Also 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.
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.
I 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.
That 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.
You 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.
It's worth pointing out though that just tracing function calls isn't good enough for the kinds of stackless coroutines that run in async Rust tasks. You need a way of mapping between the async tasks and the compiler emitted traces.
All 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.
Tsarp | 2 hours ago
jeffbee | 2 hours ago
MomsAVoxell | 2 hours ago
jeffbee | 2 hours ago
MomsAVoxell | an hour ago
nicoburns | 2 hours ago
jeffbee | 2 hours ago
Veserv | an hour ago
You 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.
jeffbee | 43 minutes ago
1: https://llvm.org/docs/XRay.html ... is there even a Rust analog to this?
duped | 19 minutes ago
It's worth pointing out though that just tracing function calls isn't good enough for the kinds of stackless coroutines that run in async Rust tasks. You need a way of mapping between the async tasks and the compiler emitted traces.
afaik, C/C++ have the same problem.
foota | an hour ago
jeffbee | 2 hours ago
cube00 | 2 hours ago
Before you say Axum is "holding it wrong" the project lives under the tokio-rs GitHub org.
rusbus | an hour ago
dist1ll | 28 minutes ago