autometrics-rs VS metrics

I’ve been working on an open source project built on OpenTelemetry and Prometheus client libraries (https://autometrics.dev). The DX of the OTel libraries is pretty painful in all the languages we’ve used. Granted, we’re using them for metrics while traces are more common, but still.

I think the fundamental issue for the DX is that it’s trying to do everything everyone might want out of all of the observability signals. That’s a useful and laudable goal but it means that everything is configurable and relatively difficult to use.

If you go with Prometheus, we’re building Autometrics to make producing and querying metrics easier for developers. It makes it trivial to instrument functions to track the request rate, error rate, and latency and then writes PromQL for you. The Autometrics libraries are thin layers on top of existing Prometheus and OpenTelemetry libraries.

Autometrics is an open source observability framework that makes it trivial to add useful metrics to your code and writes Prometheus queries for you to help you understand the data. This feature shows the power that comes from pairing code instrumentation with automatically writing queries (and the queries it writes are a whole lot more complicated than what you'd want to write by hand!).

How do people tend to get metrics off of embedded devices?

I’m working on https://github.com/autometrics-dev/autometrics-rs and people asked whether it could be used in embedded contexts but I wasn’t sure how you’d hook up the device to something like Prometheus.

I just opened these two issues for [supporting `prometheus-client`](https://github.com/fiberplane/autometrics-rs/issues/25) and another for [supporting exemplars](https://github.com/fiberplane/autometrics-rs/issues/26).

The most important character of Metriki, that different it from metrics, is it aggregates metrics on the client side so you can record data at a fixed interval while retaining its statistical information.