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 twenty-four 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. 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 a remoteWrite: section under data/config.yaml/prometheus.

   3. Add an endpoint URL and authentication credentials in this section:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          prometheus:
            remoteWrite:
            - url: "https://remote-write-endpoint.example.com" 1
              <endpoint_authentication_credentials> 2

      1

      The URL of the remote write endpoint.

      2

      The authentication method and credentials for the endpoint. Currently supported authentication methods are AWS Signature Version 4, authentication using HTTP an Authorization request header, basic authentication, OAuth 2.0, and TLS client. See Supported remote write authentication settings below for sample configurations of supported authentication methods.

   4. Add write relabel configuration values after the authentication credentials:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          prometheus:
            remoteWrite:
            - url: "https://remote-write-endpoint.example.com"
              <endpoint_authentication_credentials>
              <your_write_relabel_configs> 1

      1

      The write relabel configuration settings.

      For <your_write_relabel_configs> substitute a list of write relabel configurations for metrics that you want to send to the remote endpoint.

      The following sample shows how to forward a single metric called my_metric:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          prometheus:
            remoteWrite:
            - url: "https://remote-write-endpoint.example.com"
              writeRelabelConfigs:
              - sourceLabels: [__name__]
                regex: 'my_metric'
                action: keep

      See the Prometheus relabel_config documentation for information about write relabel configuration options.

2. Save the file to apply the changes. The pods affected by the new configuration restart automatically.

   Warning

   Saving changes to a monitoring ConfigMap object might redeploy the pods and other resources in the related project. Saving changes might also restart the running monitoring processes in that project.

Procedure

1. Edit the ConfigMap object.

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

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

   2. Add a secrets: section under data/config.yaml/alertmanager/secrets with the following configuration:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          alertmanager:
            secrets: 1
            - <secret_name_1> 2
            - <secret_name_2>

      1

      This section contains the secrets to be mounted into Alertmanager. The secrets must be located within the same namespace as the Alertmanager object.

      2

      The name of the Secret object that contains authentication credentials for the receiver. If you add multiple secrets, place each one on a new line.

      The following sample config map settings configure Alertmanager to use two Secret objects named test-secret and test-secret-api-token:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          alertmanager:
            enabled: true
            secrets:
            - test-secret
            - test-api-receiver-token

2. Save the file to apply the changes to the ConfigMap object. The new configuration is applied automatically.

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. Define a map of labels you want to add for every metric under data/config.yaml:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          prometheus:
            externalLabels:
              <key>: <value> 1

      1

      Substitute <key>: <value> with a map of key-value pairs where <key> is a unique name for the new label and <value> is its value.

      Warning

      • Do not use prometheus or prometheus_replica as key names, because they are reserved and will be overwritten.
      • Do not use cluster or managed_cluster as key names. Using them can cause issues where you are unable to see data in the developer dashboards.
      Note

      In the openshift-user-workload-monitoring project, Prometheus handles metrics and Thanos Ruler handles alerting and recording rules. Setting externalLabels for prometheus in the user-workload-monitoring-config ConfigMap object will only configure external labels for metrics and not for any rules.

      For example, to add metadata about the region and environment to all time series and alerts related to user-defined projects, use the following example:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          prometheus:
            externalLabels:
              region: eu
              environment: prod

2. Save the file to apply the changes. The new 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring namespace:

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

2. Add values for the following settings under data/config.yaml/thanosRuler to configure pod topology spread constraints:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: user-workload-monitoring-config
     namespace: openshift-user-workload-monitoring
   data:
     config.yaml: |
       thanosRuler:
         topologySpreadConstraints:
         - maxSkew: 1 1
           topologyKey: monitoring 2
           whenUnsatisfiable: ScheduleAnyway 3
           labelSelector:
             matchLabels: 4
               app.kubernetes.io/name: thanos-ruler

   1

   Specify a numeric value for maxSkew, which defines the degree to which pods are allowed to be unevenly distributed. This field is required, and the value must be greater than zero. The value specified has a different effect depending on what value you specify for whenUnsatisfiable.

   2

   Specify a key of node labels for topologyKey. This field is required. Nodes that have a label with this key and identical values are considered to be in the same topology. The scheduler will try to put a balanced number of pods into each domain.

   3

   Specify a value for whenUnsatisfiable. This field is required. Available options are DoNotSchedule and ScheduleAnyway. Specify DoNotSchedule if you want the maxSkew value to define the maximum difference allowed between the number of matching pods in the target topology and the global minimum. Specify ScheduleAnyway if you want the scheduler to still schedule the pod but to give higher priority to nodes that might reduce the skew.

   4

   Specify a value for matchLabels. This value is used to identify the set of matching pods to which to apply the constraints.

3. Save the file to apply the changes automatically.

   Warning

   When you save changes to the user-workload-monitoring-config config map, the pods and other resources in the openshift-user-workload-monitoring project might be redeployed. The running monitoring processes in that project might also restart.

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 stack component for which you are setting a log level. For user workload monitoring, available component values are alertmanager, prometheus, prometheusOperator, and thanosRuler.

      2

      The log level to apply to the component. The available values are error, warn, info, and debug. The default value is info.

2. Save the file to apply the changes. The pods for the component restart 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 in the openshift-user-workload-monitoring project:

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

2. Add queryLogFile: <path> for prometheus under data/config.yaml:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: user-workload-monitoring-config
     namespace: openshift-user-workload-monitoring
   data:
     config.yaml: |
       prometheus:
         queryLogFile: <path> 1

   1

   The full path to the file in which queries will be logged.

3. Save the file to apply the changes.

   Warning

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

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

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

5. Read the query log:

   $ oc -n openshift-user-workload-monitoring exec prometheus-user-workload-0 -- cat <path>

   Important

   Revert the setting in the config map after you have examined the logged query information.

Procedure

1. From the OpenShift Cluster Manager Hybrid Cloud Console, select a cluster.
2. Click the Settings tab.

3. Click the Enable user workload monitoring check box to unselect the option, and then click Save.

   User workload monitoring is disabled. The Prometheus, Prometheus Operator, and Thanos Ruler components are stopped in the openshift-user-workload-monitoring project.

Procedure

1. Add the label to the project namespace:

   $ oc label namespace my-project 'openshift.io/user-monitoring=false'

2. To re-enable monitoring, remove the label from the namespace:

   $ oc label namespace my-project 'openshift.io/user-monitoring-'

   Note

   If there were any active monitoring targets for the project, it may take a few minutes for Prometheus to stop scraping them after adding the label.

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.

Procedure

1. In the Administrator perspective, select Observe → Targets. The Metrics targets page opens with a list of all service endpoint targets that are being scraped for metrics.

   This page shows details about targets for default Red Hat OpenShift Service on AWS and user-defined projects. This page lists the following information for each target:

   • Service endpoint URL being scraped
   • ServiceMonitor component being monitored
   • The up or down status of the target
   • Namespace
   • Last scrape time
   • Duration of the last scrape

2. Optional: The list of metrics targets can be long. To find a specific target, do any of the following:

   Option	Description
   Filter the targets by status and source.	Select filters in the Filter list. The following filtering options are available: Status filters: Up. The target is currently up and being actively scraped for metrics. Down. The target is currently down and not being scraped for metrics. Source filters: Platform. Platform-level targets relate only to default Red Hat OpenShift Service on AWS projects. These projects provide core Red Hat OpenShift Service on AWS functionality. User. User targets relate to user-defined projects. These projects are user-created and can be customized.
   Search for a target by name or label.	Enter a search term in the Text or Label field next to the search box.
   Sort the targets.	Click one or more of the Endpoint Status, Namespace, Last Scrape, and Scrape Duration column headers.

3. Click the URL in the Endpoint column for a target to navigate to its Target details page. This page provides information about the target, including the following:

   • The endpoint URL being scraped for metrics
   • The current Up or Down status of the target
   • A link to the namespace
   • A link to the ServiceMonitor details
   • Labels attached to the target
   • The most recent time that the target was scraped for metrics

1. Open the Red Hat OpenShift Service on AWS web console and go to the Observe → Alerting → Alerts page.
2. Optional: Search for alerts by name by 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. Click the name of an alert to view its Alert details page. The page includes a graph that illustrates alert time series data. It also provides the following information about the alert:

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

1. Go 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, State, and Creator column headers.

5. Select the name of a silence to view 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. Go 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 view 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. Go to the Observe → <project_name> → Alerts page.

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

   • Alert details can be viewed by clicking a greater than symbol (>) next to an alert name and then selecting the alert from the list.

   • Silence details can be viewed by clicking 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 clicking the menu next to an alert in the Alerts page and then clicking View Alerting Rule.

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 Administrator perspective in the Red Hat OpenShift Service on AWS web console, navigate to Observe → Dashboards.
2. Choose a dashboard in the Dashboard list. Some dashboards, such as etcd and Prometheus dashboards, produce additional sub-menus when selected.

3. Optional: Select a time range for the graphs in the Time Range list.

   • Select a pre-defined time period.

   • Set a custom time range by selecting Custom time range in the Time Range list.

     1. Input or select the From and To dates and times.
     2. Click Save to save the custom time range.
4. Optional: Select a Refresh Interval.
5. Hover over each of the graphs within a dashboard to display detailed information about specific items.

Procedure

1. In the Developer perspective in the Red Hat OpenShift Service on AWS web console, navigate to Observe → Dashboard.
2. Select a project from the Project: drop-down list.

3. Select a dashboard from the Dashboard drop-down list to see the filtered metrics.

   Note

   All dashboards produce additional sub-menus when selected, except Kubernetes / Compute Resources / Namespace (Pods).

4. Optional: Select a time range for the graphs in the Time Range list.

   • Select a pre-defined time period.

   • Set a custom time range by selecting Custom time range in the Time Range list.

     1. Input or select the From and To dates and times.
     2. Click Save to save the custom time range.
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. Extract an authentication token by running the following command:

   $ TOKEN=$(oc whoami -t)

2. Extract the alertmanager-main API route URL by running the following command:

   $ HOST=$(oc -n openshift-monitoring get route alertmanager-main -ojsonpath={.spec.host})

3. Query the service API receivers for Alertmanager by running the following command:

   $ curl -H "Authorization: Bearer $TOKEN" -k "https://$HOST/api/v2/receivers"

Procedure

1. Retrieve the bearer token by running the following the command:

   $ TOKEN=$(oc whoami -t)

2. Get the Prometheus federation route URL by running the following command:

   $ HOST=$(oc -n openshift-monitoring get route prometheus-k8s-federate -ojsonpath={.spec.host})

3. Query metrics from the /federate route. The following example command queries up metrics:

   $ curl -G -k -H "Authorization: Bearer $TOKEN" https://$HOST/federate --data-urlencode 'match[]=up'

   Example output

   # TYPE up untyped
   up{apiserver="kube-apiserver",endpoint="https",instance="10.0.143.148:6443",job="apiserver",namespace="default",service="kubernetes",prometheus="openshift-monitoring/k8s",prometheus_replica="prometheus-k8s-0"} 1 1657035322214
   up{apiserver="kube-apiserver",endpoint="https",instance="10.0.148.166:6443",job="apiserver",namespace="default",service="kubernetes",prometheus="openshift-monitoring/k8s",prometheus_replica="prometheus-k8s-0"} 1 1657035338597
   up{apiserver="kube-apiserver",endpoint="https",instance="10.0.173.16:6443",job="apiserver",namespace="default",service="kubernetes",prometheus="openshift-monitoring/k8s",prometheus_replica="prometheus-k8s-0"} 1 1657035343834
   ...

Procedure

1. Extract an authentication token to connect to Prometheus by running the following command:

   $ TOKEN=$(oc whoami -t)

2. Extract the thanos-querier API route URL by running the following command:

   $ HOST=$(oc -n openshift-monitoring get route thanos-querier -ojsonpath={.spec.host})

3. Set the namespace to the namespace in which your service is running by using the following command:

   $ NAMESPACE=ns1

4. Query the metrics of your own services in the command line by running the following command:

   $ curl -H "Authorization: Bearer $TOKEN" -k "https://$HOST/api/v1/query?" --data-urlencode "query=up{namespace='$NAMESPACE'}"

   The output shows the status for each application pod that Prometheus is scraping:

   Example output

   {"status":"success","data":{"resultType":"vector","result":[{"metric":{"__name__":"up","endpoint":"web","instance":"10.129.0.46:8080","job":"prometheus-example-app","namespace":"ns1","pod":"prometheus-example-app-68d47c4fb6-jztp2","service":"prometheus-example-app"},"value":[1591881154.748,"1"]}]}}

Procedure

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

   1. From the Developer perspective in the 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 metrics are displayed in a chart.

      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.

Procedure

1. In the Administrator perspective, navigate to Observe → Metrics.

2. Enter a Prometheus Query Language (PromQL) query in the Expression field. The following example queries help to identify high cardinality metrics that might result in high disk space consumption:

   • By running the following query, you can identify the ten jobs that have the highest number of scrape samples:

     topk(10, max by(namespace, job) (topk by(namespace, job) (1, scrape_samples_post_metric_relabeling)))

   • By running the following query, you can pinpoint time series churn by identifying the ten jobs that have created the most time series data in the last hour:

     topk(10, sum by(namespace, job) (sum_over_time(scrape_series_added[1h])))

3. Investigate the number of unbound label values assigned to metrics with higher than expected scrape sample counts:

   • If the metrics relate to a user-defined project, review the metrics key-value pairs assigned to your workload. These are implemented through Prometheus client libraries at the application level. Try to limit the number of unbound attributes referenced in your labels.
   • If the metrics relate to a core Red Hat OpenShift Service on AWS project, create a Red Hat support case on the Red Hat Customer Portal.

4. Review the TSDB status using the Prometheus HTTP API by following these steps when logged in as a dedicated-admin:

   1. Get the Prometheus API route URL by running the following command:

      $ HOST=$(oc -n openshift-monitoring get route prometheus-k8s -ojsonpath={.spec.host})

   2. Extract an authentication token by running the following command:

      $ TOKEN=$(oc whoami -t)

   3. Query the TSDB status for Prometheus by running the following command:

      $ curl -H "Authorization: Bearer $TOKEN" -k "https://$HOST/api/v1/status/tsdb"

      Example output

      "status": "success","data":{"headStats":{"numSeries":507473,
      "numLabelPairs":19832,"chunkCount":946298,"minTime":1712253600010,
      "maxTime":1712257935346},"seriesCountByMetricName":
      [{"name":"etcd_request_duration_seconds_bucket","value":51840},
      {"name":"apiserver_request_sli_duration_seconds_bucket","value":47718},
      ...