一步步教你在Kubernetes上部署Metrics Server

本文将介绍如何在 Kubernetes 集群上部署 Metrics Server，并使用它来监控集群中各种资源的使用情况。

1. 前提条件

Kubernetes 集群已经运行，并且 kubectl 工具已经正确配置。
Metrics Server 的版本符合 Kubernetes 版本要求。例如，如果 Kubernetes 版本为 1.22，则应使用与之对应的 Metrics Server 版本。

2. 部署 Metrics Server

2.1下载 Metrics Server 的部署文件：

$ wget https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

2.2编辑下载的部署文件，并添加 `--kubelet-insecure-tls` 参数，以便 Metrics Server 可以使用不安全的 TLS 连接与 kubelet 通信。

$ vim components.yaml

找到以下部分：

containers:
- name: metrics-server
  image: k8s.gcr.io/metrics-server/metrics-server:v0.5.0
  command:
    - /metrics-server
    - --kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname
    - --kubelet-insecure-tls   # 添加此行

或者直接使用下面的代码:

apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  labels:
    k8s-app: metrics-server
    rbac.authorization.k8s.io/aggregate-to-admin: "true"
    rbac.authorization.k8s.io/aggregate-to-edit: "true"
    rbac.authorization.k8s.io/aggregate-to-view: "true"
  name: system:aggregated-metrics-reader
rules:
- apiGroups:
  - metrics.k8s.io
  resources:
  - pods
  - nodes
  verbs:
  - get
  - list
  - watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  labels:
    k8s-app: metrics-server
  name: system:metrics-server
rules:
- apiGroups:
  - ""
  resources:
  - nodes/metrics
  verbs:
  - get
- apiGroups:
  - ""
  resources:
  - pods
  - nodes
  verbs:
  - get
  - list
  - watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server-auth-reader
  namespace: kube-system
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: extension-apiserver-authentication-reader
subjects:
- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server:system:auth-delegator
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: system:auth-delegator
subjects:
- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: system:metrics-server
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: system:metrics-server
subjects:
- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system
---
apiVersion: v1
kind: Service
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server
  namespace: kube-system
spec:
  ports:
  - name: https
    port: 443
    protocol: TCP
    targetPort: https
  selector:
    k8s-app: metrics-server
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server
  namespace: kube-system
spec:
  selector:
    matchLabels:
      k8s-app: metrics-server
  strategy:
    rollingUpdate:
      maxUnavailable: 0
  template:
    metadata:
      labels:
        k8s-app: metrics-server
    spec:
      containers:
      - args:
        - --cert-dir=/tmp
        - --secure-port=4443
        - --kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname
        - --kubelet-use-node-status-port
        - --metric-resolution=15s
        - --kubelet-insecure-tls 
        image: bitnami/metrics-server:latest
        imagePullPolicy: IfNotPresent
        livenessProbe:
          failureThreshold: 3
          httpGet:
            path: /livez
            port: https
            scheme: HTTPS
          periodSeconds: 10
        name: metrics-server
        ports:
        - containerPort: 4443
          name: https
          protocol: TCP
        readinessProbe:
          failureThreshold: 3
          httpGet:
            path: /readyz
            port: https
            scheme: HTTPS
          initialDelaySeconds: 20
          periodSeconds: 10
        resources:
          requests:
            cpu: 100m
            memory: 200Mi
        securityContext:
          allowPrivilegeEscalation: false
          readOnlyRootFilesystem: true
          runAsNonRoot: true
          runAsUser: 1000
        volumeMounts:
        - mountPath: /tmp
          name: tmp-dir
      nodeSelector:
        kubernetes.io/os: linux
      priorityClassName: system-cluster-critical
      serviceAccountName: metrics-server
      volumes:
      - emptyDir: {}
        name: tmp-dir
---
apiVersion: apiregistration.k8s.io/v1
kind: APIService
metadata:
  labels:
    k8s-app: metrics-server
  name: v1beta1.metrics.k8s.io
spec:
  group: metrics.k8s.io
  groupPriorityMinimum: 100
  insecureSkipTLSVerify: true
  service:
    name: metrics-server
    namespace: kube-system
  version: v1beta1
  versionPriority: 100

2.3 部署 Metrics Server：

$ kubectl apply -f components.yaml

2.4 等待 Metrics Server 部署完成：

$ kubectl get deployment metrics-server -n kube-system

输出示例：

NAME             READY   UP-TO-DATE   AVAILABLE   AGE
metrics-server   1/1     1            1           2m

如果 READY 的值为 1/1，则表示 Metrics Server 部署成功。

3. 使用 Metrics Server

现在，Metrics Server 已经在 Kubernetes 集群中部署成功。可以使用 kubectl 命令来获取集群中的度量指标。

3.1 获取节点的 CPU 使用情况：

$ kubectl top node

3.2 获取命名空间中的 Pod 的 CPU 和内存使用情况：

kubectl top pod -n <namespace>

3.3 获取命名空间中的部署的 CPU 和内存使用情况：

$ kubectl top deploy -n <namespace>

总结

在 Kubernetes 集群中部署 Metrics Server 可以实现对集群中各种资源的实时监控和度量指标收集，从而帮助管理员和开发人员更好地管理和优化 Kubernetes 应用程序的性能和可靠性。通过本文所述的步骤，可以轻松部署 Metrics Server 并使用它来监控 Kubernetes 集群中的资源使用情况。

上次更新时间 13 3 月, 2023 at 09:59 上午