Create a managed cluster using Cluster API Provider for Google Cloud Platform (CAPG)

In the previous blog, I explained how to create and manage Kubernetes with cluster API locally with the help of docker infrastructure.

In this blog, I will explain how to create and manage the k8s with Cluster API in the google cloud.

Note – Throughout the blog, I will use Kubernetes version 1.22.9 and it is recommended to use the version of our OS image created by the image builder. You can check from kubernetes.json and use that.

Step 1 –

  • Create the kind cluster –
kind create cluster --image kindest/node:v1.22.9 --wait 5m

Step 2 –

Follow image builder for GCP steps and build an image.

Step 3 –

  • Export the following env variables – (reference)
export GCP_PROJECT_ID=<YOUR PROJECT ID>
export GOOGLE_APPLICATION_CREDENTIALS=<PATH TO GCP CREDENTIALS>
export GCP_B64ENCODED_CREDENTIALS=$( cat /path/to/gcp-credentials.json | base64 | tr -d '\n' )

export CLUSTER_TOPOLOGY=true
export GCP_REGION="us-east4"
export GCP_PROJECT="<YOU GCP PROJECT NAME>"
export KUBERNETES_VERSION=1.22.9
export IMAGE_ID=projects/$GCP_PROJECT/global/images/<IMAGE ID>
export GCP_CONTROL_PLANE_MACHINE_TYPE=n1-standard-2
export GCP_NODE_MACHINE_TYPE=n1-standard-2
export GCP_NETWORK_NAME=default
export CLUSTER_NAME=test

Step 4 –

setup the network in this example we are using the default network so we will create some router/nats for our workload cluster to have internet access.

gcloud compute routers create "${CLUSTER_NAME}-myrouter" --project="${GCP_PROJECT}" --region="${GCP_REGION}" --network="default"

gcloud compute routers nats create "${CLUSTER_NAME}-mynat" --project="${GCP_PROJECT}" --router-region="${GCP_REGION}" --router="${CLUSTER_NAME}-myrouter" --nat-all-subnet-ip-ranges --auto-allocate-nat-external-ips

Step 5 –

  • Initialize the infrastructure
clusterctl init --infrastructure gcp
  • Generate the workload cluster config and apply it
clusterctl generate cluster $CLUSTER_NAME --kubernetes-version v1.22.9 > workload-test.yaml

kubectl apply -f workload-test.yaml
  • View the cluster and its resources
$ clusterctl describe cluster $CLUSTER_NAME
NAME                                                               READY  SEVERITY  REASON                 SINCE  MESSAGE
/test                                                              False  Info      WaitingForKubeadmInit  5s
├─ClusterInfrastructure - GCPCluster/test
└─ControlPlane - KubeadmControlPlane/test-control-plane            False  Info      WaitingForKubeadmInit  5s
  └─Machine/test-control-plane-x57zs                               True                                    31s
    └─MachineInfrastructure - GCPMachine/test-control-plane-7xzw2
  • Check the status of the control plane
$ kubectl get kubeadmcontrolplane
NAME                 CLUSTER   INITIALIZED   API SERVER AVAILABLE   REPLICAS   READY   UPDATED   UNAVAILABLE   AGE    VERSION
test-control-plane   test                                           1                  1         1             2m9s   v1.22.9

Note – The controller plane won’t be ready until the next step when I install the CNI (Container Network Interface).

Step 6 –

  • Get the kubeconfig for the workload cluster
$ clusterctl get kubeconfig $CLUSTER_NAME > workload-test.kubeconfig
  • Apply the cni
kubectl --kubeconfig=./workload-test.kubeconfig \
  apply -f https://docs.projectcalico.org/v3.20/manifests/calico.yaml
  • Wait a bit and you should see this when getting the kubeadmcontrolplane
$ kubectl get kubeadmcontrolplane
NAME                 CLUSTER   INITIALIZED   API SERVER AVAILABLE   REPLICAS   READY   UPDATED   UNAVAILABLE   AGE     VERSION
test-control-plane   test      true          true                   1          1       1         0             6m33s   v1.22.9


$ kubectl get nodes --kubeconfig=./workload-test.kubeconfig
NAME                       STATUS   ROLES                  AGE   VERSION
test-control-plane-7xzw2   Ready    control-plane,master   62s   v1.22.9

Step 7 –

  • Edit the MachineDeployment in the workload-test.yaml it has 0 replicas add the replicas you want to have your nodes, in this case, we used 2. Apply the workload-test.yaml
$ kubectl apply -f workload-test.yaml
  • After a few minutes, you should see something like this –
$ clusterctl describe cluster $CLUSTER_NAME
NAME                                                               READY  SEVERITY  REASON  SINCE  MESSAGE
/test                                                              True                     15m
├─ClusterInfrastructure - GCPCluster/test
├─ControlPlane - KubeadmControlPlane/test-control-plane            True                     15m
│ └─Machine/test-control-plane-x57zs                               True                     19m
│   └─MachineInfrastructure - GCPMachine/test-control-plane-7xzw2
└─Workers
  └─MachineDeployment/test-md-0                                    True                     10m
    └─2 Machines...                                                True                     13m    See test-md-0-68bd55744b-qpk67, test-md-0-68bd55744b-tsgf6

$ kubectl get nodes --kubeconfig=./workload-test.kubeconfig
NAME                       STATUS   ROLES                  AGE   VERSION
test-control-plane-7xzw2   Ready    control-plane,master   21m   v1.22.9
test-md-0-b7766            Ready    <none>                 17m   v1.22.9
test-md-0-wsgpj            Ready    <none>                 17m   v1.22.9

Yaaa! Now we have a Kubernetes cluster in the GCP with 1 control pannel with 2 worker nodes.

Step 8 –

Delete what you have created –

$ kubectl delete cluster $CLUSTER_NAME

$ gcloud compute routers nats delete "${CLUSTER_NAME}-mynat" --project="${GCP_PROJECT}" \
    --router-region="${GCP_REGION}" --router="${CLUSTER_NAME}-myrouter"

$ gcloud compute routers delete "${CLUSTER_NAME}-myrouter" --project="${GCP_PROJECT}" \
    --region="${GCP_REGION}"

$ kind delete cluster

Stateful vs Stateless Application

State – It is the session data that is been generated at the time of a client connects to a server.

  • Stateful – In stateful architecture let’s imagine a scenario of a eCommerce website that has 3 servers and there are 3 user connected to three servers and all the user session is store in the servers accordingly. Now let’s imagine a scenario of one of the three server goes down and all the state information about the users that were associated with it also lost. Now all that users have to do all the process again after establishing connection to a new server. So that’s a big issue in scalability and availability.
  • Stateless – In stateless architecture all the state information about the client is been stored in the shared storage connected to all the servers. Now let’s suppose a server goes down and and because all the state information is shared so if a client establish a connection with new server all the state information can be restored from the shared storage.

In conceptual we are going stateless but basically the we cannot eliminate the state entirely. There must be some place we are storing the states. We call it shared storage.

To be continued