What Is the Difference Between Multiprocessing and Multithreading in Python?

Python provides both multithreading and multiprocessing to execute multiple tasks concurrently, but they work in different ways.

Multithreading

Multithreading runs multiple threads within the same process. All threads share the same memory space, making communication between them faster and easier.

Best for:

• I/O-bound tasks
• File operations
• Network requests
• Database queries

import threading

def task():
    print("Thread is running")

t1 = threading.Thread(target=task)
t1.start()
t1.join()

Multiprocessing

Multiprocessing creates multiple independent processes. Each process has its own memory space and can run on different CPU cores.

Best for:

• CPU-intensive tasks
• Data processing
• Mathematical computations
• Machine learning workloads

Example:

from multiprocessing import Process

def task():
    print("Process is running")

p1 = Process(target=task)
p1.start()
p1.join()

Main Difference

• **Multithreading** uses multiple threads within a single process and shares memory.
• **Multiprocessing** uses multiple processes, each with its own memory.
• Multithreading is ideal for I/O-bound tasks, while multiprocessing is better for CPU-bound tasks.