Questo contenuto non è disponibile nella lingua selezionata.

Chapter 2. Configuring the monitoring stack


The OpenShift Container Platform 4 installation program provides only a low number of configuration options before installation. Configuring most OpenShift Container Platform 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.

2.1. 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.

2.2. Maintenance and support for monitoring

The supported way of configuring OpenShift Container Platform 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.

2.2.1. 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 OpenShift Container Platform monitoring stack are not meant to be used by any other resources, as there are no guarantees about their backward compatibility.

    Note

    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 OpenShift Container Platform 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 OpenShift Container Platform. A custom instance is a Prometheus custom resource (CR) managed by the Prometheus Operator.
  • 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.
Note

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

2.2.2. Support policy for monitoring Operators

Monitoring Operators ensure that OpenShift Container Platform 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.
Warning

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.

2.3. Preparing to configure the monitoring stack

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

2.3.1. Creating a cluster monitoring config map

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

Note

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 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

2.3.2. 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.

Note

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 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
      Note

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

2.4. Configuring the monitoring stack

In OpenShift Container Platform 4.10, 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 OpenShift Container 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).

Procedure

  1. Edit the ConfigMap object.

    • To configure core OpenShift Container Platform 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 OpenShift Container Platform 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.
        Note

        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.

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

    Note

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

    Warning

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

Additional resources

2.5. 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 2.1. 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

Note

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

2.6. 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.

2.6.1. 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.

2.6.2. 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.

Note

You cannot add a node selector constraint directly to an existing scheduled pod.

Prerequisites

  • If you are configuring core OpenShift Container 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).

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 OpenShift Container Platform 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.
        Note

        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.
        Note

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

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

    Note

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

    Warning

    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

2.7. 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 OpenShift Container 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).

Procedure

  1. Edit the ConfigMap object:

    • To assign tolerations to a component that monitors core OpenShift Container Platform 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"
  2. Save the file to apply the changes. The new component placement configuration is applied automatically.

    Note

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

    Warning

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

Additional resources

2.8. Configuring a dedicated service monitor

You can configure OpenShift Container Platform 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.

2.8.1. 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.

    Warning

    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.

2.9. 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.

Note

2.9.1. 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.
  • 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.
  • Configure local persistent storage.

    Note

    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.

    Important

    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.

2.9.2. 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 OpenShift Container 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).

Procedure

  1. Edit the ConfigMap object:

    • To configure a PVC for a component that monitors core OpenShift Container Platform 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 OpenShift Container Platform 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
        Note

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

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

    Note

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

    Warning

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

2.9.3. Resizing a persistent storage volume

OpenShift Container Platform 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 OpenShift Container 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.
    • You have configured at least one PVC for core OpenShift Container Platform 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 OpenShift Container Platform 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 OpenShift Container Platform 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:

      Note

      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
        Note

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

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

    Warning

    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.

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

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

2.9.4. Modifying the retention time for Prometheus metrics data

By default, the OpenShift Container Platform 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 OpenShift Container Platform 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 OpenShift Container Platform 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 retention time configuration under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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 OpenShift Container Platform components:

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

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. Add your retention time configuration under data/config.yaml:

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

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

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            prometheus:
              retention: 24h
  2. Save the file to apply the changes. The pods affected by the new configuration are restarted automatically.

    Note

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

    Warning

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

2.9.5. 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 installed the OpenShift CLI (oc).
  • 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.
Warning

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.

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.

2.10. 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 OpenShift Container 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 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.

    Caution

    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:

    $ 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"
            <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:

      basicAuth:
        username:
          <usernameSecret>
        password:
          <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:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: cluster-monitoring-config
        namespace: openshift-monitoring
      data:
        config.yaml: |
          prometheusK8s:
            remoteWrite:
            - url: "https://remote-write.endpoint"
              basicAuth:
                username:
                  name: remoteWriteAuth
                  key: user
                password:
                  name: remoteWriteAuth
                  key: password
    • The following example configures client TLS authentication:

      tlsConfig:
        ca:
          <caSecret>
        cert:
          <certSecret>
        keySecret:
          <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:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: cluster-monitoring-config
        namespace: openshift-monitoring
      data:
        config.yaml: |
          prometheusK8s:
            remoteWrite:
            - url: "https://remote-write.endpoint"
              tlsConfig:
                ca:
                  secret:
                    name: selfsigned-mtls-bundle
                    key: ca.crt
                cert:
                  secret:
                    name: selfsigned-mtls-bundle
                    key: client.crt
                keySecret:
                  name: selfsigned-mtls-bundle
                  key: client.key
  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"
            <endpoint_authentication_credentials>
            <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:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: cluster-monitoring-config
      namespace: openshift-monitoring
    data:
      config.yaml: |
        prometheusK8s:
          remoteWrite:
          - url: "https://remote-write.endpoint"
            writeRelabelConfigs:
            - sourceLabels: [__name__]
              regex: 'my_metric'
              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:

    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"
            <endpoint_authentication_credentials>
            <write_relabel_configs>
    Note

    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.

    Note

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

    Warning

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

Additional resources

2.11. 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
Note

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.

2.11.1. 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.

Warning

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:

    $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
  2. Add the enforcedSampleLimit configuration to data/config.yaml to limit the number of samples that can be accepted per target scrape in user-defined projects:

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

    Note

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

    Warning

    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.

2.11.2. 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
Red Hat logoGithubRedditYoutubeTwitter

Formazione

Prova, acquista e vendi

Community

Informazioni sulla documentazione di Red Hat

Aiutiamo gli utenti Red Hat a innovarsi e raggiungere i propri obiettivi con i nostri prodotti e servizi grazie a contenuti di cui possono fidarsi.

Rendiamo l’open source più inclusivo

Red Hat si impegna a sostituire il linguaggio problematico nel codice, nella documentazione e nelle proprietà web. Per maggiori dettagli, visita ilBlog di Red Hat.

Informazioni su Red Hat

Forniamo soluzioni consolidate che rendono più semplice per le aziende lavorare su piattaforme e ambienti diversi, dal datacenter centrale all'edge della rete.

© 2024 Red Hat, Inc.