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

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`

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

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

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`

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

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

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

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

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

            }

        ]

    }

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

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。

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

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：

kubelet_volume_stats_capacity_bytes

kubelet_volume_stats_available_bytes

kubelet_volume_stats_used_bytes

kubelet_volume_stats_inodes

kubelet_volume_stats_inodes_free

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。

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