對比
node-exporter用於採集服務器層面的運行指標，包括機器的loadavg、filesystem、meminfo等基礎監控，類似於傳統主機監控維度的zabbix-agent。
metric-server/heapster是從api-server中獲取CPU、內存使用率這種監控指標，並把他們發送給存儲後端，如InfluxDB或雲廠商，他當前的核心作用是：爲HPA等組件提供決策指標支持。
kube-state-metrics關注於獲取Kubernetes各種資源的最新狀態，如Deployment或者DaemonSet。
例如：
我調度了多少個Replicas？現在可用的有幾個？
多少個Pod是running/stopped/terminated狀態？
Pod重啓了多少次？
我有多少job在運行中?
這些指標都由kube-state-metrics提供。
之所以沒有把kube-state-metrics納入到metric-server的能力中，是因爲他們的關注點本質上是不一樣的。
metric-server僅僅是獲取、格式化現有數據，寫入特定的存儲，實質上是一個監控系統。
kube-state-metrics是將Kubernetes的運行狀況在內存中做了個快照，並且獲取新的指標，但他沒有能力導出這些指標。
部署metric-server
下載metric-server部署的yaml文件到本地。
wget https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.3.7/components.yaml
拉取metric-server的鏡像到本地：
# docker pull zhaoqinchang/metrics-server:0.3.7 0.3.7: Pulling from zhaoqinchang/metrics-server 9ff2acc3204b: Pull complete 9d14b55ff9a0: Pull complete Digest: sha256:c0efe772bb9e5c289db6cc4bc2002c268507d0226f2a3815f7213e00261c38e9 Status: Downloaded newer image for zhaoqinchang/metrics-server:0.3.7 docker.io/zhaoqinchang/metrics-server:0.3.7
修改components.yaml文件爲如下內容：
# cat components.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"] --- 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 --- 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 --- 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 --- 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: zhaoqinchang/metrics-server:0.3.7 #修改鏡像爲剛剛拉取下來的鏡像 imagePullPolicy: IfNotPresent args: - --cert-dir=/tmp - --secure-port=4443 command: #添加以下三行command命令 - /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 --- 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 --- 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
部署metric-server：
# kubectl apply -f components.yaml 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.apps/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
查看metric.k8s.io是否出現在Kubernetes集羣的API羣組列表中：
# kubectl api-versions | grep metrics
metrics.k8s.io/v1beta1
使用
kubectl top命令可顯示節點和Pod對象的資源使用信息，它依賴於集羣中的資源指標API來收集各項指標數據。它包含有Node和Pod兩個子命令，可分別顯示Node對象和Pod對象的相關資源佔用率。
列出Node資源佔用率命令的語法格式爲“kubectl top node [-l label | NAME]”，例如下面顯示所有節點的資源佔用狀況的結果中顯示了各節點累計CPU資源佔用時長及百分比，以及內容空間佔用量及佔用比例。必要時，也可以在命令直接給出要查看的特定節點的標識，以及使用標籤選擇器進行節點過濾。
[root@master metric]# kubectl top nodes NAME CPU(cores) CPU% MEMORY(bytes) MEMORY% master 282m 14% 1902Mi 51% node-02 70m 3% 1371Mi 37% node-03 121m 1% 892Mi 11%
而名稱空間級別的Pod對象資源佔用率的使用方法會略有不同，使用時，一般應該跟定名稱空間及使用標籤選擇器過濾出目標Pod對象。例如，下面顯示kube-system名稱空間下的Pod資源使用狀況：
[root@master metric]# kubectl top pods -n kube-system NAME CPU(cores) MEMORY(bytes) etcd-master 32m 300Mi kube-apiserver-master 86m 342Mi kube-controller-manager-master 30m 48Mi kube-flannel-ds-l5ghn 5m 10Mi kube-flannel-ds-rqlm2 4m 12Mi kube-flannel-ds-v92r9 4m 14Mi kube-proxy-7vjcv 18m 15Mi kube-proxy-xrz8f 13m 21Mi kube-proxy-zpwn6 1m 14Mi kube-scheduler-master 7m 17Mi metrics-server-5549c7694f-7vb66 2m 14Mi
kubectl top命令爲用戶提供簡潔、快速獲取Node對象及Pod對象系統資源佔用狀況的接口，是集羣運行和維護的常用命令之一。
