Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统

prometheus安装和配置

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架构图

　　从上图可发现，Prometheus整个生态圈组成主要包括prometheus server，Exporter，pushgateway，alertmanager，grafana，Web ui界面，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

安装node-exporter组件

​ node-exporter是采集机器（物理机、虚拟机、云主机等）的监控指标数据，能够采集到的指标包括CPU, 内存，磁盘，网络，文件数等信息。

实验环境

​ 一个master节点，一个node节点。

在master节点操作

cat >node-export.yaml  <<EOF
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: node-exporter
  namespace: monitor-sa
  labels:
    name: node-exporter
spec:
  selector:
    matchLabels:
     name: node-exporter
  template:
    metadata:
      labels:
        name: node-exporter
    spec:
      hostPID: true
      hostIPC: true
      hostNetwork: true
      containers:
      - name: node-exporter
        image: prom/node-exporter:v0.16.0
        ports:
        - containerPort: 9100
        resources:
          requests:
            cpu: 0.15
        securityContext:
          privileged: true
        args:
        - --path.procfs
        - /host/proc
        - --path.sysfs
        - /host/sys
        - --collector.filesystem.ignored-mount-points
        - '"^/(sys|proc|dev|host|etc)($|/)"'
        volumeMounts:
        - name: dev
          mountPath: /host/dev
        - name: proc
          mountPath: /host/proc
        - name: sys
          mountPath: /host/sys
        - name: rootfs
          mountPath: /rootfs
      tolerations:
      - key: "node-role.kubernetes.io/master"
        operator: "Exists"
        effect: "NoSchedule"
      volumes:
        - name: proc
          hostPath:
            path: /proc
        - name: dev
          hostPath:
            path: /dev
        - name: sys
          hostPath:
            path: /sys
        - name: rootfs
          hostPath:
            path: /
EOF

通过node-exporter采集数据

curl http://主机ip:9100/metrics

在k8s集群中部署promethues

1. 创建namespace、sa账号，在k8s集群的master节点操作

   kubectl create ns monitor-sa
   kubectl create serviceaccount monitor -n monitor-sa
   #把sa账号monitor通过clusterrolebing绑定到clusterrole上
   kubectl create clusterrolebinding moniror-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor
   

2. 创建数据目录

   # 在k8s集群的任何一个node节点操作，本实验在node1上操作
   mkdir /data
   chmod 777 /data/
   

3. 安装prometheus，在master节点操作

   #创建一个configmap存储卷，用来存放prometheus配置信息
   #prometheus-cfg.yaml
   kind: ConfigMap
   apiVersion: v1
   metadata:
     labels:
       app: prometheus
     name: prometheus-config
     namespace: monitor-sa
   data:
     prometheus.yml: |
       global:
         scrape_interval: 15s
         scrape_timeout: 10s
         evaluation_interval: 1m
       scrape_configs:
       - job_name: 'kubernetes-node'
         kubernetes_sd_configs:
         - role: node
         relabel_configs:
         - source_labels: [__address__]
           regex: '(.*):10250'
           replacement: ':9100'
           target_label: __address__
           action: replace
         - action: labelmap
           regex: __meta_kubernetes_node_label_(.+)
       - job_name: 'kubernetes-node-cadvisor'
         kubernetes_sd_configs:
         - role:  node
         scheme: https
         tls_config:
           ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
         bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
         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//proxy/metrics/cadvisor
       - job_name: 'kubernetes-apiserver'
         kubernetes_sd_configs:
         - role: endpoints
         scheme: https
         tls_config:
           ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
         bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
         relabel_configs:
         - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
           action: keep
           regex: default;kubernetes;https
       - job_name: 'kubernetes-service-endpoints'
         kubernetes_sd_configs:
         - role: endpoints
         relabel_configs:
         - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
           action: keep
           regex: true
         - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
           action: replace
           target_label: __scheme__
           regex: (https?)
         - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
           action: replace
           target_label: __metrics_path__
           regex: (.+)
         - source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
           action: replace
           target_label: __address__
           regex: ([^:]+)(?::\d+)?;(\d+)
           replacement: :
         - action: labelmap
           regex: __meta_kubernetes_service_label_(.+)
         - source_labels: [__meta_kubernetes_namespace]
           action: replace
           target_label: kubernetes_namespace
         - source_labels: [__meta_kubernetes_service_name]
           action: replace
           target_label: kubernetes_name
   ---
   #通过deployment部署prometheus
   #prometheus-deploy.yaml
   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: prometheus-server
     namespace: monitor-sa
     labels:
       app: prometheus
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: prometheus
         component: server
     template:
       metadata:
         labels:
           app: prometheus
           component: server
         annotations:
           prometheus.io/scrape: 'false'
       spec:
         nodeName: node1
         serviceAccountName: monitor
         containers:
         - name: prometheus
           image: prom/prometheus:v2.2.1
           imagePullPolicy: IfNotPresent
           command:
             - prometheus
             - --config.file=/etc/prometheus/prometheus.yml
             - --storage.tsdb.path=/prometheus
             - --storage.tsdb.retention=720h
           ports:
           - containerPort: 9090
             protocol: TCP
           volumeMounts:
           - mountPath: /etc/prometheus/prometheus.yml
             name: prometheus-config
             subPath: prometheus.yml
           - mountPath: /prometheus/
             name: prometheus-storage-volume
         volumes:
           - name: prometheus-config
             configMap:
               name: prometheus-config
               items:
                 - key: prometheus.yml
                   path: prometheus.yml
                   mode: 0644
           - name: prometheus-storage-volume
             hostPath:
              path: /data
              type: Directory
   

   注意：通过上面命令生成的promtheus-cfg.yaml文件会有一些问题，$1和$2这种变量在文件里没有，需要在k8s的master1节点打开promtheus-cfg.yaml文件，手动把$1和$2这种变量写进文件里，promtheus-cfg.yaml文件需要手动修改部分如下：

   22行的replacement: ':9100'变成replacement: '${1}:9100'
   42行的replacement: /api/v1/nodes//proxy/metrics/cadvisor变成
                 replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
   73行的replacement:  变成replacement: $1:$2
   

   给prometheus pod 创建一个service

   cat  > prometheus-svc.yaml << EOF
   ---
   apiVersion: v1
   kind: Service
   metadata:
     name: prometheus
     namespace: monitor-sa
     labels:
       app: prometheus
   spec:
     type: NodePort
     ports:
       - port: 9090
         targetPort: 9090
         protocol: TCP
     selector:
       app: prometheus
       component: server
   EOF
   

   #查看service在物理机映射的端口
   kubectl	get svc -n monitor-sa
   
   #访问prometheus web ui 界面
   http://172.16.9.3:30426/graph
   #点击页面的Status->Targets，可看到如下,说明我们配置的服务发现可以正常采集数据
   

   prometheus热更新

   #为了每次修改配置文件可以热加载prometheus，也就是不停止prometheus，就可以使配置生效，如修改prometheus-cfg.yaml，想要使配置生效可用如下热加载命令：
   
   curl -X POST http://10.244.1.125:9090/-/reload

   #10.244.1.66是prometheus的pod的ip地址

   #热加载速度比较慢，可以暴力重启prometheus，如修改上面的prometheus-cfg.yaml文件之后，可执行如下强制删除：

   kubectl delete -f prometheus-cfg.yaml

   kubectl delete -f prometheus-deploy.yaml

   然后再通过apply更新：

   kubectl apply -f prometheus-cfg.yaml

   kubectl apply -f prometheus-deploy.yaml

   注意：

   线上最好热加载，暴力删除可能造成监控数据的丢失

Grafana安装和配置

下载安装Grafana需要的镜像

上传heapster-grafana-amd64_v5_0_4.tar.gz镜像到k8s的各个master节点和k8s的各个node节点，然后在各个节点手动解压：

docker load -i heapster-grafana-amd64_v5_0_4.tar.gz

镜像所在的百度网盘地址如下：

链接：https://pan.baidu.com/s/1TmVGKxde_cEYrbjiETboEA 提取码：052u

在k8s的master节点创建grafana.yaml

cat  >grafana.yaml <<  EOF
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:v5.0.4
        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:
  - port: 80
    targetPort: 3000
  selector:
    k8s-app: grafana
  type: NodePort
EOF

通过kubectl get sac -n cube-system看到grafana暴漏的苏主机端口是32351，我们可以访问k8s集群的master节点ip:32351即可访问grafana的web界面

Grafana界面接入prometheus数据源

1. 登录Grafana,172.16.9.3:32351，账号密码都是admin

2. 配置grafana界面，选择create your first data source

   Name:Prometheus
   Type:Prometheus
   HTTP出的URL：http://prometheus.monitor-sa.svc:9090
   

   点击左下角Save&Test，出现Data source is working，说明prometheus数据源成功的被grafana接入了。

   导入监控模板，可在如下链接搜索
   
   https://grafana.com/dashboards?dataSource=prometheus&search=kubernetes
   
   也可直接导入node_exporter.json监控模板，这个可以把node节点指标显示出来，node_exporter.json在百度网盘地址如下：

   链接：https://pan.baidu.com/s/1vF1kAMRbxQkUGPlZt91MWg 提取码：kyd6
   

   还可直接导入docker_rev1.json，可以把容器相关的数据展示出来
   
   docker_rev1.json在百度网盘地址如下

   链接：https://pan.baidu.com/s/17o_nja5N2R-g9g5PkJ3aFA 提取码：vinv
   

   导入监控模版步骤：点击左侧+号下面的Import，选择Upload json file,选择一个本地的json文件即可。

安装配置kube-state-metrics组件

​ 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。

安装kube-state-metrics组件

1. 创建sa，并对sa授权，在master节点操作

   cat > kube-state-metrics-rbac.yaml <<EOF
   ---
   apiVersion: v1
   kind: ServiceAccount
   metadata:
     name: kube-state-metrics
     namespace: kube-system
   ---
   apiVersion: rbac.authorization.k8s.io/v1
   kind: ClusterRole
   metadata:
     name: kube-state-metrics
   rules:
   - apiGroups: [""]
     resources: ["nodes", "pods", "services", "resourcequotas", "replicationcontrollers", "limitranges", "persistentvolumeclaims", "persistentvolumes", "namespaces", "endpoints"]
     verbs: ["list", "watch"]
   - apiGroups: ["extensions"]
     resources: ["daemonsets", "deployments", "replicasets"]
     verbs: ["list", "watch"]
   - apiGroups: ["apps"]
     resources: ["statefulsets"]
     verbs: ["list", "watch"]
   - apiGroups: ["batch"]
     resources: ["cronjobs", "jobs"]
     verbs: ["list", "watch"]
   - apiGroups: ["autoscaling"]
     resources: ["horizontalpodautoscalers"]
     verbs: ["list", "watch"]
   ---
   apiVersion: rbac.authorization.k8s.io/v1
   kind: ClusterRoleBinding
   metadata:
     name: kube-state-metrics
   roleRef:
     apiGroup: rbac.authorization.k8s.io
     kind: ClusterRole
     name: kube-state-metrics
   subjects:
   - kind: ServiceAccount
     name: kube-state-metrics
     namespace: kube-system
   EOF
   

2. 安装cube-state-metrics组件，在master节点操作

   cat > kube-state-metrics-deploy.yaml <<EOF
   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: kube-state-metrics
     namespace: kube-system
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: kube-state-metrics
     template:
       metadata:
         labels:
           app: kube-state-metrics
       spec:
         serviceAccountName: kube-state-metrics
         containers:
         - name: kube-state-metrics
   #        image: gcr.io/google_containers/kube-state-metrics-amd64:v1.3.1
           image: quay.io/coreos/kube-state-metrics:v1.9.0
           ports:
           - containerPort: 8080
   EOF
   

3. 创建service，在master节点操作

   cat >kube-state-metrics-svc.yaml <<EOF
   apiVersion: v1
   kind: Service
   metadata:
     annotations:
       prometheus.io/scrape: 'true'
     name: kube-state-metrics
     namespace: kube-system
     labels:
       app: kube-state-metrics
   spec:
     ports:
     - name: kube-state-metrics
       port: 8080
       protocol: TCP
     selector:
       app: kube-state-metrics
   EOF
   

   在Grafana web界面导入kubernetes Cluster和kubernetes cluster monitoring

   链接：https://pan.baidu.com/s/1QAMqT8scsXx-lzEPI6MPgA
   提取码：i4yd
   

安装和配置Alertmanager-发送报警到qq邮箱

在k8s的master节点创建alertmanager-cm.yaml文件

cat >alertmanager-cm.yaml <<EOF
kind: ConfigMap
apiVersion: v1
metadata:
  name: alertmanager
  namespace: monitor-sa
data:
  alertmanager.yml: |-
    global:
      resolve_timeout: 1m
      smtp_smarthost: 'smtp.163.com:25'
      smtp_from: '[e-mail]'
      smtp_auth_username: '15011572657'
      smtp_auth_password: 'BDBPRMLNZGKWRFJP'
      smtp_require_tls: false
    route:
      group_by: [alertname]
      group_wait: 10s
      group_interval: 10s
      repeat_interval: 10m
      receiver: default-receiver
    receivers:
    - name: 'default-receiver'
      email_configs:
      - to: '[e-mail]'
        send_resolved: true
EOF

Alertmanager配置文件解释说明:

smtp_smarthost: 'smtp.163.com:25'
#用于发送邮件的邮箱的SMTP服务器地址+端口
smtp_from: '[e-mail]'
#这是指定从哪个邮箱发送报警
smtp_auth_username: '15011572657'
#这是发送邮箱的认证用户，不是邮箱名
smtp_auth_password: 'BDBPRMLNZGKWRFJP'
#这是发送邮箱的授权码而不是登录密码
email_configs:
   - to: '[e-mail]'
#to后面指定发送到哪个邮箱，我发送到我的qq邮箱，大家需要写自己的邮箱地址，不应该跟smtp_from的邮箱名字重复

在master节点重新生成prometheus-cfg.yaml文件

kind: ConfigMap
apiVersion: v1
metadata:
  labels:
    app: prometheus
  name: prometheus-config
  namespace: monitor-sa
data:
  prometheus.yml: |
    rule_files:
    - /etc/prometheus/rules.yml
    alerting:
      alertmanagers:
      - static_configs:
        - targets: ["localhost:9093"]
    global:
      scrape_interval: 15s
      scrape_timeout: 10s
      evaluation_interval: 1m
    scrape_configs:
    - job_name: 'kubernetes-node'
      kubernetes_sd_configs:
      - role: node
      relabel_configs:
      - source_labels: [__address__]
        regex: '(.*):10250'
        replacement: '${1}:9100'
        target_label: __address__
        action: replace
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)
    - job_name: 'kubernetes-node-cadvisor'
      kubernetes_sd_configs:
      - role:  node
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
      relabel_configs:
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)
      - target_label: __address__
        replacement: kubernetes.default.svc:443
      - source_labels: [__meta_kubernetes_node_name]
        regex: (.+)
        target_label: __metrics_path__
        replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
    - job_name: 'kubernetes-apiserver'
      kubernetes_sd_configs:
      - role: endpoints
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
      relabel_configs:
      - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
        action: keep
        regex: default;kubernetes;https
    - job_name: 'kubernetes-service-endpoints'
      kubernetes_sd_configs:
      - role: endpoints
      relabel_configs:
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
        action: replace
        target_label: __scheme__
        regex: (https?)
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
        action: replace
        target_label: __metrics_path__
        regex: (.+)
      - source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
        action: replace
        target_label: __address__
        regex: ([^:]+)(?::\d+)?;(\d+)
        replacement: $1:$2
      - action: labelmap
        regex: __meta_kubernetes_service_label_(.+)
      - source_labels: [__meta_kubernetes_namespace]
        action: replace
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_service_name]
        action: replace
        target_label: kubernetes_name
    - job_name: kubernetes-pods
      kubernetes_sd_configs:
      - role: pod
      relabel_configs:
      - action: keep
        regex: true
        source_labels:
        - __meta_kubernetes_pod_annotation_prometheus_io_scrape
      - action: replace
        regex: (.+)
        source_labels:
        - __meta_kubernetes_pod_annotation_prometheus_io_path
        target_label: __metrics_path__
      - action: replace
        regex: ([^:]+)(?::\d+)?;(\d+)
        replacement: $1:$2
        source_labels:
        - __address__
        - __meta_kubernetes_pod_annotation_prometheus_io_port
        target_label: __address__
      - action: labelmap
        regex: __meta_kubernetes_pod_label_(.+)
      - action: replace
        source_labels:
        - __meta_kubernetes_namespace
        target_label: kubernetes_namespace
      - action: replace
        source_labels:
        - __meta_kubernetes_pod_name
        target_label: kubernetes_pod_name
    - job_name: 'kubernetes-schedule'
      scrape_interval: 5s
      static_configs:
      - targets: ['172.16.9.3:10251']
    - job_name: 'kubernetes-controller-manager'
      scrape_interval: 5s
      static_configs:
      - targets: ['172.16.9.3:10252']
    - job_name: 'kubernetes-kube-proxy'
      scrape_interval: 5s
      static_configs:
      - targets: ['172.16.9.3:10249','172.16.9.4:10249']
    - job_name: 'kubernetes-etcd'
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
        cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
        key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
      scrape_interval: 5s
      static_configs:
      - targets: ['172.16.9.3:2379']
  rules.yml: |
    groups:
    - name: example
      rules:
      - alert: kube-proxy的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
      - alert:  kube-proxy的cpu使用率大于90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$lables.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{{$labels.job}}组件的cpu使用率超过90%"
      - alert: etcd的cpu使用率大于80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$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: "{{ $value }}"
          threshold: "1000"
      - alert: Pod_restarts
        expr: kube_pod_container_status_restarts_total{namespace=~"kube-system|default|monitor-sa"} > 0
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "在{{$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}}采集的"
          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: "{{ $value }}"
          threshold: "1"
      - alert: Pod_terminated
        expr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|monitor-sa"} == 1
        for: 2s
        labels:
          team: admin
        annotations:
          description: "空间{{$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 }}],需要排查处理"
      - 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: "{{$labels.mountpoint}} 流入网络带宽过高！"
          description: "{{$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}}%)"

同样需要手动添加$的变量。

在k8smaster节点重新生成一个prometheus-deploy.yaml文件

cat >prometheus-deploy.yaml <<EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus-server
  namespace: monitor-sa
  labels:
    app: prometheus
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prometheus
      component: server
    #matchExpressions:
    #- {key: app, operator: In, values: [prometheus]}
    #- {key: component, operator: In, values: [server]}
  template:
    metadata:
      labels:
        app: prometheus
        component: server
      annotations:
        prometheus.io/scrape: 'false'
    spec:
      nodeName: node1
      serviceAccountName: monitor
      containers:
      - name: prometheus
        image: prom/prometheus:v2.2.1
        imagePullPolicy: IfNotPresent
        command:
        - "/bin/prometheus"
        args:
        - "--config.file=/etc/prometheus/prometheus.yml"
        - "--storage.tsdb.path=/prometheus"
        - "--storage.tsdb.retention=24h"
        - "--web.enable-lifecycle"
        ports:
        - containerPort: 9090
          protocol: TCP
        volumeMounts:
        - mountPath: /etc/prometheus
          name: prometheus-config
        - mountPath: /prometheus/
          name: prometheus-storage-volume
        - name: k8s-certs
          mountPath: /var/run/secrets/kubernetes.io/k8s-certs/etcd/
      - name: alertmanager
        image: prom/alertmanager:v0.14.0
        imagePullPolicy: IfNotPresent
        args:
        - "--config.file=/etc/alertmanager/alertmanager.yml"
        - "--log.level=debug"
        ports:
        - containerPort: 9093
          protocol: TCP
          name: alertmanager
        volumeMounts:
        - name: alertmanager-config
          mountPath: /etc/alertmanager
        - name: alertmanager-storage
          mountPath: /alertmanager
        - name: localtime
          mountPath: /etc/localtime
      volumes:
        - name: prometheus-config
          configMap:
            name: prometheus-config
        - name: prometheus-storage-volume
          hostPath:
           path: /data
           type: Directory
        - name: k8s-certs
          secret:
           secretName: etcd-certs
        - name: alertmanager-config
          configMap:
            name: alertmanager
        - name: alertmanager-storage
          hostPath:
           path: /data/alertmanager
           type: DirectoryOrCreate
        - name: localtime
          hostPath:
           path: /usr/share/zoneinfo/Asia/Shanghai
EOF

生成一个etch-certs，这个在部署prometheus需要

kubectl -n monitor-sa create secret generic etcd-certs --from-file=/etc/kubernetes/pki/etcd/server.key  --from-file=/etc/kubernetes/pki/etcd/server.crt --from-file=/etc/kubernetes/pki/etcd/ca.crt

更新yaml文件，查看部署是否成功。

在k8smaster节点上重新生成一个alertmanager-svc.yaml文件

cat >alertmanager-svc.yaml <<EOF
---
apiVersion: v1
kind: Service
metadata:
  labels:
    name: prometheus
    kubernetes.io/cluster-service: 'true'
  name: alertmanager
  namespace: monitor-sa
spec:
  ports:
  - name: alertmanager
    nodePort: 30066
    port: 9093
    protocol: TCP
    targetPort: 9093
  selector:
    app: prometheus
  sessionAffinity: None
  type: NodePort
EOF

#查看service在物理机映射的端口

kubectl get svc -n monitor-sa

访问prometheus界面，点击alerts，把controller-manager的cpu使用率大于90%展开，可看到status为FIRING，表示prometheus已经将告警发给alertmanager，在Alertmanager 中可以看到有一个 alert。

登录alertmanager web界面查看

配置alertmanager报警-发送报警到钉钉

1. 创建钉钉机器人

   打开电脑版钉钉，创建一个群，创建自定义机器人，按如下步骤创建
   https://ding-doc.dingtalk.com/doc#/serverapi2/qf2nxq
   
   我创建的机器人如下：
   群设置-->智能群助手-->添加机器人-->自定义-->添加
   
   机器人名称：kube-event
   接收群组：钉钉报警测试
   
   安全设置：
   自定义关键词：cluster1
   
   上面配置好之后点击完成即可，这样就会创建一个kube-event的报警机器人，创建机器人成功之后怎么查看webhook，按如下：
   
   点击智能群助手，可以看到刚才创建的kube-event这个机器人，点击kube-event，就会进入到kube-event机器人的设置界面
   出现如下内容：
   机器人名称：kube-event
   接受群组：钉钉报警测试
   消息推送：开启
   webhook：https://oapi.dingtalk.com/robot/send?access_token=9c03ff1f47b1d15a10d852398cafb84f8e81ceeb1ba557eddd8a79e5a5e5548e
   安全设置：
   自定义关键词：cluster1
   

2. 安装钉钉的webhook插件，在master节点操作

   tar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz
   #压缩包地址
   #链接：https://pan.baidu.com/s/1_HtVZsItq2KsYvOlkIP9DQ
   #提取码：d59o
   cd prometheus-webhook-dingtalk-0.3.0.linux-amd64
   
   #启动钉钉报警插件
   nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=4372b6419ff1f198a9732dfb9f469f8c7eb7310dec00ede726a7ecd9d235c9b9" &
   
   #对原来的文件做备份
   cp alertmanager-cm.yaml alertmanager-cm.yaml.bak
   
   #重新生成一个新的alertmanager-cm.yaml文件
   cat >alertmanager-cm.yaml <<EOF
   kind: ConfigMap
   apiVersion: v1
   metadata:
     name: alertmanager
     namespace: monitor-sa
   data:
     alertmanager.yml: |-
       global:
         resolve_timeout: 1m
         smtp_smarthost: 'smtp.163.com:25'
         smtp_from: '[e-mail]'
         smtp_auth_username: '15011572657'
         smtp_auth_password: 'BDBPRMLNZGKWRFJP'
         smtp_require_tls: false
       route:
         group_by: [alertname]
         group_wait: 10s
         group_interval: 10s
         repeat_interval: 10m
         receiver: cluster1
       receivers:
       - name: cluster1
         webhook_configs:
         - url: 'http://192.168.124.16:8060/dingtalk/cluster1/send'
           send_resolved: true
   EOF
   
   #通过kubectl apply使配置生效
   kubectl delete -f alertmanager-cm.yaml
   kubectl  apply  -f alertmanager-cm.yaml
   kubectl delete -f prometheus-cfg.yaml
   kubectl apply  -f prometheus-cfg.yaml
   kubectl delete  -f prometheus-deploy.yaml
   kubectl apply  -f  prometheus-deploy.yaml
   #通过上面步骤，就可以实现钉钉报警了
   

   参考链接：https://mp.weixin.qq.com/s/I1-xfxuny_S8DHchkXHSpQ