B2.2.4 Explain the concept of a queue as a "first in, first out" (FIFO) data structure

FIFO — First In, First Out

A queue is a linear data structure where elements are added at the rear (back) and removed from the front. The first element added is the first one to be removed — like a line at a ticket counter.

Core Operations

Operation	Action
Enqueue	Add an element to the rear of the queue
Dequeue	Remove an element from the front of the queue
Front	View the element at the front without removing it
Rear	View the element at the rear without removing it
isEmpty()	Check if the queue has any elements
isFull()	Check if the queue has reached its maximum capacity

Error Conditions

Condition	Meaning
Overflow	Attempting to Enqueue when the queue is full
Underflow	Attempting to Dequeue from an empty queue

Implementation in Python

from collections import deque

queue = deque()

# Enqueue
queue.append("A")   # queue = ['A']
queue.append("B")   # queue = ['A', 'B']
queue.append("C")   # queue = ['A', 'B', 'C']

# Front and Rear
print(queue[0])     # 'A' — front
print(queue[-1])    # 'C' — rear

# Dequeue
queue.popleft()     # removes 'A' — first in, first out

Applications of Queues

Application	How it uses a queue
Banking — Ticket counter	Customers join at the rear, served from the front
CPU task scheduling	OS schedules processes in arrival order (FIFO)
IO Buffers	Buffers store data between slow devices and the CPU
Call centres	Incoming calls are queued and answered in order
Print queue	Print jobs line up and are processed in order
Network packet queuing	Packets wait in queues before being forwarded

Queues in Asynchronous I/O

Queues are essential in asynchronous I/O, where data is being produced faster than it can be consumed — the queue acts as a buffer to prevent data loss.

Real-world example — downloading a large file:

Server → Network Buffer (queue) → Program processing
   Fast                           Slow

If the server must wait for the program to finish processing each chunk before sending the next → the network sits idle and throughput drops.

If the server sends each chunk into a queue and immediately continues → both sides run independently, maximizing efficiency. The producer (server) never blocks, and the consumer (program) processes at its own pace.