The DevOps Toolkit Series (volumes 4, 5, and 6): Kubernetes
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The DevOps Toolkit Series (volumes 4, 5, and 6): Kubernetes

The DevOps 2.3 Toolkit: Kubernetes
The DevOps 2.4 Toolkit: Continuous Deployment To Kubernetes
The DevOps 2.5 Toolkit: Monitoring, Logging, and Auto-Scaling Kubernetes

About the Bundle

From Kubernetes fundamentals through continuous deeployment and until advanced monitoring, alerting and scaling

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About the Books

The DevOps 2.3 Toolkit: Kubernetes

The DevOps 2.3 Toolkit: Kubernetes

Deploying and managing highly-available and fault-tolerant applications at scale
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The goal of this book is not to convince you to adopt Kubernetes but to provide a detailed overview of its features. I want you to become confident in your Kubernetes knowledge and only then choose whether to embrace it. That is, unless you already made up your mind and stumbled upon this book in search of Kubernetes guidance.

The plan is to cover all aspect behind Kubernetes, from basic to advanced features. We'll go not only through the tools behind the official project but also third-party add-ons. I hope that, by the time you finish reading this book, you will be able to call yourself "Kubernetes ninja". I cannot say that you will know everything there is to know about the Kubernetes ecosystem. That would be impossible to accomplish since its growing faster than any single person could follow. What I can say is that you will be very confident in running a Kubernetes cluster of any scale in production.

Like all my other books, this one is very hands-on. There will be just enough theory for you to understand the principles behind each topic. The book is packed with examples, so I need to give you a heads up. Do not buy this book if you're planning to read it on a bus or in bed before going to sleep. You will need to be in front of your computer. A terminal will be your best friend. `kubectl` will be your lover.

The book assumes that you feel comfortable with containers, especially Docker. We won't go into details how to build an image, what is container registry, and how to write Dockerfile. I hope you already know all that. If that's not the case, you might want to postpone reading this and learn at least basic container operations. This book is about things that happen after you built your images and stored them in a registry.

This book is about running containers at scale and not panicking when problems arise. It is about the present and the future of software deployment and monitoring. It's about embracing the challenges and staying ahead of the curve.

The DevOps 2.4 Toolkit: Continuous Deployment To Kubernetes

The DevOps 2.4 Toolkit: Continuous Deployment To Kubernetes

Continuously deploying applications with Jenkins to a Kubernetes cluster
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Just like the other books I wrote, this one does not have a fixed scope. I did not start with an index. I didn't write a summary of each chapter in an attempt to define the scope. I do not do such things. There is only a high-level goal to explore continuous delivery and deployment inside Kubernetes clusters. What I did do, though, was to set a few guidelines.

The first guideline is that all the examples will be tested on all major Kubernetes platforms. Well, that might be a bit far-fetched. I'm aware that any sentence that mentions "all" together with "Kubernetes" is bound to be incorrect. New platforms are popping out like mushrooms after rain. Still, what I can certainly do is to choose a few of the most commonly used ones.

Minikube and Docker for Mac or Windows should undoubtedly be there for those who prefer to "play" with Docker locally.

AWS is the biggest hosting provider so Kubernetes Operations (kops) must be included as well.

Since it would be silly to cover only un-managed cloud, I had to include managed Kubernetes clusters as well. Google Kubernetes Engine (GKE) is the obvious choice. It is the most stable and features rich managed Kubernetes solution. Adding GKE to the mix means that Azure Container Service (AKS) and Amazon's Elastic Container Service (EKS) should be included as well so that we can have the "big trio" of the hosting vendors that offer managed Kubernetes. Unfortunately, even though AKS is available, it is, at this moment (June 2018), still too unstable and it's missing a lot of features. So, I'm forced to scale down from the trio to the GKE and EKS duo as representatives of managed Kubernetes we'll explore.

Finally, a possible on-prem solution should be included as well. Since OpenShift shines in that area, the choice was relatively easy.

All in all, I decided to test everything in minikube and Docker for Mac locally, AWS with kops as the representative of a cluster in the cloud, GKE for managed Kubernetes clusters, and OpenShift (with minishift) as a potential on-prem solution. That, in itself, already constitutes a real challenge that might prove to be more than I can chew. Still, making sure that all the examples work with all those platforms and solutions should provide some useful insights.

Some of you already chose the Kubernetes flavor you'll use. Others might still wonder whether to adopt one or the other. Even though the comparison of different Kubernetes platforms is not the primary scope of the book, I'll do my best to explain the differences as they come.

To summarize the guidelines, it explores continuous delivery and deployment in Kubernetes using Jenkins. All the examples are tested in minikube, Docker for Mac (or Windows), AWS with kops, GKE, OpenShift with minishift, and EKS.

The DevOps 2.5 Toolkit: Monitoring, Logging, and Auto-Scaling Kubernetes

The DevOps 2.5 Toolkit: Monitoring, Logging, and Auto-Scaling...

Making Resilient, Self-Adaptive, And Autonomous Kubernetes Clusters
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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.

The second phase is often automation. Once we become comfortable with how Kubernetes works and we are running production loads, we can move to automation. We often adopt some form of continuous delivery (CD) or continuous deployment (CDP). We create Pods with the tools we need, we build our software and container images, we run tests, and we deploy to production. When we're finished, most of our processes are automated, and we do not perform manual deployments to Kubernetes anymore. We can say that things are working and I'm not even touching my keyboard. I did my best to provide some insights into CD and CDP with Kubernetes in The DevOps 2.4 Toolkit: Continuous Deployment To Kubernetes.

The third phase is in many cases related to monitoring, alerting, logging, and scaling. The fact that we can run (almost) anything in Kubernetes and that it will do its best to make it fault tolerant and highly available, does not mean that our applications and clusters are bulletproof. We need to monitor the cluster, and we need alerts that will notify us of potential issues. When we do discover that there is a problem, we need to be able to query metrics and logs of the whole system. We can fix an issue only once we know what the root cause is. In highly dynamic distributed systems like Kubernetes, that is not as easy as it looks.

Further on, we need to learn how to scale (and de-scale) everything. The number of Pods of an application should change over time to accommodate fluctuations in traffic and demand. Nodes should scale as well to fulfill the needs of our applications.

Kubernetes already has the tools that provide metrics and visibility into logs. It allows us to create auto-scaling rules. Yet, we might discover that Kuberentes alone is not enough and that we might need to extend our system with additional processes and tools. This phase is the subject of this book. By the time you finish reading it, you'll be able to say that your clusters and applications are truly dynamic and resilient and that they require minimal manual involvement. We'll try to make our system self-adaptive.

I mentioned the fourth phase. That, dear reader, is everything else. The last phase is mostly about keeping up with all the other goodies Kubernetes provides. It's about following its roadmap and adapting our processes to get the benefits of each new release.

About the Author

Viktor Farcic
Viktor Farcic

Viktor Farcic is a Developer Advocate at CloudBees and a member of the Docker Captains group.

His big passions are Microservices, Continuous Deployment and Test-Driven Development (TDD).

He often speaks at community gatherings and conferences.

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