kubernetes 1.15安装部署metrics-server插件

kubernetes 1.15安装部署metrics-server插件

简单介绍：

如果使用kubernetes的自动扩容功能的话，那首先得有一个插件，然后该插件将收集到的信息（cpu、memory..）与自动扩容的设置的值进行比对，自动调整pod数量。关于该插件，在kubernetes的早些版本中采用的是heapster，1.13版本正式发布后，丢弃了heapster，官方推荐采用metrics-sever。

操作步骤：

1、 准备相关yaml文件

aggregated-metrics-reader.yaml

--- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: system:aggregated-metrics-reader labels: rbac.authorization.k8s.io/aggregate-to-view: "true" rbac.authorization.k8s.io/aggregate-to-edit: "true" rbac.authorization.k8s.io/aggregate-to-admin: "true" rules: - apiGroups: ["metrics.k8s.io"] resources: ["pods", "nodes"] verbs: ["get", "list", "watch"]

auth-delegator.yaml

--- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: 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

auth-reader.yaml

--- apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: 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

metrics-apiservice.yaml

--- apiVersion: apiregistration.k8s.io/v1beta1 kind: APIService metadata: name: v1beta1.metrics.k8s.io spec: service: name: metrics-server namespace: kube-system group: metrics.k8s.io version: v1beta1 insecureSkipTLSVerify: true groupPriorityMinimum: 100 versionPriority: 100

metrics-server-deployment.yaml

--- apiVersion: v1 kind: ServiceAccount metadata: name: metrics-server namespace: kube-system --- apiVersion: apps/v1 kind: Deployment metadata: name: metrics-server namespace: kube-system labels: k8s-app: metrics-server spec: selector: matchLabels: k8s-app: metrics-server template: metadata: name: metrics-server labels: k8s-app: metrics-server spec: serviceAccountName: metrics-server volumes: # mount in tmp so we can safely use from-scratch images and/or read-only containers - name: tmp-dir emptyDir: {} containers: - name: metrics-server image: mirrorgooglecontainers/metrics-server-amd64:v0.3.2 imagePullPolicy: IfNotPresent args: - --cert-dir=/tmp - --secure-port=4443 - /metrics-server - --kubelet-preferred-address-types=InternalIP - --kubelet-insecure-tls ports: - name: main-port containerPort: 4443 protocol: TCP securityContext: readOnlyRootFilesystem: true runAsNonRoot: true runAsUser: 1000 volumeMounts: - name: tmp-dir mountPath: /tmp nodeSelector: kubernetes.io/os: linux

metrics-server-service.yaml

--- apiVersion: v1 kind: Service metadata: name: metrics-server namespace: kube-system labels: kubernetes.io/name: "Metrics-server" kubernetes.io/cluster-service: "true" spec: selector: k8s-app: metrics-server ports: - port: 443 protocol: TCP targetPort: main-port

resource-reader.yaml

--- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: system:metrics-server rules: - apiGroups: - "" resources: - pods - nodes - nodes/stats - namespaces - configmaps verbs: - get - list - watch --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: 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

2、 应用所有配置文件到系统中

[root@k8s-master 1.8+]# kubectl apply -f . clusterrole.rbac.authorization.k8s.io/system:aggregated-metrics-reader created clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created serviceaccount/metrics-server created deployment.extensions/metrics-server created service/metrics-server created clusterrole.rbac.authorization.k8s.io/system:metrics-server created clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created

过个一两分钟（下载镜像和获取数据都会耗时）检查metrics-server的状态

[root@k8s-master 1.8+]# kubectl get po -n kube-system NAME READY STATUS RESTARTS AGE calico-node-b78m4 1/1 Running 0 176m calico-node-r5mlj 1/1 Running 0 3h6m calico-node-z5tdh 1/1 Running 0 176m coredns-fb8b8dccf-6mgks 1/1 Running 0 3h21m coredns-fb8b8dccf-cbtlx 1/1 Running 0 3h21m etcd-k8s-master 1/1 Running 0 3h20m kube-apiserver-k8s-master 1/1 Running 0 3h20m kube-controller-manager-k8s-master 1/1 Running 0 3h20m kube-proxy-c9xd2 1/1 Running 0 3h21m kube-proxy-fp2r2 1/1 Running 0 176m kube-proxy-lrsw7 1/1 Running 0 176m kube-scheduler-k8s-master 1/1 Running 0 3h20m metrics-server-7579f696d8-pgcc4 1/1 Running 0 99s [root@k8s-master 1.8+]# kubectl top node NAME CPU(cores) CPU% MEMORY(bytes) MEMORY% k8s-master 179m 8% 1660Mi 43% k8s-node1 81m 4% 908Mi 23% k8s-node2 78m 3% 1036Mi 26%

看的出来，metrics-server已经正常running，并且能够获取节点的信息。

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