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 post-installation.

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 project. To configure additional routes for 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.

  • Modifying the monitoring stack Grafana instance.

  • Installing custom Prometheus instances on OKD.

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

  • Modifying Alertmanager configurations by using the AlertmanagerConfig CRD in Prometheus Operator.

Backward compatibility for metrics, recording rules, or alerting rules is not guaranteed.

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:

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

Setting a CVO override puts the entire cluster in an unsupported state and prevents the monitoring stack from being reconciled to its intended state. This impacts the reliability features built into Operators and prevents updates from being received. Reported issues must be reproduced after removing any overrides for support to proceed.

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.

When you save your changes to the cluster-monitoring-config ConfigMap object, some or all of the pods in the openshift-monitoring project might be redeployed. It can sometimes take a while for these components to redeploy.

Prerequisites

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

  • You have installed the OpenShift CLI (oc).

Procedure

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

    1. $ 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:

      1. apiVersion: v1
      2. kind: ConfigMap
      3. metadata:
      4.   name: cluster-monitoring-config
      5.   namespace: openshift-monitoring
      6. data:
      7.   config.yaml: |

    2. Apply the configuration to create the ConfigMap object:

      1. $ 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.

When you save your changes to the user-workload-monitoring-config ConfigMap object, some or all of the pods in the openshift-user-workload-monitoring project might be redeployed. It can sometimes take a while for these components to redeploy. You can create and configure the config map before you first enable monitoring for user-defined projects, to prevent having to redeploy the pods often.

Prerequisites

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

  • You have installed the OpenShift CLI (oc).

Procedure

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

    1. $ 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:

      1. apiVersion: v1
      2. kind: ConfigMap
      3. metadata:
      4.   name: user-workload-monitoring-config
      5.   namespace: openshift-user-workload-monitoring
      6. data:
      7.   config.yaml: |

    2. Apply the configuration to create the ConfigMap object:

      1. $ 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

Configuring the monitoring stack

In OKD 4.9, 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 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 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:

        1. $ 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>:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       <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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusK8s: (1)
        9.       volumeClaimTemplate:
        10.        spec:
        11.          storageClassName: fast
        12.          volumeMode: Filesystem
        13.          resources:
        14.            requests:
        15.              storage: 40Gi
        1Defines 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:

        1. $ 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>:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       <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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheus: (1)
        9.       retention: 24h (2)
        10.       resources:
        11.         requests:
        12.           cpu: 200m (3)
        13.           memory: 2Gi (4)
        1Defines the Prometheus component and the subsequent lines define its configuration.
        2Configures a twenty-four hour data retention period for the Prometheus instance that monitors user-defined projects.
        3Defines a minimum resource request of 200 millicores for the Prometheus container.
        4Defines 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

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:

Table 1. Configurable monitoring components
Componentcluster-monitoring-config config map keyuser-workload-monitoring-config config map key

Prometheus Operator

prometheusOperator

prometheusOperator

Prometheus

prometheusK8s

prometheus

Alertmanager

alertmanagerMain

kube-state-metrics

kubeStateMetrics

openshift-state-metrics

openshiftStateMetrics

Grafana

grafana

Telemeter Client

telemeterClient

Prometheus Adapter

k8sPrometheusAdapter

Thanos Querier

thanosQuerier

Thanos Ruler

thanosRuler

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

Moving monitoring components to different nodes

You can move any of the monitoring stack components to specific nodes.

Prerequisites

  • If you are configuring core OKD monitoring components:

    • You have access to the cluster as a user with the cluster-admin 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 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 move a component that monitors core OKD projects:

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

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       nodeSelector:
        10.         <node_key>: <node_value>
        11.         <node_key>: <node_value>
        12.         <...>

        Substitute <component> accordingly and substitute <node_key>: <node_value> with the map of key-value pairs that specifies a group of 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 core OKD projects to specific nodes that are labeled nodename: controlplane1, nodename: worker1, nodename: worker2, and nodename: worker2, use:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusOperator:
        9.       nodeSelector:
        10.         nodename: controlplane1
        11.     prometheusK8s:
        12.       nodeSelector:
        13.         nodename: worker1
        14.         nodename: worker2
        15.     alertmanagerMain:
        16.       nodeSelector:
        17.         nodename: worker1
        18.         nodename: worker2
        19.     kubeStateMetrics:
        20.       nodeSelector:
        21.         nodename: worker1
        22.     grafana:
        23.       nodeSelector:
        24.         nodename: worker1
        25.     telemeterClient:
        26.       nodeSelector:
        27.         nodename: worker1
        28.     k8sPrometheusAdapter:
        29.       nodeSelector:
        30.         nodename: worker1
        31.         nodename: worker2
        32.     openshiftStateMetrics:
        33.       nodeSelector:
        34.         nodename: worker1
        35.     thanosQuerier:
        36.       nodeSelector:
        37.         nodename: worker1
        38.         nodename: worker2

    • 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:

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       nodeSelector:
        10.         <node_key>: <node_value>
        11.         <node_key>: <node_value>
        12.         <...>

        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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusOperator:
        9.       nodeSelector:
        10.         nodename: worker1
        11.     prometheus:
        12.       nodeSelector:
        13.         nodename: worker1
        14.         nodename: worker2
        15.     thanosRuler:
        16.       nodeSelector:
        17.         nodename: worker1
        18.         nodename: worker2

  1. Save the file to apply the changes. The components affected by 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 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 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 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 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:

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

      2. Specify tolerations for the component:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       tolerations:
        10.         <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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     alertmanagerMain:
        9.       tolerations:
        10.       - key: "key1"
        11.         operator: "Equal"
        12.         value: "value1"
        13.         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:

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

      2. Specify tolerations for the component:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       tolerations:
        10.         <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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     thanosRuler:
        9.       tolerations:
        10.       - key: "key1"
        11.         operator: "Equal"
        12.         value: "value1"
        13.         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

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.

If you are running cluster monitoring with an attached PVC for Prometheus, you might experience OOM kills during cluster upgrade. When persistent storage is in use for Prometheus, Prometheus memory usage doubles during cluster upgrade and for several hours after upgrade is complete. To avoid the OOM kill issue, allow worker nodes with double the size of memory that was available prior to the upgrade. For example, if you are running monitoring on the minimum recommended nodes, which is 2 cores with 8 GB of RAM, increase memory to 16 GB. For more information, see BZ#1925061.

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. For information on system requirements for persistent storage, see Prometheus database storage requirements.

  • Make sure you have a persistent volume (PV) ready to be claimed by the persistent volume claim (PVC), one PV for each replica. Because Prometheus has two replicas and Alertmanager has three replicas, you need five PVs to support the entire monitoring stack. The PVs should be available from the Local Storage Operator. This does not apply if you enable dynamically provisioned storage.

  • Use the block type of storage.

  • Configure local persistent storage.

    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.

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 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 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:

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       volumeClaimTemplate:
        10.         spec:
        11.           storageClassName: <storage_class>
        12.           resources:
        13.             requests:
        14.               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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusK8s:
        9.       volumeClaimTemplate:
        10.         spec:
        11.           storageClassName: local-storage
        12.           resources:
        13.             requests:
        14.               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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     alertmanagerMain:
        9.       volumeClaimTemplate:
        10.         spec:
        11.           storageClassName: local-storage
        12.           resources:
        13.             requests:
        14.               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:

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       volumeClaimTemplate:
        10.         spec:
        11.           storageClassName: <storage_class>
        12.           resources:
        13.             requests:
        14.               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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheus:
        9.       volumeClaimTemplate:
        10.         spec:
        11.           storageClassName: local-storage
        12.           resources:
        13.             requests:
        14.               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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     thanosRuler:
        9.       volumeClaimTemplate:
        10.         spec:
        11.           storageClassName: local-storage
        12.           resources:
        13.             requests:
        14.               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.

    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 OKD monitoring stack configures the retention time for Prometheus data to be 15 days. You can modify the retention time to change how soon the data is deleted.

Prerequisites

  • If you are configuring core OKD monitoring components:

    • You have access to the cluster as a user with the cluster-admin 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 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 for the Prometheus instance that monitors core OKD projects:

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

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

      2. Add your retention time configuration under data/config.yaml:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusK8s:
        9.       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 core OKD components:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusK8s:
        9.       retention: 24h

    • To modify the retention time 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:

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

      2. Add your retention time configuration under data/config.yaml:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheus:
        9.       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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheus:
        9.       retention: 24h

  1. Save the file to apply the changes. 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

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 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 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 for the remote write endpoint.

    To reduce security risks, avoid sending metrics to an endpoint via unencrypted HTTP or without using authentication.

Procedure

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

    1. $ 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:

    1. apiVersion: v1
    2. kind: ConfigMap
    3. metadata:
    4.   name: cluster-monitoring-config
    5.   namespace: openshift-monitoring
    6. data:
    7.   config.yaml: |
    8.     prometheusK8s:
    9.       remoteWrite:
    10.       - url: "https://remote-write.endpoint"
    11.         <endpoint_authentication_credentials>

    For endpoint_authentication_credentials substitute the credentials for the endpoint. Currently supported authentication methods are basic authentication (basicAuth) and client TLS (tlsConfig) authentication.

    • The following example configures basic authentication:

      1. basicAuth:
      2.   username:
      3.     <usernameSecret>
      4.   password:
      5.     <passwordSecret>

      Substitute <usernameSecret> and <passwordSecret> accordingly.

      The following sample shows basic authentication configured with remoteWriteAuth for the name values and user and password for the key values. These values contain the endpoint authentication credentials:

      1. apiVersion: v1
      2. kind: ConfigMap
      3. metadata:
      4.   name: cluster-monitoring-config
      5.   namespace: openshift-monitoring
      6. data:
      7.   config.yaml: |
      8.     prometheusK8s:
      9.       remoteWrite:
      10.       - url: "https://remote-write.endpoint"
      11.         basicAuth:
      12.           username:
      13.             name: remoteWriteAuth
      14.             key: user
      15.           password:
      16.             name: remoteWriteAuth
      17.             key: password

    • The following example configures client TLS authentication:

      1. tlsConfig:
      2.   ca:
      3.     <caSecret>
      4.   cert:
      5.     <certSecret>
      6.   keySecret:
      7.     <keySecret>

      Substitute <caSecret>, <certSecret>, and <keySecret> accordingly.

      The following sample shows a TLS authentication configuration using selfsigned-mtls-bundle for the name values and ca.crt for the ca key value, client.crt for the cert key value, and client.key for the keySecret key value:

      1. apiVersion: v1
      2. kind: ConfigMap
      3. metadata:
      4.   name: cluster-monitoring-config
      5.   namespace: openshift-monitoring
      6. data:
      7.   config.yaml: |
      8.     prometheusK8s:
      9.       remoteWrite:
      10.       - url: "https://remote-write.endpoint"
      11.       tlsConfig:
      12.         ca:
      13.           secret:
      14.             name: selfsigned-mtls-bundle
      15.             key: ca.crt
      16.         cert:
      17.           secret:
      18.             name: selfsigned-mtls-bundle
      19.             key: client.crt
      20.         keySecret:
      21.           name: selfsigned-mtls-bundle
      22.           key: client.key

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

    1. apiVersion: v1
    2. kind: ConfigMap
    3. metadata:
    4.   name: cluster-monitoring-config
    5.   namespace: openshift-monitoring
    6. data:
    7.   config.yaml: |
    8.     prometheusK8s:
    9.       remoteWrite:
    10.       - url: "https://remote-write.endpoint"
    11.         <endpoint_authentication_credentials>
    12.         <write_relabel_configs>

    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:

    1. apiVersion: v1
    2. kind: ConfigMap
    3. metadata:
    4.   name: cluster-monitoring-config
    5.   namespace: openshift-monitoring
    6. data:
    7.   config.yaml: |
    8.     prometheusK8s:
    9.       remoteWrite:
    10.       - url: "https://remote-write.endpoint"
    11.         writeRelabelConfigs:
    12.         - source_labels: [__name__]
    13.           regex: 'my_metric'
    14.           action: keep

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

  5. If required, configure remote write for the Prometheus instance that monitors user-defined projects by changing the name and namespace metadata values as follows:

    1. apiVersion: v1
    2. kind: ConfigMap
    3. metadata:
    4.   name: user-workload-monitoring-config
    5.   namespace: openshift-user-workload-monitoring
    6. data:
    7.   config.yaml: |
    8.     prometheus:
    9.       remoteWrite:
    10.       - url: "https://remote-write.endpoint"
    11.         <endpoint_authentication_credentials>
    12.         <write_relabel_configs>

    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.

  6. Save the file to apply the changes to the ConfigMap object. 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.

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.

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

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

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 cluster-admin 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 user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

    1. $ 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:

    1. apiVersion: v1
    2. kind: ConfigMap
    3. metadata:
    4.   name: user-workload-monitoring-config
    5.   namespace: openshift-user-workload-monitoring
    6. data:
    7.   config.yaml: |
    8.     prometheus:
    9.       enforcedSampleLimit: 50000 (1)
    1A 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.

    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 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:

    1. apiVersion: monitoring.coreos.com/v1
    2. kind: PrometheusRule
    3. metadata:
    4.   labels:
    5.     prometheus: k8s
    6.     role: alert-rules
    7.   name: monitoring-stack-alerts (1)
    8.   namespace: ns1 (2)
    9. spec:
    10.   groups:
    11.   - name: general.rules
    12.     rules:
    13.     - alert: TargetDown (3)
    14.       annotations:
    15.         message: '{{ printf "%.4g" $value }}% of the {{ $labels.job }}/{{ $labels.service
    16.           }} targets in {{ $labels.namespace }} namespace are down.' (4)
    17.       expr: 100 * (count(up == 0) BY (job, namespace, service) / count(up) BY (job,
    18.         namespace, service)) > 10
    19.       for: 10m (5)
    20.       labels:
    21.         severity: warning (6)
    22.     - alert: ApproachingEnforcedSamplesLimit (7)
    23.       annotations:
    24.         message: '{{ $labels.container }} container of the {{ $labels.pod }} pod in the {{ $labels.namespace }} namespace consumes {{ $value | humanizePercentage }} of the samples limit budget.' (8)
    25.       expr: scrape_samples_scraped/50000 > 0.8 (9)
    26.       for: 10m (10)
    27.       labels:
    28.         severity: warning (11)
    1Defines the name of the alerting rule.
    2Specifies the user-defined project where the alerting rule will be deployed.
    3The TargetDown alert will fire if the target cannot be scraped or is not available for the for duration.
    4The message that will be output when the TargetDown alert fires.
    5The conditions for the TargetDown alert must be true for this duration before the alert is fired.
    6Defines the severity for the TargetDown alert.
    7The ApproachingEnforcedSamplesLimit alert will fire when the defined scrape sample threshold is reached or exceeded for the specified for duration.
    8The message that will be output when the ApproachingEnforcedSamplesLimit alert fires.
    9The 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.
    10The conditions for the ApproachingEnforcedSamplesLimit alert must be true for this duration before the alert is fired.
    11Defines the severity for the ApproachingEnforcedSamplesLimit alert.

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

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

Additional resources

Configuring external alertmanager instances

The OKD monitoring stack includes a local Alertmanager instance that routes alerts from Prometheus. You can add external Alertmanager instances by configuring the cluster-monitoring-config config map in either the openshift-monitoring project or the user-workload-monitoring-config project.

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

  • You have installed the OpenShift CLI (oc).

  • 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 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 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.

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:

        1. $ 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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusK8s:
        9.       additionalAlertmanagerConfigs:
        10.       - <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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusK8s:
        9.       additionalAlertmanagerConfigs:
        10.       - scheme: https
        11.         pathPrefix: /
        12.         timeout: "30s"
        13.         apiVersion: v1
        14.         bearerToken:
        15.           name: alertmanager-bearer-token
        16.           key: token
        17.         tlsConfig:
        18.           key:
        19.             name: alertmanager-tls
        20.             key: tls.key
        21.           cert:
        22.             name: alertmanager-tls
        23.             key: tls.crt
        24.           ca:
        25.             name: alertmanager-tls
        26.             key: tls.ca
        27.         staticConfigs:
        28.         - external-alertmanager1-remote.com
        29.         - 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:

        1. $ 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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>:
        9.       additionalAlertmanagerConfigs:
        10.       - <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:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.    thanosRuler:
        9.      additionalAlertmanagerConfigs:
        10.     - scheme: https
        11.       pathPrefix: /
        12.       timeout: "30s"
        13.       apiVersion: v1
        14.       bearerToken:
        15.         name: alertmanager-bearer-token
        16.         key: token
        17.       tlsConfig:
        18.         key:
        19.           name: alertmanager-tls
        20.           key: tls.key
        21.         cert:
        22.           name: alertmanager-tls
        23.           key: tls.crt
        24.         ca:
        25.           name: alertmanager-tls
        26.           key: tls.ca
        27.       staticConfigs:
        28.       - external-alertmanager1-remote.com
        29.       - external-alertmanager1-remote2.com

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

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

Attaching additional labels to your time series and alerts

Using the external labels feature of Prometheus, you can attach custom labels to all time series and alerts leaving Prometheus.

Prerequisites

  • If you are configuring core OKD monitoring components:

    • You have access to the cluster as a user with the cluster-admin 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 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 attach custom labels to all time series and alerts leaving the Prometheus instance that monitors core OKD projects:

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

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusK8s:
        9.       externalLabels:
        10.         <key>: <value> (1)
        1Substitute <key>: <value> with a map of key-value pairs where <key> is a unique name for the new label and <value> is its value.

        Do not use prometheus or prometheus_replica as key names, because they are reserved and will be overwritten.

        For example, to add metadata about the region and environment to all time series and alerts, use:

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheusK8s:
        9.       externalLabels:
        10.         region: eu
        11.         environment: prod

    • To attach custom labels to all time series and alerts leaving the Prometheus instance that monitors user-defined projects:

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

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheus:
        9.       externalLabels:
        10.         <key>: <value> (1)
        1Substitute <key>: <value> with a map of key-value pairs where <key> is a unique name for the new label and <value> is its value.

        Do not use prometheus or prometheus_replica as key names, because they are reserved and will be overwritten.

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     prometheus:
        9.       externalLabels:
        10.         region: eu
        11.         environment: prod

  1. Save the file to apply the changes. The new 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

Setting log levels for monitoring components

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

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

  • 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

  • If you are setting a log level for Prometheus Operator, Prometheus, or Thanos Querier in the openshift-monitoring project:

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

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

  • If you are setting a log level for Prometheus Operator, Prometheus, or Thanos Ruler in the openshift-user-workload-monitoring project:

    • You have access to the cluster as a user with the cluster-admin 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 set a log level for a component in the openshift-monitoring project:

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

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: cluster-monitoring-config
        5.   namespace: openshift-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>: (1)
        9.       logLevel: <log_level> (2)
        1The monitoring component that you are applying a log level to.
        2The log level to apply to the component.

    • To set a log level for a component in the openshift-user-workload-monitoring project:

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

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

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

        1. apiVersion: v1
        2. kind: ConfigMap
        3. metadata:
        4.   name: user-workload-monitoring-config
        5.   namespace: openshift-user-workload-monitoring
        6. data:
        7.   config.yaml: |
        8.     <component>: (1)
        9.       logLevel: <log_level> (2)
        1The monitoring component that you are applying a log level to.
        2The log level to apply to the component.

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

    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.

  2. 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:

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

    Example output

    1.         - --log-level=debug

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

    1. $ 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.

Additional resources

Disabling the default Grafana deployment

By default, a read-only Grafana instance is deployed with a collection of dashboards displaying cluster metrics. The Grafana instance is not user-configurable.

You can disable the Grafana deployment, causing the associated resources to be deleted from the cluster. You might do this if you do not need these dashboards and want to conserve resources in your cluster. You will still be able to view metrics and dashboards included in the web console. Grafana can be safely enabled again at any time.

Prerequisites

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

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

  • You have installed the OpenShift CLI (oc).

Procedure

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

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

  2. Add enabled: false for the grafana component under data/config.yaml:

    1. apiVersion: v1
    2. kind: ConfigMap
    3. metadata:
    4.   name: cluster-monitoring-config
    5.   namespace: openshift-monitoring
    6. data:
    7.   config.yaml: |
    8.     grafana:
    9.       enabled: false

  3. Save the file to apply the changes. The resources will begin to be removed automatically when you apply the change.

    This change results in some components, including Prometheus and the Thanos Querier, being restarted. This might lead to previously collected metrics being lost if you have not yet followed the steps in the “Configuring persistent storage” section.

  4. Check that the Grafana pod is no longer running. The following example lists the status of pods in the openshift-monitoring project:

    1. $ oc -n openshift-monitoring get pods

    It may take a few minutes after applying the change for these pods to terminate.

Additional resources

Disabling the local Alertmanager

A local Alertmanager that routes alerts from Prometheus instances is enabled by default in the openshift-monitoring project of the OKD monitoring stack.

If you do not need the local Alertmanager, you can disable it by configuring the cluster-monitoring-config config map in the openshift-monitoring project.

Prerequisites

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

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

  • You have installed the OpenShift CLI (oc).

Procedure

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

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

  2. Add enabled: false for the alertmanagerMain component under data/config.yaml:

    1. apiVersion: v1
    2. kind: ConfigMap
    3. metadata:
    4.   name: cluster-monitoring-config
    5.   namespace: openshift-monitoring
    6. data:
    7.   config.yaml: |
    8.     alertmanagerMain:
    9.       enabled: false

  3. Save the file to apply the changes. The Alertmanager instance is disabled automatically when you apply the change.

Additional resources

Next steps