...Hasn't multiprocessing been replaced by concurrent.futures?

I have been using concurrent.futures for a while now, but ChatGPT keeps mentioning multiprocessing and not concurrent futures.... so I had to ask...

Great question — and you're not alone in wondering this! The short answer is:

No, multiprocessing hasn't been replaced by concurrent.futures, but rather, concurrent.futures builds on top of it in a more modern, simplified interface.

Let me break it down for clarity:

🔧 multiprocessing vs concurrent.futures

Feature	multiprocessing	concurrent.futures
Introduced in	Python 2.6	Python 3.2
Level of abstraction	Low-level	High-level (simpler API)
Process management	Manual (you manage Process)	Automatically managed via ProcessPoolExecutor
Also supports threads?	❌ No (processes only)	✅ Yes (ThreadPoolExecutor)
Good for...	Fine-grained control	Ease of use and quick parallelism

✅ How concurrent.futures Uses multiprocessing

When you use:

from concurrent.futures import ProcessPoolExecutor

You're still using the multiprocessing module behind the scenes — concurrent.futures just provides:

• Cleaner syntax

• Automatic handling of pools

• Easier result collection

• Exception handling with futures

🧠 Example: Multiprocessing vs. Concurrent Futures

🔹 Using multiprocessing.Process

from multiprocessing import Process 
def task(x):
	print(f"Running task with {x}") 
p = Process(target=task, args=(10,)) 
p.start() 
p.join()

🔹 Using concurrent.futures.ProcessPoolExecutor

from concurrent.futures import ProcessPoolExecutor 
def task(x):
	return x * x 

with ProcessPoolExecutor() as executor: 
	results = executor.map(task, [1, 2, 3, 4]) 
	print(list(results)) # [1, 4, 9, 16]

✅ Much simpler for multiple tasks!

📝 TL;DR

• multiprocessing is still a core module, fully supported and powerful.

• concurrent.futures is a higher-level wrapper that makes parallelism easier.

• For simple parallel tasks, use concurrent.futures.

• For advanced control (pipes, shared memory, etc.), stick with multiprocessing.