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…

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.

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Improving your code quality by linking Azure DevOps with SonarCloud

Introduction

In a customer workshop earlier this week, Hans mentioned a very nice tool (SonarCloud). He used it “in his previous life and was very enthusiastic about it. So this immediately triggered my curiosity… 😉 As it is free for public projects, I investigated how easy it was to integrate into my existing pipelines. Which turned out to be quite easy! After browsing around a bit on how to integrate it into a YAML pipeline, I can proudly say that VMchooser is now fully hooked up with SonarCloud.

However, it did confirm my suspicion, that I am a lousy developer! 😉 Though better lousy code fulfilling a purpose than having no alternative at all?!?

Anyhow, today’s post is about the experience of moving existing pipelines to SonarCloud and investigate the results you get out of it.

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Is Azure a tier 3 datacenter? And what about Service Levels in a broader sense…

Introduction

Everyone who has been working with cloud, and involved with tenders, has had the follow question (in one form or another) ; “Has the cloud datacenter achieved a tier 3 (or higher) classification? In today’s post we will delve into the specifics linked to the ask ; Why do organizations ask the question, and how does it related to cloud?

What is a “Tier 3 Datacenter”?

To better understand the concept of data-center tiers, it is important to understand that several organizations (like the Telecommunications Industry Association (TIA) and the Uptime Institute) have defined standards for data-centers.

Uptime Institute created the standard Tier Classification System as a means to effectively evaluate data center infrastructure in terms of a business’ requirements for system availability. The Tier Classification System provides the data center industry with a consistent method to compare typically unique, customized facilities based on expected site infrastructure performance, or uptime. Furthermore, Tiers enables companies to align their data center infrastructure investment with business goals specific to growth and technology strategies.
Source ; https://uptimeinstitute.com/tiers

Which typically consists of several tiers…

Four tiers are defined by the Uptime Institute :

  • Tier I : lacks redundant IT equipment, with 99.671% availability, maximum of 1729 minutes annual downtime
  • Tier II : adds redundant infrastructure, with 99.741% availability (1361 minutes)
  • Tier III : adds more data paths, duplicate equipment, and that all IT equipment must be dual-powered, with 99.982% availability (95 minutes)
  • Tier IV : all cooling equipment is independently dual-powered; adds Fault-tolerance, with 99.995% availability (26 minutes)

Source ; https://en.wikipedia.org/wiki/Data_center#Uptime_Institute_-_Data_Center_Tier_Standards 

So it is a classification for organizations to understand the quality of the data-center, and be able to take a given availability into account. Though it is important to understand, that this relates to “datacenter housing” (colocation) and not to the cloud service models! Why is this statement important? As on top of that housing, additional services will be delivered by cloud providers to achieve service models like IaaS, PaaS, SaaS, …

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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 I  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 DevOps ;

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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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… 😦

 

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.  So let’s delve into it shall we?

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