Opinion – Cloud Native, Cloud Native and Cloud Native? What I like about, and my two cents on, running Containers, Kubernetes and/or Serverless

Introduction

In the beginning of the month I posted about my experience of moving VMchooser from “Serverless” to “Containers”. As in, moving from one way of implementing a CloudNative architecture to another… Since then, I have actually moved back to “Serverless”.  Though the cogwheels in my head have been turning 24/7 on how to put everything around this into perspective. Yesterday Yves made a tweet (reply) that really made something click inside of my head…

@LaurentGoderre @DanRosanova @sfeldman @Microsoft @MicrosoftAzure Azure is itself an orchestrator, it provisions st… twitter.com/i/web/status/1…

—
Yves Goeleven (@YvesGoeleven) February 22, 2020

In today’s post I’m going to try to do a “brain dump” of several thoughts that have been floating around in my mind. Where I hope this will help you in your journey of “finding your perfect rock”. Here I will indicate what I like about the various options and what my typical advice would be to organizations looking to do a given option.

Leveraging Azure Tags and Azure Graph for deploying to your Blue/Green environments

Introduction

For this post I am assuming you are pretty familiar with the concept of deployment strategies (if not check out this post by Etienne). Now these are typically seen from an application deployment level, where platforms (like for instance Kubernetes) typically have out-of-the box mechanisms in place to do this. Now what if you would want to do this on an “infrastructure level”, like for instance the Kubernetes version of Azure Kubernetes Service. We could do an in-place upgrade, which will carefully cordon and drain the nodes. Though what if things go bad? We could do a Canary, Blue/Green, A/B, Shadow, … on cluster level too? Though how would we tackle the infrastructure point of view of this? That is the base for today’s post!

 

Architecture at hand

For today’s post we’ll leverage the following high level architecture ;

This project leverages Terraform under the hood. Things like DNS, Traffic Manager, Key Vault, CosmosDB, etc are “statefull’ where its lifecycle is fully managed by Terraform. On the other hand, our kubernetes clusters are “stateless” from an Infrastructure-as-Code point-of-view. We deploy them via Terraform, though do not keep track of them… All the lifecycle management is done on operating on the associated tags afterwards.

 

Community-Tool-of-the-day

The drawing above was not created in Visio for once. The above was made leveraging CloudSkew, which was created by Mithun Shanbhag. Always awesome to see community contributions, which we can only applaud!

Unified monitoring view in Kubernetes : Linking infrastructure monitoring with application monitoring

Introduction

Typically you notice that there are two dimensions / viewpoints when it comes to monitoring. On one side there is a team that wants to view everything related to the “infrastructure”, like for instance the kubernetes cluster. On the other hand, there is the typical application performance monitoring that starts from the application side. Sadly enough, in a lot of cases, those two are separated islands… 😦

 

As you might know, on the Azure front you can do Application Performance Monitoring with Application Insights and there is like a really awesome integration with Azure Monitor (“Log Analytics”) from the container space (kubernetes). Though I see you thinking it… Two separate solutions. Though, what a lot of people forget, is that they are actually using “Log Analytics” under the hood. And… That you can query across workspaces in Log Analytics! Which means that you can join the two and have an aggregated view to span both worlds.

 

Let’s take a look!

For this test, I’ve created a k8s cluster which is linked to a separate log analytics work-space. Where next to it, there is an application (Azure Function) inside of a docker container that is linked to Application Insights.

 

Enabling Azure Active Directory support in OpenShift (Origin)

Introduction

In today’s post we’ll go through the steps to get Azure Active Directory (AAD) integrated in RedHat’s OpenShift. So that we can use the AAD identity we all love in OpenShift too.

 

Prerequisites

For the next steps, I’m assuming you already have an OpenShift deployment up & running. If not, check out this repository!

Azure Custom Vision AI : From training to deploying the container export on the Azure Kuberenetes Service (AKS)

Introduction

Todays post will be the backend tour of “Frietjes-of-Niet” (translated from Dutch : “French-Frites-or-Not?”). A big part of the mission of Azure is about democratizing technology so it becomes accessible to organizations in order for them to achieve more. AI (Artificial Intelligence) is a key part of that vision.

 

End-to-End Flow

What will be the flow for today?

• We’ll train a model to recognize fries
• Next we’ll be exporting that model to be used as a container
• Afterwards we’ll build that container
• To end with deploying (and testing) it onto AKS

Sound cool? Let’s get to it..

Taking a glance at Rancher’s ability to manage the Azure Kubernetes Service (AKS)

Introduction

Pfew, it’s odd to admit that it has been a while since I’ve posted about Rancher. Though today is as good a day as any to pick up that thread… So today we’ll go through give or take the same objective as in the past, where we’ll notice that the integration has improved significantly with the arrival of AKS! Let’s get today’s post underway and deploy AKS from our Rancher control plane.

Preparation

Before the below started, I already had the following things ready ;

• A service principal with appropriate rights
• Installed Rancher 2.0

Replatforming Azure Functions into an Azure Functions Container

Introduction

A while ago I talked about  “Faas/Serverless” in relation to vendor lock-in. Today we’ll be continuing in that road, where we’ll be doing a small proof-of-concept (PoC). In this PoC, we’ll be replatforming existing Azure Functions code into an Azure Functions container!

 

Things to know

Since Azure Functions 2.0 (in preview at the time of writing this post), you are able to leverage containers. Though be aware that there are several known issues. Do check them out first before embarking on your journey!

 

Testdriving 2.0

So first, we’ll start off with testing the Azure Functions Core Tools!  If you’re looking to follow this guide, be sure to have the Azure Functions Core Tools installed, which also depends on .NET Core 2.0 and Nodejs. Once you have those installed, do a “func –help”, and you’ll see what capabilities are at hand…