Unconstant Conjunction latest posts

Annotating Deployments in Grafana Using the Process Start Time Metric

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:

Process start time annotations on a Grafana panel

(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.

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Introducing openmetrics: A Opinionated Prometheus Client for R

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:

Grafana Dashboard

Adding these to an existing Plumber API is extremely simple:


srv <- plumber::plumb("plumber.R")
srv <- register_plumber_metrics(srv)

There is also built-in support for Shiny:

app <- shiny::shinyApp(...)
app <- register_shiny_metrics(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.

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Three Useful Endpoints for Any Plumber API

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: /_ping, /_version, and /_sessioninfo.

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A Bayesian Estimate of BackBlaze's Hard Drive Failure Rates

    13 February 2020 // tagged
Bayesian Hard Drive Failure Rates

Each quarter the backup service BackBlaze publishes data on the failure rate of its hundreds of thousands of hard drives, most recently on February 11th. Since the failure rate of different models can vary widely, these posts sometimes make a splash in the tech community. They’re also notable as the only large public dataset on drive failures:

BackBlaze 2019 Annualized Hard Drive Failure Rates

One of the things that strikes me about the presentation above is that BackBlaze uses simple averages to compute the “Annualized Failure Rate” (AFR), despite the fact that the actual count data vary by orders of magnitude, down to a single digit. This might lead us to question the accuracy for smaller samples; in fact, the authors are sensitive to this possibility and suppress data from drives with less than 5,000 days of operation in Q4 2019 (although they are detailed in the text of the article and available in their public datasets).

This looks like a perfect use case for a Bayesian approach: we want to combine a prior expectation of the failure rate (which might be close to the historical average across all drives) with observed failure events to produce a more accurate estimate for each model.

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Browsing Twitch.tv From Emacs

A video of my presentation from EmacsConf 2019 is now available. You can check out the recording below or see the slides here.

Browser-based applications can sometimes punish even new machines. In 2015, due to limited hardware, I was no longer able to use the popular video streaming site Twitch.tv to follow eSports. I investigated some alternatives at the time, but they lacked discovery and curation features, and so I decided to write a full-fledged Twitch client in my favourite text editor, Emacs. Years later, I still use this little bit of Emacs Lisp almost every day.

The talk discusses how I was able to use the richness of the built-in Emacs features and some community packages to build this client, as well as the various bumps along the way.

The code is available on GitHub.

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