This document is a quick guide to setting up the monitor for Longhorn.

Longhorn natively exposes metrics in Prometheus text format on a REST endpoint http://LONGHORN_MANAGER_IP:PORT/metrics.

You can use any collecting tools such as Prometheus, Graphite, Telegraf to scrape these metrics then visualize the collected data by tools such as Grafana.

See Longhorn Metrics for Monitoring for available metrics.

High-level Overview

The monitoring system uses Prometheus for collecting data and alerting, and Grafana for visualizing/dashboarding the collected data.

• Prometheus server which scrapes and stores time-series data from Longhorn metrics endpoints. The Prometheus is also responsible for generating alerts based on configured rules and collected data. Prometheus servers then send alerts to an Alertmanager.
• AlertManager then manages those alerts, including silencing, inhibition, aggregation, and sending out notifications via methods such as email, on-call notification systems, and chat platforms.
• Grafana which queries Prometheus server for data and draws a dashboard for visualization.

The below picture describes the detailed architecture of the monitoring system.

There are 2 unmentioned components in the above picture:

• Longhorn Backend service is a service pointing to the set of Longhorn manager pods. Longhorn’s metrics are exposed in Longhorn manager pods at the endpoint http://LONGHORN_MANAGER_IP:PORT/metrics.
• Prometheus operator makes running Prometheus on top of Kubernetes very easy. The operator watches 3 custom resources: ServiceMonitor, Prometheus ,and AlertManager. When you create those custom resources, Prometheus Operator deploys and manages the Prometheus server, AlertManager with the user-specified configurations.

Installation

This document uses the default namespace for the monitoring system. To install on a different namespace, change the field namespace: <OTHER_NAMESPACE> in manifests.

Install Prometheus Operator

Follow instructions in Prometheus Operator - Quickstart.

NOTE: You may need to choose a release that is compatible with the Kubernetes version of the cluster.

Install Longhorn ServiceMonitor

1. Create a ServiceMonitor for Longhorn manager.

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

   Longhorn ServiceMonitor has a label selector app: longhorn-manager for selecting Longhorn backend service.

   Longhorn ServiceMonitor is included in the Prometheus custom resource so that the Prometheus server can discover all Longhorn manager pods and their endpoints.

Install and configure Prometheus AlertManager

1. Create a highly available Alertmanager deployment with 3 instances.

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

2. The Alertmanager instances will not start unless a valid configuration is given. See Prometheus - Configuration for more explanation.

   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 }}
   

   Save the above Alertmanager config in a file called alertmanager.yaml and create a secret from it using kubectl.

   Alertmanager instances require the secret resource naming to follow the format alertmanager-<ALERTMANAGER_NAME>. In the previous step, the name of the Alertmanager is longhorn, so the secret name must be alertmanager-longhorn

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

3. To be able to view the web UI of the Alertmanager, expose it through a Service. A simple way to do this is to use a Service of type NodePort.

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

   After creating the above service, you can access the web UI of Alertmanager via a Node’s IP and the port 30903.

   Use the above NodePort service for quick verification only because it doesn’t communicate over the TLS connection. You may want to change the service type to ClusterIP and set up an Ingress-controller to expose the web UI of Alertmanager over a TLS connection.

Install and configure Prometheus server

1. Create PrometheusRule custom resource to define alert conditions. See more examples about Longhorn alert rules at Longhorn Alert Rule Examples.

   apiVersion: monitoring.coreos.com/v1
   kind: PrometheusRule
   metadata:
     labels:
       prometheus: longhorn
       role: alert-rules
     name: prometheus-longhorn-rules
     namespace: default
   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
   

   See Prometheus - Alerting rules for more information.

2. If RBAC authorization is activated, Create a ClusterRole and ClusterRoleBinding for the Prometheus Pods.

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

   apiVersion: rbac.authorization.k8s.io/v1
   kind: ClusterRole
   metadata:
     name: prometheus
     namespace: default
   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/v1
   kind: ClusterRoleBinding
   metadata:
     name: prometheus
   roleRef:
     apiGroup: rbac.authorization.k8s.io
     kind: ClusterRole
     name: prometheus
   subjects:
   - kind: ServiceAccount
     name: prometheus
     namespace: default
   

3. Create a Prometheus custom resource. Notice that we select the Longhorn service monitor and Longhorn rules in the spec.

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

4. To be able to view the web UI of the Prometheus server, expose it through a Service. A simple way to do this is to use a Service of type NodePort.

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

   After creating the above service, you can access the web UI of the Prometheus server via a Node’s IP and the port 30904.

   At this point, you should be able to see all Longhorn manager targets as well as Longhorn rules in the targets and rules section of the Prometheus server UI.

   Use the above NodePort service for quick verification only because it doesn’t communicate over the TLS connection. You may want to change the service type to ClusterIP and set up an Ingress controller to expose the web UI of the Prometheus server over a TLS connection.

Setup Grafana

1. Create Grafana datasource ConfigMap.

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

   NOTE: change field url if you are installing the monitoring stack in a different namespace. http://prometheus-longhorn.<NAMESPACE>.svc:9090"

2. Create Grafana Deployment.

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: grafana
     namespace: default
     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. Create Grafana Service.

   apiVersion: v1
   kind: Service
   metadata:
     name: grafana
     namespace: default
   spec:
     selector:
       app: grafana
     type: ClusterIP
     ports:
       - port: 3000
         targetPort: 3000
   

4. Expose Grafana on NodePort 32000.

   kubectl -n default patch svc grafana --type='json' -p '[{"op":"replace","path":"/spec/type","value":"NodePort"},{"op":"replace","path":"/spec/ports/0/nodePort","value":32000}]'
   

   Use the above NodePort service for quick verification only because it doesn’t communicate over the TLS connection. You may want to change the service type to ClusterIP and set up an Ingress controller to expose Grafana over a TLS connection.

5. Access the Grafana dashboard using any node IP on port 32000.

   # Default Credential
   User: admin
   Pass: admin
   

6. Setup Longhorn dashboard.

   Once inside Grafana, import the prebuilt Longhorn example dashboard.

   See Grafana Lab - Export and import for instructions on how to import a Grafana dashboard.

   You should see the following dashboard at successful setup: