As I continue to mess around with various ways of installing and running Kubernetes in my home lab using Rancher I keep coming up with different ways to solve similar problems. Each time I set it up using different host OSs I learn a bit more which my primary goal. The latest iteration uses CentOS 8 and allows for iSCSI based persistent storage to work properly. I want to use CentOS 8 because it includes a newer kernel required for doing buildx based multi-arch builds. In this post, I’d like to go through the process of setting up CentOS 8 with Docker and what utilities to install to support NFS and iSCSI based persistent storage so that it works properly with Rancher.

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I keep doing more multi-architecture builds using buildx and continue to find good information out there to help refine the process. Here is a post I found I thought I’d share that discusses how to build multi-architecture using AWS Graviton2 based instances which are ARM based. https://www.smartling.com/resources/product/building-multi-architecture-docker-images-on-arm-64-bit-aws-graviton2/. I haven’t officially tried this yet but the same process should also work on a Pi4 with the 64bit PiOS installed.

Under some conditions, you may find that your Docker in Docker builds will hang our stall out, especially when you combine DIND based builds and Kubernetes. The fix for this isn’t always obvious because it doesn’t exactly announce itself. After a bit of searching, I came across a post that described the issue in great detail located at https://medium.com/@liejuntao001/fix-docker-in-docker-network-issue-in-kubernetes-cc18c229d9e5.

As described, the issue is actually due to the MTU the DIND service uses when it starts. By default, it uses 1500. Unfortunately, a lot of Kubernetes overlay networks will set a smaller MTU of around 1450. Since DIND is a service running on an overlay network it needs to use an MTU equal to or smaller than the overlay network in order to work properly. If your build process happens to download a file that is larger than the Maximum Transmission Unit then it will wait indefinitely for data that will never arrive. This is because DIND, and the app using it, thinks the MTU is 1500 when it is actually 1450.

Anyway, this isn’t about what MTU is or how it works, it’s about how to configure a Gitlab based job that is using the DIND service with a smaller MTU. Thankfully it’s easy to do.

In your .gitlab-ci.yml file where you enable the dind service add a command or parameter to pass to Gitlab, like this:

Build Image:
  image: docker
  services:
    - name: docker:dind
      command: ["--mtu 1000"]
  variables:
    DOCKER_DRIVER: overlay2
    DOCKER_TLS_CERTDIR: ""
    DOCKER_HOST: tcp://localhost:2375

This example shown will work if you are using a Kubernetes based Gitlab Runner. With this added, you should find that your build stalls go away and everything works as expected.

When Docker first came out it was a real mind bender of an experience for me. I simply couldn’t wrap my head around what a Docker image was, how it was different from a virtual machine and so on. “Why not just install the software from rpm?” I said.

I also struggled with how the app in the container was running inside of something and didn’t have access to anything. At the time I saw this just as a silly hurdle that made it more difficult than it should be to get something running rather than a core benefit of using containers.

Over time I got to know Docker and containers better. I gained an understanding of now images are created, how they could be given restricted resources, easily shared and so on. I started creating my own containers to further understand the process, got to know multi stage builds and so in.

Although I had gained a better understanding of the container itself I still couldn’t find a good use case for containers in my line of work. I was too used to creating VMs that ran a static set of services that rarely changed. Docker containers still seemed like another packaging format that has few additional advantages. It wasn’t until I started playing with container orchestration that things really started to click.

With container orchestration, and in particular Kubernetes, the power and convenience of containers becomes much harder to ignore. Orchestration was definitely the missing piece of the puzzle for me that sealed the deal. This is because orchestration solves a number common issues with running larger software infrastructure. One of the biggest issues that Kubernetes solves is how to swap out the running application with little fuss. By simply declaring that a running workload should update the Docker image in use Kubernetes will go through the process of starting the new container, waiting for it to be ready, adding it to the load balancer and then draining connections from the old container. While it’s true you can achieve all of that with a traditional setup it requires a lot more effort. This feature alone is what sold me on using Kubernetes at all and from there my current state of container acceptance.

With Kubernetes revealing the huge potential of containers I’ve since come back to exploring them for other uses outside of orchestration. Now, core features of containers that once bothered me are seen as advantages. I still see containers as a packaging format but one that works equally well on macOS and Windows as well as it does on Linux or in Kubernetes. As an “expert” I can provide a container to a user that has everything installed for some to tool. Previously this may have required me to write extensive documentation detailing the requirements, installation process and finally the configuration of whatever software it took to meet the user’s needs. A process that may end up failing or not work at all because the end user is using a different operating system or because of some other environment specific reason. With containers, if it works for me there is a much greater chance it will work for someone else as well.

Today I find myself building more and more containers for use in CI/CD pipelines. I see them as little utilities that I can chain together to create a larger solution. Similar to the Unix philosophy, I am creating containers that do one thing and do it well. These small containers are easy to maintain, easy to document and easy to use. And this, I believe, is one of the core strengths of containers. They encapsulate a solution into something that is easier to understand. Even though a container is technically more bloated because it contains not only the application itself but also all of its requirements, the end result is something that is ultimately easier to understand. Like writing code, you can write the most incredible for loop ever devised but if the next person can’t understand it is it still a good solution?

Throughout my career I’ve always enjoyed trying out new things to see how I can apply them to everyday problems or how they can be used to create great new opportunities. Docker was one the first things that I really struggled to understand and initially I thought “this is it, this is the tech my kids will understand that I won’t.” Today, however, I can see what a game changer containers are. When properly constructed containers are easier to understand, easier to share with others and easier to document. These are powerful reasons too use containers. There are new hurdles to overcome, like how to maintain them for security, but all things have tradeoffs and it’s up to us to decide which ones are worth it.

 

Although I’m more than comfortable using command line tools to manage things there are times where a GUI is just more convenient. Pruning old containers, images and volumes in Docker are all things that are easier much to manage under a new tool I saw via twitter the other day. Portainer promises to make the task of managing Docker a bit easier and they’ve made good progress on delivering on that promise.  Getting up and running with it is incredibly simple because, as you’d expect, it’s available as a Docker image.  Simply issue the following this slightly Mac specific command:

docker run -d -p 9000:9000 -v "/var/run/docker.sock:/var/run/docker.sock" -v portainer:/data --name portainer portainer/portainer

This will get Portainer up and running on your system. If you’re on a Linux system you can skip mapping docker.sock. The other mapping just gives a persistent store for the little bit of data Portainer generates.  For full documentation visit their documentation site.

Found myself with an odd situation when running Jenkins using Docker. The time displayed was correct but claimed it was UTC which lead to some inconsistent behavior. The best way to resolve this is to force the Docker container to use the correct timezone from the host system.

To do so, add the following to your run command:

-v /etc/timezone:/etc/timezone -v /etc/localtime:/etc/localtime