Async Techniques and Examples in Python Transcripts
Chapter: Course conclusion and review
Lecture: Review: Threads
0:00 In contrast to the cooperative nature
0:02 of asyncio type of concurrency on a single thread
0:06 thread based parallelism lets the operating system
0:09 decide when our code gets to run.
0:10 We have a single process and that process
0:12 has a main thread which may kick off additional threads
0:15 spawn a couple additional pieces of work and then
0:18 wait for them to complete and keep on running.
0:20 And the important thing is it's up to the OS to figure out
0:24 when a thread runs, not up to our particular process.
0:27 Although the GIL does have something to say about
0:29 it doesn't it, so here's the mental picture for threads.
0:32 Here's the programming model, so we're going to start
0:34 with the built in threading library.
0:36 There's no external things to install here.
0:39 We're going to have some function we want to run asynchronously
0:41 but this is just normal Python code.
0:44 It doesn't do any sort of async await or stuff like
0:46 that, it's just code and instead of running on the main
0:49 thread, we're going to run somewhere else.
0:51 So given this, we'd like to create a bit of work
0:54 that's going to run it so we set up the targets
0:56 the arguments and if we want it not keep the
0:59 process alive or be canceled more easily
1:01 we can say it's a daemon thread.
1:04 And then we just start it, potentially do some
1:07 other work while it's running, or start other
1:09 threads, things like that and then we're
1:10 going to just wait for it to complete so we say work.join.
1:13 Of course we can give it a timeout.
1:16 We were just saying wait forever until it finishes.