# cat prometheus.svc.yml
---
kind: Service
apiVersion: v1
metadata:
labels:
app: prometheus
name: prometheus
namespace: kube-system
spec:
type: NodePort
ports:
- port: 9090
targetPort: 9090
nodePort: 30003
selector:
app: prometheus
5、通过上述yaml文件创建相应的对象
# kubectl create -f rbac-setup.yaml
# kubectl create -f configmap.yaml
# kubectl create -f prometheus.deploy.yml
# kubectl create -f prometheus.svc.yml
Node-exporter对应的nodeport端口为31672，通过访问:31672/metrics 可以看到对应的metrics
prometheus对应的nodeport端口为30003，通过访问:30003/target 可以看到prometheus已经成功连接上了k8s的apiserver
可以在prometheus的WEB界面上提供了基本的查询K8S集群中每个POD的CPU使用情况，查询条件如下： sum by (pod_name)( rate(container_cpu_usage_seconds_total{image!="", pod_name!=""}[1m] ) )
上述的查询有出现数据，说明node-exporter往prometheus中写入数据正常，接下来我们就可以部署grafana组件，实现更友好的webui展示数据了。
五、部署grafana组件
1、grafana deployment配置文件
# cat grafana-deploy.yaml
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: grafana-core
namespace: kube-system
labels:
app: grafana
component: core
spec:
replicas: 1
template:
metadata:
labels:
app: grafana
component: core
spec:
containers:
- image: grafana/grafana:4.2.0
name: grafana-core
imagePullPolicy: IfNotPresent
# env:
resources:
# keep request = limit to keep this container in guaranteed class
limits:
cpu: 100m
memory: 100Mi
requests:
cpu: 100m
memory: 100Mi
env:
# The following env variables set up basic auth twith the default admin user and admin password.
- name: GF_AUTH_BASIC_ENABLED
value: "true"
- name: GF_AUTH_ANONYMOUS_ENABLED
value: "false"
# - name: GF_AUTH_ANONYMOUS_ORG_ROLE
# value: Admin
# does not really work, because of template variables in exported dashboards:
# - name: GF_DASHBOARDS_JSON_ENABLED
# value: "true"
readinessProbe:
httpGet:
path: /login
port: 3000
# initialDelaySeconds: 30
# timeoutSeconds: 1
volumeMounts:
- name: grafana-persistent-storage
mountPath: /var
volumes:
- name: grafana-persistent-storage
emptyDir: {}
2、grafana service配置文件