Python Multithreading and Multiprocessing

1. Multithreading: Lightweight Concurrency

Threads run concurrently within the same process, sharing memory space. Python's Global Interpreter Lock (GIL) limits threads to one execution at a time, making it ideal for I/O-bound tasks but not for CPU-intensive ones.

Example: A Simple Threaded Program

import threading

def print_numbers():
    for i in range(5):
        print(f"Number: {i}")

# Create and start threads
thread1 = threading.Thread(target=print_numbers)
thread2 = threading.Thread(target=print_numbers)

thread1.start()
thread2.start()
thread1.join()
thread2.join()

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2. Multiprocessing: True Parallelism

Multiprocessing creates separate processes with individual memory space, bypassing the GIL. It’s best for CPU-bound tasks, like data processing or simulations.

Example: Multiprocessing Basics

from multiprocessing import Process

def print_numbers():
    for i in range(5):
        print(f"Number: {i}")

if __name__ == "__main__":
    process1 = Process(target=print_numbers)
    process2 = Process(target=print_numbers)

    process1.start()
    process2.start()
    process1.join()
    process2.join()

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When to Use Which

• Use multithreading for tasks like file I/O, database operations, or network requests.
• Use multiprocessing for tasks like image processing, machine learning, or data analysis.

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Final Thoughts: Threads vs. Processes

With threads, Python multitasks within a single process. With processes, Python achieves true parallelism across multiple cores. Together, they make your code efficient and scalable.
_ ? Cheers to mastering concurrency in Python!_

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