ubuntu

Python在Ubuntu中如何实现并发编程

小樊
55
2025-05-05 21:22:58
栏目: 编程语言

在Ubuntu中,Python可以通过多种方式实现并发编程。以下是一些常用的方法:

1. 多线程(Threading)

Python的threading模块允许你创建和管理线程。

import threading

def worker():
    """线程执行的任务"""
    print(f"Thread {threading.current_thread().name} is running")

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

for thread in threads:
    thread.join()

2. 多进程(Multiprocessing)

Python的multiprocessing模块允许你创建和管理进程,每个进程都有自己的Python解释器实例,因此可以避免GIL(全局解释器锁)的限制。

from multiprocessing import Process

def worker():
    """进程执行的任务"""
    print(f"Process {multiprocessing.current_process().name} is running")

processes = []
for i in range(5):
    process = Process(target=worker)
    processes.append(process)
    process.start()

for process in processes:
    process.join()

3. 异步编程(Asyncio)

Python的asyncio模块提供了一种基于事件循环的并发编程模型,适用于I/O密集型任务。

import asyncio

async def worker():
    """异步任务"""
    print("Worker is running")
    await asyncio.sleep(1)
    print("Worker is done")

async def main():
    tasks = [worker() for _ in range(5)]
    await asyncio.gather(*tasks)

asyncio.run(main())

4. 协程(Coroutines)

协程是一种特殊的函数,可以在执行过程中暂停并在稍后恢复。Python的asyncio库提供了对协程的支持。

import asyncio

async def coroutine_example():
    print("Coroutine started")
    await asyncio.sleep(1)
    print("Coroutine ended")

asyncio.run(coroutine_example())

5. 第三方库

还有一些第三方库可以帮助实现并发编程,例如:

使用gevent

from gevent import monkey; monkey.patch_all()
import gevent

def worker():
    """协程任务"""
    print(f"Worker {gevent.getcurrent()} is running")
    gevent.sleep(1)
    print(f"Worker {gevent.getcurrent()} is done")

jobs = [gevent.spawn(worker) for _ in range(5)]
gevent.joinall(jobs)

使用concurrent.futures

from concurrent.futures import ThreadPoolExecutor

def worker():
    """线程任务"""
    print(f"Thread {threading.current_thread().name} is running")
    return "Done"

with ThreadPoolExecutor(max_workers=5) as executor:
    futures = [executor.submit(worker) for _ in range(5)]
    for future in concurrent.futures.as_completed(futures):
        print(future.result())

选择哪种方法取决于你的具体需求和应用场景。对于I/O密集型任务,asynciogevent通常是更好的选择;而对于CPU密集型任务,multiprocessing可能更合适。

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