Seastar VS rust

I used glommio as the async executor (instead of something like tokio), and it is wonderful. For people wondering whether it's "good enough" or to use C++ and seastar (as I have thought about a lot before starting this project), take the leap of faith, it's fast - both in terms of run time and to code.

b) It's proven now e.g Seastar, Glommio that the fastest way to run a multi-threaded application is to have one instance with one thread pinned per CPU core. Then to have fibers/lightweight threads on top handling all of the asynchronous code. Your approach of lots of instances is the slowest so there will be a ton of unnecessary thread context-switching.

The most common example is DPDK [1]. It's a framework for building bespoke networking stacks that are usable from userspace, without involving the kernel.

You'll find DPDK mentioned a lot in the networking/HPC/data center literature. An example of a backend framework that uses DPDK is the seastar framework [2]. Also, I recently stumbled upon a paper for efficient RPC networks in data centers [3].

If you want to learn more, the p99 conference by ScyllaDB has tons of speakers talking about some interesting challenges.

[1] https://www.dpdk.org/.

[2] https://github.com/scylladb/seastar

[3] https://github.com/erpc-io/eRPC

I assume Tokio itself, see e.g monoio or glommio, but also Seastar for C++.

Seastar which is a thread per core runtime written by the Scylla devs thats used in both Redpanda and Scylla as the underlying runtime. https://github.com/scylladb/seastar

I’ve seen frameworks for c++ (https://seastar.io/) and rust (https://github.com/actix/actix) which support what you’re describing out of the box.

If you're interested in scaling and asynchronous programming in c++ I highly recommend you investigate the SeaStar application framework. You wouldn't build a web service with SeaStar, rather you would build the infrastructure that you would use to build the web service on top of. https://github.com/scylladb/seastar

C++ also allows us to control as much as possible from the platform. Through the efficiency of our own code, combined with the amazing Seastar framework and other best-in-class libraries, Redpanda speaks directly to the hardware. It only depends on the Linux kernel to launch the process, after which Redpanda is very deterministic in terms of performance, runtime characteristics, memory utilization, and CPU speed. We own the entire end-to-end experience, which provides safety and allows Redpanda to build impactful products.

The Seastar framework offers a great HTTP server implementation, which is used by ScyllaDB and Redpanda. However, Seastar doesn’t have an HTTP client library that can be easily used with Seastar framework. So we made one.

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.

There’s also other reasons. For example, take binary search:

* prefetch + cmov. These should be part of the STL but languages and compilers struggle to emit the cmov properly (Rust’s been broken for 6 years: https://github.com/rust-lang/rust/issues/53823). Prefetch is an interesting one because while you do optimize the binary search in a micro benchmark, you’re potentially putting extra pressure on the cache with “garbage” data which means it’s a greedy optimization that might hurt surrounding code. Probably should have separate implementations as binary search isn’t necessarily always in the hot path.

* Eytzinger layout has additional limitations that are often not discussed when pointing out “hey this is faster”. Adding elements is non-trivial since you first have to add + sort (as you would for binary search) and then rebuild a new parallel eytzinger layout from scratch (i.e. you’d have it be an index of pointers rather than the values themselves which adds memory overhead + indirection for the comparisons). You can’t find the “insertion” position for non-existent elements which means it can’t be used for std::lower_bound (i.e. if the element doesn’t exist, you just get None back instead of Err(position where it can be slotted in to maintain order).

Basically, optimizations can sometimes rely on changing the problem domain so that you can trade off features of the algorithm against the runtime. These kinds of algorithms can be a bad fit for a standard library which aims to be a toolbox of “good enough” algorithms and data structures for problems that appear very very frequently. Or they could be part of the standard library toolkit just under a different name but you also have to balance that against maintenance concerns.

But that said, - and again I might be a bit biased - Groovy is too slow for me! I compared it to Rust in this LinkedIn post and it was waaaaay slow. Keep in mind that subjectively comparing programming languages might be a tricky business. But at the end, it will be up to your use case/project to prefer a language over the other.

13. Rust - $87,012

Yes, actually.

https://github.com/rust-lang/rust/blob/d0ea1d767925d53b2230e...

Limited to the rust codebase itself, but I'm sure the developers would force it on everyone else if they thought they could get away with it.