When you're at a point of tuning Tokio, consider taking a look at ef_vi/DPDK + SPDK
replyOne great use of agentic coding is being able to add and very granular tracing instrumentation to help with these sort of optimizations.
replyAlso a great way to make sure that your app spends most of its time in observability overhead. For example even the latency histogram that the OP mentions is wildly expensive.
replyThat just sounds like bad tracing implementations. A good tracing implementation should be able to drive gigabytes per second of trace logs to memory. If you are generating it slow enough to allow actual offload then you should be in the 1—10% range even if you are saturating your offload.
replyYou should, of course, upper bound this overhead by switching to a full time travel debugging solution, thus tracing everything, when you get to the 10-30% range.
The only way you get to “majority” is if your trace implementation is slower than time travel debugging and provides less information, but then why choose something worse in every dimension.
I'm just reporting from the trenches here. I think you are suggesting that everyone is aware of and capable of using state-of-the-art (from 20 years ago) tracing schemes like XRay[1], when in reality they are not. Most projects would be well-served by any basic profiler but even profiling is apparently for wizards, because I've seen a lot of projects that will resort to manually annotating functions with OTel trace spans, which are ~millions of times more expensive than function calls. Even eBPF uprobe/uretprobe is 100x more expensive than XRay, at a minimum. HotSpot's JFR is like a miracle compared to what people suffer through to diagnose Rust+Tokio.
reply1: https://llvm.org/docs/XRay.html ... is there even a Rust analog to this?
One legitimately great thing about LLMs is that it makes it feasible to add these kind of tracing instrumentations temporarily for profiling and then throw them away so they never reach source control let alone production.
replyI can get an LLM to trace my incomprehensible Tokio application which was also written by an LLM, which is why I don't understand its behavior. Truly the future we were promised.
replyJust curious, why? Is this true even if you did something like a per-CPU histogram that uses atomic ops to increment?
replyIf you’re not using eBPF to trace your app you’re doing it wrong.
replyThe low cost of eBPF tracing is another myth.
reply1) Its no myth, but you can definitely foot-bullet into doing it wrong, and 2) it's a far better path to take than in-app telemetry.
replyAll of the significant server applications I have encountered in the industry have suffered from the same problem, which surprised their authors but seemed obvious to me: the application was spending the majority of its CPU time doing meta-work like entering and leaving epoll, stealing work from itself, etc. There are principles for writing Tokio servers and these are good points in the OP but I think they are little-known and too easy to violate.
replyI can't say I'm surprised when I see the 100+ function stack traces that Axum built on Tokio produces.
replyBefore you say Axum is "holding it wrong" the project lives under the tokio-rs GitHub org.
Note that most of those end up getting inlined in practice
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