Difference between Linear and Non-linear Data Structures
Last Updated :
28 Feb, 2023
Linear Data Structure:
Data structure where data elements are arranged sequentially or linearly where each and every element is attached to its previous and next adjacent is called a linear data structure. In linear data structure, single level is involved. Therefore, we can traverse all the elements in single run only. Linear data structures are easy to implement because computer memory is arranged in a linear way. Its examples are array, stack, queue, linked list, etc.
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.
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.
Non-linear Data Structure:
Data structures where data elements are not arranged sequentially or linearly are called non-linear data structures. In a non-linear data structure, single level is not involved. Therefore, we can't traverse all the elements in single run only. Non-linear data structures are not easy to implement in comparison to linear data structure. It utilizes computer memory efficiently in comparison to a linear data structure. Its examples 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.
Difference between Linear and Non-linear Data Structures:
| S.NO | Linear Data Structure | Non-linear Data Structure |
|---|
| 1. | In a linear data structure, data elements are arranged in a linear order where each and every element is attached to its previous and next adjacent. | In a non-linear data structure, data elements are attached in hierarchically manner. |
| 2. | In linear data structure, single level is involved. | Whereas in non-linear data structure, multiple levels are involved. |
| 3. | Its implementation is easy in comparison to non-linear data structure. | While its implementation is complex in comparison to linear data structure. |
| 4. | In linear data structure, data elements can be traversed in a single run only. | While in non-linear data structure, data elements can't be traversed in a single run only. |
| 5. | In a linear data structure, memory is not utilized in an efficient way. | While in a non-linear data structure, memory is utilized in an efficient way.
|
| 6. | Its examples are: array, stack, queue, linked list, etc. | While its examples are: trees and graphs. |
| 7. | Applications of linear data structures are mainly in application software development. | Applications of non-linear data structures are in Artificial Intelligence and image processing. |
| 8. | Linear data structures are useful for simple data storage and manipulation. | Non-linear data structures are useful for representing complex relationships and data hierarchies, such as in social networks, file systems, or computer networks. |
| 9. | Performance is usually good for simple operations like adding or removing at the ends, but slower for operations like searching or removing elements in the middle. | Performance can vary depending on the structure and the operation, but can be optimized for specific operations. |
Similar Reads
DSA Tutorial - Learn Data Structures and Algorithms
DSA (Data Structures and Algorithms) is the study of organizing data efficiently using data structures like arrays, stacks, and trees, paired with step-by-step procedures (or algorithms) to solve problems effectively. Data structures manage how data is stored and accessed, while algorithms focus on
7 min read
Quick Sort
QuickSort is a sorting algorithm based on the Divide and Conquer that picks an element as a pivot and partitions the given array around the picked pivot by placing the pivot in its correct position in the sorted array. It works on the principle of divide and conquer, breaking down the problem into s
12 min read
Merge Sort - Data Structure and Algorithms Tutorials
Merge sort is a popular sorting algorithm known for its efficiency and stability. It follows the divide-and-conquer approach. It works by recursively dividing the input array into two halves, recursively sorting the two halves and finally merging them back together to obtain the sorted array. Merge
14 min read
Breadth First Search or BFS for a Graph
Given a undirected graph represented by an adjacency list adj, where each adj[i] represents the list of vertices connected to vertex i. Perform a Breadth First Search (BFS) traversal starting from vertex 0, visiting vertices from left to right according to the adjacency list, and return a list conta
15+ min read
Bubble Sort Algorithm
Bubble Sort is the simplest sorting algorithm that works by repeatedly swapping the adjacent elements if they are in the wrong order. This algorithm is not suitable for large data sets as its average and worst-case time complexity are quite high.We sort the array using multiple passes. After the fir
8 min read
Binary Search Algorithm - Iterative and Recursive Implementation
Binary Search Algorithm is a searching algorithm used in a sorted array by repeatedly dividing the search interval in half. The idea of binary search is to use the information that the array is sorted and reduce the time complexity to O(log N). Binary Search AlgorithmConditions to apply Binary Searc
15 min read
Insertion Sort Algorithm
Insertion sort is a simple sorting algorithm that works by iteratively inserting each element of an unsorted list into its correct position in a sorted portion of the list. It is like sorting playing cards in your hands. You split the cards into two groups: the sorted cards and the unsorted cards. T
9 min read
Data Structures Tutorial
Data structures are the fundamental building blocks of computer programming. They define how data is organized, stored, and manipulated within a program. Understanding data structures is very important for developing efficient and effective algorithms. What is Data Structure?A data structure is a st
2 min read
Dijkstra's Algorithm to find Shortest Paths from a Source to all
Given a weighted undirected graph represented as an edge list and a source vertex src, find the shortest path distances from the source vertex to all other vertices in the graph. The graph contains V vertices, numbered from 0 to V - 1.Note: The given graph does not contain any negative edge. Example
12 min read
Selection Sort
Selection Sort is a comparison-based sorting algorithm. It sorts an array by repeatedly selecting the smallest (or largest) element from the unsorted portion and swapping it with the first unsorted element. This process continues until the entire array is sorted.First we find the smallest element an
8 min read