Any good resources to clear things up?
The best mental model for me always was to think: Here's an await, that means "interpreter, go and do something else that's currently waiting while this is not done yet."
And that's all about IO, because what you can wait on is essentially IO.
By the way, I really wish there was a better story to executors in async python. To think we still have the same queue/pickle-based multiprocessing to send async tasks to another core is kind of sad. Hoping for 3.12 and beyond there.
[edit] one really neat example that helped me get asyncio was Guido van Rossum's crawler in 500 lines of python [1]. A lot of the syntax is deprecated now, but it's still a great walk-through
[1] http://aosabook.org/en/500L/a-web-crawler-with-asyncio-corou...
Basically, your program will normally halt when doing I/O, and won't proceed until that I/O is done. During that halt, your program is doing nothing (no cpu being used). With asyncio, you can schedule multiple tasks to run, so if one is halted doing I/O, another can run.
Edit: And AFAIK, the GIL does not come into play at all with async. Only when multithreading.
In the sense that the GIL is still held and you can have at most one path of Python code executing at a time regardless of whether you use async or threads, sure. Most blocking I/O was already releasing the GIL so the difference is purely in how you can design your modules; for any reasoning about performance the GIL behaves the same way whether you use asyncio or not.
Very few locking and care is needed with asyncio, as opposed to using threads. Race conditions are basically not a thing if you write reasonably idiomatic code.
It might be (or not) faster than using threads, but that's not the main benefit in my view - this easiness of use is.
It doesn't necessarily improve performance. It's just a much easier way to do non-blocking I/O (note that blocking and threaded I/O is easier to do but much much heavier).
It's needed when you're spending a lot of time waiting for an I/O request to complete (network/HTTP requests, disk reads/writes, database reads/writes)
> what happens under the hood,
https://tenthousandmeters.com/blog/python-behind-the-scenes-...
(Please read the entire blog post - It goes through the necessary concepts like generators, event loops, & coroutines)
> does it actually help with performance,
Refer to the first answer: You'll see improved performances if your workloads are mainly comprised of waiting for other stuff to complete. If you're compute-heavy, it'll be better to use the 'multiprocessing' library instead.
> sometimes the GIL comes into play and sometimes it doesn't,
The GIL comes into play when you have a lot of compute-heavy tasks: Otherwise, you'll rarely encounter it.
It's only when you have that many compute-heavy tasks that you start to use the 'multiprocessing' library.
> why do we ever use threads at all if there's a GIL,
Threads exist because it was there before asyncio & event loops came into Python.
> why is it called asyncio if we can use it for anything.
Its name came from PEP 3156, proposing the asyncio library back in 2012.
https://peps.python.org/pep-3156/
As for why, asynchronous I/O stands in contrast to synchronous I/O, where the program/thread had to wait for the I/O request to complete before it can do anything else. Making tasks asynchronous allows it to do other stuff while it waits for a task's request to complete, increasing CPU & I/O utilization.
> My mind is kind of scattered and people seems to be using a lot of async Python for some reason. Any good resources to clear things up?
Highly recommend this video from mcoding: It's fairly simple & goes through a sample implementation.
https://www.youtube.com/watch?v=ftmdDlwMwwQ
Also, this article:
If you ever coded in sockets in C (and its a good exercise to do so), you probably have at some point ran across `select` which is essentially a non blocking way to check which sockets have data available to read, and then sequentially read the data. This gives the ability for a program to appear parallelized in the sense that it can handle multiple client connections, but its not truly parallel. Different clients can be handled at different time depending on which order they connect, which is asynchronous in nature (versus processing each client in sequence and waiting on each one to connect and disconnect before moving on to the next one)
Async in Python is basically this concept, with a core fundamental feature of time limited execution. Functions can say that they are pausing for x seconds, allowing other functions to run, or functions can say that they give a certain function x seconds to run before resuming execution. If you async code (along with any library you may use) doesn't contain any sleeps or timeouts, its exactly equivalent to synchronous code (since the event loop never really recieves a message that it can suspend a routine or cancel it). With sleeps and timeouts, you gain control over things that can potentially block, both from a caller perspective of not having a function call block your own, and from a callee perspective of not making your function blocking.
The use case is for it is that it is good for I/O bound operations like Threading is, but with the addition that you don't have to worry about synchronization or race conditions, since by design your code will have predictable access patterns. The downside is that your code and any libraries that you use within your code has to be implemented as async libraries, and any library that is async has to have async wrappers around the calls to its methods, which in turn means that your entire code has to be async.
Threading with Python is generally not useful, as its not true parallelism because of GIL. GIL allows only one thread in Python to run. Threading is safer in Python because of this, however it obviously has drawbacks. In general its best used if you want asyncio like performance with a library that is not written with async, since GIL is smart enough to detect when a thread is waiting for input and switch context.
True parallelism in Python is achieved with multiprocessing, however the use case is a little different. Rather than spinning off processes, you generally launch a bunch of worker processes up front (to avoid the larger overhead), then use smart scheduling to distribute work between these processes. Here though you do have to worry about race conditions and synchronization, and use things like locks and mutexes.