Advanced Python Tutorial: Multithreading vs Multiprocessing

Advanced Python Tutorial 7: Multithreading & Multiprocessing

🧠 What’s the Difference?

Multithreading uses multiple threads in the same process — great for I/O-bound tasks. But it's limited by the GIL (Global Interpreter Lock).

Multiprocessing uses separate processes — ideal for CPU-bound tasks since it bypasses the GIL.

📌 Use Cases

• Multithreading: Downloading files, web scraping, network I/O.
• Multiprocessing: Image processing, large numerical computations, simulations.

⚙️ Example: Multithreading

We’ll run two threads that simulate I/O by sleeping for a second.

import threading
import time

def download(name):
    print(f"{name} starting")
    time.sleep(1)
    print(f"{name} done")

t1 = threading.Thread(target=download, args=("Thread 1",))
t2 = threading.Thread(target=download, args=("Thread 2",))
t1.start()
t2.start()
t1.join()
t2.join()
print("Both threads done")
    

⚡ Example: Multiprocessing

This time, we’ll use the multiprocessing module to run processes in parallel.

import multiprocessing
import time

def compute(name):
    print(f"{name} starting")
    time.sleep(1)
    print(f"{name} done")

if __name__ == "__main__":
    p1 = multiprocessing.Process(target=compute, args=("Process 1",))
    p2 = multiprocessing.Process(target=compute, args=("Process 2",))
    p1.start()
    p2.start()
    p1.join()
    p2.join()
    print("Both processes done")
    

📊 Key Takeaways

• 🧵 Use threads for tasks that wait (network, disk, APIs).
• 🧠 Use processes for heavy CPU computation.
• 🚫 GIL limits true parallelism in threads but not in processes.