# coroutine

The Post Created(Updated) On 08/8/2022，Please note the timeliness of the article!

I’ve been coming across the concept of concurrent threads lately, and every time I come across it, I’m confused. Just try to understand this into the thread outside of the strange guy, and finally look at it is not too difficult, simply said is a micro-thread.

# Description

• Concurrent threads, also known as micro-threads, slim threads, also known as user-level threads, on the basis of not opening threads to complete multiple tasks, that is, in the case of a single thread to complete multiple tasks, multiple tasks in a certain order of alternate execution.

# Implementation method

• Individuals are currently the main technical language is Python, so the following are written based on Python.

# # The code implementation of the concurrent yield

• Commonly understood as long as you see only one yield keyword inside the def means that it is a concurrent process.
import time

def work1():
while True:
print("----work1---")
yield
time.sleep(0.5)

def work2():
while True:
print("----work2---")
yield
time.sleep(0.5)

def main():
w1 = work1()
w2 = work2()
while True:
next(w1)
next(w2)

if __name__ == "__main__":
main()


## greenlet implementation

• Install
pip install greenlet

• implement
import time
import greenlet

# task1
def work1():
for i in range(5):
print("work1...")
time.sleep(0.2)
# Switch to work2 to execute the corresponding task
g2.switch()

# Task 2
def work2():
for i in range(5):
print("work2...")
time.sleep(0.2)
# Switch to the first worker to execute the corresponding task
g1.switch()

if __name__ == '__main__':
# Create a concurrent process to specify the corresponding task
g1 = greenlet.greenlet(work1)
g2 = greenlet.greenlet(work2)

# Switch to the first worker to perform the corresponding task
g1.switch()


## gevent

• Install
pip install gevent

• Implement
import gevent
import time
from gevent import monkey

# Patch to make the gevent framework recognize time-consuming operations, e.g. time.sleep, network request delay
monkey.patch_all()

# Task 1
def work1(num):
for i in range(num):
print("work1....")
time.sleep(0.2)
# gevent.sleep(0.2)

# Task 1
def work2(num):
for i in range(num):
print("work2....")
time.sleep(0.2)
# gevent.sleep(0.2)

if __name__ == '__main__':
# Create a concurrent process to specify the corresponding task
g1 = gevent.spawn(work1, 3)
g2 = gevent.spawn(work2, 3)

# The main thread waits for the concurrent thread to finish executing before exiting the program
g1.join()
g2.join()


# The relationship between processes, threads, and concurrent threads

• A process has at least one thread, and a process can have multiple threads inside it
• A thread can have multiple concurrent threads inside it

# Comparison of processes, threads, and concurrent threads

1. process is a unit of resource allocation
2. threads are the unit of OS scheduling
3. process switching requires the most resources and is very inefficient
4. thread switching requires average resources and has average efficiency (without considering GIL of course)
5. the co-process switch task resources are small, high efficiency
6. multi-process, multi-thread may be parallel depending on the number of cpu cores, but the concurrent thread is in a thread, so it is concurrent

# Summary

• Processes, threads, and concurrent threads are all capable of multi-tasking, so you can choose to use them according to your actual development needs
• Since threads and concurrent threads require few resources, the chances of using threads and concurrent threads are the highest
• Opening a concurrent process requires the least resources

# Reference

Note that section 6.3 of the reference, the personal implementation results are not quite consistent with the reference article.

Translated with www.DeepL.com/Translator (free version)

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https://blog.emperinter.info/2022/08/08/coroutine

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