How to use the threading module for multi-thread management in Python 3.x

How to use the threading module for multi-thread management in Python 3.x

Introduction:
In the computer field, multi-threading is an important programming model that can improve the concurrency and execution efficiency of the program. . The Python language provides the threading module to facilitate developers to manage multi-threads. This article will introduce how to use the threading module for multi-thread programming and demonstrate the use of multi-threading through examples.

1. threading module overview
   Threading is Python's standard library module for multi-threaded programming, which provides a series of operations such as creating, starting, managing and controlling threads. In the threading module, the following classes are mainly used:
2. Thread: represents a thread object, used to create and manage threads
3. Lock: used for mutex locks between threads to avoid Conflicts caused by multiple threads accessing shared resources at the same time
4. Condition: used for condition variables between threads to achieve communication between threads
5. Event: used for event notification mechanism between threads
6. Timer: timer for thread timing execution
7. Semaphore: semaphore for controlling the number of concurrent threads
8. Simple multi-threading example
   The following example demonstrates A simple multi-threaded application scenario assumes that there is a shared resource count, and multiple threads operate on it at the same time. In order to avoid conflicts, Lock needs to be used for locking operations.

import threading

count = 0  # 共享资源
lock = threading.Lock()  # 互斥锁

def increase():
    global count
    for _ in range(100000):
        lock.acquire()  # 加锁
        count += 1
        lock.release()  # 解锁

def decrease():
    global count
    for _ in range(100000):
        lock.acquire()  # 加锁
        count -= 1
        lock.release()  # 解锁

if __name__ == '__main__':
    # 创建两个线程
    t1 = threading.Thread(target=increase)
    t2 = threading.Thread(target=decrease)

    # 启动线程
    t1.start()
    t2.start()

    # 等待线程结束
    t1.join()
    t2.join()

    # 输出结果
    print("count:", count)

In the above example, we created two threads t1 and t2, calling the increase() and decrease() functions respectively to operate the shared resource count. Since Lock is used, no conflict occurs. Finally, the value of the result count is output.

3. Thread synchronization
   In multi-threaded programming, it is often necessary to synchronize threads to ensure orderly execution between threads. The threading module provides the Condition class to implement condition variables between threads and realize communication between threads. The following example demonstrates the use of thread synchronization.

import threading

count = 0  # 共享资源
lock = threading.Lock()  # 互斥锁
condition = threading.Condition()  # 条件变量

def produce():
    global count
    while True:
        with condition:
            if count >= 10:
                condition.wait()  # 释放锁并等待条件变量
            count += 1
            print("Produced 1 item")
            condition.notify()  # 通知等待的线程

def consume():
    global count
    while True:
        with condition:
            if count <= 0:
                condition.wait()  # 释放锁并等待条件变量
            count -= 1
            print("Consumed 1 item")
            condition.notify()  # 通知等待的线程

if __name__ == '__main__':
    # 创建两个线程
    t1 = threading.Thread(target=produce)
    t2 = threading.Thread(target=consume)

    # 启动线程
    t1.start()
    t2.start()

    # 等待线程结束
    t1.join()
    t2.join()

In the above example, we created two threads t1 and t2, calling the produce() and consume() functions respectively to simulate the producer and consumer scenarios. By using the Condition class, synchronization and communication between threads are achieved. When the counter count does not meet the condition, the thread waits and continues to execute other threads until the condition is met, notifying the waiting thread.

Summary:
This article introduces how to use the threading module for multi-thread management in Python 3.x. The basic operations of multi-threading and the use of thread synchronization are demonstrated through sample code. Reasonable use of multi-threading can improve program execution efficiency and concurrency, but at the same time, attention must be paid to thread safety and data sharing issues. In actual applications, just choose the appropriate multi-threading solution according to specific needs.

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