GitHub - alexellis/k3sup: bootstrap K3s over SSH in < 60s 🚀
Description: Continuous Delivery, DevOps, ALM and IoT in a Mostly Microsoft Azure World
This is the sixth post in a series where I'm taking a fresh look at how to deploy a dockerized application to Azure Kubernetes Service (AKS) using Azure Pipelines after having previously blogged about this in 2018 . The list of posts in this series is as follows:
One of the problems with running applications in containers in an orchestration system such as Kubernetes is that it can be harder to understand what is happening when things go wrong. So while instrumenting your application for telemetry and diagnostic information should be fairly high on your to do list anyway, this is even more so when running application is containers. Whilst there are lots of third party offerings in the telemetry and diagnostics space in this post I take a look at what's available for
If you are following along with this series you may recall that in the last post we configured an Azure DevOps Pipeline Environment for the Kubernetes cluster. It turns out that these are great for quickly taking a peek at the health of the components deployed to a cluster. For example, this is what's displayed for the MegaStore.SaveSaleHandler deployment and pods:
GitHub - alexellis/k3sup: bootstrap K3s over SSH in < 60s 🚀