Deep understanding of coroutine functions in python
The content of this article is to share with you an in-depth understanding of the coroutine function in python, which has a certain reference value. Friends in need can refer to it
Concept:
According to the definition given by Wikipedia, "Coroutines are computer program components that generate subroutines for non-preemptive multitasking. Coroutines allow different entry points to pause or start program execution at different locations." From a technical perspective, "a coroutine is a function that you can pause execution of." If you understand it as "just like a generator", then you're thinking right.
Coroutine, also known as micro-thread, looks like a subroutine, but it is different from a subroutine. During the execution process, it can interrupt the current subroutine. After the program executes other subroutines, it returns to execute the previous subroutine, but its related information is still the same as before.
Coroutines are different from threads. Threads are preemptive scheduling, while coroutines are collaborative scheduling. Coroutines need to do their own scheduling.
Subroutine calls always have one entrance and one return, and the calling sequence is clear. The calling of coroutines is different from subroutines. Coroutines also look like subprograms, but during execution, they can be interrupted inside the subprogram, then switch to executing other subprograms, and then return to continue execution at the appropriate time.
Advantages of coroutines:
The advantage of coroutines is extremely high execution efficiency. Because subroutine switching is not thread switching, but is controlled by the program itself, there is no overhead of thread switching. Compared with multi-threading, the greater the number of threads, the more obvious the performance advantages of coroutines. It is very suitable for performing coroutine multitasking.
Coroutines have no thread safety issues. A process can have multiple coroutines at the same time, but only one coroutine is active, and the activation and dormancy of coroutines are controlled by programmers through programming, not by the operating system.
Generator implements coroutine principle
Example:
def func(n): index=0 if index<=n: c=yield 1 print("task------{}".format(c)) index+=1f=func(3) n=next(f) print(n)try: n=f.send(5)#程序就直接结束了 print("n是{}".format(n))except StopIteration as e: pass
输出打印:1task------5
Explanation:
Obviously func is a generator, and the send method has a parameter that specifies the return value of the last suspended yield statement.
send requires exception handling.
In general, the only difference between the send method and the next method is that when executing the send method, the return value of the last pending yield statement will first be set through parameters to achieve Interaction with generator methods. However, it should be noted that before a generator object executes the next method, since no yield statement is suspended, an error will be reported when executing the send method.
When the parameter of the send method is None, it is completely equivalent to the next method.
The generator implements the producer and consumer patterns:
def cunsumer(): while True: n=yield 3 if not n: return print('cunsumer{}'.format(n))def product(c): c.send(None) n=0 while n<5: n=n+1 r=c.send(n) print("product{}".format(r)) c.close() c=cunsumer() product(c)
打印: cunsumer1 product3 cunsumer2 product3 cunsumer3 product3 cunsumer4 product3 cunsumer5 product3
Explanation:
Execute first in the producer The purpose of c.send(None) is to let the consumer hang up first, and then use send to pass the value. The first time 1 is passed, the consumer prints 1, and the producer prints r which is the value after the consumer's yield.
Introduction of greenlet
Although CPython (standard Python) can implement coroutines through generators, it is not very convenient to use.
At the same time, Stackless Python, a derivative of Python, implements native coroutines, which is more convenient to use.
So, everyone began to take out the coroutine code in Stackless and make it into a CPython expansion package.
This is the origin of greenlet, so greenlet is a C extension library that implements native coroutines at the bottom.
Code description:
from greenlet import greenletimport randomimport timedef Producer(): while True: item = random.randint(0,10) print("生产了{}".format(item)) c.switch(item)#切换到消费者,并将item传入消费者 time.sleep(1)def consumer(): print('我先执行') #p.switch() while True: item = p.switch()#切换到生产者,并且等待生产者传入item print('消费了{}'.format(item)) c = greenlet(consumer)#将一个普通函数变成一个协程p = greenlet(Producer) c.switch()#让消费者先进入暂停状态(只有恢复了才能接收数据)
Value of greenlet:
High-performance native coroutine
Explicit switching with clearer semantics
Wrap functions directly into coroutines and maintain the original code style
gevent coroutine
Although we have a callback programming model based on epoll, it is difficult to use.
Even though we can make complex encapsulation with generator coroutine to simplify programming difficulty.
But there is still a big problem: encapsulation is difficult, and the existing code must be almost completely rewritten
gevent, by encapsulating the two libraries libev (based on epoll) and greenlet.
Help us encapsulate it and allow us to use coroutines in a thread-like manner.
So that we can make full use of the power of epoll and coroutines without rewriting the original code.
Code diagram:
from gevent import monkey;monkey.patch_all()#会把python标准库当中一些阻塞操作变成非阻塞import geventdef test1(): print("11") gevent.sleep(4)#模拟爬虫请求阻塞 print("33")def test2(): print("22") gevent.sleep(4) print("44") gevent.joinall([gevent.spawn(test1),gevent.spawn(test2)])#joinall 阻塞当前协程,执行给定的greenlet#spawn 启动协程,参数就是函数的名字
The value of gevent:
Switch to another coroutine to continue execution when it encounters blocking !
Use epoll-based libev to avoid blocking.
Use efficient coroutines based on gevent to switch execution.
Only switches when encountering blocking. There is no round-robin overhead or thread overhead.
gevent implements concurrent server
from gevent import monkey;monkey.patch_all() #建议放在首行,会把python标准库当中一些阻塞操作变成非阻塞import geventimport socket server=socket.socket() server.bind(('',6666)) server.listen(5) print("开始监听")def readable(con,addr): print("客户端{}接入".format(addr)) while True: data=con.recv(1024) if data: print(data) else: con.close() breakwhile True: con,addr=server.accept() gevent.spawn(readable,con,addr)#将readable函数变为协程,并且把con和addr传入其中。
gevent coroutine communication
gevent also has its own queue. The usage is basically the same as threading.
Producer and consumer patterns based on gevent and queue
from gevent import monkey;monkey.patch_all()import geventfrom gevent.queue import Queueimport randomdef producter(queue): while True: item=random.randint(0,99) print('生产了{}'.format(item)) queue.put(item) gevent.sleep(1)def comuser(queue): while True: item=queue.get() print('消费了{}'.format(item)) queue=Queue() p=gevent.spawn(producter,queue) c=gevent.spawn(comuser,queue) gevent.joinall([p,c])
打印: 生产了33消费了33生产了95消费了95生产了92消费了92...
相关推荐:
The above is the detailed content of Deep understanding of coroutine functions in python. For more information, please follow other related articles on the PHP Chinese website!

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

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

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

Hot Topics

How to download DeepSeek Xiaomi? Search for "DeepSeek" in the Xiaomi App Store. If it is not found, continue to step 2. Identify your needs (search files, data analysis), and find the corresponding tools (such as file managers, data analysis software) that include DeepSeek functions.

The key to using DeepSeek effectively is to ask questions clearly: express the questions directly and specifically. Provide specific details and background information. For complex inquiries, multiple angles and refute opinions are included. Focus on specific aspects, such as performance bottlenecks in code. Keep a critical thinking about the answers you get and make judgments based on your expertise.

Just use the search function that comes with DeepSeek. Its powerful semantic analysis algorithm can accurately understand the search intention and provide relevant information. However, for searches that are unpopular, latest information or problems that need to be considered, it is necessary to adjust keywords or use more specific descriptions, combine them with other real-time information sources, and understand that DeepSeek is just a tool that requires active, clear and refined search strategies.

DeepSeek is not a programming language, but a deep search concept. Implementing DeepSeek requires selection based on existing languages. For different application scenarios, it is necessary to choose the appropriate language and algorithms, and combine machine learning technology. Code quality, maintainability, and testing are crucial. Only by choosing the right programming language, algorithms and tools according to your needs and writing high-quality code can DeepSeek be successfully implemented.

Detailed explanation of DeepSeekAPI access and call: Quick Start Guide This article will guide you in detail how to access and call DeepSeekAPI, helping you easily use powerful AI models. Step 1: Get the API key to access the DeepSeek official website and click on the "Open Platform" in the upper right corner. You will get a certain number of free tokens (used to measure API usage). In the menu on the left, click "APIKeys" and then click "Create APIkey". Name your APIkey (for example, "test") and copy the generated key right away. Be sure to save this key properly, as it will only be displayed once

PiNetwork is about to launch PiBank, a revolutionary mobile banking platform! PiNetwork today released a major update on Elmahrosa (Face) PIMISRBank, referred to as PiBank, which perfectly integrates traditional banking services with PiNetwork cryptocurrency functions to realize the atomic exchange of fiat currencies and cryptocurrencies (supports the swap between fiat currencies such as the US dollar, euro, and Indonesian rupiah with cryptocurrencies such as PiCoin, USDT, and USDC). What is the charm of PiBank? Let's find out! PiBank's main functions: One-stop management of bank accounts and cryptocurrency assets. Support real-time transactions and adopt biospecies

This article explores the quantitative trading functions of the three major exchanges, Binance, OKX and Gate.io, aiming to help quantitative traders choose the right platform. The article first introduces the concepts, advantages and challenges of quantitative trading, and explains the functions that excellent quantitative trading software should have, such as API support, data sources, backtesting tools and risk control functions. Subsequently, the quantitative trading functions of the three exchanges were compared and analyzed in detail, pointing out their advantages and disadvantages respectively, and finally giving platform selection suggestions for quantitative traders of different levels of experience, and emphasizing the importance of risk assessment and strategic backtesting. Whether you are a novice or an experienced quantitative trader, this article will provide you with valuable reference

DeepSeekR1 model local deployment guide: Unlocking data analysis and prediction potential DeepSeek is a powerful data analysis and prediction tool, and its R1 model can provide efficient and accurate model support for various application scenarios. This guide will provide you with detailed instructions on how to deploy the DeepSeekR1 model in your on-premises environment to help you get started quickly and take advantage of its power. System requirements for the local deployment step preparation stage of the DeepSeekR1 model: Please make sure that your local machine meets the minimum system configuration requirements of DeepSeek (for specific requirements, please refer to the official DeepSeek document). Software installation: Download and install the installation package provided by DeepSeek and follow the installation guide
