‘It’s not how you start it’s how you finish’, is a phrase you may hear a coach utter to his team but it is surprisingly applicable in the Tech industry as well. All of us were newbies at some point in time. We’ve all faced challenges while trying to learn something new. However, reaching the end of the road to proficiency requires you to push through those tough times and I am here to let you know that it is possible even when entering Kubernetes (k8s) as a Kubernewbie. My background prior this undertaking was Design and Development (DevSigner), and not Ops. However, by the end of this journey I made the transition and gained a new moniker, DevOpsSigner.
Initially, this blog post was going to serve as a comprehensive intro to Kubernetes but, after some thought (and a few rewrites), I realized that this would not be possible in a single post. It was better to approach this post from the angle of what this is not meant to be. This post is not a comprehensive introduction to Kubernetes, nor is it a tutorial or even a guide. It is more akin to a testimonial, a recap of our experience coming into Kubernetes and how we went from Kubernewbies to a fully deployed production application in a Kubernetes cluster in a few weeks.
With no prior experience with Kubernetes or Devops it made the process of learning and ramping up even more challenging. When entering a new domain, like Devops, there is a load of domain specific knowledge (DSK) you do not possess that others have acquired over time. That DSK is what you use to evaluate, explore, build solutions and use technologies in that domain. Without it you could go down a path of reinventing the wheel. Every domain has very complex problems that people are trying to solve, have solved or are improving on. Fortunately for us and you that solution maybe primed and applicable to the need / issue / requirement that you have. Kubernetes was that soltion for us.
Kubernetes is a solution to a complex problem in its domain and by using Kubernetes you cut a chunk of complexity in this initial DSK acquisition and can start your Kubernewbie and Devops journey, ‘standing on the shoulders of giants’. Kubernetes gifts you a system with a set of primitives, an API, and a set of hooks to build and focus on things unique to your needs. Joe Beda said it best in that ‘Kubernetes gives you a Platform to make a Platform’. We will talk about how we used this ‘Platform Builder’ (Kubernetes) if you will, later, but first let us define what Kubernetes is in general.
Kubernetes.io introduces Kubernetes as “an open-source system for automating deployment, scaling, and management of containerized applications.” Kubernetes builds upon 15 years of experience of running production workloads at Google which was influenced by Google’s internal systems Borg & Omega. So what does Google know about production workloads you ask? Well around 2015, Google was spinning up approximately 2 billion containers per week. Yes, you read that correctly, 2 billion per week. I don’t know about you, but I have not seen 2 billion of anything in even a year.
So, it is fair to say that Google has a tremendous amount of experience utilizing containers in production. Fortunately, some brave souls started to open-source that expertise. Harnessing that experience, then Google employees Joe Beda, Brendan Burns andCraig McLuckie took on the challenge. Brendan Burns started the initial prototypes which, combined with work from Joe Beda and Craig McLuckie, resulted in the first commit to the Kubernetes repo in 2014.
Kubernetes was created to bring the idea of dynamic, container-centric, managed, scheduled-cluster thinking outside of Google. With the exponential growth and usage of containers globally, Kubernetes strives to give everyone a production level solution fashioned from Google itself. There is no denying Google’s experience with containers and containerized applications. But what is a container and what does a containerized application mean in this context?
For now, let’s say that a containerized application is an application that is within a container. A container, by definition from Webster’s Dictionary, is “an object that can be used to hold or transport something.” In relation to technology, that “something” is everything that a piece of software needs to run in a particular environment, such as application code, utilities, configuration and dependencies. So a proposed Tech definition is: a container is an abstraction: an Operating-system-level-virtualization that allows you to run an application and its dependencies in resource-isolated processes.
Diving even deeper into this tech definition, an Operating-system-level virtualization, also known as containerization (containerized application), refers to an operating system feature in which the kernel allows the existence of multiple resource-isolated user-space instances. Those instances are the containers that and share one Host Operating system across them. Since the containers encapsulate everything an application needs to run this makes moving and running the software in different environments possible. So with all these definitions in hand, my final description of a container would be: a Container is a isolated-resource that is used to transport everything an application needs to run.
That was an intense explanation of what a container is, but container technology is a fundamental technology and a catalyst for the building of Kubernetes. Kelsey Hightower said, “People will soon learn that containers only solve the software packaging and distribution problem. Containers don't manage anything; they need to be managed”. Kubernetes presents a management solution for containers. Lets now assume you have some containers built and ready for use, you can have anywhere from one to one-thousand of them to deploy -- and you’ll need a way to deploy, manage and even scale the system using these containers.
That was the position we found ourselves in with a new project that we were hired to develop. We were hired to build a React application and a backend application. The backend application was developed using Ruby, which would be a Microservice / module living in an overall system of modules, powered by an API-Gateway called Okapi. We needed our own production and sandbox environments that were fully functional versions of the overall Microservice Architecture. Having this would allow us rapid development and iteration on our module, a place to demo all the new features we would be implementing, and a sandbox location for experimentation. Since our module was going to be part of a larger system made up of multiple modules/containers we needed a deployment and management solution. Enter Kubernetes.
We researched a few options for a container management solution prior to settling on Kubernetes. Many of which are capable of being a solution to container management but ultimately we went with Kubernetes because of a few things:
- Documentation. The Kubernetes documentation is extensive.
- Self Healing. An essential feature of Kubernetes is ‘self-healing’, the system will always try to keep your cluster of deployed pods ( which hold containers) running at the desired scale you define. Yes, you can ‘set it and forget it’ in Tech.
- Declarative. Kubernetes is a declarative system and uses files written in YAML or JSON to define how you want the various parts of the system to look. There are more advanced solutions, rather than hand rolling and editing multiple YAML files, but these are used at the basic level.
- Community. There were several members from the Kubernetes community who were extremely helpful and receptive to answering questions as we ramped up our experience.
Now that we’d settled on using Kubernetes, we needed to decide on where to host our Kubernetes clusters, and whether we wanted to go with a managed or unmanaged Kubernetes hosting solution. A managed solution has Provisioning, Security, Load Balancing, Upgrading, and Monitoring that is handled by them. An unmanaged solution will require you to do more of the leg work to setup all the previously mentioned points. However, not all managed Kubernetes hosting solutions are the same, so we researched a few. One that was pretty awesome was Platform9, which is a good turn key managed solution, but we ended up going with Google Kubernetes Engine (previously Google Container Engine).
Google Kubernetes Engine (GKE) is a managed solution that abstracts away the aforementioned Provisioning, Security, Load Balancing, Upgrading, and Monitoring of running a Kubernetes cluster, but not so much that you cannot configure (and even swap) an alternate technology for one of those parts. However, staying as close as you can in the GKE system and using all the features within GKE will make your life much easier. Now that we’ve decided on using Kubernetes for orchestration, and GKE as the managed solution for hosting the cluster, let’s Ship it. Well, not so fast.
Before we actually shipped to GKE, we flexed our Kubernetes muscles and experimented with Kubernetes using Minikube. The Minikube readme says that,
“Minikube is a tool that makes it easy to run Kubernetes locally. Minikube runs
a single-node Kubernetes cluster inside a VM on your laptop for users looking to try out Kubernetes or develop with it day-to-day,”.
We used Minikube to initially test standing up and deploying all the modules. Minikube allowed us to get familiar with certain parts of Kubernetes, known as Resource Types such as Pods, Services, Replica Sets, Deployments, Config Maps etc and go through some of the examples in the Kubernetes documentation and around the web.
During this k8s exploration period using MiniKube we became very familiar with using another tool called 'kubectl'. kubectl is a command-line interface (CLI) for executing commands against a running Kubernetes cluster in this case the running cluster was the single-node Minikube instance but could be any running k8s cluster. kubectl has a laundry list of commands but our initial use was to ‘apply’ our YAML files and as time progress we started to use more of the advanced commands of kubectl. One resource that I wish I had during this exploration phase was a book called ‘Kubernetes Up and Running’ which I read later but would suggest it to anyone that is new to k8s. Despite not having the book at the time this step was crucial to gaining a basic understanding of Kubernetes and standing up a cluster before shipping the full ancolata to GKE, which we did soon thereafter.
We popped some champagne and celebrated the win of successfully standing up a Kubernetes cluster with a fully functional API-Gateway and modules (including our module). Our clusters were running great, but after a few weeks and having to swap out and redeploy parts of it (and on occasion having to redeploy the entire cluster), we needed to find a solution to automate the process of tearing down and standing up our cluster. Again, we did some market research, but we couldn’t find anything that would fit our needs at the time; so we ended up building Okubi, as mentioned earlier using the ‘Platform to build a Platform’. Okubi gives us a command line interface for tearing down and standing up our cluster.
We use Okubi for things like:
Generating module YAML files from a template.
Changing configuration values foreach environment from a configuration file.
Setting priority level to modules.This will allow explicit ordering of when certain modules are deployed.
Order the deployment of the certain parts of the system to the cluster.
Run execution steps during the standup specific to this infrastructure.
Okubi is great, but it took hitting the pain points of doing all this manually before we found a solution that worked for us and built an abstraction. Definitely have to give a big shout out to Joe LaSala for all his hardwork in Okubi. We have some enhancements that we will like to introduce within Okubi but currently it is serving us well with cluster deployment. While having to redeploy modules in the cluster or redeploy the cluster we often found ourselves debugging containers.
To debug containers one method that we use was to tail the logs of the container. Which essentially watches the logs and prints them to the terminal. Though you can run a command in kubectl to tail the log of a container. We started to use Stern because of some added features. Stern simply describes itself as Multi pod container log tailing for Kubernetes. This was a feature that we used along with tailing multiple containers within a Kubernetes “Pod”. It maybe possible to do the same with kubectl but do not quote me. However, using Stern made this task fairly simple with easy to remember CLI commands.
Tailing logs is very useful but then we wondered if we could somehow interact with the code while it was running in the cluster. Then we found Telepresence. Telepresence is an open source tool that lets you run a single service locally, while connecting that service to a remote Kubernetes cluster. So, by using Telepresence we were able to change the reality in your running cluster and tests code locally as though our local code was running in the cluster. In addition, we were able to find usage for Telepresence during active feature development. Yes, this all sounds quite magical and it did to me as first so let me try to explain how we used it.
First, use case was when we saw some buggy behavior and wanted to debug that code. We would then switch to our sandbox cluster context utilizing another tool called Kubectx. Kubectx says it is a way to switch between clusters and namespaces in kubectl and that is exactly right. Since our sandbox mirrors our production cluster we would then Telepresence into the sandbox cluster and then set debuggers within our local in order to step through the code during execution. Once we found the offending code that was causing the bug we would then create a PR with the fix.
Second use-case as I mentioned earlier was the usage of Telepresence during active feature development. This is one usage that came in handy for me several times. Let’s say I was task with developing a feature in the module. That new feature would then be consumed by the React frontend that I aforementioned React application that we had underdevelopment as well. Now, call me crazy but even though I have all these awesome testing tools at my disposal and full test coverage around my code because we do not ship code without tests. I still want to test it live. Hooray for Telepresence.
I would follow the same steps of making sure I was in the correct sandbox context and then Telepresence in that cluster. In addition, I would then spin up the React application locally and make sure it was making it calls to the sandbox URL. This would give me a live React, a Telepresenced Ruby Module and a fully running Kubernetes cluster. I could manually test out what I was developing and perform a full end to end test before I pushed to out a PR. I did not use this all the time but on the occasions when I had to reach for this technique it was worth its weight in Gold.
Well, that is all that I have for this post. Honestly, I could keep writing but this is supposed to be a blogPost and not a blogBook. So, I will keep the additional topics for another blogPost. Overall, I would say that the journey from a Kubernewbie was very challenging and extremely rewarding. You have to push your limits and go into uncharted territory in order to grow. Have faith in your abilities, ask questions and don’t get discouraged when you can’t figure something out. There is still much to learn but that is nature of the Beast which we call technology. If you have any question you can always hit me on twitter at @elrickvm or send an email. Be blessed and happy coding!
Photo by chuttersnap on Unsplash.