K8S集群实现prometheus+grafana+alertmanager监控报警

 Prometheus概述                                                                                                                                                                 

Prometheus是一个开源的系统监控和报警系统，现在已经加入到CNCF基金会，成为继k8s之后第二个在CNCF托管的项目，在kubernetes容器管理系统中，通常会搭配prometheus进行监控，同时也支持多种exporter采集数据，还支持pushgateway进行数据上报，Prometheus性能足够支撑上万台规模的集群

 

 

 

prometheus特点                                                                                                                                                                                                                                      

---

 

1、多维度数据模型
    时间序列数据由metrics名称和键值对来组成
    可以对数据进行聚合，切割等操作
    所有的metrics都可以设置任意的多维标签。
2、灵活的查询语言（PromQL）
    可以对采集的metrics指标进行加法，乘法，连接等操作；

3、可以直接在本地部署，不依赖其他分布式存储；

4、通过基于HTTP的pull方式采集时序数据；

5、可以通过中间网关pushgateway的方式把时间序列数据推送到prometheus server端；

6、可通过服务发现或者静态配置来发现目标服务对象（targets）。

7、有多种可视化图像界面，如Grafana等。

8、高效的存储，每个采样数据占3.5 bytes左右，300万的时间序列，30s间隔，保留60天，消耗磁盘大概200G

 

prometheus组件介绍                                                                                                                                                                                                                               

---

1.Prometheus Server: 用于收集和存储时间序列数据。

2.Client Library: 客户端库，检测应用程序代码，当Prometheus抓取实例的HTTP端点时，客户端库会将所有跟踪的metrics指标的当前状态发送到prometheus server端。

3.Exporters: prometheus支持多种exporter，通过exporter可以采集metrics数据，然后发送到prometheus server端

4.Alertmanager: 从 Prometheus server 端接收到 alerts 后，会进行去重，分组，并路由到相应的接收方，发出报警，常见的接收方式有：电子邮件，微信，钉钉, slack等。

5.Grafana：监控仪表盘

6.pushgateway: 各个目标主机可上报数据到pushgatewy，然后prometheus server统一从pushgateway拉取数据。

Prometheus server由三个部分组成，Retrieval，Storage，PromQL

   Retrieval负责在活跃的target主机上抓取监控指标数据

   Storage存储主要是把采集到的数据存储到磁盘中

   PromQL是Prometheus提供的查询语言模块。

 

---

prometheus工作流程：                                                                                                                                                                                                                            

1.  Prometheus  server可定期从活跃的（up）目标主机上（target）拉取监控指标数据，目标主机的监控数据可通过配置静态job或者服务发现的方式被prometheus server采集到，这种方式默认的pull方式拉取指标；也可通过pushgateway把采集的数据上报到prometheus server中；还可通过一些组件自带的exporter采集相应组件的数据；

2.Prometheus server把采集到的监控指标数据保存到本地磁盘或者数据库；

3.Prometheus采集的监控指标数据按时间序列存储，通过配置报警规则，把触发的报警发送到alertmanager

4.Alertmanager通过配置报警接收方，发送报警到邮件，微信或者钉钉等

5.Prometheus 自带的web ui界面提供PromQL查询语言，可查询监控数据

6.Grafana可接入prometheus数据源，把监控数据以图形化形式展示出

 

部署Prometheus                                                                                                                                                                                                                                       

1、为Prometheus创建命名空间

[root@master1 prom]# cat ns_monitor.yaml 
---
apiVersion: v1
kind: Namespace
metadata:
  name: ns-monitor
  labels:
    name: ns-monitor
[root@master1 prom]# kubectl apply -f ns_monitor.yaml 
namespace/ns-monitor created

2、部署node-exporter

#拉取镜像到私服仓库（该仓库权限为公开）

[root@master1 prom]# docker pull prom/node-exporter

[root@master1 prom]# docker tag docker.io/prom/node-exporter:latest 192.168.24.33:32800/base/node-exporter

[root@master1 prom]# docker push 192.168.24.33:32800/base/node-exporter

3、node-exporter yaml #配置tolerations使其在master节点也启动一个pod

#node-exporter 文件内容
---
kind: DaemonSet
apiVersion: apps/v1
metadata:
  name: node-exporter
  namespace: ns-monitor
  labels:
    app: node-exporter
spec:
##版本历史记录
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: node-exporter
  template:
    metadata:
      labels:
        app: node-exporter
    spec:
      containers:
        - name: node-exporter
          image: 192.168.24.33:32800/base/node-exporter
          ports:
            - containerPort: 9100
              protocol: TCP
      hostNetwork: true
      hostPID: true
      tolerations:
        - effect: NoSchedule
          operator: Exists
---
kind: Service
apiVersion: v1
metadata:
  labels:
    app: node-exporter
  name: node-exporter-service
  namespace: ns-monitor
spec:
  type: ClusterIP
  ports:
    - port: 9100
      targetPort: 9100
  selector:
    app: node-exporter
  clusterIP: None

4、 部署node_exporter 访问 {nodeIP}:9100/metrics 查看指标信息 (这里不需要集群IP 所以为None)

[root@master1 prom]# kubectl apply -f node_porter.yaml 
daemonset.apps/node-exporter created
service/node-exporter-service created
[root@master1 prom]# kubectl get pod,svc -n ns-monitor
NAME                      READY   STATUS    RESTARTS   AGE
pod/node-exporter-8qjgn   1/1     Running   0          33s
pod/node-exporter-j72gm   1/1     Running   0          33s
pod/node-exporter-sdn52   1/1     Running   0          33s

NAME                            TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)    AGE
service/node-exporter-service   ClusterIP   None         <none>        9100/TCP   33s

5、部署prometheus

# 创建prometheus的configmap 存放Prometheus的配置文件内容

[root@master1 prom]# cat prometheus_cm.yaml
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-conf
  namespace: ns-monitor
  labels:
    app: prometheus
data:
  prometheus.yml: |-
    # my global config
    global:
      scrape_interval:     15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
      evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
      # scrape_timeout is set to the global default (10s).

    # Alertmanager configuration
    alerting:
      alertmanagers:
      - static_configs:        
        - targets:          
          # - alertmanager:9093

    # Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
    rule_files:
      # - "first_rules.yml"
      # - "second_rules.yml"

    # A scrape configuration containing exactly one endpoint to scrape:
    # Here it's Prometheus itself.
    scrape_configs:
      # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
      - job_name: 'prometheus'
        # metrics_path defaults to '/metrics'
        # scheme defaults to 'http'.
        static_configs:
          - targets: ['prometheus-service.ns-monitor:9090']
      - job_name: "node"
        static_configs:
          # 这里应该填写node IP+port 不应填写集群IP或者Service name
          - targets: ['node-exporter-service.ns-monitor:9100']
      - job_name: "kube-state-metrics"
        static_configs:
          - targets: ['kube-state-metrics.ns-monitor:8080']
      - job_name: 'grafana'        
        static_configs:
          - targets: ['grafana-service.ns-monitor:3000']
      - job_name: kubernetes
        honor_timestamps: true
        metrics_path: /metrics
        scheme: http
        static_configs:
          - targets: ['prometheus-service.ns-monitor:9090']
        metric_relabel_configs:
          - target_label: cluster
            replacement: kubernetes
      - job_name: 'kubernetes-cadvisor'
        # Default to scraping over https. If required, just disable this or change to
        # `http`.
        scheme: https

        # This TLS & bearer token file config is used to connect to the actual scrape
        # endpoints for cluster components. This is separate to discovery auth
        # configuration because discovery & scraping are two separate concerns in
        # Prometheus. The discovery auth config is automatic if Prometheus runs inside
        # the cluster. Otherwise, more config options have to be provided within the
        # <kubernetes_sd_config>.
        tls_config:
          ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
        bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token

        metric_relabel_configs:
        - source_labels: [instance]
          separator: ;
          regex: (.+)
          target_label: node
          replacement: $1
          action: replace

        kubernetes_sd_configs:
        - role: node
        relabel_configs:
        - action: labelmap
          regex: __meta_kubernetes_node_label_(.+)
        - target_label: __address__
          replacement: kubernetes.default.svc:443
        - source_labels: [__meta_kubernetes_node_name]
          regex: (.+)
          target_label: __metrics_path__
          replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
      # Scrape config for Kubelet cAdvisor.
      #
      # This is required for Kubernetes 1.7.3 and later, where cAdvisor metrics
      # (those whose names begin with 'container_') have been removed from the
      # Kubelet metrics endpoint.  This job scrapes the cAdvisor endpoint to
      # retrieve those metrics.
      #
      # In Kubernetes 1.7.0-1.7.2, these metrics are only exposed on the cAdvisor
      # HTTP endpoint; use "replacement: /api/v1/nodes/${1}:4194/proxy/metrics"
      # in that case (and ensure cAdvisor's HTTP server hasn't been disabled with
      # the --cadvisor-port=0 Kubelet flag).
      #
      # This job is not necessary and should be removed in Kubernetes 1.6 and
      # earlier versions, or it will cause the metrics to be scraped twice.

      # Scrape config for service endpoints.
      #
      # The relabeling allows the actual service scrape endpoint to be configured
      # via the following annotations:
      #
      # * `prometheus.io/scrape`: Only scrape services that have a value of `true`
      # * `prometheus.io/scheme`: If the metrics endpoint is secured then you will need
      # to set this to `https` & most likely set the `tls_config` of the scrape config.
      # * `prometheus.io/path`: If the metrics path is not `/metrics` override this.
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-rules
  namespace: ns-monitor
  labels:
    app: prometheus
data:
  cpu-usage.rule: |
    groups:
      - name: NodeCPUUsage
        rules:
          - alert: NodeCPUUsage
            expr: (100 - (avg by (instance) (irate(node_cpu{name="node-exporter",mode="idle"}[5m])) * 100)) > 75
            for: 2m
            labels:
              severity: "page"
            annotations:
              summary: "{{$labels.instance}}: High CPU usage detected"
              description: "{{$labels.instance}}: CPU usage is above 75% (current value is: {{ $value }})"

 #基于consul自动发现 实现批量添加 （需搭建consul服务）

      - job_name: 'consul-prometheus'
        consul_sd_configs:
        - server: '192.168.24.31:8500'
          services: [] # 抓取所有服务
        relabel_configs:
          - source_labels: ['__meta_consul_service']
            regex: "consul"  #匹配为"consul" 的service 不抓取consul服务
            action: drop
         # tags: ['service'] # 根据tag筛选要捕捉的服务

# tags 与 relabel_configs 功能重合 都是筛选或者删除匹配的标签服务

 

 

6、创建rbac权限

[root@master1 prom]# cat prometheus_rbac.yaml 
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
  name: prometheus
rules:
  - apiGroups: [""] # "" indicates the core API group    
    resources:
      - nodes      
      - nodes/proxy      
      - services      
      - endpoints      
      - pods    
    verbs:
      - get
      - watch
      - list
  - apiGroups:
      - extensions
    resources:
      - ingresses
    verbs:
      - get
      - watch
      - list
  - nonResourceURLs: ["/metrics"]
    verbs:
      - get
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: prometheus
  namespace: ns-monitor
  labels:
    app: prometheus
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
  name: prometheus
subjects:
- kind: ServiceAccount
  name: prometheus
  namespace: ns-monitor
roleRef:
  kind: ClusterRole
  name: prometheus
  apiGroup: rbac.authorization.k8s.io



[root@master1 prom]# kubectl apply -f prometheus_rbac.yaml 
clusterrole.rbac.authorization.k8s.io/prometheus created
serviceaccount/prometheus created
clusterrolebinding.rbac.authorization.k8s.io/prometheus created

7、创建prometheus PV PVC # 生产环境建议使用nfs 存储数据 否则pod 数据会丢失

# 测试为搭建nfs 使用本地目录作为pv的挂载
[root@master1 prom]# mkdir /data/prometheus/ -p

[root@master1 prom]# kubectl create -f prometheus_pv.yaml 
persistentvolume/prometheus-data-pv created
persistentvolumeclaim/prometheus-data-pvc created

[root@master1 prom]# cat prometheus_pv.yaml 
---
apiVersion: v1
kind: PersistentVolume
metadata:
  name: "prometheus-data-pv"
  labels:
    name: prometheus-data-pv
    release: stable
spec:
  capacity:
    storage: 5Gi
  accessModes:
    - ReadWriteOnce
  persistentVolumeReclaimPolicy: Recycle
  hostPath:
    path: /data/prometheus
#  nfs:
#    path: /nfs/prometheus/data
#    server: x.x.x.x

---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: prometheus-data-pvc
  namespace: ns-monitor
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 5Gi
  selector:
    matchLabels:
      name: prometheus-data-pv
      release: stable

8、部署prometheus # 生产环境建议使用nfs 存储数据 否则pod 数据会丢失

[root@master1 prom]# cat prometheus.yaml 
---
kind: Deployment
apiVersion: apps/v1
metadata:
  labels:
    app: prometheus
  name: prometheus
  namespace: ns-monitor
spec:
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      serviceAccountName: prometheus
      securityContext:
        runAsUser: 0
        fsGroup: 0
      containers:
        - name: prometheus
          image: 192.168.24.33:32800/base/prometheus
          volumeMounts:
            - mountPath: /prometheus
              name: prometheus-data-volume
            - mountPath: /etc/prometheus/prometheus.yml
              name: prometheus-conf-volume
              subPath: prometheus.yml
            - mountPath: /etc/prometheus/rules
              name: prometheus-rules-volume
          ports:
            - containerPort: 9090
              protocol: TCP
      volumes:
        - name: prometheus-data-volume
          persistentVolumeClaim:
            claimName: prometheus-data-pvc
        - name: prometheus-conf-volume
          configMap:
            name: prometheus-conf
        - name: prometheus-rules-volume
          configMap:
            name: prometheus-rules
      tolerations:
        - key: node-role.kubernetes.io/master
          effect: NoSchedule

---
apiVersion: v1
kind: Service
metadata:
  annotations:
    prometheus.io/scrape: 'true'
  labels:
    app: prometheus
  name: prometheus-service
  namespace: ns-monitor
spec:
  ports:
    - port: 9090
      targetPort: 9090
  selector:
    app: prometheus
  type: NodePort

查看是否部署成功

[root@master1 prom]# kubectl get pods,svc -n ns-monitor
NAME                              READY   STATUS    RESTARTS   AGE
pod/node-exporter-8qjgn           1/1     Running   0          87m
pod/node-exporter-j72gm           1/1     Running   0          87m
pod/node-exporter-sdn52           1/1     Running   0          87m
pod/prometheus-785989754c-bz7jg   1/1     Running   0          69s

NAME                            TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
service/node-exporter-service   ClusterIP   None            <none>        9100/TCP         87m
service/prometheus-service      NodePort    10.102.249.50   <none>        9090:31072/TCP   69s

 

 9、安装配置kube-state-metrics组件

#kube-state-metrics是什么？

kube-state-metrics通过监听API Server生成有关资源对象的状态指标，比如Deployment、Node、Pod，需要注意的是kube-state-metrics只是简单的提供一个metrics数据，并不会存储这些指标数据，所以我们可以使用Prometheus来抓取这些数据然后存储，主要关注的是业务相关的一些元数据，比如Deployment、Pod、副本状态等；调度了多少个replicas？现在可用的有几个？多少个Pod是running/stopped/terminated状态？Pod重启了多少次？我有多少job在运行中。

#metrics cluster-role yaml

[root@master1 metrics]# cat cluster-role.yaml 
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  labels:
    app.kubernetes.io/name: kube-state-metrics
    app.kubernetes.io/version: v1.9.8
  name: kube-state-metrics
rules:
- apiGroups:
  - ""
  resources:
  - configmaps
  - secrets
  - nodes
  - pods
  - services
  - resourcequotas
  - replicationcontrollers
  - limitranges
  - persistentvolumeclaims
  - persistentvolumes
  - namespaces
  - endpoints
  verbs:
  - list
  - watch
- apiGroups:
  - extensions
  resources:
  - daemonsets
  - deployments
  - replicasets
  - ingresses
  verbs:
  - list
  - watch
- apiGroups:
  - apps
  resources:
  - statefulsets
  - daemonsets
  - deployments
  - replicasets
  verbs:
  - list
  - watch
- apiGroups:
  - batch
  resources:
  - cronjobs
  - jobs
  verbs:
  - list
  - watch
- apiGroups:
  - autoscaling
  resources:
  - horizontalpodautoscalers
  verbs:
  - list
  - watch
- apiGroups:
  - authentication.k8s.io
  resources:
  - tokenreviews
  verbs:
  - create
- apiGroups:
  - authorization.k8s.io
  resources:
  - subjectaccessreviews
  verbs:
  - create
- apiGroups:
  - policy
  resources:
  - poddisruptionbudgets
  verbs:
  - list
  - watch
- apiGroups:
  - certificates.k8s.io
  resources:
  - certificatesigningrequests
  verbs:
  - list
  - watch
- apiGroups:
  - storage.k8s.io
  resources:
  - storageclasses
  - volumeattachments
  verbs:
  - list
  - watch
- apiGroups:
  - admissionregistration.k8s.io
  resources:
  - mutatingwebhookconfigurations
  - validatingwebhookconfigurations
  verbs:
  - list
  - watch
- apiGroups:
  - networking.k8s.io
  resources:
  - networkpolicies
  verbs:
  - list
  - watch

# metrics rolebinding yaml

[root@master1 metrics]# cat cluster-role-binding.yaml 
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  labels:
    app.kubernetes.io/name: kube-state-metrics
    app.kubernetes.io/version: v1.9.8
  name: kube-state-metrics
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: kube-state-metrics
subjects:
- kind: ServiceAccount
  name: kube-state-metrics
  namespace: ns-monitor

#sa yaml

[root@master1 metrics]# cat service-account.yaml 
apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    app.kubernetes.io/name: kube-state-metrics
    app.kubernetes.io/version: v1.9.8
  name: kube-state-metrics
  namespace: ns-monitor

#deploy.yaml

[root@master1 metrics]# cat deployment.yaml 
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app.kubernetes.io/name: kube-state-metrics
    app.kubernetes.io/version: v1.9.8
  name: kube-state-metrics
  namespace: ns-monitor
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/name: kube-state-metrics
  template:
    metadata:
      labels:
        app.kubernetes.io/name: kube-state-metrics
        app.kubernetes.io/version: v1.9.8
    spec:
      containers:
      - image: registry.cn-shenzhen.aliyuncs.com/starsl/kube-state-metrics:v1.9.8
        livenessProbe:
          httpGet:
            path: /healthz
            port: 8080
          initialDelaySeconds: 5
          timeoutSeconds: 5
        name: kube-state-metrics
        ports:
        - containerPort: 8080
          name: http-metrics
        - containerPort: 8081
          name: telemetry
        readinessProbe:
          httpGet:
            path: /
            port: 8081
          initialDelaySeconds: 5
          timeoutSeconds: 5
      nodeSelector:
        beta.kubernetes.io/os: linux
      serviceAccountName: kube-state-metrics

---
apiVersion: v1
kind: Service
metadata:
#  annotations:
#    prometheus.io/scrape: 'true'
  labels:
    app.kubernetes.io/name: kube-state-metrics
    app.kubernetes.io/version: v1.9.8
  name: kube-state-metrics
  namespace: ns-monitor
spec:
  clusterIP: None
  ports:
  - name: http-metrics
    port: 8080
    targetPort: http-metrics
  - name: telemetry
    port: 8081
    targetPort: telemetry
  selector:
    app.kubernetes.io/name: kube-state-metrics

# kubectl apply -f 相关yaml文件 然后查看服务运行状态 成功即可

[root@master1 metrics]# kubectl get pods -n ns-monitor
NAME                                  READY   STATUS    RESTARTS   AGE
kube-state-metrics-78d7c9bd46-g9bmv   1/1     Running   0          16h

 

 10、部署grafana

[root@master1 prom]# docker pull grafana/grafana:latest

[root@master1 prom]# docker tag docker.io/grafana/grafana:latest 192.168.24.33:32800/base/grafana
[root@master1 prom]# docker push 192.168.24.33:32800/base/grafana

grafana.yaml # 生产环境建议使用nfs 存储数据 否则pod 数据会丢失

[root@master1 prom]# cat grafana.yaml 
apiVersion: v1
kind: PersistentVolume
metadata:
  name: "grafana-data-pv"
  labels:
    name: grafana-data-pv
    release: stable
spec:
  capacity:
    storage: 5Gi
  accessModes:
    - ReadWriteOnce
  persistentVolumeReclaimPolicy: Recycle
#  nfs:
#    path: /nfs/grafana/data
#    server: 192.168.115.210
  hostPath:
    path: /data/grafana

---

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: grafana-data-pvc
  namespace: ns-monitor
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 5Gi
  selector:
    matchLabels:
      name: grafana-data-pv
      release: stable
---
kind: Deployment
apiVersion: apps/v1
metadata:
  labels:
    app: grafana
  name: grafana
  namespace: ns-monitor
spec:
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: grafana
  template:
    metadata:
      labels:
        app: grafana
    spec:
      securityContext:
        runAsUser: 0
        fsGroup: 0
      containers:
        - name: grafana
          image: 192.168.24.33:32800/base/grafana
          env:
            - name: GF_AUTH_BASIC_ENABLED
              value: "true"
            - name: GF_AUTH_ANONYMOUS_ENABLED
              value: "false"
          readinessProbe:
            httpGet:
              path: /login
              port: 3000
          volumeMounts:
            - mountPath: /var/lib/grafana
              name: grafana-data-volume
          ports:
            - containerPort: 3000
              protocol: TCP
      volumes:
        - name: grafana-data-volume
          persistentVolumeClaim:
            claimName: grafana-data-pvc
---
kind: Service
apiVersion: v1
metadata:
  labels:
    app: grafana
  name: grafana-service
  namespace: ns-monitor
spec:
  ports:
    - port: 3000
      targetPort: 3000
  selector:
    app: grafana
  type: NodePort

查看服务状态

[root@master1 prom]# kubectl get pods,svc -n ns-monitor -o wide
NAME                              READY   STATUS    RESTARTS   AGE     IP              NODE      NOMINATED NODE   READINESS GATES
pod/grafana-6f5f9d57c6-rxgzn      1/1     Running   0          4m20s   10.244.1.57     node1     <none>           <none>
pod/node-exporter-8qjgn           1/1     Running   0          124m    192.168.24.32   node1     <none>           <none>
pod/node-exporter-j72gm           1/1     Running   0          124m    192.168.24.33   24d33     <none>           <none>
pod/node-exporter-sdn52           1/1     Running   0          124m    192.168.24.31   master1   <none>           <none>
pod/prometheus-785989754c-bz7jg   1/1     Running   0          38m     10.244.2.23     24d33     <none>           <none>

NAME                            TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE     SELECTOR
service/grafana-service         NodePort    10.110.28.180   <none>        3000:32276/TCP   4m20s   app=grafana
service/node-exporter-service   ClusterIP   None            <none>        9100/TCP         124m    app=node-exporter
service/prometheus-service      NodePort    10.102.249.50   <none>        9090:31072/TCP   38m     app=prometheus

登陆grafana面板 默认账号密码为admin/admin 

 

 #配置Prometheus为数据源

 

#grafana官网查找一个关于kubernetes的 exporter模板 导入到grafana

#本次导入的模板为https://grafana.com/grafana/dashboards/13105  

# dashboard id 为13105

 

 #导入后点击load载入 然后选择指标来源为Prometheus 加载模板 以下为效果图

 

 

 

 

 #安装配置AlertManager报警

#步骤后直接执行yaml文件 这里没放kubectl apply -f 的步骤

1、拉取官方镜像推送到仓库
2、创建alertmanager的configmap
3、部署alertmanager服务
4、配置Prometheus设置alertmanager地址以及报警规则

1、拉取官方镜像推送到仓库

docker pull prom/alertmanager

docker tag docker.io/prom/alertmanager:latest 192.168.24.33:32800/base/alertmanager

docker push 192.168.24.33:32800/base/alertmanager

2、创建alertmanager的configmap

[root@master1 prom]# cat alertmanager_cm.yaml 
kind: ConfigMap
apiVersion: v1
metadata:
  name: alertmanager
  namespace: ns-monitor
data:
  alertmanager.yml: |-
    global:
#每1分钟检查是否恢复
      resolve_timeout: 1m
      smtp_smarthost: 'smtp.163.com:25'
      smtp_from: 'jinpeng_chen27@163.com'
      smtp_auth_username: 'jinpeng_chen27'
# auth_password 并不是邮箱密码 只是邮箱的授权码
      smtp_auth_password: 'PWVCTWNVHOEDEMXJ'
      smtp_require_tls: false
    route:
#采用哪个标签来作为分组依据
      group_by: [alertname]
#组告警等待时间 如果有同组告警一起发出
      group_wait: 10s
#两组告警的时间间隔
      group_interval: 10s
#重复告警的间隔时间
      repeat_interval: 10m
# 设置默认接收人
      receiver: default-receiver
    receivers:
    - name: 'default-receiver'
      email_configs:
      - to: '1585742649@qq.com'
        send_resolved: true

 3、部署alertmanager服务 #如果是生产环境 挂在目录最好为nfs

[root@master1 prom]# cat alertmanager.yaml 
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: alertmanager
  namespace: ns-monitor
  labels:
    app: alertmanager
spec:
  replicas: 1
  selector:
    matchLabels:
      app: alertmanager
  template:
    metadata:
      labels:
        app: alertmanager
    spec:
      securityContext:
        runAsUser: 0
        fsGroup: 0
      #serviceAccountName: alertmanager
      containers:
        - name: alertmanager
          image: 192.168.24.33:32800/base/alertmanager
          args:
          - "--config.file=/etc/alertmanager/alertmanager.yml"
          - "--log.level=debug"
          ports:
            - containerPort: 9093
          volumeMounts:
          - name: alertmanager-config
            mountPath: /etc/alertmanager
          - name: alertmanager-storage
            mountPath: /alertmanager
          - name: localtime
            mountPath: /etc/localtime
      volumes:
        - name: alertmanager-config
          configMap:
            name: alertmanager
        - name: alertmanager-storage
          hostPath:
           path: /data/alertmanager
           type: DirectoryOrCreate
        - name: localtime
          hostPath:
           path: /usr/share/zoneinfo/Asia/Shanghai

---
apiVersion: v1
kind: Service
metadata:
  labels:
    name: alertmanager
  name: alertmanager-service
  namespace: ns-monitor
spec:
  ports:
    - port: 9093
      protocol: TCP
      targetPort: 9093
  selector:
    app: alertmanager
  sessionAffinity: None
  type: NodePor

4、配置Prometheus设置alertmanager地址以及报警规则

报警规则相关文档文献

阿里云  https://help.aliyun.com/document_detail/176180.html

腾讯云 https://cloud.tencent.com/document/product/1416/56011

#报警规则的yml文件

#prometheus_rule.yaml

---
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-rules
  namespace: ns-monitor
  labels:
    app: prometheus
data:
  rules.yml: |
    groups:
    - name: example
      rules:
      - alert: kube-proxy的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
      - alert:  kube-proxy的cpu使用率大于90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
      - alert: scheduler的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
      - alert:  scheduler的cpu使用率大于90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
      - alert: controller-manager的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
      - alert:  controller-manager的cpu使用率大于90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
      - alert: apiserver的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
      - alert:  apiserver的cpu使用率大于90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率���过90%"
      - alert: etcd的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
      - alert:  etcd的cpu使用率大于90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
      - alert: kube-state-metrics的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"
          value: "{{ $value }}%"
          threshold: "80%"      
      - alert: kube-state-metrics的cpu使用率大于90%
        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"
          value: "{{ $value }}%"
          threshold: "90%"      
      - alert: coredns的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"
          value: "{{ $value }}%"
          threshold: "80%"      
      - alert: coredns的cpu使用率大于90%
        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"
          value: "{{ $value }}%"
          threshold: "90%"      
      - alert: kube-proxy打开句柄数>600
        expr: process_open_fds{job=~"kubernetes-kube-proxy"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
          value: "{{ $value }}"
      - alert: kube-proxy打开句柄数>1000
        expr: process_open_fds{job=~"kubernetes-kube-proxy"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
          value: "{{ $value }}"
      - alert: kubernetes-schedule打开句柄数>600
        expr: process_open_fds{job=~"kubernetes-schedule"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
          value: "{{ $value }}"
      - alert: kubernetes-schedule打开句柄数>1000
        expr: process_open_fds{job=~"kubernetes-schedule"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
          value: "{{ $value }}"
      - alert: kubernetes-controller-manager打开句柄数>600
        expr: process_open_fds{job=~"kubernetes-controller-manager"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
          value: "{{ $value }}"
      - alert: kubernetes-controller-manager打开句柄数>1000
        expr: process_open_fds{job=~"kubernetes-controller-manager"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
          value: "{{ $value }}"
      - alert: kubernetes-apiserver打开句柄数>600
        expr: process_open_fds{job=~"kubernetes-apiserver"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
          value: "{{ $value }}"
      - alert: kubernetes-apiserver打开句柄数>1000
        expr: process_open_fds{job=~"kubernetes-apiserver"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
          value: "{{ $value }}"
      - alert: kubernetes-etcd打开句柄数>600
        expr: process_open_fds{job=~"kubernetes-etcd"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
          value: "{{ $value }}"
      - alert: kubernetes-etcd打开句柄数>1000
        expr: process_open_fds{job=~"kubernetes-etcd"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
          value: "{{ $value }}"
      - alert: coredns
        expr: process_open_fds{k8s_app=~"kube-dns"}  > 600
        for: 2s
        labels:
          severity: warnning 
        annotations:
          description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过600"
          value: "{{ $value }}"
      - alert: coredns
        expr: process_open_fds{k8s_app=~"kube-dns"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过1000"
          value: "{{ $value }}"
      - alert: kube-proxy
        expr: process_virtual_memory_bytes{job=~"kubernetes-kube-proxy"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
          value: "{{ $value }}"
      - alert: scheduler
        expr: process_virtual_memory_bytes{job=~"kubernetes-schedule"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
          value: "{{ $value }}"
      - alert: kubernetes-controller-manager
        expr: process_virtual_memory_bytes{job=~"kubernetes-controller-manager"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
          value: "{{ $value }}"
      - alert: kubernetes-apiserver
        expr: process_virtual_memory_bytes{job=~"kubernetes-apiserver"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
          value: "{{ $value }}"
      - alert: kubernetes-etcd
        expr: process_virtual_memory_bytes{job=~"kubernetes-etcd"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
          value: "{{ $value }}"
      - alert: kube-dns
        expr: process_virtual_memory_bytes{k8s_app=~"kube-dns"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 使用虚拟内存超过2G"
          value: "{{ $value }}"
      - alert: HttpRequestsAvg
        expr: sum(rate(rest_client_requests_total{job=~"kubernetes-kube-proxy|kubernetes-kubelet|kubernetes-schedule|kubernetes-control-manager|kubernetes-apiservers"}[1m]))  > 1000
        for: 2s
        labels:
          team: admin
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): TPS超过1000"
          value: "{{ $value }}"
          threshold: "1000"   
      - alert: Pod_restarts
        expr: sum (increase (kube_pod_container_status_restarts_total{}[2m])) by (namespace,pod) >0
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "在{{$labels.namespace}}名称空间下发现{{$labels.pod}}这个pod下的容器{{$labels.container}}被重启,这个监控指标是由{{$labels.instance}}采集"
          value: "{{ $value }}"
          threshold: "0"
      - alert: Pod_waiting
        expr: kube_pod_container_status_waiting_reason{namespace=~"kube-system|default"} == 1
        for: 2s
        labels:
          team: admin
        annotations:
          description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}启动异常等待中"
          value: "{{ $value }}"
          threshold: "1"   
      - alert: Pod_terminated
        expr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|ns-monitor"} == 1
        for: 2s
        labels:
          team: admin
        annotations:
          description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}被删除"
          value: "{{ $value }}"
          threshold: "1"
      - alert: Etcd_leader
        expr: etcd_server_has_leader{job="kubernetes-etcd"} == 0
        for: 2s
        labels:
          team: admin
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): 当前没有leader"
          value: "{{ $value }}"
          threshold: "0"
      - alert: Etcd_leader_changes
        expr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0
        for: 2s
        labels:
          team: admin
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): 当前leader已发生改变"
          value: "{{ $value }}"
          threshold: "0"
      - alert: Etcd_failed
        expr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0
        for: 2s
        labels:
          team: admin
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}}): 服务失败"
          value: "{{ $value }}"
          threshold: "0"
      - alert: Etcd_db_total_size
        expr: etcd_debugging_mvcc_db_total_size_in_bytes{job="kubernetes-etcd"} > 10000000000
        for: 2s
        labels:
          team: admin
        annotations:
          description: "组件{{$labels.job}}({{$labels.instance}})：db空间超过10G"
          value: "{{ $value }}"
          threshold: "10G"
      - alert: Endpoint_ready
        expr: kube_endpoint_address_not_ready{namespace=~"kube-system|default"} == 1
        for: 2s
        labels:
          team: admin
        annotations:
          description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.endpoint}}不可用"
          value: "{{ $value }}"
          threshold: "1"
    - name: 物理节点状态-监控告警
      rules:
      - alert: 物理节点cpu使用率
        expr: 100-avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) by(instance)*100 > 90
        for: 2s
        labels:
          severity: ccritical
        annotations:
          summary: "{{ $labels.instance }}cpu使用率过高"
          description: "{{ $labels.instance }}的cpu使用率超过90%,当前使用率[{{ $value }}],需要排查处理" 
      - alert: 物理节点内存使用率
        expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes + node_memory_Buffers_bytes + node_memory_Cached_bytes)) / node_memory_MemTotal_bytes * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{ $labels.instance }}内存使用率过高"
          description: "{{ $labels.instance }}的内存使用率超过90%,当前使用率[{{ $value }}],需要排���处理"
      - alert: InstanceDown
        expr: up == 0
        for: 2s
        labels:
          severity: critical
        annotations:   
          summary: "{{ $labels.instance }}: 服务器宕机"
          description: "{{ $labels.instance }}: 服务器延时超过2分钟"
      - alert: 物理节点磁盘的IO性能
        expr: 100-(avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} 流入磁盘IO使用率过高！"
          description: "{{$labels.mountpoint }} 流入磁盘IO大于60%(目前使用:{{$value}})"
      - alert: 入网流量带宽
        expr: ((sum(rate (node_network_receive_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} 流入网络带宽��高！"
          description: "{{$labels.mountpoint }}流入网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"
      - alert: 出网流量带宽
        expr: ((sum(rate (node_network_transmit_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} 流出网络带宽过高！"
          description: "{{$labels.mountpoint }}流出网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"
      - alert: TCP会话
        expr: node_netstat_Tcp_CurrEstab > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} TCP_ESTABLISHED过高！"
          description: "{{$labels.mountpoint }} TCP_ESTABLISHED大于1000%(目前使用:{{$value}}%)"
      - alert: 磁盘容量
        expr: 100-(node_filesystem_free_bytes{fstype=~"ext4|xfs"}/node_filesystem_size_bytes {fstype=~"ext4|xfs"}*100) > 80
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} 磁盘分区使用率过高！"
          description: "{{$labels.mountpoint }} 磁盘分区使用大于80%(目前使用:{{$value}}%)"

 

#重新配置prometheus配置文件的configmap  添加alertmanager 地址 以及添加报警规则文件

[root@master1 prom]# cat prometheus_cm.yaml
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-conf
  namespace: ns-monitor
  labels:
    app: prometheus
data:
  prometheus.yml: |-
    # my global config
    global:
      scrape_interval:     15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
      evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
      # scrape_timeout is set to the global default (10s).

    # Alertmanager configuration
    alerting:
      alertmanagers:
      - static_configs:        
        - targets: ['alertmanager-service:9093']

    # Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
    rule_files:
      - "/etc/prometheus/rules.yml"
      # - "first_rules.yml"
      # - "second_rules.yml"

    # A scrape configuration containing exactly one endpoint to scrape:
    # Here it's Prometheus itself.
    scrape_configs:
      # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
      - job_name: 'prometheus'
        # metrics_path defaults to '/metrics'
        # scheme defaults to 'http'.
        static_configs:
          - targets: ['prometheus-service.ns-monitor:9090']
      - job_name: "node1"
        static_configs:
          - targets: ['192.168.24.32:9100']
      - job_name: "node2"
        static_configs:
          - targets: ['192.168.24.33:9100']
      - job_name: "master1"
        static_configs:
          - targets: ['192.168.24.31:9100']
      - job_name: "kube-state-metrics"
        static_configs:
          - targets: ['kube-state-metrics.ns-monitor:8080']
      - job_name: 'grafana'        
        static_configs:
          - targets: ['grafana-service.ns-monitor:3000']
      - job_name: kubernetes
        honor_timestamps: true
        metrics_path: /metrics
        scheme: http
        static_configs:
          - targets: ['prometheus-service.ns-monitor:9090']
        metric_relabel_configs:
          - target_label: cluster
            replacement: kubernetes
      - job_name: 'kubernetes-cadvisor'
        # Default to scraping over https. If required, just disable this or change to
        # `http`.
        scheme: https

        # This TLS & bearer token file config is used to connect to the actual scrape
        # endpoints for cluster components. This is separate to discovery auth
        # configuration because discovery & scraping are two separate concerns in
        # Prometheus. The discovery auth config is automatic if Prometheus runs inside
        # the cluster. Otherwise, more config options have to be provided within the
        # <kubernetes_sd_config>.
        tls_config:
          ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
        bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token

        metric_relabel_configs:
        - source_labels: [instance]
          separator: ;
          regex: (.+)
          target_label: node
          replacement: $1
          action: replace

        kubernetes_sd_configs:
        - role: node
        relabel_configs:
        - action: labelmap
          regex: __meta_kubernetes_node_label_(.+)
        - target_label: __address__
          replacement: kubernetes.default.svc:443
        - source_labels: [__meta_kubernetes_node_name]
          regex: (.+)
          target_label: __metrics_path__
          replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
      # Scrape config for Kubelet cAdvisor.
      #
      # This is required for Kubernetes 1.7.3 and later, where cAdvisor metrics
      # (those whose names begin with 'container_') have been removed from the
      # Kubelet metrics endpoint.  This job scrapes the cAdvisor endpoint to
      # retrieve those metrics.
      #
      # In Kubernetes 1.7.0-1.7.2, these metrics are only exposed on the cAdvisor
      # HTTP endpoint; use "replacement: /api/v1/nodes/${1}:4194/proxy/metrics"
      # in that case (and ensure cAdvisor's HTTP server hasn't been disabled with
      # the --cadvisor-port=0 Kubelet flag).
      #
      # This job is not necessary and should be removed in Kubernetes 1.6 and
      # earlier versions, or it will cause the metrics to be scraped twice.

      # Scrape config for service endpoints.
      #
      # The relabeling allows the actual service scrape endpoint to be configured
      # via the following annotations:
      #
      # * `prometheus.io/scrape`: Only scrape services that have a value of `true`
      # * `prometheus.io/scheme`: If the metrics endpoint is secured then you will need
      # to set this to `https` & most likely set the `tls_config` of the scrape config.
      # * `prometheus.io/path`: If the metrics path is not `/metrics` override this.

# 重新部署Prometheus服务

[root@master1 prom]# cat prometheus.yaml 
---
kind: Deployment
apiVersion: apps/v1
metadata:
  labels:
    app: prometheus
  name: prometheus
  namespace: ns-monitor
spec:
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      serviceAccountName: prometheus
      securityContext:
        runAsUser: 0
        fsGroup: 0
      containers:
        - name: prometheus
          image: 192.168.24.33:32800/base/prometheus
          args:
          - --web.enable-lifecycle
          - --config.file=/etc/prometheus/prometheus.yml
          volumeMounts:
            - mountPath: /prometheus
              name: prometheus-data-volume
            - mountPath: /etc/prometheus/prometheus.yml
              name: prometheus-conf-volume
              subPath: prometheus.yml
            - mountPath: /etc/prometheus/rules.yml
              name: prometheus-rules-volume
              subPath: rules.yml
            - mountPath: /etc/localtime
              name: date-config
          ports:
            - containerPort: 9090
      volumes:
        - name: date-config
          hostPath:
            path: /etc/localtime
        - name: prometheus-data-volume
          persistentVolumeClaim:
            claimName: prometheus-data-pvc
        - name: prometheus-conf-volume
          configMap:
            name: prometheus-conf
        - name: prometheus-rules-volume
          configMap:
            name: prometheus-rules
      tolerations:
        - key: node-role.kubernetes.io/master
          effect: NoSchedule

---
apiVersion: v1
kind: Service
metadata:
  annotations:
    prometheus.io/scrape: 'true'
  labels:
    app: prometheus
  name: prometheus-service
  namespace: ns-monitor
spec:
  ports:
    - port: 9090
      targetPort: 9090
  selector:
    app: prometheus
  type: NodePort

#查看Prometheus的alerts接口

 

 #访问alertmanager服务 查看告警邮件是否发出 这里为http://192.168.24.31:31313/#/alerts

 

 #登录邮箱验证

 

 ## 邮件告警方式完成