In my last blog post, I hope I convinced you why you should be creating your own Kubernetes cluster for personal usage. Now, we can tackle the fun part of creating the cluster. What is a Kubernetes cluster? via GIPHY We can start by answering the most important question: what is a Kubernetes cluster? A Kubernetes cluster is a collection of physical resources on which we run the Kubernetes container management software.
From my last blog post, you know Kubernetes manages the blog you are reading right now. But I violated pretty much all the rules of good blogging by only briefly discussing why I wanted to start running my own production Kubernetes cluster in the first place, and how exactly I made that happen. I think I was just excited it was working and I wanted to share… via GIPHY Now, I’m writing to remedy my excited self’s mistakes.
I’ve avidly followed the Kubernetes project since it was the basis of my undergraduate thesis in 2015. But despite all my reading and minikube experimentation, I felt I was missing out the important lessons you can only learn from using a technology to run real applications in production. I acutely felt this pain when contributing code to the Kubernetes ecosystem, as I was able to fix bugs, but didn’t have knowledge and empathy around the production user’s experience.
After a Saturday of disk partitioning, a trip to CVS for an emergency thumb drive, and much Googling, I’m writing this blog post from a MacBook Air running Ubuntu 17.10. I won’t write an entire tutorial on the installation, as some great ones already exist (in particular, I benefitted greatly from this tutorial from CalTech and this tutorial from Christopher Berner). Instead, I’ll share why I installed Ubuntu 17.10 on my MacBook Air, some tips I found helpful during the installation, and the remaining shortcomings I hope to address in the coming weeks.
For the past couple of months, I’ve been working on Sheepdoge, a tool for managing your personal Unix machines with Ansible. It’s like boxen, but for Ansible. One new sheepdoge feature I’m particularly excited about is the use of concurrent.futures during sheepdoge install. concurrent.futures provides a high-level API for executing code asynchronously, making adding thread/process based concurrency trivial. It is a standard library python module as of 3.2, and is available on python 2.
Over the course of my final year at Williams College, I spent a lot of time working on a distributed systems thesis with Professor Jeannie Albrecht. My thesis is entitled “Predictive Pod Auto-scaling in the Kubernetes Container Cluster Manager”, and it is entirely open-source. The written portion, presentation slides, and evaluation code can be found on my Github. Additionally, our contributions to Google’s open-source cluster container manager Kubernetes can be found on my fork.