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This article mainly introduces "Python multithreading and multi-processes are used in what scenarios". In daily operations, I believe many people have doubts about the use of Python multithreading and multiprocesses in what scenarios. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the doubts of "Python multithreading and multi-processes in what scenarios." Next, please follow the editor to study!

Differences between Python multithreading and multiprocess

Python multithreading cannot use CPU multicore resources, that is, only one thread uses CPU resources at a time, so using Python multithreading is not concurrent.

If you want to make full use of CPU multi-core resources and achieve multi-concurrency, you need Python multi-process!

In other words: only Python multi-processes can make use of CPU multi-core resources to achieve real multi-concurrency!

Python multithreading and multiprocess application scenarios

Since Python multithreading cannot be concurrent, what's the point of being there?

In fact, Python multithreading and multiprocess have their own application scenarios:

Python multithreading is suitable for Istroke O-intensive scenarios, such as solving network IO and disk IO blocking problems, such as file read and write, network data transfer, etc.

Python multi-process is more suitable for computing-intensive scenarios, multi-concurrency, a large number of computing tasks and so on.

Note: Python multithreading and multiprocess in the usual development process, you need to pay attention to use, if you use Python multithreading to deal with computing-intensive tasks, it is even slower than the actual single-process processing performance! So pay attention to the type of scene.

Talk about Python multithreading, global interpreter lock (GIL)

Why can't Python multithreading use CPU multicore resources?

Why does Python multithreading have only one thread using CPU resources at the same time?

It is precisely because Python has a global interpreter lock (GIL, full name Global Interpreter Lock), which makes Python multi-thread unable to use CPU multi-core resources, ensuring that only one thread is using CPU resources at the same time; when IO blocking occurs, unlock and release CPU resources so that other threads can apply for locks and use CPU resources.

Python concurrent programming

Python multi-process programming module library: multiprocessing module, Python built-in multi-process processing library, using similar to the thread library threading.Thread.

Module libraries used in Python multithreaded programming:

Thread module Python is built-in, which is relatively low-level and is not recommended.

Threading module Python built-in

Multiprocessing.dummy module Python built-in

Add:

Multiprocessing module and multiprocessing.dummy module

The difference between the two is that the former is multi-process and the latter is multi-threaded, but their programming interfaces are exactly the same.

So it is very convenient to switch the code in multi-thread and multi-process!

At this point, the study on "what scenarios Python multithreading and multi-processes are used in" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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