Chapter 3. Configuring the monitoring stack
This section explains what configuration is supported, shows how to configure the monitoring stack for user-defined projects, and demonstrates several common configuration scenarios.
Not all configuration parameters for the monitoring stack are exposed. Only the parameters and fields listed in the Config map reference for the Cluster Monitoring Operator are supported for configuration.
3.1. Maintenance and support for monitoring
Not all configuration options for the monitoring stack are exposed. The only supported way of configuring Red Hat OpenShift Service on AWS monitoring is by configuring the Cluster Monitoring Operator (CMO) using the options described in the Config map reference for the Cluster Monitoring Operator. 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 the Config map reference for the Cluster Monitoring Operator, your changes will disappear because the CMO automatically reconciles any differences and resets any unsupported changes back to the originally defined state by default and by design.
Installing another Prometheus instance is not supported by the Red Hat Site Reliability Engineers (SRE).
3.1.1. Support considerations for monitoring
Backward compatibility for metrics, recording rules, or alerting rules is not guaranteed.
The following modifications are explicitly not supported:
- Installing custom Prometheus instances on Red Hat OpenShift Service on AWS. A custom instance is a Prometheus custom resource (CR) managed by the Prometheus Operator.
-
Modifying the default platform monitoring components. You should not modify any of the components defined in the
cluster-monitoring-config
config map. Red Hat SRE uses these components to monitor the core cluster components and Kubernetes services.
3.1.2. Support version matrix for monitoring components
The following matrix contains information about versions of monitoring components for Red Hat OpenShift Service on AWS 4.12 and later releases:
Red Hat OpenShift Service on AWS | Prometheus Operator | Prometheus | Metrics Server | Alertmanager | kube-state-metrics agent | monitoring-plugin | node-exporter agent | Thanos |
---|---|---|---|---|---|---|---|---|
4.17 | 0.75.2 | 2.53.1 | 0.7.1 | 0.27.0 | 2.13.0 | 1.0.0 | 1.8.2 | 0.35.1 |
4.16 | 0.73.2 | 2.52.0 | 0.7.1 | 0.26.0 | 2.12.0 | 1.0.0 | 1.8.0 | 0.35.0 |
4.15 | 0.70.0 | 2.48.0 | 0.6.4 | 0.26.0 | 2.10.1 | 1.0.0 | 1.7.0 | 0.32.5 |
4.14 | 0.67.1 | 2.46.0 | N/A | 0.25.0 | 2.9.2 | 1.0.0 | 1.6.1 | 0.30.2 |
4.13 | 0.63.0 | 2.42.0 | N/A | 0.25.0 | 2.8.1 | N/A | 1.5.0 | 0.30.2 |
4.12 | 0.60.1 | 2.39.1 | N/A | 0.24.0 | 2.6.0 | N/A | 1.4.0 | 0.28.1 |
The openshift-state-metrics agent and Telemeter Client are OpenShift-specific components. Therefore, their versions correspond with the versions of Red Hat OpenShift Service on AWS.
3.2. Configuring the monitoring stack
In Red Hat OpenShift Service on AWS, you can configure the stack that monitors workloads for user-defined projects by using the user-workload-monitoring-config
ConfigMap
object. Config maps configure the Cluster Monitoring Operator (CMO), which in turn configures the components of the stack.
Prerequisites
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap
object exists. This object is created by default when the cluster is created. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
ConfigMap
object.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.
Save the file to apply the changes to the
ConfigMap
object.WarningDifferent configuration changes to the
ConfigMap
object result in different outcomes:- The pods are not redeployed. Therefore, there is no service outage.
The affected pods are redeployed:
- For single-node clusters, this results in temporary service outage.
- For multi-node clusters, because of high-availability, the affected pods are gradually rolled out and the monitoring stack remains available.
- Configuring and resizing a persistent volume always results in a service outage, regardless of high availability.
Each procedure that requires a change in the config map includes its expected outcome.
Additional resources
-
Configuration reference for the
user-workload-monitoring-config
config map
3.3. Configurable monitoring components
This table shows the monitoring components you can configure and the keys used to specify the components in the user-workload-monitoring-config
ConfigMap
objects.
Do not modify the monitoring components in the cluster-monitoring-config
ConfigMap
object. Red Hat Site Reliability Engineers (SRE) use these components to monitor the core cluster components and Kubernetes services.
Component | user-workload-monitoring-config config map key |
---|---|
Alertmanager |
|
Prometheus Operator |
|
Prometheus |
|
Thanos Ruler |
|
3.4. 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.
3.4.1. 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.
3.4.2. Moving monitoring components to different nodes
You can move any of the components that monitor workloads for user-defined projects to specific worker nodes. It is not permitted to move components to control plane or infrastructure nodes.
Prerequisites
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap
object exists. This object is created by default when the cluster is created. -
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: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 automatically moved to the new nodes, and the pods affected by the new configuration are redeployed.
Additional resources
-
See the Kubernetes documentation for details on the
nodeSelector
constraint
3.5. Assigning tolerations to monitoring components
You can assign tolerations to the components that monitor user-defined projects, to enable moving them to tainted worker nodes. Scheduling is not permitted on control plane or infrastructure nodes.
Prerequisites
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap
object exists in theopenshift-user-workload-monitoring
namespace. This object is created by default when the cluster is created. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
ConfigMap
object: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 pods affected by the new configuration are automatically redeployed.
Additional resources
- See the Kubernetes documentation on taints and tolerations
3.6. Managing CPU and memory resources for monitoring components
You can ensure that the containers that run monitoring components have enough CPU and memory resources by specifying values for resource limits and requests for those components.
You can configure these limits and requests for core platform monitoring components in the openshift-monitoring
namespace and for the components that monitor user-defined projects in the openshift-user-workload-monitoring
namespace.
3.6.1. About specifying limits and requests for monitoring components
You can configure resource limits and request settings for core platform monitoring components and for the components that monitor user-defined projects, including the following components:
- Alertmanager (for core platform monitoring and for user-defined projects)
- kube-state-metrics
- monitoring-plugin
- node-exporter
- openshift-state-metrics
- Prometheus (for core platform monitoring and for user-defined projects)
- Metrics Server
- Prometheus Operator and its admission webhook service
- Telemeter Client
- Thanos Querier
- Thanos Ruler
By defining resource limits, you limit a container’s resource usage, which prevents the container from exceeding the specified maximum values for CPU and memory resources.
By defining resource requests, you specify that a container can be scheduled only on a node that has enough CPU and memory resources available to match the requested resources.
3.6.2. Specifying limits and requests for monitoring components
To configure CPU and memory resources, specify values for resource limits and requests in the appropriate ConfigMap
object for the namespace in which the monitoring component is located:
-
The
cluster-monitoring-config
config map in theopenshift-monitoring
namespace for core platform monitoring -
The
user-workload-monitoring-config
config map in theopenshift-user-workload-monitoring
namespace for components that monitor user-defined projects
Prerequisites
If you are configuring core platform monitoring components:
-
You have access to the cluster as a user with the
cluster-admin
cluster role. -
You have created a
ConfigMap
object namedcluster-monitoring-config
.
-
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 access to the cluster as a user with the
-
You have installed the OpenShift CLI (
oc
).
Procedure
To configure core platform monitoring components, edit the
cluster-monitoring-config
config map object in theopenshift-monitoring
namespace:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
Add values to define resource limits and requests for each core platform monitoring component you want to configure.
ImportantMake sure that the value set for a limit is always higher than the value set for a request. Otherwise, an error will occur, and the container will not run.
Example
apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | alertmanagerMain: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi prometheusK8s: resources: limits: cpu: 500m memory: 3Gi requests: cpu: 200m memory: 500Mi prometheusOperator: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi metricsServer: resources: requests: cpu: 10m memory: 50Mi limits: cpu: 50m memory: 500Mi kubeStateMetrics: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi telemeterClient: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi openshiftStateMetrics: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi thanosQuerier: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi nodeExporter: resources: limits: cpu: 50m memory: 150Mi requests: cpu: 20m memory: 50Mi monitoringPlugin: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi prometheusOperatorAdmissionWebhook: resources: limits: cpu: 50m memory: 100Mi requests: cpu: 20m memory: 50Mi
- Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
Additional resources
3.7. Configuring persistent storage
Run cluster monitoring with persistent storage to gain the following benefits:
- Protect your metrics and alerting data from data loss by storing them in a persistent volume (PV). As a result, they can survive pods being restarted or recreated.
- Avoid getting duplicate notifications and losing silences for alerts when the Alertmanager pods are restarted.
For production environments, it is highly recommended to configure persistent storage.
In multi-node clusters, you must configure persistent storage for Prometheus, Alertmanager, and Thanos Ruler to ensure high availability.
3.7.1. Persistent storage prerequisites
- Use the block type of storage.
3.7.2. Configuring a persistent volume claim
To use a persistent volume (PV) for monitoring components, you must configure a persistent volume claim (PVC).
Prerequisites
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap
object exists. This object is created by default when the cluster is created. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
ConfigMap
object: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>: 1 volumeClaimTemplate: spec: storageClassName: <storage_class> 2 resources: requests: storage: <amount_of_storage> 3
See the Kubernetes documentation on PersistentVolumeClaims for information on how to specify
volumeClaimTemplate
.The following example configures a PVC that claims 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: my-storage-class 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 automatically redeployed and the new storage configuration is applied.
WarningWhen you update the config map with a PVC configuration, the affected
StatefulSet
object is recreated, resulting in a temporary service outage.
3.7.3. Modifying the retention time and size for Prometheus metrics data
By default, Prometheus retains metrics data for the following durations:
- Core platform monitoring: 15 days
- Monitoring for user-defined projects: 24 hours
You can modify the retention time for the Prometheus instance that monitors user-defined projects, to change how soon the data is deleted. You can also set the maximum amount of disk space the retained metrics data uses. If the data reaches this size limit, Prometheus deletes the oldest data first until the disk space used is again below the limit.
Note the following behaviors of these data retention settings:
-
The size-based retention policy applies to all data block directories in the
/prometheus
directory, including persistent blocks, write-ahead log (WAL) data, and m-mapped chunks. -
Data in the
/wal
and/head_chunks
directories counts toward the retention size limit, but Prometheus never purges data from these directories based on size- or time-based retention policies. Thus, if you set a retention size limit lower than the maximum size set for the/wal
and/head_chunks
directories, you have configured the system not to retain any data blocks in the/prometheus
data directories. - The size-based retention policy is applied only when Prometheus cuts a new data block, which occurs every two hours after the WAL contains at least three hours of data.
-
If you do not explicitly define values for either
retention
orretentionSize
, retention time defaults to 15 days for core platform monitoring and 24 hours for user-defined project monitoring. Retention size is not set. -
If you define values for both
retention
andretentionSize
, both values apply. If any data blocks exceed the defined retention time or the defined size limit, Prometheus purges these data blocks. -
If you define a value for
retentionSize
and do not defineretention
, only theretentionSize
value applies. -
If you do not define a value for
retentionSize
and only define a value forretention
, only theretention
value applies. -
If you set the
retentionSize
orretention
value to0
, the default settings apply. The default settings set retention time to 15 days for core platform monitoring and 24 hours for user-defined project monitoring. By default, retention size is not set.
Data compaction occurs every two hours. Therefore, a persistent volume (PV) might fill up before compaction, potentially exceeding the retentionSize
limit. In such cases, the KubePersistentVolumeFillingUp
alert fires until the space on a PV is lower than the retentionSize
limit.
Prerequisites
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap
object exists. This object is created by default when the cluster is created. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
ConfigMap
object: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 and size configuration under
data/config.yaml
:apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: retention: <time_specification> 1 retentionSize: <size_specification> 2
- 1
- The retention time: a number directly followed by
ms
(milliseconds),s
(seconds),m
(minutes),h
(hours),d
(days),w
(weeks), ory
(years). You can also combine time values for specific times, such as1h30m15s
. - 2
- The retention size: a number directly followed by
B
(bytes),KB
(kilobytes),MB
(megabytes),GB
(gigabytes),TB
(terabytes),PB
(petabytes), orEB
(exabytes).
The following example sets the retention time to 24 hours and the retention size to 10 gigabytes for the Prometheus instance that monitors user-defined projects:
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: retention: 24h retentionSize: 10GB
- Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
3.7.4. Modifying the retention time for Thanos Ruler metrics data
By default, for user-defined projects, Thanos Ruler automatically retains metrics data for 24 hours. You can modify the retention time to change how long this data is retained by specifying a time value in the user-workload-monitoring-config
config map in the openshift-user-workload-monitoring
namespace.
Prerequisites
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap
object exists. This object is created by default when the cluster is created. -
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 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 are automatically redeployed.
Additional resources
3.8. 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
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap
object exists. This object is created by default when the cluster is created. -
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.
ImportantRed Hat only provides information for configuring remote write senders and does not offer guidance on configuring receiver endpoints. Customers are responsible for setting up their own endpoints that are remote-write compatible. Issues with endpoint receiver configurations are not included in Red Hat production support.
You have set up authentication credentials in a
Secret
object for the remote write endpoint. You must create the secret in theopenshift-user-workload-monitoring
namespace.WarningTo reduce security risks, use HTTPS and authentication to send metrics to an endpoint.
Procedure
Edit the
ConfigMap
object: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 a
remoteWrite:
section underdata/config.yaml/prometheus
, as shown in the following example:apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://remote-write-endpoint.example.com" 1 <endpoint_authentication_credentials> 2
- 1
- The URL of the remote write endpoint.
- 2
- The authentication method and credentials for the endpoint. Currently supported authentication methods are AWS Signature Version 4, authentication using HTTP an
Authorization
request header, basic authentication, OAuth 2.0, and TLS client. See Supported remote write authentication settings below for sample configurations of supported authentication methods.
Add write relabel configuration values after the authentication credentials:
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://remote-write-endpoint.example.com" <endpoint_authentication_credentials> writeRelabelConfigs: - <your_write_relabel_configs> 1
- 1
- Add configuration for metrics that you want to send to the remote endpoint.
Example of forwarding a single metric called
my_metric
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://remote-write-endpoint.example.com" writeRelabelConfigs: - sourceLabels: [__name__] regex: 'my_metric' action: keep
Example of forwarding metrics called
my_metric_1
andmy_metric_2
inmy_namespace
namespaceapiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://remote-write-endpoint.example.com" writeRelabelConfigs: - sourceLabels: [__name__,namespace] regex: '(my_metric_1|my_metric_2);my_namespace' action: keep
- Save the file to apply the changes. The new configuration is applied automatically.
3.8.1. Supported remote write authentication settings
You can use different methods to authenticate with a remote write endpoint. Currently supported authentication methods are AWS Signature Version 4, basic authentication, authorization, OAuth 2.0, and TLS client. The following table provides details about supported authentication methods for use with remote write.
Authentication method | Config map field | Description |
---|---|---|
AWS Signature Version 4 |
| This method uses AWS Signature Version 4 authentication to sign requests. You cannot use this method simultaneously with authorization, OAuth 2.0, or Basic authentication. |
Basic authentication |
| Basic authentication sets the authorization header on every remote write request with the configured username and password. |
authorization |
|
Authorization sets the |
OAuth 2.0 |
|
An OAuth 2.0 configuration uses the client credentials grant type. Prometheus fetches an access token from |
TLS client |
| A TLS client configuration specifies the CA certificate, the client certificate, and the client key file information used to authenticate with the remote write endpoint server using TLS. The sample configuration assumes that you have already created a CA certificate file, a client certificate file, and a client key file. |
3.8.2. Example remote write authentication settings
The following samples show different authentication settings you can use to connect to a remote write endpoint. Each sample also shows how to configure a corresponding Secret
object that contains authentication credentials and other relevant settings. Each sample configures authentication for use with monitoring user-defined projects in the openshift-user-workload-monitoring
namespace.
3.8.2.1. Sample YAML for AWS Signature Version 4 authentication
The following shows the settings for a sigv4
secret named sigv4-credentials
in the openshift-user-workload-monitoring
namespace.
apiVersion: v1 kind: Secret metadata: name: sigv4-credentials namespace: openshift-user-workload-monitoring stringData: accessKey: <AWS_access_key> 1 secretKey: <AWS_secret_key> 2 type: Opaque
The following shows sample AWS Signature Version 4 remote write authentication settings that use a Secret
object named sigv4-credentials
in the openshift-user-workload-monitoring
namespace:
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://authorization.example.com/api/write" sigv4: region: <AWS_region> 1 accessKey: name: sigv4-credentials 2 key: accessKey 3 secretKey: name: sigv4-credentials 4 key: secretKey 5 profile: <AWS_profile_name> 6 roleArn: <AWS_role_arn> 7
- 1
- The AWS region.
- 2 4
- The name of the
Secret
object containing the AWS API access credentials. - 3
- The key that contains the AWS API access key in the specified
Secret
object. - 5
- The key that contains the AWS API secret key in the specified
Secret
object. - 6
- The name of the AWS profile that is being used to authenticate.
- 7
- The unique identifier for the Amazon Resource Name (ARN) assigned to your role.
3.8.2.2. Sample YAML for Basic authentication
The following shows sample Basic authentication settings for a Secret
object named rw-basic-auth
in the openshift-user-workload-monitoring
namespace:
apiVersion: v1 kind: Secret metadata: name: rw-basic-auth namespace: openshift-user-workload-monitoring stringData: user: <basic_username> 1 password: <basic_password> 2 type: Opaque
The following sample shows a basicAuth
remote write configuration that uses a Secret
object named rw-basic-auth
in the openshift-user-workload-monitoring
namespace. It assumes that you have already set up authentication credentials for the endpoint.
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://basicauth.example.com/api/write" basicAuth: username: name: rw-basic-auth 1 key: user 2 password: name: rw-basic-auth 3 key: password 4
3.8.2.3. Sample YAML for authentication with a bearer token using a Secret
Object
The following shows bearer token settings for a Secret
object named rw-bearer-auth
in the openshift-user-workload-monitoring
namespace:
apiVersion: v1
kind: Secret
metadata:
name: rw-bearer-auth
namespace: openshift-user-workload-monitoring
stringData:
token: <authentication_token> 1
type: Opaque
- 1
- The authentication token.
The following shows sample bearer token config map settings that use a Secret
object named rw-bearer-auth
in the openshift-user-workload-monitoring
namespace:
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | enableUserWorkload: true prometheus: remoteWrite: - url: "https://authorization.example.com/api/write" authorization: type: Bearer 1 credentials: name: rw-bearer-auth 2 key: token 3
3.8.2.4. Sample YAML for OAuth 2.0 authentication
The following shows sample OAuth 2.0 settings for a Secret
object named oauth2-credentials
in the openshift-user-workload-monitoring
namespace:
apiVersion: v1 kind: Secret metadata: name: oauth2-credentials namespace: openshift-user-workload-monitoring stringData: id: <oauth2_id> 1 secret: <oauth2_secret> 2 type: Opaque
The following shows an oauth2
remote write authentication sample configuration that uses a Secret
object named oauth2-credentials
in the openshift-user-workload-monitoring
namespace:
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://test.example.com/api/write" oauth2: clientId: secret: name: oauth2-credentials 1 key: id 2 clientSecret: name: oauth2-credentials 3 key: secret 4 tokenUrl: https://example.com/oauth2/token 5 scopes: 6 - <scope_1> - <scope_2> endpointParams: 7 param1: <parameter_1> param2: <parameter_2>
- 1 3
- The name of the corresponding
Secret
object. Note thatClientId
can alternatively refer to aConfigMap
object, althoughclientSecret
must refer to aSecret
object. - 2 4
- The key that contains the OAuth 2.0 credentials in the specified
Secret
object. - 5
- The URL used to fetch a token with the specified
clientId
andclientSecret
. - 6
- The OAuth 2.0 scopes for the authorization request. These scopes limit what data the tokens can access.
- 7
- The OAuth 2.0 authorization request parameters required for the authorization server.
3.8.2.5. Sample YAML for TLS client authentication
The following shows sample TLS client settings for a tls
Secret
object named mtls-bundle
in the openshift-user-workload-monitoring
namespace.
apiVersion: v1 kind: Secret metadata: name: mtls-bundle namespace: openshift-user-workload-monitoring data: ca.crt: <ca_cert> 1 client.crt: <client_cert> 2 client.key: <client_key> 3 type: tls
The following sample shows a tlsConfig
remote write authentication configuration that uses a TLS Secret
object named mtls-bundle
.
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://remote-write-endpoint.example.com" tlsConfig: ca: secret: name: mtls-bundle 1 key: ca.crt 2 cert: secret: name: mtls-bundle 3 key: client.crt 4 keySecret: name: mtls-bundle 5 key: client.key 6
- 1 3 5
- The name of the corresponding
Secret
object that contains the TLS authentication credentials. Note thatca
andcert
can alternatively refer to aConfigMap
object, thoughkeySecret
must refer to aSecret
object. - 2
- The key in the specified
Secret
object that contains the CA certificate for the endpoint. - 4
- The key in the specified
Secret
object that contains the client certificate for the endpoint. - 6
- The key in the specified
Secret
object that contains the client key secret.
3.8.3. Example remote write queue configuration
You can use the queueConfig
object for remote write to tune the remote write queue parameters. The following example shows the queue parameters with their default values for monitoring for user-defined projects in the openshift-user-workload-monitoring
namespace.
Example configuration of remote write parameters with default values
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://remote-write-endpoint.example.com" <endpoint_authentication_credentials> queueConfig: capacity: 10000 1 minShards: 1 2 maxShards: 50 3 maxSamplesPerSend: 2000 4 batchSendDeadline: 5s 5 minBackoff: 30ms 6 maxBackoff: 5s 7 retryOnRateLimit: false 8 sampleAgeLimit: 0s 9
- 1
- The number of samples to buffer per shard before they are dropped from the queue.
- 2
- The minimum number of shards.
- 3
- The maximum number of shards.
- 4
- The maximum number of samples per send.
- 5
- The maximum time for a sample to wait in buffer.
- 6
- The initial time to wait before retrying a failed request. The time gets doubled for every retry up to the
maxbackoff
time. - 7
- The maximum time to wait before retrying a failed request.
- 8
- Set this parameter to
true
to retry a request after receiving a 429 status code from the remote write storage. - 9
- The samples that are older than the
sampleAgeLimit
limit are dropped from the queue. If the value is undefined or set to0s
, the parameter is ignored.
Additional resources
- Setting up remote write compatible endpoints (Prometheus documentation)
- Tuning remote write settings (Prometheus documentation)
3.9. Adding cluster ID labels to metrics
If you manage multiple Red Hat OpenShift Service on AWS clusters and use the remote write feature to send metrics data from these clusters to an external storage location, you can add cluster ID labels to identify the metrics data coming from different clusters. You can then query these labels to identify the source cluster for a metric and distinguish that data from similar metrics data sent by other clusters.
This way, if you manage many clusters for multiple customers and send metrics data to a single centralized storage system, you can use cluster ID labels to query metrics for a particular cluster or customer.
Creating and using cluster ID labels involves three general steps:
- Configuring the write relabel settings for remote write storage.
- Adding cluster ID labels to the metrics.
- Querying these labels to identify the source cluster or customer for a metric.
3.9.1. Creating cluster ID labels for metrics
You can create cluster ID labels for metrics by editing the settings in the user-workload-monitoring-config
config map in the openshift-user-workload-monitoring
namespace.
Prerequisites
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap object exists. This object is created by default when the cluster is created. -
You have installed the OpenShift CLI (
oc
). - You have configured remote write storage.
Procedure
Edit the
ConfigMap
object: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
In the
writeRelabelConfigs:
section underdata/config.yaml/prometheus/remoteWrite
, add cluster ID relabel configuration values:apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://remote-write-endpoint.example.com" <endpoint_authentication_credentials> writeRelabelConfigs: 1 - <relabel_config> 2
The following sample shows how to forward a metric with the cluster ID label
cluster_id
in user-workload monitoring:apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: remoteWrite: - url: "https://remote-write-endpoint.example.com" writeRelabelConfigs: - sourceLabels: - __tmp_openshift_cluster_id__ 1 targetLabel: cluster_id 2 action: replace 3
- 1
- The system initially applies a temporary cluster ID source label named
__tmp_openshift_cluster_id__
. This temporary label gets replaced by the cluster ID label name that you specify. - 2
- Specify the name of the cluster ID label for metrics sent to remote write storage. If you use a label name that already exists for a metric, that value is overwritten with the name of this cluster ID label. For the label name, do not use
__tmp_openshift_cluster_id__
. The final relabeling step removes labels that use this name. - 3
- The
replace
write relabel action replaces the temporary label with the target label for outgoing metrics. This action is the default and is applied if no action is specified.
- Save the file to apply the changes. The new configuration is applied automatically.
Additional resources
3.10. Controlling the impact of unbound metrics attributes in user-defined projects
Developers can create labels to define attributes for metrics in the form of key-value pairs. The number of potential key-value pairs corresponds to the number of possible values for an attribute. An attribute that has an unlimited number of potential values is called an unbound attribute. For example, a customer_id
attribute is unbound because it has an infinite number of possible values.
Every assigned key-value pair has a unique time series. The use of many unbound attributes in labels can result in an exponential increase in the number of time series created. This can impact Prometheus performance and can consume a lot of disk space.
A dedicated-admin
can use the following measures to control the impact of unbound metrics attributes in user-defined projects:
- Limit the number of samples that can be accepted per target scrape in user-defined projects
- Limit the number of scraped labels, the length of label names, and the length of label values
- Create alerts that fire when a scrape sample threshold is reached or when the target cannot be scraped
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.
3.10.1. Setting scrape sample and label limits for user-defined projects
You can limit the number of samples that can be accepted per target scrape in user-defined projects. You can also limit the number of scraped labels, the length of label names, and the length of label values.
If you set sample or label limits, no further sample data is ingested for that target scrape after the limit is reached.
Prerequisites
-
You have access to the cluster as a user with the
dedicated-admin
role. -
The
user-workload-monitoring-config
ConfigMap
object exists. This object is created by default when the cluster is created. -
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.
Add the
enforcedLabelLimit
,enforcedLabelNameLengthLimit
, andenforcedLabelValueLengthLimit
configurations todata/config.yaml
to limit the number of scraped labels, the length of label names, and the length of label values in user-defined projects:apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: enforcedLabelLimit: 500 1 enforcedLabelNameLengthLimit: 50 2 enforcedLabelValueLengthLimit: 600 3
- 1
- Specifies the maximum number of labels per scrape. The default value is
0
, which specifies no limit. - 2
- Specifies the maximum length in characters of a label name. The default value is
0
, which specifies no limit. - 3
- Specifies the maximum length in characters of a label value. The default value is
0
, which specifies no limit.
- Save the file to apply the changes. The limits are applied automatically.