are placed one after another. Each item has a clear place in the sequence, which helps organize data for basic tasks like adding or removing elements.
Because every part follows the same line of progression, retrieval complexity can stay low when working with moderate or predictable data sizes. These structures also use memory straightforwardly, reducing some of the overhead seen in more intricate arrangements.
Mastering linear data structures like arrays, stacks, and queues is the first step toward advanced tech careers. Ready to level up? Explore these top programs that build on your foundation:
Let’s see this better with the help of a linear data structure example:
When elements are arranged in a single path, it becomes possible to traverse all of them in one pass. That makes tasks like searching, iterating, or updating far more approachable.
Linear data structures stand out because they arrange elements in a single, orderly sequence. This straightforward layout often leads to simpler logic when adding, removing, or updating information.
Many of these structures also keep memory usage predictable, which can be a strong advantage in programs with moderate or steadily growing data sizes.
Let’s now examine these defining features in detail to understand what is Linear Data Structure better.
This property ensures that each element retains its position based on when it was added. Any new entry joins the end (or top, for certain structures), so earlier entries stay ahead in the sequence. Consistency in ordering makes it simpler to process items in the exact sequence you introduced them.
Let’s understand this through some example examples and scenarios:
Traversal refers to systematically going through each element from start to finish. Because the structure follows a single path, you can examine all elements without skipping any. This method is especially useful for tasks such as computing totals or searching for a specific data point.
Here are some example scenarios to understand this better:
Searching can involve either going through each element until a match is found or applying indexed access in a fixed-size structure. The approach depends on how the structure stores its elements. In arrays, direct indexing pinpoints an element quickly, whereas linked lists require a step-by-step approach.
Let’s understand this through some simple examples:
Adding or removing elements can occur at defined points like the front or back. Depending on the chosen structure, insertion or removal might shift other elements or simply adjust a pointer to maintain the sequence.
This design keeps you informed about where the element is placed or removed, which helps reduce confusion.
Here are some examples to help you understand this better:
Some linear data structures, such as arrays, store elements in contiguous blocks of memory. Others, like linked lists, distribute elements across different locations while connecting them through references or pointers.
The right choice usually depends on whether a program needs an easy-to-resize structure or a consistent, contiguous block.
Check out some examples to understand this characteristic better:
Read this to know the Difference between Data Science and Data Analytics!
Linear data structures can vary in how they store elements, handle insertion or deletion, and manage memory. Each type addresses different scenarios and priorities, such as the need for random access, dynamic growth, or strict ordering.
Understanding the distinct patterns in each subtype makes it easier to choose the one that best meets a project's requirements.
An array is a fundamental linear data structure that arranges elements in contiguous memory locations. Every element in an array shares the same data type, and each position can be accessed through its index.
Because these positions lie side by side, certain tasks, for instance, iterating through all elements, can be straightforward. However, arrays usually require knowing their size at the start, so they may not expand easily once created.
When handled correctly, they are known for providing fast lookup times for elements by index and supporting clear, orderly data storage.
The three main types of Arrays and their examples are listed below.
A one-dimensional array arranges elements in a single row, each accessed by a single index. This layout's straightforward nature suits tasks where data can be handled in a direct, linear format. Once initialized, it keeps its defined size unless you use a dynamic variant provided by certain programming languages.
Here’s an example of one-dimensional Arrays.
Consider storing the marks of several students in one class. Each position in the array represents a different student:
Indexes: [0] [1] [2] [3] [4]
Elements: [85] [90] [78] [92] [88]
By referencing marks[i], you can directly read or modify each student’s data without scanning the entire structure.
Also Read: One Dimensional Arrays in C: Definition, Types and Example
A two-dimensional array stacks data in rows and columns, essentially forming a matrix. Each element is addressed by two indices — one for the row and another for the column. This pattern is often used in tabular data storage, image matrices, or any setup that benefits from a row-column classification.
Consider a table of student marks for multiple subjects:
Index |
[0][0] |
[0][1] |
[1][0] |
[1][1] |
Subjects | 85 | 90 | 78 | 82 |
Also Read: Two-Dimensional Array in C Programming with Example
Multi-dimensional arrays extend beyond two dimensions, allowing you to map data in multiple layers, like three-dimensional (3D) or higher. This approach can be practical for advanced data visualization or modeling tasks involving more than rows and columns.
Envision a 3D array for storing color channels (Red, Green, Blue) of small images:
Dimension |
0 |
1 |
2 |
Pixel (x, y) | (R,G,B) | (R,G,B) | (R,G,B) |
These configurations provide a structured way to represent complex data, although the code can grow more intricate as dimensions increase.
Also Read: Multidimensional Array in Java
A linked list in a data structure arranges its items through nodes, where each node contains data and at least one reference (pointer) to another node. This design spares you from allocating a fixed block of memory in advance since new nodes can be created as needed.
Each node can be placed anywhere in memory, which makes inserting or removing elements easier because you do not have to shift every item in the list. Although retrieving elements is generally slower than in arrays since you must follow pointers, linked lists perform well in situations where frequent changes to the sequence are expected.
The three main types and their examples have been discussed below.
1. Singly Linked List
A singly linked list – also known as a linear list in data structure – connects each node to exactly one other node, called the next node. The final node indicates the end of the sequence by pointing to nothing. This style streamlines storage needs because only one pointer is maintained, and you can add or remove nodes at either the head or tail without shuffling all data.
Let’s understand linear list in data structure through an example: Managing a Media Playlist
Each node contains a song title and a pointer to the next track.
Diagram Representation:
HEAD -> [Song A] -> [Song B] -> [Song C] -> NULL
2. Doubly Linked List
A doubly linked list enhances the concept by giving each node two pointers: one pointing to the next node and one to the previous node. This two-way structure supports direct movement forward or backward, allowing certain operations — like reverse traversal or immediate node removal — to be more direct.
Here’s an example for better understanding: Browsing Photos in a Gallery
Each node represents an image with a forward pointer (next) and a backward pointer (prev).
Diagram Representation:
NULL <- [Photo 1] <-> [Photo 2] <-> [Photo 3] -> NULL
3. Circular Linked List
A circular linked list forms a loop by connecting the final node back to the first. There is no null pointer; instead, the chain continues around. This setup can be helpful if you want to cycle through elements repeatedly without resetting to the start.
Let’s understand this with an example: Rotating Through Players in a Board Game
Nodes represent each participant, looping back to the first after the last player.
Diagram Representation:
┌───────────────┐
HEAD -> [Player A] -> [Player B] -> [Player C]
└───────────────┘
(loops back)
A stack in data structure follows a Last-In, First-Out (LIFO) order, where the most recent item added is the first one removed. Elements only join or leave the top of the stack, which means you do not rearrange or shift all stored items.
This design works particularly well when the order of removal must mirror the order of insertion, as in undo operations or nested function calls. Stacks often appear wherever you need to keep track of a reversing sequence of actions or manage nested processes.
1. Fixed Size Stack
A fixed-size stack has a predefined capacity. Once it holds the maximum number of elements, attempting to push one more will cause an overflow error.
Example: Limited Undo History in a Simple Drawing Application
Diagram Representation (Capacity = 5):
Top -> [ Action 5 ]
[ Action 4 ]
[ Action 3 ]
[ Action 2 ]
[ Action 1 ] <-- Oldest remains at bottom
2. Dynamic Size Stack
A dynamic size stack expands (and may also shrink) according to the number of items it must hold. This allows more flexibility in storing data sequences without being restricted by an initial limit.
Example: Extensive Command Records in an Advanced Code Editor
Diagram Representation (Size Grows as Actions Increase):
Top -> [ Action 7 ]
[ Action 6 ]
[ Action 5 ]
...
[ Action 1 ] <-- The earliest action
Also Read: How to Implement Stacks in Data Structure? Stack Operations Explained
A queue is a linear data structure that follows the First-In, First-Out (FIFO) principle. You place new elements at the back (known as the rear), and items leave from the front. Because the earliest added item is always the first to exit, queues are well-suited to scenarios where order matters, such as managing tasks, requests, or resources.
This structure ensures that each element is served in the same sequence in which it was enqueued, supporting fair distribution or systematic processing.
Below are the five main types of queues in data structures.
1. Input Restricted Queue
This type of queue only accepts new entries from one end, while removal can occur from either the front or the rear. It grants more flexibility in how elements exit, yet insertion remains controlled at a single point.
Restaurant Orders Example
Imagine a restaurant that only accepts new orders at one counter (the rear), yet completed orders can be picked up either from the kitchen side (front) or canceled at the end:
Rear -> [New Order #101] -> [Order #99] -> [Order #100] <- Front
Indexes might look like this:
Index |
[0] |
[1] |
[2] |
Data | #101 | #99 | #100 |
Ends | Rear | (middle) | Front |
upGrad’s Exclusive Data Science Webinar for you –
How to Build Digital & Data Mindset
2. Output Restricted Queue
This is nearly the opposite of the input restricted queue. It allows inserting items at both the front and the rear, but removal can happen only from one end (usually the front). It fits scenarios where rapid insertion from multiple directions is needed but still keeps a single exit path.
Shared Printer Example
Several users might enqueue print jobs from different points, while jobs are always dequeued from one fixed side when the printer is ready:
Front -> [Job #12] -> [Job #13] -> [Job #14] <- Rear
↑ ↑
Insertions can happen at both ends
This is what the indexes might look like:
Index |
[0] |
[1] |
[2] |
Data | Job #12 | Job #13 | Job #14 |
Ends | Front | (middle) | Rear |
3. Circular Queue
A circular queue links the last position back to the first, stopping the issue of “unused slots” in a simple array-based queue once the rear index hits the end. When space becomes free at the front, the rear can loop around.
Printer Task Buffer Example
Consider a printer queue tracked in an array of size 4:
Index |
[0] |
[1] |
[2] |
[3] |
Data | Task #A | Task #B | (empty) | Task #C |
Front | 0 | |||
Rear | 2 |
4. Double-Ended Queue (Deque)
A double-ended queue (deque) allows enqueuing and dequeuing at both the front and the rear. This flexibility is beneficial if you need to quickly push or pop items from either side without the restrictions imposed by typical queues.
Web Browser’s History Navigation Example
A user’s browsing history can be treated like a deque:
Front <-> [Page 1] <-> [Page 2] <-> [Page 3] <-> [Page 4] <-> Rear
Indexes might reflect quick additions at both ends:
Index |
[0] |
[1] |
[2] |
[3] |
Data | Page 1 | Page 2 | Page 3 | Page 4 |
Ends | Front | Rear |
5. Priority Queue
A priority queue reorders elements based on their priority rather than strictly following FIFO. Higher-priority items get removed first, regardless of when they arrived.
System Tasks Scheduling Example
Processes might arrive at different times, each with a priority value:
Priorities: High | Low | High | Medium
Data: Task#8 | Task#9| Task#10| Task#11
Want to understand the difference between stacks and queues in data structures? Check out upGrad’s free Tutorial, Stack vs Queue: Unlocking the Differences!
Linear data structures store elements in a single progression, where each item has a clear predecessor and successor (except the first and last).
Non-linear structures arrange elements in branching or interconnected ways, creating more complex relationships. Because of these contrasts, their usage can differ significantly, especially in terms of memory arrangement, traversal patterns, and ease of operations.
Here’s a tabulated snapshot of linear vs non-linear data structures:
Aspect |
Linear Data Structures |
Non-Linear Data Structures |
Arrangement of Elements | Organized in a continuous or direct sequence, where each item typically has one predecessor and one successor. | Organized in a hierarchical or interconnected manner, allowing branching paths and multi-level relationships. |
Levels | Involves a single level, so all items lie along the same path. | Involves multiple levels, often forming tree-like or network-like connections. |
Implementation Complexity | Easier to implement because of their one-dimensional layout (e.g., arrays, linked lists). | More complex to implement due to hierarchical or networked structures (e.g., trees, graphs). |
Traversal | Allows a single-run approach (you can visit all elements by following a straight path). | Requires specialized algorithms like depth-first or breadth-first to traverse multiple branches or connections. |
Memory Utilization | Can be less efficient if size is fixed (arrays) or if many nodes use pointers (linked lists). | Often makes more efficient use of space when dealing with intricate or branching data sets, though managing pointers can be complex. |
Data Access | Supports simpler indexing (arrays) or linear pointer-following (linked lists), but random access is generally easier in arrays. | Access depends on traversals, with no simple index-based lookup. Elements may be found through algorithms like DFS or BFS in trees/graphs. |
Examples | Arrays, Stacks, Queues, Linked Lists | Trees (binary, AVL, etc.), Graphs (directed, undirected) |
Applications | Effective for basic data storage, application software development, and straightforward operations like searching or sorting. | Suited for AI, image processing, hierarchical file systems, social networks, and scenarios that need complex relationships or multiple paths. |
Performance Considerations | Generally good for smaller tasks or when you need direct indexing (as in arrays) or frequent insertions (as in linked lists). | Excels at representing nested or interconnected data but comes with more overhead and specialized traversal methods. |
By examining these differences, you can decide whether a single-level, linear layout (like arrays or linked lists) or a branching, multi-level approach (like trees or graphs) better suits a specific problem.
Linear data structures arrange elements in a sequence, making it easy to traverse, insert, or delete data in an organized way. While they’re simple and widely used, they come with both advantages and limitations depending on the task or scale.
Let’s break them down in more detail,
Linear data structures have a straightforward layout, making them easier to understand and use compared to complex, multi-level structures.
Example: Arrays and linked lists follow a straightforward order, making them beginner-friendly.
Arrays offer constant-time access through indexing, while other linear structures allow sequential traversal in a clear order.
Example: Arrays offer direct access via index; linked lists follow a logical chain.
Some linear structures, such as linked lists, can grow or shrink during execution without needing a fixed size defined upfront.
Example: Linked lists don’t need predefined sizes, unlike arrays.
Many programming languages provide built-in support or libraries for linear structures, enabling quick and reliable implementation.
Example: Python’s list, Java’s ArrayList, or LinkedList make implementation faster.
Basic operations like searching and sorting are well-studied and have efficient, documented algorithms for linear structures.
Example: Linear search, bubble sort, and binary search work effectively on these structures.
Arrays require a predefined size during initialization. This means you either risk running out of space or wasting memory if the allocated size isn’t fully used.
Example: If you declare an array with 100 slots but only store 30 elements, 70 slots remain unused.
When inserting or deleting elements from an array (especially in the middle), all subsequent elements must be shifted, which increases processing time.
Example: Deleting the 2nd element in a 1,000-element array requires shifting 998 elements.
Structures like linked lists don't support direct indexing, so finding a specific value requires checking each element one by one. This results in a linear time complexity.
Example: Searching for an item in a 50-node linked list may take up to 50 steps.
Unlike arrays, data structures like linked lists don't allow random access, which can slow down operations that need to retrieve elements by index frequently.
Example: Accessing the 10th node in a linked list involves traversing the first 9 nodes every time.
Static structures like arrays may reserve more memory than actually required, leading to inefficient space utilization.
Example: Allocating an array of 200 elements for a program that only uses 40 creates significant unused memory.
Linear data structures play a crucial role in solving real-world problems, from managing databases to optimizing processes in software systems. Let’s look at their key applications across different industries.
Arrays are widely used in programming for their simplicity and efficiency in storing sequential data. Their fixed size and direct indexing make them ideal for various practical applications:
While arrays are great for static data, dynamic structures like linked lists offer more flexibility for evolving datasets.
Linked lists excel in dynamic environments where data structures grow or shrink during runtime. Their ability to efficiently modify elements makes them versatile:
For specific operations like reversing data or managing undo actions, stacks are often the preferred choice.
Stacks are specialized data structures that operate on the LIFO (Last In, First Out) principle, making them indispensable for managing temporary states and recursive tasks:
While stacks are best for LIFO tasks, queues handle sequential operations and scheduling tasks more effectively.
Queues operate on the FIFO (First In, First Out) principle, making them perfect for maintaining order in processes and ensuring fairness in execution:
These practical applications highlight how linear data structures, such as arrays, linked lists, stacks, and queues, effectively solve real-world challenges.
Mastering linear data structures is a crucial step in programming. Here’s how you can learn and implement them effectively:
Practical Steps to Learn:
Learn various programming languages for free with upGrad’s free courses, such as Learn Basic Python Programming and Core Java Basics, today!
To maximize the efficiency of linear data structures, it’s essential to follow best practices that ensure optimal performance and resource utilization. Here are a few tips and practices to get you started:
1. Choose the Right Structure for Your Use Case:
2. Optimize Operations for Time and Space Complexity:
3. Avoid Common Pitfalls:
By applying these practices, you can effectively manage linear data structures for a wide range of applications, ensuring efficient and scalable solutions.
Learn the basics of data structures with upGrad’s free Data Structures & Algorithms course, and get ahead of your peers.
Understanding the difference between linear and nonlinear data structures is essential for choosing the right tool for your programming tasks. While linear structures arrange data sequentially, nonlinear ones follow hierarchical or networked formats.
Here’s a quick comparison between linear and nonlinear data structures:
Feature |
Linear Data Structures |
Nonlinear Data Structures |
Data Arrangement | Stored in a sequential order | Stored in a hierarchical or interconnected manner |
Traversal | Traversed in a single run (start to end) | Multiple traversal paths are possible |
Examples | Arrays, Linked Lists, Stacks, Queues | Trees, Graphs |
Memory Usage | Memory is mostly contiguous | Memory is often non-contiguous |
Implementation Simplicity | Easier to implement and debug | More complex in terms of structure and logic |
Use Cases | Ideal for linear tasks like scheduling or buffering | Suitable for modeling hierarchies (tree) or networks (graph) |
Learning linear data structures is essential for a successful career in software development and programming. Whether you're a beginner learning the basics or an experienced developer aiming to refine your skills, upGrad offers a range of specialized courses that focus on data structures and algorithms to help you achieve your goals.
At upGrad, you can choose from hands-on programs featuring expert mentorship and personalized feedback. This enables you to gain practical experience and become confident in solving complex problems.
Here are some additional courses offered by upGrad that can help you strengthen your understanding of data structures.
Enroll today and take the first step toward mastering linear data structures with upGrad! Get personalized counseling from upGrad’s experts to help you choose the right program for your goals. You can also visit your nearest upGrad offline career center to kickstart your future!
Unlock the power of data with our popular Data Science courses, designed to make you proficient in analytics, machine learning, and big data!
Elevate your career by learning essential Data Science skills such as statistical modeling, big data processing, predictive analytics, and SQL!
Stay informed and inspired with our popular Data Science articles, offering expert insights, trends, and practical tips for aspiring data professionals!
763 articles published
Rohit Sharma shares insights, skill building advice, and practical tips tailored for professionals aiming to achieve their career goals.
Get Free Consultation
By submitting, I accept the T&C and
Privacy Policy
Start Your Career in Data Science Today
Top Resources