prom-client VS statsd

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.

This is an external stats collecting system built by Etsy, they blogged about it and how it works and it's also open source, you can set it up in django through a third party app called django-statsd.