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
, andPrometheusRule
objects in theopenshift-*
andkube-*
projects. Modifying any resources or objects deployed in the
openshift-monitoring
oropenshift-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.NoteThe 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-*
, andkube-*
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.
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.
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.
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
Check whether the
cluster-monitoring-config
ConfigMap
object exists:$ oc -n openshift-monitoring get configmap cluster-monitoring-config
If the
ConfigMap
object does not exist: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: |
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.
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
Check whether the
user-workload-monitoring-config
ConfigMap
object exists:$ oc -n openshift-user-workload-monitoring get configmap user-workload-monitoring-config
If the
user-workload-monitoring-config
ConfigMap
object does not exist: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: |
Apply the configuration to create the
ConfigMap
object:$ oc apply -f user-workload-monitoring-config.yaml
NoteConfigurations 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
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.
-
You have access to the cluster as a user with the
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 theuser-workload-monitoring-config-edit
role in theopenshift-user-workload-monitoring
project. -
You have created the
user-workload-monitoring-config
ConfigMap
object.
-
You have access to the cluster as a user with the
-
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
ConfigMap
object.To configure core OpenShift Container Platform monitoring components:
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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:
Edit the
user-workload-monitoring-config
ConfigMap
object in theopenshift-user-workload-monitoring
project:$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
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.
NoteThe Prometheus config map component is called
prometheusK8s
in thecluster-monitoring-config
ConfigMap
object andprometheus
in theuser-workload-monitoring-config
ConfigMap
object.
Save the file to apply the changes to the
ConfigMap
object. The pods affected by the new configuration are restarted automatically.NoteConfigurations applied to the
user-workload-monitoring-config
ConfigMap
object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.WarningWhen changes are saved to a monitoring config map, the pods and other resources in the related project might be redeployed. The running monitoring processes in that project might also be restarted.
Additional resources
- See Preparing to configure the monitoring stack for steps to create monitoring config maps
- Enabling monitoring for user-defined projects
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:
Component | cluster-monitoring-config config map key | user-workload-monitoring-config config map key |
---|---|---|
Prometheus Operator |
|
|
Prometheus |
|
|
Alertmanager |
| |
kube-state-metrics |
| |
openshift-state-metrics |
| |
Grafana |
| |
Telemeter Client |
| |
Prometheus Adapter |
| |
Thanos Querier |
| |
Thanos Ruler |
|
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.
Additional resources
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.
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.
-
You have access to the cluster as a user with the
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 theuser-workload-monitoring-config-edit
role in theopenshift-user-workload-monitoring
project. -
You have created the
user-workload-monitoring-config
ConfigMap
object.
-
You have access to the cluster as a user with the
-
You have installed the OpenShift CLI (
oc
).
Procedure
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>
Edit the
ConfigMap
object:To move a component that monitors core OpenShift Container Platform projects:
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
Specify the node labels for the
nodeSelector
constraint for the component underdata/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.
NoteIf monitoring components remain in a
Pending
state after configuring thenodeSelector
constraint, check the pod events for errors relating to taints and tolerations.
To move a component that monitors user-defined projects:
Edit the
user-workload-monitoring-config
ConfigMap
object in theopenshift-user-workload-monitoring
project:$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
Specify the node labels for the
nodeSelector
constraint for the component underdata/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.
NoteIf monitoring components remain in a
Pending
state after configuring thenodeSelector
constraint, check the pod events for errors relating to taints and tolerations.
Save the file to apply the changes. The components specified in the new configuration are moved to the new nodes automatically.
NoteConfigurations applied to the
user-workload-monitoring-config
ConfigMap
object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.WarningWhen you save changes to a monitoring config map, the pods and other resources in the project might be redeployed. The running monitoring processes in that project might also restart.
Additional resources
- See Preparing to configure the monitoring stack for steps to create monitoring config maps
- Enabling monitoring for user-defined projects
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.
-
You have access to the cluster as a user with the
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 theuser-workload-monitoring-config-edit
role in theopenshift-user-workload-monitoring
project. -
You have created the
user-workload-monitoring-config
ConfigMap
object.
-
You have access to the cluster as a user with the
-
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
ConfigMap
object:To assign tolerations to a component that monitors core OpenShift Container Platform projects:
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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 tonode1
with the keykey1
and the valuevalue1
. This prevents monitoring components from deploying pods onnode1
unless a toleration is configured for that taint. The following example configures thealertmanagerMain
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:
Edit the
user-workload-monitoring-config
ConfigMap
object in theopenshift-user-workload-monitoring
project:$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
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 tonode1
with the keykey1
and the valuevalue1
. This prevents monitoring components from deploying pods onnode1
unless a toleration is configured for that taint. The following example configures thethanosRuler
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"
Save the file to apply the changes. The new component placement configuration is applied automatically.
NoteConfigurations applied to the
user-workload-monitoring-config
ConfigMap
object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.WarningWhen changes are saved to a monitoring config map, the pods and other resources in the related project might be redeployed. The running monitoring processes in that project might also be restarted.
Additional resources
- See Preparing to configure the monitoring stack for steps to create monitoring config maps
- Enabling monitoring for user-defined projects
- See the OpenShift Container Platform documentation on taints and tolerations
- See the Kubernetes documentation on taints and tolerations
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
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
namespace:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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 totrue
to deploy a dedicated service monitor that exposes the kubelet/metrics/resource
endpoint.
Save the file to apply the changes automatically.
WarningWhen you save changes to a
cluster-monitoring-config
config map, the pods and other resources in theopenshift-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.
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 thevolumeMode
parameter when you configure the persistent volume. Configure local persistent storage.
NoteIf you use a local volume for persistent storage, do not use a raw block volume, which is described with
volumeMode: Block
in theLocalVolume
object. Prometheus cannot use raw block volumes.ImportantPrometheus 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.
-
You have access to the cluster as a user with the
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 theuser-workload-monitoring-config-edit
role in theopenshift-user-workload-monitoring
project. -
You have created the
user-workload-monitoring-config
ConfigMap
object.
-
You have access to the cluster as a user with the
-
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
ConfigMap
object:To configure a PVC for a component that monitors core OpenShift Container Platform projects:
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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:
Edit the
user-workload-monitoring-config
ConfigMap
object in theopenshift-user-workload-monitoring
project:$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
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
NoteStorage requirements for the
thanosRuler
component depend on the number of rules that are evaluated and how many samples each rule generates.
Save the file to apply the changes. The pods affected by the new configuration are restarted automatically and the new storage configuration is applied.
NoteConfigurations applied to the
user-workload-monitoring-config
ConfigMap
object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.WarningWhen 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.
-
You have access to the cluster as a user with the
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 theuser-workload-monitoring-config-edit
role in theopenshift-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.
-
You have access to the cluster as a user with the
Procedure
Edit the
ConfigMap
object:To resize a PVC for a component that monitors core OpenShift Container Platform projects:
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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
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:
NoteYou can resize the volumes for the Thanos Ruler and Prometheus instances that monitor user-defined projects.
Edit the
user-workload-monitoring-config
ConfigMap
object in theopenshift-user-workload-monitoring
project:$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
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
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
NoteStorage requirements for the
thanosRuler
component depend on the number of rules that are evaluated and how many samples each rule generates.
Save the file to apply the changes. The pods affected by the new configuration restart automatically.
WarningWhen 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.
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
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.
-
You have access to the cluster as a user with the
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 theuser-workload-monitoring-config-edit
role in theopenshift-user-workload-monitoring
project. -
You have created the
user-workload-monitoring-config
ConfigMap
object.
-
You have access to the cluster as a user with the
-
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
ConfigMap
object:To modify the retention time for the Prometheus instance that monitors core OpenShift Container Platform projects:
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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 byms
(milliseconds),s
(seconds),m
(minutes),h
(hours),d
(days),w
(weeks), ory
(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:
Edit the
user-workload-monitoring-config
ConfigMap
object in theopenshift-user-workload-monitoring
project:$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
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 byms
(milliseconds),s
(seconds),m
(minutes),h
(hours),d
(days),w
(weeks), ory
(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
Save the file to apply the changes. The pods affected by the new configuration are restarted automatically.
NoteConfigurations applied to the
user-workload-monitoring-config
ConfigMap
object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.WarningWhen 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 theuser-workload-monitoring-config-edit
role in theopenshift-user-workload-monitoring
project. -
You have created the
user-workload-monitoring-config
ConfigMap
object.
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
Edit the
user-workload-monitoring-config
ConfigMap
object in theopenshift-user-workload-monitoring
project:$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
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), ory
(years). You can also combine time values for specific times, such as1h30m15s
. The default is24h
.
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
- Save the file to apply the changes. The pods affected by the new configuration automatically restart.
Additional resources
- See Preparing to configure the monitoring stack for steps to create monitoring config maps.
- Enabling monitoring for user-defined projects
- Understanding persistent storage
- Optimizing storage
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.
-
You have access to the cluster as a user with the
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 theuser-workload-monitoring-config-edit
role in theopenshift-user-workload-monitoring
project. -
You have created the
user-workload-monitoring-config
ConfigMap
object.
-
You have access to the cluster as a user with the
-
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.
CautionTo reduce security risks, avoid sending metrics to an endpoint via unencrypted HTTP or without using authentication.
Procedure
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
-
Add a
remoteWrite:
section underdata/config.yaml/prometheusK8s
. 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 thename
values anduser
andpassword
for thekey
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 thename
values andca.crt
for theca
key
value,client.crt
for thecert
key
value, andclient.key
for thekeySecret
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
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.
If required, configure remote write for the Prometheus instance that monitors user-defined projects by changing the
name
andnamespace
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>
NoteThe Prometheus config map component is called
prometheusK8s
in thecluster-monitoring-config
ConfigMap
object andprometheus
in theuser-workload-monitoring-config
ConfigMap
object.Save the file to apply the changes to the
ConfigMap
object. The pods affected by the new configuration restart automatically.NoteConfigurations applied to the
user-workload-monitoring-config
ConfigMap
object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.WarningSaving 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
- See Setting up remote write compatible endpoints for steps to create a remote write compatible endpoint (such as Thanos).
- See Tuning remote write settings for information about how to optimize remote write settings for different use cases.
- For information about additional optional fields, please refer to the API documentation.
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
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.
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 theuser-workload-monitoring-config-edit
role in theopenshift-user-workload-monitoring
project. -
You have created the
user-workload-monitoring-config
ConfigMap
object. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
user-workload-monitoring-config
ConfigMap
object in theopenshift-user-workload-monitoring
project:$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
Add the
enforcedSampleLimit
configuration todata/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.
Save the file to apply the changes. The limit is applied automatically.
NoteConfigurations applied to the
user-workload-monitoring-config
ConfigMap
object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.WarningWhen changes are saved to the
user-workload-monitoring-config
ConfigMap
object, the pods and other resources in theopenshift-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 theuser-workload-monitoring-config-edit
role in theopenshift-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
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 thefor
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 specifiedfor
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 of50000
. Thefor
duration must also have passed before the alert will fire. The<number>
in the expressionscrape_samples_scraped/<number> > <threshold>
must match theenforcedSampleLimit
value defined in theuser-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.
Apply the configuration to the user-defined project:
$ oc apply -f monitoring-stack-alerts.yaml
Additional resources
- Creating a user-defined workload monitoring config map
- Enabling monitoring for user-defined projects
- See Determining why Prometheus is consuming a lot of disk space for steps to query which metrics have the highest number of scrape samples.