Knowing that HorizontalPodAutoscaler (HPA) manages auto-scaling of our applications, the question might arise regarding replicas. Should we define them in our Deployments and StatefulSets, or should we rely solely on HPA to manage them? Instead of answering that question directly, we'll explore different combinations and, based on results, define the strategy.
First, let's see how many Pods we have in our cluster right now.
You might not be able to use the same commands since they assume that go-demo-5 application is already running, that the cluster has HPA enabled, that you cloned the code, and a few other things. I presented the outputs so that you can follow the logic without running the same commands.
The output is as follows.
We can see that there are two replicas of the api Deployment, and three replicas of the db StatefulSets. Continue reading →
Kubernetes is probably the biggest project we know. It is vast, and yet many think that after a few weeks or months of reading and practice they know all there is to know about it. It's much bigger than that, and it is growing faster than most of us can follow. How far did you get in Kubernetes adoption?
From my experience, there are four main phases in Kubernetes adoption.
In the first phase, we create a cluster and learn intricacies of Kube API and different types of resources (e.g., Pods, Ingress, Deployments, StatefulSets, and so on). Once we are comfortable with the way Kubernetes works, we start deploying and managing our applications. By the end of this phase, we can shout "look at me, I have things running in my production Kubernetes cluster, and nothing blew up!" I explained most of this phase in The DevOps 2.3 Toolkit: Kubernetes. Continue reading →
Let's paint a high-level picture of the continuous delivery pipeline. To be more precise, we'll draw a diagram instead of painting anything. But, before we dive into a continuous delivery diagram, we'll refresh our memory with the one we used before for describing continuous deployment.
The continuous deployment pipeline contains all the steps from pushing a commit to deploying and testing a release in production.
Continuous delivery removes one of the stages from the continuous deployment pipeline. We do NOT want to deploy a new release automatically. Instead, we want humans to decide whether a release should be upgraded in production. If it should, we need to decide when will that happen. Those (human) decisions are, in our case, happening as Git operations. We'll comment on them soon. For now, the important note is that the deploy stage is now removed from pipelines residing in application repositories. Continue reading →
The difference between continuous integration, delivery, and deployment is not in processes, but in the level of confidence we have in them.
The continuous deployment process is relatively easy to explain, even though implementation might get tricky. We'll split our requirements into two groups. We'll start with a discussion about the overall goals that should be applied to the whole process. To be more precise, we'll talk about what I consider non-negotiable requirements. Continue reading →
Soon after I started working on The DevOps 2.3 Toolkit: Kubernetes, I realized that a single book could only scratch the surface. Kubernetes is vast, and no single book can envelop even all the core components. If we add community projects, the scope becomes even more extensive. Then we need to include hosting vendors and different ways to set up and manage Kubernetes. That would inevitably lead us to third-party solutions like OpenShift, Rancher, and DockerEE, to name a few. It doesn't end there. We'd need to explore other types of community and third-party additions like those related to networking and storage. And don't forget the processes like, for example, continuous delivery and deployment. All those things could not be explored in a single book so The DevOps 2.3 Toolkit: Kubernetes ended up being an introduction to Kubernetes. It can serve as the base for exploring everything else.
The moment I published the last chapter of The DevOps 2.3 Toolkit: Kubernetes, I started working on the next material. A lot of ideas and tryouts came out of it. It took me a while until the subject and the form of the forthcoming book materialized. After a lot of consultation with the readers of the previous book, the decision was made to explore continuous delivery and deployment processes in a Kubernetes cluster. The high-level scope of the book you are reading right now was born. Continue reading →
Picture me as a young teenager. After school, we'd go a courtyard and play soccer. That was an exciting sight. A random number of us to be running around the yard without any orchestration. There was no offense and no defense. We'd just run after a ball. Everyone moves forward towards the ball, someone kicks it to the left, and we move in that direction, only to start running back because someone kicked the ball again. The strategy was simple. Run towards the ball, kick it if you can, wherever you can, repeat. To this day I do not understand how did anyone manage to score. It was a complete randomness applied to a bunch of kids. There was no strategy, no plan, and no understanding that winning required coordination. Even a goalkeeper would be in random locations on the field. If he'd catch the ball around the goal he's guarding, he'd continue running with the ball in front of him. Most of the goals were scored by shooting at an empty goalpost. It was "every man for himself" type of ambition. Each one of us hoped to score and bring glory to his or her name. Fortunately, the main objective was to have fun so winning as a team did not matter that much. If we were a "real" team, we'd need a coach. We'd need someone to tell us what the strategy is, who should do what, and when to go into the offense or fall back to defend the goalpost. We'd need someone to orchestrate us. The field (a cluster) had a random number of people (services) with the common goal (to win). Since everyone could join the game at any time, the number of people (services) was continually changing. Someone would be injured and would have to be replaced or, when there was no replacement, the rest of us would have to take over his tasks (self-healing). Those football games can be easily translated into clusters. Just as we needed someone to tell us what to do (a coach), clusters need something to orchestrate all the services and resources. Both need not only to make up-front decisions, but also to continuously watch the game/cluster, and adapt the strategy/scheduling depending on the internal and external influences. We needed a coach and clusters need a scheduler. They need a framework that will decide where a service should be deployed and make sure that it maintains the desired run-time specification. Continue reading →
The article that follows is an extract from the last chapter of The DevOps 2.2 Toolkit: Self-Sufficient Docker Clusters book. It provides a good summary into the processes and tools we explored in the quest to build a self-sufficient cluster that can (mostly) operate without humans.
We split the tasks that a self-sufficient system should perform into those related to services and those oriented towards infrastructure. Even though some of the tools are used in both groups, the division between the two allowed us to keep a clean separation between infrastructure and services running on top of it. Continue reading →
If you liked this article, you might be interested in The DevOps 2.2 Toolkit: Self-Sufficient Docker Clusters book. The book goes beyond Docker and schedulers and tries to explore ways for building self-adaptive and self-healing Docker clusters. If you are a Docker user and want to explore advanced techniques for creating clusters and managing services, this book might be just what you're looking for.
Please get a copy from Amazon, LeanPub, or look for it through your favorite book seller.
Give the book a try and let me know what you think.
A self-sufficient system is a system capable of healing and adaptation. Healing means that the cluster will always be in the designed state. As an example, if a replica of a service goes down, the system needs to bring it back up again. Adaptation, on the other hand, is about modifications of the desired state so that the system can deal with changed conditions. A simple example would be increased traffic. When it happens, services need to be scaled up. When healing and adaptation are automated, we get self-healing and self-adaptation. Together, they both a self-sufficient system that can operate without human intervention.
How does a self-sufficient system look? What are its principal parts? Who are the actors? Continue reading →