Getting Started with Cluster API Locally: A Developer's Guide to CAPD

Interested in Cluster API for automating Kubernetes cluster management but hesitant because you think you need a cloud environment? Good news! You can actually try Cluster API right on your local machine with just Docker. In this guide, we'll walk through setting up a testing environment using CAPD (Cluster API Provider Docker).

Prerequisites

You'll need the following tools installed:

• Docker
• kubectl
• kind
• clusterctl

Overview

Here's what we'll cover:

1. Creating a management cluster using kind
2. Initializing Cluster API (CAPD)
3. Creating a workload cluster
4. Setting up CNI

Let's dive in!

1. Creating the Management Cluster

First, we'll create a management cluster using kind that will serve as the foundation for CAPD.

Create a configuration file that enables access to the host's Docker socket:

# kind-cluster-with-extramounts.yaml
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
networking:
  ipFamily: dual
nodes:
- role: control-plane
  extraMounts:
    - hostPath: /var/run/docker.sock
      containerPath: /var/run/docker.sock

Now, create the kind cluster using this configuration:

kind create cluster --config kind-cluster-with-extramounts.yaml

You should see output similar to this:

Creating cluster "kind" ...
 ✓ Ensuring node image (kindest/node:v1.31.0) 🖼
 ✓ Preparing nodes 📦  
 ✓ Writing configuration 📜 
 ✓ Starting control-plane 🕹️ 
 ✓ Installing CNI 🔌 
 ✓ Installing StorageClass 💾 
Set kubectl context to "kind-kind"

2. Initializing Cluster API

Next, let's install Cluster API on our management cluster:

export CLUSTER_TOPOLOGY=true && clusterctl init --infrastructure docker

This command installs all necessary components. If successful, you'll see:

Fetching providers
Installing cert-manager version="v1.16.0"
Waiting for cert-manager to be available...
Installing provider="cluster-api" version="v1.8.5" targetNamespace="capi-system"
Installing provider="bootstrap-kubeadm" version="v1.8.5" targetNamespace="capi-kubeadm-bootstrap-system"
Installing provider="control-plane-kubeadm" version="v1.8.5" targetNamespace="capi-kubeadm-control-plane-system"
Installing provider="infrastructure-docker" version="v1.8.5" targetNamespace="capd-system"
Your management cluster has been initialized successfully!

3. Creating a Workload Cluster

Now let's create a Kubernetes cluster for testing. We'll name it "muscat" 🍇:

clusterctl generate cluster muscat \
  --flavor development \
  --kubernetes-version v1.31.0 \
  --control-plane-machine-count=3 \
  --worker-machine-count=3 \
  > muscat.yaml
kubectl apply -f muscat.yaml

Check the cluster status:

kubectl get cluster

NAME     CLUSTERCLASS   PHASE         AGE     VERSION
muscat   quick-start    Provisioned   5m47s   v1.31.0

4. Setting Up CNI

Finally, let's enable networking functionality by installing Calico:

# First, get the kubeconfig
clusterctl get kubeconfig muscat > kubeconfig.muscat.yaml
# Install Calico
kubectl --kubeconfig=./kubeconfig.muscat.yaml apply -f https://raw.githubusercontent.com/projectcalico/calico/v3.27.0/manifests/calico.yaml

After a few moments, all nodes should be in Ready state:

kubectl --kubeconfig=./kubeconfig.muscat.yaml get nodes
NAME                            STATUS   ROLES           AGE     VERSION
muscat-md-0-9mhvz-4xxcd-42nh8   Ready    <none>          4m20s   v1.31.0
muscat-md-0-9mhvz-4xxcd-8hghp   Ready    <none>          4m25s   v1.31.0
muscat-md-0-9mhvz-4xxcd-mxg7k   Ready    <none>          4m20s   v1.31.0
muscat-r65sn-592c8              Ready    control-plane   3m35s   v1.31.0
muscat-r65sn-xrzfl              Ready    control-plane   4m41s   v1.31.0
muscat-worker-08sx08            Ready    <none>          4m17s   v1.31.0
muscat-worker-u6l39f            Ready    <none>          4m17s   v1.31.0
muscat-worker-ydhg40            Ready    <none>          4m17s   v1.31.0

Conclusion

You now have a fully functional Cluster API testing environment on your local machine! Here's what we've accomplished:

• A management cluster (kind) running Cluster API
• A workload cluster with three control plane nodes and three worker nodes
• All this running locally with just Docker - no cloud provider needed!

You're now ready to start experimenting with various Cluster API features in this local environment. Happy clustering! 🎉