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Cluster API GCP Prototype

The Cluster API GCP prototype implements the Cluster API for GCP.

Getting Started

Prerequisites

Follow the steps listed at CONTRIBUTING.md to:

  1. Build the gcp-deployer tool
  2. Generate base machines.yaml file configured for your GCP project
  3. Login with gcloud gcloud auth application-default login

Limitation

gcp-deployer tool only supports Kubernetes version 1.9 or newer.

Creating a cluster

  1. Optional update machines.yaml to give your preferred GCP zone in each machine's providerConfig field.
  2. Optional: Update cluster.yaml to set a custom cluster name.
  3. Create a cluster: ./gcp-deployer create -c cluster.yaml -m machines.yaml -s machine_setup_configs.yaml.
    1. Note: The --machinesetup or -s flag is set to machine_setup_configs.yaml by default.

During cluster creation, you can watch the machine resources get created in Kubernetes, see the corresponding virtual machines created in GCP, and then finally see nodes join the cluster:

$ watch -n 5 "kubectl get machines"
$ watch -n 5 "gcloud compute instances list"
$ watch -n 5 "kubectl get nodes"

Interacting with your cluster

Once you have created a cluster, you can interact with the cluster and machine resources using kubectl:

$ kubectl get clusters
$ kubectl get machines
$ kubectl get machines -o yaml

Scaling your cluster

You can add machines to your cluster using kubectl apply or kubectl create.

Upgrading your cluster

By default, your cluster will initially be running Kubernetes version 1.9.4. You can upgrade the control plane or nodes using kubectl edit or you can run the upgrader tool to upgrade your entire cluster with a single command.

Node repair

To test node repair, first pick a node, ssh into it, and "break" it by killing the kubelet process:

$ node=$(kubectl get nodes --no-headers | grep -v master | head -n 1 | awk '{print $1}')
$ gcloud compute ssh $node --zone us-central1-f
# sudo systemctl stop kubelet.service
# sudo systemctl daemon-reload

Then run the node repair tool to find the broken node (using the dry run flag) and fix it.

Deleting a cluster

To delete your cluster run ./gcp-deployer delete

How does the prototype work?

Right now, the Cluster and Machine objects are stored as resources in an extension apiserver, which connected with main apiserver through api aggregation. We deploy the extension API server and controller manager as a pod inside the cluster. Like other resources in Kubernetes, machine controller as part of controller manager is responsible to reconcile the actual vs. desired machine state. Bootstrapping and in-place upgrading is handled by kubeadm.

Machine Setup Configs

The machine_setup_configs.yaml file defines what machine setups are supported (i.e. what machine definitions are allowed in machines.yaml). Here is an example yaml with a master config and a node config. You are free to edit this file to support other machine setups (i.e. different versions, images, or startup scripts). The yaml is unmarshalled into a machinesetup.configList.

Startup Scripts

Right now, the startup scripts must be bash scripts. There is a set of environment variables that is concatenated to the beginning of the startup script. You can find them here and use them in your startup script as needed.