


How to use the thread module to create and manage threads in Python 2.x
Introduction:
In multi-threaded programming, we often need to create and manage multiple threads to implement concurrently executed tasks. Python provides the thread module to support multi-threaded programming. This article will introduce how to use the thread module to create and manage threads, and provide some code examples.
- Thread module overview:
The thread module provides some thread-related functions and classes for creating and managing threads. The following is a brief introduction to commonly used thread module functions and classes:
- thread.start_new_thread(function, args[, kwargs]): Create a new thread and execute the function function, args and kwargs are parameters passed to functions.
- thread.allocate_lock(): Create a new lock object for synchronization between threads.
- thread.exit(): The thread exits and ends the execution of the thread.
- thread.get_ident(): Get the identifier of the current thread.
- thread.interrupt_main(): Interrupt the execution of the main thread.
- thread.stack_size([size]): Get or set the thread stack size.
- Example of creating a thread:
The following example demonstrates how to use the thread module to create a thread.
import thread import time # 定义线程执行的函数 def print_time(threadName, delay): count = 0 while count < 5: time.sleep(delay) count += 1 print("%s: %s" % (threadName, time.ctime(time.time()))) # 创建两个线程 try: thread.start_new_thread(print_time, ("Thread-1", 2,)) thread.start_new_thread(print_time, ("Thread-2", 4,)) except: print("Error: 无法启动线程") # 主线程等待子线程结束 while 1: pass
Running the above code will create two threads that print the current time every 2 seconds and 4 seconds respectively. The main thread will wait for the child thread to end.
- Thread synchronization and locking:
In thread programming, it is often necessary to ensure the correct cooperation between multiple threads to avoid competition and conflicts. The thread module provides lock objects to achieve synchronization between threads. The following example shows how to use locks to guarantee mutually exclusive execution of threads.
import thread import time # 全局变量 counter = 0 lock = thread.allocate_lock() # 线程函数 def increment_counter(threadName, delay): global counter while True: lock.acquire() counter += 1 print("%s: %d" % (threadName, counter)) lock.release() time.sleep(delay) # 创建两个线程 try: thread.start_new_thread(increment_counter, ("Thread-1", 1,)) thread.start_new_thread(increment_counter, ("Thread-2", 2,)) except: print("Error: 无法启动线程") # 主线程等待子线程结束 while 1: pass
The above code creates two threads, increments the counter variable at different speeds and prints the results. Through the use of locks, mutually exclusive access to counter between threads is ensured, and race conditions are avoided.
Conclusion:
This article introduces the basic method of using the thread module to create and manage threads in Python 2.x, and provides some code examples. It is important to understand and master multi-threaded programming to improve the performance and responsiveness of your application. In actual development, you can also use more advanced and flexible multi-threading libraries, such as the threading module, which provides more functions and easier-to-use interfaces, but the basic principles and ideas are similar.
Reference materials:
- Python thread module documentation: https://docs.python.org/2/library/thread.html
The above is the detailed content of How to use the thread module to create and manage threads in Python 2.x. For more information, please follow other related articles on the PHP Chinese website!

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

Python provides a variety of ways to download files from the Internet, which can be downloaded over HTTP using the urllib package or the requests library. This tutorial will explain how to use these libraries to download files from URLs from Python. requests library requests is one of the most popular libraries in Python. It allows sending HTTP/1.1 requests without manually adding query strings to URLs or form encoding of POST data. The requests library can perform many functions, including: Add form data Add multi-part file Access Python response data Make a request head

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

PDF files are popular for their cross-platform compatibility, with content and layout consistent across operating systems, reading devices and software. However, unlike Python processing plain text files, PDF files are binary files with more complex structures and contain elements such as fonts, colors, and images. Fortunately, it is not difficult to process PDF files with Python's external modules. This article will use the PyPDF2 module to demonstrate how to open a PDF file, print a page, and extract text. For the creation and editing of PDF files, please refer to another tutorial from me. Preparation The core lies in using external module PyPDF2. First, install it using pip: pip is P

This tutorial demonstrates how to leverage Redis caching to boost the performance of Python applications, specifically within a Django framework. We'll cover Redis installation, Django configuration, and performance comparisons to highlight the bene

Natural language processing (NLP) is the automatic or semi-automatic processing of human language. NLP is closely related to linguistics and has links to research in cognitive science, psychology, physiology, and mathematics. In the computer science

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

WebStorm Mac version
Useful JavaScript development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

SublimeText3 Chinese version
Chinese version, very easy to use

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

Dreamweaver Mac version
Visual web development tools
