Azure DevOps : Operational validation with Approval Gates & Azure Monitor Alerts

Introduction

After having migrated VMchooser from a fully Serverless infrastructure to Containers, I am currently doing the opposite move. As I can start off the same code base to basically run different deployment options in Azure. Where I found that the serverless deployment added more value for me compared to a lower cost profile. That being said, one of the big learnings I had this week is that while having an automated landscape with Terraform, some changes are rather intrusive… Where I should have checked the output of the terraform plan stage, I failed to do so. Which resulted in downtime for VMchooser. So IĀ  was looking for way to do operational validation in the least intrusive and re-usable way. This led me to a solution where the Azure DevOps pipelines would leverage the health-check used in the Traffic manager deployment. This was already part of the deployment of course and in this a key aspect of understanding if the deployment was healthy or not.

 

Gates

In order to add validation steps in our deployment process, we can leverage the concept of Gates in Azure DevOpsĀ ;

Gates allow automatic collection of health signals from external services, and then promote the release when all the signals are successful at the same time or stop the deployment on timeout. Typically, gates are used in connection with incident management, problem management, change management, monitoring, and external approval systems.

As most of the health parameters vary over time, regularly changing their status from healthy to unhealthy and back to healthy. To account for such variations, all the gates are periodically re-evaluated until all of them are successful at the same time. The release execution and deployment does not proceed if all gates do not succeed in the same interval and before the configured timeout. The following diagram illustrates the flow of gate evaluation where, after the initial stabilization delay period and three sampling intervals, the deployment is approved.

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Cloud Native Options – Personal experience when moving from Serverless to Containers

Introduction

Over the last months I have migrated VMchooser from a purely serverless implementation towards a container based one. The main reason for me to do this, was (like always) the learning effect that comes with such a refactoring. This post will run you through the various aspects that I encountered and hopefully give you a better understanding between both implementation options of a CloudNative architecture. I will divert a bit from the pure “X vs Y” comparison and also touch topics that typically come with the business discussions when thinking about both in terms of a strategic approach.

 

CloudNative?

First of all… In terms of semantics, there are some definitions floating around. When you look at the CNCF, then it seems to solely revolve around containers. Though when you look at Azure, the definition broadens up a bit… In reality, it depends a lot on the context of the person/organization you are speaking too. So do not be surprised if for some organizations the scope of CloudNative is “limited” to containers. Where for others this might be about leveraging the PaaS cloud services in a “Serverless” manner.

 

It kinda makes me think about the following “cartoon” (Source ; Simon Wardley) from a few years ago ;

Anyhow, be aware that there are different views on the semantics of “Cloud Native” and be crisp on your own when making conscious decisions.

 

Strategic Design Principle ; Portability

The design principle of software portability is high on the radar with about every organization I talk to. Here I always highlight two dimensions to take into consideration ;

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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!

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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… šŸ˜¦

 

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.

 

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Test driving the newly announced Visual Studio Online

Introduction

This week Ignite kicked off with a series of announcements (as always). One of those was “Visual Studio Online“…

 

In my eagerness I wanted to test drive it and check out what the developer experience would be, and if it could replace my development station in Azure.Ā  So let’s delve into it shall we?

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Improving the security & compatibility aspects of package management with native GitHub features

Introduction

Did you know almost every piece of software depends on OpenSource? Not sure… What libraries is your software using? Bingo! šŸ˜‰

Now we all know that package management can be a true hell. Tracking everything and ensure you are up-to-date to achieve the needed security level is hard. Next to that, there is always the risk that your build will break to moving to a library version.

What if we could enhance that flow a bit? You guessed it… Today’s post will be around how we can leverage native GitHub features to help us in this area!

 

Let’s hit the slopes!

For this walk-through, we’ll use the following ;

  • an existing code repository, where we’ve forked CoreUI’s VueJS repo
  • GitHub’s actions to run a workflow on every pull request
  • GitHub’s automated security feature that will send pull requests to us when it detects security issues

Want to test this one out or follow along? Browse to the following sample repository ; https://github.com/beluxappdev/CoreUI-VueJS-GitHubSecurityDemo

So let’s fork this sample repository!

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Taking a look at Github Enterprise Server & Github Connect

Introduction

For today’s post we’re going to take a look at GitHub Connect … It’s the link between the On-Premises installation of GitHub Enterprise Server and the popular SaaS offering (as we all have come to love it) called GitHub. šŸ˜‰

 

Installing GitHub Enterprise Server (on Azure)

So my journey for today started with registering for the GitHub Enterprise Trial, where I decided to install it on Azure… as my “On Premises” location.

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Hardening your storage account with Private Link / Endpoint

Introduction

Earlier this week, a new capability called “Azure Private Link” (and also “Azure Private Endpoint”) went into public preview. As a nice copy & past from the documentation page ;

Azure Private Link enables you to access Azure PaaS Services (for example, Azure Storage and SQL Database) and Azure hosted customer/partner services over a Private Endpoint in your virtual network. Traffic between your virtual network and the service traverses over the Microsoft backbone network, eliminating exposure from the public Internet. You can also create your own Private Link Service in your virtual network (VNet) and deliver it privately to your customers. The setup and consumption experience using Azure Private Link is consistent across Azure PaaS, customer-owned, and shared partner services.

As always, we’ll take this one out for a spin! For this we’ll see if we can access a storage account privately (from a virtual machine) over the VNET.

 

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Taking the user delegation SAS tokens for a spin

Introduction

A few days ago the preview for the “User delegation SAS token” has seen the light. In today’s post, we’ll take a first glance on this new capability! Though why should we care about this feature? You can now create SAS tokens based on the scoped permissions of an AAD user, instead of linked towards the storage account key. From a security perspective this is REALLY awesome, cause you can harden the scope of a possible even more.

 

Bibliography

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