![]() info ( "Main : wait for the thread to finish" ) 20 # x.join() 21 logging. info ( "Main : before running thread" ) 18 x. Thread ( target = thread_function, args = ( 1 ,)) 17 logging. info ( "Main : before creating thread" ) 16 x = threading. basicConfig ( format = format, level = logging. ![]() info ( "Thread %s : finishing", name ) 9 10 if _name_ = "_main_" : 11 format = " %(asctime)s : %(message)s " 12 logging. info ( "Thread %s : starting", name ) 7 time. start():ġ import logging 2 import threading 3 import time 4 5 def thread_function ( name ): 6 logging. To start a separate thread, you create a Thread instance and then tell it to. Thread, in this module, nicely encapsulates threads, providing a clean interface to work with The Python standard library provides threading, which contains most of the primitives you’ll see in this article. Now that you’ve got an idea of what a thread is, let’s learn how to make one. So, let’s stop talking about threading and start using it! Starting a Using threading in them helps to make the design cleaner and easier to reason about. Most of the examples you’ll learn about in this tutorial are not necessarily going to run faster because they use threads. If you are running a standard Python implementation, writing in only Python, and have a CPU-bound problem, you shouldĬheck out the multiprocessing module instead.Īrchitecting your program to use threading can also provide gains in design clarity. If you are running on a different Python implementation, check with the documentation too see how it handles threads. If your threads are written in C they have the ability to release the GIL and run concurrently. This is true for code written in Python and running on the standard CPython implementation. Time waiting for external events might not run faster at all. Problems that require heavy CPU computation and spend little Tasks that spend much of their time waiting for external events are generally good candidates for threading. This is due to interactions with the GIL that essentially limit one Python thread to run at a time. Using multiprocessing which comes with some extra overhead.īecause of the way CPython implementation of Python works, threading may not speed up all tasks. Getting multiple tasks running simultaneously requires a non-standard implementation of Python, writing some of your code in a different language, or The threads may be running on different processors, but they will only be running one at a time. It’s tempting to think of threading as having two (or more) different processors running on your program, each one doing an independent task at the same time. But for most Python 3 implementations the different threads do notĪctually execute at the same time: they merely appear to. This means that your program will have two things happening at once. What Is a Thread?Ī thread is a separate flow of execution. If you’re not sure if you want to use Python threading, asyncio, or multiprocessing, then you can check out Speed Up Your Python Program With Concurrency.Īll of the sources used in this tutorial are available to you in the If you need a refresher, you can start with the Python Learning Paths and get up to speed. This article assumes you’ve got the Python basics down pat and that you’re using at least version 3.6 to run the examples. How to use the common tools that Python threading provides.How to create threads and wait for them to finish.If you’ve got some experience in Python and want to speed up your program using Python threading allows you to have different parts of your program run concurrently and can simplify your design.
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