pprof VS tokio

The pprof format is not tied to Go. From my understanding, it's used within Google across multiple languages. The format is defined in the pprof repository[0], and the visualization tool is source-language agnostic. I've seen libraries in numerous languages (e.g. Python, Java) to publish profiles in pprof format. This is an indicator the pprof format has become de-facto. Grafana Pyroscope[1] is a tool that's capable of parsing the pprof format, agnostic to the source programming language, and has instructions for Go, Java, Python, Ruby, node.js, Rust, and .NET.

My understanding is that you're searching for a combination of the profiles, metrics, and tracing. Caddy supports all 3.

[0] https://github.com/google/pprof/blob/main/doc/README.md

[1] https://grafana.com/docs/pyroscope/latest/

metrics and tracing need to be manually enabled (for now, perhaps)

Because we couldn't identify the issue using the results we got from Callgrind, we reached for another profiler, gperftools. It's a sampling profiler and therefor it has a smaller impact on the application's performance in exchange for less accurate call statistics. After filtering out the unimportant parts and visualizing the rest with pprof, it was evident that something strange was happening with the send function. It took only 71 milliseconds with the previous implementation and more than 900 milliseconds with the new implementation of our Bolt server. It was very suspicious, but based on Callgrind, its cost was almost the same as before. We were confused as the two results seemed to conflict with each other.

github.com - google/pprof

The application performances in a specific code path (e.g. gdb, pprof, …).

pprof is https://github.com/google/pprof, it's a very useful tool in golang , and really really really convenient

Go also has pretty good out of the box profiling (pprof[0]) and third-party runtime debugging (delv[1]) that can be used both remotely and local.

These tools also have decent editor integration and can be use hand in hand:

https://blog.jetbrains.com/go/2019/04/03/profiling-go-applic...

https://blog.jetbrains.com/go/2020/03/03/how-to-find-gorouti...

[0] https://github.com/google/pprof

[1] https://github.com/go-delve/delve

And what we do in Google Cloud is that we still use the pprof. But it's a kind of forked version of the pprof because the visualization part is totally different. So we give that tool as the Cloud Profiler. So that is the product name. And then, the difference between the pprof and a Cloud Profiler is that Cloud Profiler provides the agent library for each famous programming language such as Java, Python, Node.js, and Go. And then what you need to do is to just write 5 to 10 lines of code in a new application. That launches the profile agent in your application as a subsidiary thread of the main thread. And then, that thread periodically collects the profile data of the application and then sends that data back to Google Cloud and the Cloud Profiler.

gprftools (https://github.com/gperftools/gperftools) can be easily plugged in using LD_PRELOAD and signal, and has nice go implemented visualization tool https://github.com/google/pprof.

Being able to control nondeterminism is particularly useful for testing and debugging. This allows creating reproducible test environments, as well as discrete-event simulation for faster-than-real-time simulation of time delays. For example, Cardano uses a simulation environment for the IO monad that closely follows core Haskell packages; Sui has a simulator based on madsim that provides an API-compatible replacement for the Tokio runtime and intercepts various POSIX API calls in order to enforce determinism. Both allow running the same code in production as in the simulator for testing.

tokio / hyper / toml / serde / serde_json / json5 / console

tokio - An asynchronous runtime for Rust

3. Tokio

The AWS SDK makes use of the async capabilities in the Tokio library. So when you see async in front of a fn that function is capable of executing asynchronously.

Petar is also looking at implementing concurrency the way it is in Go to have a fully functional virtual machine as it is in the spec. This would likely attract more external contributors to developing the VM. One advantage of Rust is that, with the concurrency model, there is already an extensive library called Tokio which he can use. Petar stresses that this isn’t easy, but he believes it’s achievable, at least as a research topic around determinism and concurrency.

Another thing to point out is that async is a thing in Rust. I'm not going to begin to dive into this paradigm in this article, but know it's handled by the awesome Tokio framework.

So I started by using Tokio, a popular async runtime. The docs and samples helped me get a simple outbound TCP connection working. The Rust async book also had a lot of good explanations, both practical and digging into the details of what a runtime does.

Regarding the quote:

> The Original Sin of Rust async programming is making it multi-threaded by default. If premature optimization is the root of all evil, this is the mother of all premature optimizations, and it curses all your code with the unholy Send + 'static, or worse yet Send + Sync + 'static, which just kills all the joy of actually writing Rust.

Agree about the melodramatic tone. I also don't think removing the Send + Sync really makes that big a difference. It's the 'static that bothers me the most. I want scoped concurrency. Something like <https://github.com/tokio-rs/tokio/issues/2596>.

Another thing I really hate about Rust async right now is the poor instrumentation. I'm having a production problem at work right now in which some tasks just get stuck. I wish I could do the equivalent of `gdb; thread apply all bt`. Looking forward to <https://github.com/tokio-rs/tokio/issues/5638> landing at least. It exists right now but is experimental and in my experience sometimes panics. I'm actually writing a PR today to at least use the experimental version on SIGTERM to see what's going on, on the theory that if it crashes oh well, we're shutting down anyway.

Neither of these complaints would be addressed by taking away work stealing. In fact, I could keep doing down my list, and taking away work stealing wouldn't really help with much of anything.

The PHP <-> Rust bindings are provided by https://github.com/Nicelocal/ext-php-rs/ (our fork of https://github.com/davidcole1340/ext-php-rs with a bunch of UX improvements :).

php-tokio's integrates the https://revolt.run event loop with the https://tokio.rs event loop; async functionality is provided by the two event loops, in combination with PHP fibers through revolt's suspension API (I could've directly used the PHP Fiber API to provide coroutine suspension, but it was a tad easier with revolt's suspension API (https://revolt.run/fibers), since it also handles the base case of suspension in the main fiber).