Itechmeat LLM Code K8S Cluster API

Kubernetes Cluster API

Kubernetes Cluster API (CAPI) is a Kubernetes sub-project focused on providing declarative APIs and tooling to simplify provisioning, upgrading, and operating multiple Kubernetes clusters.

Overview

Started by SIG Cluster Lifecycle, Cluster API uses Kubernetes-style APIs and patterns to automate cluster lifecycle management. The infrastructure (VMs, networks, load balancers, VPCs) and Kubernetes configuration are defined declaratively, enabling consistent and repeatable cluster deployments across environments.

Why Cluster API?

While kubeadm reduces installation complexity, it doesn't address day-to-day cluster management:

• How to consistently provision infrastructure across providers and locations?
• How to automate cluster lifecycle (upgrades, deletion)?
• How to scale processes to manage any number of clusters?

Cluster API addresses these gaps with declarative, Kubernetes-style APIs that automate cluster creation, configuration, and management.

Goals

• Manage lifecycle (create, scale, upgrade, destroy) of Kubernetes-conformant clusters via declarative API
• Work in different environments (on-premises and cloud)
• Define common operations with swappable implementations
• Reuse existing ecosystem components (cluster-autoscaler, node-problem-detector)
• Provide transition path for existing tools to adopt incrementally

Non-Goals

• Add APIs to Kubernetes core
• Manage infrastructure unrelated to Kubernetes clusters
• Force all lifecycle products to use these APIs
• Manage non-CAPI provisioned clusters
• Manage single cluster spanning multiple providers
• Configure machines after create/upgrade

Quick Navigation

When to Use

• Provisioning Kubernetes clusters across multiple infrastructure providers
• Managing cluster lifecycle (create, scale, upgrade, destroy)
• Automating cluster operations with declarative APIs
• Implementing GitOps workflows for cluster management
• Building custom infrastructure providers

Core Concepts

Architecture

┌─────────────────────────────────────────┐
│         Management Cluster              │
│  ┌─────────────┐  ┌─────────────────┐   │
│  │ CAPI Core   │  │ Infrastructure  │   │
│  │ Controllers │  │ Provider        │   │
│  └─────────────┘  └─────────────────┘   │
│  ┌─────────────┐  ┌─────────────────┐   │
│  │  Bootstrap  │  │  Control Plane  │   │
│  │  Provider   │  │  Provider       │   │
│  └─────────────┘  └─────────────────┘   │
└─────────────────────┬───────────────────┘
                      │ manages
          ┌───────────┴───────────┐
          ▼                       ▼
┌─────────────────┐     ┌─────────────────┐
│ Workload        │     │ Workload        │
│ Cluster 1       │     │ Cluster N       │
└─────────────────┘     └─────────────────┘

Key Components

Core Resources

Quick Start

# Install clusterctl
curl -L https://github.com/kubernetes-sigs/cluster-api/releases/download/v1.12.0/clusterctl-linux-amd64 -o clusterctl
chmod +x clusterctl
sudo mv clusterctl /usr/local/bin/

# Initialize management cluster
clusterctl init --infrastructure docker

# Create workload cluster
clusterctl generate cluster my-cluster --kubernetes-version v1.32.0 --control-plane-machine-count 1 --worker-machine-count 3 | kubectl apply -f -

# Get cluster kubeconfig
clusterctl get kubeconfig my-cluster > my-cluster.kubeconfig

# Delete cluster
kubectl delete cluster my-cluster

Common Workflows

Cluster Lifecycle

# Create cluster from template
clusterctl generate cluster prod-cluster \
  --infrastructure aws \
  --kubernetes-version v1.32.0 \
  --control-plane-machine-count 3 \
  --worker-machine-count 5 \
  | kubectl apply -f -

# Scale workers
kubectl scale machinedeployment prod-cluster-md-0 --replicas=10

# Upgrade Kubernetes version
kubectl patch cluster prod-cluster --type merge -p '{"spec":{"topology":{"version":"v1.33.0"}}}'

# Move cluster to new management cluster
clusterctl move --to-kubeconfig target-mgmt.kubeconfig

Health Monitoring

apiVersion: cluster.x-k8s.io/v1beta1
kind: MachineHealthCheck
metadata:
  name: my-cluster-mhc
spec:
  clusterName: my-cluster
  maxUnhealthy: 40%
  nodeStartupTimeout: 10m
  selector:
    matchLabels:
      cluster.x-k8s.io/cluster-name: my-cluster
  unhealthyConditions:
    - type: Ready
      status: "False"
      timeout: 5m
    - type: Ready
      status: Unknown
      timeout: 5m

Critical Prohibitions

• Do NOT modify management cluster directly without proper backup
• Do NOT delete Machine objects directly (use MachineDeployment scale)
• Do NOT mix provider versions without checking compatibility
• Do NOT skip cluster upgrade steps (control plane before workers)
• Do NOT ignore MachineHealthCheck alerts

Scripts

Go-based tools in scripts/. Run via go run ./tool-name from the scripts directory.

Assets

Reusable templates in assets/:

• Cluster templates: cluster-minimal.yaml, cluster-production.yaml, cluster-clusterclass.yaml, clusterclass-example.yaml
• Provider configs: docker-quickstart.yaml, aws-credentials.yaml, azure-credentials.yaml, provider-matrix.md
• Operations: upgrade-checklist.md, migration-v1beta2.md, troubleshooting-flow.md, security-audit-report.md, dr-backup-restore.md, etcd-backup.yaml
• GitOps: argocd-cluster-app.yaml, flux-kustomization.yaml, gitops-rbac.yaml
• Monitoring: prometheus-alerts.yaml

Links

• Documentation
• GitHub
• Releases

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