The comment you are responding to prefers green threads to be managed like goroutines, where the code looks synchronous, but really it's cooperative multitasking managed by the runtime, to explicit async/await.

But then you criticize "code that looks synchronous but is really async". So you prefer the explicit "async" keywords? What exactly is your preferred model here?

Goroutines feel like old-school, threaded code to me. I spawn a goroutine and interact with other “threads” through well defined IPC. I can’t tell if I’m spawning a green thread or a “real” system thread.

C#’s async/await is different IMO and I prefer the other model. I think the async-concept gets overused (at my workplace at least).

If you know Haskell, I would compare it to overuse of laziness, when strictness would likely use fewer resources and be much easier to reason about. I see many of the same problems/bugs with async/await..

An obvious advantage of doing it that way is you don’t need any runtime/OS-level support. Eg your runtime doesn’t need to even have a concept of threads. It works on bare metal embedded.

Another advantage is that it’s fully cooperative model. No magic preemption. You control the points where the switch can happen, there is no magic stuff suddenly running in background and messing up the state.

It is nothing like what you just described

Problem is it that it self reinforces and before you look every little function is suddenly async.

The irony is that it is used where you want to write in a synchronous style...

Async in C# is awesome, and there's nothing stopping you from writing sync code where appropriate or using threads if you want proper multi threading. Async is primarily used to avoid blocking for non-cpu-bound work, like waiting for API/db/filesystem etc. If you use it everywhere then it's used everywhere, if you don't then it isn't. For a lot of apps it makes sense to use it a lot, like in web apis that do lots of db calls and such. This incurs some overhead but it has the benefit of avoiding blocked threads so that no threads sit idle waiting for I/O.

You can imagine in a web API receiving a large number of requests per second there's a lot of this waiting going on and if threads were idle waiting for responses you wouldn't be able to handle nearly as much throughout.

Used to be well-hidden cooperative, these days it's preemptive.

But this appears to be describing languages with green threads, rather than languages that make async explicit.

You may think of use of an async keyword as explicit async code but that is very much not the case.

If you want to see async code without the keyword, most of the code of Linux is asynchronous.

Thank you for explaining much more clearly than I could.

> none of the function coloring stuff

And it’s this part that I don’t like (and see colleagues struggling to implement correctly at work).

Kernel-style async code, where everything is explicit:

* You write a poller that opens up queues and reads structs representing work

* Your functions are not tagged as "async" but they do not block

* When those functions finish, you explicitly put that struct in another queue based on the result

Async-await code, where the runtime is implicit:

* All async functions are marked and you await them if they might block

* A runtime of some sort handles queueing and runnability

Green threads, where all asynchrony is implicit:

* Functions are functions and can block

* A runtime wraps everything that can block to switch to other local work before yielding back to the kernel

which are no different from app POV from kernel threads, or any threads for that matter.

the whole async stuff came up because context switch per event is way more expensive than just shoveling down a page of file descriptor state.

thus poll, kqueue, epoll, io_uring, whatever.

think of it as batch processing

Let me try to clarify my point of view:

I don’t mean that async/await is more or less explicit than goroutines. I mean regular threaded code is more explicit than async/await code, and I prefer that.

I see colleagues struggle to correctly analyze resource usage for instance. Someone tries to parallelize some code (perhaps naiively) by converting it to async/await and then run out of memory.

Again, I don’t mean to judge anyone. I just observe that the async/await-flavor has more bugs in the code bases I work on.

More explicit in what sense? I've written both regular threaded Python and async/await Python. Only the latter shows me precisely where the context switches occur.

Everything is in a run loop that does not exist in my codebase.

The context switching points are obvious but the execution environment is opaque.

At least that's how it looks to me.

Green threads are better (IMHO), because they actually do hide all the machinery. As a developer in a language with mature green threads (Erlang), I don't have to know about the machinery[1], I just write code that blocks from my perspective and BEAM makes magic happen. As I understand it, that's the model for Java's Project Loom aka Java Green Threads 2: 2 Green 2 Threads. The first release had some issues with the machinery, but I think I read the second release was much better, and I haven't seen much since... I'm not a Cafe Babe, so I don't follow Java that closely.

[1] It's always nice to know about the machinery, but I don't have to know about it, and I was able to get started pretty quick and figure out the machinery later.

If even this does not help, rm -rf is your friend.

I would say that green threads still have "function coloring stuff", we just decided that every function will be async-colored.

Now, what happens if you try to cross an FFI-border and try to call a function that knows nothing about your green-thread runtime is an entirely different story...

To be fair that also happens with other solutions.

: Yes I know about service workers, but they are not threads in the sense that there is no shared memory. It is good for some types of parallelization problems, but not others because of all the memory copying required.

2: Yes I know about SharedArrayBuffer and there is a bunch of proposals to add support for locks and all that fun stuff to them, which also brings all the complexity back.

https://nodejs.org/en/learn/asynchronous-work/overview-of-bl...

I will agree with what some is said a above, BEAM is pretty great. I have been using it recently through Elixir.

DESPITE THAT: even if you're doing everything "right" (TM) -- using a single thread and doing all your networking I/O sequentially is simply slow as hell. A very very good example of this is bottle.py. Lets say you host a static web server with bottle.py. Every single web request for files leads to sequential loading, which makes page load times absolutely laughable. This isn't the case for every python web frame work, but it seems to be a common theme to me. (Cause: single thread, event loop.)

With asyncio, the most consistent behavior I've had with it seems to be to avoid having multiple processes and then running event loops inside them. Even though this approach seems like its necessary (or at least threading) to avoid the massive down sides of the event loop. But yeah, you have to keep everything simple. In my own library I use a single event loop and don't do anything fancy. I've learned the hard way how asyncio punishes trying to improve it. It's a damn cool piece of software, just has some huge limitations for performance.