# Types of Data Structures: Simple to Complex

## Types of Data Structures

In this article, we will discuss various types of data structures. Data structures are the fundamental elements that make up every computer programme. They give us the ability to save and organise data in a way that makes it simpler to both access and change information. We can categorize data structures based on various criteria such as: primitive and non-primitive, linear and non-linear, static and dynamic etc.

### Primitive vs Non-Primitive Data Structures:

Primitive data structures are the foundational sorts of data that are included in the majority of programming languages. Integers, floating-point numbers, characters, and Boolean values are some examples of these. They are regarded as primitive since it is not possible to partition them into more specific data types.

Non-primitive data structures are more complicated than primitive data structures, and they can be constructed from a mixture of primitive data types and additional non-primitive data structures. Arrays, linked lists, stacks, queues, trees, and graphs are all examples of non-primitive data structures. Other examples include queues and trees. These data structures can be accessed and modified in a variety of ways, as well as used to store and organise huge amounts of data. They can also be used to store and arrange data.

Primitive data structures are the fundamental components that make up all programming languages. Non-primitive data structures, on the other hand, are more complicated and can be composed of primitive data types as well as other non-primitive data structures.

### Linear vs Non-Linear Data Structures:

Each element in a linear data structure only has one predecessor and one successor, and the elements are kept in a linear or sequential order. Arrays, linked lists, stacks, and queues are a few examples of linear data structures.

On the other hand, non-linear data structures are those in which the elements are stored in a hierarchical or tree-like structure rather than in a linear or sequential fashion. The non-linear data structures heaps, trees, and graphs are a few examples.

The way the elements are arranged and accessible is the primary distinction between linear and non-linear data structures. In linear data structures, elements are accessed sequentially and in order, one after the other. Depending on the particular structure, there are several ways to retrieve elements in non-linear data structures.

In conclusion, while non-linear data structures arrange data in a hierarchical or tree-like structure, linear data structures organise data in a linear or sequential fashion.

### Static vs Dynamic Data Structures:

The allocation and management of data memory differ between static and dynamic data structures.

The size of static data structures is predetermined at build time and cannot be altered at runtime. Arrays and static linked lists are two instances of static data structures. Static data structures have a fixed size, and if the quantity of data being stored is not known in advance, this might result in inefficient use of memory.

On the other side, dynamic data structures are those whose size can be modified live. Dynamic arrays, linked lists, stacks, and queues are a few examples of these types of data structures. Dynamic data structures can be more effective in terms of memory utilisation because they allocate memory as needed on the fly.

As their size is defined and known in advance, static data structures have the potential to be faster to access than dynamic data structures. They might, however, be less adaptable and require more memory.

Dynamic data structures can be slower to access because they must allocate and deallocate memory as needed, but they are more adaptable and efficient in terms of memory utilisation.

In conclusion, dynamic data structures can change in size while being run, whereas static data structures have a fixed size at compile time. Dynamic structures can be more memory-efficient and flexible but longer to access, whereas static structures can be faster to access but less flexible.

### Array

An index is used to access the elements stored in an array, which is a simple data structure that can hold a collection of elements that are all of the same types.

Linked lists are comparable to arrays, but they differ in that their size can be changed on the fly and they are made up of nodes that each point to the following node in the list.

### Stack and Queue

Stacks and queues are examples of linear data structures. They permit components to be added and withdrawn in a predetermined order. In stacks, the principle known as Last In First Out, or LIFO, is followed, while in queues, the First In First Out, or FIFO principle is followed.

### Tree

A tree is a hierarchical data structure that is made up of nodes that are connected to one another by edges, with a single node known as the root located at the very top of the tree. Both the hierarchical relationships they indicate and the search operations they enable are possible applications for these nodes.

### Graph

Nodes and the edges that connect them make up the building blocks of graphs, which are non-linear data structures. They are frequently utilised in social networks, transportation systems, and computer networks, where they are used to represent the intricate interactions that exist between various objects or entities.

It is essential for effective and efficient programming to have a thorough understanding of data structures, and having such an understanding is necessary for the development of complex algorithms and software applications. Understanding the advantages and disadvantages of various data structures is essential.