> 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).
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.