Python server programming: Introduction to Python's built-in concurrency module

Python server programming: Introduction to Python's built-in concurrency module

With the popularity of the Internet and mobile devices, network applications increasingly require high-performance server programs. As an efficient and scalable programming language, Python is playing an increasingly important role in network programming. This article will focus on introducing Python's built-in concurrency module to help readers better understand the concurrency mechanism in Python server programming.

1. Threads

Python has a built-in threading module, which provides support for multi-threaded programming. By creating threads, multiple tasks can be executed concurrently in a program. The following code shows how to use the threading module to create and start threads:

import threading

def worker(num):
    """线程执行的函数"""
    print('Worker %d is starting...' % num)

threads = []
for i in range(5):
    t = threading.Thread(target=worker, args=(i,))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

In the above code, 5 threads are created by calling the threading.Thread() function, and the execution function of each thread is designated as worker (). Finally, start these threads by calling the start() method, and then call the join() method to wait for the thread to complete execution.

It should be noted that threads in Python are implemented based on the native threads of the operating system (ie, one-to-one thread model). On some operating systems, threads can consume large amounts of system resources, even when idle. Therefore, when using threads, the number of threads should be rationally utilized according to the specific situation to avoid wastage of resources.

2. Process

Python has a built-in multiprocessing module, which provides support for inter-process concurrency and communication. By using the multiprocessing module, you can execute multiple processes concurrently in a program to better utilize the performance of multi-core CPUs. The following code shows how to use the multiprocessing module to create and start a process:

import multiprocessing

def worker(num):
    """进程执行的函数"""
    print('Worker %d is starting...' % num)

processes = []
for i in range(5):
    p = multiprocessing.Process(target=worker, args=(i,))
    processes.append(p)
    p.start()

for p in processes:
    p.join()

In the above code, 5 processes are created by calling the multiprocessing.Process() function, and the execution function of each process is designated as worker (). Finally, start these processes by calling the start() method, and then call the join() method to wait for the process to complete execution.

It should be noted that inter-process communication needs to be implemented using Queue, Pipe and other methods. These methods can be found in the multiprocessing module.

3. Coroutine

Coroutine is a lightweight concurrency mechanism that can implement switching execution of multiple subprograms in one process. Python has a built-in asyncio module, which provides support for coroutine concurrency. By using the asyncio module, asynchronous IO and efficient network programming can be achieved. The following code shows how to use the asyncio module to implement coroutines:

import asyncio

async def worker(num):
    """协程执行的函数"""
    print('Worker %d is starting...' % num)
    await asyncio.sleep(1)
    print('Worker %d is finished.' % num)

async def main():
    """协程调度器"""
    tasks = [asyncio.create_task(worker(i)) for i in range(5)]
    await asyncio.gather(*tasks)

asyncio.run(main())

In the above code, async is used to define a coroutine function worker(), and async is used to define a coroutine scheduler main(). Create 5 coroutine tasks through the asyncio.create_task() function, and use the asyncio.gather() function for concurrent execution. Finally, the coroutine scheduler is run through the asyncio.run() function.

It should be noted that coroutine programs are extremely flexible and efficient, but require high programming capabilities and professional knowledge.

4. Summary

Python’s three built-in concurrency modules (threading, multiprocessing, asyncio) can provide different concurrency mechanisms in different scenarios. When writing a server program, you need to choose appropriate concurrency modules and concurrency mechanisms based on actual needs to improve the performance and reliability of the program.

It is worth noting that Python is not a specialized server programming language, so when writing high-performance server programs, other factors need to be considered, such as the number of connections, concurrency, request response time, etc. . By rationally selecting concurrency modules and designing effective algorithms, the performance of the server program can be optimized and a good user experience can be provided.

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