Learn Queue Data Structure - Revision Notes

Introduction to Queue Data Structure

A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The element that is added first is the one to be removed first. A real-life example of a queue is a line of people waiting for a bus; the first person in line is the first to get on the bus.

Importance and Applications of Queue Data Structure

→ CPU Scheduling: Queues are used in operating systems for managing processes in a scheduled order.
Example: In CPU scheduling algorithms like Round Robin, processes are stored in a queue, and the CPU processes them in the order they arrive.

→ Print Spooling: Queues manage print jobs in printers.
Example: When multiple print jobs are sent to a printer, they are stored in a queue and processed sequentially.

→ Handling Requests: Web servers use queues to handle incoming requests from clients.
Example: In web servers, incoming HTTP requests are queued and processed in the order they arrive.

Key Operations of Queue Data Structure

→ Enqueue: Add an element to the end of the queue.
→ Dequeue: Remove the front element from the queue.
→ Front: Get the front element of the queue without removing it.
→ Rear: Get the rear element of the queue without removing it.
→ isEmpty: Check if the queue is empty.
→ isFull: Check if the queue is full (for fixed-size queues).

Enqueue Operation in Queue Data Structure

The enqueue operation adds an element to the end of the queue. Here's the algorithm and pseudocode:

Algorithm:

1. Check if the queue is full.
2. If the queue is full, throw an overflow error.
3. If the queue is not full, add the element to the end of the queue.
4. Update the rear pointer.

Pseudocode:

function enqueue(queue, element):
    if queue is full:
        throw overflow error
    else:
        queue[rear] = element
        rear = (rear + 1) % queue.size
    

Dequeue Operation in Queue Data Structure

The dequeue operation removes the front element from the queue. Here's the algorithm and pseudocode:

Algorithm:

1. Check if the queue is empty.
2. If the queue is empty, throw an underflow error.
3. If the queue is not empty, remove the front element from the queue.
4. Update the front pointer.

Pseudocode:

function dequeue(queue):
    if queue is empty:
        throw underflow error
    else:
        element = queue[front]
        front = (front + 1) % queue.size
        return element
    

Front Operation in Queue Data Structure

The front operation returns the front element of the queue without removing it. Here's the algorithm and pseudocode:

Algorithm:

1. Check if the queue is empty.
2. If the queue is empty, throw an underflow error.
3. If the queue is not empty, return the front element of the queue.

Pseudocode:

function front(queue):
    if queue is empty:
        throw underflow error
    else:
        return queue[front]
    

Rear Operation in Queue Data Structure

The rear operation returns the rear element of the queue without removing it. Here's the algorithm and pseudocode:

Algorithm:

1. Check if the queue is empty.
2. If the queue is empty, throw an underflow error.
3. If the queue is not empty, return the rear element of the queue.

Pseudocode:

function rear(queue):
    if queue is empty:
        throw underflow error
    else:
        return queue[(rear - 1 + queue.size) % queue.size]
    

isEmpty Operation in Queue Data Structure

The isEmpty operation checks if the queue is empty. Here's the algorithm and pseudocode:

Algorithm:

1. Check if the front and rear pointers are equal.
2. If they are equal, return true.
3. Otherwise, return false.

Pseudocode:

function isEmpty(queue):
    if front == rear:
        return true
    else:
        return false
    

isFull Operation in Queue Data Structure

The isFull operation checks if the queue is full. Here's the algorithm and pseudocode:

Algorithm:

1. Check if the next position of the rear pointer is equal to the front pointer.
2. If they are equal, return true.
3. Otherwise, return false.

Pseudocode:

function isFull(queue):
    if (rear + 1) % queue.size == front:
        return true
    else:
        return false
    

Time Complexity and Space Complexity

Time Complexity: For enqueue and dequeue operations, the time complexity is O(1) because these operations are performed in constant time.

Space Complexity: The space complexity is O(n), where n is the number of elements in the queue, due to the storage of elements.

Advantages of Queue Data Structure

→ Simple Implementation: Easy to implement and use.
→ Efficient Memory Management: Dynamically managed in linked list implementation.
→ Supports Various Applications: Natural fit for scheduling, buffering, and resource management.

Disadvantages of Queue Data Structure

→ Limited Access: Only front and rear elements can be accessed directly.
→ Fixed Size Limitation: In fixed-size array implementation, the size cannot be changed.
→ Complex Implementation: More complex compared to stack due to additional pointer management.