pprof-rs VS pprof

On top of others' specific recommendations, don't forget to profile! Tools like perf on Linux and pprof within Rust will tell you which functions are taking the most time.

https://github.com/tikv/pprof-rs: Seems to work nicely per se, but I cant seem to find any useful information in the flamegraph for my setting. I see mostly functions in std::thread but cant find the time it costs to render stuff or to do the actual computations which should be the most time consuming things. Not sure whether this is necessarily something wrong with pprof-rs, maybe I'm just bad at finding stuff in the flamegraph svg or bevys ECS is making this hard.

Have you looked into using pprof?

The library doesn't actually do any profiling (The profiler for Rust is pprof-rs: https://github.com/tikv/pprof-rs) but it's goal is to manage data returned by profilers (abstracted behind a Backend) and send this data to a Pyroscope Server (or exported to flamegraph, though this is being implemented in the commandline application).

The libunwind part is actually not related to overhead, this is just a nuance of the way that pprof-rs unwinds stack traces.

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.