tokio-uring VS rr

While Mio will probably not implement uring in its current design, there's https://github.com/tokio-rs/tokio-uring if you want to use io_uring in Rust.

It's still in development, but the Tokio team seems intent on getting good io_uring support at least!

As the README states, the Rust implementation requires a kernel newer than the one that shipped with Ubuntu 20.04 so I think it'll be a while before we'll see significant development among major libraries.

That's what the pool is for: https://github.com/tokio-rs/tokio-uring/blob/master/src/buf/fixed/pool.rs

Tokio supports io_uring (https://github.com/tokio-rs/tokio-uring), so perhaps when it's mature and battle-tested, it'd be easier to transition to it if Cloudflare aren't using it already.

- Tokio suffers from a similar problem

Eg via tokio-uring.

I strongly recommend you to look into io-uring and use async executors that take advantages of it: - tokio-uring (not recommended as it is still undergoing development) - monoio - glommio

This is my thinking as well. Specifically, I realized that if you don’t use tasks, but rather futures and join, than structured concurrency just works out (at the cost of less efficient poll). In a single-threaded/thread-per-core runtime, tasks could have the same semantics as futures. Somewhat elaborated here: https://github.com/tokio-rs/tokio-uring/issues/81

There's a new API on Linux called io_uring that has performance benefits, but most executors don't use it yet, except executors meant specifically to harness the power of io_uring like tokio-uring and Glommio

Breaking it down a bit further- Rust's async is zero-cost, and there's no way to write faster equivalent code to the language construct in Rust (and presumably other LLVM languages). Tokio introduces abstractions over OS APIs (indirectly) and provides a runtime. The runtime isn't zero cost, but it is likely to be better optimized for "standard" situations than a homebrewed solution, and its primary competition is in the form of other large async runtimes. On the other hand, Tokio's IO routines are (AFAIK) about as well written as one can get with blocking OS APIs, and the only competitors in that space are projects like tokio-uring that use APIs more well suited for asynchronous usage.

I think this tool must share a lot techniques and use cases with rr. I wonder how it compares in various aspects.

https://rr-project.org/

rr "sells" as a "reversible debugger", but it obviously needs the determinism for its record and replay to work, and AFAIK it employs similar techniques regarding system call interception and serializing on a single CPU. The reversible debugger aspect is built on periodic snapshotting on top of it and replaying from those snapshots, AFAIK. They package it in a gdb compatible interface.

Hermit also lists record/replay as a motivation, although it doesn't list reversible debugging in general.

Interesting. Perhaps you can inspect the disassembly of the function in question when using Graal and HotSpot. It is likely related to that.

Another debugging technique we use for heisenbugs is to see if `rr` [1] can reproduce it. If it can then that's great as it allows you to go back in time to debug what may have caused the bug. But `rr` is often not great for concurrency bugs since it emulates a single-core machine. Though debugging a VM is generally a nightmare. What we desperately need is a debugger that can debug both the VM and the language running on top of it. Usually it's one or the other.

> In general I’d argue you haven’t fixed a bug unless you understand why it happened and why your fix worked, which makes this frustrating, since every indication is that the bug exists within proprietary code that is out of my reach.

Were you using Oracle GraalVM? GraalVM community edition is open source, so maybe it's worth checking if it is reproducible in that.

[1]: https://github.com/rr-debugger/rr

https://rr-project.org/ had the same problem. They use the retired conditional branch counter instead of instruction counter, and then instruction steeping until at the correct address.

That'll work great for your Distributed QSort Incorporated startup, where the only product is a sorting algorithm.

Formal software verification is very useful. But what can be usefully formalized is rather limited, and what can be formalized correctly in practice is even more limited. That means you need to restrict your scope to something sane and useful. As a result, in the real world running thousands of tests is practically useful. (Well, it depends on what those tests are; it's easy to write 1000s of tests that either test the same thing, or only test the things that will pass and not the things that would fail.) They are especially useful if running in a mode where the unexpected happens often, as it sounds like this system can do. (It's reminiscent of rr's chaos mode -- https://rr-project.org/ linking to https://robert.ocallahan.org/2016/02/introducing-rr-chaos-mo... )

The approach of check-pointing computation such that it is resumable and restartable sounds similar to a time-traveling debugger, like rr or WinDbg:

https://rr-project.org/

https://learn.microsoft.com/windows-hardware/drivers/debugge...

https://github.com/rr-debugger/rr?tab=readme-ov-file#system-...