Digital tasks, whether simple like browsing photos or complex like navigating a tricky route with Google Maps, use data structures. These are the building blocks of programming. Simply put, these structures refer to how information is arranged and managed.
As the number of internet users increases, the data volume is also growing at an unprecedented rate. According to Cybercrime Magazine, the world will have 200 zettabytes of data by 2025. This makes data structures more important than ever. Let’s explore the two common types of data structures.
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What is a Linear Data Structure?
A linear data structure follows a clear, straightforward path for arranging the elements. The elements are arranged sequentially, with each having a predecessor (except the first) and a successor (except the last). It’s the simplest form of data arrangement, as it makes retrieving, adding, and removing elements in the database easier compared to other intricate data structures.
Let’s take a look at a linear data structure example.
For example, to arrange the marks of 15 students in your class, you store their marks in a straight line. This is called the linear data structure model. When searching for a specific student’s mark in the sheet, you can use the student’s roll number as an index and spot the target student’s marks easily instead of going through all the scores.
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Key Features of Linear Data Structure
In a linear data structure, the data is organised in a one-directional order, making it not only accessible but also easy to update. Let’s explore the key features of a linear data structure:
- Stays in Order: Each data element remains in the same place where they were stored. They remain in place unless their position is altered manually. A new element joins the top or the bottom of the sequence.
- Transversal: You follow a single path when going over the data arranged in a linear data structure format. With everything organised in a single direction, you go through each element without skipping any. For example, when reading a book, you turn to page 2, 3, 4, 5, and so on.
- Insertion and Removal: New data elements can be added in the front or back. Depending on the structure, addition or removal can either shift other elements or maintain their sequence.
- Memory Usage: Linear data structures have predictable memory usage, making it easier for you to determine the total space your data will occupy. For instance, if each element in the list consumes 7 bytes, and you have 10 such elements to store, they will take up 70 bytes of memory.
- Access: You can access data either sequentially (going through the list step-by-step) or use indexing to pinpoint the exact element. The indexing approach is common in arrays. Linked lists require you to go over every element to find the target piece of information.
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Types of Linear Data Structures
Data in the linear structure is stored, organised, and managed in different ways. Understanding each type will help you choose a format that fits your requirements. These include:
Arrays
An array stores data side-by-side. Each element has an index, also known as a position number, which helps you locate the specific piece of data quickly. An array has homogeneous elements, meaning each element is of the same data type. These are mainly used when you need to keep the data organised neatly and find your target element easily.
An array follows a zero-based indexing model, in which the first element has the index of zero, followed by one, two, three, and so on. For example, you can store the scores of 5 students in this order in an array.
[67, 40, 98, 82, 70]
The index of the first student is zero. The scores of the fourth and fifth students are 82 and 70, respectively.
Linked List
Think of linked lists like a train’s carriages. Each one stores elements and is connected to another carriage. In linked lists, the data is stored in nodes. Since there’s no fixed memory space for these nodes, it’s easier to create new nodes, delete the existing ones, or make changes without shuffling the other elements. This linear approach is used in cases where data is changed frequently.
Stack
A stack follows the LIFO (Last-In, First-Out) order. It’s used for temporary storage. One example is the stack of plates in a restaurant. The last plate you arrange in a stack is the first you take out when needed. If you need an element placed lower in the stack, you can’t do it without removing the elements at the top first.
Queues
Queues follow the FIFO (First-In, First-Out) order. The new data is added to the back, and the elements placed in the front leave first. The approach is effective in situations where the order matters. For example, an IT department typically follows the FIFO (First-In-First-Out) approach, which addresses customer queries based on the order in which they are received.
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What is a Non-Linear Data Structure?
A non-linear data structure organises data in a hierarchical structure instead of a single, straight line. The structure is mainly used to make sorting and managing complex data easier. Family trees and road maps, for example, use non-linear data structures. They are comparatively more difficult than linear data structures, but offer great flexibility. The data is arranged in either a tree or a graph format.
Key Features of Non-linear Data Structure
Non-linear structures are useful for real-world data with complex relationships that linear formats can’t represent.
- Network Organisation: Instead of a single, one-directional path, elements are connected as branches of a tree. A family tree is a prime example of a non-linear data structure, representing different family branches. It’s used in places where one element has many connections.
- Best for Complex Data: Non-linear data structure streamlines the presentation of complex connections, such as maps featuring different city routes. These are shown in graph format.
- Flexible: Adding or removing content in a non-linear data structure does not change the sequence of the structure. For example, it’s easier to add a new employee to an organisational chart using the non-linear structure.
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Conclusion
Linear and non-linear data structures might sound like complex scientific terms, but they are the best ways to organise, store, and manage data. They are further divided into different categories, which makes data management effortless.
Once you know the different types of data structures, their meanings, and differences, it will be easier to use the right approach for your next coding project. To learn more about these types, sign up for courses from Amity University Online that provide a detailed understanding of data structure.
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