Ŀ¼ 设置 Prometheus 和 Grafana 来监控 Longhorn 将 Longhorn 指标集成到 Rancher 监控系统中 Longhorn 监控指标 支持 Kubelet Volume 指标 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
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: from: smarthost: # SMTP authentication information. auth_username: auth_identity: auth_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: channel: 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。
