Longhorn，企业级云原生容器分布式存储 - 监控(Prometheus+AlertManager+Grafana)

内容来源于官方 Longhorn 1.1.2 英文技术手册。

系列

• Longhorn 是什么?
• Longhorn 企业级云原生容器分布式存储解决方案设计架构和概念
• Longhorn 企业级云原生容器分布式存储-部署篇
• Longhorn 企业级云原生容器分布式存储-券(Volume)和节点(Node)
• Longhorn，企业级云原生容器分布式存储-K8S 资源配置示例

目录

1. 设置 Prometheus 和 Grafana 来监控 Longhorn
2. 将 Longhorn 指标集成到 Rancher 监控系统中
3. Longhorn 监控指标
4. 支持 Kubelet Volume 指标
5. Longhorn 警报规则示例

设置 Prometheus 和 Grafana 来监控 Longhorn

概览

Longhorn 在 REST 端点 http://LONGHORN_MANAGER_IP:PORT/metrics 上以 Prometheus 文本格式原生公开指标。

有关所有可用指标的说明，请参阅 Longhorn's metrics。

您可以使用 Prometheus, Graphite, Telegraf 等任何收集工具来抓取这些指标，然后通过 Grafana 等工具将收集到的数据可视化。

本文档提供了一个监控 Longhorn 的示例设置。监控系统使用 Prometheus 收集数据和警报，使用 Grafana 将收集的数据可视化/仪表板(visualizing/dashboarding)。 高级概述来看，监控系统包含：

• Prometheus 服务器从 Longhorn 指标端点抓取和存储时间序列数据。Prometheus 还负责根据配置的规则和收集的数据生成警报。Prometheus 服务器然后将警报发送到 Alertmanager。
• AlertManager 然后管理这些警报(alerts)，包括静默(silencing)、抑制(inhibition)、聚合(aggregation)和通过电子邮件、呼叫通知系统和聊天平台等方法发送通知。
• Grafana 向 Prometheus 服务器查询数据并绘制仪表板进行可视化。

下图描述了监控系统的详细架构。

上图中有 2 个未提及的组件：

• Longhorn 后端服务是指向 Longhorn manager pods 集的服务。Longhorn 的指标在端点 http://LONGHORN_MANAGER_IP:PORT/metrics 的 Longhorn manager pods 中公开。
• Prometheus operator 使在 Kubernetes 上运行 Prometheus 变得非常容易。operator 监视 3 个自定义资源：ServiceMonitor、Prometheus 和 AlertManager。当用户创建这些自定义资源时，Prometheus Operator 会使用用户指定的配置部署和管理 Prometheus server, AlerManager。

安装

按照此说明将所有组件安装到 monitoring 命名空间中。要将它们安装到不同的命名空间中，请更改字段 namespace: OTHER_NAMESPACE

创建 monitoring 命名空间

apiVersion: v1
kind: Namespace
metadata:
  name: monitoring

安装 Prometheus Operator

部署 Prometheus Operator 及其所需的 ClusterRole、ClusterRoleBinding 和 Service Account。

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  labels:
    app.kubernetes.io/component: controller
    app.kubernetes.io/name: prometheus-operator
    app.kubernetes.io/version: v0.38.3
  name: prometheus-operator
  namespace: monitoring
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus-operator
subjects:
- kind: ServiceAccount
  name: prometheus-operator
  namespace: monitoring
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  labels:
    app.kubernetes.io/component: controller
    app.kubernetes.io/name: prometheus-operator
    app.kubernetes.io/version: v0.38.3
  name: prometheus-operator
  namespace: monitoring
rules:
- apiGroups:
  - apiextensions.k8s.io
  resources:
  - customresourcedefinitions
  verbs:
  - create
- apiGroups:
  - apiextensions.k8s.io
  resourceNames:
  - alertmanagers.monitoring.coreos.com
  - podmonitors.monitoring.coreos.com
  - prometheuses.monitoring.coreos.com
  - prometheusrules.monitoring.coreos.com
  - servicemonitors.monitoring.coreos.com
  - thanosrulers.monitoring.coreos.com
  resources:
  - customresourcedefinitions
  verbs:
  - get
  - update
- apiGroups:
  - monitoring.coreos.com
  resources:
  - alertmanagers
  - alertmanagers/finalizers
  - prometheuses
  - prometheuses/finalizers
  - thanosrulers
  - thanosrulers/finalizers
  - servicemonitors
  - podmonitors
  - prometheusrules
  verbs:
  - '*'
- apiGroups:
  - apps
  resources:
  - statefulsets
  verbs:
  - '*'
- apiGroups:
  - ""
  resources:
  - configmaps
  - secrets
  verbs:
  - '*'
- apiGroups:
  - ""
  resources:
  - pods
  verbs:
  - list
  - delete
- apiGroups:
  - ""
  resources:
  - services
  - services/finalizers
  - endpoints
  verbs:
  - get
  - create
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - nodes
  verbs:
  - list
  - watch
- apiGroups:
  - ""
  resources:
  - namespaces
  verbs:
  - get
  - list
  - watch
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app.kubernetes.io/component: controller
    app.kubernetes.io/name: prometheus-operator
    app.kubernetes.io/version: v0.38.3
  name: prometheus-operator
  namespace: monitoring
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/component: controller
      app.kubernetes.io/name: prometheus-operator
  template:
    metadata:
      labels:
        app.kubernetes.io/component: controller
        app.kubernetes.io/name: prometheus-operator
        app.kubernetes.io/version: v0.38.3
    spec:
      containers:
      - args:
        - --kubelet-service=kube-system/kubelet
        - --logtostderr=true
        - --config-reloader-image=jimmidyson/configmap-reload:v0.3.0
        - --prometheus-config-reloader=quay.io/prometheus-operator/prometheus-config-reloader:v0.38.3
        image: quay.io/prometheus-operator/prometheus-operator:v0.38.3
        name: prometheus-operator
        ports:
        - containerPort: 8080
          name: http
        resources:
          limits:
            cpu: 200m
            memory: 200Mi
          requests:
            cpu: 100m
            memory: 100Mi
        securityContext:
          allowPrivilegeEscalation: false
      nodeSelector:
        beta.kubernetes.io/os: linux
      securityContext:
        runAsNonRoot: true
        runAsUser: 65534
      serviceAccountName: prometheus-operator
---
apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    app.kubernetes.io/component: controller
    app.kubernetes.io/name: prometheus-operator
    app.kubernetes.io/version: v0.38.3
  name: prometheus-operator
  namespace: monitoring
---
apiVersion: v1
kind: Service
metadata:
  labels:
    app.kubernetes.io/component: controller
    app.kubernetes.io/name: prometheus-operator
    app.kubernetes.io/version: v0.38.3
  name: prometheus-operator
  namespace: monitoring
spec:
  clusterIP: None
  ports:
  - name: http
    port: 8080
    targetPort: http
  selector:
    app.kubernetes.io/component: controller
    app.kubernetes.io/name: prometheus-operator

安装 Longhorn ServiceMonitor

Longhorn ServiceMonitor 有一个标签选择器 app: longhorn-manager 来选择 Longhorn 后端服务。

稍后，Prometheus CRD 可以包含 Longhorn ServiceMonitor，以便 Prometheus server 可以发现所有 Longhorn manager pods 及其端点。

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: longhorn-prometheus-servicemonitor
  namespace: monitoring
  labels:
    name: longhorn-prometheus-servicemonitor
spec:
  selector:
    matchLabels:
      app: longhorn-manager
  namespaceSelector:
    matchNames:
    - longhorn-system
  endpoints:
  - port: manager

安装和配置 Prometheus AlertManager

1. 使用 3 个实例创建一个高可用的 Alertmanager 部署：

   apiVersion: monitoring.coreos.com/v1
   kind: Alertmanager
   metadata:
     name: longhorn
     namespace: monitoring
   spec:
     replicas: 3
   

2. 除非提供有效配置，否则 Alertmanager 实例将无法启动。有关 Alertmanager 配置的更多说明，请参见此处。下面的代码给出了一个示例配置：

   global:
     resolve_timeout: 5m
   route:
     group_by: [alertname]
     receiver: email_and_slack
   receivers:
   - name: email_and_slack
     email_configs:
     - to: <the email address to send notifications to>
       from: <the sender address>
       smarthost: <the SMTP host through which emails are sent>
       # SMTP authentication information.
       auth_username: <the username>
       auth_identity: <the identity>
       auth_password: <the password>
       headers:
         subject: 'Longhorn-Alert'
       text: |-
         {{ range .Alerts }}
           *Alert:* {{ .Annotations.summary }} - `{{ .Labels.severity }}`
           *Description:* {{ .Annotations.description }}
           *Details:*
           {{ range .Labels.SortedPairs }} • *{{ .Name }}:* `{{ .Value }}`
           {{ end }}
         {{ end }}
     slack_configs:
     - api_url: <the Slack webhook URL>
       channel: <the channel or user to send notifications to>
       text: |-
         {{ range .Alerts }}
           *Alert:* {{ .Annotations.summary }} - `{{ .Labels.severity }}`
           *Description:* {{ .Annotations.description }}
           *Details:*
           {{ range .Labels.SortedPairs }} • *{{ .Name }}:* `{{ .Value }}`
           {{ end }}
         {{ end }}
   

   将上述 Alertmanager 配置保存在名为 alertmanager.yaml 的文件中，并使用 kubectl 从中创建一个 secret。

   Alertmanager 实例要求 secret 资源命名遵循 alertmanager-{ALERTMANAGER_NAME} 格式。
   
   在上一步中，Alertmanager 的名称是 longhorn，所以 secret 名称必须是 alertmanager-longhorn

   $ kubectl create secret generic alertmanager-longhorn --from-file=alertmanager.yaml -n monitoring
   

3. 为了能够查看 Alertmanager 的 Web UI，请通过 Service 公开它。一个简单的方法是使用 NodePort 类型的 Service ：

   apiVersion: v1
   kind: Service
   metadata:
     name: alertmanager-longhorn
     namespace: monitoring
   spec:
     type: NodePort
     ports:
     - name: web
       nodePort: 30903
       port: 9093
       protocol: TCP
       targetPort: web
     selector:
       alertmanager: longhorn
   

   创建上述服务后，您可以通过节点的 IP 和端口 30903 访问 Alertmanager 的 web UI。

   使用上面的 NodePort 服务进行快速验证，因为它不通过 TLS 连接进行通信。您可能希望将服务类型更改为 ClusterIP，并设置一个 Ingress-controller 以通过 TLS 连接公开 Alertmanager 的 web UI。

安装和配置 Prometheus server

1. 创建定义警报条件的 PrometheusRule 自定义资源。

   apiVersion: monitoring.coreos.com/v1
   kind: PrometheusRule
   metadata:
     labels:
       prometheus: longhorn
       role: alert-rules
     name: prometheus-longhorn-rules
     namespace: monitoring
   spec:
     groups:
     - name: longhorn.rules
       rules:
       - alert: LonghornVolumeUsageCritical
         annotations:
           description: Longhorn volume {{$labels.volume}} on {{$labels.node}} is at {{$value}}% used for
             more than 5 minutes.
           summary: Longhorn volume capacity is over 90% used.
         expr: 100 * (longhorn_volume_usage_bytes / longhorn_volume_capacity_bytes) > 90
         for: 5m
         labels:
           issue: Longhorn volume {{$labels.volume}} usage on {{$labels.node}} is critical.
           severity: critical
   

   有关如何定义警报规则的更多信息，请参见https://prometheus.io/docs/prometheus/latest/configuration/alerting_rules/#alerting-rules

2. 如果激活了 RBAC 授权，则为 Prometheus Pod 创建 ClusterRole 和 ClusterRoleBinding：

   apiVersion: v1
   kind: ServiceAccount
   metadata:
     name: prometheus
     namespace: monitoring
   

   apiVersion: rbac.authorization.k8s.io/v1beta1
   kind: ClusterRole
   metadata:
     name: prometheus
     namespace: monitoring
   rules:
   - apiGroups: [""]
     resources:
     - nodes
     - services
     - endpoints
     - pods
     verbs: ["get", "list", "watch"]
   - apiGroups: [""]
     resources:
     - configmaps
     verbs: ["get"]
   - nonResourceURLs: ["/metrics"]
     verbs: ["get"]
   

   apiVersion: rbac.authorization.k8s.io/v1beta1
   kind: ClusterRoleBinding
   metadata:
     name: prometheus
   roleRef:
     apiGroup: rbac.authorization.k8s.io
     kind: ClusterRole
     name: prometheus
   subjects:
   - kind: ServiceAccount
     name: prometheus
     namespace: monitoring
   

3. 创建 Prometheus 自定义资源。请注意，我们在 spec 中选择了 Longhorn 服务监视器(service monitor)和 Longhorn 规则。

   apiVersion: monitoring.coreos.com/v1
   kind: Prometheus
   metadata:
     name: prometheus
     namespace: monitoring
   spec:
     replicas: 2
     serviceAccountName: prometheus
     alerting:
       alertmanagers:
         - namespace: monitoring
           name: alertmanager-longhorn
           port: web
     serviceMonitorSelector:
       matchLabels:
         name: longhorn-prometheus-servicemonitor
     ruleSelector:
       matchLabels:
         prometheus: longhorn
         role: alert-rules
   

4. 为了能够查看 Prometheus 服务器的 web UI，请通过 Service 公开它。一个简单的方法是使用 NodePort 类型的 Service：

   apiVersion: v1
   kind: Service
   metadata:
     name: prometheus
     namespace: monitoring
   spec:
     type: NodePort
     ports:
     - name: web
       nodePort: 30904
       port: 9090
       protocol: TCP
       targetPort: web
     selector:
       prometheus: prometheus
   

   创建上述服务后，您可以通过节点的 IP 和端口 30904 访问 Prometheus server 的 web UI。

   此时，您应该能够在 Prometheus server UI 的目标和规则部分看到所有 Longhorn manager targets 以及 Longhorn rules。

   使用上述 NodePort service 进行快速验证，因为它不通过 TLS 连接进行通信。您可能希望将服务类型更改为 ClusterIP，并设置一个 Ingress-controller 以通过 TLS 连接公开 Prometheus server 的 web UI。

安装 Grafana

1. 创建 Grafana 数据源配置：

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: grafana-datasources
     namespace: monitoring
   data:
     prometheus.yaml: |-
       {
           "apiVersion": 1,
           "datasources": [
               {
                  "access":"proxy",
                   "editable": true,
                   "name": "prometheus",
                   "orgId": 1,
                   "type": "prometheus",
                   "url": "http://prometheus:9090",
                   "version": 1
               }
           ]
       }
   

2. 创建 Grafana 部署：

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: grafana
     namespace: monitoring
     labels:
       app: grafana
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: grafana
     template:
       metadata:
         name: grafana
         labels:
           app: grafana
       spec:
         containers:
         - name: grafana
           image: grafana/grafana:7.1.5
           ports:
           - name: grafana
             containerPort: 3000
           resources:
             limits:
               memory: "500Mi"
               cpu: "300m"
             requests:
               memory: "500Mi"
               cpu: "200m"
           volumeMounts:
             - mountPath: /var/lib/grafana
               name: grafana-storage
             - mountPath: /etc/grafana/provisioning/datasources
               name: grafana-datasources
               readOnly: false
         volumes:
           - name: grafana-storage
             emptyDir: {}
           - name: grafana-datasources
             configMap:
                 defaultMode: 420
                 name: grafana-datasources
   

3. 在 NodePort 32000 上暴露 Grafana：

   apiVersion: v1
   kind: Service
   metadata:
     name: grafana
     namespace: monitoring
   spec:
     selector:
       app: grafana
     type: NodePort
     ports:
       - port: 3000
         targetPort: 3000
         nodePort: 32000
   

   使用上述 NodePort 服务进行快速验证，因为它不通过 TLS 连接进行通信。您可能希望将服务类型更改为 ClusterIP，并设置一个 Ingress-controller 以通过 TLS 连接公开 Grafana。

4. 使用端口 32000 上的任何节点 IP 访问 Grafana 仪表板。默认凭据为：

   User: admin
   Pass: admin
   

5. 安装 Longhorn dashboard

   进入 Grafana 后，导入预置的面板：https://grafana.com/grafana/dashboards/13032

   有关如何导入 Grafana dashboard 的说明，请参阅 https://grafana.com/docs/grafana/latest/reference/export_import/

   成功后，您应该会看到以下 dashboard：

将 Longhorn 指标集成到 Rancher 监控系统中

关于 Rancher 监控系统

使用 Rancher，您可以通过与领先的开源监控解决方案 Prometheus 的集成来监控集群节点、Kubernetes 组件和软件部署的状态和进程。

有关如何部署/启用 Rancher 监控系统的说明，请参见https://rancher.com/docs/rancher/v2.x/en/monitoring-alerting/

将 Longhorn 指标添加到 Rancher 监控系统

如果您使用 Rancher 来管理您的 Kubernetes 并且已经启用 Rancher 监控，您可以通过简单地部署以下 ServiceMonitor 将 Longhorn 指标添加到 Rancher 监控中：

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: longhorn-prometheus-servicemonitor
  namespace: longhorn-system
  labels:
    name: longhorn-prometheus-servicemonitor
spec:
  selector:
    matchLabels:
      app: longhorn-manager
  namespaceSelector:
    matchNames:
    - longhorn-system
  endpoints:
  - port: manager

创建 ServiceMonitor 后，Rancher 将自动发现所有 Longhorn 指标。

然后，您可以设置 Grafana 仪表板以进行可视化。

Longhorn 监控指标

Volume(卷)

指标名	说明	示例
longhorn_volume_actual_size_bytes	对应节点上卷的每个副本使用的实际空间	longhorn_volume_actual_size_bytes{node="worker-2",volume="testvol"} 1.1917312e+08
longhorn_volume_capacity_bytes	此卷的配置大小（以 byte 为单位）	longhorn_volume_capacity_bytes{node="worker-2",volume="testvol"} 6.442450944e+09
longhorn_volume_state	本卷状态： 1=creating, 2=attached, 3=Detached, 4=Attaching, 5=Detaching, 6=Deleting	longhorn_volume_state{node="worker-2",volume="testvol"} 2
longhorn_volume_robustness	本卷的健壮性: 0=unknown, 1=healthy, 2=degraded, 3=faulted	longhorn_volume_robustness{node="worker-2",volume="testvol"} 1

Node(节点)

指标名	说明	示例
longhorn_node_status	该节点的状态： 1=true, 0=false	longhorn_node_status{condition="ready",condition_reason="",node="worker-2"} 1
longhorn_node_count_total	Longhorn 系统中的节点总数	longhorn_node_count_total 4
longhorn_node_cpu_capacity_millicpu	此节点上的最大可分配 CPU	longhorn_node_cpu_capacity_millicpu{node="worker-2"} 2000
longhorn_node_cpu_usage_millicpu	此节点上的 CPU 使用率	longhorn_node_cpu_usage_millicpu{node="pworker-2"} 186
longhorn_node_memory_capacity_bytes	此节点上的最大可分配内存	longhorn_node_memory_capacity_bytes{node="worker-2"} 4.031229952e+09
longhorn_node_memory_usage_bytes	此节点上的内存使用情况	longhorn_node_memory_usage_bytes{node="worker-2"} 1.833582592e+09
longhorn_node_storage_capacity_bytes	本节点的存储容量	longhorn_node_storage_capacity_bytes{node="worker-3"} 8.3987283968e+10
longhorn_node_storage_usage_bytes	该节点的已用存储	longhorn_node_storage_usage_bytes{node="worker-3"} 9.060941824e+09
longhorn_node_storage_reservation_bytes	此节点上为其他应用程序和系统保留的存储空间	longhorn_node_storage_reservation_bytes{node="worker-3"} 2.519618519e+10

Disk(磁盘)

指标名	说明	示例
longhorn_disk_capacity_bytes	此磁盘的存储容量	longhorn_disk_capacity_bytes{disk="default-disk-8b28ee3134628183",node="worker-3"} 8.3987283968e+10
longhorn_disk_usage_bytes	此磁盘的已用存储空间	longhorn_disk_usage_bytes{disk="default-disk-8b28ee3134628183",node="worker-3"} 9.060941824e+09
longhorn_disk_reservation_bytes	此磁盘上为其他应用程序和系统保留的存储空间	longhorn_disk_reservation_bytes{disk="default-disk-8b28ee3134628183",node="worker-3"} 2.519618519e+10

Instance Manager(实例管理器)

指标名	说明	示例
longhorn_instance_manager_cpu_usage_millicpu	这个 longhorn 实例管理器的 CPU 使用率	longhorn_instance_manager_cpu_usage_millicpu{instance_manager="instance-manager-e-2189ed13",instance_manager_type="engine",node="worker-2"} 80
longhorn_instance_manager_cpu_requests_millicpu	在这个 Longhorn 实例管理器的 kubernetes 中请求的 CPU 资源	longhorn_instance_manager_cpu_requests_millicpu{instance_manager="instance-manager-e-2189ed13",instance_manager_type="engine",node="worker-2"} 250
longhorn_instance_manager_memory_usage_bytes	这个 longhorn 实例管理器的内存使用情况	longhorn_instance_manager_memory_usage_bytes{instance_manager="instance-manager-e-2189ed13",instance_manager_type="engine",node="worker-2"} 2.4072192e+07
longhorn_instance_manager_memory_requests_bytes	这个 longhorn 实例管理器在 Kubernetes 中请求的内存	longhorn_instance_manager_memory_requests_bytes{instance_manager="instance-manager-e-2189ed13",instance_manager_type="engine",node="worker-2"} 0

Manager(管理器)

指标名	说明	示例
longhorn_manager_cpu_usage_millicpu	这个 Longhorn Manager 的 CPU 使用率	longhorn_manager_cpu_usage_millicpu{manager="longhorn-manager-5rx2n",node="worker-2"} 27
longhorn_manager_memory_usage_bytes	这个 Longhorn Manager 的内存使用情况	longhorn_manager_memory_usage_bytes{manager="longhorn-manager-5rx2n",node="worker-2"} 2.6144768e+07

支持 Kubelet Volume 指标

关于 Kubelet Volume 指标

Kubelet 公开了以下指标：

1. kubelet_volume_stats_capacity_bytes
2. kubelet_volume_stats_available_bytes
3. kubelet_volume_stats_used_bytes
4. kubelet_volume_stats_inodes
5. kubelet_volume_stats_inodes_free
6. kubelet_volume_stats_inodes_used

这些指标衡量与 Longhorn 块设备内的 PVC 文件系统相关的信息。

它们与 longhorn_volume_* 指标不同，后者测量特定于 Longhorn 块设备(block device)的信息。

您可以设置一个监控系统来抓取 Kubelet 指标端点以获取 PVC 的状态并设置异常事件的警报，例如 PVC 即将耗尽存储空间。

一个流行的监控设置是 prometheus-operator/kube-prometheus-stack,，它抓取 kubelet_volume_stats_* 指标并为它们提供仪表板和警报规则。

Longhorn CSI 插件支持

在 v1.1.0 中，Longhorn CSI 插件根据 CSI spec 支持 NodeGetVolumeStats RPC。

这允许 kubelet 查询 Longhorn CSI 插件以获取 PVC 的状态。

然后 kubelet 在 kubelet_volume_stats_* 指标中公开该信息。

Longhorn 警报规则示例

我们在下面提供了几个示例 Longhorn 警报规则供您参考。请参阅此处获取所有可用 Longhorn 指标的列表并构建您自己的警报规则。

apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  labels:
    prometheus: longhorn
    role: alert-rules
  name: prometheus-longhorn-rules
  namespace: monitoring
spec:
  groups:
  - name: longhorn.rules
    rules:
    - alert: LonghornVolumeActualSpaceUsedWarning
      annotations:
        description: The actual space used by Longhorn volume {{$labels.volume}} on {{$labels.node}} is at {{$value}}% capacity for
          more than 5 minutes.
        summary: The actual used space of Longhorn volume is over 90% of the capacity.
      expr: (longhorn_volume_actual_size_bytes / longhorn_volume_capacity_bytes) * 100 > 90
      for: 5m
      labels:
        issue: The actual used space of Longhorn volume {{$labels.volume}} on {{$labels.node}} is high.
        severity: warning
    - alert: LonghornVolumeStatusCritical
      annotations:
        description: Longhorn volume {{$labels.volume}} on {{$labels.node}} is Fault for
          more than 2 minutes.
        summary: Longhorn volume {{$labels.volume}} is Fault
      expr: longhorn_volume_robustness == 3
      for: 5m
      labels:
        issue: Longhorn volume {{$labels.volume}} is Fault.
        severity: critical
    - alert: LonghornVolumeStatusWarning
      annotations:
        description: Longhorn volume {{$labels.volume}} on {{$labels.node}} is Degraded for
          more than 5 minutes.
        summary: Longhorn volume {{$labels.volume}} is Degraded
      expr: longhorn_volume_robustness == 2
      for: 5m
      labels:
        issue: Longhorn volume {{$labels.volume}} is Degraded.
        severity: warning
    - alert: LonghornNodeStorageWarning
      annotations:
        description: The used storage of node {{$labels.node}} is at {{$value}}% capacity for
          more than 5 minutes.
        summary:  The used storage of node is over 70% of the capacity.
      expr: (longhorn_node_storage_usage_bytes / longhorn_node_storage_capacity_bytes) * 100 > 70
      for: 5m
      labels:
        issue: The used storage of node {{$labels.node}} is high.
        severity: warning
    - alert: LonghornDiskStorageWarning
      annotations:
        description: The used storage of disk {{$labels.disk}} on node {{$labels.node}} is at {{$value}}% capacity for
          more than 5 minutes.
        summary:  The used storage of disk is over 70% of the capacity.
      expr: (longhorn_disk_usage_bytes / longhorn_disk_capacity_bytes) * 100 > 70
      for: 5m
      labels:
        issue: The used storage of disk {{$labels.disk}} on node {{$labels.node}} is high.
        severity: warning
    - alert: LonghornNodeDown
      annotations:
        description: There are {{$value}} Longhorn nodes which have been offline for more than 5 minutes.
        summary: Longhorn nodes is offline
      expr: longhorn_node_total - (count(longhorn_node_status{condition="ready"}==1) OR on() vector(0))
      for: 5m
      labels:
        issue: There are {{$value}} Longhorn nodes are offline
        severity: critical
    - alert: LonghornIntanceManagerCPUUsageWarning
      annotations:
        description: Longhorn instance manager {{$labels.instance_manager}} on {{$labels.node}} has CPU Usage / CPU request is {{$value}}% for
          more than 5 minutes.
        summary: Longhorn instance manager {{$labels.instance_manager}} on {{$labels.node}} has CPU Usage / CPU request is over 300%.
      expr: (longhorn_instance_manager_cpu_usage_millicpu/longhorn_instance_manager_cpu_requests_millicpu) * 100 > 300
      for: 5m
      labels:
        issue: Longhorn instance manager {{$labels.instance_manager}} on {{$labels.node}} consumes 3 times the CPU request.
        severity: warning
    - alert: LonghornNodeCPUUsageWarning
      annotations:
        description: Longhorn node {{$labels.node}} has CPU Usage / CPU capacity is {{$value}}% for
          more than 5 minutes.
        summary: Longhorn node {{$labels.node}} experiences high CPU pressure for more than 5m.
      expr: (longhorn_node_cpu_usage_millicpu / longhorn_node_cpu_capacity_millicpu) * 100 > 90
      for: 5m
      labels:
        issue: Longhorn node {{$labels.node}} experiences high CPU pressure.
        severity: warning

在https://prometheus.io/docs/prometheus/latest/configuration/alerting_rules/#alerting-rules

查看有关如何定义警报规则的更多信息。

公众号：黑客下午茶