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Deployment strategies affect everyone, no matter whether we are focused only on a single aspect of the application lifecycle or we are in full control. The way we deploy affects the architecture, testing, monitoring, and many other aspects. And not only that, but we can say that architecture, testing, and monitoring affect the way we deploy. All those things are closely related and affect each other.
We’ll discuss different deployment strategies and answer a couple of questions. Is your application stateful or stateless? Does its architecture permit scaling? How do you roll back? How do you scale up and down? Do you need your application to run always? Should you use Kubernetes Deployments instead of, let’s say, StatefulSets? Answers to those questions will not serve much unless we are familiar with some of the most commonly used deployment strategies. Not only that knowledge will help us choose which one to pick, but they might even influence the architecture of our applications.
Your cluster is bound to run quite a few third-party applications. They need to be installed and managed somehow. This article offers one possible way to install and maintain third-party applications using Jenkins X Apps. We’ll use Istio as an example of such an application and try to figure out how to convert its official Helm charts into Jenkins X Apps and, during that process, explore some of the benefits they provide.
When Jenkins appeared, its pipelines were called FreeStyle jobs. There was no way to describe them in code, and they were not kept in version control. We were creating and maintaining those jobs through Jenkins UI by filling input fields, marking checkboxes, and selecting values from drop-down lists. The results were impossible-to-read XML files stored in the Jenkins home directory. Nevertheless, that approach was so great (compared to what existed at the time) that Jenkins become widely adopted overnight. But, that was many years ago and what was great over a decade ago is not necessarily as good today. As a matter of fact, FreeStyle jobs are the antithesis of the types of jobs we should be writing today. Tools that create code through drag-and-drop methods are extinct. Not having code in version control is a cardinal sin. Not being able to use our favorite IDE or code editor is unacceptable. Hence, the Jenkins community created Jenkins pipelines.
Versioning is one of those things that can be done in many different ways. Almost every team I worked with came up with their own versioning schema. When starting a new project, quite often we would spend time debating how we are going to version our releases. And yet, coming up with our own versioning schema is usually a waste of time. The goals of versioning are simple. We need a unique identifier of a release as well as an indication of whether a change breaks backward compatibility. Given that others already agreed on the format that fulfills those objectives, the best we can do, just as with many other things related to software development, is to use the convention. Otherwise, we are probably wasting our time reinventing the wheel without understanding that a few things are likely going to go wrong down the line.
Unlike GKE, EKS does not come with Cluster Autoscaler. We’ll have to configure it ourselves. We’ll need to add a few tags to the Autoscaling Group dedicated to worker nodes, to put additional permissions to the Role we’re using, and to install Cluster Autoscaler.
Continuous Deployment is about making a decision to do things right. It is a clear goal and a proof that the changes across all levels were successful. The primary obstacle is thinking that we can get there without drastic changes in the application’s architecture, processes, and culture. Tools are the least of our problems.
I spend a significant chunk of my time helping companies improve their systems. The most challenging part of my job is going back home after an engagement knowing that the next time I visit the same company, I will discover that there was no significant improvement. I cannot say that is not partly my fault. It certainly is. I might not be very good at what I do. Or maybe I am not good at conveying the right message. Maybe my advice was wrong. There can be many reasons for those failures, and I do admit that they are probably mostly my fault. Still, I cannot shake the feeling that my failures are caused by something else. I think that the root cause is in false expectations.
Every once in a while I get asked a variation of the same question. “Viktor, how do we create a learning culture in our company?” In most cases, my answer receives a bad reception. The short version of it is to “stop doing what you’re doing, and get out of the way.”
Engineers love learning. Most of us became engineers because we like to “play” with tech. And yet, people in many organizations complain that they are not allowed to innovate and that they don’t have time to learn. If my statement that engineers are playful kind of people, lack of innovation cannot be attributed to them, so we need to look for causes somewhere else.
Any system that intends to be fully automated and self-sufficient must be capable of self-healing and self-adaptation. As a minimum, it needs to be able to monitor itself and perform certain actions both on service and infrastructure levels.
Two axes can represent the set of actions a system might execute. One group of actions be represented through the differences between infrastructure and services. The other axis can be explained by the type of activities, with self-healing on one end, and self-adaptation on the other.
Let’s face it. The systems we are creating are not perfect. Sooner or later, one of our applications will fail, one of our services will not be able to handle the increased load, one of our commits will introduce a fatal bug, a piece of hardware will break, or something entirely unexpected will happen.
How do we fight the unexpected? Most of us are trying to develop a bullet proof system. We are attempting to create what no one did before. We strive for the ultimate perfection, hoping that the result will be a system that does not have any bugs, is running on hardware that never fails, and can handle any load. Here’s a tip. There is no such thing as perfection. No one is perfect, and nothing is without fault. That does not mean that we should not strive for perfection. We should, when time and resources are provided. However, we should also embrace the inevitable, and design our systems not to be perfect, but able to recuperate from failures, and able to predict likely future. We should hope for the best but prepare for the worst.