What is The Difference Between Linear And Non Linear Data Structure?
A data structure refers to organizing and storing data in a computer's memory in a way that enables efficient access, manipulation, and retrieval of the data. Data structures are fundamental concepts in computer science and are extensively used in programming and software development to solve various computational problems.
There are two categories of data structure - linear data structure and non-linear data structure. In real life, linear data structure is used to develop software, and non-linear data structure is used in image processing and artificial intelligence.
In this article, we will understand the difference between linear and non-linear data structures.
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What is a linear data structure?
A linear data structure is a type of data structure that stores the data linearly or sequentially. In the linear data structure, data is arranged in such a way that one element is adjacent to its previous and the next element. It includes the data at a single level such that we can traverse all data into a single run.
The implementation of the linear data structure is always easy as it stores the data linearly. The common examples of linear data types are Stack, Queue, Array, LinkedList, and Hashmap, etc.
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Here, we have briefly explained every linear data type below:
1. Stack
Users can push/pop data from a single end of the stack. Users can insert the data into the stack via push operation and remove data from the stack via pop operation. The stack follows the rule of LIFO (last in first out). Users can access all the stack data from the top of the stack in a linear manner.
In real-life problems, the stack data structure is used in many applications. For example, the All web browser uses the stack to store the backward/forward operations.
2. Queue
Queue data structure stores the data in a linear sequence. Queue data structure follows the FIFO rule, which means first-in-first-out. It is similar to the stack data structure, but it has two ends. In the queue, we can perform insertion operations from the rear using the Enqueue method and deletion operations from the front using the deque method.
Escalator is one of the best real-life examples of the queue.
3. Array
The array is the most used Linear data type. The array stores the objects of the same data type in a linear fashion. Users can use an array to construct all linear or non-linear data structures. For example, Inside the car management software to store the car names array of the strings is useful.
We can access the element of the array by the index of elements. In an array, the index always starts at 0. To prevent memory wastage, users should create an array of dynamic sizes.
4. LinkedList
LinkedList data structure stores the data in the form of a node. Every linked list node contains the element value and address pointer. The address pointer of the LinkedList consists of the address of the next node. It can store unique or duplicate elements.
What is a non-linear data structure?
In a non-linear data structure, data is connected to its previous, next, and more elements like a complex structure. In simple terms, data is not organized sequentially in such a type of data structure.
It is one type of Non-primitive data structure. In non-linear data structures, data is not stored linear manner. There are multiple levels of nonlinear data structures. It is also called a multilevel data structure. It is important to note here that we can't implement non-linear data structures as easily as linear data structures.
1. Tree
A tree data structure is an example of a nonlinear data structure. It is a collection of nodes where each node consists of the data element. Every tree structure contains a single root node.
In the tree, every child node is connected to the parent node. Every parent and child node has a parent-child node relationship. In the tree, Nodes remaining at the last level are called leaf nodes, and other nodes are called internal nodes.
Types of trees:
- Binary tree
- Binary search tree
- AVL tree
- Red-black tree
The Heap data structure is also a non-linear tree-based data structure, and it uses the complete binary tree to store the data.
2. Graph
The graph contains vertices and edges. Every vertex of the graph stores the data element. Edges are used to build a vertices relationship. Social networks are real-life examples of graph data structure.
Here, we can consider the user as a vertex, and the relation between him/her with other users is called an edge. There is no limitation for building the relationship between any two vertexes of the graph like a tree. We can implement the graph data structure using the array or LinkedList.
Types of the graph:
- Simple Graph
- Un-directed Graph
- Directed Graph
- Weighted Graph
3. Hashmap
Hashmaps store data as key-value pairs. It can be either linear or non-linear data structure. We can use the hashmap to map the value with its keys and search value efficiently from it.
The above-explained data structures are fundamental data structures.
Difference between linear data structure and non-linear data structure
Here, we have explained the key difference between linear and non-linear data structure in the table format.
| Linear data structure | non-linear data structure |
| The linear relationship is present between data elements. | Data elements have a hierarchical relationship. |
| Every element makes a connection to only its previous and next elements. | Every element makes a connection to more than two elements. |
| We can traverse it in a single run as it is linear. | We can't traverse it in a single run as it is a non-linear structure. |
| It is a single-level data structure. | It is a multi-level data structure. |
| The utilization of memory is not efficient. | Memory utilization is efficient. |
| Time complexity increases if we need to traverse through a large dataset. | Time complexity doesn't change much if we need to traverse through the large input size. |
| It is used to build an operating system and compiler design. | All Social networks, telephone networks, Artificial intelligence, and image processing are using non-linear data structures. |
| Examples: Array, stack, queue, LinkedList | Example: Graph, map, tree, heap data structure |
| It is easy to implement. | It is complex to implement. However, we can implement it easily, as nonlinear data structures include a powerful algorithm to implement them. |
The main use of the data structure is to build efficient algorithms such as sorting algorithms. Stack, queue, array, etc., have a linear relationship between the data elements, while graph, tree, hashmap, etc., have a complex relationship with the data.
FAQs about Linear and Non-Linear Data Structures
1. Which data structure is non-linear?
A linear data structure is where elements are organized sequentially. In this each element has a predecessor (except for the first element) and a successor (except for the last element). Examples of non-linear data structures are linked-list, arrays, queues, etc.
2. How to choose between linear and non-linear data structures?
The choice between linear and non-linear data structures depends on the nature of the data and the specific operations or algorithms you need to perform on that data. For example, graphs (non-linear data structure) are used for modeling networks (social networks, transportation networks), pathfinding algorithms, and data modeling.
3. What are queues used for?
Queues are used for managing and organizing data in a way that follows the First-In-First-Out (FIFO) principle. They find applications in scenarios where the order of processing or execution matters and where elements need to be processed in the order they were added. For example, in task scheduling algorithms.
4. What are linked lists?
A linked list is a linear data structure used to organize and store a collection of elements called nodes. Each node contains two main components: data and a reference to the next node in the sequence. Linked lists are helpful when the number of elements in a data structure may change frequently. A linked list has a head (starting node) and a tail (last node).
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