Do you know, Queue in data structures is used to develop online food delivery applications like Domino’s, Swiggy and Zomato? This is because it serves the purpose of the First-Come-First-Serve strategy of software development. So, in this tutorial, you are going to discover queues as an abstract data structure to understand their functionalities and applications.
Why Learn Queue in Data Structure?
Nowadays, several virtual messaging applications serve the obligation of keeping in touch with friends and family. And while using them, you must have observed that these applications maintain the hierarchy of your text messages irrespective of whether the person is online or offline. This leads you to the question: how are these messaging applications maintaining the order of text messages?
The answer to this question lies in the queue in data structures. All messaging applications maintain a queue for each user, containing the messages to be delivered to the user. When the user connects to the network, the messages in the queue get delivered. And later, the empty queue is deleted.
This example clearly illustrates the importance of queues. So, look at the definition of this data structure.
What Is Queue in Data Structures?
Queue in data structures is a linear collection of different data types which follow a specific order while performing various operations. It can only be modified by the addition of data entities at one end or the removal of data entities at another. By convention, the end where insertion is performed is called Rear, and the end at which deletion takes place is known as the Front.
These constraints of queue make it a First-In-First-Out (FIFO) data structure, i.e., the data element inserted first will be accessed first, and the data element inserted last will be accessed last. This is equivalent to the requirement that once an additional data element is added, all previously added elements must be removed before the new element can be removed. That’s why more abstractly, a queue in a data structure is considered being a sequential collection.
Once you are clear with the key terms related to a queue data structure, you will now look at its representation details.
Queue Representation
Before dealing with the representation of a queue, examine the real-life example of the queue to understand it better. The movie ticket counter is an excellent example of a queue where the customer that came first will be served first. Also, the barricades of the movie ticket counter stop in-between disruption to attain different operations at different ends.
The queue in the data structure acts the same as the movie ticket counter. Both the ends of this abstract data structure remain open. Further, the insertion and deletion processes also operate analogously to the wait-up line for tickets.
The following diagram tries to explain queue representation as a data structure:
A queue can be implemented using Arrays, Linked-lists, Pointers, and Structures. The implementation using one-dimensional arrays is the easiest method of all the mentioned methods.
With this understanding of queue representation, look at the different operations that can be performed on the queues in data structures.
Basic Operations for Queue in Data Structure
Unlike arrays and linked lists, elements in the queue cannot be operated from their respective locations. They can only be operated at two data pointers, front and rear. Also, these operations involve standard procedures like initializing or defining data structure, utilizing it, and then wholly erasing it from memory. Here, you must try to comprehend the operations associated with queues:
- Enqueue() - Insertion of elements to the queue.
- Dequeue() - Removal of elements from the queue.
- Peek() - Acquires the data element available at the front node of the queue without deleting it.
- isFull() - Validates if the queue is full.
- isNull() - Checks if the queue is empty.
When you define the queue data structure, it remains empty as no element is inserted into it. So, both the front and rear pointer should be set to -1 (Null memory space). This phase is known as data structure declaration in the context of programming.
First, understand the operations that allow the queue to manipulate data elements in a hierarchy.
Enqueue() Operation
The following steps should be followed to insert (enqueue) data element into a queue -
- Step 1: Check if the queue is full.
- Step 2: If the queue is full, Overflow error.
- Step 3: If the queue is not full, increment the rear pointer to point to the next available empty space.
- Step 4: Add the data element to the queue location where the rear is pointing.
- Step 5: Here, you have successfully added 7, 2, and -9.
Dequeue() Operation
Obtaining data from the queue comprises two subtasks: access the data where the front is pointing and remove the data after access. You should take the following steps to remove data from the queue -
- Step 1: Check if the queue is empty.
- Step 2: If the queue is empty, Underflow error.
- Step 3: If the queue is not empty, access the data where the front pointer is pointing.
- Step 4: Increment front pointer to point to the next available data element.
- Step 5: Here, you have removed 7, 2, and -9 from the queue data structure.
Now that you have dealt with the operations that allow manipulation of data entities, you will encounter supportive functions of the queues -
Peek() Operation
This function helps in extracting the data element where the front is pointing without removing it from the queue. The algorithm of Peek() function is as follows-
- Step 1: Check if the queue is empty.
- Step 2: If the queue is empty, return “Queue is Empty.”
- Step 3: If the queue is not empty, access the data where the front pointer is pointing.
- Step 4: Return data.
isFull() Operation
This function checks if the rear pointer is reached at MAXSIZE to determine that the queue is full. The following steps are performed in the isFull() operation -
- Step 1: Check if rear == MAXSIZE - 1.
- Step 2: If they are equal, return “Queue is Full.”
- Step 3: If they are not equal, return “Queue is not Full.”
isNull() Operation
The algorithm of the isNull() operation is as follows -
- Step 1: Check if the rear and front are pointing to null memory space, i.e., -1.
- Step 2: If they are pointing to -1, return “Queue is empty.”
- Step 3: If they are not equal, return “Queue is not empty.”
Now that you have covered all the queue operations, you will discover a few applications of the queue.
Applications of Queue
Queue, as the name suggests, is utilized when you need to regulate a group of objects in order. This data structure caters to the need for First Come First Serve problems in different software applications. The scenarios mentioned below are a few systems that use the queue data structure to serve their needs -
- Printers: Queue data structure is used in printers to maintain the order of pages while printing.
- Interrupt handling in computes: The interrupts are operated in the same order as they arrive, i.e., interrupt which comes first, will be dealt with first.
- Process scheduling in Operating systems: Queues are used to implement round-robin scheduling algorithms in computer systems.
- Switches and Routers: Both switch and router interfaces maintain ingress (inbound) and egress (outbound) queues to store packets.
- Customer service systems: It develops call center phone systems using the concepts of queues.
Conclusion
A queue is an object which is used to manipulate the ordered collection of different data types. You have seen various queue operations like Enqueue(), Dequeue(), isFull(), isNull() and Peek(). It is recommended to apply queue when there is a need for the FCFS (First Come First Serve) approach in software development. You can also utilize them in situations where data does not need a synchronous transfer.
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On that note, do you have any questions related to this tutorial on queue in data structure? If you do, please don’t hesitate to place them as comments towards the end of this page. We will get to them and respond soon!
