What is Queue in Data Structure?

In data structure, a queue is a linear collection of elements that follows the First In First Out principle. It plays a vital role in CPU scheduling, memory management, and algorithm design. From managing print jobs to handling asynchronous tasks in software systems, queues are used to maintain order and control data flow. 

In this guide, you will explore what a queue in data structure is, its key operations, different types like circular and priority queues, and how to implement them in Java and Python with clear code examples.

Table of Contents:

• What Is Queue Data Structure?
• Representation of Queues in Data Structure
• What Is the Need for a Queue in Data Structure?
• Queue Operations in Data Structure
• Types of Queues in Data Structure
• Applications of Queue in Data Structures
• Stack vs Queue
• Common Queue Interview Questions

What Is Queue Data Structure?

“By definition, it is a defined linear data structure that stores elements in a specific order. A queue allows operation at both ends, and operations are performed here based on the FIFO (First In, First Out) principle. “

Following the FIFO principle in the queue means the first element added to the queue is also the first one to leave. 

In other words, elements are inserted at the rear of the queue and removed from the front only. In computing, queues act as a temporary holding space used for tasks, messages, or resources that need to be handled in the order they arrive.

Representation of Queues in Data Structure

A Queue data structure has both ends open, hence data is accessible from both its sides (at the  front for deletion and the back for insertion)

The following queue diagram explains the queue representation as a data structure:

What Is the Need for a Queue in Data Structure?

Queues are mostly used as buffers in computer systems where a speed mismatch between two communicating devices can be seen, such as between a CPU and keyboard for an instruction or between two different network devices.

They are an essential part of operating system algorithms, such as CPU Scheduling and Memory management, including standard algorithms like Breadth-First Search (BFS) in graphs and level-order traversal in trees.

Characteristics of a Queue:

• Queues can handle multiple data 
• They are fast and flexible.
• Cannot access arbitrary elements; only the front can be accessed directly.

Real-life examples of queues include a line of people waiting for their turn to get a meal at a mess or a sequence of airplanes waiting for landing instructions.

In the above illustration, the person at the front of the queue is the first to receive a meal, while the one at the rear is the last to receive a meal. 

Other Real-Life Examples of Queues:

1. Managing people on an escalator
2. Queuing vehicles at a car wash
3. Organizing customers in a cashier line
4. Regulating traffic flow through one-way exits
5. Vehicles at a toll plaza

Queue Operations in Data Structure

Some basic operations supported by queues are Enqueue and Dequeue. Let’s discuss them quickly:

1. Enqueue

The enqueue operation adds an element to the rear end of the queue.

Algorithm:

• Check if the queue has reached its maximum capacity.
• If the queue is initially empty, set front to 0.
• Move the rear pointer forward and insert the new element.

Example (Array-based Queue):

Before enqueue:
Queue = [10, 20, 30]
Front = 0, Rear = 2

Operation:
Enqueue(40)

After enqueue:
Queue = [10, 20, 30, 40]
Front = 0, Rear = 3

Note: The rear pointer moves right as new elements are added.

2. Dequeue

The dequeue operation removes the element from the front end of the queue.

Algorithm:

• Check if the queue is empty.
• Retrieve the element at the front.
• Move the front pointer forward.
• If the queue becomes empty after removal, reset both pointers.

Example (Array-based Queue):

Before dequeue:
Queue = [10, 20, 30, 40]
Front = 0, Rear = 3

Operation:
Dequeue()

After dequeue:
Queue = [20, 30, 40]
Front = 1, Rear = 3

When we call dequeue(), the element 10 at index 0 is removed, and the front pointer moves one step forward.

Now, 20 is the front of the queue. The memory slot at index 0 is logically removed from the active queue.

3. Peek

The peek operation returns the element at the front without removing it.

Algorithm:

• Check if the queue is empty.
• Return the element at the front.

Example (Array-based Queue):

Before peek:
Queue = [10, 20, 30, 40]
Front = 0, Rear = 3

Operation:
Peek()

After peek:
Queue = [10, 20, 30, 40]
Front = 0, Rear = 3
Peeked value: 10

4. isFull

Used to check whether the queue has reached its maximum capacity (only in case of static implementation).

Algorithm:

• Compare the current size with the capacity.

Example (Array-based Queue):

Before isFull:
Queue = [10, 20, 30, 40]
Front = 0, Rear = 3

Operation:
isFull()

After isFull:
Queue = [10, 20, 30, 40]
Front = 0, Rear = 3
Is Full: True

5. isEmpty

Used to check whether the queue is empty.

Algorithm:

• Return true if the number of elements is 0.

Example (Array-based Queue):

Before isEmpty:
Queue = [10, 20, 30, 40]
Front = 0, Rear = 3

Operation:
isEmpty()

After isEmpty:
Queue = [10, 20, 30, 40]
Front = 0, Rear = 3
Is Empty: False

Every operation preserves the FIFO ordering and guarantees that data moves in a predictable, structured manner.

Bonus Tip:
Always check underflow (empty queue) and overflow (full queue) conditions prior to executing dequeue or enqueue operations, particularly when working with fixed-size structures.

Example Code: Array Implementation of Queue

A queue in data structure can be implemented using linked lists, arrays, or vectors. For the sake of simplicity, we will perform it using a fixed-size array and a circular queue. 

1. Queue Data Structure Example in C++

Output:

3 added to the queue.
6 added to the queue.
9 added to the queue.
Front item: 3
3 removed from the queue.
6 removed from the queue.
Front after removal: 9

2. Queue Data Structure Example in Java

Output:

3 added to the queue.
6 added to the queue.
9 added to the queue.
Front item: 3
3 removed from the queue.
6 removed from the queue.
Front after removal: 9

3. Queue Data Structure Example in Python

Output:

3 added to the queue.
6 added to the queue.
9 added to the queue.
Front item: 3
3 removed from the queue.
6 removed from the queue.
Front after removal: 9

Explanation: What are the Current Codes doing?

Each of the three code examples (C++, Java, Python) implements a Queue using a fixed-size array, with the following features:

1. Circular indexing to handle wrap-around using % capacity
2. enqueue() adds an element to the rear
3. dequeue() removes an element from the front
4. peek() shows the front element
5. Overflow and underflow conditions are handled
6. The queue size is fixed and controlled by a capacity variable

These are all array-based implementations of a circular queue in cpp queue, Java, and Python.

Types of Queues in Data Structure

All queues are not created equal. The practice employs different types of queues depending on what is required at that time.

1. Linear Queue

It is the standard queue where elements are inserted at the rear and removed from the front. But once it’s full, it never reuses freed space even if the front is dequeued.

2. Circular Queue

It is also known as a Ring Buffer and is an incremental improvement over the linear queue in data structure for circular-shaped storage. Once the data insertion reaches the end, it wraps back around over itself, reusing space freed at the front. 

Do You Know?
Circular queues are the most common data structures in memory-constrained environments like embedded systems and device drivers.

3. Double-Ended Queue (Deque)

Insertion and deletion are possible here at either end. This flexibility is useful in many applications, such as in caching, real-time scheduling, and some complex algorithms, such as sliding window maximum.

4. Priority Queue

Elements are processed according to priority rather than their arrival order. These are indispensable in operating systems and network routers. It is used in algorithms such as Dijkstra’s shortest path and Prim’s algorithm, along with data compression techniques like Huffman coding.

Applications of Queue in Data Structures

The queue data structure is based on the First In First Out model, which has a simple but powerful effect, and it becomes an integral part of many real-world application systems as well as core algorithms.

1. Job Scheduling

Queues are used by operating systems to organize a variety of tasks, such as keystrokes, mouse clicks, and process scheduling. Jobs arrive in the queue and are processed serially, one at a time. In round-robin scheduling, each process receives an identical amount of time slice to use through the queue mechanism.

2. Multiprogramming

When more than one program is in memory at the same time, they are managed by a structure called the ready queue. These processes line up for a CPU allocation. The processor fetches and executes them based on the scheduling algorithms, thereby ensuring efficient multitasking.

Did You Know?

Ready queue in the DS is very important in the modern operating system. It maximizes resource allocation by allowing the processor to retrieve jobs in a structured and deadlock-free way.

3. Graph and Tree Traversal

Algorithms such as BFS (Breadth First Search) and Level Order Traversal in trees or graphs are typical examples that heavily rely on queues. Nodes are added to the queue as they’re discovered, then processed one by one, which makes queues ideal for layered exploration.

4. Buffer Management

Buffer spaces are handled by queues in network systems and I/O processing. Temporary queues are used for packets that are temporarily stored in the queue while they’re being sent or processed. This way, communication flows smoothly even with different speeds for sender and receiver.

Bonus Tip:
Queues are used in real-time systems to prevent data loss. This happens by acting as a temporary holding space for incoming data until the system is ready to handle it.

Stack vs Queue

A queue is an Abstract Data Type (ADT) similar to a stack. However, the only difference between the two lies in the fact that a queue ADT in data structure allows operation on both ends, while a stack only allows at one end, called the top. 

Thus, data is inserted in the queue from one end and removed from it using the other end, as shown in the queue diagram. While in a stack, the data inserted first will be processed last and vice versa. Queues are very frequently used in most programming languages via libraries, built-in, or custom implementations.

Do you know?

A queue is a sequential data structure, like an array, but in an array, any element can be directly accessed using its index. In a queue, only the front element can be accessed at a time.

While both are linear data structures, their behavior is opposite in terms of order.

Do You Know?
Undo functionality in text editors as well as IDEs uses stack functionality. 

Common Queue Interview Questions

Queues often appear in technical interviews. Some common questions include, but are not limited to

• Implement a queue using two stacks
• Design a circular queue with fixed capacity
• Reverse the first k elements of a queue
• Simulate a ticket counter using queue logic
• Apply BFS traversal on a graph

Bonus Tip:
When solving queue problems in interviews, focus on edge cases like empty queue, one-element queue, and overflow conditions. These are where most bugs hide.

Conclusion

The queue data structure is more than just a coding concept. It reflects how real-world systems manage order, timing, and resource management. Whether you’re building a CPU scheduler, a graph traversal algorithm, or simply managing requests in a web server, understanding queues is essential.

As with many core data structures, queues are simple in design but powerful in application. Their real strength lies in their predictability, efficiency, and wide relevance across domains.

What is Queue in Data Structure – FAQs