> That sounds like a decent amount of work for a service
5+ years ago I wrote a real-time transcoding, muxing streaming radio service that did 5000 simultaneous connections with inline, per-client ad spot injection (every 30 seconds in my benchmark). Using C and Lua. On 2 Xeon E3 cores--1 core for all the stream transcoding, muxing, and HTTP/RTSP setup, 1 core for the Lua controller (which was mostly idle). The ceiling was handling all the NIC IRQs.
While I think what I did was cool, I know people can eke much more performance out of their hardware than I can. And I wasn't even trying too hard--my emphasis is always on writing clear code and simple abstractions (though that often translates into cache-friendly code).
At my day job, in the past two months I've seen two services in a "scalable" k8s clusters fall over because the daemons were running with file descriptor ulimits of 1024. "Highly concurrent" Go-based daemons. For all the emphasis on scale, apparently none of the engineers had yet hit the teeny, tiny 1024 descriptor limit.
We really do need to raise our expectations a little.
I haven't written any Rust but I have recently helped someone writing a concurrent Rust-based reverse proxy service debug their Rust code and from my vantage point I have some serious criticisms of Tokio. Some of the decisions are clearly premature optimization chosen by people who probably haven't actually developed and pushed into production a process that handles 10s of thousands of concurrent connections, single-threaded or multi-threaded. At least not without a team of people debugging things and pushing it along. For example, their choice of defaulting to edge-triggered instead of level-triggered notification shows a failure to appreciate the difficulties of managing backpressure, or debugging lost edge-triggered readiness state. These are hard lessons to learn, but people don't often learn them because in practice it's cheaper and easier to scale up with EC2 than it is to actually write a solid piece of software.