You might want to expose metrics from these scripts or reports to Prometheus in order to improve monitoring and alerting on failures, but many of these processes are not around long enough to run a webserver that Prometheus can pull from.
This is where the Pushgateway comes in. It allows you to push metrics to a centralised location where they can be aggregated and then scraped by Prometheus itself. But beware: there are a limited number of use cases for pushing metrics, and you should always prefer pull-based methods when possible.Continue Reading →
Grafana sports a feature called Annotations that allow you to label a timestamp on a dashboard with meaningful events – most commonly deployments, campaigns, or outages:
(In this case annotating the simulated deployment of a Fluent Bit container, which I’ve used to forward container logs out of the cluster.)
Annotations can be input manually, but the only recommendations I’ve seen to generate them automatically is to use something like Loki, or teaching your CI/CD system to interact with Grafana’s web API. However, if you’re running a simple Prometheus + Grafana stack (say, using the Prometheus Operator on Kubernetes), you might be reticent to add more complexity to your setup just to get deployment annotations.
Fortunately, there’s a simpler alternative for this narrow case: you can use the
process_start_time_seconds metric from Prometheus to get an approximate idea
of when apps or pods were started. I haven’t seen this approach recommended
elsewhere, which is the purpose of this post.
My openmetrics package is now available on
CRAN. The package makes it
possible to add predefined and custom “metrics” to any R web application and
expose them on a
/metrics endpoint, where they can be consumed by
Prometheus itself is a hugely popular, open-source monitoring and metrics aggregation tool that is widely used in the Kubernetes ecosystem, usually alongside Grafana for visualisation.
To illustrate, the following is a real Grafana dashboard built from the default metrics exposed by the package for Plumber APIs:
Adding these to an existing Plumber API is extremely simple:
library(openmetrics) srv <- plumber::plumb("plumber.R") srv <- register_plumber_metrics(srv) srv$run()
There is also built-in support for Shiny:
app <- shiny::shinyApp(...) app <- register_shiny_metrics(app) app
openmetrics is designed to be “batteries included” and offer good built-in metrics for existing applications, but it is also possible (and encouraged!) to add custom metrics tailored to your needs, and to expose them to Prometheus even if you are not using Plumber or Shiny.
More detailed usage information is available in the package’s
The Plumber package is a popular way to make R models or other code accessible to others with an HTTP API. It’s easy to get started using Plumber, but it’s not always clear what to do after you have a basic API up and running.
This post shares three simple endpoints I’ve used on dozens of Plumber APIs to
make them easier to debug and deploy in development and production environments: