1、淺析整個監控流程

heapster以k8s內置的cAdvisor作為數據源收集集群信息，並匯總出有價值的性能數據(Metrics)：cpu、內存、網絡流量等，然後將這些數據輸出到外部存儲，如InfluxDB，最後就可以通過相應的UI界面顯示出來，如grafana。 另外heapster的數據源和外部存儲都是可插拔的，所以可以很靈活的組建出很多監控方案，如：Heapster+ElasticSearch+Kibana等等。

2、創建k8s資源對象

使用官方提供的yml文件有一些小問題，請參考以下改動和說明：

2.1、創建InfluxDB資源對象

apiVersion: apps/v1
kind:
 
 Deployment
metadata:
  name: monitoring-influxdb
  namespace: kube-system
spec:
  replicas: 1
  selector:
    matchLabels:
      task: monitoring
      k8s-app: influxdb
  template:
    metadata:
      labels:
        task: monitoring
        k8s-app: influxdb
    spec:
      containers:
      - name:
 
 influxdb
        image: k8s.gcr.io/heapster-influxdb-amd64:v1.3.3
        volumeMounts:
        - mountPath: /data
          name: influxdb-storage
      volumes:
      - name: influxdb-storage
        emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
  labels:
    task: monitoring
    kubernetes.io/cluster
 
-service: ‘true‘
    kubernetes.io/name: monitoring-influxdb
  name: monitoring-influxdb
  namespace: kube-system
spec:
  type: NodePort
  ports:
  - nodePort: 31001
    port: 8086
    targetPort: 8086
  selector:
    k8s-app: influxdb

註意：這裏我們使用NotePort暴露monitoring-influxdb服務在主機的31001端口上，那麽InfluxDB服務端的地址：http://[host-ip]:31001 ，記下這個地址，以便創建heapster和為grafana配置數據源時，可以直接使用。

2.1、創建Grafana資源對象

apiVersion: apps/v1
kind: Deployment
metadata:
  name: monitoring-grafana
  namespace: kube-system
spec:
  replicas: 1
  selector:
    matchLabels:
      task: monitoring
      k8s-app: grafana
  template:
    metadata:
      labels:
        task: monitoring
        k8s-app: grafana
    spec:
      containers:
      - name: grafana
        image: k8s.gcr.io/heapster-grafana-amd64:v4.4.3
        ports:
        - containerPort: 3000
          protocol: TCP
        volumeMounts:
        - mountPath: /etc/ssl/certs
          name: ca-certificates
          readOnly: true
        - mountPath: /var
          name: grafana-storage
        env:
        - name: INFLUXDB_HOST
          value: monitoring-influxdb
        - name: GF_SERVER_HTTP_PORT
          value: "3000"
          # The following env variables are required to make Grafana accessible via
          # the kubernetes api-server proxy. On production clusters, we recommend
          # removing these env variables, setup auth for grafana, and expose the grafana
          # service using a LoadBalancer or a public IP.
        - name: GF_AUTH_BASIC_ENABLED
          value: "false"
        - name: GF_AUTH_ANONYMOUS_ENABLED
          value: "true"
        - name: GF_AUTH_ANONYMOUS_ORG_ROLE
          value: Admin
        - name: GF_SERVER_ROOT_URL
          # If you‘re only using the API Server proxy, set this value instead:
          # value: /api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
          value: /
      volumes:
      - name: ca-certificates
        hostPath:
          path: /etc/ssl/certs
      - name: grafana-storage
        emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
  labels:
    # For use as a Cluster add-on (https://github.com/kubernetes/kubernetes/tree/master/cluster/addons)
    # If you are NOT using this as an addon, you should comment out this line.
    kubernetes.io/cluster-service: ‘true‘
    kubernetes.io/name: monitoring-grafana
  name: monitoring-grafana
  namespace: kube-system
spec:
  # In a production setup, we recommend accessing Grafana through an external Loadbalancer
  # or through a public IP.
  # type: LoadBalancer
  # You could also use NodePort to expose the service at a randomly-generated port
  type: NodePort
  ports:
  - nodePort: 30108
    port: 80
    targetPort: 3000
  selector:
    k8s-app: grafana

註意：這裏我們使用NotePort暴露monitoring-grafana服務在主機的30108上，那麽Grafana服務端的地址：http://registry.wuling.com:30108 ，通過瀏覽器訪問，為Grafana修改數據源，如下：

標紅的地方，為上一步記錄下的InfluxDB服務端的地址。

2.2、創建Heapster資源對象

apiVersion: v1
kind: ServiceAccount
metadata:
  name: heapster
  namespace: kube-system
---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: heapster
  namespace: kube-system
spec:
  replicas: 1
  selector:
    matchLabels:
      task: monitoring
      k8s-app: heapster
  template:
    metadata:
      labels:
        task: monitoring
        k8s-app: heapster
    spec:
      serviceAccountName: heapster
      containers:
      - name: heapster
        image: k8s.gcr.io/heapster-amd64:v1.4.2
        imagePullPolicy: IfNotPresent
        command:
        - /heapster
        - --source=kubernetes:https://kubernetes.default 
        - --sink=influxdb:http://150.109.39.33:31001  # 這裏填寫剛剛記錄下的InfluxDB服務端的地址。
---
apiVersion: v1
kind: Service
metadata:
  labels:
    task: monitoring
    # For use as a Cluster add-on (https://github.com/kubernetes/kubernetes/tree/master/cluster/addons)
    # If you are NOT using this as an addon, you should comment out this line.
    kubernetes.io/cluster-service: ‘true‘
    kubernetes.io/name: Heapster
  name: heapster
  namespace: kube-system
spec:
  ports:
  - port: 80
    targetPort: 8082
  selector:
    k8s-app: heapster

--source 為heapster指定獲取集群信息的數據源。參考：https://github.com/kubernetes/heapster/blob/master/docs/source-configuration.md
--sink 為heaster指定後端存儲，這裏我們使用InfluxDB，其他的，請參考：https://github.com/kubernetes/heapster/blob/master/docs/sink-owners.md
這裏heapster留下了一個的坑，請繼續往下看，當我部署完heapster，通過查看Heapster容器組的鏡像發現：

很多人都以為是https或者k8s配置的問題，於是去就慌忙的去配置InSecure http方式，導致坑越來越深，透明度越來越低，更是無從下手，我也是這樣弄了很久，都較上勁了，此處省略一萬字。。。，當這些路子都走遍了，再次品讀下面的原文：

才發現是權限的問題，heaster默認使用一個令牌(Token)與ApiServer進行認證，通過查看heapster.yml發現 serviceAccountName: heapster ，現在明白了吧，就是heaster沒有權限，那麽如何授權呢-----給heaster綁定一個有權限的角色就行了，如下：

apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
  name: heapster
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-admin
subjects:
- kind: ServiceAccount
  name: heapster
  namespace: kube-system

當創建heapster資源的時候，直接把這段代碼加上，就行了。

3、查看監控詳情

3.1、通過dashboard查看集群概況

整個監控方案部署成功後，從上圖可以看到，在不同粒度/維度下，dashboard上可以呈現對象的具體CPU和內存使用率。

3.2、通過Grafana查看集群詳情(cpu、memory、filesystem)

詳解k8s一個完整的監控方案(Heapster+Grafana+InfluxDB) - kubernetes