Interview question

What is the difference between threading and multiprocessing in Python? Python में threading और multiprocessing में क्या अंतर है?

Answer

Aspect	threading	multiprocessing
Memory	Shared memory space	Separate memory per process
GIL impact	Limited by GIL for CPU-bound work	Bypasses GIL - true parallelism
Overhead	Lightweight, fast to create	Heavier, slower to create (new process)
Best for	I/O-bound tasks (network, files)	CPU-bound tasks (computation)

import threading
import multiprocessing
import time

def cpu_task(n):
    return sum(i * i for i in range(n))

# Threading - limited by GIL for CPU-bound work
start = time.time()
threads = [threading.Thread(target=cpu_task, args=(10_000_000,)) for _ in range(4)]
for t in threads: t.start()
for t in threads: t.join()
print(f'Threading: {time.time() - start:.2f}s')

# Multiprocessing - true parallelism across CPU cores
if __name__ == '__main__':
    start = time.time()
    processes = [multiprocessing.Process(target=cpu_task, args=(10_000_000,)) for _ in range(4)]
    for p in processes: p.start()
    for p in processes: p.join()
    print(f'Multiprocessing: {time.time() - start:.2f}s')
    # Noticeably FASTER on a multi-core machine because each process
    # has its own Python interpreter and GIL - no contention between them

# Sharing data between processes requires special mechanisms (unlike threads)
from multiprocessing import Queue
q = Queue()
q.put('hello')
print(q.get())  # 'hello' - processes don't share memory, so need explicit IPC

पहलू	threading	multiprocessing
Memory	Shared memory	अलग memory per process
GIL impact	CPU-bound काम में limited	GIL bypass - true parallelism
Overhead	हल्का, तेज़ बनता है	भारी, धीमा बनता है
सबसे अच्छा	I/O-bound tasks	CPU-bound tasks

import threading
import multiprocessing
import time

def cpu_task(n):
    return sum(i * i for i in range(n))

# Threading - GIL से limited
start = time.time()
threads = [threading.Thread(target=cpu_task, args=(10_000_000,)) for _ in range(4)]
for t in threads: t.start()
for t in threads: t.join()

# Multiprocessing - true parallelism
if __name__ == '__main__':
    start = time.time()
    processes = [multiprocessing.Process(target=cpu_task, args=(10_000_000,)) for _ in range(4)]
    for p in processes: p.start()
    for p in processes: p.join()
    # Multi-core पर काफ़ी तेज़

from multiprocessing import Queue
q = Queue()
q.put('hello')
print(q.get())

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