Python multi-threading and multi-process: from beginner to proficient, creating high-performance applications

In computer science, Multi-threading and multi-process are the two basic ways of Concurrent programming. MultiThreads execute multiple tasks in parallel and share the same memory space, while multiple processes execute multiple tasks in parallel, and each task has its own independent memory space.

1. python Multi-threading

Multiple threads in Python are implemented through the threading module. The threading module provides a variety of multi-threading related classes and functions, including the Thread class, the Lock class and the Semaphore class, etc.

The following is a simple Python multi-threading example:

import threading

def task(i):
print(f"Task {i} is running...")

if __name__ == "__main__":
threads = []
for i in range(5):
thread = threading.Thread(target=task, args=(i,))
threads.append(thread)

for thread in threads:
thread.start()

for thread in threads:
thread.join()

In this example, we create 5 threads, each thread executes the task function. task The function prints a message indicating that the task is running.

2. Python multi-process

Multiple processes in Python are implemented through the multiprocessing module. The multiprocessing module provides a variety of multi-process related classes and functions, including the Process class, the Manager class and the Pool class, etc.

The following is a simple Python multi-process example:

import multiprocessing

def task(i):
print(f"Task {i} is running...")

if __name__ == "__main__":
processes = []
for i in range(5):
process = multiprocessing.Process(target=task, args=(i,))
processes.append(process)

for process in processes:
process.start()

for process in processes:
process.join()

In this example, we created 5 processes, each executing the task function. task The function prints a message indicating that the task is running.

3. The difference between Python multi-threading and multi-process

The main differences between Python multi-threading and multi-process are:

• Multiple threads share the same memory space, while each task of multi-process has its own independent memory space.
• The switching overhead of multi-threads is less than the switching overhead of multi-processes.
• Multiple threads are more likely to have deadlocksLocks, but multi-processes will not have deadlocks.

4. Python multi-threading and multi-process application scenarios

The application scenarios of Python multi-threading and multi-process mainly include:

• Multi-threading is suitable for computing-intensive tasks, such as numerical calculations, image processing, etc.
• Multiple processes are suitable for I/O-intensive tasks, such as file reading and writing, networkcommunication, etc.

5. Python multi-threading and multi-process Performance optimization

Performance of Python multi-threading and multi-processOptimizationMainly include the following aspects:

• Use thread pool and process pool to manage threads and processes.
• Use locks and semaphores to synchronize access between threads and processes.
• Avoid sharing data between multiple threads and processes.
• Use GIL lock to prevent multiple threads from executing the same Python bytecode at the same time.

6. Summary

Python multi-threading and multi-process are the two basic ways of concurrencyprogramming. They have their own advantages, disadvantages and application scenarios. In actualdevelopment, it is necessary to choose the appropriate concurrent programming method according to specific needs.

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