tracing VS flamegraph

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)

You can install cargo-flamegraph with cargo install flamegraph. There are some underlying requirements to be able to use cargo-flamegraph; you will want to take a look at the repo here to make sure you have the right dependencies.

I tried to diagnose the issue with flamegraph, but unfortunately the flamegraph didn't show anything beyond the next call for some reason

having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go)

I'm currently implementing a solution for the first part of the day 16. It work but it is really slow... I'd like to : - understand why - having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go) - eventually have some clue/solution/idea - have general feedback on what in my "coding style" is not appropriate for rust (I come from java/kotlin/ts even if I've already coded a bit in c/c++) : for example I love iterator & sequence but i feel they are not really suited to overuse in rust (mostly because of async & result).

Use a profiler. Flamegraph is a good way to visualise profiler output. This lets you identify which functions are taking up a large amount of time - and hence helps you identify where to focus your optimisation efforts.

You tried trying to see what takes the most time under load via flames? https://github.com/flamegraph-rs/flamegraph

Well, in cases like that the answer is straight forward, use a profiler like https://github.com/flamegraph-rs/flamegraph

I don't see any obvious problems with your algorithm. I've had luck using cargo-flamegraph to identify the slow parts of my code. That's going to show you which parts to focus on improving the performance of!

From the readme of cargo flamegraph: