chiamon VS client_python

I’m a bit of a monitoring nerd, and when I first got into chia I wrote the chia-exporter for Prometheus and put together chiamon, which just glues together a bunch of industry-standard monitoring tools (which I was already running anyways). Seeing people struggle with installing and configuring all that showed me that there’s a need for a simpler solution for less-technical people who only care about monitoring chia. More than I was willing or able to take on, so kudos. I probably don’t have a lot of time to help with your project either, but I’d be happy to help out where I can.

Hey, I also have something similar - but sadly not for plotting (yet).https://imgur.com/a/rPuqK3c.Used https://github.com/retzkek/chiamon and customized it a bit.

Thanks! I don't have the resources (or motivation) to test and package for all the different ecosystems that chia can run on. I myself am using a niche system - freebsd. If there was community interest in building it out maybe a github project could be in the future. Besides, there are already some great setups out there using grafana.

There are tools like ChiaMon that you could use; https://github.com/retzkek/chiamon

I use Grafana and Prometheus, with various exporters to collect metrics on everything. Add promtail and Loki for logs to really spice it up. See my example setup: https://github.com/retzkek/chiamon

The goal is to have some kind of dashboard (like chiamon) with all active/inactive plotters and be able to control them through the network. Ideally even upload the finished plots directly to the NAS through the network. Following the HiveOS example.

Here you go: https://stackoverflow.com/questions/75652326/celery-spawn-si...

Plus some adjacent discussion on GitHub: https://github.com/prometheus/client_python/issues/902

Hope that helps!

Prometheus, which is also a CNCF open source project, collects metrics data by scraping HTTP endpoints and then stores that data in a time series database that uses a multidimensional model. It’s a powerful tool for gathering metrics about your application and it also includes alerting functionality that you can use to notify your teams when issues come up. Prometheus includes a client library for Python.

We have a java one but the principle is the same. Install the Prometheus client ( https://github.com/prometheus/client_python) ,create the metrics you want, then push jmx settings to Prometheus.

The code above is copied from the official documentation of prometheus_client which simply creates a new metric named request_processing_seconds that measures the time spent on that particular request. We'll cover other types of metrics later in this post.

Just use the client? https://github.com/prometheus/client_python

I've experimented with the official Prometheus python client, i really really like the way they use decorators to instrument. I've tried to measure latency with multiple types of metrics (histogram, & summary), i see the value in both of them, but the one that between fits my objective is the histogram metric type. Great!

Now is a good time to expand your event loop by adding metrics collection of the event handler functions and also use that endpoint as a liveness probe. E.g. https://github.com/prometheus/client_python just add the event handled, success/error and the duration as a histogram (look for examples of tracking http requests served)

I've spent along time trying to figure out a bug that I'm facing while using Prometheus from its python client.

The official Prometheus Python client library makes this easy, no need to worry about the export file format.