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This article focuses on "how Python threaded programming highlights the new features of the language". Interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how Python thread programming highlights the new features of the language.

Programming with Python threads

Thread programming can be easily done in Python by combining threads with queues. This article will examine the use of threads and queues at the same time to create some simple but effective patterns to solve problems that require concurrent processing.

Introduction

For Python, there is no shortage of concurrency options, and its standard library includes support for threads, processes, and asynchronous Icano. In many cases, Python simplifies the use of various concurrency methods by creating high-level modules such as async, threads, and child processes. In addition to the standard library, there are some third-party solutions, such as Twisted, Stackless, and process modules.

This article focuses on threads that use Python and uses some practical examples to illustrate. Although there are many good online resources detailing thread API, this article attempts to provide some practical examples to illustrate some common thread usage patterns.

The global interpreter lock (Global Interpretor Lock) indicates that the Python interpreter is not thread-safe. The current thread must hold a global lock for secure access to the Python object. Because only one thread can get the Python object / C API, the interpreter regularly releases and reacquires the lock for every 100bytecode instruction. The frequency with which the interpreter checks for thread switching can be controlled by the sys.setcheckinterval () function.

In addition, the lock will be released and regained based on the potential blocking Iripple O operation. For more detailed information, see Gil and Threading State and Threading the Global Interpreter Lock in the Resources section to note that applications with limited GIL,CPU will not benefit from the use of threads. When using Python, it is recommended that you use processes, or mix them to create processes and threads.

It is important to first understand the difference between a process and a thread. Threads differ from processes in that they share state, memory, and resources. For threads, this simple difference is both its strength and its weakness. On the one hand, threads are lightweight and easy to communicate with each other, but on the other hand, they also bring a variety of problems, including deadlocks, race conditions, and high complexity. Fortunately, because of GIL and queue modules, threading implementation in the Python language is much less complex than in other languages.

Use Python threads

To continue with this article, I assume that you have installed Python2.5 or later, because many of the examples in this article will use the new features of the Python language that appear only after Python2.5. To start threads that use the Python language, we will start with a simple "Hello World" example:

Hello_threads_example import threading import datetime class ThreadClass (threading.Thread): def run (self): now = datetime.datetime.now () print "% s says Hello World at time:% s"% (self.getName (), now) for i in range (2): t = ThreadClass () t.start ()

If you run this example, you will get the following output:

# python hello_threads.py Thread-1 says Hello World at time: 2008-05-13 13 22buret 50.252069 Thread-2 says Hello World at time: 2008-05-13 13 22 50.252576 so far, I believe you have a deeper understanding of "how Python thread programming highlights the new features of the language". You might as well do it in practice! Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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