Creating namespaces and initial cluster configuration on vSphere 7 with Tanzu Kubernetes Grid Service (TKGS)

Following from our last three posts. There are three primary ways of standing up Kubernetes on vSphere. Each with there own benefits and drawbacks. This post will be the final post of our tree part post looking at VMware’s TKGS.

  • Tanzu Kubernetes Grid (TKG)
    Deploy Kubernetes via Tanzu (TKG) without the need for a licensed Tanzu supervisor cluster. This does not provide a load balancer.
  • Tanzu Kubernetes Grid Service (TKGS)
    Deploy and operate Tanzu Kubernetes clusters natively in vSphere with HA Proxy as the load balancer. Without VMware Harbor as a container repository.
    - Deploying and configuring HA Proxy
    - Deploying workloads via the supervisor cluster
    - Creating namespaces and initial cluster configuration (this post)
  • VMware Tanzu Kubernetes Grid Integrated Edition (TKGI)
    Fully featured Tanzu deployment with NSX-T.
    - Deploying and configuring NSX-T
    - Deploying workloads via the supervisor cluster
    - Creating namespaces and initial cluster configuration


Deploying TKGS Namespaces

From our last post the context we were using was that of our supervisor cluster. For this next step we’ll create our namespace and then use this new namespace as our next context from which we’re going to stand up our Kubernetes cluster.

Namespace creation

Select our primary cluster used by our supervisor cluster. Give your namespace a name. We’re using production. However, you may choose to dive these up by teams or projects. Or maybe if you’re resource constrained everything will just live under one namespace.

Once you’ve created your new namespace. Click on the namespace name which will take you to the namespace overview pane.

Adding permissions

Adding storage

Awesome, we’re ready to create our cluster.

Creating our cluster spec

$ kubectl vsphere logout
$ kubectl vsphere login --server= --insecure-skip-tls-verify

Here you’ll be prompted for your login credentials and once you’ve successfully authenticated you should be able to see the production context listed.

$ kubectl config use-context production

Now in order for us to create our Kubernetes cluster we need to create a spec to let the supervisor cluster know what to provision. Below is an example spec you can use. This spec will create one control plane and 3 worker nodes. If you’re planning on deploying a larger cluster then you can simply increase the number of workers and I wold set the control plane nodes to 3. To provide some redundancy. Create this file as tkgs-cluster-production.yaml .

Anything defined within the spec can be updated at a later date. Some downtime will be incurred. However, the cluster will come back up.

kind: TanzuKubernetesCluster
name: zercurity
namespace: production
version: v1.18.5
count: 1
class: best-effort-small
storageClass: tanzu-storage-policy
count: 3
class: best-effort-small
storageClass: tanzu-storage-policy

The storageClass will need to be the name of your storage policy. This will be all lower case. If you have used spaces then use hyphens as a substitute. You can check the policy name using $ kubectl get sc command. Which will retrieve the available storage classes. You can then copy over the name.

As for the class this represents the instance size you want to provision. You can get a list of the available instance types like so:

$ kubectl get virtualmachineclasses

The best-effort prefix will thinly provision resources. Where as the guaranteed prefix will allocate all the resources required to the VM. You can get a detailed breakdown on the VM class with:

$ kubectl describe virtualmachineclasses best-effort-small

Once you’ve created your spec file. Deploy it like so:

$ kubectl apply -f tkgs-cluster-production.yaml

If there are no errors the shell will return. You can check on the status of the creation of the cluster with the command:

$ kubectl get tanzukubernetescluster
$ kubectl get cluster

The status of the creation of he cluster will also be visible from the GUI.


$ kubectl get machines
$ kubectl get virtualmachines
$ kubectl get cluster
$ kubectl describe tanzukubernetescluster

On the last command. Check that all the add-ons have the status of applied.

Error: ErrImagePull

If you still have issues. Then try simplifying your deployment. We ended up having to create a separate cluster to troubleshoot some inter-VLAN routing issues.

Creating a default storage class

helm install harbor bitnami/harbor --set global.storageClass=tanzu-storage-policy ...

To overcome this we need to update the running cluster spec to set our default storageClass.

kubectl edit tanzukubernetescluster zercurity

Then under the settings section add:

fullVersion: v1.18.5+vmware.1-tkg.1.c40d30d
version: v1.18.5
name: antrea
serviceDomain: cluster.local
defaultClass: tanzu-storage-policy

Once you’ve made the changes highlighted in bold. Exit the editor and then you can check you’re cluster storage spec (you’ll need to login to the namespace first. See the accessing the cluster section).

$ hugh@hugh-ubuntu-dev-2004:~$ kubectl get scNAME                             PROVISIONER              
tanzu-storage-policy (default) ...

Accessing the cluster

$ kubectl vsphere logout
$ kubectl vsphere login --server= --insecure-skip-tls-verify --tanzu-kubernetes-cluster-namespace production --tanzu-kubernetes-cluster-name zercurity

Once you’ve logged in. Set the context to your new cluster.

$ kubectl config use-context zercurity

You can then check the status of all pods.

kubectl get pod -A

Deploying harbor

As we’ve mentioed before Harbor cannot be deployed as part of TKGS through the vSphere managment portal. The option for Container repository simply isn’t there unless you’re using NSX-T. You can however, still deploy Harbor via helm.

$ kubectl create ns helm$ helm install harbor bitnami/harbor \
--set harborAdminPassword='adminpass' \
--set global.storageClass=tanzu-storage-policy \
--set service.type=LoadBalancer \
--set externalURL=harbor.test.corp \
--set service.tls.commonName=harbor.test.corp \
-n harbor
$ helm uninstall harbor -n harbor

You can check on the deployment status like so:

$ kubectl get pod -n harbor
harbor-chartmuseum-657b95d5f7-fxzll 1/1 Running 0 9d
harbor-clair-586d8cf9f6-rhzzd 2/2 Running 0 9d
harbor-core-5cd79cc5f6-2r2sw 1/1 Running 4 9d
harbor-jobservice-b6fff8654-kvnmn 1/1 Running 5 9d
harbor-nginx-55d7d6d846-vfr6c 1/1 Running 0 9d
harbor-notary-server-8695c547f5-hrvft 1/1 Running 0 9d
harbor-notary-signer-5647c4968c-pqwmc 1/1 Running 0 9d
harbor-portal-54cc4dbc8c-dgswz 1/1 Running 0 9d
harbor-postgresql-0 1/1 Running 0 9d
harbor-redis-master-0 1/1 Running 0 9d
harbor-registry-dd67784b8-hbthw 2/2 Running 0 9d
harbor-trivy-0 1/1 Running 0 9d

Once everything is running you can get the external IP address of the LoadBalancer service:

$ kubectl get svc -n harbor

If you connect to this address you should see the harbor service up and running:

Its all over!

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