【探索实战】Kurator多集群统一应用分发实战：从环境搭建到业务落地全流程

# 下载并安装Karmada
curl -fsSL https://raw.githubusercontent.com/karmada-io/karmada/master/hack/local-up-karmada.sh | bash

# 配置Karmada集群上下文
export KUBECONFIG="$HOME/.kube/config:/etc/karmada/karmada-apiserver.config"
kubectl config use-context karmada-apiserver

# 添加Kurator Helm仓库
helm repo add kurator https://kurator-dev.github.io/helm-charts/
helm repo update

# 安装Kurator核心组件
helm install kurator kurator/kurator --namespace kurator-system --create-namespace

# 验证安装结果
kubectl get pods -n kurator-system
# 预期输出：kurator-controller-manager等Pod状态为Running

# 下载对应系统版本的kuratorctl（以Linux为例）
wget https://github.com/kurator-dev/kurator/releases/download/v0.5.0/kuratorctl-linux-amd64.tar.gz
tar -zxvf kuratorctl-linux-amd64.tar.gz
mv kuratorctl /usr/local/bin/

# 验证安装
kuratorctl version
# 预期输出：kuratorctl version v0.5.0

# fleet.yaml
apiVersion: fleet.kurator.dev/v1alpha1
kind: Fleet
metadata:
  name: demo-fleet
  namespace: default
spec:
  clusters:
  - name: cluster-a  # 生产集群名称
    kubeconfig: |-  # 生产集群kubeconfig内容（需base64解码后填入）
      {{base64_decode "你的集群A kubeconfig base64编码"}}
  - name: cluster-b  # 测试集群名称
    kubeconfig: |-
      {{base64_decode "你的集群B kubeconfig base64编码"}}

kubectl apply -f fleet.yaml

# 验证集群纳管状态
kuratorctl get fleet demo-fleet
# 预期输出：cluster-a与cluster-b状态为Ready

# 通过kuratorctl查看集群列表
kuratorctl get clusters

# 在管理集群查看工作集群节点
kubectl --context karmada-apiserver get nodes --all-namespaces

# 初始化Nginx Chart
helm create nginx-demo

# values.yaml核心配置
replicaCount: 1  # 默认副本数
image:
  repository: nginx
  tag: 1.23-alpine
service:
  type: NodePort
  port: 80

spec:
  replicas: {{ .Values.replicaCount }}
  # 其余配置省略

# 打包Chart
helm package nginx-demo

# 安装Kurator ChartMuseum（内置Chart仓库）
helm install chartmuseum kurator/chartmuseum --namespace kurator-system

# 推送Chart到仓库
curl --data-binary "@nginx-demo-0.1.0.tgz" http://chartmuseum.kurator-system.svc.cluster.local:8080/api/charts

# nginx-application.yaml
apiVersion: apps.kurator.dev/v1alpha1
kind: Application
metadata:
  name: nginx-app
  namespace: default
spec:
  # 指定分发的Fleet（即之前创建的demo-fleet）
  fleet: demo-fleet
  # 应用来源（Helm Chart）
  source:
    helm:
      repo: http://chartmuseum.kurator-system.svc.cluster.local:8080
      chart: nginx-demo
      version: 0.1.0
  # 多集群差异化配置
  overrides:
  - clusterName: cluster-a  # 生产集群
    values: |
      replicaCount: 2  # 生产集群2副本
  - clusterName: cluster-b  # 测试集群
    values: |
      replicaCount: 1  # 测试集群1副本
  # 分发策略
  placement:
    clusterAffinity:
      clusterNames:
      - cluster-a
      - cluster-b

kubectl apply -f nginx-application.yaml

# 查看应用分发状态
kuratorctl get application nginx-app

# 查看Karmada分发的资源绑定状态
kubectl --context karmada-apiserver get resourcebindings

# 验证生产集群（cluster-a）
kubectl --context cluster-a get pods -n default
# 预期输出：2个nginx pod，状态为Running

# 验证测试集群（cluster-b）
kubectl --context cluster-b get pods -n default
# 预期输出：1个nginx pod，状态为Running

# 验证服务可访问性
# 生产集群NodePort访问
curl http://<cluster-a-node-ip>:<node-port>
# 测试集群NodePort访问
curl http://<cluster-b-node-ip>:<node-port>
# 均应返回Nginx默认页面

spec:
  source:
    helm:
      repo: http://chartmuseum.kurator-system.svc.cluster.local:8080
      chart: nginx-demo
      version: 0.1.1  # 升级后的Chart版本
  overrides:
  - clusterName: cluster-a
    values: |
      replicaCount: 2
      image:
        tag: 1.25-alpine  # 升级镜像
  - clusterName: cluster-b
    values: |
      replicaCount: 1
      image:
        tag: 1.25-alpine

kubectl apply -f nginx-application.yaml
# 验证升级后镜像版本
kubectl --context cluster-a describe pod <nginx-pod-name> | grep Image

# 通过kuratorctl触发回滚
kuratorctl rollout undo application nginx-app --to-revision 1

# 验证回滚结果
kubectl --context cluster-b describe pod <nginx-pod-name> | grep Image
# 预期输出：镜像为1.23-alpine

kubectl create clusterrolebinding admin-binding --clusterrole=cluster-admin --user=kubeconfig-user

overrides:
- clusterName: cluster-b
  values: |
    replicaCount: 1
    resources:
      requests:
        cpu: 100m  # 降低CPU请求
        memory: 128Mi