Types of Data Structures

In the ever-evolving field of software development, mastering Data Structures and Algorithms (DSA) is crucial. Data structures are essential for organizing, managing, and storing data efficiently, enabling quick access and modification. This article provides an overview of key data structures, their characteristics, and use cases.

There are two types of data structures:

1. Linear data structure.
2. Non-linear data structure.

Linear Data Structure:

Linear data structures are those in which the elements are arranged in a sequential manner, where each element is connected to its previous and next elements, except for the first and last elements. The linear arrangement implies that there is a direct relationship between the elements based on their order. Examples of linear data structures include:

• Arrays
• Linked lists
• Stacks
• Queues

Non-linear Data Structure:

Non-linear data structures do not have a sequential arrangement of elements. Elements are not arranged in a sequential manner like linear data structures. Instead, they have complex relationships among their elements. Examples of non-linear data structures include:

• Trees
• Graphs
• Hash Tables

Linear Data Structures:

?? Arrays

Definition: Arrays are collections of elements stored at contiguous memory locations.

Characteristics:

• Fixed size.
• Random access of elements.
• Homogeneous elements.

Use Cases:

• Used in situations where the size of the dataset is known and fixed.
• Commonly used in implementing other data structures like stacks, queues, and matrices.

?? Linked Lists

Definition: A linked list is a collection of nodes where each node contains data and a reference (or link) to the next node in the sequence.

Characteristics:

• Dynamic size.
• Efficient insertion and deletion.
• Sequential access.

Types:

• Singly Linked List.
• Doubly Linked List.
• Circular Linked List.

Use Cases:

• Implementing data structures like stacks, queues, and graphs.
• Managing memory in dynamic data storage.

?? Stacks

Definition: A stack is a linear data structure that follows the Last In, First Out (LIFO) principle.

Characteristics:

• Elements can be added (pushed) and removed (popped) only from the top.
• Efficient for operations like undo mechanisms, syntax parsing, and function call management. Use Cases:
• Expression evaluation and syntax parsing.
• Memory management in recursive algorithms.

?? Queues

Definition: A queue is a linear data structure that follows the First In, First Out (FIFO) principle.

Characteristics:

• Elements are added at the rear and removed from the front.
• Efficient for scenarios requiring order maintenance.

Types:

• Simple Queue.
• Circular Queue.
• Priority Queue.
• Deque (Double-Ended Queue).

Use Cases:

• Order processing and task scheduling.
• Managing resources in printers and CPU scheduling.

Non-linear Data Structure:

?? Trees

Definition: A tree is a hierarchical data structure consisting of nodes, with a root node and child nodes forming a parent-child relationship.

Characteristics:

• Non-linear structure.
• Efficient for hierarchical data representation.

Types:

• Binary Tree.
• Binary Search Tree (BST).
• AVL Tree.
• B-Tree.
• Trie.

Use Cases:

• Database indexing.
• Organizing hierarchical data like file systems.

?? Graphs

Definition: A graph is a collection of nodes (vertices) connected by edges.

Characteristics:

• Non-linear structure.
• Can be directed or undirected.
• Suitable for representing complex relationships.

Types:

• Directed Graph.
• Undirected Graph.
• Weighted Graph.
• Unweighted Graph.

Use Cases:

• Social networks, recommendation systems, and network routing.

Hash Tables

Definition: A hash table is a data structure that maps keys to values using a hash function.

Characteristics:

• Efficient for searching, insertion, and deletion operations.
• Handles collisions using techniques like chaining and open addressing.

Use Cases:

• Implementing associative arrays and database indexing.
• Caching and data retrieval systems.