内容来源于官方 Longhorn 1.1.2 英文技术手册。

系列

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=Deletinglonghorn_volume_state{node="worker-2",volume="testvol"} 2
longhorn_volume_robustness本卷的健壮性: 0=unknown, 1=healthy, 2=degraded, 3=faultedlonghorn_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=falselonghorn_node_status{condition="ready",condition_reason="",node="worker-2"} 1
longhorn_node_count_totalLonghorn 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 CPUlonghorn_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
# 分布式 # kubernetes # java # 大数据
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