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Configuring the monitoring stack

After you configure the monitoring stack, you can review common configuration scenarios and configure monitoring of user-defined projects.

Maintenance and support for monitoring

The supported way of configuring OpenShift Dedicated Monitoring is by using the options described in this document. Do not use other configurations, as they are unsupported.

Installing another Prometheus instance is not supported by the Red Hat Site Reliability Engineers (SREs).

Configuration paradigms can change across Prometheus releases, and such cases can only be handled gracefully if all configuration possibilities are controlled. If you use configurations other than those described in this section, your changes will disappear because the cluster-monitoring-operator reconciles any differences. The Operator resets everything to the defined state by default and by design.

Support considerations for monitoring user-defined projects

The following modifications are explicitly not supported:

  • Installing custom Prometheus instances on OpenShift Dedicated

Configuring the monitoring stack

In OpenShift Dedicated, you can configure the stack that monitors workloads for user-defined projects by using the user-workload-monitoring-config ConfigMap object. Config maps configure the Cluster Monitoring Operator (CMO), which in turn configures the components of the stack.

Prerequisites

  • You have access to the cluster as a user with the dedicated-admin role.

  • You have created the user-workload-monitoring-config ConfigMap object.

  • You have installed the OpenShift CLI (oc).

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.

    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.

Configurable monitoring components

This table shows the monitoring components you can configure and the keys used to specify the components in the user-workload-monitoring-config ConfigMap objects:

Table 1. Configurable monitoring components
Component user-workload-monitoring-config config map key

Prometheus Operator

prometheusOperator

Prometheus

prometheus

Thanos Ruler

thanosRuler

Moving monitoring components to different nodes

You can move any of the components that monitor workloads for user-defined projects to specific worker nodes. It is not permitted to move components to control plane or infrastructure nodes.

Prerequisites

  • You have access to the cluster as a user with the dedicated-admin role.

  • You have created the user-workload-monitoring-config ConfigMap object.

  • You have installed the OpenShift CLI (oc).

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.

      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.

      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.

    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.
Additional resources

Assigning tolerations to components that monitor user-defined projects

You can assign tolerations to the components that monitor user-defined projects, to enable moving them to tainted worker nodes. Scheduling is not permitted on control plane or infrastructure nodes.

Prerequisites

  • You have access to the cluster as a user with the dedicated-admin role.

  • You have created the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring namespace.

  • The OpenShift CLI (oc) is installed.

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.

    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.
Additional resources

Configuring persistent storage

Running cluster monitoring with persistent storage means that your metrics are stored to a persistent volume (PV) and can survive a pod being restarted or recreated. This is ideal if you require your metrics data to be guarded from data loss. For production environments, it is highly recommended to configure persistent storage. Because of the high IO demands, it is advantageous to use local storage.

See Recommended configurable storage technology.

Persistent storage prerequisites

  • Use the block type of storage.

Configuring a local persistent volume claim

For monitoring components to use a persistent volume (PV), you must configure a persistent volume claim (PVC).

Prerequisites

  • You have access to the cluster as a user with the dedicated-admin role.

  • You have created the user-workload-monitoring-config ConfigMap object.

  • You have installed the OpenShift CLI (oc).

Procedure

  1. To configure a PVC for 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. Add your PVC configuration 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>:
      volumeClaimTemplate:
        spec:
          storageClassName: <storage_class>
          resources:
            requests:
              storage: <amount_of_storage>

      See the Kubernetes documentation on PersistentVolumeClaims for information on how to specify volumeClaimTemplate.

      The following example configures a PVC that claims local persistent storage for the Prometheus instance that monitors user-defined projects:

      apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    prometheus:
      volumeClaimTemplate:
        spec:
          storageClassName: local-storage
          resources:
            requests:
              storage: 40Gi

      In the above example, the storage class created by the Local Storage Operator is called local-storage.

      The following example configures a PVC that claims local persistent storage for Thanos Ruler:

      apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    thanosRuler:
      volumeClaimTemplate:
        spec:
          storageClassName: local-storage
          resources:
            requests:
              storage: 40Gi

  2. Save the file to apply the changes. The pods affected by the new configuration are restarted automatically and the new storage configuration is applied.

    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.

Modifying the retention time for Prometheus metrics data

By default, the OpenShift Dedicated monitoring stack configures the retention time for Prometheus data to be 15 days. You can modify the retention time for the Prometheus instance that monitors user-defined projects, to change how soon the data is deleted.

Prerequisites

  • You have access to the cluster as a user with the dedicated-admin role.

  • You have created the user-workload-monitoring-config ConfigMap object.

  • You have installed the OpenShift CLI (oc).

Procedure

  1. To modify the retention time for the Prometheus instance 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. Add your retention time configuration under data.config.yaml:

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

      Substitute <time_specification> with a number directly followed by ms (milliseconds), s (seconds), m (minutes), h (hours), d (days), w (weeks), or y (years).

      The following example sets the retention time to 24 hours for the Prometheus instance that monitors user-defined projects:

      apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    prometheus:
      retention: 24h

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

    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.
Additional resources

Controlling the impact of unbound metrics attributes in user-defined projects

Developers can create labels to define attributes for metrics in the form of key-value pairs. The number of potential key-value pairs corresponds to the number of possible values for an attribute. An attribute that has an unlimited number of potential values is called an unbound attribute. For example, a customer_id attribute is unbound because it has an infinite number of possible values.

Every assigned key-value pair has a unique time series. The use of many unbound attributes in labels can result in an exponential increase in the number of time series created. This can impact Prometheus performance and can consume a lot of disk space.

A dedicated-admin can use the following measure to control the impact of unbound metrics attributes in user-defined projects:

  • Limit the number of samples that can be accepted per target scrape in user-defined projects

Limiting scrape samples can help prevent the issues caused by adding many unbound attributes to labels. Developers can also prevent the underlying cause by limiting the number of unbound attributes that they define for metrics. Using attributes that are bound to a limited set of possible values reduces the number of potential key-value pair combinations.

Setting a scrape sample limit for user-defined projects

You can limit the number of samples that can be accepted per target scrape in user-defined projects.

If you set a sample limit, no further sample data is ingested for that target scrape after the limit is reached.
Prerequisites

  • You have access to the cluster as a user with the dedicated-admin role.

  • You have created the user-workload-monitoring-config ConfigMap object.

  • You have installed the OpenShift CLI (oc).

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 the enforcedSampleLimit configuration to data.config.yaml to limit the number of samples that can be accepted per target scrape in user-defined projects:

    apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    prometheus:
      enforcedSampleLimit: 50000 (1)
    1 A value is required if this parameter is specified. This enforcedSampleLimit example limits the number of samples that can be accepted per target scrape in user-defined projects to 50,000.

  3. Save the file to apply the changes. The limit is applied automatically.

    When changes are saved to the user-workload-monitoring-config ConfigMap object, the pods and other resources in the openshift-user-workload-monitoring project might be redeployed. The running monitoring processes in that project might also be restarted.
Additional resources

Setting log levels for monitoring components

You can configure the log level for Prometheus Operator, Prometheus, and Thanos Ruler.

The following log levels can be applied to each of those components in the user-workload-monitoring-config ConfigMap object:

  • debug. Log debug, informational, warning, and error messages.

  • info. Log informational, warning, and error messages.

  • warn. Log warning and error messages only.

  • error. Log error messages only.

The default log level is info.

Prerequisites

  • You have access to the cluster as a user with the dedicated-admin role.

  • You have created the user-workload-monitoring-config ConfigMap object.

  • You have installed the OpenShift CLI (oc).

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.

    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

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

Next steps