Queue in c++: A First-In-First-Out (FIFO) Approach

Data structures play a pivotal role in computer programming, enabling efficient data organization and manipulation. Among these structures, the queue emerges as a versatile and widely-applied concept with a multitude of applications. A queue is a linear data structure governed by the First-In-First-Out (FIFO) principle, where the first element inserted is the first to be removed. In this blog, we embark on a journey into the realm of queues, delving into their definition, implementation, operations, and real-world significance in C++. We will explore how queue in C++ serve as essential tools in solving intricate problems and optimizing algorithms, making them indispensable for modern programming tasks. The world of queue in C++ awaits us, promising a deeper understanding of data management and problem-solving prowess.

Introduction to Queue in C++

A queue is a linear data structure that stores elements in a specific order. It adheres to the FIFO principle, meaning the first element added to the queue is the first one to be removed. This behavior is similar to standing in a line (queue) for a ticket, where the first person in line gets the first ticket and leaves the line.

Queues are widely used in various real-world scenarios, such as task scheduling, process management, and data buffering. They play a crucial role in algorithms like breadth-first search (BFS), simulation, and scheduling applications.

Implementation Queue in C++

In C++, you can implement a queue using different data structures like arrays and linked lists. The Standard Template Library (STL) in C++ provides a built-in container for queues, making implementation easier and efficient.

Explanation:

1. We start by defining a template class Queue that represents the queue data structure. The class has private member variables arr, front, rear, capacity, and size.
2. The arr is a dynamically allocated array of type T, used to store the elements of the queue. front and rear are pointers to the front and rear positions of the queue, respectively. capacity stores the maximum capacity of the queue, and size tracks the current number of elements in the queue.
3. In the constructor, we initialize the queue by allocating memory for the array arr with the specified capacity. We also set front to 0, rear to -1 (since the queue is empty initially), and size to 0.
4. The isEmpty method checks whether the queue is empty by checking if size is 0, and the isFull method checks if the queue is full by comparing size with capacity.
5. The getSize method returns the current number of elements in the queue (size).
6. The enqueue method is used to add elements to the back (rear) of the queue. It first checks if the queue is full using the isFull method. If the queue is not full, it increments rear (with wrap-around) to point to the next position in the circular array, and then assigns the new element to that position. Finally, it increments the size.
7. The dequeue method is used to remove the element from the front of the queue. It first checks if the queue is empty using the isEmpty method. If the queue is not empty, it increments front (with wrap-around) to point to the next position in the circular array. Finally, it decrements the size.
8. The getFront method returns the element at the front of the queue without removing it. If the queue is empty, it displays a message and returns a default value of type T.
9. In the main function, we create a Queue of integers named myQueue with a capacity of 5.
10. We then enqueue the elements 10, 20, and 30 into the myQueue using the enqueue method.
11. Next, we use the getFront method to retrieve and print the front element of the queue, which is 10.
12. We dequeue an element using the dequeue method.
13. Again, we use the getFront method to retrieve and print the front element of the queue after dequeue, which is now 20.
14. Lastly, we use the getSize method to get the current number of elements in the queue, which is 2.

Out put

Queue Operations

1. Enqueue (Push): This operation is used to add an element to the back (rear) of the queue.

2. Dequeue (Pop): This operation is used to remove the element from the front (front) of the queue.

3. Front: This operation returns the element at the front of the queue without removing it.

4. Empty: This operation checks if the queue is empty or not.

5. Size: This operation returns the number of elements in the queue.

Time Complexity of Queue in C++

Explanation:

1. Enqueue (Push): Adding an element to the back of the queue takes constant time O(1). Since we maintain a rear pointer, we can directly insert the new element at the rear position without traversing the entire queue.

2. Dequeue (Pop): Removing an element from the front of the queue also takes constant time O(1). We maintain a front pointer, allowing us to easily remove the front element without the need for traversal.

3. Front (Peek): Accessing the front element without removing it is a constant-time operation O(1). We can directly access the element using the front pointer.

4. Empty: Checking if the queue is empty involves a simple comparison of the size with 0. It is a constant-time operation O(1).

5. Size: Determining the number of elements in the queue is a constant-time operation O(1). We keep track of the size as elements are enqueued or dequeued, so retrieving the size does not require iterating through the queue.

The table illustrates that all fundamental queue operations have a time complexity of O(1), making queues an efficient data structure for scenarios where FIFO behavior is essential. The constant time complexity ensures that these operations perform consistently well, even for large queues, making queues valuable in various real-world applications.

Space Complexity of Queue in C++

Explanation:

1. Array-based Queue:

• The space complexity for storing the elements in an array-based queue is O(n), where n is the number of elements in the queue. As the number of elements increases, more space is required to store them in the array.
• Additionally, we need constant space O(1) to store the front and rear pointers. These pointers do not depend on the number of elements and remain the same regardless of the number of elements in the queue.

2. Linked List Queue:

• The space complexity for storing the elements in a linked list queue is also O(n), where n is the number of elements in the queue. Each element (node) in the linked list requires memory space to store the data and the pointer to the next node.
• Similarly to the array-based queue, we need constant space O(1) to store the front and rear pointers in the linked list queue.

Both implementations have a space complexity directly proportional to the number of elements in the queue, which is O(n). The space complexity of queues grows linearly with the number of elements they store, making them efficient for managing a dynamic collection of elements.

Some Code examples:

1. Enqueue (Push):

This operation is used to add an element to the back (rear) of the queue.

This code example demonstrates how to add elements to the back (rear) of the queue using the push() method. We start with an empty queue and use the push() method to add three elements: 10, 20, and 30.

2. Dequeue (Pop):

This operation is used to remove the element from the front (front) of the queue.

This code example shows how to remove the element from the front of the queue using the pop() method. After enqueueing three elements (10, 20, and 30), we use the pop() method to remove the front element (10).

3. Front:

This operation returns the element at the front of the queue without removing it.

This code example demonstrates how to access the element at the front of the queue without removing it using the front() method. After enqueueing three elements (10, 20, and 30), we use the front() method to access the front element (10) and print it.

Output:

4. Empty:

This operation checks if the queue is empty or not.

This code example checks if the queue is empty using the empty() method. Since we haven’t enqueued any elements, the queue will be empty, and the code will print “Queue is empty.”

Output

5. Size:

This operation returns the number of elements in the queue.

This code example shows how to get the number of elements in the queue using the size() method. After enqueueing three elements (10, 20, and 30), we use the size() method to get the size of the queue (which is 3) and print it.

Output

Queue Applications

1. Process Scheduling: Queues are used in operating systems for process scheduling, where processes are placed in a queue and executed based on priority and time-sharing.

2. Print Spooling: In printers, a queue is used to manage print jobs. The print spooler organizes the print jobs in a queue and processes them one by one.

3. Breadth-First Search (BFS): BFS traversal of graphs and trees is often implemented using queues to explore the nodes layer by layer.

4. Task Management: Queues are used in task management systems to prioritize and execute tasks in a specific order.

5. Buffering of Data: Queues are used to buffer data in various data processing systems to balance data flow.

Advantages of Queue:

1. FIFO Order: The First-In-First-Out (FIFO) behavior of queues makes them ideal for scenarios where elements need to be processed in the order they arrive. This property is valuable in tasks like task scheduling, process management, and handling print jobs.

2. Simple Operations: Queue operations (enqueue and dequeue) have a constant time complexity O(1) in most implementations, making them efficient for adding and removing elements from the front and back of the queue.

3. Dynamic Size: Queues can dynamically grow and shrink as elements are added and removed, making them suitable for applications with varying input sizes or dynamic data streams.

4. Synchronization and Scheduling: In concurrent programming, queues are widely used for synchronization and communication between threads. They facilitate orderly execution of tasks and data exchange among threads.

5. Efficient Memory Allocation: Queue implementations using linked lists efficiently manage memory allocation by allocating memory only when needed. This can save memory compared to fixed-size arrays.

Limitations of Queue:

1. Random Access: Queues do not support random access to elements. Accessing or modifying elements in the middle of the queue requires dequeuing all elements before the desired element.

2. Fixed Capacity (Array-based Queue): In array-based implementations, the queue has a fixed capacity. Once the capacity is reached, no more elements can be added unless the queue is resized or re-implemented.

3. Memory Overhead (Linked List Queue): Linked list implementations require additional memory for storing pointers, which can lead to higher memory overhead compared to arrays.

4. Not Suitable for All Data Retrieval Operations: If there is a need to frequently access elements other than the front or back, a different data structure like an array or linked list might be more appropriate.

5. Not Efficient for Search Operations: Searching for a specific element in a queue is not efficient, as it requires traversing the entire queue in the worst case.

Despite these limitations, queues remain a powerful and indispensable data structure for various applications, especially when the order of data processing is crucial or synchronization is required between different parts of a program. Understanding the strengths and limitations of queues allows programmers to make informed decisions when choosing the appropriate data structure for their specific use cases.

Some Use of Queue:

1. Task Scheduling: In operating systems, queues are used for task scheduling, where processes or threads are placed in a queue and executed based on priority or scheduling algorithms like Round Robin.

2. Print Spooling: In printers, a queue is used to manage print jobs. The print spooler organizes print requests in a queue and processes them one by one, ensuring orderly printing.

3. Breadth-First Search (BFS): Queues are fundamental in graph traversal algorithms like BFS. BFS explores all vertices at the same level before moving to the next level, which is efficiently managed using a queue.

4. Simulation: In simulation modeling, queues are used to represent waiting lines or service systems, such as in traffic simulations or network models.

5. Buffering: Queues are employed as buffers in data processing systems, allowing the flow of data to be balanced and preventing data loss or congestion.

6. Synchronization and Communication: In concurrent programming, queues are used for synchronization and communication between threads. They provide a safe way to exchange data or messages among different threads.

7. Network Routing: In computer networks, queues are used to manage data packets waiting to be transmitted. They help control traffic and prevent data packet loss.

8. Call Center Management: In call centers, queues are used to hold incoming customer calls, ensuring calls are processed in the order they arrive.

9. CPU and I/O Scheduling: In computer systems, queues are utilized for scheduling CPU processes and handling I/O requests.

10.Task Management: Queues are used in various task management systems, like job scheduling in batch processing systems or managing tasks in multi-process applications.

The versatility of queues and their ability to efficiently manage data in a FIFO manner make them valuable tools in diverse applications across computer science, telecommunications, simulations, and many other fields.

Conclusion

In conclusion, queues are an essential data structure with widespread applications in various computer science and real-world scenarios. Their First-In-First-Out (FIFO) behavior makes them suitable for tasks that require organized data processing. Whether it’s managing processes, handling print jobs, or implementing search algorithms, queues provide a valuable tool for efficient and organized data management. C++’s Standard Template Library (STL) further simplifies queue implementation, making it convenient and efficient for programmers to use queues in their applications. Understanding the power of queues and incorporating them into your algorithms can significantly enhance the efficiency and performance of your programs.

Want a good reference book for DSA in C++?

you can check out: Introduction to Algorithms by Thomas H. Cormen(Author), Charles E. Leiserson(Author), Ronald L. Rivest (Author), Clifford Stein (Author).

This is a classic and widely regarded as one of the most comprehensive and authoritative books on algorithms. It covers a broad range of algorithms and data structures and provides in-depth explanations and analyses. Although the examples in the book use pseudocode, many students and programmers have successfully applied the concepts in C++.

check 4rth fourth edition price: Hardcover price             Kindel Edition price.

 check 3rd edition price:  Paper Back   

you can also check out for DSA in java: Data Structures and Algorithms Made Easy by Narasimha Karumanchi(Author)

This book is a part of a series that covers data structures and algorithms using different programming languages, including Java, C, and Python. So, if you specifically want to learn data structures and algorithms in Java, you can look for the Java version of this book. It will provide examples and explanations using Java programming language to illustrate various concepts related to data structures and algorithms.

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Best problem-solving book in c++: Problem Solving with C++ by Walter Savitch

Although not specifically a DSA book, this resource introduces C++ programming through problem-solving techniques. It is beginner-friendly and includes some coverage of basic data structures and algorithms.

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Cracking the Coding Interview: 189 Programming Questions and Solutions [paperback] McDowell, Gayle Laakmann

In this book, you’ll find a wide range of array-related problems, including array manipulation, searching, sorting, dynamic programming, and more. Each problem is explained thoroughly, and the solutions are provided with step-by-step explanations and code examples in multiple programming languages, including C++.

The book also includes valuable tips and strategies for approaching coding interviews, making it a useful resource for anyone preparing for technical interviews at top tech companies.

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