Day 39: Ignoring GIL Assumptions

🐍 Python Mistakes Everyone Makes ❌

Day 39: Ignoring GIL Assumptions

Python makes multithreading look easy — but under the hood, there’s a critical detail many developers overlook: the Global Interpreter Lock (GIL).

Ignoring it can lead to slower programs instead of faster ones.

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❌ The Mistake

Using threads to speed up CPU-bound work.

import threading

def work():
    total = 0
    for i in range(10_000_000):
         total += i

threads = [threading.Thread(target=work) for _ in range(4)]

for t in threads: 

   t.start()
for t in threads: 

  t.join()

This looks parallel — but it isn’t.

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❌ Why This Fails

• Python has a Global Interpreter Lock (GIL)

• Only one thread executes Python bytecode at a time

• CPU-bound tasks do not run in parallel

• Threads add context-switching overhead

• Performance can be worse than single-threaded code

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🧠 What the GIL Really Means

• Threads are great for I/O-bound tasks

• Threads are bad for CPU-bound tasks

• Multiple CPU cores ≠ parallel Python threads

The GIL protects memory safety, but limits CPU parallelism.

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✅ The Correct Way

Use multiprocessing for CPU-bound work.

from multiprocessing import Pool

def work(n):
    total = 0
   for i in range(n):
         total += i
 return total

if __name__ == "__main__":
    with Pool(4) as p: 

       p.map(work, [10_000_000] * 4)

Why this works:

• Each process has its own Python interpreter

• No shared GIL

• True parallel execution across CPU cores

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🧠 When to Use What

Task Type	Best Choice
I/O-bound (network, files)	threading, asyncio
CPU-bound (math, loops)	multiprocessing
Mixed workloads	Combine wisely

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🧠 Simple Rule to Remember

🐍 Threads ≠ CPU parallelism in Python
🐍 GIL blocks parallel bytecode execution
🐍 Use multiprocessing for CPU-heavy tasks

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🚀 Final Takeaway

Threads won’t make CPU-heavy Python code faster.
Understanding the GIL helps you choose the right concurrency model — and avoid hidden performance traps.

Know the limits. Write smarter Python. 🐍⚡