cluster-api-provider-digitalocean

README

Kubernetes cluster-api-provider-digitalocean Project

This repository hosts a concrete implementation of a provider for DigitalOcean for the cluster-api project.

Project Status

This project is currently Work-in-Progress and may not be production ready. There is no backwards-compatibility guarantee at this point. Checkout the Features portion of the README for details about the project status.

Getting Started

Prerequisites

In order to create a cluster using clusterctl, you need the following tools installed on your local machine:

• kubectl, which can be done by following this tutorial
• minikube and the appropriate minikube driver. Minikube version < 0.28 is recommend due to an upstream bug (see #79 for more details) We recommend kvm2 driver for Linux and virtualbox for macOS.
• DigitalOcean API Access Token generated and set as the DIGITALOCEAN_ACCESS_TOKEN environment variable,
• Go toolchain installed and configured, needed in order to compile the clusterctl binary,
• cluster-api-provider-digitalocean repository cloned:

git clone https://github.com/kubermatic/cluster-api-provider-digitalocean $(go env GOPATH)/src/github.com/kubermatic/cluster-api-provider-digitalocean

Building clusterctl

The clusterctl tool is used to bootstrap an Kubernetes cluster from zero. Currently, we have not released binaries, so you need to compile it manually.

Compiling is done by invoking the compile Make target:

make compile

This command generates three binaries: clusterctl, machine-controller and cluster-controller, in the ./bin directory. In order to bootstrap the cluster, you only need the clusterctl binary.

The clusterctl can also be compiled manually, such as:

cd $(go env GOPATH)/github.com/kubermatic/cluster-api-provider-digitalocean/clusterctl
go install

Creating a Cluster

To create your first cluster using cluster-api-provider-digitalocean, you need to use the clusterctl. It takes the following four manifests as input:

• cluster.yaml - defines Cluster properties, such as Pod and Services CIDR, Services Domain, etc.
• machines.yaml - defines Machine properties, such as machine size, image, tags, SSH keys, enabled features, as well as what Kubernetes version will be used for each machine.
• provider-components.yaml - contains deployment manifest for controllers, userdata used to bootstrap machines, a secret with SSH key for the machine-controller and a secret with DigitalOcean API Access Token.
• [Optional] addons.yaml - used to deploy additional components once the cluster is bootstrapped, such as DigitalOcean Cloud Controller Manager and DigitalOcean CSI plugin.

The manifests can be generated automatically by using the generate-yaml.sh script, located in the clusterctl/examples/digitalocean directory:

cd clusterctl/examples/digitalocean
./generate-yaml.sh
cd ../..

The result of the script is an out directory with generated manifests and a generated SSH key to be used by the machine-controller. More details about how it generates manifests and how to customize them can be found in the README file in clusterctl/examples/digitalocean directory.

Once you have manifests generated, you can create a cluster using the following command. Make sure to replace the value of vm-driver flag with the name of your actual minikube driver.

./bin/clusterctl create cluster \
    --provider digitalocean \
    --vm-driver kvm2 \
    -c ./clusterctl/examples/digitalocean/out/cluster.yaml \
    -m ./clusterctl/examples/digitalocean/out/machines.yaml \
    -p ./clusterctl/examples/digitalocean/out/provider-components.yaml \
    -a ./clusterctl/examples/digitalocean/out/addons.yaml

More details about the create cluster command can be found by invoking help:

./bin/clusterctl create cluster --help

The clusterctl's workflow is:

• Create a Minikube bootstrap cluster,
• Deploy the cluster-api-controller, digitalocean-machine-controller and digitalocean-cluster-controller, on the bootstrap cluster,
• Create a Master, download kubeconfig file, and deploy controllers on the Master,
• Create other specified machines (nodes),
• Deploy addon components (digitalocean-cloud-controller-manager and csi-digitalocean),
• Remove the local Minikube cluster.

To learn more about the process and how each component work, check out the diagram in cluster-api repostiory.

Interacting With Your New Cluster

clusterctl downloads the kubeconfig file in your current directory from the cluster automatically. You can use it with kubectl to interact with your cluster:

kubectl --kubeconfig kubeconfig get nodes
kubectl --kubeconfig kubeconfig get all --all-namespaces

Upgrading the Cluster

Upgrading Master is currently not possible automatically (by updating the Machine object) as Update method is not fully implemented. More details can be found in issue #32.

Workers can be upgraded by updating the appropriate Machine object for that node. Workers are upgraded by replacing nodes—first the old node is removed and then a new one with new properties is created.

To ensure non-disturbing maintenance we recommend having at least 2+ worker nodes at the time of upgrading, so another node can take tasks from the node being upgraded. The node that is going to be upgraded should be marked unschedulable and drained, so there are no pods running and scheduled.

# Make node unschedulable.
kubectl --kubeconfig kubeconfig cordon <node-name>
# Drain all pods from the node.
kubectl --kubeconfig kubeconfig drain <node-name>

Now that you prepared node for upgrading, you can proceed with editing the Machine object:

kubectl --kubeconfig kubeconfig edit machine <node-name>

This opens the Machine manifest such as the following one, in your default text editor. You can choose editor by setting the EDITOR environment variable.

There you can change machine properties, including Kubernetes (kubelet) version.

apiVersion: cluster.k8s.io/v1alpha1
kind: Machine
metadata:
  creationTimestamp: 2018-09-14T11:02:16Z
  finalizers:
  - machine.cluster.k8s.io
  generateName: digitalocean-fra1-node-
  generation: 3
  labels:
    set: node
  name: digitalocean-fra1-node-tzzgm
  namespace: default
  resourceVersion: "5"
  selfLink: /apis/cluster.k8s.io/v1alpha1/namespaces/default/machines/digitalocean-fra1-node-tzzgm
  uid: a41f83ad-b80d-11e8-aeef-0242ac110003
spec:
  metadata:
    creationTimestamp: null
  providerConfig:
    ValueFrom: null
    value:
      backups: false
      image: ubuntu-18-04-x64
      ipv6: false
      monitoring: true
      private_networking: true
      region: fra1
      size: s-2vcpu-2gb
      sshPublicKeys:
      - ssh-rsa AAAA
      tags:
      - machine-2
  versions:
    kubelet: 1.11.3
status:
  lastUpdated: null
  providerStatus: null

Saving changes to the Machine object deletes the old machine and then creates a new one. After some time, a new machine will be part of your Kubernetes cluster. You can track progress by watching list of nodes. Once new node appears and is Ready, upgrade has finished.

watch -n1 kubectl get nodes

Deleting the Cluster

To delete Master and confirm all relevant resources are deleted from the cloud, we're going to use doctl—DigitalOcean CLI. You can also use DigitalOcean Cloud Control Panel or API instead of `doctl.

First, save the Droplet ID of Master, as we'll use it later to delete the control plane machine:

export MASTER_ID=$(kubectl --kubeconfig=kubeconfig get machines -l set=master -o jsonpath='{.items[0].metadata.annotations.droplet-id}')

Now, delete all Workers in the cluster by removing all Machine object with label set=node:

kubectl --kubeconfig=kubeconfig delete machines -l set=node

You can confirm are nodes deleted by checking list of nodes. After some time, only Master should be present:

kubectl --kubeconfig=kubeconfig get nodes

Then, delete all Services and PersistentVolumeClaims, so all Load Balancers and Volumes in the cloud are deleted:

kubectl --kubeconfig=kubeconfig delete svc --all
kubectl --kubeconfig=kubeconfig delete pvc --all

Finally, we can delete the Master using doctl and $MASTER_ID environment variable we set earlier:

doctl compute droplet delete $MASTER_ID

You can use doctl to confirm that Droplets, Load Balancers and Volumes relevant to the cluster are deleted:

doctl compute droplet list
doctl compute load-balancer list
doctl compute volume list

Development

This portion of the README file contains information about the development process.

Building Controllers

The following Make targets can be used to build controllers:

• make compile compiles all three components: clusterctl, machine-controller and cluster-controller
• make build runs dep ensure and installs machine-controller and cluster-controller
• make all runs code generation, dep ensure, install machine-controller and cluster-controller, and build machine-controller and cluster-controller Docker images

The make generate target runs code generation.

Building and Pushing Images

We're using two Quay.io repositories for hosting cluster-api-provider-digitalocean images: quay.io/kubermatic/digitalocean-machine-controller and quay.io/kubermatic/digitalocean-cluster-controller.

Before building and pushing images to the registry, make sure to replace the version number in the machine-controller and cluster-controller Makefiles, so you don't overwrite existing images!

The images are built using the make images target, which runs dep ensures and build images. There is also make images-nodep target, which doesn't run dep ensure, and is used by the CI.

The images can be pushed to the registry using the make push and make push-nodep targets. Both targets build images, so you don't need to run make images before.

Code Style Guidelines

In order for the pull request to get accepted, the code must pass gofmt, govet and gometalinter checks. You can run all checks by invoking the make check Makefile target.

Dependency Management

This project uses dep for dependency management. Before pushing the code to GitHub, make sure your vendor directory is in sync by running dep ensure, or otherwise CI will fail.

Testing

Unit tests can be invoked by running the make test-unit Makefile target. Integration and End-to-End tests are currently not implemented.