This is a tutorial for how to use the VS Code Remote-Containers extension to containerize your development environment. First, I will discuss my reasons for separating my programming environment and why virtual machines didn’t work. Then, I’ll show a simple example using a containerized Python development environment. Finally, I’ll give you my reasons why containerizing the development environment fits what I’m looking for in a solution.
In this post, we will set up continuous deployment using Azure’s Deployment Center. Continuous Deployment is used to shorten the release cycle and quickly get code pushed to its target environment. This is especially useful when code is completed in small increments. Automated testing should be used as part of this process to produce stable code. This blog will focus on the continuous deployment.
So you want to host a web application on Azure with minimal overhead, but how is this done? Azure makes it possible by running an App Service using Docker containers. Setting up an App Service is simple and can be accomplished with a few steps.
In this blog, I’ll explain the steps necessary to generate a Docker image in Azure. Then, we will deploy a web application based on an image we generate. We host the application with the following steps:
1. Create a Container Registry
2. Build a Docker image
3. Create a Web App
I was pushing a new Docker image tag for each application code commit, and the admins of the private registry were getting annoyed at how much space I was using.
Yes, I know there are strategies to clean up old tags but I first wanted to reduce the impact of the tags I was pushing. With the right layering strategy, I knew I could reduce the net registry size increase of consecutive tag pushes.
I wanted to only push what had actually changed in the application. In addition to reducing the impact on the registry, having smaller tag deltas could possibly speed up rolling deployments since nodes could potentially have less to download.
For the last few years, Docker containers have been all the rage in the DevOps world. After all, what’s not to like? They allow you to strip out 99% of stuff in your VM and just deploy your code.
Containers can save resources, speed deployment, scale well and offer more fault tolerance. But how do you manage them?
In my experience, the Docker Machine and Docker Swarm stack hasn’t lived up my to expectations. It has a limited API, no support for monitoring and logging, and much more manual scaling. AWS’s EC2 containers scale well, but you’ll be locked into Amazon.
In my opinion, the best current stack for Docker containers includes Kubernetes and OpenShift. In this blog I will give a brief introduction to Kubernetes + OpenShift with an eye for what they do well…