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Goldilocks Zone for AKS

In this blog, we will look at the process used by Microsoft Azure to add support for new Kubernetes versions for their "Managed" Azure Kubernetes Service (AKS). We will also look at recommendations for customers on things they need to consider to operate their AKS clusters at scale without issues.

Azure's AKS managed Kubernetes is supported globally in 60+ regions. As one can imagine, it is not practical to update software in all these regions in one fell swoop. The AKS team at Microsoft employs a Safe Deployment Practice (SDP) where new releases are rolled out gradually in phases. This means that any given time, something new is being rolled out to some region.

Note

The AKS team maintains a Release Tracker that provides visibility to customers that require it.

Considerations for In-Place Upgrades to Amazon EKS v1.24

Recently, AWS added support for Kubernetes v1.24 for their Amazon EKS offering. One significant change with this version is the removal of Dockershim as the Container Runtime (CRI). Amazon EKS clusters v1.24 onwards are standardized on "containerd".

New Amazon EKS v1.24 clusters are provisioned with containerd. Watch a brief video showcasing how customers can use Rafay to configure and provision an Amazon EKS v1.24 cluster.

When EKS clusters are upgraded to v1.24, the nodes in the EKS cluster's data plane are seamlessly migrated from "Dockershim" to "containerd".

graph LR
  A[Dockershim] --> B[Containerd];

Although this transition is mostly "behind the scenes" for users, the transition from Dockershim -> Containerd can cause disruptions to deployed applications that may be dependent on Docker. In this blog, we will look at what Rafay has done to protect our customers during an in-place upgrade to EKS v1.24.

Considerations for Windows Containers on Kubernetes

With increasing adoption of Kubernetes in organizations, we are seeing interest from a number of customers that would like to deploy and operate their "legacy Windows applications" on Kubernetes as well.

In this blog, we have attempted to capture our learnings from working with customers that use the Rafay Kubernetes Operations Platform to deploy and operate Kubernetes clusters with Windows based containerized applications.

Kubernetes Cluster Insights for Platform Teams

Many customers of the Rafay Kubernetes Operations Platform are "Platform Teams". In many cases, the first priority for these platform teams is to "take over and standardize" existing Kubernetes clusters in active use by application teams.

However, one of the challenges they run into with the take over process is nobody in the team has complete clarity into what resources already exist on the cluster and for what purpose. Identifying an accurate list manually can be extremely error prone and time consuming for both the platform teams as well as the various application teams resulting in delays in adoption and standardization efforts.

Deploying Backstage in Kubernetes With Enterprise-Grade Governance and Automation

Introduction To Backstage

Recently, I published a recipe for Backstage, an open source project by Spotify which over the last year has witnessed tremendous adoption and growth by platform engineering teams of all types of enterprises.

Some of the key features of Backstage include:

  • an easy-to-use interface for developers
  • extensible plugin ecosystem (for ex. plugins available for GitHub Actions, ArgoCD, AWS, and more)
  • ability to easily build and publish tech documentation
  • native Kubernetes plugin for cloud-native apps
  • ability to compose different developer workflows into an Internal Developer Portal (IDP)

Spinning up cost effective clusters for training sessions

We have been running a number of internal and external (with partners/customers) enablement sessions over the last few weeks to provide "hands-on, labs based training" on some recently introduced capabilities in the Rafay Kubernetes Operations Platform.

Here's what we setup for those enablement sessions:

  • Each attendee was provided with their own Kubernetes cluster
  • We spun up ~25 "ephemeral" Kubernetes clusters on Digital Ocean (for life of the session)
  • We needed the clusters to be provisioned in just a few minutes for the training exercise
  • Each attendee had their own dedicated "Project" in the Rafay Org

A question that we frequently got asked after those enablement sessions was "I would love to run similar sessions with my extended team, how much did it cost to run those clusters?".

Our total spend for ~25 ephemeral clusters on Digital Ocean for these enablement sessions was less than $15. It was no wonder there has been so much interest in this.

We decided that it would help everyone if we shared the automation scripts and the methodology we have been using to provision Digital Ocean clusters and to import them to Rafay's platform here.

Digital Ocean

Cluster Blueprints and Drift Detection

Around three years back, we noticed many of our customers struggling with enterprise wide standardization of their Kubernetes clusters. Every cluster in their Organization was a snowflake and they were looking for a way to enforce that every cluster had a "baseline set of add-ons". This prompted us to develop Cluster Blueprints which has turned out to be one of the most heavily used features in our platform.

In this blog, we will describe a superpower setting in the cluster blueprints feature that we see customers use heavily for their production clusters to secure against unplanned drift.

Blueprints Icon

Multi-tenancy: Best practices for shared Kubernetes clusters

Some of the key questions that platform teams have to think about very early on in their K8s journey are:

  • How many clusters should I have? What is the right number for my organization?
  • Should I set up dedicated or shared clusters for my application teams?
  • What are the governance controls that need to be in place?

The model that customers are increasingly adopting is to standardize on shared clusters as the default and create a dedicated cluster only when certain considerations are met.

graph LR
  A[Request for compute from Application teams] --> B[Evaluate against list of considerations] --> C[Dedicated or shared clusters];

A few example scenarios for which Platform teams often end up setting up dedicated clusters are:

  • Application has low latency requirements (target SLA/SLO is significantly different from others)
  • Application has specific requirements that are unique to it (e.g. GPU worker nodes, CNI plugin)
  • Based on Type of environment - ‘Prod’ has a dedicated clusters and 'Dev', 'Test' environments have shared clusters

With shared clusters (which is the most cost efficient and therefore the default model in most customer environments), there are certain challenges that platform teams have to solve for around security and operational efficiencies.

Considerations for In-Place Upgrades to Amazon EKS v1.23

Earlier this year, AWS added support for Kubernetes v1.23 for their Amazon EKS offering. One significant change with this version is with the Container Storage Interface (CSI) for working with AWS Elastic Block Store (Amazon EBS) volumes.

Specifically, the updates to the CSI driver require customers to take action to ensure a seamless upgrade process for EKS clusters from previous versions. The CSI was developed in Kubernetes to replace the in-tree driver. With the CSI, there is now a simplified plug-in model that makes it easier for storage providers to decouple their releases from the Kubernetes release cycle.

graph LR
  A[In-Tree Storage Driver] --> B[CSI Plugin for EBS CSI];

In a nutshell, this transition is good for Amazon EKS users because they do not have to upgrade Kubernetes versions for their EKS clusters just to get some additional functionality or bug fixes for EBS storage via the "in-tree driver".