Configuring the monitoring stack

Maintenance and support for monitoring
- Support considerations for monitoring
Configuring the monitoring stack
Configurable monitoring components
Using node selectors to move monitoring components
- How node selectors work with other constraints
- Moving monitoring components to different nodes
Assigning tolerations to monitoring components
Managing CPU and memory resources for monitoring components
- About specifying limits and requests for monitoring components
- Specifying limits and requests for monitoring components
Configuring persistent storage
Configuring remote write storage
- Supported remote write authentication settings
- Example remote write authentication settings
Adding cluster ID labels to metrics
- Creating cluster ID labels for metrics
Controlling the impact of unbound metrics attributes in user-defined projects
- Setting scrape sample and label limits for user-defined projects
Configuring external Alertmanager instances
Configuring secrets for Alertmanager
- Adding a secret to the Alertmanager configuration
Attaching additional labels to your time series and alerts
Using pod topology spread constraints for monitoring
- Configuring pod topology spread constraints
Setting log levels for monitoring components
Enabling the query log file for Prometheus

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.

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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 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 Cluster Monitoring Operator 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).

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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.

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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 the openshift-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. The pods affected by the new configuration are restarted automatically.

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

Additional resources

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

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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.

Table 1. Configurable monitoring components
Component	user-workload-monitoring-config config map key
Alertmanager	`alertmanager`
Prometheus Operator	`prometheusOperator`
Prometheus	`prometheus`
Thanos Ruler	`thanosRuler`

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.

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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

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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 the openshift-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 under data/config.yaml:

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

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

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

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

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

Additional resources

See the Kubernetes documentation for details on the nodeSelector constraint

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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 the openshift-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 the openshift-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 to node1 with the key key1 and the value value1. This prevents monitoring components from deploying pods on node1 unless a toleration is configured for that taint. The following example configures the thanosRuler component to tolerate the example taint:

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

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

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

Additional resources

See the Kubernetes documentation on taints and tolerations

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.

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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)
Prometheus Adapter
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.

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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 the openshift-monitoring namespace for core platform monitoring
The user-workload-monitoring-config config map in the openshift-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 named cluster-monitoring-config.
If you are configuring components that monitor user-defined projects:
- You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
You have installed the OpenShift CLI (oc).

Procedure

To configure core platform monitoring components, edit the cluster-monitoring-config config map object in the openshift-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.

Make 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
    k8sPrometheusAdapter:
      resources:
        limits:
          cpu: 500m
          memory: 1Gi
        requests:
          cpu: 200m
          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 automatically.

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

Additional resources

Kubernetes requests and limits documentation

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. Because of the high IO demands, it is advantageous to use local storage.

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Persistent storage prerequisites

Use the block type of storage.

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Configuring a persistent volume claim

For monitoring components to use a persistent volume (PV), 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 the openshift-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 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: gp3
          resources:
            requests:
              storage: 40Gi

The above example uses the gp3 storage class.

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: gp3
          resources:
            requests:
              storage: 10Gi

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

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

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

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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 or retentionSize, 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 and retentionSize, both values apply. If any data blocks exceed the defined retention time or the defined size limit, Prometheus purges these data blocks.
If you define a value for retentionSize and do not define retention, only the retentionSize value applies.
If you do not define a value for retentionSize and only define a value for retention, only the retention value applies.
If you set the retentionSize or retention value to 0, 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 the openshift-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), or `y` (years). You can also combine time values for specific times, such as `1h30m15s`.
2	The retention size: a number directly followed by `B` (bytes), `KB` (kilobytes), `MB` (megabytes), `GB` (gigabytes), `TB` (terabytes), `PB` (petabytes), or `EB` (exabytes).

The following example sets the retention time to 24 hours and the retention size to 10 gigabytes for the Prometheus instance that monitors user-defined projects:

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

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

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

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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 the openshift-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), or `y` (years). You can also combine time values for specific times, such as `1h30m15s`. The default is `24h`.

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

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

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

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

Additional resources

Understanding persistent storage

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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.

Red 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 the openshift-user-workload-monitoring namespace.

To 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 the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```
Add a remoteWrite: section under data/config.yaml/prometheus.

Add an endpoint URL and authentication credentials in this section:

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

1	The URL of the remote write endpoint.
2	The authentication method and credentials for the endpoint. Currently supported authentication methods are AWS Signature Version 4, authentication using HTTP an `Authorization` request header, basic authentication, OAuth 2.0, and TLS client. See Supported remote write authentication settings below for sample configurations of supported authentication methods.

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>
        <your_write_relabel_configs> (1)

1	The write relabel configuration settings.

For <your_write_relabel_configs> substitute a list of write relabel configurations for metrics that you want to send to the remote endpoint.

The following sample shows how to forward a single metric called my_metric:

apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    prometheus:
      remoteWrite:
      - url: "https://remote-write-endpoint.example.com"
        writeRelabelConfigs:
        - sourceLabels: [__name__]
          regex: 'my_metric'
          action: keep

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

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

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

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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

Authentication method	Config map field	Description
AWS Signature Version 4	`sigv4`	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	`basicAuth`	Basic authentication sets the authorization header on every remote write request with the configured username and password.
authorization	`authorization`	Authorization sets the `Authorization` header on every remote write request using the configured token.
OAuth 2.0	`oauth2`	An OAuth 2.0 configuration uses the client credentials grant type. Prometheus fetches an access token from `tokenUrl` with the specified client ID and client secret to access the remote write endpoint. You cannot use this method simultaneously with authorization, AWS Signature Version 4, or Basic authentication.
TLS client	`tlsConfig`	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.

AWS Signature Version 4

sigv4

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

basicAuth

Basic authentication sets the authorization header on every remote write request with the configured username and password.

authorization

authorization

Authorization sets the Authorization header on every remote write request using the configured token.

OAuth 2.0

oauth2

An OAuth 2.0 configuration uses the client credentials grant type. Prometheus fetches an access token from tokenUrl with the specified client ID and client secret to access the remote write endpoint. You cannot use this method simultaneously with authorization, AWS Signature Version 4, or Basic authentication.

TLS client

tlsConfig

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.

edit

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.

Example 1. Sample YAML for AWS Signature Version 4 authentication

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

1	The AWS API access key.
2	The AWS API secret key.

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 (2)
            key: secretKey (4)
          profile: <AWS_profile_name> (5)
          roleArn: <AWS_role_arn> (6)

1	The AWS region.
2	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.
4	The key that contains the AWS API secret key in the specified `Secret` object.
5	The name of the AWS profile that is being used to authenticate.
6	The unique identifier for the Amazon Resource Name (ARN) assigned to your role.

Example 2. Sample YAML for basic authentication

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

1	The username.
2	The password.

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 (1)
            key: password (3)

1	The name of the `Secret` object that contains the authentication credentials.
2	The key that contains the username in the specified `Secret` object.
3	The key that contains the password in the specified `Secret` object.

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

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

1	The authentication type of the request. The default value is `Bearer`.
2	The name of the `Secret` object that contains the authentication credentials.
3	The key that contains the authentication token in the specified `Secret` object.

Example 4. Sample YAML for OAuth 2.0 authentication

The following shows sample OAuth 2.0 settings for a Secret object named oauth2-credentials in the openshift-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)
  token: <oauth2_authentication_token> (3)
type: Opaque

1	The Oauth 2.0 ID.
2	The OAuth 2.0 secret.
3	The OAuth 2.0 token.

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 (1)
            key: secret (2)
          tokenUrl: https://example.com/oauth2/token (3)
          scopes: (4)
          - <scope_1>
          - <scope_2>
          endpointParams: (5)
            param1: <parameter_1>
            param2: <parameter_2>

1	The name of the corresponding `Secret` object. Note that `ClientId` can alternatively refer to a `ConfigMap` object, although `clientSecret` must refer to a `Secret` object.
2	The key that contains the OAuth 2.0 credentials in the specified `Secret` object.
3	The URL used to fetch a token with the specified `clientId` and `clientSecret`.
4	The OAuth 2.0 scopes for the authorization request. These scopes limit what data the tokens can access.
5	The OAuth 2.0 authorization request parameters required for the authorization server.

Example 5. Sample YAML for TLS client authentication

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

1	The CA certificate in the Prometheus container with which to validate the server certificate.
2	The client certificate for authentication with the server.
3	The client key.

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 (1)
              key: client.crt (3)
          keySecret:
            name: mtls-bundle (1)
            key: client.key (4)

1	The name of the corresponding `Secret` object that contains the TLS authentication credentials. Note that `ca` and `cert` can alternatively refer to a `ConfigMap` object, though `keySecret` must refer to a `Secret` object.
2	The key in the specified `Secret` object that contains the CA certificate for the endpoint.
3	The key in the specified `Secret` object that contains the client certificate for the endpoint.
4	The key in the specified `Secret` object that contains the client key secret.

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.

edit

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.

edit

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 the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```

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

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

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

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

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

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

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

Additional resources

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

edit

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.

edit

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 the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```
Add the enforcedSampleLimit configuration to data/config.yaml to limit the number of samples that can be accepted per target scrape in user-defined projects:
```
apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    prometheus:
      enforcedSampleLimit: 50000 (1)
```
1 A value is required if this parameter is specified. This enforcedSampleLimit example limits the number of samples that can be accepted per target scrape in user-defined projects to 50,000.

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

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

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

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

When changes are saved to the user-workload-monitoring-config ConfigMap object, the pods and other resources in the openshift-user-workload-monitoring project might be redeployed. The running monitoring processes in that project might also be restarted.

edit

Configuring external Alertmanager instances

The Red Hat OpenShift Service on AWS monitoring stack includes a local Alertmanager instance that routes alerts from Prometheus. You can add external Alertmanager instances to route alerts for user-defined projects.

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

Prerequisites

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 config map in the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```
Add a <component>/additionalAlertmanagerConfigs: section under data/config.yaml/.

Add the configuration details for additional Alertmanagers in this section:

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

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

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

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

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

edit

Configuring secrets for Alertmanager

The Red Hat OpenShift Service on AWS monitoring stack includes Alertmanager, which routes alerts from Prometheus to endpoint receivers. If you need to authenticate with a receiver so that Alertmanager can send alerts to it, you can configure Alertmanager to use a secret that contains authentication credentials for the receiver.

For example, you can configure Alertmanager to use a secret to authenticate with an endpoint receiver that requires a certificate issued by a private Certificate Authority (CA). You can also configure Alertmanager to use a secret to authenticate with a receiver that requires a password file for Basic HTTP authentication. In either case, authentication details are contained in the Secret object rather than in the ConfigMap object.

edit

Adding a secret to the Alertmanager configuration

You can add secrets to the Alertmanager configuration for user-defined projects by editing the user-workload-monitoring-config config map in the openshift-user-workload-monitoring project.

After you add a secret to the config map, the secret is mounted as a volume at /etc/alertmanager/secrets/<secret_name> within the alertmanager container for the Alertmanager pods.

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 created the secret to be configured in Alertmanager in the openshift-user-workload-monitoring project.
You have installed the OpenShift CLI (oc).

Procedure

Edit the ConfigMap object.

Edit the user-workload-monitoring-config config map in the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```

Add a secrets: section under data/config.yaml/alertmanager/secrets with the following configuration:

apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    alertmanager:
      secrets: (1)
      - <secret_name_1> (2)
      - <secret_name_2>

1	This section contains the secrets to be mounted into Alertmanager. The secrets must be located within the same namespace as the Alertmanager object.
2	The name of the `Secret` object that contains authentication credentials for the receiver. If you add multiple secrets, place each one on a new line.

The following sample config map settings configure Alertmanager to use two Secret objects named test-secret and test-secret-api-token:

apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    alertmanager:
      enabled: true
      secrets:
      - test-secret
      - test-api-receiver-token

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

edit

Attaching additional labels to your time series and alerts

You can attach custom labels to all time series and alerts leaving Prometheus by using the external labels feature of Prometheus.

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 the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```

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

apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    prometheus:
      externalLabels:
        <key>: <value> (1)

1	Substitute `<key>: <value>` with a map of key-value pairs where `<key>` is a unique name for the new label and `<value>` is its value.

Do not use prometheus or prometheus_replica as key names, because they are reserved and will be overwritten.
Do not use cluster or managed_cluster as key names. Using them can cause issues where you are unable to see data in the developer dashboards.

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

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

apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    prometheus:
      externalLabels:
        region: eu
        environment: prod

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

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

edit

Using pod topology spread constraints for monitoring

You can use pod topology spread constraints to control how the pods for user-defined monitoring are spread across a network topology when Red Hat OpenShift Service on AWS pods are deployed in multiple availability zones.

Pod topology spread constraints are suitable for controlling pod scheduling within hierarchical topologies in which nodes are spread across different infrastructure levels, such as regions and zones within those regions. Additionally, by being able to schedule pods in different zones, you can improve network latency in certain scenarios.

Additional resources

Kubernetes Pod Topology Spread Constraints documentation

edit

Configuring pod topology spread constraints

You can configure pod topology spread constraints for all the pods for user-defined monitoring to control how pod replicas are scheduled to nodes across zones. This ensures that the pods are highly available and run more efficiently, because workloads are spread across nodes in different data centers or hierarchical infrastructure zones.

You can configure pod topology spread constraints for monitoring pods by using the user-workload-monitoring-config config map.

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 config map in the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```

Add the following settings under the data/config.yaml field to configure pod topology spread constraints:

apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    <component>: (1)
      topologySpreadConstraints:
      - maxSkew: <n> (2)
        topologyKey: <key> (3)
        whenUnsatisfiable: <value> (4)
        labelSelector: (5)
          <match_option>

1	Specify a name of the component for which you want to set up pod topology spread constraints.
2	Specify a numeric value for `maxSkew`, which defines the degree to which pods are allowed to be unevenly distributed.
3	Specify a key of node labels for `topologyKey`. Nodes that have a label with this key and identical values are considered to be in the same topology. The scheduler tries to put a balanced number of pods into each domain.
4	Specify a value for `whenUnsatisfiable`. Available options are `DoNotSchedule` and `ScheduleAnyway`. Specify `DoNotSchedule` if you want the `maxSkew` value to define the maximum difference allowed between the number of matching pods in the target topology and the global minimum. Specify `ScheduleAnyway` if you want the scheduler to still schedule the pod but to give higher priority to nodes that might reduce the skew.
5	Specify `labelSelector` to find matching pods. Pods that match this label selector are counted to determine the number of pods in their corresponding topology domain.

Example configuration for Thanos Ruler

apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    thanosRuler:
      topologySpreadConstraints:
      - maxSkew: 1
        topologyKey: monitoring
        whenUnsatisfiable: ScheduleAnyway
        labelSelector:
          matchLabels:
            app.kubernetes.io/name: thanos-ruler

Save the file to apply the changes automatically.

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

edit

Setting log levels for monitoring components

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

The following log levels can be applied to the relevant component in the user-workload-monitoring-config ConfigMap objects:

debug. Log debug, informational, warning, and error messages.
info. Log informational, warning, and error messages.
warn. Log warning and error messages only.
error. Log error messages only.

The default log level is info.

Prerequisites

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 the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```

Add logLevel: <log_level> for a 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)
      logLevel: <log_level> (2)

1	The monitoring stack component for which you are setting a log level. For user workload monitoring, available component values are `alertmanager`, `prometheus`, `prometheusOperator`, and `thanosRuler`.
2	The log level to apply to the component. The available values are `error`, `warn`, `info`, and `debug`. The default value is `info`.

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

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

Confirm that the log-level has been applied by reviewing the deployment or pod configuration in the related project. The following example checks the log level in the prometheus-operator deployment in the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring get deploy prometheus-operator -o yaml | grep "log-level"
```
Example output
```
        - --log-level=debug
```
Check that the pods for the component are running. The following example lists the status of pods in the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring get pods
```
If an unrecognized logLevel value is included in the ConfigMap object, the pods for the component might not restart successfully.

edit

Enabling the query log file for Prometheus

You can configure Prometheus to write all queries that have been run by the engine to a log file.

Because log rotation is not supported, only enable this feature temporarily when you need to troubleshoot an issue. After you finish troubleshooting, disable query logging by reverting the changes you made to the ConfigMap object to enable the feature.

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 the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
```

Add queryLogFile: <path> for prometheus under data/config.yaml:

apiVersion: v1
kind: ConfigMap
metadata:
  name: user-workload-monitoring-config
  namespace: openshift-user-workload-monitoring
data:
  config.yaml: |
    prometheus:
      queryLogFile: <path> (1)

1	The full path to the file in which queries will be logged.

Save the file to apply the changes.

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

Verify that the pods for the component are running. The following example command lists the status of pods in the openshift-user-workload-monitoring project:
```
$ oc -n openshift-user-workload-monitoring get pods
```
Read the query log:
```
$ oc -n openshift-user-workload-monitoring exec prometheus-user-workload-0 -- cat <path>
```
Revert the setting in the config map after you have examined the logged query information.