prom-client VS prometheus-net

However, for this tutorial, the Prom-client package will be used as it has user-friendly documentation, a large user base and strong community support. Here is a link to its documentation and npm page.

prom-client

This approach will work as long as you don’t need to run multiple instances of the application in cluster mode. For collecting metrics in cluster mode, prom-client provides a good example with metric aggregation. But what if you’re running the application through the process manager PM2, where you don’t have access to the running cluster?

You would need to build a custom endpoint if it's a custom application that Prometheus can scrape but there are many libraries for that like prom-client for NodeJS or prometheus-net for C#. The list of client libraries can be found here.

For node, try this: https://github.com/siimon/prom-client

The metric itself is process_start_time_seconds (from the prom-client NodeJS package), which returns the timestamp in seconds corresponding to the start time of the process.

Consumer lag will quickly show any functional or technical issue with your event stream. By using the code examples from this blogpost, you can avoid having to dive into the SDKs yourself. Of course, you can adopt the metric collection to send the metric to the logs or to another metrics system like prometheus, datadog, or open telemetry.

There is a node client library for backend dev.

You might want to look into prometheus https://github.com/prometheus-net/prometheus-net, https://prometheus.io/.

You would need to build a custom endpoint if it's a custom application that Prometheus can scrape but there are many libraries for that like prom-client for NodeJS or prometheus-net for C#. The list of client libraries can be found here.

You could probably combine a HangFire Job Filter with the Prometheus C# library and log the things you want. Looks pretty extensible.

prometheus-net

I have a websocket server that hoards memory during days, till the point that Kubernetes eventually kills it. We monitor it using prometheous-net.

A library that makes this really easy for us is prometheus-net - we can create a minimal API that exposes metrics quickly and demonstrate a few different ways of creating metric data

// example taken from https://github.com/prometheus-net/prometheus-net#quick-start private static readonly Counter ProcessedJobCount = Metrics .CreateCounter("myapp_jobs_processed_total", "Number of processed jobs."); ... ProcessJob(); ProcessedJobCount.Inc();