k8s内通过Prometheus监控外部节点资源

前提

• 已部署k8s集群
• 集群内部署prometheus

步骤

• 在外部节点安装node-exporter

  docker run -d --name node-exporter -p 9100:9100 prom/node-exporter

  启动完成可以访问http://IP:9100/metrics.
• 创建endpoints

  apiVersion: v1
  kind: Endpoints
  metadata:
  name: node-data
  namespace: kubesphere-monitoring-system
  labels:
    app.kubernetes.io/name: node-data
  subsets:
  - addresses:
      - ip: 192.168.3.17
      - ip: 192.168.3.19
      - ip: 192.168.3.20
    ports:
      - port: 9100
        name: http

• 创建service

  apiVersion: v1
  kind: Service
  metadata:
  name: node-data
  namespace: kubesphere-monitoring-system
  labels:
    app.kubernetes.io/name: node-data
  spec:
  ports:
    - port: 9100
      name: http

• 创建ServiceMonitor

  apiVersion: monitoring.coreos.com/v1
  kind: ServiceMonitor
  metadata:
  name: node-exporter-data
  namespace: kubesphere-monitoring-system
  spec:
  endpoints:
  - port: http
  namespaceSelector:
    matchNames:
    - kubesphere-monitoring-system
  selector:
    matchLabels:
      app.kubernetes.io/name: node-data

  创建完成可以prometheus web页面查看是否有新建的targets.

  PromQL

1. 计算CPU使用率

   # 1m查不到数据可以设置成3m,5m
   (1-(sum(increase(node_cpu_seconds_total{mode="idle"}[1m]))by(instance))/(sum(increase(node_cpu_seconds_total[1m]))by(instance)))*100

2. 内存使用率

   (1-(node_memory_MemAvailable_bytes{}/(node_memory_MemTotal_bytes{})))*100

3. 磁盘分区

   # 注意mountpoint
   100 - (node_filesystem_free_bytes{mountpoint="/",fstype=~"ext4|xfs"} / node_filesystem_size_bytes{mountpoint="/",fstype=~"ext4|xfs"} * 100)