Category Archives: Continuous Integration, Delivery and Deployment

Building A Self-Sufficient Docker Cluster

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?
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The Ten Commandments Of Continuous Delivery

The Ten Commandments Of Continuous Delivery

Everyone wants to implement continuous delivery. After all, the benefits are too big to be ignored. Increase the speed of delivery, increase the quality, decrease the costs, free people to dedicate time on what brings value, and so on and so forth. Those improvements are like music to any decision maker. Especially if that person has a business background. If a tech geek can articulate the benefits continuous delivery brings to the table, when he asks a business representative for a budget, the response is almost always “Yes! Do it.”

A continuous delivery project will start. Tests will be written. Builds will be scripted. Deployments will be automated. Everything will be tied into an automated pipeline and triggered on every commit. Everyone will enter a state of nirvana as soon as all that is done. There will be a huge inauguration party with a vice president having the honor to be the first one to press the button that will deploy the first release to production. Isn’t that a glorious plan everyone should be proud of?
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The DevOps Toolkit Series Are Born

The DevOps 2.1 ToolkitAt the beginning of 2016, I published The DevOps 2.0 Toolkit. It took me a long time to finish it. Much longer than I imagined.

I started by writing blog posts in TechnologyConversations.com. They become popular and I received a lot of feedback. Through them, I clarified the idea behind the book. The goal was to provide a guide for those who want to implement DevOps practices and tools. At the same time, I did not want to write a material usable to any situation. I wanted to concentrate only on people that truly want to implement the latest and greatest practices. I hoped to make it go beyond the “traditional” DevOps. I wished to show that the DevOps movement matured and evolved over the years and that we needed a new name. A reset from the way DevOps is implemented in some organizations. Hence the name, The DevOps 2.0 Toolkit.
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Docker Flow – Walkthrough

Docker Flow is a project aimed towards creating an easy to use continuous deployment flow. It depends on Docker Engine, Docker Compose, Consul, and Registrator. Each of those tools is proven to bring value and are recommended for any Docker deployment.

The goal of the project is to add features and processes that are currently missing inside the Docker ecosystem. The project, at the moment, solves the problems of blue-green deployments, relative scaling, and proxy service discovery and reconfiguration. Many additional features will be added soon.

The current list of features is as follows.

Blue-Green Deployment

Traditionally, we deploy a new release by replacing the current one. The old release is stopped, and the new one is brought up in its place. The problem with this approach is the downtime occurring from the moment the old release is stopped until the new one is fully operational. No matter how quickly you try to do this process, there will be some downtime. That might be only a millisecond, or it can last for minutes or, in extreme situations, even hours. Having monolithic applications introduces additional problems like, for example, the need to wait a considerable amount of time until the application is initialized. People tried to solve this issue in various ways, and most of them used some variation of the blue-green deployment process. The idea behind it is simple. At any time, one of the releases should be running meaning that, during the deployment process, we must deploy a new release in parallel with the old one. The new and the old releases are called blue and green.
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