As a Python programming enthusiast, I often encounter scenarios that require processing large amounts of data or executing time-consuming tasks. Today I'd like to share my practical experience with Python multiprocessing programming, hoping to help you better understand and apply this powerful feature.

Why We Need It

Speaking of multiprocessing, you might ask: why do we need it? Let me give you a vivid example.

Imagine you're eating at a restaurant. If there's only one chef handling all the tasks like cutting vegetables, cooking, and packaging simultaneously, the efficiency would be very low. But if we have multiple chefs, each responsible for different tasks, the overall efficiency would greatly improve. This is the core idea of multiprocessing.

In the computer world, single-process is like a restaurant with only one chef, while multiprocessing allows us to run multiple tasks simultaneously, fully utilizing modern computer's multi-core processors.

Basic Concepts

What is a Process

A process is a fundamental concept in computing - it's the smallest unit of program execution. Each process has its own independent memory space, just like each chef has their own workstation.

import os

def print_process_info():
    print(f"Current Process ID: {os.getpid()}")
    print(f"Parent Process ID: {os.getppid()}")

print_process_info()

This code shows how to get basic process information. Each process has a unique ID, allowing us to distinguish and manage different processes.

Difference Between Processes and Threads

You might have heard about threads, so what's the difference between processes and threads? I think the simplest way to understand is: a process is like an independent restaurant, while threads are like waiters in the restaurant. A restaurant (process) can have multiple waiters (threads) who share the restaurant's resources.

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