If you’ve read my blog before, you’ll probably know that I am huge fan of Docker and containers. When was the last time you installed software onto bare metal? Other than your laptop, chances are you haven’t for a long time. Virtualization has transformed how we think about resources in the datacenter, greatly increasing the density and utilization of resources. The next evolution in density is containers - just what VMs are to physical servers, containers are to VMs. Soon, almost no-one will work against VMs anymore - we’ll all be in containers. At least, that’s the potential.

In Part 1 I looked at how to develop multi-container apps using Kubernetes (k8s) - and more specifically, minikube, which is a full k8s environment that runs a single node on a VM on your laptop. In that post I walk through cloning this repo (be sure to look at the docker branch) which contains two containers: a DotNet Core API container and a frontend SPA (Aurelia) container (also hosted as static files in a DotNet Core app). I show how to build the containers locally and get them running in minikube, taking advantage of ConfigMaps to handle configuration.

I am a huge fan of Aurelia – and that was even when I was working with it in the beta days. I recently had to do some development to display d3 graphs, and needed a simple SPA app. Of course I decided to use Aurelia. During development, I was again blown away by how well thought out Aurelia is – and using some new (to me) tooling, the experience was super. In this post I’ll walk through the tools that I used as well as the build/release pipeline that I set up to host the site in Azure.

DevOps is a journey that every team should at least have started by now. Most of the engagements I have been on in the last year or so have been in the build/release automation space. There are still several practices that I think teams must invest in to remain competitive – unit testing during builds and integration testing during releases are crucial foundations for more advanced DevOps, which I’ve blogged about (a lot) before. However, Application Performance Monitoring (APM) is also something that I believe is becoming more and more critical to successful DevOps teams. And one application of monitoring is hypothesis driven development.

I have a build/release task pack in the marketplace. I’ve just added a new task that allows you to add tags to builds or releases in the pipeline, inspired by my friend and fellow MVP Rene van Osnabrugge’s excellent post.

A good DevOps pipeline should utilize the principle of build once, deploy many times. In fact, I’d go so far as to say it’s essential for a good DevOps pipeline. That means that you have to have a way to manage your configuration in such a way that the package coming out of the build process is tokenized somehow so that when you release to different environments you can inject environment-specific values. Easier said that done – until now.

The other day I was doing a POC to run some Selenium tests in a Release. I came across some Selenium docker images that I thought would be perfect – you can spin up a Selenium grid (or hub) container and then join as many node containers as you want to (the node container is where the tests will actually run). The really cool thing about the node containers is that the container is configured with a browser (there are images for Chrome and Firefox) meaning you don’t have to install and configure a browser or manually run Selenium to join the grid. Just fire up a couple containers and you’re ready to test!

In part 1 I introduced some thoughts as to how good architecture makes DevOps easier. And how good DevOps drives better architecture – a symbiotic relationship. I discussed how good source control structure, branching strategies, loosely coupled architectures and package management can make DevOps easier. In this post I’ll share some thoughts along the same lines for infrastructure as code, database design and management, monitoring and test automation.

(Read part 2 here)