# difference between linear and non linear data structure

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The data structure means relationship between the multiple data. It is a simple kind of technique to manage, organize, and efficiently sort the data. It includes suitable algorithms to implement abstract data types.

There are two categories of data structure. One is a linear data structure, and another is a non-linear data structure.

In the real-life, linear data structure is used to develop software, and non-linear data structure is used in image processing and artificial intelligence.

Every programmer should have data structure knowledge and we will get it through this article.

## What is a Linear data structure?

The 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 the linear data types are Stack, Queue, Array, LinkedList, and Hashmap, etc.

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 that 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 operation from the rare using the Enqueue method and deletion operation from the front using the deque method.

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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.

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.

## Linear Data Structures

A Linear data structure have data elements arranged in sequential manner and each member element is connected to its previous and next element. This connection helps to traverse a linear data structure in a single level and in single run. Such data structures are easy to implement as computer memory is also sequential. Examples of linear data structures are List, Queue, Stack, Array etc.

## Non-linear Data Structures

A non-linear data structure has no set sequence of connecting all its elements and each element can have multiple paths to connect to other elements. Such data structures supports multi-level storage and often cannot be traversed in single run. Such data structures are not easy to implement but are more efficient in utilizing computer memory. Examples of non-linear data structures are Tree, BST, Graphs etc.

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Following are the important differences between Linear Data Structures and Non-linear Data Structures.

Sr. No. Key Linear Data Structures Non-linear Data Structures
1 Data Element Arrangement In linear data structure, data elements are sequentially connected and each element is traversable through a single run. In non-linear data structure, data elements are hierarchically connected and are present at various levels.
2 Levels In linear data structure, all data elements are present at a single level. In non-linear data structure, data elements are present at multiple levels.
3 Implementation complexity Linear data structures are easier to implement. Non-linear data structures are difficult to understand and implement as compared to linear data structures.
4 Traversal Linear data structures can be traversed completely in a single run. Non-linear data structures are not easy to traverse and needs multiple runs to be traversed completely.
5 Memory utilization Linear data structures are not very memory friendly and are not utilizing memory efficiently. Non-linear data structures uses memory very efficiently.
6 Time Complexity the Time complexity of linear data structure often increases with increase in size. Time complexity of non-linear data structure often remain with increase in size.
7 Examples Array, List, Queue, Stack. Graph, Map, Tree.

### Comparison Chart

Linear Data Structure Non-Linear Data Structure
Every item is related to its previous and next time Every item is attached with many other items.
Data is arranged in a linear sequence. Data is not arranged in sequence
Data items can be traversed in a single run. Data cannot be traversed in a single run.
Examples: Linked List, Stack, Queue, etc. Examples: Trees, graphs, etc.
Implementation is easy. Implementation is difficult.
Memory utilization Ineffective Memory utilization Effective

#### Linear data structures

• A data structure is said to be linear if its elements are connected in a linear fashion by means of logical or in sequence memory locations.
• There are two ways to represent a linear data structure in memory,
• Static memory allocation
• Dynamic memory allocation
• The possible operations on the linear data structure are Traversal, Insertion, Deletion, Searching, Sorting and Merging.
• Examples of Linear Data Structure are Stack and Queue.
• Stack: Stack is a data structure in which insertion and deletion operations are performed at one end only.
• The insertion operation is referred to as ‘PUSH’ and deletion operation is referred to as ‘POP’ operation.
• A stack is also called as Last in First out (LIFO) data structure.
• Queue: The data structure which permits the insertion at one end and Deletion at another end, known as Queue.
• The end at which deletion occurs is known as FRONT end and another end at which insertion occurs is known as a REAR end
• A queue is also called a First in First out (FIFO) data structure.
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#### Nonlinear data structures

• Nonlinear data structures are those data structures in which data items are not arranged in a sequence.
• Examples of Non-linear Data Structure are Tree and Graph.
• TreeA tree can be defined as a finite set of data items (nodes) in which data items are arranged in branches and sub-branches according to requirements.
• Trees represent the hierarchical relationship between various elements
• Tree consists of nodes connected by an edge, the node represented by a circle and edge lives connecting to the circle.
• GraphGraph is a collection of nodes (Information) and connecting edges (Logical relation) between nodes.
• A tree can be viewed as a restricted graph.
• Graphs have many types:
• Un-directed Graph
• Directed Graph
• Mixed Graph
• Multi-Graph
• Simple Graph
• Null Graph
• Weighted Graph