If you agree that Rust's memory safety is hugely beneficial in cryptography, then I'd love to hear other (sub)domains (e.g., kernel code, networking code, blockchain, ...etc) that necessitate tricky data structures, making Zig a better fit than Rust (which, IIUC, is your main point here).
I don’t have a “zig/rust is better than rust/zig” point here. The point was to say you have to judge Rust’s success against the unsafe code it occasionally forces you to write (you don’t get to hand wave it away), especially since a great example of such unsafe code is data structures code, and that is where a lot of my memory errors occur. Then we got diverted into some minor points about how much benefit does memory safety at compile time give you in different domains.
I wouldn’t build a company or long-term project around Zig right now, since it’s quite young, so if the choice was just those two, I’d probably always pick Rust. But ignoring that, since Zig will probably “graduate” in a year or two: both are good for all those domains, and I’d be hard-pressed to say one is better or worse than the other without further constraints. Zig certainly seems less indirect and more lightweight, and has less of an immediate cognitive burden, but Rust’s additional burden there is providing some additional value. I really don’t like Rust’s allocation story though — AFAIK there are plenty of allocations in std that can only be controlled by swapping out the global allocator, possibly even at link time. C++ is clunky on this front too, just in a different way. C is “better” simply because the standard library is so meager, but that has never been a problem for me in practice.
> You have to judge Rust’s success against the unsafe code it occasionally forces you to write (you don’t get to hand wave it away).
Agreed.
> I really don’t like Rust’s allocation story though.
I presume this is because of the lack of ergonomic handling of OOM? Only time I've run into OOM was when training deep learning models (in Python backed by C++) on a GPU, but if I were to do that in Rust, I'm pretty sure I'd be able to avoid the memory leaks that caused OOM since allocations in a deep learning training loop are almost always deterministic.
So I would love to learn about the domains where you think OOM is a huge issue (apart from kernel code, which I'm already familiar with).
Btw, thanks for all the replies! I'm learning with every one.
It's not at all about OOM, actually. It's about performance, and to a lesser degree, simplifying code or architecture. Zig's FixedBufferAllocator and ArenaAllocator are very simple and very useful, and they conform to the Allocator interface that's used for every bit of allocation in std and non-std. Anyone who has written a lot of C or a certain kind of C++ will be very familiar with the concepts, and will have used them a lot. They're nothing revolutionary, but not having a "default" global allocator and having a culture of passing the Allocator as a parameter means there's a nice way to actually use these things without always having to rewrite/modify someone else's code -- all Zig code I've seen passes the Allocator as an argument. This also means you have some idea of when allocations occur, which is another thing I often want to know (I used to work on ultra low latency systems, measuring in nanoseconds -- no allocations allowed in the hot path).
