Types of Data Structure: Primitive & Non-Primitive Data Structure

Any application or software has two components at its foundation: data and algorithm. Data as an asset needs to be efficiently stored and easily accessed. It is done with the help of data structures. In this blog, we will discuss different types of data structures to help you build a strong foundation and optimize your application or program.

What is Data Structure?

A data structure is a specialized and systematic format of storing, processing and organizing data in the memory of a computer. It is stored in a way that it can be accessed and used efficiently. It is essential for algorithms because of several data structure types. You can learn more about data structures with an online Data Structures and Algorithms course.

2 Different Types of Data Structures

Various types of data structures allow storing data in the computer memory in such a way that the data can be accessed quickly for the required calculations. 

Two broad data structure types based on the type of operations they are required to perform are primitive and non-primitive data structures. They have further classifications which we will discuss in subsequent sections. 

1. Primitive Data Structure

The primitive data structures are also known as built-in data structures as they are basic data structures in many high-level programming languages. They operate directly according to the machine’s instructions. The following are the various types of data structures that are primitive. 

1. Boolean: The data in this type is either true or false or positive or negative. Boolean type depicts whether the data is positive or negative. Here binary variables, logical values, and algebra are used. 
2. Integer: In this data structure type, both positive and negative numbers along with zero are stored. These integers help with the arithmetic operations. 
3. Floating-point Numbers: In this type of DS, approximate values are used in the form of a formula to allow the trade-off between range and precision. They include real numbers positive, negative, fractions, and numbers with decimal points. 
4. Character: It stores information in a fixed-length field or variable-length string. The fixed-length field is used by the CHAR data type. They are extended with spaces on output but the variable-length string is not. Numbers, alphabets, and special characters are all included but represented inside single or double inverted commas. 
5. Pointer: The pointer data structure stores the memory address of another variable as its value. 

2. Non-Primitive Data Structure

Non-primitive data structures are complex data structures that use primitive data structures to form a structure of their own. This is why they are also known as derived types. Though in-built support is provided for these data structures by many programming languages, they are mostly user-defined. Non-primitive data structures can be further classified into linear and non-linear data structures. 

Linear Data Structure

The linear data structure sequentially stores the values, that is, each value is linked to the previous one. The following are the categories of linear data structure. 

• Array: In a fundamental data structure,
  • Array stores a collection of data items in a linear sequence and contiguous memory location.
  • Each data item is called an “element”. Retrieval of data is easy with this data structure due to the storage of elements in contiguous memory locations. 
  • Each array element has a unique associated index value with which it can be accessed. 
  • The size of an array can be expressed mathematically as Length*Element size. 
  • Here length is the number of elements in the array and the element size is the memory size of the individual element. 
  • Arrays can be used to store data in tabular format, in financial analysis, in scientific computing, to represent graphs, CPU scheduling, in management systems in libraries, in online ticket booking systems, etc. 
• Linked List: The linked list data structure:
  • Uses data memory allocation to store its elements in different locations. Despite being in different locations, these elements are arranged in a sequence and linked with one another. 
  • The element contains a data item and a link or reference to the subsequent item on the same list. This is why they are most preferred when one has to handle dynamic data elements. 
  • There are four major types of lists in data structures, singly linked lists, doubly linked lists, circular linked lists, and circular doubly linked lists. 
  • Linked lists can be used to implement stacks, graphs, etc., to perform arithmetic operations on long integers, for representation of sparse metrics, in memory management.
• Stack: The stack data structure is:
  • One of the types of data structure that uses LIFO or Last In First Out to store and retrieve data elements. It simply means that the element stored last in the stack will be retrieved first.
  • Stack uses two operations for implementing the LIFO principle, push and pop.
  • The push() method inserts the element in the stack and the pop() method retrieves one or more elements from the stack. 
  • Stacks are used to maintain the call logs in mobile phones, to keep track of visited pages on a browser, etc. 
• Queue: The queue data structure:
  • Uses the FIFO or First In First Out principle. 
  • Here the element stored first or the oldest element in the structure is retrieved first. It is retrieved by the dequeue() method. 
  • It is used for handling website traffic, asynchronous transfer of data, switching to multiple applications on an operating system, etc. 

Also Read: Types of Queues in Data Structure

Non-linear Data Structure

In the non-linear data structure, the elements are stored in a non-linear or non-sequential manner. It is complicated since values are arranged at multiple levels, that is, each value is connected to one or more values. The following are the types of non-linear data structures. 

• Graph: The graph is a node-based type of DS that has a list of other nodes that are linked in the graph. It has two components, vertices, and edges. The edges are used to connect vertices. This data structure is used for network representation. The graph data structure is used to represent the flow of computation, in modeling graphs, in Google Maps, to study molecules of physics and chemistry, etc.
• Tree: A subtype of the graph that represents its elements in a hierarchical order. A tree uses a node-like structure to make a hierarchical form and each node here represents a value. The root node is the uppermost node of the tree and the leaf node is the node at the bottom of the tree. This data structure imposes a rule that nodes of a tree do not create loops in the data structure. A tree is used for indexing databases, scanning, parsing, and generating code in compiler design, in routers in computer networks, social networking sites, etc. 

File Data Structure

The file data structure allows the user to perform read, write, and append operations on files. A file is a collection of records and one can store data in it using the .txt format. Though it is an easy method for creating and retrieving data, it is not suitable for handling large amounts of data. 

Importance of Data Structure

Some of the reasons why data structures are important are as follows-

• They allow the storage and retrieval of data quickly and efficiently.
• The code becomes more understandable with various data structure types.
• They help improve the performance of the code by reducing the time and space complexity of algorithms. 
• They are useful in handling bulk data.

Conclusion

Knowledge of data structure is essential for data scientists and engineers. It is a systematic way of organizing data in a specialized format that allows the users to store, process, and retrieve data quickly and efficiently. Therefore, to ensure the smooth running of your application program or software, you should be well-versed with the various types of data structures available.

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