Python多线程编程全解析:基础到高级用法

 

Python中有多线程的支持。Python的threading模块提供了多线程编程的基本工具。在下面,我将列举一些基础的多线程用法和一些高级用法,并提供相应的源代码,其中包含中文注释。

基础用法:

创建和启动线程

import threading
import time

# 定义一个简单的线程类
class MyThread(threading.Thread):
    def run(self):
        for _ in range(5):
            print(threading.current_thread().name, "is running")
            time.sleep(1)

# 创建两个线程实例
thread1 = MyThread(name="Thread-1")
thread2 = MyThread(name="Thread-2")

# 启动线程
thread1.start()
thread2.start()

# 主线程等待所有子线程结束
thread1.join()
thread2.join()

print("Main thread exiting")

线程同步 - 使用锁

import threading

# 共享资源
counter = 0

# 创建锁
counter_lock = threading.Lock()

# 定义一个简单的线程类
class MyThread(threading.Thread):
    def run(self):
        global counter
        for _ in range(5):
            with counter_lock:  # 使用锁保护临界区
                counter += 1
                print(threading.current_thread().name, "Counter:", counter)

# 创建两个线程实例
thread1 = MyThread(name="Thread-1")
thread2 = MyThread(name="Thread-2")

# 启动线程
thread1.start()
thread2.start()

# 主线程等待所有子线程结束
thread1.join()
thread2.join()

print("Main thread exiting")

高级用法:

使用线程池

import concurrent.futures
import time

# 定义一个简单的任务函数
def task(name):
    print(f"{name} is running")
    time.sleep(2)
    return f"{name} is done"

# 使用线程池
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
    # 提交任务给线程池
    future_to_name = {executor.submit(task, f"Thread-{i}"): f"Thread-{i}" for i in range(5)}

    # 获取任务结果
    for future in concurrent.futures.as_completed(future_to_name):
        name = future_to_name[future]
        try:
            result = future.result()
            print(f"{name}: {result}")
        except Exception as e:
            print(f"{name}: {e}")

使用Condition进行线程间通信

import threading
import time

# 共享资源
shared_resource = None

# 创建条件变量
condition = threading.Condition()

# 定义一个写线程
class WriterThread(threading.Thread):
    def run(self):
        global shared_resource
        for _ in range(5):
            with condition:
                shared_resource = "Write data"
                print("Writer wrote:", shared_resource)
                condition.notify()  # 通知等待的线程
                condition.wait()  # 等待其他线程通知

# 定义一个读线程
class ReaderThread(threading.Thread):
    def run(self):
        global shared_resource
        for _ in range(5):
            with condition:
                while shared_resource is None:
                    condition.wait()  # 等待写线程通知
                print("Reader read:", shared_resource)
                shared_resource = None
                condition.notify()  # 通知写线程

# 创建写线程和读线程
writer_thread = WriterThread()
reader_thread = ReaderThread()

# 启动线程
writer_thread.start()
reader_thread.start()

# 主线程等待所有子线程结束
writer_thread.join()
reader_thread.join()

print("Main thread exiting")

这些例子涵盖了一些基础和高级的多线程用法。请注意,在Python中由于全局解释器锁(GIL)的存在,多线程并不能充分利用多核处理器。如果需要充分利用多核处理器,可以考虑使用multiprocessing模块进行多进程编程。

 

热门相关:抗日之全能兵王   抗日之特战兵王   官策   陆先生偏要以婚相许   陆先生偏要以婚相许