Chapter 5. Monitoring user-defined projects

Procedure

1. Edit the ConfigMap object.

   1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

      $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config

   2. Add your configuration under data.config.yaml as a key-value pair <component_name>: <component_configuration>:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          <component>:
            <configuration_for_the_component>

      Substitute <component> and <configuration_for_the_component> accordingly.

      The following example ConfigMap object configures a data retention period and minimum container resource requests for Prometheus. This relates to the Prometheus instance that monitors user-defined projects only:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          prometheus: 1
            retention: 24h 2
            resources:
              requests:
                cpu: 200m 3
                memory: 2Gi 4

      1

      Defines the Prometheus component and the subsequent lines define its configuration.

      2

      Configures a 24 hour data retention period for the Prometheus instance that monitors user-defined projects.

      3

      Defines a minimum resource request of 200 millicores for the Prometheus container.

      4

      Defines a minimum pod resource request of 2 GiB of memory for the Prometheus container.

2. Save the file to apply the changes to the ConfigMap object. The pods affected by the new configuration are restarted automatically.

   Warning

   When changes are saved to a monitoring config map, the pods and other resources in the related project might be redeployed. The running monitoring processes in that project might also be restarted.

Procedure

1. To move a component that monitors user-defined projects, edit the ConfigMap object:

   1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

      $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config

   2. Specify the nodeSelector constraint for the component under data.config.yaml:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          <component>:
            nodeSelector:
              <node_key>: <node_value>
              <node_key>: <node_value>
              <...>

      Substitute <component> accordingly and substitute <node_key>: <node_value> with the map of key-value pairs that specifies the destination nodes. Often, only a single key-value pair is used.

      The component can only run on nodes that have each of the specified key-value pairs as labels. The nodes can have additional labels as well.

      Important

      Many of the monitoring components are deployed by using multiple pods across different nodes in the cluster to maintain high availability. When moving monitoring components to labeled nodes, ensure that enough matching nodes are available to maintain resilience for the component. If only one label is specified, ensure that enough nodes contain that label to distribute all of the pods for the component across separate nodes. Alternatively, you can specify multiple labels each relating to individual nodes.

      Note

      If monitoring components remain in a Pending state after configuring the nodeSelector constraint, check the pod logs for errors relating to taints and tolerations.

      For example, to move monitoring components for user-defined projects to specific worker nodes labeled nodename: worker1, nodename: worker2, and nodename: worker2, use:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          prometheusOperator:
            nodeSelector:
              nodename: worker1
          prometheus:
            nodeSelector:
              nodename: worker1
              nodename: worker2
          thanosRuler:
            nodeSelector:
              nodename: worker1
              nodename: worker2

2. Save the file to apply the changes. The components affected by the new configuration are moved to the new nodes automatically.

   Warning

   When changes are saved to a monitoring config map, the pods and other resources in the related project might be redeployed. The running monitoring processes in that project might also be restarted.

Procedure

1. Edit the ConfigMap object:

   1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

      $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config

   2. Specify tolerations for the component:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          <component>:
            tolerations:
              <toleration_specification>

      Substitute <component> and <toleration_specification> accordingly.

      For example, oc adm taint nodes node1 key1=value1:NoSchedule adds a taint to node1 with the key key1 and the value value1. This prevents monitoring components from deploying pods on node1 unless a toleration is configured for that taint. The following example configures the thanosRuler component to tolerate the example taint:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          thanosRuler:
            tolerations:
            - key: "key1"
              operator: "Equal"
              value: "value1"
              effect: "NoSchedule"

2. Save the file to apply the changes. The new component placement configuration is applied automatically.

   Warning

   When changes are saved to a monitoring config map, the pods and other resources in the related project might be redeployed. The running monitoring processes in that project might also be restarted.

Procedure

1. Edit the ConfigMap object:

   1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

      $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config

   2. Add logLevel: <log_level> for a component under data.config.yaml:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          <component>: 1
            logLevel: <log_level> 2

      1

      The monitoring component that you are applying a log level to.

      2

      The log level to apply to the component.

2. Save the file to apply the changes. The pods for the component restarts automatically when you apply the log-level change.

   Warning

   When changes are saved to a monitoring config map, the pods and other resources in the related project might be redeployed. The running monitoring processes in that project might also be restarted.

3. Confirm that the log level has been applied by reviewing the deployment or pod configuration in the related project. The following example checks the log level in the prometheus-operator deployment in the openshift-user-workload-monitoring project:

   $ oc -n openshift-user-workload-monitoring get deploy prometheus-operator -o yaml |  grep "log-level"

   Example output

           - --log-level=debug

4. Check that the pods for the component are running. The following example lists the status of pods in the openshift-user-workload-monitoring project:

   $ oc -n openshift-user-workload-monitoring get pods

   Note

   If an unrecognized loglevel value is included in the ConfigMap object, the pods for the component might not restart successfully.

Procedure

1. Edit the user-workload-monitoring-config ConfigMap object:

   $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config

2. Add enabled: true and enableAlertmanagerConfig: true in the alertmanager section under data/config.yaml:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: user-workload-monitoring-config
     namespace: openshift-user-workload-monitoring
   data:
     config.yaml: |
       alertmanager:
         enabled: true 1
         enableAlertmanagerConfig: true 2

   1

   Set the enabled value to true to enable a dedicated instance of the Alertmanager for user-defined projects in a cluster. Set the value to false or omit the key entirely to disable the Alertmanager for user-defined projects. If you set this value to false or if the key is omitted, user-defined alerts are routed to the default platform Alertmanager instance.

   2

   Set the enableAlertmanagerConfig value to true to enable users to define their own alert routing configurations with AlertmanagerConfig objects.

3. Save the file to apply the changes. The dedicated instance of Alertmanager for user-defined projects starts automatically.

1. Open the OpenShift Dedicated web console and navigate to the Observe → Alerting → Alerts page.
2. Optional: Search for alerts by name using the Name field in the search list.
3. Optional: Filter alerts by state, severity, and source by selecting filters in the Filter list.
4. Optional: Sort the alerts by clicking one or more of the Name, Severity, State, and Source column headers.

5. Select the name of an alert to navigate to its Alert Details page. The page includes a graph that illustrates alert time series data. It also provides information about the alert, including:

   • A description of the alert
   • Messages associated with the alerts
   • Labels attached to the alert
   • A link to its governing alerting rule
   • Silences for the alert, if any exist

1. Navigate to the Observe → Alerting → Silences page.
2. Optional: Filter the silences by name using the Search by name field.
3. Optional: Filter silences by state by selecting filters in the Filter list. By default, Active and Pending filters are applied.
4. Optional: Sort the silences by clicking one or more of the Name, Firing Alerts, and State column headers.

5. Select the name of a silence to navigate to its Silence Details page. The page includes the following details:

   • Alert specification
   • Start time
   • End time
   • Silence state
   • Number and list of firing alerts

1. Navigate to the Observe → Alerting → Alerting Rules page.
2. Optional: Filter alerting rules by state, severity, and source by selecting filters in the Filter list.
3. Optional: Sort the alerting rules by clicking one or more of the Name, Severity, Alert State, and Source column headers.

4. Select the name of an alerting rule to navigate to its Alerting Rule Details page. The page provides the following details about the alerting rule:

   • Alerting rule name, severity, and description
   • The expression that defines the condition for firing the alert
   • The time for which the condition should be true for an alert to fire
   • A graph for each alert governed by the alerting rule, showing the value with which the alert is firing
   • A table of all alerts governed by the alerting rule

1. Navigate to the Observe → <project_name> → Alerts page.

2. View details for an alert, silence, or an alerting rule:

   • Alert Details can be viewed by selecting > to the left of an alert name and then selecting the alert in the list.

   • Silence Details can be viewed by selecting a silence in the Silenced By section of the Alert Details page. The Silence Details page includes the following information:

     • Alert specification
     • Start time
     • End time
     • Silence state
     • Number and list of firing alerts
   • Alerting Rule Details can be viewed by selecting View Alerting Rule in the menu on the right of an alert in the Alerts page.

Procedure

1. Print the currently active Alertmanager configuration into the file alertmanager.yaml:

   $ oc -n openshift-user-workload-monitoring get secret alertmanager-user-workload --template='{{ index .data "alertmanager.yaml" }}' | base64 --decode > alertmanager.yaml

2. Edit the configuration in alertmanager.yaml:

   route:
     receiver: Default
     group_by:
     - name: Default
     routes:
     - matchers:
       - "service = prometheus-example-monitor" 1
       receiver: <receiver> 2
   receivers:
   - name: Default
   - name: <receiver>
   #  <receiver_configuration>

   1

   Specifies which alerts match the route. This example shows all alerts that have the service="prometheus-example-monitor" label.

   2

   Specifies the receiver to use for the alerts group.

3. Apply the new configuration in the file:

   $ oc -n openshift-user-workload-monitoring create secret generic alertmanager-user-workload --from-file=alertmanager.yaml --dry-run=client -o=yaml |  oc -n openshift-user-workload-monitoring replace secret --filename=-

Procedure

1. In the Developer perspective in the OpenShift Dedicated web console, navigate to Observe → Dashboard.
2. Choose a project in the Project: list.
3. Choose a workload in the All Workloads list.
4. Optional: Select a time range for the graphs in the Time Range list.
5. Optional: Select a Refresh Interval.
6. Hover over each of the graphs within a dashboard to display detailed information about specific items.

Procedure

1. Query the metric name and verify that the project is correct:

   1. From the Developer perspective in the OpenShift Container Platform web console, select Observe → Metrics.
   2. Select the project that you want to view metrics for in the Project: list.

   3. Choose a query from the Select Query list, or run a custom PromQL query by selecting Show PromQL.

      The Select Query pane shows the metric names.

      Queries must be done on a per-project basis. The metrics that are shown relate to the project that you have selected.

2. Verify that the pod that you want metrics from is actively serving metrics. Run the following oc exec command into a pod to target the podIP, port, and /metrics.

   $ oc exec <sample_pod> -n <sample_namespace> -- curl <target_pod_IP>:<port>/metrics

   Note

   You must run the command on a pod that has curl installed.

   The following example output shows a result with a valid version metric.

   Example output

     % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                    Dload  Upload   Total   Spent    Left  Speed
   # HELP version Version information about this binary-- --:--:-- --:--:--     0
   # TYPE version gauge
   version{version="v0.1.0"} 1
   100   102  100   102    0     0  51000      0 --:--:-- --:--:-- --:--:-- 51000

   An invalid output indicates that there is a problem with the corresponding application.

3. If you are using a PodMonitor CRD, verify that the PodMonitor CRD is configured to point to the correct pods using label matching. For more information, see the Prometheus Operator documentation.

4. If you are using a ServiceMonitor CRD, and if the /metrics endpoint of the pod is showing metric data, follow these steps to verify the configuration:

   1. Verify that the service is pointed to the correct /metrics endpoint. The service labels in this output must match the services monitor labels and the /metrics endpoint defined by the service in the subsequent steps.

      $ oc get service

      Example output

      apiVersion: v1
      kind: Service 1
      metadata:
        labels: 2
          app: prometheus-example-app
        name: prometheus-example-app
        namespace: ns1
      spec:
        ports:
        - port: 8080
          protocol: TCP
          targetPort: 8080
          name: web
        selector:
          app: prometheus-example-app
        type: ClusterIP

      1

      Specifies that this is a service API.

      2

      Specifies the labels that are being used for this service.

   2. Query the serviceIP, port, and /metrics endpoints to see if the same metrics from the curl command you ran on the pod previously:

      1. Run the following command to find the service IP:

         $ oc get service -n <target_namespace>

      2. Query the /metrics endpoint:

         $ oc exec <sample_pod> -n <sample_namespace> -- curl <service_IP>:<port>/metrics

         Valid metrics are returned in the following example.

         Example output

         % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                        Dload  Upload   Total   Spent    Left  Speed
         100   102  100   102    0     0  51000      0 --:--:-- --:--:-- --:--:--   99k
         # HELP version Version information about this binary
         # TYPE version gauge
         version{version="v0.1.0"} 1

   3. Use label matching to verify that the ServiceMonitor object is configured to point to the desired service. To do this, compare the Service object from the oc get service output to the ServiceMonitor object from the oc get servicemonitor output. The labels must match for the metrics to be displayed.

      For example, from the previous steps, notice how the Service object has the app: prometheus-example-app label and the ServiceMonitor object has the same app: prometheus-example-app match label.

5. If everything looks valid and the metrics are still unavailable, please contact the support team for further help.