Advantages & Disadvantages of Data Structure – The Complete Guide

Data structures are the backbone and silent architects of modern computing. The vast amounts of data we see on the Internet and the seamless organization of the data we access are all supported by the efficient application of various data structures. 

In computer science, data structures determine the efficiency and performance of all algorithms. Data structures are widely used in industries like Computer Science, Software Engineering, Data Analytics, Data Science, Artificial Intelligence, Machine Learning, Web Development, Networking, Geographic Information Systems, and many others.

In this blog, we will examine the advantages and disadvantages of data structures and explore their potential for various applications.

Overview of Data Structure

Data structures are specialized formats for storing, organizing, analyzing, and retrieving data in all fields related to computer science. They simplify data handling by arranging data for a specific purpose. The functioning of data structures is based on two core elements: algorithms and data. 

Algorithms process data into programming-usable formats, while data represents information. Therefore, using appropriate data structures is essential for creating fast and efficient algorithms, which result in more comprehensible code. Enrolling in an online data structures and algorithms course will help you advance your ability to work with complex data structures.

Classification of Data Structures

Before we learn the benefits of data structure, let us understand the types of data structures. The type of data used in a data structure determines its type. Every programming language (PL/SQL, Python, C++, etc.) has data types to create data structures. According to the data types, the data structure is divided into two categories, namely:

• Primitive Data Structures
• Non-Primitive Data Structures

Primitive Data Structures

This type of data structure stores the value of a particular data type. For example, an integer data structure can store the value of only an integer. A primitive data structure cannot be NULL. The following are the primitive data structures:

•  Integer: It represents a whole number data type with no decimal places. 

For example, int num = 30

•  Float: It is a data type with decimal precision. 

For example, float num = 2346.9875

•  Character: It represents a single character. 

For example, char name = “M”

•  Boolean: It returns true or false values for a condition specified. 

For example, boolean value = “true”

Non-Primitive Data Structures

These data structures are capable of storing values of more than one data type. The size of the non-primitive data structure depends on the type of data it will store. For example, a list (a non-primitive data structure) can store the values of various data types. It can contain a NULL value. The following are some of the non-primitive data structures:

•  List: A list is a linear type of data structure. This data structure holds an ordered list of elements. 

For example,

list_of_networks [“Airtel”, ”Jio”, “Idea”]

•  Arrays: An array is also a linear data structure that stores a collection of data having the same data type. It has a fixed size, which means it stores data in a contagious memory location of the same data type (int, float, boolean, text, and others). 

For example,

int[] numbers = {1, 2, 3, 4, 5}

•  Queue: A qQueue is a data structure that arranges items in a specific order and follows the First-In-First-Out (FIFO) method for accessing elements. It is commonly employed in building priority queuing systems and handling threads in multithreading scenarios.

For example,

String[] names = {"Alice", "Charlie", "David", "Emily"};

•  Stack: Based on the principle ‘last in, first out’ or LIFO, a stack is an abstract data type that is composed of homogenous pieces. It is used for push and pop operations that are applied on top of the stack. While the pop operation is responsible for removing an element from the top spot, the push operation adds an element to the stack.

For example, 

stack = [1,2,3,4,5]

• Tree: This is a non-linear and hierarchical data structure. A tree is a hierarchical structure with nodes connected by edges, with the root being the top node and child nodes below it.

For example, 

tree = Tree(1, Tree(2, 2.1, 2.2), Tree(3, 3.1))

You can also consider enrolling in an online data structures and algorithms course to advance your ability to work with complex data structures.

Advantages of Data Structure

Data structures offer a wide range of benefits for creating and maintaining code in programming languages. Some of these advantages are:

1) Efficient Storage

Data structures provide efficient storage by organizing the data effectively, which facilitates quick retrieval and maintenance of the data in the system. The memory allocation takes place according to the data types used in the data structure. 

For example, if it is a long integer data type, 8 bytes get allocated to the memory to store it; arrays allocate memory in a continuous block; linked lists use pointers to allocate memory; and so on. In a nutshell, data structures facilitate efficient storage of data on disks in a manner that makes it easy to retrieve the data.

2) Easy Data Processing

Various data structures are used for specific purposes like organizing, processing, retrieving, and storing data. These structures enable users to access and work with data efficiently. Data structures simplify data processing and enable faster sorting and searching for specific data within a large data set. The data structures convert raw data into a machine-readable format and develop algorithms for data processing.

3) Develop Algorithms 

Algorithms for data structures help organize and access information in a structured manner. These algorithms take into account the format of the data as well as any actions that can be performed on it. Their goal is to find the most efficient way to store and manipulate data within the structure while also allowing for easy navigation. By utilizing these algorithms, complex issues can be resolved with efficiency.

4) Reusability of Data

One of the fundamental advantages of data structure is that it offers data reusability. It enables the creation of data in specific formats, which can then be stored in libraries, allowing different clients to utilize and access the data as needed. Therefore, data can be reused in multiple ways and purposes. This makes it easier to create efficient and dynamic algorithms that can be used for different applications.

5) Provide Built-in Functions

Different programming languages offer diverse data structures equipped with a variety of built-in functions. These functions make the most efficient use of specific databases and enhance data manipulation capabilities. For example, data structures provide built-in functions, such as search, sort, filter, and merge, which enable us to manipulate data more effectively.

6) Supports Data Abstraction

The abstract data type in data structures helps support data abstraction. Data abstraction is the process of hiding internal functions and displaying relevant and basic information. An abstract data type supports the use of complex data structures with complex functions. They can customize any data structure according to how it will be used and enable reusing code by calling its functions without writing repetitive code. Examples of abstract data structures include lists, queues, stacks, etc.

7) Data Structure Selection for Optimal Performance

When dealing with large data sets, the effectiveness of a program heavily relies on the employed data structure. Hence, selecting the appropriate data structure becomes paramount. For instance, linked lists, stacks, and queues are commonly utilized to store data in programs. 

Linked lists excel at efficient data insertion and deletion, stacks are ideal for temporary data storage, and queues are well-suited for organizing data in a specific order. Similarly, a tree data structure aids in the visualization, implementation, and manipulation of hierarchical data sets. 

Graph data structure processes, models, and visualizes real-life problems in other instances. Tailoring the data structure to match the data storage and access requirements allows for optimal performance and efficiency in the program’s execution.

8) Saves Programmer’s Time

Data structures streamline the process of organizing and accessing data, which helps save time. Developers can access data quickly and efficiently without having to manually search through large amounts of data by selecting the appropriate data structure for their program. As a result, they can concentrate more on the program’s functionality and less on data storage and retrieval. Moreover, data structures can be easily reused for other applications, saving more time for developers by not having to write code again.

9) Practical Applications

Data structures have several practical uses, such as dynamic memory allocation, process scheduling, dictionaries, and file system organization. It enables the allocation and deallocation of memory blocks as needed during program execution, reducing memory wastage. Further, spatial data structures are utilized in geographic information systems. 

10) Increased Data Security

You can implement security features into data structures to ensure data integrity and application security. Some of these features are encrypted search trees, access control lists, and support for multi-party computation (SMPC). By incorporating these features, the data structures can detect tampering, prevent unauthorized access, and maintain confidentiality. Therefore, integrating data structures with cyber security frameworks can create a robust defense against cyber attacks.  

Disadvantages of Data Structure

Having discussed the advantages of data structure in detail, let us look at some of its disadvantages:

1) Difficult to Handle for Beginners

Working on simple and complex data structures requires good programming skills and experience. A new developer may find it difficult to handle complex data structures.

2) Slower Data Structure Access

Although different data types are available to allocate memory within the system, some of the more complex data structures’ memory access might become slow and sluggish at times.

3) Initial Quality Testing Takes Time

Building algorithms is a necessary step in the process of designing a data structure from scratch. Initially, it takes a lot of quality testing time, especially if the complexity of the data structure is high.

4) High Maintenance

Handling large data sets, especially big data, requires the use of complex data structures and algorithms along with physical infrastructure. This will require a high cost of maintenance for the smooth functioning of the programs.

5) Requires Comprehensive Planning

Implementing and managing data structures without prior preparation and planning is challenging. You need sophisticated calculations and tremendous efforts to outline the use of data structures in your program.

Advantages and Disadvantages of Array in Data Structure

Arrays are fundamental building blocks in data structures that have benefits and limitations. Let us discuss these advantages and disadvantages to make informed decisions while implementing these in programming. 

Here are the advantages of the array in data structure: 

• Direct Accessibility to Elements: The time required to access an element in an array is constant and does not depend on the array’s size. Therefore, arrays provide direct access to any element in a collection.
• Efficient Storage: Elements of an array are stored in contiguous memory locations. Therefore, memory allocation happens in one block with minimal memory fragmentation, ensuring storage efficiency.
• Quick Data Retrieval: Since data in arrays is stored in contiguous memory, it enables quick data retrieval. It eliminates the need for complex data structures or algorithms. 
• Compatibility With Hardware: Arrays are compatible with most hardware architectures, making them a versatile data structure for programming. 
• Easy Implementation: You can easily understand and implement these data structures. It is ideal for new programmers who are beginning in the field. 
• Versatility: Arrays can store various data types, such as integers, characters, and floating-point numbers. They are also used to store complex data structures like pointers and objects. 

Now let us take a look at the disadvantages of an array in data structure: 

• Insertion and Deletion Issues: In an array, insertion or deletion points must be shifted to adapt to the change. This makes these tasks inefficient and time-consuming. 
• Fixed Size: The size of the arrays is specified at the time of the creation. Therefore, if you need to increase the size of the array, you have to copy the data from the old array to the new one. 
• Lack of Flexibility: Arrays offer limited support for complex data types, making them inflexible compared to trees and lists. 
• Challenges in Memory Allocation: If the size of the array is too large, you may encounter memory allocation problems. This is because a system may not have enough memory to store the array, leading the program to crash. 

Advantages and Disadvantages of Queue in Data Structure

Queues are pivotal in data management and processing. Understanding the strengths and limitations of queues is essential for harnessing their full potential in computational tasks. 

Here are the major advantages of queues in data structures: 

• Efficient Data Management: A queue data structure can efficiently manage large amounts of data. You can perform operations like insertion and deletion smoothly because queues follow the first in first out rule. 
• Simple Implementation: Queues are helpful in the implementation of other data structures and minimize the complexity of the process. 
• Maintain Order: Queue ensures the order of the elements is intact, making it the ideal choice for operations that require the elements to be processed in the same order they were added. 
• Support for Buffering Operations: This data structure provides support for buffering operations, which helps handle asynchronous data transfers. 
• Easy to Understand: Queues are easy to understand and implement. This makes them a good choice for novice programmers.

Here are some disadvantages of queues in data structures: 

• Synchronization Issues: In a multithreaded environment, queues can have issues. When multiple threads try to access or modify the same queue simultaneously, it can produce inconsistent results. 
• Limited Random Access: Another disadvantage of queues is that they do not allow random access to elements, making it difficult to modify elements in the middle. If you have to make changes to such an element, you will have to remove the elements before it. 
• Overhead Memory: They need a large amount of overhead memory to store pointers that connect to the elements, wasting a lot of memory when storing several small elements. 
• Limited Storage Capacity: Queues have a limited capacity. If they are at full capacity, you will have to remove some elements before adding new ones. In these cases, it may lead to overflow and data loss. 
• Unsuitable for Search Operations: Queues are not suitable for search operations because they follow the first in first out (FIFO) principle. To search for a particular element, you will have to dequeue each element until you find the element you were searching for. 

Conclusion

Data structures are the foundation for writing code. Developing an application requires fast computation, which is why data structures are crucial. By understanding the advantages and disadvantages of data structures, developers can select the best data structures for building programs and mitigate potential loopholes. Enhance your programming skills by learning to build data structures for various applications and programs.

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