kubernetes

I realized I haven't posted about my DrupalCon Seattle 2019 session titled Everything I know about Kubernetes I learned from a cluster of Raspberry Pis, so I thought I'd remedy that. First, here's a video of the recorded session:

.embed-container { position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 100%; } .embed-container iframe, .embed-container object, .embed-container embed { position: absolute; top: 0; left: 0; width: 100%; height: 100%; }

The original Pi Dramble 6-node cluster, running the LAMP stack.

Note: Please note that the Tower Operator this post references is currently in early alpha status, and has no official support from Red Hat. If you are planning on using Tower for production and have a Red Hat Ansible Automation subscription, you should use one of the official Tower installation methods. Someday the operator may become a supported install method, but it is not right now.

I have been building a variety of Kubernetes Operators using the Operator SDK. Operators make managing applications in Kubernetes (and OpenShift/OCP) clusters very easy, because you can capture the entire application lifecycle in the Operator's logic.

It wouldn't surprise me if you've never heard of Mcrouter. Described by Facebook as "a memcached protocol router for scaling memcached deployments", it's not the kind of software that everyone needs.

There are many scenarios where a key-value cache is necessary, and in probably 90% of them, running a single Redis or Memcached instance would adequately serve the application's needs. There are more exotic use cases, though, where you need better horizontal scaling and consistency.

I've been working a bit with Red Hat lately, and one of the products that has intrigued me is their OpenShift Kubernetes platform; it's kind of like Kubernetes, but made to be more palatable and UI-driven... at least that's my initial take after taking it for a spin both using Minishift (which works with OpenShift 3.x), and CRC (which works with OpenShift 4.x).

Because it took me a bit of time to figure out a few details in testing things with OpenShift 4.1 and CRC, I thought I'd write up a blog post detailing my learning process. It might help someone else who wants to get things going locally!

CRC System Requirements

First things first, you need a decent workstation to run OpenShift 4. The minimum requirements are 4 vCPUs, 8 GB RAM, and 35 GB disk space. And even with that, I constantly saw HyperKit (the VM backend CRC uses) consuming 100-200% CPU and 12+ GB of RAM (sheesh!).

Kubernetes is taking over the world of infrastructure management, at least for larger-scale operations, and best practices have started to solidify. One of those best practices is the cultivation of Custom Resource Definitions (CRDs) to describe your applications in a Kubernetes-native way, and Operators to manage your the Custom Resources running on your Kubernetes clusters.

In the Drupal community, Kubernetes uptake has been somewhat slow, but is on the rise. Just like with Docker adoption for local development, the tooling and documentation has been slowly percolating. For example, Tess Flynn from TEN7 has been boldly going where no one has gone before (oops, wrong scifi series!) using the Force to promote Drupal usage in a Kubernetes environment.

Since 2014, I've been working for Acquia, doing some fun work with a great team in Professional Services. I started out managing some huge Drupal site builds for Acquia clients, and ended up devoting all my time for the past couple years to some major infrastructure projects, diving deeper into operations work, Ansible, AWS, Docker, and Kubernetes in production.

In that same time period, I began work on my second book, Ansible for Kubernetes, but have not had the dedicated time to get too deep into writing—especially now that I have three young kids. When I started writing Ansible for DevOps, I had one newborn!

For production applications running in containerized infrastructure (e.g. Kubernetes, ECS, Docker Swarm, etc.)—and even for more traditional infrastructure with multiple application servers (for horizontal scalability), it is important to have centralized, persistent logging of some sort or another.

Some services like the ELK/EFK stack, SumoLogic, and Splunk offer a robust feature set for full text searching, filtering, and 'log intelligence'. On the other end of the spectrum, you can use a simple aggregator like rsyslogd or CloudWatch Logs without a fancy system on top if you just need basic central log storage.

But when I'm debugging something in a Kubernetes cluster—especially something like an internal service which I may not want to have logging everything to a central logging system (for cost or performance reasons)—it's often helpful to see all the logs from all pods in a Deployment or Replication Controller at the same time.

You can always stream logs from a single Pod with the command:

Update: Since posting this, there have been some interesting new developments in this area, for example:

• There is now a Drupal/Kubernetes SIG which meets every other Wednesday.
• There are Kubernetes Drupal Operators which can manage Drupal instances in Kubernetes; I maintain the geerlingguy/drupal-operator but there are a couple others out there in development.

Since 2014, I've been working on various projects which containerized Drupal in a production environment. There have always been a few growing pains—there will for some time, as there are so few places actually using Docker or containers in a production environment (at least in a 'cloud native' way, without tons of volume mounts), though this is changing. It was slow at first, but it's becoming much more rapid.

I am working on integrating a few Laravel PHP applications into a new Kubernetes architecture, and every now and then we hit a little snag. For example, the app developers noticed that when their cron job ran (php artisan schedule:run), the MySQL container in the cluster would drop an error message like:

In Kubernetes, I had the Laravel app CronJob set up like so:

If you're running Kubernetes clusters at scale, it pays to have good monitoring in place. Typical tools I use in production like Prometheus and Alertmanager are extremely useful in monitoring critical metrics, like "is my cluster almost out of CPU or Memory?"

But I also have a number of smaller clusters—some of them like my Raspberry Pi Dramble have very little in the way of resources available for hosting monitoring internally. But I still want to be able to say, at any given moment, "how much CPU or RAM is available inside the cluster? Can I fit more Pods in the cluster?"

So without further ado, I'm now using the following script, which is slightly adapted from a script found in the Kubernetes issue Need simple kubectl command to see cluster resource usage:

Usage is pretty easy, just make sure you have your kubeconfig configured so kubectl commands are working on the cluster, then run: