virt-v2v VS loom

In virt-v2v we eventually enforced that every module file also has a corresponding interface file: https://github.com/libguestfs/virt-v2v/blob/master/check-mli...

You can look at the project yourself: https://github.com/libguestfs/virt-v2v I've been writing OCaml for 20+ years and C for 40 years.

I develop in OCaml from time to time, and it's pretty practical. Separate compilation, makes small-ish binaries that most people wouldn't know weren't written in C/C++, easily call out to C if you need to. We steer clear of the more complex language features like functors because they confuse most programmers.

Here's an example of one very widely used production application: https://github.com/libguestfs/virt-v2v/tree/master/v2v

XPath is the real killer feature for XML. I don't think it's possible to use it in this particular example, but in the more generally useful cases where you want to pull (eg) all subnodes with key matching a particular string, XPath is great.

Here's it being used in real code (search for "xpath_"):

https://github.com/libguestfs/virt-v2v/blob/master/v2v/parse...

https://github.com/libguestfs/virt-v2v/blob/master/v2v/parse...

> Mutexes are very cheap in the uncontended case

It was a while ago I was deep into this mess so forgive any ignorance–but–iirc the thread-mutex dogma[1] has many pitfalls despite being so widely used. Primarily they’re easy to misuse (deadlocks, holding a lock across a suspend point), and have unpredictable performance because they span so far into compiler, OS and CPU territory (instruction reordering, cache line invalidation, mode switches etc). Also on Arm it’s unclear if mutices are as cheap because of the relaxed memory order(?). Finally code with mutices are hard to test exhaustively, and are prone to heisenbugs.

Now, many if not most of the above apply to anything with atomics, so lock-free/wait-free won’t help either. There’s a reason why a lot of concurrency is ~phd level on the theoretical side, as well as deeply coupled with the gritty realities of hardware/compilers/os on the engineering side.

That said, I still think there’s room for a slightly expanded concurrency toolbox for mortals. For instance, a well implemented concurrent queue can be a significant improvement for many workflows, perhaps even with native OS support (io_uring style)?. Another exciting example is concurrency permutation test frameworks[2] for atomics that reorder operations in order to synthetically trigger rare logical race conditions. I’ve also personally had great experience with the Golang race detector. I hope we see some convergence on some of this stuff within a few years. Concurrency is still incredibly hard to get right.

[1]: I say this only because CS degrees has preached mutices to as the silver bullet for decades.

[2]: https://github.com/tokio-rs/loom

Of course atomics are absolutely essential for some of the libraries we take for granted, such as Arc and Tokio. But if you start reading the code and comments and issues and PRs around code like that, you'll see how much work it took to mature them to the point we can now rely on them. That's why tools like Loom exist.

loom and shuttle can help you narrow down the problem.

Unless you are writing unsafe code, you generally don't need to use sanitizers. If you do write unsafe code, checking it with a sanitizer would be a great idea. Two most useful tools here I think are miri and loom.

https://github.com/tokio-rs/loom perhaps? It also models weak memory reordering, but takes some work to integrate into existing apps.

For triggering race conditions in compiled binaries, you could try https://robert.ocallahan.org/2016/02/introducing-rr-chaos-mo....

There is Loom[1] (part of the Tokio project) for exhaustively testing multithreaded code. Though as far as I can tell it is designed for debugging threads, not async tasks.

[1] https://github.com/tokio-rs/loom

That tool seems similar to https://github.com/tokio-rs/loom, insofar as detecting potential locking errors. These are useful during development, but could still miss production cases (as dev never perfectly matches production). This crate is meant to not have to worry about possibly deadlocking.

Rust doesn't catch memory ordering errors, which can result in behavioral bugs in safe Rust and data races and memory unsafety in unsafe Rust. But Loom is an excellent tool for catching ordering errors, though its UnsafeCell API differs from std's (and worse yet, some people report Loom returns false positives/negatives in some cases: https://github.com/tokio-rs/loom/issues/180, possibly https://github.com/tokio-rs/loom/issues/166).