What are the advantages of python multithreading? Six advantages to help you understand multi-threading

For those who are exposed to the python programming language for the first time, when they first started learning python multi-threading, they had relatively little understanding of python multi-threading. In this article Let's take a look at the advantages of python multi-threading. Without further ado, let’s start learning in this article.

python multi-threading

Multi-threading is similar to executing multiple different programs at the same time. Multi-threading has the following advantages:

1. Using threads can Put the tasks in the program that take a long time to the background for processing.

2. The user interface can be more attractive, so that if the user clicks a button to trigger the processing of certain events, a progress bar can pop up to show the progress of the processing

3. Program The running speed may be accelerated

4. Threads are more useful in the implementation of some waiting tasks such as user input, file reading and writing, and network sending and receiving data. In this case we can release some precious resources such as memory usage and so on.

5. Threads can be preempted (interrupted).

6. A thread can be temporarily put on hold (also called sleeping) while other threads are running - this is the thread's retreat.

Multi-threading is used for IO-intensive applications, such as sockets, crawlers, and web

Threads are still different from processes during execution. Each independent thread has an entry point for program execution, a sequential execution sequence, and an exit point for the program. However, threads cannot execute independently and must exist in the application program, and the application program provides multiple thread execution control.

Each thread has its own set of CPU registers, called the thread's context, which reflects the state of the CPU registers the thread last ran.

The instruction pointer and stack pointer register are the two most important registers in the thread context. The thread always runs in the process context. These addresses are used to mark the memory in the address space of the process that owns the thread.

The above is all the content described in this article. This article mainly introduces the relevant knowledge of multi-threading in python. I hope you can use the information to understand the above content. I hope what I have described in this article will be helpful to you and make it easier for you to learn python.

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