I recently ran into an issue causing the vCenter server to crash almost daily. What seemed to be a random vCenter issue initially, turned out to be related to CAPV (Cluster API Provider vSphere), running on some of our Kubernetes clusters. That was also an edge case I had not seen before, so I decided to document and share it here.
Initially, the issue we were witnessing on the vCenter server was the following:
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With the release of TKG 2.3, the Grafana package was finally updated from version 7.5.x to 9.5.1.
If you have deployed the new Grafana package (9.5.1+vmware.2-tkg.1) or upgraded your existing one to this version, you may have run into error messages in your Grafana dashboards.
For example, in the TKG Kubernetes cluster monitoring default dashboard, you may have run into the Failed to call resource error when opening the dashboard and noticed that a lot of the data is missing.
Tanzu Kubernetes Grid (TKG) is one of the few platforms providing out-of-the-box support and streamlined deployment of Windows Kubernetes clusters. VMware is actively investing in this area and constantly improving the support and capabilities around Windows on Kubernetes.
Unlike Linux-based clusters, for which VMware provides pre-packaged base OS images (typically based on Ubuntu and Photon OS), VMware cannot offer Windows pre-packaged images, primarily due to licensing restrictions, I suppose. Therefore, building your own Windows base OS image is one of the prerequisites for deploying a TKG Windows workload cluster. Fortunately, VMware leverages the upstream Image Builder project - a fantastic collection of cross-provider Kubernetes virtual machine image-building utilities intended to simplify and streamline the creation of base OS images for Kubernetes.
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I recently had to demonstrate Tanzu Kubernetes Grid and its GPU integration capabilities. Developing a good use case and assembling the demo required some preliminary research.
During my research, I reached out to Jay Vyas, staff engineer at VMware, SIG Windows lead for Kubernetes, a Kubernetes legend, and an awesome guy in general. :) For those who don’t know Jay, he is also one of the authors of the fantastic book Core Kubernetes (look it up!).
Thanks to TeraSky’s education program, I recently attended KubeCon & CloudNativeCon Europe 2022 in Valencia, Spain.
The experience was incredible. While there were many interesting technical sessions on many exciting topics, I was most curious about Backstage - which has sparked my interest ever since I started exploring VMware Tanzu Application Platform (TAP).
I decided to attend a session entitled “Backstage: Restoring Order to Your Chaos”, given by Spotify software engineer Dave Zolotusky. Going into the session, I was stunned by the huge line of people trying to get into the room. That was something I had never seen before in any other session. Fortunately, I was lucky enough to secure one of the last seats.
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Over the years of working with Tanzu Kubernetes Grid (TKG), one tool has stood out as a game-changer for resource customization: Carvel’s ytt. Whether tailoring cluster manifests, customizing TKG packages, or addressing unique deployment requirements, ytt has consistently been a fundamental part of the workflow. Its flexibility, power, and declarative approach make it an essential tool for anyone working deeply with Kubernetes in a TKG ecosystem.
But what exactly is ytt? Short for YAML Templating Tool, ytt is part of the Carvel suite of tools designed for Kubernetes resource management. It provides a powerful, programmable approach to templating YAML configurations by combining straightforward data values, overlays, and scripting capabilities. Unlike many traditional templating tools, ytt prioritizes structure and intent, making it easier to maintain, validate, and debug configurations—particularly in complex, large-scale Kubernetes environments.
This post continues our two-part series on automating LDAP configuration for Harbor Registry. In the previous post, we demonstrated how to achieve this using Ansible, running externally. However, external automation has its challenges, such as firewall restrictions or limited API access in some cases/environments.
Note: make sure you review the previous post as it provides a lot of additional background and clarifications on this process, LDAPS configuration, and more.
Here, we explore an alternative approach using Terraform, running the automation directly inside the Kubernetes cluster hosting Harbor. This method leverages native Kubernetes scheduling capabilities for running the configuration job in a fully declarative approach and does not require any network access to Harbor from the machine running the job.
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