pprof VS tracing

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.

The video description is as follows:

In this stream, we peel back the crust on the tracing crate — https://github.com/tokio-rs/tracing/ — and explore its interface, structure, and mechanisms. We talk about spans, events, their attributes and fields, and how to think about them in async code. We also dig into what subscribers are, how they pick up events, and how you can construct your own subscribers through the layer abstraction. For more details about tracing, see https://docs.rs/tracing/latest/tracing/.

> What's been your biggest issues around ergonomics/amenities for OpenTelemetry?

I can't speak generally, but in the Rust ecosystem the various crates don't play well together. Here's one example: <https://github.com/tokio-rs/tracing/issues/2648> There are four crates involved (tracing-attributes, tracing-opentelemetry, opentelemetry, and opentelemetry-datadog) and none of them fit properly into any of the others.

The tracing (logging) mechanism in an asynchronous codebase (tracing).

Tracing is Tokio's alternative for async code.

At a technical level, in Rust, both [tracing]https://crates.io/crates/tracing) and log are entire ecosystems (though for the latter at least there's also third party logging frameworks), and there's at least a bridge from log to tracing.

I am using https://github.com/tokio-rs/tracing for logging purposes in my application. I would like to develop a feature wherein logs should be saved to a database table (via sea-orm). Something similar is this, but it does not solve my needs fully.

I've used the tracing infrastructure with tracing_flame to profile some hot paths in async code: https://github.com/tokio-rs/tracing/tree/master/tracing-flame

Oh nice! IIRC when I checked, it was the Unicode tables that smashed the code size. I recently hit the same issue with the tracing crate, where a crate feature (for env var filtering) pulled in regex and my binary was suddenly 1MB bigger.

I know about tools such as tracing, jaeger or tracy. While having a complete tracing could be a potential solution, these tools don't work with no_std.

tracing by itself only outputs log data, you need to consume them in a subscriber, the tracing-subscriber crate exists for this. (example)