[VorpalWay]

Outside of Embassy in embedded, tokio is the only realistic choice though, because it is likely that any third party async crate has a dependency on it already. Yes, smol, monio, glommio etc exist, but they are marginalised (and as far as I can tell they don't really help that much with mixed IO / compute workloads).

In fact, async/await in Rust falls apart with a mixed IO / compute workload since scheduling is cooperative. As soon as you want preemption (most of the time for what I do), it is not the right choice.

Seeing how embassy (embedded async rust) handles preemption reinforces this: it uses a separate scheduler per preemption level. This works fine, but is a bit clunky. Basically you are at this point just using async to help write a state machine per preemptive thread, which can be useful for some code patterns (in particular those common in embedded, where you are often waiting for IO). But to talk between threads you are back to channels, mutexes etc.

[oersted]

Yes I do agree. It's not that Tokio has taken over just due to community momentum. There's a certain subtle lock-in that happens due to how the Rust type system and dependency system work together. It's a hard problem to address.

Regarding mixed IO/compute and preemptive scheduling, well that's what threads are. They are not as lightweight, but they are there and they are quite ergonomic to use in Rust.

I could even imagine an async executor that simply starts a thread per task, so that you can keep the nice syntax, but that's obviously not ideal from a performance standpoint. Tokio already dispatches tasks to a thread pool, it is not completely cooperative, it's the sensible way to do this. And there's always tokio::spawn_blocking too.

PS: Actually, it is underrated how well designed classic threading is in Rust. It was just going out of vogue when they did it, but they addressed so much of the complexity that was around for years in Java, C++ and the like. They did actually mostly achieve the holy grail of fearless concurrency and with a more ergonomic design.

[imtringued]

I don't really know much about tokio ecosystem lock-in, but async is fundamentally an ecosystem split. In principle nothing stops you from running computationally expensive code inside an async context, it just needs to be designed to support self pre-emption. That means if you have an async JSON parser it needs to await inside long running loops or recursive functions.

[random17]

How do you properly mix IO/compute (in any language)? In Rust what I’ve done in the past is have two Tokio runtimes, one for IO and one for compute. I know you can also use Rayon but the abstractions are not always flexible/convenient enough.

But in either case the boundary between the two types of async work is never easy to cross.

[Animats]

Threads.

I have a graphics program that has about eight different threads doing different things. The draw thread is high priority and does only the things that have to be done on the draw thread. The update thread, medium priority, is doing change events that affect the scene. The movement thread is doing repeated per-frame movement, computed in parallel with drawing. The asset-loading threads, low priority, are making blocking HTTPS requests to get assets from remote servers, and decompressing them, the biggest compute load. Plus there are a few other threads that do various intermittent tasks.

This works well in Rust because the language catches locking errors that cause race conditions. Doing this in C++ would be tough.

You don't really need async until you have thousands of things waiting.

[oersted]

You can use tokio::spawn_blocking to separate compute-heavy stuff, it gets handled in a separate thread pool. You shouldn’t need two tokio runtimes, that sounds problematic.

> How do you properly mix IO/compute (in any language)?

Good question, it’s a really hard problem.

In principle you are meant to use threads, the OS will automatically switch to other work while you are waiting for IO, like with async. But there are difficulties with threads too: memory overhead, switching overhead, thread limits… Although I am getting the feeling that they have a much worse reputation than they deserve. More projects should try switching to threads and actually measuring the difference.

[quietbritishjim]

In Python Trio, you would have a thread pool for each type of computation (potentially a "pool" of 1 thread shared between all tasks if that works for your application). You would await trio.to_thread.run_sync(...) [1] (pass it a normal non-async function, and a token representing the thread pool). This is a pretty simple wrapper that takes care of queuing the work to the thread pool, waiting for it to complete and for a message to be passed back to the Trio thread.

It works for simple situations. It certainly preserves backpressure, although a slow CPU task can impact other users of the same thread pool.

[1] https://trio.readthedocs.io/en/stable/reference-core.html#tr...