Introduction

Python developers often face the big question:
Should I use multithreading or multiprocessing for concurrency?

While both approaches allow you to run tasks concurrently, they solve different problems:

• Multithreading → best for I/O-bound tasks (networking, file I/O).

• Multiprocessing → best for CPU-bound tasks (number crunching, computations).

This article breaks down the differences, use cases, and practical code examples so you’ll know which one to use.

Core Concept: Concurrency vs Parallelism

• Concurrency → multiple tasks make progress in overlapping time (switching context).

• Parallelism → multiple tasks literally run at the same time on different CPU cores.

⚡ Python’s GIL (Global Interpreter Lock) is why threads can’t achieve true parallelism for CPU tasks.

Python Multithreading

• Uses the threading module.

• Multiple threads run within the same process memory.

• Best for I/O-bound tasks (waiting for network, disk, etc.).

Example:

import threading, time

def task(name):
    print(f"Thread {name} starting")
    time.sleep(2)
    print(f"Thread {name} finished")

threads = [threading.Thread(target=task, args=(i,)) for i in range(3)]

for t in threads: t.start()
for t in threads: t.join()

✔ Efficient for tasks that spend time waiting.

Python Multiprocessing

• Uses the multiprocessing module.

• Spawns separate processes → each has its own memory and GIL.

• Best for CPU-bound tasks (math, image processing, ML).

Example:

from multiprocessing import Process
import os

def worker(num):
    print(f"Process {num} running on PID: {os.getpid()}")

processes = [Process(target=worker, args=(i,)) for i in range(3)]

for p in processes: p.start()
for p in processes: p.join()

✔ True parallelism on multi-core CPUs.

Comparison Table: Multithreading vs Multiprocessing

Practical Use Cases

✅ Multithreading:

• Web scraping with multiple requests

• File downloads/uploads

• Real-time UI updates

✅ Multiprocessing:

• Image/video processing

• Machine learning model training

• Heavy mathematical computations

Tips & Common Pitfalls

• Rule of Thumb:

  • I/O-bound → Threads

  • CPU-bound → Processes

• Don’t use too many processes → high memory overhead.

• Use ThreadPoolExecutor / ProcessPoolExecutor for simplicity.

• Beware of deadlocks in threading if locks are misused.

• Be careful with pickling issues when passing data in multiprocessing.

FAQ Section

❓ Why doesn’t Python multithreading improve CPU-heavy performance?
Because of the GIL—only one thread can execute Python bytecode at a time.

❓ Can I mix multiprocessing and multithreading?
Yes. Some apps (like web servers) use multiprocessing for scaling and threads inside each process.

❓ Which is faster: threading or multiprocessing?

• For I/O-bound tasks → threading is faster (less overhead).

• For CPU-bound tasks → multiprocessing is faster (parallel cores).

❓ How do threads and processes communicate?

• Threads → shared variables, queue.Queue().

• Processes → multiprocessing.Queue, Pipe.

❓ What’s easier: asyncio or threading?

• asyncio → great for scalable I/O with coroutines.

• threading → simpler for beginners, blocking calls.

???? Cheat Sheet

Interview Questions

1. What is the GIL in Python? Why does it matter?

• A lock that allows only one thread to execute Python bytecode at a time. Affects threading, not multiprocessing.

2. How do you choose between multithreading and multiprocessing?

• I/O-bound → Threads

• CPU-bound → Processes

3. How do processes avoid the GIL limitation?
Each process has its own Python interpreter and GIL.

4. Can threads run in true parallel in Python?
No, due to GIL. They only achieve concurrency, not parallelism.

5. Show an example of ThreadPoolExecutor vs ProcessPoolExecutor.

from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor

def work(x): return x*x

with ThreadPoolExecutor() as tpool:
    print(list(tpool.map(work, range(5))))

with ProcessPoolExecutor() as ppool:
    print(list(ppool.map(work, range(5))))

6. What are race conditions? How do you handle them?

• When multiple threads access shared data incorrectly. Use threading.Lock.

7. What’s the downside of multiprocessing?

• Higher memory overhead and slower inter-process communication.

Conclusion / Summary

Python Multithreading vs Multiprocessing:

• Use multithreading for I/O-bound tasks.

• Use multiprocessing for CPU-bound tasks.

• The GIL limits threading, but not multiprocessing.

• Use Executors (ThreadPoolExecutor/ProcessPoolExecutor) for cleaner code.

By understanding both, you can choose the right tool for performance and scalability in your Python projects.