"Be careful with mutexes" is good advice, but I'm surprised it doesn't explicitly call out the various channels that tokio provides as alternatives (detailed here: https://docs.rs/tokio/latest/tokio/sync/index.html). There are a variety of options that fit different use cases, and you don't even need to enable the runtime feature to use them (e.g. if you want to do a single check for completion rather than await). I'd estimate that at least half of the bottlenecks I've seen with mutexes when using tokio could have been avoided by not even using a mutex at all and instead passing the data that's truly needed across different tasks with some type of channel.
The other trick I've used a few times that's a bit hacky but can get the job done is when reading a snapshot of the data under a mutex is enough without needing to prevent other changes; if that's the case, you can just clone the data and drop the mutex to allow other uses move forward at the cost of the data potentially being stale.
I don't think there is a ton of overlap. tokio is appropriate for general userspace apps, ranging anywhere from a CLI, GUI, API or web app. DPDK and SPDK are specialized fast paths for building network data paths and storage solutions that come with tradeoffs: DPDK uses poll mode drivers, outside of the operating system, which have various implications including busy waiting and taking over the interface. That is why DPDK is fast, no kernel/userspace context switching and copies, and the drivers are tuned for the polling model. But it's not a general purpose building block.
It all depends on what you are doing. I do embedded with strict realtime requirements. CPU pinning would not be an option. I have also done software that should use as little resources as possible (but still be quick) to coexist with other software on the same hardware.
All of these are different, valid, meanings of high performance. You need context. An interactive IDE is yet another thing that needs to be high performance in yet another way.
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.
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.
Huh, it seems xray puts blank trampolines all over your binary? That sounds pretty nifty but I would expect it to be pretty language agnostic, ish? Adding support should be doable for Rust as well, right? Anyways, pretty nifty.
I am by no means an expert, but I've recently improved performance for some code and used tracy. They have rust bindings as well. It's pretty cool and it seems to be low overhead. Wonder if I can couple it with something like xray? Tracy is more the tracing library + tracing interpretations/aquisition tool.
Edit: apparently rust already supports xray natively on the nightly.
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.
The difference is nobody in the C++ community believes that a dominant asynchronous executor library exists, and there is not a pervasive belief that it would be helpful.
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.
Reading the clock every time you jump into a closure is in fact incredibly wasteful, and is exacerbated by chopping work up into tiny chunks for questionable reasons.
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.
The other trick I've used a few times that's a bit hacky but can get the job done is when reading a snapshot of the data under a mutex is enough without needing to prevent other changes; if that's the case, you can just clone the data and drop the mutex to allow other uses move forward at the cost of the data potentially being stale.
All of these are different, valid, meanings of high performance. You need context. An interactive IDE is yet another thing that needs to be high performance in yet another way.
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.
1: https://llvm.org/docs/XRay.html ... is there even a Rust analog to this?
I am by no means an expert, but I've recently improved performance for some code and used tracy. They have rust bindings as well. It's pretty cool and it seems to be low overhead. Wonder if I can couple it with something like xray? Tracy is more the tracing library + tracing interpretations/aquisition tool.
Edit: apparently rust already supports xray natively on the nightly.
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.
Before you say Axum is "holding it wrong" the project lives under the tokio-rs GitHub org.