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Custom Virtual Machine Class Types with vSphere with Tanzu

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When you deploy a Tanzu Kubernetes Grid (TKG) Cluster using the integrated TKG Service in vSphere with Tanzu, you can specify a Virtual Machine Class Type which determines the amount of CPU and Memory resources that are allocated for both the Control Plane and/or Worker Node VMs for your TKG Cluster.

Here is a sample YAML specification that uses the best-effort-xsmall VM class type for both Control Plane and Worker Node, but you can certainly override and choose different classes based on your requirements.

apiVersion: run.tanzu.vmware.com/v1alpha1
kind: TanzuKubernetesCluster
metadata:
  name: william-tkc-01
  namespace: primp-industries
spec:
  distribution:
    version: v1.17.8+vmware.1-tkg.1.5417466
  settings:
    network:
      cni:
        name: antrea
      pods:
        cidrBlocks:
        - 193.0.2.0/16
      serviceDomain: managedcluster.local
      services:
        cidrBlocks:
        - 195.51.100.0/12
  topology:
    controlPlane:
      class: best-effort-xsmall
      count: 1
      storageClass: vsan-default-storage-policy
    workers:
      class: best-effort-xsmall
      count: 3
      storageClass: vsan-default-storage-policy

Today, the are a total of 16 VM Class types that you can select from, however these are not customizable which is something that has been coming up more recently. The vSphere with Tanzu team is aware of this request and is working on a solution that not only makes customizing CPU and Memory easier but also supporting storage customization. As you can see from the table below, 16GB is only supported configuration today.


In the mean time, if you need a supported path for customizing your TKG Guest Clusters, one option is to use the TKG Standalone / MultiCloud CLI, which can be used with a vSphere with Tanzu Cluster. You will need to deploy an additional TKG Management Cluster (basically a few VMs), but once you have that, you can override CPU, Memory and Storage of both the Control Plane and Worker Nodes using the following environment variables:

  • VSPHERE_WORKER_NUM_CPUS
  • VSPHERE_WORKER_MEM_MIB
  • VSPHERE_WORKER_DISK_GIB
  • VSPHERE_CONTROL_PLANE_NUM_CPUS
  • VSPHERE_CONTROL_PLANE_MEM_MIB
  • VSPHERE_CONTROL_PLANE_DISK_GIB

If you are interested, the easiest way to get started is by using my TKG Demo Appliance Fling which was just recently updated to the latest TKG 1.2 release which has support for K8s v1.19 which is currently not available on vSphere with Tanzu.

Now, you might ask, would it be possible to create your own custom VM class types using vSphere with Tanzu? Well .... keep reading to find out 🙂

Disclaimer: This is not officially supported by VMware, use at your own risk. These custom changes can potentially impact upgrades or automatically be reverted upon the next update or upgrade. You have been warned.

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Tanzu Kubernetes Grid (TKG) Demo Appliance 1.2.0

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Happy to share that the Tanzu Kubernetes Grid (TKG) Demo Appliance Fling has been updated to support the latest TKG 1.2.0 release which just came out a couple of weeks ago. The TKG Workshop Guide has been updated to reflect all new TKG 1.2 changes along with an updated vSphere Content Library containing all the OVA required to get started. As mentioned in the workshop guide, you can use either a VMware Cloud on AWS SDDC (1-Node) or a vSphere 6.7 Update 3/vSphere 7.0+ environment.

The most notable change with this version is actually within TKG itself which now uses kube-vip to replace the functionality that the HAProxy VM used to provide. What this means when deploying either a TKG Management or Workload Cluster is that you will need to specify an IP Address which will be used for the Virtual IP endpoint of the K8s Cluster as shown in the screenshot below.

tkg init -i vsphere -p dev --name tkg-mgmt --vsphere-controlplane-endpoint-ip 192.168.2.10


Using the TKG Demo Appliance, you can deploy both v1.19.1 and v1.18.8 K8s Clusters. To exercise a TKG Cluster upgrade workflow, you just have to run these three simple commands:

export VSPHERE_TEMPLATE=photon-3-kube-v1.18.8_vmware.1
tkg create cluster tkg-cluster-01 --plan=dev --kubernetes-version=v1.18.8+vmware.1 --vsphere-controlplane-endpoint-ip 192.168.2.11
tkg upgrade cluster tkg-cluster-01


There has been a lot of demand for TKG on VMware Cloud on AWS, so that is where I have spent the bulk of my testing not to mention where it was originally developed. You can also deploy the TKG Demo Appliance in an on-premises vSphere environment running 6.7 Update 3 or newer.

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Customizing Kubernetes cluster template (Dev/Prod) plans in Tanzu Kubernetes Grid 1.2

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With previous releases of Tanzu Kubernetes Grid (TKG), if you needed to apply special OS customizations that were applied to the deployed Control Plane and Worker Node VMs, such as injecting commands to handle network proxy or dealing with insecure container registry, your only option was to hand edit the default TKG Dev/Prod YAML templates. Not only was this error prone but because the templates can change from each release, it was difficult to manage and test until you attempted a deployment.

One of the newest features with the release of TKG 1.2 is official support for customizing the Kubernetes (K8s) Cluster Templates Plans using YTT (YAML Templating Tooling) which allows users to provide custom data that can then be patched/overlay to an existing YAML file. YTT itself is part of a larger toolset for building, creating and configuring deployments for K8s called Carvel. The Domain Specific Language (DSL) that YTT uses was not exactly intuitive but since the official TKG documentation had an example to start with, I was able to mostly figure my way through along with some tips from the #carvel Slack channel.

So what was I trying to do? I was working on updating my TKG Demo Appliance Fling to the latest 1.2 release and part of the setup required adding an entry to /etc/hosts file on all TKG VMs that are deployed. Instead of directly messing with the YAML templates, there is now a new "overlay" YAML file in ~/.tkg/providers/infrastructure-vsphere/ytt/vsphere-overlay.yaml which can be used to make such changes.

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