Difference Between Queue and Collestions in Python

A queue is a data structure which follows a FIFO (First In First Out) delivery method that is, the order of a set of messages is strictly preserved. It processes all messages exactly once, hence, there's no duplication of messages.

The advantage of a FIFO system over a standard delivery method is because of a queue's ability to support unlimited throughput due to the application of batch-processing of queues. FIFO has high throughput (amount of items passing through a process) and can process a lot more messages than average.

In Python, queues can be implemented via two main libraries, collections and queue libraries. Though they are used in queue implementation, there are many differences between the two which can be optimal for different use-case scenarios. The main differences are listed below:

Key Differences

CATEGORY	queue.Queue()	collections.deque()
Synchronisation	'queue.Queue' provides a safe operation to process multithreading operations since it implements synchronously.	'collections.deque' is not synchronised by default, hence, it may not be applicable for operations where multiple threads may be used.
Functionality	'queue.Queue' is specifically used for queue implementation along with the FIFO delivery system where elements are added at the end. It provides functions such as put() and get() that are used for enqueuing and dequeuing elements present in a queue.	In the case of 'collections.deque' it is more of a standard implementation implementing both concepts of LIFO and FIFO, hence, it can also be used for stack operations. Functions such as append(), pop(), and popleft() give you the ability to add and delete elements from both sides.
Miscellaneous	'queue.Queue' doesn't provide much other than the basic queue functions.	'collections.deque' provides more functionalities to modify or change a given queue with functions like rotate() to rotate the queue in any way, and remove() to remove specific elements from the deque.
Performance	Complexity-wise, 'collections.deque' is faster due to it using doubly-linked lists. Due to this, it has O(1) complexity due to which it can perform operations way faster.	In 'queue.Queue' it is slightly slower due to its synchronization overhead especially during multithreading processes.

queue.Queue

In Python, the queue module provides a 'Queue()' class that provides a data structure. The Queue() class has a queue method that works by implementing the queue data structures.

Syntax

Queue().put(value)
Queue().get(value)

Here, value is the element enqueued in the queue using the put() function. To remove the element from the queue we use the get() function to perform the dequeue operation.

Algorithm

• Import the library.

• Initialize a queue.

• Enqueue some elements.

• Check whether they are empty or not.

• If not empty, dequeue the elements and print them.

Example

#import the necessary library
from queue import Queue

#assign an object to the queue class
m_q=Queue()

#Enqueue elements in a queue
m_q.put(20)
m_q.put(40)
m_q.put(60)

#Dequeuing the elements and printing them
while not m_q.empty():
  i=m_q.get()
  print(i)

Output

20
40
60

We first import the queue module and assign an object for the queue class. Then we enqueue in elements using the put() function. Then, we initialize a while loop to keep dequeuing elements until the queue is empty. The dequeued elements are then printed as output.

collections.deque

In Python, the collections.deque class is used to bring in a double-ended queue data structure. “Deque” stands for “double-ended queue”. In this, elements can be added or removed from both sides of the queue efficiently.

Syntax

deque().append(20)
deque().appendleft(60)
deque().pop()
deque().popleft()

Here we use the deque (double-ended queue) to showcase enqueuing and dequeuing operations on both sides of a deque.

20 is appended normally, and then 60 is appended into the deque at the left of 20. After which, the dequeue operation can be performed on either side using the pop() and popleft() functions.

Algorithm

• Import the library.

• Initialize the deque.

• Add elements from either the left side or the right side.

• Pop the elements.

• Print the queue.

Example

from collections import deque

# Create a new deque
my_deque = deque()

# Add elements to the deque
my_deque.append(20)  # Add to the right end
my_deque.appendleft(40)  # Add to the left end
my_deque.append(60) #Add to right end
print("Initial deque: \n",my_deque)

# Remove elements from the deque
right_element = my_deque.pop()  # Remove from the right end
print("\nAfter removing element from right: \n",my_deque)

left_element = my_deque.popleft()  # Remove from the left end
print("\nAfter removing element from left :\n", my_deque)

# Print the deque's contents
my_deque.pop()
print("\nFinal deque: \n",my_deque)

Output

Initial deque: 
 deque([40, 20, 60])

After removing element from right: 
 deque([40, 20])

After removing element from left :
 deque([20])

Final deque: 
 deque([])

Some of its advantages are

Less complexity	collections.deque()' has less complexity due to the usage of doubly-linked lists and has an O(1) complexity, which is suitable in cases of insertions and deletions from both sides of the queue.
Dynamic size	Unlike regular queues, where the capacity is defined beforehand and storage is quite rigid, collections.deque can change its queue size dynamically. There is no need to resize or copy elements when maximum capacity is reached.

Conclusion

Both 'queue.Queue' and 'collections.deque' are used in queue implementation. But, performance-wise, they provide a very minuscule difference in performing different operations. But, 'collections.deque' is widely used due to its additional functionality. Besides cases where synchronisation is key for multithreading examples, 'collections.deque' are mostly used.