MIO VS glommio

Was playing around with mio (https://github.com/tokio-rs/mio) (not that mio itself is very important here!) and was trying to implement a simple something that I've done in java before: a Reactor that you can register ReactorClients with that will get callback whenever there are events on the corresponding socket etc.

How does it compare to mio?

TcpStream gets its wake behavior by delegating to the fd wakers. The Unix wakers have a few implementations, for different platforms. On Linux and Android, epoll is used.

The real implementation details of the I/O event queue is done in mio as u/hniksic pointed out, but that's more comparable with libuv which is certainly a huge part of the Node runtime. mio and libuv have a lot of similarities (at least they used to).

My example was "twice by one developer", not "twice across all indexed repos."

A spot check shows that quite a few in your link are used specifically to ensure correct handling of Rust multi-level breaks work syntax, like https://github.com/rust-lang/rust-analyzer/blob/master/crate... , https://github.com/rust-lang/rustfmt/blob/master/tests/sourc... , https://github.com/rust-lang/rust/blob/master/src/tools/rust... , https://github.com/rust-lang/rust/blob/master/src/tools/rust... and likely more.

Another is a translation of BASIC code to Rust, using break as a form of goto. https://github.com/coding-horror/basic-computer-games/blob/e...

The example at https://github.com/tokio-rs/mio/blob/master/tests/tcp.rs is a nice one

    // Wait for our TCP stream to connect

In my program, I have about 6 different components that follow the pattern below. Basically, the components run a thread while polling on crossbeam channels, file descriptors or sockets. For polling, I am using Mio (https://github.com/tokio-rs/mio).

On the code side it's pretty much the same as C++. You have a module that defines an interface and per-platform implementations that are included depending on a "configuration conditional checks" #[cfg(target_os = "linux")] macro.

https://github.com/tokio-rs/mio/blob/c6b5f13adf67483d927b176...

Made possible by Wasi support for Mio https://github.com/tokio-rs/mio/pull/1576

Indeed! In practice it's done through the polling operation: instead of a separate poll for op1 and op2, the program essentially tells the OS "wake me when either op1 or op2 is ready" (through the epoll syscall on Linux). The mio crate implements this, and the example on the readme is basically the same loop, but written with this polling strategy in mind.

I used glommio as the async executor (instead of something like tokio), and it is wonderful. For people wondering whether it's "good enough" or to use C++ and seastar (as I have thought about a lot before starting this project), take the leap of faith, it's fast - both in terms of run time and to code.

My understanding is you always need a runtime, somethings needs to drive the async flow. But there are others on the market, just not without the.. market domination... of tokio.

https://github.com/smol-rs/smol looks promising simply for being minimal

https://github.com/bytedance/monoio looks potentially easier to work with than tokio

https://github.com/DataDog/glommio is built around linux io_uring and seems somewhat promising for performance reasons.

I haven't played with any of these yet, because Tokio is unfortunately the path of least resistance. And a bit viral in how it's infected tings.

I think you missed one which is based on io_uring [1].

In my benchmarks with a slightly tweaked version it was 2x faster than Nginx and and 30x faster than Python's SimpleHttpServer.

[1] https://github.com/DataDog/glommio/blob/master/examples/hype...

b) It's proven now e.g Seastar, Glommio that the fastest way to run a multi-threaded application is to have one instance with one thread pinned per CPU core. Then to have fibers/lightweight threads on top handling all of the asynchronous code. Your approach of lots of instances is the slowest so there will be a ton of unnecessary thread context-switching.

I assume Tokio itself, see e.g monoio or glommio, but also Seastar for C++.

https://github.com/DataDog/glommio Rust thread per core library.

> Few of us have really figured out io_uring. But that doesn't mean it is slower.

seastar.io is a high level framework that I believe has "figured out" io_uring, with additional caveats the framework imposes (which is honestly freeing).

Additionally the rust equivalent: https://github.com/DataDog/glommio

This use case is perfect for https://github.com/DataDog/glommio which is a thread-per-core runtime that is appropriate for latency sensitive code.

It's worth mentioning: Under "Async Executors", for "io_uring" there is only "Glommio"

I recently found out that ByteDance has a competitor library which supposedly has better performance:

https://github.com/bytedance/monoio

https://github.com/DataDog/glommio/issues/554

FYI, Datadog has a Rust library for scheduling things to run thread-per-core with io_uring

It'd be really useful for DB use cases:

https://github.com/DataDog/glommio