Configuring the monitoring stack

The OKD 4 installation program provides only a low number of configuration options before installation. Configuring most OKD framework components, including the cluster monitoring stack, happens postinstallation.

This section explains what configuration is supported, shows how to configure the monitoring stack, and demonstrates several common configuration scenarios.

Prerequisites

• The monitoring stack imposes additional resource requirements. Consult the computing resources recommendations in Scaling the Cluster Monitoring Operator and verify that you have sufficient resources.

Maintenance and support for monitoring

The supported way of configuring OKD Monitoring is by configuring it using the options described in this document. Do not use other configurations, as they are unsupported. Configuration paradigms might 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

The following modifications are explicitly not supported:

• Creating additional ServiceMonitor, PodMonitor, and PrometheusRule objects in the openshift-* and kube-* projects.

• Modifying any resources or objects deployed in the openshift-monitoring or openshift-user-workload-monitoring projects. The resources created by the OKD monitoring stack are not meant to be used by any other resources, as there are no guarantees about their backward compatibility.

  The Alertmanager configuration is deployed as a secret resource in the openshift-monitoring namespace. If you have enabled a separate Alertmanager instance for user-defined alert routing, an Alertmanager configuration is also deployed as a secret resource in the openshift-user-workload-monitoring namespace. To configure additional routes for any instance of Alertmanager, you need to decode, modify, and then encode that secret. This procedure is a supported exception to the preceding statement.

• Modifying resources of the stack. The OKD monitoring stack ensures its resources are always in the state it expects them to be. If they are modified, the stack will reset them.

• Deploying user-defined workloads to openshift-*, and kube-* projects. These projects are reserved for Red Hat provided components and they should not be used for user-defined workloads.

• Enabling symptom based monitoring by using the Probe custom resource definition (CRD) in Prometheus Operator.

• Installing custom Prometheus instances on OKD. A custom instance is a Prometheus custom resource (CR) managed by the Prometheus Operator.

Support policy for monitoring Operators

Monitoring Operators ensure that OKD monitoring resources function as designed and tested. If Cluster Version Operator (CVO) control of an Operator is overridden, the Operator does not respond to configuration changes, reconcile the intended state of cluster objects, or receive updates.

While overriding CVO control for an Operator can be helpful during debugging, this is unsupported and the cluster administrator assumes full control of the individual component configurations and upgrades.

Overriding the Cluster Version Operator

The spec.overrides parameter can be added to the configuration for the CVO to allow administrators to provide a list of overrides to the behavior of the CVO for a component. Setting the spec.overrides[].unmanaged parameter to true for a component blocks cluster upgrades and alerts the administrator after a CVO override has been set:

Disabling ownership via cluster version overrides prevents upgrades. Please remove overrides before continuing.

Preparing to configure the monitoring stack

You can configure the monitoring stack by creating and updating monitoring config maps.

Creating a cluster monitoring config map

To configure core OKD monitoring components, you must create the cluster-monitoring-config ConfigMap object in the openshift-monitoring project.

Prerequisites

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

• You have installed the OpenShift CLI (oc).

Procedure

1. Check whether the cluster-monitoring-config ConfigMap object exists:

   $ oc -n openshift-monitoring get configmap cluster-monitoring-config

2. If the ConfigMap object does not exist:

   1. Create the following YAML manifest. In this example the file is called cluster-monitoring-config.yaml:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: cluster-monitoring-config
        namespace: openshift-monitoring
      data:
        config.yaml: |

   2. Apply the configuration to create the ConfigMap object:

      $ oc apply -f cluster-monitoring-config.yaml

Creating a user-defined workload monitoring config map

To configure the components that monitor user-defined projects, you must create the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project.

Prerequisites

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

• You have installed the OpenShift CLI (oc).

Procedure

1. Check whether the user-workload-monitoring-config ConfigMap object exists:

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

2. If the user-workload-monitoring-config ConfigMap object does not exist:

   1. Create the following YAML manifest. In this example the file is called user-workload-monitoring-config.yaml:

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

   2. Apply the configuration to create the ConfigMap object:

      $ oc apply -f user-workload-monitoring-config.yaml

      Configurations applied to the user-workload-monitoring-config ConfigMap object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.

Additional resources

• Enabling monitoring for user-defined projects

Configuring the monitoring stack

In OKD 4.14, you can configure the monitoring stack using the cluster-monitoring-config or user-workload-monitoring-config ConfigMap objects. Config maps configure the Cluster Monitoring Operator (CMO), which in turn configures the components of the stack.

Prerequisites

• If you are configuring core OKD monitoring components:

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

  • You have created the cluster-monitoring-config ConfigMap object.

• If you are configuring components that monitor user-defined projects:

  • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

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

• You have installed the OpenShift CLI (oc).

Procedure

1. Edit the ConfigMap object.

   • To configure core OKD monitoring components:

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

        $ oc -n openshift-monitoring edit configmap cluster-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: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>:
              <configuration_for_the_component>

        Substitute <component> and <configuration_for_the_component> accordingly.

        The following example ConfigMap object configures a persistent volume claim (PVC) for Prometheus. This relates to the Prometheus instance that monitors core OKD components only:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s: (1)
              volumeClaimTemplate:
                spec:
                  storageClassName: fast
                  volumeMode: Filesystem
                  resources:
                    requests:
                      storage: 40Gi

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

   • To configure components that monitor user-defined projects:

     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.

        The Prometheus config map component is called prometheusK8s in the cluster-monitoring-config ConfigMap object and prometheus in the user-workload-monitoring-config ConfigMap object.

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

   Configurations applied to the user-workload-monitoring-config ConfigMap object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.

   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

• Configuration reference for the cluster-monitoring-config config map

• Configuration reference for the user-workload-monitoring-config config map

• See Preparing to configure the monitoring stack for steps to create monitoring config maps

• Enabling monitoring for user-defined projects

Configurable monitoring components

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

Using node selectors to move monitoring components

By using the nodeSelector constraint with labeled nodes, you can move any of the monitoring stack components to specific nodes. By doing so, you can control the placement and distribution of the monitoring components across a cluster.

By controlling placement and distribution of monitoring components, you can optimize system resource use, improve performance, and segregate workloads based on specific requirements or policies.

How node selectors work with other constraints

If you move monitoring components by using node selector constraints, be aware that other constraints to control pod scheduling might exist for a cluster:

• Topology spread constraints might be in place to control pod placement.

• Hard anti-affinity rules are in place for Prometheus, Thanos Querier, Alertmanager, and other monitoring components to ensure that multiple pods for these components are always spread across different nodes and are therefore always highly available.

When scheduling pods onto nodes, the pod scheduler tries to satisfy all existing constraints when determining pod placement. That is, all constraints compound when the pod scheduler determines which pods will be placed on which nodes.

Therefore, if you configure a node selector constraint but existing constraints cannot all be satisfied, the pod scheduler cannot match all constraints and will not schedule a pod for placement onto a node.

To maintain resilience and high availability for monitoring components, ensure that enough nodes are available and match all constraints when you configure a node selector constraint to move a component.

Additional resources

• Understanding how to update labels on nodes

• Placing pods on specific nodes using node selectors

• Placing pods relative to other pods using affinity and anti-affinity rules

• Controlling pod placement by using pod topology spread constraints

• Configuring pod topology spread constraints for monitoring

• Kubernetes documentation about node selectors

Moving monitoring components to different nodes

To specify the nodes in your cluster on which monitoring stack components will run, configure the nodeSelector constraint in the component’s ConfigMap object to match labels assigned to the nodes.

Prerequisites

• If you are configuring core OKD monitoring components:

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

  • You have created the cluster-monitoring-config ConfigMap object.

• If you are configuring components that monitor user-defined projects:

  • You have access to the cluster as a user with the cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

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

• You have installed the OpenShift CLI (oc).

Procedure

1. If you have not done so yet, add a label to the nodes on which you want to run the monitoring components:

   $ oc label nodes <node-name> <node-label>

2. Edit the ConfigMap object:

   • To move a component that monitors core OKD projects:

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

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>: (1)
              nodeSelector:
                <node-label-1> (2)
                <node-label-2> (3)
                <...>

        1	Substitute <component> with the appropriate monitoring stack component name.
        2	Substitute <node-label-1> with the label you added to the node.
        3	Optional: Specify additional labels. If you specify additional labels, the pods for the component are only scheduled on the nodes that contain all of the specified labels.

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

   • To move a component that monitors user-defined projects:

     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 node labels for 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>: (1)
              nodeSelector:
                <node-label-1> (2)
                <node-label-2> (3)
                <...>

        1	Substitute <component> with the appropriate monitoring stack component name.
        2	Substitute <node-label-1> with the label you added to the node.
        3	Optional: Specify additional labels. If you specify additional labels, the pods for the component are only scheduled on the nodes that contain all of the specified labels.

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

1. Save the file to apply the changes. The components specified in the new configuration are moved to the new nodes automatically.

   Configurations applied to the user-workload-monitoring-config ConfigMap object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.

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

Additional resources

• See Preparing to configure the monitoring stack for steps to create monitoring config maps

• Enabling monitoring for user-defined projects

• Understanding how to update labels on nodes

• Placing pods on specific nodes using node selectors

• See the Kubernetes documentation for details on the nodeSelector constraint

Assigning tolerations to monitoring components

You can assign tolerations to any of the monitoring stack components to enable moving them to tainted nodes.

Prerequisites

• If you are configuring core OKD monitoring components:

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

  • You have created the cluster-monitoring-config ConfigMap object.

• If you are configuring components that monitor user-defined projects:

  • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

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

• You have installed the OpenShift CLI (oc).

Procedure

1. Edit the ConfigMap object:

   • To assign tolerations to a component that monitors core OKD projects:

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

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

     2. Specify tolerations for the component:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-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 alertmanagerMain component to tolerate the example taint:

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

   • To assign tolerations to a component that monitors user-defined projects:

     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"

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

   Configurations applied to the user-workload-monitoring-config ConfigMap object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.

   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

• See Preparing to configure the monitoring stack for steps to create monitoring config maps

• Enabling monitoring for user-defined projects

• See the OKD documentation on taints and tolerations

• See the Kubernetes documentation on taints and tolerations

Setting the body size limit for metrics scraping

By default, no limit exists for the uncompressed body size for data returned from scraped metrics targets. You can set a body size limit to help avoid situations in which Prometheus consumes excessive amounts of memory when scraped targets return a response that contains a large amount of data. In addition, by setting a body size limit, you can reduce the impact that a malicious target might have on Prometheus and on the cluster as a whole.

After you set a value for enforcedBodySizeLimit, the alert PrometheusScrapeBodySizeLimitHit fires when at least one Prometheus scrape target replies with a response body larger than the configured value.

Prerequisites

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

• You have installed the OpenShift CLI (oc).

Procedure

1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring namespace:

   $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

2. Add a value for enforcedBodySizeLimit to data/config.yaml/prometheusK8s to limit the body size that can be accepted per target scrape:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: cluster-monitoring-config
     namespace: openshift-monitoring
   data:
     config.yaml: |-
       prometheusK8s:
         enforcedBodySizeLimit: 40MB (1)

   1

   Specify the maximum body size for scraped metrics targets. This enforcedBodySizeLimit example limits the uncompressed size per target scrape to 40 megabytes. Valid numeric values use the Prometheus data size format: B (bytes), KB (kilobytes), MB (megabytes), GB (gigabytes), TB (terabytes), PB (petabytes), and EB (exabytes). The default value is 0, which specifies no limit. You can also set the value to automatic to calculate the limit automatically based on cluster capacity.

3. Save the file to apply the changes automatically.

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

Additional resources

• Prometheus scrape configuration documentation

Configuring a dedicated service monitor

You can configure OKD core platform monitoring to use dedicated service monitors to collect metrics for the resource metrics pipeline.

When enabled, a dedicated service monitor exposes two additional metrics from the kubelet endpoint and sets the value of the honorTimestamps field to true.

By enabling a dedicated service monitor, you can improve the consistency of Prometheus Adapter-based CPU usage measurements used by, for example, the oc adm top pod command or the Horizontal Pod Autoscaler.

Enabling a dedicated service monitor

You can configure core platform monitoring to use a dedicated service monitor by configuring the dedicatedServiceMonitors key in the cluster-monitoring-config ConfigMap object in the openshift-monitoring namespace.

Prerequisites

• You have installed the OpenShift CLI (oc).

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

• You have created the cluster-monitoring-config ConfigMap object.

Procedure

1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring namespace:

   $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

2. Add an enabled: true key-value pair as shown in the following sample:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: cluster-monitoring-config
     namespace: openshift-monitoring
   data:
     config.yaml: |
       k8sPrometheusAdapter:
         dedicatedServiceMonitors:
           enabled: true (1)

   1	Set the value of the enabled field to true to deploy a dedicated service monitor that exposes the kubelet /metrics/resource endpoint.

3. Save the file to apply the changes automatically.

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

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 or alerting 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.

Persistent storage prerequisites

• Dedicate sufficient local persistent storage to ensure that the disk does not become full. How much storage you need depends on the number of pods.

• Verify that you have a persistent volume (PV) ready to be claimed by the persistent volume claim (PVC), one PV for each replica. Because Prometheus and Alertmanager both have two replicas, you need four PVs to support the entire monitoring stack. The PVs are available from the Local Storage Operator, but not if you have enabled dynamically provisioned storage.

• Use Filesystem as the storage type value for the volumeMode parameter when you configure the persistent volume.

  If you use a local volume for persistent storage, do not use a raw block volume, which is described with volumeMode: Block in the LocalVolume object. Prometheus cannot use raw block volumes.

  Prometheus does not support file systems that are not POSIX compliant. For example, some NFS file system implementations are not POSIX compliant. If you want to use an NFS file system for storage, verify with the vendor that their NFS implementation is fully POSIX compliant.

Configuring a local persistent volume claim

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

Prerequisites

• If you are configuring core OKD monitoring components:

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

  • You have created the cluster-monitoring-config ConfigMap object.

• If you are configuring components that monitor user-defined projects:

  • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

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

• You have installed the OpenShift CLI (oc).

Procedure

1. Edit the ConfigMap object:

   • To configure a PVC for a component that monitors core OKD projects:

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

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

     2. Add your PVC configuration for the component under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-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 core OKD components:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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 Alertmanager:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            alertmanagerMain:
              volumeClaimTemplate:
                spec:
                  storageClassName: local-storage
                  resources:
                    requests:
                      storage: 10Gi

   • To configure a PVC for a component that monitors user-defined projects:

     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: 10Gi

        Storage requirements for the thanosRuler component depend on the number of rules that are evaluated and how many samples each rule generates.

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

   Configurations applied to the user-workload-monitoring-config ConfigMap object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.

Resizing a persistent storage volume

OKD does not support resizing an existing persistent storage volume used by StatefulSet resources, even if the underlying StorageClass resource used supports persistent volume sizing. Therefore, even if you update the storage field for an existing persistent volume claim (PVC) with a larger size, this setting will not be propagated to the associated persistent volume (PV).

However, resizing a PV is still possible by using a manual process. If you want to resize a PV for a monitoring component such as Prometheus, Thanos Ruler, or Alertmanager, you can update the appropriate config map in which the component is configured. Then, patch the PVC, and delete and orphan the pods. Orphaning the pods recreates the StatefulSet resource immediately and automatically updates the size of the volumes mounted in the pods with the new PVC settings. No service disruption occurs during this process.

Prerequisites

• You have installed the OpenShift CLI (oc).

• If you are configuring core OKD monitoring components:

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

  • You have created the cluster-monitoring-config ConfigMap object.

  • You have configured at least one PVC for core OKD monitoring components.

• If you are configuring components that monitor user-defined projects:

  • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

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

  • You have configured at least one PVC for components that monitor user-defined projects.

Procedure

1. Edit the ConfigMap object:

   • To resize a PVC for a component that monitors core OKD projects:

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

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

     2. Add a new storage size for the PVC configuration for the component under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>: (1)
              volumeClaimTemplate:
                spec:
                  storageClassName: <storage_class> (2)
                  resources:
                    requests:
                      storage: <amount_of_storage> (3)

        1	Specify the core monitoring component.
        2	Specify the storage class.
        3	Specify the new size for the storage volume.

        The following example configures a PVC that sets the local persistent storage to 100 gigabytes for the Prometheus instance that monitors core OKD components:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              volumeClaimTemplate:
                spec:
                  storageClassName: local-storage
                  resources:
                    requests:
                      storage: 100Gi

        The following example configures a PVC that sets the local persistent storage for Alertmanager to 40 gigabytes:

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

   • To resize a PVC for a component that monitors user-defined projects:

     You can resize the volumes for the Thanos Ruler and Prometheus instances that monitor user-defined projects.

     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. Update the PVC configuration for the monitoring 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)
              volumeClaimTemplate:
                spec:
                  storageClassName: <storage_class> (2)
                  resources:
                    requests:
                      storage: <amount_of_storage> (3)

        1	Specify the core monitoring component.
        2	Specify the storage class.
        3	Specify the new size for the storage volume.

        The following example configures the PVC size to 100 gigabytes 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: 100Gi

        The following example sets the PVC size to 20 gigabytes 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: 20Gi

        Storage requirements for the thanosRuler component depend on the number of rules that are evaluated and how many samples each rule generates.

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

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

2. Manually patch every PVC with the updated storage request. The following example resizes the storage size for the Prometheus component in the openshift-monitoring namespace to 100Gi:

   $ for p in $(oc -n openshift-monitoring get pvc -l app.kubernetes.io/name=prometheus -o jsonpath='{range .items[*]}{.metadata.name} {end}'); do \
     oc -n openshift-monitoring patch pvc/${p} --patch '{"spec": {"resources": {"requests": {"storage":"100Gi"}}}}'; \
     done

3. Delete the underlying StatefulSet with the --cascade=orphan parameter:

   $ oc delete statefulset -l app.kubernetes.io/name=prometheus --cascade=orphan

Modifying the retention time and size for Prometheus metrics data

By default, Prometheus automatically retains metrics data for 11 days. You can modify the retention time for Prometheus to change how soon the data is deleted. You can also set the maximum amount of disk space the retained metrics data uses. If the data reaches this size limit, Prometheus deletes the oldest data first until the disk space used is again below the limit.

Note the following behaviors of these data retention settings:

• The size-based retention policy applies to all data block directories in the /prometheus directory, including persistent blocks, write-ahead log (WAL) data, and m-mapped chunks.

• Data in the /wal and /head_chunks directories counts toward the retention size limit, but Prometheus never purges data from these directories based on size- or time-based retention policies. Thus, if you set a retention size limit lower than the maximum size set for the /wal and /head_chunks directories, you have configured the system not to retain any data blocks in the /prometheus data directories.

• The size-based retention policy is applied only when Prometheus cuts a new data block, which occurs every two hours after the WAL contains at least three hours of data.

• If you do not explicitly define values for either retention or retentionSize, retention time defaults to 11 days, and retention size is not set.

• If you define values for both retention and retentionSize, both values apply. If any data blocks exceed the defined retention time or the defined size limit, Prometheus purges these data blocks.

• If you define a value for retentionSize and do not define retention, only the retentionSize value applies.

• If you do not define a value for retentionSize and only define a value for retention, only the retention value applies.

Prerequisites

• If you are configuring core OKD monitoring components:

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

  • You have created the cluster-monitoring-config ConfigMap object.

• If you are configuring components that monitor user-defined projects:

  • A cluster administrator has enabled monitoring for user-defined projects.

  • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

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

• You have installed the OpenShift CLI (oc).

Procedure

1. Edit the ConfigMap object:

   • To modify the retention time and size for the Prometheus instance that monitors core OKD projects:

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

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

     2. Add the retention time and size configuration under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              retention: <time_specification> (1)
              retentionSize: <size_specification> (2)

        1	The retention time: a number directly followed by ms (milliseconds), s (seconds), m (minutes), h (hours), d (days), w (weeks), or y (years). You can also combine time values for specific times, such as 1h30m15s.
        2	The retention size: a number directly followed by B (bytes), KB (kilobytes), MB (megabytes), GB (gigabytes), TB (terabytes), PB (petabytes), and EB (exabytes).

        The following example sets the retention time to 24 hours and the retention size to 10 gigabytes for the Prometheus instance that monitors core OKD components:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              retention: 24h
              retentionSize: 10GB

   • To modify the retention time and size for the Prometheus instance that monitors user-defined projects:

     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 retention time and size 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> (1)
              retentionSize: <size_specification> (2)

        1	The retention time: a number directly followed by ms (milliseconds), s (seconds), m (minutes), h (hours), d (days), w (weeks), or y (years). You can also combine time values for specific times, such as 1h30m15s.
        2	The retention size: a number directly followed by B (bytes), KB (kilobytes), MB (megabytes), GB (gigabytes), TB (terabytes), PB (petabytes), or EB (exabytes).

        The following example sets the retention time to 24 hours and the retention size to 10 gigabytes 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
              retentionSize: 10GB

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

Modifying the retention time for Thanos Ruler metrics data

By default, for user-defined projects, Thanos Ruler automatically retains metrics data for 24 hours. You can modify the retention time to change how long this data is retained by specifying a time value in the user-workload-monitoring-config config map in the openshift-user-workload-monitoring namespace.

Prerequisites

• You have access to the cluster as a user with the cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

• A cluster administrator has enabled monitoring for user-defined projects.

• 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 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: |
       thanosRuler:
         retention: <time_specification> (1)

   1	Specify the retention time in the following format: a number directly followed by ms (milliseconds), s (seconds), m (minutes), h (hours), d (days), w (weeks), or y (years). You can also combine time values for specific times, such as 1h30m15s. The default is 24h.

   The following example sets the retention time to 10 days for Thanos Ruler data:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: user-workload-monitoring-config
     namespace: openshift-user-workload-monitoring
   data:
     config.yaml: |
       thanosRuler:
         retention: 10d

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

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

Additional resources

• Creating a cluster monitoring config map

• Prometheus database storage requirements

• Recommended configurable storage technology

• Understanding persistent storage

• Optimizing storage

• Configure local persistent storage

• Enabling monitoring for user-defined projects

Configuring remote write storage

You can configure remote write storage to enable Prometheus to send ingested metrics to remote systems for long-term storage. Doing so has no impact on how or for how long Prometheus stores metrics.

Prerequisites

• If you are configuring core OKD monitoring components:

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

  • You have created the cluster-monitoring-config ConfigMap object.

• If you are configuring components that monitor user-defined projects:

  • You have access to the cluster as a user with the cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

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

• You have installed the OpenShift CLI (oc).

• You have set up a remote write compatible endpoint (such as Thanos) and know the endpoint URL. See the Prometheus remote endpoints and storage documentation for information about endpoints that are compatible with the remote write feature.

• You have set up authentication credentials in a Secret object for the remote write endpoint. You must create the secret in the same namespace as the Prometheus object for which you configure remote write: the openshift-monitoring namespace for default platform monitoring or the openshift-user-workload-monitoring namespace for user workload monitoring.

  To reduce security risks, use HTTPS and authentication to send metrics to an endpoint.

Procedure

1. Edit the ConfigMap object:

   • To configure remote write for the Prometheus instance that monitors core OKD projects:

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

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

     2. Add a remoteWrite: section under data/config.yaml/prometheusK8s.

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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 in an Authorization request header, Basic authentication, OAuth 2.0, and TLS client. See Supported remote write authentication settings for sample configurations of supported authentication methods.

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              remoteWrite:
              - url: "https://remote-write-endpoint.example.com"
                <endpoint_authentication_credentials>
                <write_relabel_configs> (1)

        1	The write relabel configuration settings.

        For <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: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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.

   • To configure remote write for the Prometheus instance that monitors user-defined projects:

     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>
                <write_relabel_configs> (1)

        1	The write relabel configuration settings.

        For <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.

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

   Configurations applied to the user-workload-monitoring-config ConfigMap object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.

   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.

Supported remote write authentication settings

You can use different methods to authenticate with a remote write endpoint. Currently supported authentication methods are AWS Signature Version 4, basic authentication, authorization, OAuth 2.0, and TLS client. The following table provides details about supported authentication methods for use with remote write.

Example remote write authentication settings

The following samples show different authentication settings you can use to connect to a remote write endpoint. Each sample also shows how to configure a corresponding Secret object that contains authentication credentials and other relevant settings. Each sample configures authentication for use with default platform monitoring in the openshift-monitoring namespace.

Example 1. Sample YAML for AWS Signature Version 4 authentication

The following shows the settings for a sigv4 secret named sigv4-credentials in the openshift-monitoring namespace.

apiVersion: v1
kind: Secret
metadata:
  name: sigv4-credentials
  namespace: openshift-monitoring
stringData:
  accessKey: <AWS_access_key> (1)
  secretKey: <AWS_secret_key> (2)
type: Opaque

The following shows sample AWS Signature Version 4 remote write authentication settings that use a Secret object named sigv4-credentials in the openshift-monitoring namespace:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      remoteWrite:
      - url: "https://authorization.example.com/api/write"
        sigv4:
          region: <AWS_region> (1)
          accessKey:
            name: sigv4-credentials (2)
            key: accessKey (3)
          secretKey:
            name: sigv4-credentials (2)
            key: secretKey (4)
          profile: <AWS_profile_name> (5)
          roleArn: <AWS_role_arn> (6)

Example 2. Sample YAML for basic authentication

The following shows sample basic authentication settings for a Secret object named rw-basic-auth in the openshift-monitoring namespace:

apiVersion: v1
kind: Secret
metadata:
  name: rw-basic-auth
  namespace: openshift-monitoring
stringData:
  user: <basic_username> (1)
  password: <basic_password> (2)
type: Opaque

The following sample shows a basicAuth remote write configuration that uses a Secret object named rw-basic-auth in the openshift-monitoring namespace. It assumes that you have already set up authentication credentials for the endpoint.

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      remoteWrite:
      - url: "https://basicauth.example.com/api/write"
        basicAuth:
          username:
            name: rw-basic-auth (1)
            key: user (2)
          password:
            name: rw-basic-auth (1)
            key: password (3)

Example 3. Sample YAML for authentication with a bearer token using a Secret Object

The following shows bearer token settings for a Secret object named rw-bearer-auth in the openshift-monitoring namespace:

apiVersion: v1
kind: Secret
metadata:
  name: rw-bearer-auth
  namespace: openshift-monitoring
stringData:
  token: <authentication_token> (1)
type: Opaque

The following shows sample bearer token config map settings that use a Secret object named rw-bearer-auth in the openshift-monitoring namespace:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    enableUserWorkload: true
    prometheusK8s:
      remoteWrite:
      - url: "https://authorization.example.com/api/write"
        authorization:
          type: Bearer (1)
          credentials:
            name: rw-bearer-auth (2)
            key: token (3)

Example 4. Sample YAML for OAuth 2.0 authentication

The following shows sample OAuth 2.0 settings for a Secret object named oauth2-credentials in the openshift-monitoring namespace:

apiVersion: v1
kind: Secret
metadata:
  name: oauth2-credentials
  namespace: openshift-monitoring
stringData:
  id: <oauth2_id> (1)
  secret: <oauth2_secret> (2)
  token: <oauth2_authentication_token> (3)
type: Opaque

The following shows an oauth2 remote write authentication sample configuration that uses a Secret object named oauth2-credentials in the openshift-monitoring namespace:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      remoteWrite:
      - url: "https://test.example.com/api/write"
        oauth2:
          clientId:
            secret:
              name: oauth2-credentials (1)
              key: id (2)
          clientSecret:
            name: oauth2-credentials (1)
            key: secret (2)
          tokenUrl: https://example.com/oauth2/token (3)
          scopes: (4)
          - <scope_1>
          - <scope_2>
          endpointParams: (5)
            param1: <parameter_1>
            param2: <parameter_2>

Example 5. Sample YAML for TLS client authentication

The following shows sample TLS client settings for a tls Secret object named mtls-bundle in the openshift-monitoring namespace.

apiVersion: v1
kind: Secret
metadata:
  name: mtls-bundle
  namespace: openshift-monitoring
data:
  ca.crt: <ca_cert> (1)
  client.crt: <client_cert> (2)
  client.key: <client_key> (3)
type: tls

The following sample shows a tlsConfig remote write authentication configuration that uses a TLS Secret object named mtls-bundle.

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      remoteWrite:
      - url: "https://remote-write-endpoint.example.com"
        tlsConfig:
          ca:
            secret:
              name: mtls-bundle (1)
              key: ca.crt (2)
          cert:
            secret:
              name: mtls-bundle (1)
              key: client.crt (3)
          keySecret:
            name: mtls-bundle (1)
            key: client.key (4)

Additional resources

• See Setting up remote write compatible endpoints for steps to create a remote write compatible endpoint (such as Thanos).

• See Tuning remote write settings for information about how to optimize remote write settings for different use cases.

• See Understanding secrets for steps to create and configure Secret objects in OKD.

• See the Prometheus REST API reference for remote write for information about additional optional fields.

Adding cluster ID labels to metrics

If you manage multiple OKD clusters and use the remote write feature to send metrics data from these clusters to an external storage location, you can add cluster ID labels to identify the metrics data coming from different clusters. You can then query these labels to identify the source cluster for a metric and distinguish that data from similar metrics data sent by other clusters.

This way, if you manage many clusters for multiple customers and send metrics data to a single centralized storage system, you can use cluster ID labels to query metrics for a particular cluster or customer.

Creating and using cluster ID labels involves three general steps:

• Configuring the write relabel settings for remote write storage.

• Adding cluster ID labels to the metrics.

• Querying these labels to identify the source cluster or customer for a metric.

Creating cluster ID labels for metrics

You can create cluster ID labels for metrics for default platform monitoring and for user workload monitoring.

For default platform monitoring, you add cluster ID labels for metrics in the write_relabel settings for remote write storage in the cluster-monitoring-config config map in the openshift-monitoring namespace.

For user workload monitoring, you edit the settings in the user-workload-monitoring-config config map in the openshift-user-workload-monitoring namespace.

Prerequisites

• If you are configuring default platform monitoring components:

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

  • You have created the cluster-monitoring-config ConfigMap object.

• If you are configuring components that monitor user-defined projects:

  • You have access to the cluster as a user with the cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

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

• You have installed the OpenShift CLI (oc).

• You have configured remote write storage.

Procedure

1. Edit the ConfigMap object:

   • To create cluster ID labels for core OKD metrics:

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

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

     2. In the writeRelabelConfigs: section under data/config.yaml/prometheusK8s/remoteWrite, add cluster ID relabel configuration values:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              remoteWrite:
              - url: "https://remote-write-endpoint.example.com"
                <endpoint_authentication_credentials>
                writeRelabelConfigs: (1)
                  - <relabel_config> (2)

        1	Add a list of write relabel configurations for metrics that you want to send to the remote endpoint.
        2	Substitute the label configuration for the metrics sent to the remote write endpoint.

        The following sample shows how to forward a metric with the cluster ID label cluster_id in default platform monitoring:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              remoteWrite:
              - url: "https://remote-write-endpoint.example.com"
                writeRelabelConfigs:
                - sourceLabels:
                  - __tmp_openshift_cluster_id__ (1)
                  targetLabel: cluster_id (2)
                  action: replace (3)

        1	The system initially applies a temporary cluster ID source label named tmp_openshift_cluster_id. This temporary label gets replaced by the cluster ID label name that you specify.
        2	Specify the name of the cluster ID label for metrics sent to remote write storage. If you use a label name that already exists for a metric, that value is overwritten with the name of this cluster ID label. For the label name, do not use tmp_openshift_cluster_id. The final relabeling step removes labels that use this name.
        3	The replace write relabel action replaces the temporary label with the target label for outgoing metrics. This action is the default and is applied if no action is specified.

   • To create cluster ID labels for user-defined project metrics:

     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. In the writeRelabelConfigs: section under data/config.yaml/prometheus/remoteWrite, add cluster ID relabel configuration values:

        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>
                writeRelabelConfigs: (1)
                  - <relabel_config> (2)

        1	Add a list of write relabel configurations for metrics that you want to send to the remote endpoint.
        2	Substitute the label configuration for the metrics sent to the remote write endpoint.

        The following sample shows how to forward a metric with the cluster ID label cluster_id in user-workload monitoring:

        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:
                  - __tmp_openshift_cluster_id__ (1)
                  targetLabel: cluster_id (2)
                  action: replace (3)

        1	The system initially applies a temporary cluster ID source label named tmp_openshift_cluster_id. This temporary label gets replaced by the cluster ID label name that you specify.
        2	Specify the name of the cluster ID label for metrics sent to remote write storage. If you use a label name that already exists for a metric, that value is overwritten with the name of this cluster ID label. For the label name, do not use tmp_openshift_cluster_id. The final relabeling step removes labels that use this name.
        3	The replace write relabel action replaces the temporary label with the target label for outgoing metrics. This action is the default and is applied if no action is specified.

1. Save the file to apply the changes to the ConfigMap object. The pods affected by the updated configuration automatically restart.

   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.

Additional resources

• For details about write relabel configuration, see Configuring remote write storage.

Configuring metrics collection profiles

By default, Prometheus collects metrics exposed by all default metrics targets in OKD components. However, you might want Prometheus to collect fewer metrics from a cluster in certain scenarios:

• If cluster administrators require only alert, telemetry, and console metrics and do not require other metrics to be available.

• If a cluster increases in size, and the increased size of the default metrics data collected now requires a significant increase in CPU and memory resources.

You can use a metrics collection profile to collect either the default amount of metrics data or a minimal amount of metrics data. When you collect minimal metrics data, basic monitoring features such as alerting continue to work. At the same time, the CPU and memory resources required by Prometheus decrease.

About metrics collection profiles

You can enable one of two metrics collection profiles:

• full: Prometheus collects metrics data exposed by all platform components. This setting is the default.

• minimal: Prometheus collects only the metrics data required for platform alerts, recording rules, telemetry, and console dashboards.

Choosing a metrics collection profile

To choose a metrics collection profile for core OKD monitoring components, edit the cluster-monitoring-config ConfigMap object.

Prerequisites

• You have installed the OpenShift CLI (oc).

• You have enabled Technology Preview features by using the FeatureGate custom resource (CR).

• You have created the cluster-monitoring-config ConfigMap object.

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

Procedure

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

   $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

2. Add the metrics collection profile setting under data/config.yaml/prometheusK8s:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: cluster-monitoring-config
     namespace: openshift-monitoring
   data:
     config.yaml: |
       prometheusK8s:
         collectionProfile: <metrics_collection_profile_name> (1)

   1	The name of the metrics collection profile. The available values are full or minimal. If you do not specify a value or if the collectionProfile key name does not exist in the config map, the default setting of full is used.

   The following example sets the metrics collection profile to minimal for the core platform instance of Prometheus:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: cluster-monitoring-config
     namespace: openshift-monitoring
   data:
     config.yaml: |
       prometheusK8s:
         collectionProfile: minimal

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

Additional resources

• See Viewing a list of available metrics for steps to view a list of metrics being collected for a cluster.

• See Enabling features using feature gates for steps to enable Technology Preview features.

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.

Cluster administrators can use the following measures 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

• Limit the number of scraped labels, the length of label names, and the length of label values

• Create alerts that fire when a scrape sample threshold is reached or when the target cannot be scraped

Setting scrape sample and label limits for user-defined projects

You can limit the number of samples that can be accepted per target scrape in user-defined projects. You can also limit the number of scraped labels, the length of label names, and the length of label values.

Prerequisites

• You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

• You have enabled monitoring for user-defined projects.

• 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. Add the enforcedLabelLimit, enforcedLabelNameLengthLimit, and enforcedLabelValueLengthLimit configurations to data/config.yaml to limit the number of scraped labels, the length of label names, and the length of label values in user-defined projects:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: user-workload-monitoring-config
     namespace: openshift-user-workload-monitoring
   data:
     config.yaml: |
       prometheus:
         enforcedLabelLimit: 500 (1)
         enforcedLabelNameLengthLimit: 50 (2)
         enforcedLabelValueLengthLimit: 600 (3)

   1	Specifies the maximum number of labels per scrape. The default value is 0, which specifies no limit.
   2	Specifies the maximum length in characters of a label name. The default value is 0, which specifies no limit.
   3	Specifies the maximum length in characters of a label value. The default value is 0, which specifies no limit.

4. Save the file to apply the changes. The limits are applied automatically.

   Configurations applied to the user-workload-monitoring-config ConfigMap object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.

   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.

Creating scrape sample alerts

You can create alerts that notify you when:

• The target cannot be scraped or is not available for the specified for duration

• A scrape sample threshold is reached or is exceeded for the specified for duration

Prerequisites

• You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

• You have enabled monitoring for user-defined projects.

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

• You have limited the number of samples that can be accepted per target scrape in user-defined projects, by using enforcedSampleLimit.

• You have installed the OpenShift CLI (oc).

Procedure

1. Create a YAML file with alerts that inform you when the targets are down and when the enforced sample limit is approaching. The file in this example is called monitoring-stack-alerts.yaml:

   apiVersion: monitoring.coreos.com/v1
   kind: PrometheusRule
   metadata:
     labels:
       prometheus: k8s
       role: alert-rules
     name: monitoring-stack-alerts (1)
     namespace: ns1 (2)
   spec:
     groups:
     - name: general.rules
       rules:
       - alert: TargetDown (3)
         annotations:
           message: '{{ printf "%.4g" $value }}% of the {{ $labels.job }}/{{ $labels.service
             }} targets in {{ $labels.namespace }} namespace are down.' (4)
         expr: 100 * (count(up == 0) BY (job, namespace, service) / count(up) BY (job,
           namespace, service)) > 10
         for: 10m (5)
         labels:
           severity: warning (6)
       - alert: ApproachingEnforcedSamplesLimit (7)
         annotations:
           message: '{{ $labels.container }} container of the {{ $labels.pod }} pod in the {{ $labels.namespace }} namespace consumes {{ $value | humanizePercentage }} of the samples limit budget.' (8)
         expr: scrape_samples_scraped/50000 > 0.8 (9)
         for: 10m (10)
         labels:
           severity: warning (11)

   1	Defines the name of the alerting rule.
   2	Specifies the user-defined project where the alerting rule will be deployed.
   3	The TargetDown alert will fire if the target cannot be scraped or is not available for the for duration.
   4	The message that will be output when the TargetDown alert fires.
   5	The conditions for the TargetDown alert must be true for this duration before the alert is fired.
   6	Defines the severity for the TargetDown alert.
   7	The ApproachingEnforcedSamplesLimit alert will fire when the defined scrape sample threshold is reached or exceeded for the specified for duration.
   8	The message that will be output when the ApproachingEnforcedSamplesLimit alert fires.
   9	The threshold for the ApproachingEnforcedSamplesLimit alert. In this example the alert will fire when the number of samples per target scrape has exceeded 80% of the enforced sample limit of 50000. The for duration must also have passed before the alert will fire. The <number> in the expression scrape_samples_scraped/<number> > <threshold> must match the enforcedSampleLimit value defined in the user-workload-monitoring-config ConfigMap object.
   10	The conditions for the ApproachingEnforcedSamplesLimit alert must be true for this duration before the alert is fired.
   11	Defines the severity for the ApproachingEnforcedSamplesLimit alert.

2. Apply the configuration to the user-defined project:

   $ oc apply -f monitoring-stack-alerts.yaml

Additional resources

• Creating a user-defined workload monitoring config map

• Enabling monitoring for user-defined projects

• See Determining why Prometheus is consuming a lot of disk space for steps to query which metrics have the highest number of scrape samples.

Configuring external Alertmanager instances

The OKD monitoring stack includes a local Alertmanager instance that routes alerts from Prometheus. You can add external Alertmanager instances to route alerts for core OKD projects or user-defined projects.

If you add the same external Alertmanager configuration for multiple clusters and disable the local instance for each cluster, you can then manage alert routing for multiple clusters by using a single external Alertmanager instance.

Prerequisites

• If you are configuring core OKD monitoring components in the openshift-monitoring project:

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

  • You have created the cluster-monitoring-config config map.

• If you are configuring components that monitor user-defined projects:

  • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.

  • You have created the user-workload-monitoring-config config map.

• You have installed the OpenShift CLI (oc).

Procedure

1. Edit the ConfigMap object.

   • To configure additional Alertmanagers for routing alerts from core OKD projects:

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

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config

     2. Add an additionalAlertmanagerConfigs: section under data/config.yaml/prometheusK8s.

     3. Add the configuration details for additional Alertmanagers in this section:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              additionalAlertmanagerConfigs:
              - <alertmanager_specification>

        For <alertmanager_specification>, substitute authentication and other configuration details for additional Alertmanager instances. Currently supported authentication methods are bearer token (bearerToken) and client TLS (tlsConfig). The following sample config map configures an additional Alertmanager using a bearer token with client TLS authentication:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              additionalAlertmanagerConfigs:
              - scheme: https
                pathPrefix: /
                timeout: "30s"
                apiVersion: v1
                bearerToken:
                  name: alertmanager-bearer-token
                  key: token
                tlsConfig:
                  key:
                    name: alertmanager-tls
                    key: tls.key
                  cert:
                    name: alertmanager-tls
                    key: tls.crt
                  ca:
                    name: alertmanager-tls
                    key: tls.ca
                staticConfigs:
                - external-alertmanager1-remote.com
                - external-alertmanager1-remote2.com

   • To configure additional Alertmanager instances for routing alerts from user-defined projects:

     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 <component>/additionalAlertmanagerConfigs: section under data/config.yaml/.

     3. Add the configuration details for additional Alertmanagers in this section:

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

        For <component>, substitute one of two supported external Alertmanager components: prometheus or thanosRuler.

        For <alertmanager_specification>, substitute authentication and other configuration details for additional Alertmanager instances. Currently supported authentication methods are bearer token (bearerToken) and client TLS (tlsConfig). The following sample config map configures an additional Alertmanager using Thanos Ruler with a bearer token and client TLS authentication:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            thanosRuler:
              additionalAlertmanagerConfigs:
              - scheme: https
                pathPrefix: /
                timeout: "30s"
                apiVersion: v1
                bearerToken:
                  name: alertmanager-bearer-token
                  key: token
                tlsConfig:
                  key:
                    name: alertmanager-tls
                    key: tls.key
                  cert:
                    name: alertmanager-tls
                    key: tls.crt
                  ca:
                    name: alertmanager-tls
                    key: tls.ca
                staticConfigs:
                - external-alertmanager1-remote.com
                - external-alertmanager1-remote2.com