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Prometheus+Grafana+Alertmanager搭建全方位的監控告警系統

本文轉載自   查看原文   2021-02-26 17:02   681    監控運維

prometheus安裝和配置

prometheus組件介紹

1.Prometheus Server: 用於收集和存儲時間序列數據。

2.Client Library: 客戶端庫，檢測應用程序代碼，當Prometheus抓取實例的HTTP端點時，客戶端庫會將所有跟蹤的metrics指標的當前狀態發送到prometheus server端。

3.Exporters: prometheus支持多種exporter，通過exporter可以采集metrics數據，然后發送到prometheus server端

4.Alertmanager: 從 Prometheus server 端接收到 alerts 后，會進行去重，分組，並路由到相應的接收方，發出報警，常見的接收方式有：電子郵件，微信，釘釘, slack等。

5.Grafana：監控儀表盤

6.pushgateway: 各個目標主機可上報數據到pushgatewy，然后prometheus server統一從pushgateway拉取數據。

prometheus架構圖

img

　　從上圖可發現，Prometheus整個生態圈組成主要包括prometheus server，Exporter，pushgateway，alertmanager，grafana，Web ui界面，Prometheus server由三個部分組成，Retrieval，Storage，PromQL 。

  • retrieval負責在活躍的target主機上抓取監控指標數據
  • storage主要是把采集到的數據存儲到磁盤中
  • promQL是prometheus提供的查詢語言模塊

prometheus工作流程

  1. Prometheus server可定期從活躍的（up）目標主機上（target）拉取監控指標數據，目標主機的監控數據可通過配置靜態job或者服務發現的方式被prometheus server采集到，這種方式默認的pull方式拉取指標；也可通過pushgateway把采集的數據上報到prometheus server中；還可通過一些組件自帶的exporter采集相應組件的數據；
  2. Prometheus server把采集到的監控指標數據保存到本地磁盤或者數據庫；
  3. Prometheus采集的監控指標數據按時間序列存儲，通過配置報警規則，把觸發的報警發送到alertmanager
  4. Alertmanager通過配置報警接收方，發送報警到郵件，微信或者釘釘等
  5. Prometheus 自帶的web ui界面提供PromQL查詢語言，可查詢監控數據
  6. Grafana可接入prometheus數據源，把監控數據以圖形化形式展示出

安裝node-exporter組件

​ node-exporter是采集機器（物理機、虛擬機、雲主機等）的監控指標數據，能夠采集到的指標包括CPU, 內存，磁盤，網絡，文件數等信息。

實驗環境

​ 一個master節點，一個node節點。

在master節點操作

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

通過node-exporter采集數據

curl http://主機ip:9100/metrics

在k8s集群中部署promethues

  1. 創建namespace、sa賬號，在k8s集群的master節點操作

    kubectl create ns monitor-sa
kubectl create serviceaccount monitor -n monitor-sa
#把sa賬號monitor通過clusterrolebing綁定到clusterrole上
kubectl create clusterrolebinding moniror-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor

  2. 創建數據目錄

    # 在k8s集群的任何一個node節點操作，本實驗在node1上操作
mkdir /data
chmod 777 /data/

  3. 安裝prometheus，在master節點操作

    #創建一個configmap存儲卷，用來存放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 
---
#通過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：通過上面命令生成的promtheus-cfg.yaml文件會有一些問題，$1和$2這種變量在文件里沒有，需要在k8s的master1節點打開promtheus-cfg.yaml文件，手動把$1和$2這種變量寫進文件里，promtheus-cfg.yaml文件需要手動修改部分如下：

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

    給prometheus pod 創建一個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在物理機映射的端口
kubectl	get svc -n monitor-sa

#訪問prometheus web ui 界面
http://172.16.9.3:30426/graph
#點擊頁面的Status->Targets，可看到如下,說明我們配置的服務發現可以正常采集數據

    prometheus熱更新

    #為了每次修改配置文件可以熱加載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熱加載命令：
    curl -X POST http://10.244.1.125:9090/-/reload

    #10.244.1.66是prometheus的pod的ip地址

    #熱加載速度比較慢，可以暴力重啟prometheus，如修改上面的prometheus-cfg.yaml文件之后，可執行如下強制刪除：

    kubectl delete -f prometheus-cfg.yaml

    kubectl delete -f prometheus-deploy.yaml

    然后再通過apply更新：

    kubectl apply -f prometheus-cfg.yaml

    kubectl apply -f prometheus-deploy.yaml

    注意：

    線上最好熱加載，暴力刪除可能造成監控數據的丟失

Grafana安裝和配置

下載安裝Grafana需要的鏡像

上傳heapster-grafana-amd64_v5_0_4.tar.gz鏡像到k8s的各個master節點和k8s的各個node節點，然后在各個節點手動解壓：
docker load -i heapster-grafana-amd64_v5_0_4.tar.gz

鏡像所在的百度網盤地址如下：

鏈接：https://pan.baidu.com/s/1TmVGKxde_cEYrbjiETboEA 提取碼：052u

在k8s的master節點創建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

通過kubectl get sac -n cube-system看到grafana暴漏的蘇主機端口是32351，我們可以訪問k8s集群的master節點ip:32351即可訪問grafana的web界面

Grafana界面接入prometheus數據源

  1. 登錄Grafana,172.16.9.3:32351，賬號密碼都是admin

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

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

    點擊左下角Save&Test，出現Data source is working，說明prometheus數據源成功的被grafana接入了。

    導入監控模板，可在如下鏈接搜索
    https://grafana.com/dashboards?dataSource=prometheus&search=kubernetes
    也可直接導入node_exporter.json監控模板，這個可以把node節點指標顯示出來，node_exporter.json在百度網盤地址如下：

    鏈接：https://pan.baidu.com/s/1vF1kAMRbxQkUGPlZt91MWg 提取碼：kyd6

    還可直接導入docker_rev1.json，可以把容器相關的數據展示出來
    docker_rev1.json在百度網盤地址如下

    鏈接：https://pan.baidu.com/s/17o_nja5N2R-g9g5PkJ3aFA 提取碼：vinv

    導入監控模版步驟：點擊左側+號下面的Import，選擇Upload json file,選擇一個本地的json文件即可。

安裝配置kube-state-metrics組件

​ kube-state-metrics通過監聽API Server生成有關資源對象的狀態指標，比如Deployment、Node、Pod，需要注意的是kube-state-metrics只是簡單的提供一個metrics數據，並不會存儲這些指標數據，所以我們可以使用Prometheus來抓取這些數據然后存儲，主要關注的是業務相關的一些元數據，比如Deployment、Pod、副本狀態等；調度了多少個replicas？現在可用的有幾個？多少個Pod是running/stopped/terminated狀態？Pod重啟了多少次？我有多少job在運行中。

安裝kube-state-metrics組件

  1. 創建sa，並對sa授權，在master節點操作

    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. 安裝cube-state-metrics組件，在master節點操作

    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. 創建service，在master節點操作

    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界面導入kubernetes Cluster和kubernetes cluster monitoring

    鏈接：https://pan.baidu.com/s/1QAMqT8scsXx-lzEPI6MPgA 
提取碼：i4yd

安裝和配置Alertmanager-發送報警到qq郵箱

在k8s的master節點創建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: '15011572657@163.com'
      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: 'y1486170457@qq.com'
        send_resolved: true
EOF

Alertmanager配置文件解釋說明:

smtp_smarthost: 'smtp.163.com:25'
#用於發送郵件的郵箱的SMTP服務器地址+端口
smtp_from: '15011572657@163.com'
#這是指定從哪個郵箱發送報警
smtp_auth_username: '15011572657'
#這是發送郵箱的認證用戶，不是郵箱名
smtp_auth_password: 'BDBPRMLNZGKWRFJP'
#這是發送郵箱的授權碼而不是登錄密碼
email_configs:
   - to: 'y1486170457@qq.com'
#to后面指定發送到哪個郵箱，我發送到我的qq郵箱，大家需要寫自己的郵箱地址，不應該跟smtp_from的郵箱名字重復

在master節點重新生成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:$2
        source_labels:
        - __address__
        - __meta_kubernetes_pod_annotation_prometheus_io_port
        target_label: __address__
      - action: labelmap
        regex: __meta_kubernetes_pod_label_(.+)
      - action: replace
        source_labels:
        - __meta_kubernetes_namespace
        target_label: kubernetes_namespace
      - action: replace
        source_labels:
        - __meta_kubernetes_pod_name
        target_label: kubernetes_pod_name
    - job_name: 'kubernetes-schedule'
      scrape_interval: 5s
      static_configs:
      - targets: ['172.16.9.3:10251']
    - job_name: 'kubernetes-controller-manager'
      scrape_interval: 5s
      static_configs:
      - targets: ['172.16.9.3:10252']
    - job_name: 'kubernetes-kube-proxy'
      scrape_interval: 5s
      static_configs:
      - targets: ['172.16.9.3:10249','172.16.9.4:10249']
    - job_name: 'kubernetes-etcd'
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
        cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
        key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
      scrape_interval: 5s
      static_configs:
      - targets: ['172.16.9.3:2379']
  rules.yml: |
    groups:
    - name: example
      rules:
      - alert: kube-proxy的cpu使用率大於80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
      - alert:  kube-proxy的cpu使用率大於90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$lables.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
      - alert: scheduler的cpu使用率大於80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
      - alert:  scheduler的cpu使用率大於90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
      - alert: controller-manager的cpu使用率大於80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
      - alert:  controller-manager的cpu使用率大於90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
      - alert: apiserver的cpu使用率大於80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
      - alert:  apiserver的cpu使用率大於90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
      - alert: etcd的cpu使用率大於80%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
      - alert:  etcd的cpu使用率大於90%
        expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
      - alert: kube-state-metrics的cpu使用率大於80%
        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.k8s_app}}組件的cpu使用率超過80%"
          value: "{{ $value }}%"
          threshold: "80%"      
      - alert: kube-state-metrics的cpu使用率大於90%
        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.k8s_app}}組件的cpu使用率超過90%"
          value: "{{ $value }}%"
          threshold: "90%"      
      - alert: coredns的cpu使用率大於80%
        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 80
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.k8s_app}}組件的cpu使用率超過80%"
          value: "{{ $value }}%"
          threshold: "80%"      
      - alert: coredns的cpu使用率大於90%
        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 90
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.k8s_app}}組件的cpu使用率超過90%"
          value: "{{ $value }}%"
          threshold: "90%"      
      - alert: kube-proxy打開句柄數>600
        expr: process_open_fds{job=~"kubernetes-kube-proxy"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
          value: "{{ $value }}"
      - alert: kube-proxy打開句柄數>1000
        expr: process_open_fds{job=~"kubernetes-kube-proxy"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
          value: "{{ $value }}"
      - alert: kubernetes-schedule打開句柄數>600
        expr: process_open_fds{job=~"kubernetes-schedule"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
          value: "{{ $value }}"
      - alert: kubernetes-schedule打開句柄數>1000
        expr: process_open_fds{job=~"kubernetes-schedule"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
          value: "{{ $value }}"
      - alert: kubernetes-controller-manager打開句柄數>600
        expr: process_open_fds{job=~"kubernetes-controller-manager"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
          value: "{{ $value }}"
      - alert: kubernetes-controller-manager打開句柄數>1000
        expr: process_open_fds{job=~"kubernetes-controller-manager"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
          value: "{{ $value }}"
      - alert: kubernetes-apiserver打開句柄數>600
        expr: process_open_fds{job=~"kubernetes-apiserver"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
          value: "{{ $value }}"
      - alert: kubernetes-apiserver打開句柄數>1000
        expr: process_open_fds{job=~"kubernetes-apiserver"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
          value: "{{ $value }}"
      - alert: kubernetes-etcd打開句柄數>600
        expr: process_open_fds{job=~"kubernetes-etcd"}  > 600
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
          value: "{{ $value }}"
      - alert: kubernetes-etcd打開句柄數>1000
        expr: process_open_fds{job=~"kubernetes-etcd"}  > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>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}}): 打開句柄數超過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}}): 打開句柄數超過1000"
          value: "{{ $value }}"
      - alert: kube-proxy
        expr: process_virtual_memory_bytes{job=~"kubernetes-kube-proxy"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
          value: "{{ $value }}"
      - alert: scheduler
        expr: process_virtual_memory_bytes{job=~"kubernetes-schedule"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
          value: "{{ $value }}"
      - alert: kubernetes-controller-manager
        expr: process_virtual_memory_bytes{job=~"kubernetes-controller-manager"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
          value: "{{ $value }}"
      - alert: kubernetes-apiserver
        expr: process_virtual_memory_bytes{job=~"kubernetes-apiserver"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
          value: "{{ $value }}"
      - alert: kubernetes-etcd
        expr: process_virtual_memory_bytes{job=~"kubernetes-etcd"}  > 2000000000
        for: 2s
        labels:
          severity: warnning
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過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}}): 使用虛擬內存超過2G"
          value: "{{ $value }}"
      - alert: HttpRequestsAvg
        expr: sum(rate(rest_client_requests_total{job=~"kubernetes-kube-proxy|kubernetes-kubelet|kubernetes-schedule|kubernetes-control-manager|kubernetes-apiservers"}[1m]))  > 1000
        for: 2s
        labels:
          team: admin
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): TPS超過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}}名稱空間下發現{{$labels.pod}}這個pod下的容器{{$labels.container}}被重啟,這個監控指標是由{{$labels.instance}}采集的"
          value: "{{ $value }}"
          threshold: "0"
      - alert: Pod_waiting
        expr: kube_pod_container_status_waiting_reason{namespace=~"kube-system|default"} == 1
        for: 2s
        labels:
          team: admin
        annotations:
          description: "空間{{$labels.namespace}}({{$labels.instance}}): 發現{{$labels.pod}}下的{{$labels.container}}啟動異常等待中"
          value: "{{ $value }}"
          threshold: "1"   
      - alert: Pod_terminated
        expr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|monitor-sa"} == 1
        for: 2s
        labels:
          team: admin
        annotations:
          description: "空間{{$labels.namespace}}({{$labels.instance}}): 發現{{$labels.pod}}下的{{$labels.container}}被刪除"
          value: "{{ $value }}"
          threshold: "1"
      - alert: Etcd_leader
        expr: etcd_server_has_leader{job="kubernetes-etcd"} == 0
        for: 2s
        labels:
          team: admin
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): 當前沒有leader"
          value: "{{ $value }}"
          threshold: "0"
      - alert: Etcd_leader_changes
        expr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0
        for: 2s
        labels:
          team: admin
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): 當前leader已發生改變"
          value: "{{ $value }}"
          threshold: "0"
      - alert: Etcd_failed
        expr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0
        for: 2s
        labels:
          team: admin
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}}): 服務失敗"
          value: "{{ $value }}"
          threshold: "0"
      - alert: Etcd_db_total_size
        expr: etcd_debugging_mvcc_db_total_size_in_bytes{job="kubernetes-etcd"} > 10000000000
        for: 2s
        labels:
          team: admin
        annotations:
          description: "組件{{$labels.job}}({{$labels.instance}})：db空間超過10G"
          value: "{{ $value }}"
          threshold: "10G"
      - alert: Endpoint_ready
        expr: kube_endpoint_address_not_ready{namespace=~"kube-system|default"} == 1
        for: 2s
        labels:
          team: admin
        annotations:
          description: "空間{{$labels.namespace}}({{$labels.instance}}): 發現{{$labels.endpoint}}不可用"
          value: "{{ $value }}"
          threshold: "1"
    - name: 物理節點狀態-監控告警
      rules:
      - alert: 物理節點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使用率過高"
          description: "{{ $labels.instance }}的cpu使用率超過90%,當前使用率[{{ $value }}],需要排查處理" 
      - alert: 物理節點內存使用率
        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 }}內存使用率過高"
          description: "{{ $labels.instance }}的內存使用率超過90%,當前使用率[{{ $value }}],需要排查處理"
      - alert: InstanceDown
        expr: up == 0
        for: 2s
        labels:
          severity: critical
        annotations:   
          summary: "{{ $labels.instance }}: 服務器宕機"
          description: "{{ $labels.instance }}: 服務器延時超過2分鍾"
      - alert: 物理節點磁盤的IO性能
        expr: 100-(avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} 流入磁盤IO使用率過高！"
          description: "{{$labels.mountpoint }} 流入磁盤IO大於60%(目前使用:{{$value}})"
      - alert: 入網流量帶寬
        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: "{{$labels.mountpoint}} 流入網絡帶寬過高！"
          description: "{{$labels.mountpoint }}流入網絡帶寬持續5分鍾高於100M. RX帶寬使用率{{$value}}"
      - alert: 出網流量帶寬
        expr: ((sum(rate (node_network_transmit_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} 流出網絡帶寬過高！"
          description: "{{$labels.mountpoint }}流出網絡帶寬持續5分鍾高於100M. RX帶寬使用率{{$value}}"
      - alert: TCP會話
        expr: node_netstat_Tcp_CurrEstab > 1000
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} TCP_ESTABLISHED過高！"
          description: "{{$labels.mountpoint }} TCP_ESTABLISHED大於1000%(目前使用:{{$value}}%)"
      - alert: 磁盤容量
        expr: 100-(node_filesystem_free_bytes{fstype=~"ext4|xfs"}/node_filesystem_size_bytes {fstype=~"ext4|xfs"}*100) > 80
        for: 2s
        labels:
          severity: critical
        annotations:
          summary: "{{$labels.mountpoint}} 磁盤分區使用率過高！"
          description: "{{$labels.mountpoint }} 磁盤分區使用大於80%(目前使用:{{$value}}%)"

同樣需要手動添加$的變量。

在k8smaster節點重新生成一個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

生成一個etch-certs，這個在部署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節點上重新生成一個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在物理機映射的端口

kubectl get svc -n monitor-sa

訪問prometheus界面，點擊alerts，把controller-manager的cpu使用率大於90%展開，可看到status為FIRING，表示prometheus已經將告警發給alertmanager，在Alertmanager 中可以看到有一個 alert。

登錄alertmanager web界面查看

配置alertmanager報警-發送報警到釘釘

  1. 創建釘釘機器人

    打開電腦版釘釘，創建一個群，創建自定義機器人，按如下步驟創建
https://ding-doc.dingtalk.com/doc#/serverapi2/qf2nxq

我創建的機器人如下：
群設置-->智能群助手-->添加機器人-->自定義-->添加

機器人名稱：kube-event
接收群組：釘釘報警測試

安全設置：
自定義關鍵詞：cluster1

上面配置好之后點擊完成即可，這樣就會創建一個kube-event的報警機器人，創建機器人成功之后怎么查看webhook，按如下：

點擊智能群助手，可以看到剛才創建的kube-event這個機器人，點擊kube-event，就會進入到kube-event機器人的設置界面
出現如下內容：
機器人名稱：kube-event
接受群組：釘釘報警測試
消息推送：開啟
webhook：https://oapi.dingtalk.com/robot/send?access_token=9c03ff1f47b1d15a10d852398cafb84f8e81ceeb1ba557eddd8a79e5a5e5548e
安全設置：
自定義關鍵詞：cluster1

  2. 安裝釘釘的webhook插件，在master節點操作

    tar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz
#壓縮包地址
#鏈接：https://pan.baidu.com/s/1_HtVZsItq2KsYvOlkIP9DQ 
#提取碼：d59o
cd prometheus-webhook-dingtalk-0.3.0.linux-amd64

#啟動釘釘報警插件
nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=4372b6419ff1f198a9732dfb9f469f8c7eb7310dec00ede726a7ecd9d235c9b9" &

#對原來的文件做備份
cp alertmanager-cm.yaml alertmanager-cm.yaml.bak

#重新生成一個新的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: '15011572657@163.com'
      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

#通過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
#通過上面步驟，就可以實現釘釘報警了

    參考鏈接：https://mp.weixin.qq.com/s/I1-xfxuny_S8DHchkXHSpQ


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