How can I optimize performance using ProcessPoolExecutor?

How can I optimize performance using ProcessPoolExecutor?

You can optimize performance by using ProcessPoolExecutor to parallelize CPU-bound tasks, allowing multiple processes to run concurrently.

What types of tasks are best suited for ProcessPoolExecutor?

It's ideal for CPU-bound tasks like data processing, image manipulation, or any task that requires heavy computation.

Can you give me an example of how to implement it?

Sure! Here's a simple example:

from concurrent.futures import ProcessPoolExecutor

def square(n):
    return n * n

with ProcessPoolExecutor() as executor:
    results = list(executor.map(square, range(10)))
print(results)

How does it compare to ThreadPoolExecutor?

ProcessPoolExecutor is better for CPU-bound tasks, while ThreadPoolExecutor is more suitable for I/O-bound tasks due to Python's Global Interpreter Lock.