upb VS rust

> There are undeniably power users for whom every last bit of performance translates to very large sums of money, and I don’t claim to know how to satisfy them otherwise.

That is the key, right there.

In 1970, C may have been considered a general-purpose programming langauge. Today, given the landscape of languages currently available, C and C++ have a much more niche role. They are appropriate for the "power users" described above, who need every last bit of performance, at the cost of more development effort.

When I'm working in C, I'm frequently watching the assembly language output closely, making sure that I'm getting the optimizations I expect. I frequently find missed optimization bugs in compilers. In these scenarios, undefined behavior is a tool that can actually help achieve my goal. The question I'm always asking myself is: what do I have to write in C to get the assembly language output I expect? Here is an example of such a journey: https://blog.reverberate.org/2021/04/21/musttail-efficient-i...

I created the https://github.com/protocolbuffers/upb project a long time ago. It's written in C, and over the years I have gotten it to a state where the speed and code size are pretty compelling. Both speed and code size are very important to the use cases where it is being used. It's a relatively small code base also. I think focused, performance-oriented kernels are the area where C makes the most sense.

More and more languages are being built on top of the "upb" C library for protobuf (https://github.com/protocolbuffers/upb) which is designed around arenas to avoid this very problem.

Currently Ruby, PHP, and Python are backed by upb, but this list may expand in the future.

Lua uses "chained scatter" (linked list, but links point to other entries in the same table, to maintain locality): https://github.com/lua/lua/blob/master/ltable.c

This is a good visual depiction of chained scatter: https://book.huihoo.com/data-structures-and-algorithms-with-...

Inspired by Lua, I did the same for upb (https://github.com/protocolbuffers/upb). I recently benchmarked upb's table vs SwissTable for a string-keyed table and found I was beating it in both insert and lookup (in insert upb is beating SwissTable by 2x).

> But yes, the serialized dict-of-arrays-of-dicts type stuff can be approached in a few ways, none of which are particularly beautiful.

For what it's worth, this sounds somewhat similar to protobuf (which also supports dicts, arrays, etc).

After spending many years trying to figure out the smallest, fastest, and simplest way to implement protobuf in https://github.com/protocolbuffers/upb, the single best improvement I found was to make the entire memory management model arena-based.

When you parse an incoming request, all the little objects (messages, arrays, maps, etc) are allocated on the arena. When you are done with it, you just free the arena.

In my experience this results in code that is both simpler and faster than trying to memory-manage all of the sub-objects independently. It also integrates nicely with existing memory-management schemes: I've been able to adapt the arena model to both Ruby (tracing GC) and PHP (refcounting) runtimes. You just have to make sure that the arena itself outlives any reference to any of the objects within.

> Google's implementations, at least C++ and Java, are a bunch of bloated crap (or maybe they're very good, but for a use case that I haven't yet encountered).

As someone who has been working on protobuf-related things for >10 years, including creating a size-focused implementation (https://github.com/protocolbuffers/upb), and has been working on the protobuf team for >5 years, I have a few thoughts on this.

I think it is true that protobuf C++ could be a lot more lean than it currently is. That's why I created upb (above) to begin with. But there's also a bit more to this story.

The protobuf core runtime is split into two parts, "lite" and "full". Basically the full runtime contains reflection support, while the lite runtime omits it. The full runtime is much larger than the lite runtime. If you don't need runtime reflection for your protos, it's better to use "lite" by using "option optimize_for = LITE_RUNTIME" in your .proto file (https://developers.google.com/protocol-buffers/docs/proto#op...). That will cut out a huge amount of overhead in your binary. On the downside, you won't get functionality that requires reflection, including text format, JSON, or DebugString().

In addition to this, even the lite runtime can get "lighter" if you compile your binary to statically link the runtime and strip unused symbols with -ffunction-sections/-fdata-sections and gc-sections in the linker. Some parts of the lite runtime are only used in unusual situations, like ExtensionSet which is only used if your protos use proto2 extensions (https://developers.google.com/protocol-buffers/docs/proto#ex...). If you avoid this stuff, the lite runtime is quite light.

However, there is also the issue of the generated code size. The size of the generated code is generally quite large, even for lite. You are getting a generated parser, serializer, CopyFrom(), MergeFrom(), etc for every message you define. If your schema is of any size, this quickly adds up and can dwarf the size of the actual runtime. For this reason, C++ also supports "option optimize_for = CODE_SIZE" which does everything reflectively instead of generating code. This means you pay the fixed size hit from the full runtime, but the generated code size is much smaller. On the downside, "optimize_for = CODE_SIZE" has a severe speed penalty.

I have long had the goal of making https://github.com/protocolbuffers/upb competitive with protobuf C++ in speed while achieving much smaller code size. With the benefit of 10 years of hindsight and many wrong turns, upb is meeting and even surpassing these goals. It is an order of magnitude smaller, both in the core runtime and the generated code, and after some recent experiments it is beginning to significantly surpass it in speed also (I want to publish these results soon, but the code is on this branch: https://github.com/protocolbuffers/upb/pull/310).

upb has downsides that prevent it from being fully "user ready" yet: the API is still not 100% stable, there is no C++ API for the generated code yet (and C APIs for protobuf are relatively verbose and painful), it has a bunch of legacy APIs sitting around that I am just on the verge of being able to finally delete, and it doesn't support proto2 extensions yet. On the upside, it is 100% conformant on every other protobuf feature, it has full binary and JSON support, it supports reflection if you want it but also lets you omit it for code size savings.

I hope 2021 is a year when I'll be able to publish more about these results, and when upb will be a more viable choice for users who want a smaller protobuf implementation.

If you haven't dipped your touch-typing fingers into Rust yet, you really owe it to yourself. Rust is a modern programming language with features that make it suitable not only for systems programming -- its original purpose, but just about any other environment, too; there are frameworks that let your build web services, web applications including user interfaces, software for embedded devices, machine learning solutions, and of course, command-line tools. Since a custom GitHub Action is essentially a command-line tool that interacts with the system through files and environment variables, Rust is perfectly suited for that as well.

Here's an example of someone citing a disagreement between CRT and shell32:

https://github.com/rust-lang/rust/issues/44650

This in addition to the Rust CVE mentioned elsewhere in the thread which was rooted in this issue:

https://blog.rust-lang.org/2024/04/09/cve-2024-24576.html

Here are some quick programs to test contrasting approaches. I don't have examples of inputs where they parse differently on hand right now, but I know they exist. This was also a problem that was frequently discussed internally when I worked at MSFT.

    #include 

> instead of choosing a certain numbered version of the random library (if I remember correctly) I let cargo download the latest version which had a completely different API.

Yeah, they didn't follow the instructions and got burned. I still think that multiple things went wrong simultaneously for that experience. I wonder if more prevalent uses of `#[doc(alias = "name")]` being leveraged by https://github.com/rust-lang/rust/pull/120730 (which now that I check only accounts for methods and not functions, I should get on that!) so that when changing APIs around people at least get a slightly better experience.

Almost fixed the compiler: https://github.com/rust-lang/rust/pull/123325

Rust: A secure, efficient, and modern programming language (omitting ten thousand words). You can simply follow the installation instructions provided on the official website.

Recently did something similar in Rust but for generating SVGs. We've adopted it for snapshot testing of cargo and rustc's output. Don't have a good PR handy for showing Github's rendering of changes in the SVG (text, side-by-side, swiping) but https://github.com/rust-lang/rust/pull/121877/files has newly added SVGs.

To see what is supported, see the screenshot in the docs: https://docs.rs/anstyle-svg/latest/anstyle_svg/

We strongly believe in Rust as a powerful language for building production-grade software, especially for systems like ours that run alongside Kubernetes.

The above Assert<{N % 2 == 1}> requires #![feature(generic_const_exprs)] and the nightly toolchain. See https://github.com/rust-lang/rust/issues/76560 for more info.