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.
- 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.
- 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()
pip install greenlet
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()
pip install gevent
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
- process is a unit of resource allocation
- threads are the unit of OS scheduling
- process switching requires the most resources and is very inefficient
- thread switching requires average resources and has average efficiency (without considering GIL of course)
- the co-process switch task resources are small, high efficiency
- 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
- 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
Note that section 6.3 of the reference, the personal implementation results are not quite consistent with the reference article.
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