Chapter 3. Configuring the monitoring stack
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. Important 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. Important Installing another Prometheus instance is not supported by the Red Hat Site Reliability Engineers (SRE). 3.1.1. Support considerations for monitoring Note 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: Table 3.1. Red Hat OpenShift Service on AWS and component versions Red Hat OpenShift Service on AWS Prometheus Operator Prometheus Metrics Server Alertmanager kube-state-metrics agent monitoring-plugin node-exporter agent Thanos 4.18 0.78.1 2.55.1 0.7.2 0.27.0 2.13.0 1.0.0 1.8.2 0.36.1 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 Note 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 the openshift-user-workload-monitoring project: USD 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. Warning Different 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 config map. Warning 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 3.2. Configurable monitoring components for user-defined projects Component user-workload-monitoring-config config map key Prometheus Operator prometheusOperator Prometheus prometheus Alertmanager alertmanager Thanos Ruler thanosRuler 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 separate workloads based on specific requirements or policies. 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, 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 Using pod topology spread constraints for monitoring Kubernetes documentation about node selectors 3.4.1. Moving monitoring components to different nodes You can move any of the components that monitor workloads for user-defined projects to specific worker nodes. Warning 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: USD oc label nodes <node_name> <node_label> 1 1 Replace <node_name> with the name of the node where you want to add the label. Replace <node_label> with the name of the wanted label. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project: USD 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. Note If monitoring components remain in a Pending state after configuring the nodeSelector constraint, check the pod events for errors relating to taints and tolerations. 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 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD 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 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 requests for the following core platform monitoring components: Alertmanager kube-state-metrics monitoring-plugin node-exporter openshift-state-metrics Prometheus Metrics Server Prometheus Operator and its admission webhook service Telemeter Client Thanos Querier You can configure resource limits and requests for the following components that monitor user-defined projects: Alertmanager Prometheus Thanos Ruler By defining the 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 the 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 To configure CPU and memory resources, specify values for resource limits and requests in the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring namespace. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. You have installed the OpenShift CLI ( oc ). Procedure Edit the user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config Add values to define resource limits and requests for each component you want to configure. Important Ensure 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 of setting resource limits and requests apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | alertmanager: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi prometheus: resources: limits: cpu: 500m memory: 3Gi requests: cpu: 200m memory: 500Mi thanosRuler: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed. 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. Important 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD 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 1 Specify the monitoring component for which you want to configure the PVC. 2 Specify an existing storage class. If a storage class is not specified, the default storage class is used. 3 Specify the amount of required storage. The following example configures a PVC that claims persistent storage for Thanos Ruler: Example PVC configuration 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 Note 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 automatically redeployed and the new storage configuration is applied. Warning When you update the config map with a PVC configuration, the affected StatefulSet object is recreated, resulting in a temporary service outage. Additional resources PersistentVolumeClaims (Kubernetes documentation about how to specify volumeClaimTemplate ) 3.7.3. Retention time and size for Prometheus metrics 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 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. Note 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. 3.7.4. Modifying retention time and size for Prometheus metrics data By default, Prometheus retains metrics data for 24 hours for monitoring for user-defined projects. You can modify the retention time for the Prometheus instance to change when the data is deleted. You can also set the maximum amount of disk space the retained metrics data uses. Note 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD 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), and EB (exabytes). The following example sets the retention time to 24 hours and the retention size to 10 gigabytes for the Prometheus instance: Example of setting retention time for Prometheus 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. 3.7.5. Modifying the retention time for Thanos Ruler metrics data By default, for user-defined projects, Thanos Ruler automatically retains metrics data for 24 hours. You can modify the retention time to change how long this data is retained by specifying a time value in the user-workload-monitoring-config config map in the openshift-user-workload-monitoring namespace. Prerequisites You have 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: USD 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 are automatically redeployed. Additional resources Understanding persistent storage 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. Important 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. Warning To reduce security risks, use HTTPS and authentication to send metrics to an endpoint. Procedure Edit the user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config Add a remoteWrite: section under data/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 in an Authorization request header, Basic authentication, OAuth 2.0, and TLS client. See Supported remote write authentication settings 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 and my_metric_2 in my_namespace namespace 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__,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 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. 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 for 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 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 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 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 3 key: password 4 1 3 The name of the Secret object that contains the authentication credentials. 2 The key that contains the username in the specified Secret object. 4 The key that contains the password in the specified Secret object. 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 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. 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 1 The Oauth 2.0 ID. 2 The OAuth 2.0 secret. 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 that ClientId can alternatively refer to a ConfigMap object, although clientSecret must refer to a Secret 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 and clientSecret . 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 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 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 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. 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 to 0s , 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 adding the write_relabel settings for remote write storage 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD 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 : 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 Configuring remote write storage 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 Configure the intervals between consecutive scrapes and between Prometheus rule evaluations Create alerts that fire when a scrape sample threshold is reached or when the target cannot be scraped Note Limiting scrape samples can help prevent the issues caused by adding many unbound attributes to labels. Developers can also prevent the underlying cause by limiting the number of unbound attributes that they define for metrics. Using attributes that are bound to a limited set of possible values reduces the number of potential key-value pair combinations. 3.10.1. Setting scrape intervals, evaluation intervals, and enforced limits for user-defined projects You can set the following scrape and label limits for user-defined projects: Limit the number of samples that can be accepted per target scrape Limit the number of scraped labels Limit the length of label names and label values You can also set an interval between consecutive scrapes and between Prometheus rule evaluations. Warning 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: USD oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config Add the enforced limit and time interval configurations to data/config.yaml : apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: enforcedSampleLimit: 50000 1 enforcedLabelLimit: 500 2 enforcedLabelNameLengthLimit: 50 3 enforcedLabelValueLengthLimit: 600 4 scrapeInterval: 1m30s 5 evaluationInterval: 1m15s 6 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. 2 Specifies the maximum number of labels per scrape. The default value is 0 , which specifies no limit. 3 Specifies the maximum character length for a label name. The default value is 0 , which specifies no limit. 4 Specifies the maximum character length for a label value. The default value is 0 , which specifies no limit. 5 Specifies the interval between consecutive scrapes. The interval must be set between 5 seconds and 5 minutes. The default value is 30s . 6 Specifies the interval between Prometheus rule evaluations. The interval must be set between 5 seconds and 5 minutes. The default value for Prometheus is 30s . Note You can also configure the evaluationInterval property for Thanos Ruler through the data/config.yaml/thanosRuler field. The default value for Thanos Ruler is 15s . Save the file to apply the changes. The limits are applied automatically. 3.11. 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config Add an additionalAlertmanagerConfigs section with configuration details under data/config.yaml/<component> : apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | <component>: 1 additionalAlertmanagerConfigs: - <alertmanager_specification> 2 2 Substitute <alertmanager_specification> with authentication and other configuration details for additional Alertmanager instances. Currently supported authentication methods are bearer token ( bearerToken ) and client TLS ( tlsConfig ). 1 Substitute <component> for one of two supported external Alertmanager components: prometheus or thanosRuler . The following sample config map configures an additional Alertmanager for Thanos Ruler by using a bearer token with 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. The pods affected by the new configuration are automatically redeployed. 3.12. 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. 3.12.1. Adding a secret to the Alertmanager configuration You can add secrets to the Alertmanager configuration 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config Add a secrets: section under data/config.yaml/alertmanager 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-basic-auth and test-secret-api-token : apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | alertmanager: secrets: - test-secret-basic-auth - test-secret-api-token Save the file to apply the changes. The new configuration is applied automatically. 3.13. 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config Define 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 key-value pairs where <key> is a unique name for the new label and <value> is its value. Warning 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. Note 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, 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 pods affected by the new configuration are automatically redeployed. 3.14. About pod topology spread constraints for monitoring You can use pod topology spread constraints to control how the monitoring pods 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. You can configure pod topology spread constraints for all the pods deployed by the Cluster Monitoring Operator 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. Additional resources Kubernetes Pod Topology Spread Constraints documentation 3.14.1. 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: USD 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. The pods affected by the new configuration are automatically redeployed. 3.15. 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 object: 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 user-workload-monitoring-config config map in the openshift-user-workload-monitoring project: USD 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. Available component values are prometheus , alertmanager , prometheusOperator , and thanosRuler . 2 The log level to set for 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 affected by the new configuration are automatically redeployed. 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 for the prometheus-operator deployment: USD 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: USD oc -n openshift-user-workload-monitoring get pods Note If an unrecognized logLevel value is included in the ConfigMap object, the pods for the component might not restart successfully. 3.16. 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. Important 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 config map in the openshift-user-workload-monitoring project: USD oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config Add the queryLogFile parameter 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 Add the full path to the file in which queries will be logged. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed. Verify that the pods for the component are running. The following sample command lists the status of pods: USD oc -n openshift-user-workload-monitoring get pods Example output ... prometheus-operator-776fcbbd56-2nbfm 2/2 Running 0 132m prometheus-user-workload-0 5/5 Running 1 132m prometheus-user-workload-1 5/5 Running 1 132m thanos-ruler-user-workload-0 3/3 Running 0 132m thanos-ruler-user-workload-1 3/3 Running 0 132m ... Read the query log: USD oc -n openshift-user-workload-monitoring exec prometheus-user-workload-0 -- cat <path> Important Revert the setting in the config map after you have examined the logged query information.
oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | <component>: <configuration_for_the_component> 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 oc label nodes <node_name> <node_label> 1 oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config 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 # oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | <component>: tolerations: <toleration_specification> 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" oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | alertmanager: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi prometheus: resources: limits: cpu: 500m memory: 3Gi requests: cpu: 200m memory: 500Mi thanosRuler: resources: limits: cpu: 500m memory: 1Gi requests: cpu: 200m memory: 500Mi oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config 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 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 oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config 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 apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: retention: 24h retentionSize: 10GB oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | thanosRuler: retention: <time_specification> 1 apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | thanosRuler: retention: 10d oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config 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 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 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 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__,namespace] regex: '(my_metric_1|my_metric_2);my_namespace' action: keep 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 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 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 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 apiVersion: v1 kind: Secret metadata: name: rw-bearer-auth namespace: openshift-user-workload-monitoring stringData: token: <authentication_token> 1 type: Opaque 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 apiVersion: v1 kind: Secret metadata: name: oauth2-credentials namespace: openshift-user-workload-monitoring stringData: id: <oauth2_id> 1 secret: <oauth2_secret> 2 type: Opaque 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> 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 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 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 oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config 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 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 oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: enforcedSampleLimit: 50000 1 enforcedLabelLimit: 500 2 enforcedLabelNameLengthLimit: 50 3 enforcedLabelValueLengthLimit: 600 4 scrapeInterval: 1m30s 5 evaluationInterval: 1m15s 6 oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | <component>: 1 additionalAlertmanagerConfigs: - <alertmanager_specification> 2 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 oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config 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> apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | alertmanager: secrets: - test-secret-basic-auth - test-secret-api-token oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: externalLabels: <key>: <value> 1 apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: externalLabels: region: eu environment: prod oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config 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> 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 oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | <component>: 1 logLevel: <log_level> 2 oc -n openshift-user-workload-monitoring get deploy prometheus-operator -o yaml | grep "log-level" - --log-level=debug oc -n openshift-user-workload-monitoring get pods oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | prometheus: queryLogFile: <path> 1 oc -n openshift-user-workload-monitoring get pods prometheus-operator-776fcbbd56-2nbfm 2/2 Running 0 132m prometheus-user-workload-0 5/5 Running 1 132m prometheus-user-workload-1 5/5 Running 1 132m thanos-ruler-user-workload-0 3/3 Running 0 132m thanos-ruler-user-workload-1 3/3 Running 0 132m oc -n openshift-user-workload-monitoring exec prometheus-user-workload-0 -- cat <path>