RStudio has an enterprise offering called RStudio Connect that is designed to host R (and now Python) content. In my experience it’s a great platform for R users using the RStudio IDE, and works particularly well for frictionless deployment of internal Shiny apps or RMarkdown reports.

But one of the things we’ve struggled with in using Connect is making Shiny app logs useful. You can view logs for individual sessions in the browser (if you know what you’re looking for), but there’s no searching or aggregation of any kind. This is the existing interface:

This is doubly frustrating for users with experience with any other log management system – a crowded field, with many commercial and open-source solutions available. These solutions typically offer not only searching but complex querying, visualisation, and ad-hoc dashboard capabilities.¹ For example, check out the landing page for Elasticsearch/Kibana, which is the most popular open-source option. Common commercial choices include Splunk and Datadog, and all of the cloud vendors have highly-integrated platforms of their own.

Unfortunately, Connect won’t work natively with any of these tools. Log management is focused on the per-session GUI.

Motivated by our own internal desire to get Shiny logs into Splunk, I wrote a Connect-specific plugin for the open-source Fluent Bit project. Fluent Bit itself is a fast, lightweight log forwarding and aggregation program with a vibrant plugin community.

Everyone gets a Kubernetes war story, right? Well, here’s mine.

At work we have a huge, business-critical C# service that I helped to port to Linux (from the Windows-only .NET framework) to run under Kubernetes. This service emits a large number of logs.

Like, a lot of logs. Many gigabytes of logs per day, in sequentially-numbered text files of 50MB each.

For various reasons the developers of this application preferred to be able to look at these logs as actual files for post-mortem analysis rather than through our existing centralised logging tools. Under Windows they would remote into a server and inspect a mounted drive with the logs.

As part of the porting effort they asked me to emulate this workflow.

The openmetrics R package now supports pushing metrics to a Prometheus Pushgateway instance, which is useful for short-lived batch scripts or RMarkdown reports.

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.

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.

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 README.