Chapter 4. Configuring user workload monitoring 4.1. Preparing to configure the user workload monitoring stack This section explains which user-defined monitoring components can be configured, how to enable user workload monitoring, and how to prepare for configuring the user workload monitoring stack. 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. The monitoring stack imposes additional resource requirements. Consult the computing resources recommendations in Scaling the Cluster Monitoring Operator and verify that you have sufficient resources. 4.1.1. 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. Table 4.1. 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 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. 4.1.2. Enabling monitoring for user-defined projects In OpenShift Container Platform, you can enable monitoring for user-defined projects in addition to the default platform monitoring. You can monitor your own projects in OpenShift Container Platform without the need for an additional monitoring solution. Using this feature centralizes monitoring for core platform components and user-defined projects. Note Versions of Prometheus Operator installed using Operator Lifecycle Manager (OLM) are not compatible with user-defined monitoring. Therefore, custom Prometheus instances installed as a Prometheus custom resource (CR) managed by the OLM Prometheus Operator are not supported in OpenShift Container Platform. 4.1.2.1. Enabling monitoring for user-defined projects Cluster administrators can enable monitoring for user-defined projects by setting the enableUserWorkload: true field in the cluster monitoring ConfigMap object. Important You must remove any custom Prometheus instances before enabling monitoring for user-defined projects. Note You must have access to the cluster as a user with the cluster-admin cluster role to enable monitoring for user-defined projects in OpenShift Container Platform. Cluster administrators can then optionally grant users permission to configure the components that are responsible for monitoring user-defined projects. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role. You have installed the OpenShift CLI ( oc ). You have created the cluster-monitoring-config ConfigMap object. You have optionally created and configured the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project. You can add configuration options to this ConfigMap object for the components that monitor user-defined projects. Note Every time you save configuration changes to the user-workload-monitoring-config ConfigMap object, the pods in the openshift-user-workload-monitoring project are redeployed. It might sometimes take a while for these components to redeploy. Procedure Edit the cluster-monitoring-config ConfigMap object: USD oc -n openshift-monitoring edit configmap cluster-monitoring-config Add enableUserWorkload: true under data/config.yaml : apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | enableUserWorkload: true 1 1 When set to true , the enableUserWorkload parameter enables monitoring for user-defined projects in a cluster. Save the file to apply the changes. Monitoring for user-defined projects is then enabled automatically. Note If you enable monitoring for user-defined projects, the user-workload-monitoring-config ConfigMap object is created by default. Verify that the prometheus-operator , prometheus-user-workload , and thanos-ruler-user-workload pods are running in the openshift-user-workload-monitoring project. It might take a short while for the pods to start: USD oc -n openshift-user-workload-monitoring get pod Example output NAME READY STATUS RESTARTS AGE prometheus-operator-6f7b748d5b-t7nbg 2/2 Running 0 3h prometheus-user-workload-0 4/4 Running 1 3h prometheus-user-workload-1 4/4 Running 1 3h thanos-ruler-user-workload-0 3/3 Running 0 3h thanos-ruler-user-workload-1 3/3 Running 0 3h Additional resources User workload monitoring first steps 4.1.2.2. Granting users permission to configure monitoring for user-defined projects As a cluster administrator, you can assign the user-workload-monitoring-config-edit role to a user. This grants permission to configure and manage monitoring for user-defined projects without giving them permission to configure and manage core OpenShift Container Platform monitoring components. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role. The user account that you are assigning the role to already exists. You have installed the OpenShift CLI ( oc ). Procedure Assign the user-workload-monitoring-config-edit role to a user in the openshift-user-workload-monitoring project: USD oc -n openshift-user-workload-monitoring adm policy add-role-to-user \ user-workload-monitoring-config-edit <user> \ --role-namespace openshift-user-workload-monitoring Verify that the user is correctly assigned to the user-workload-monitoring-config-edit role by displaying the related role binding: USD oc describe rolebinding <role_binding_name> -n openshift-user-workload-monitoring Example command USD oc describe rolebinding user-workload-monitoring-config-edit -n openshift-user-workload-monitoring Example output Name: user-workload-monitoring-config-edit Labels: <none> Annotations: <none> Role: Kind: Role Name: user-workload-monitoring-config-edit Subjects: Kind Name Namespace ---- ---- --------- User user1 1 1 In this example, user1 is assigned to the user-workload-monitoring-config-edit role. 4.1.3. Enabling alert routing for user-defined projects In OpenShift Container Platform, an administrator can enable alert routing for user-defined projects. This process consists of the following steps: Enable alert routing for user-defined projects: Use the default platform Alertmanager instance. Use a separate Alertmanager instance only for user-defined projects. Grant users permission to configure alert routing for user-defined projects. After you complete these steps, developers and other users can configure custom alerts and alert routing for their user-defined projects. Additional resources Understanding alert routing for user-defined projects 4.1.3.1. Enabling the platform Alertmanager instance for user-defined alert routing You can allow users to create user-defined alert routing configurations that use the main platform instance of Alertmanager. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role. A cluster administrator has enabled monitoring for user-defined projects. You have installed the OpenShift CLI ( oc ). Procedure Edit the cluster-monitoring-config ConfigMap object: USD oc -n openshift-monitoring edit configmap cluster-monitoring-config Add enableUserAlertmanagerConfig: true in the alertmanagerMain section under data/config.yaml : apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | # ... alertmanagerMain: enableUserAlertmanagerConfig: true 1 # ... 1 Set the enableUserAlertmanagerConfig value to true to allow users to create user-defined alert routing configurations that use the main platform instance of Alertmanager. Save the file to apply the changes. The new configuration is applied automatically. 4.1.3.2. Enabling a separate Alertmanager instance for user-defined alert routing In some clusters, you might want to deploy a dedicated Alertmanager instance for user-defined projects, which can help reduce the load on the default platform Alertmanager instance and can better separate user-defined alerts from default platform alerts. In these cases, you can optionally enable a separate instance of Alertmanager to send alerts for user-defined projects only. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role. You have enabled monitoring for user-defined projects. You have installed the OpenShift CLI ( oc ). Procedure Edit the user-workload-monitoring-config ConfigMap object: USD oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config Add enabled: true and enableAlertmanagerConfig: true in the alertmanager section under data/config.yaml : apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | alertmanager: enabled: true 1 enableAlertmanagerConfig: true 2 1 Set the enabled value to true to enable a dedicated instance of the Alertmanager for user-defined projects in a cluster. Set the value to false or omit the key entirely to disable the Alertmanager for user-defined projects. If you set this value to false or if the key is omitted, user-defined alerts are routed to the default platform Alertmanager instance. 2 Set the enableAlertmanagerConfig value to true to enable users to define their own alert routing configurations with AlertmanagerConfig objects. Save the file to apply the changes. The dedicated instance of Alertmanager for user-defined projects starts automatically. Verification Verify that the user-workload Alertmanager instance has started: # oc -n openshift-user-workload-monitoring get alertmanager Example output NAME VERSION REPLICAS AGE user-workload 0.24.0 2 100s 4.1.3.3. Granting users permission to configure alert routing for user-defined projects You can grant users permission to configure alert routing for user-defined projects. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role. You have enabled monitoring for user-defined projects. The user account that you are assigning the role to already exists. You have installed the OpenShift CLI ( oc ). Procedure Assign the alert-routing-edit cluster role to a user in the user-defined project: USD oc -n <namespace> adm policy add-role-to-user alert-routing-edit <user> 1 1 For <namespace> , substitute the namespace for the user-defined project, such as ns1 . For <user> , substitute the username for the account to which you want to assign the role. Additional resources Configuring alert notifications 4.1.4. Granting users permissions for monitoring for user-defined projects As a cluster administrator, you can monitor all core OpenShift Container Platform and user-defined projects. You can also grant developers and other users different permissions: Monitoring user-defined projects Configuring the components that monitor user-defined projects Configuring alert routing for user-defined projects Managing alerts and silences for user-defined projects You can grant the permissions by assigning one of the following monitoring roles or cluster roles: Table 4.2. Monitoring roles Role name Description Project user-workload-monitoring-config-edit Users with this role can edit the user-workload-monitoring-config ConfigMap object to configure Prometheus, Prometheus Operator, Alertmanager, and Thanos Ruler for user-defined workload monitoring. openshift-user-workload-monitoring monitoring-alertmanager-api-reader Users with this role have read access to the user-defined Alertmanager API for all projects, if the user-defined Alertmanager is enabled. openshift-user-workload-monitoring monitoring-alertmanager-api-writer Users with this role have read and write access to the user-defined Alertmanager API for all projects, if the user-defined Alertmanager is enabled. openshift-user-workload-monitoring Table 4.3. Monitoring cluster roles Cluster role name Description Project monitoring-rules-view Users with this cluster role have read access to PrometheusRule custom resources (CRs) for user-defined projects. They can also view the alerts and silences in the Developer perspective of the OpenShift Container Platform web console. Can be bound with RoleBinding to any user project. monitoring-rules-edit Users with this cluster role can create, modify, and delete PrometheusRule CRs for user-defined projects. They can also manage alerts and silences in the Developer perspective of the OpenShift Container Platform web console. Can be bound with RoleBinding to any user project. monitoring-edit Users with this cluster role have the same privileges as users with the monitoring-rules-edit cluster role. Additionally, users can create, read, modify, and delete ServiceMonitor and PodMonitor resources to scrape metrics from services and pods. Can be bound with RoleBinding to any user project. alert-routing-edit Users with this cluster role can create, update, and delete AlertmanagerConfig CRs for user-defined projects. Can be bound with RoleBinding to any user project. Additional resources CMO services resources Granting users permission to configure monitoring for user-defined projects Granting users permission to configure alert routing for user-defined projects 4.1.4.1. Granting user permissions by using the web console You can grant users permissions for the openshift-monitoring project or their own projects, by using the OpenShift Container Platform web console. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role. The user account that you are assigning the role to already exists. Procedure In the Administrator perspective of the OpenShift Container Platform web console, go to User Management RoleBindings Create binding . In the Binding Type section, select the Namespace Role Binding type. In the Name field, enter a name for the role binding. In the Namespace field, select the project where you want to grant the access. Important The monitoring role or cluster role permissions that you grant to a user by using this procedure apply only to the project that you select in the Namespace field. Select a monitoring role or cluster role from the Role Name list. In the Subject section, select User . In the Subject Name field, enter the name of the user. Select Create to apply the role binding. 4.1.4.2. Granting user permissions by using the CLI You can grant users permissions for the openshift-monitoring project or their own projects, by using the OpenShift CLI ( oc ). Important Whichever role or cluster role you choose, you must bind it against a specific project as a cluster administrator. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role. The user account that you are assigning the role to already exists. You have installed the OpenShift CLI ( oc ). Procedure To assign a monitoring role to a user for a project, enter the following command: USD oc adm policy add-role-to-user <role> <user> -n <namespace> --role-namespace <namespace> 1 1 Substitute <role> with the wanted monitoring role, <user> with the user to whom you want to assign the role, and <namespace> with the project where you want to grant the access. To assign a monitoring cluster role to a user for a project, enter the following command: USD oc adm policy add-cluster-role-to-user <cluster-role> <user> -n <namespace> 1 1 Substitute <cluster-role> with the wanted monitoring cluster role, <user> with the user to whom you want to assign the cluster role, and <namespace> with the project where you want to grant the access. 4.1.5. Excluding a user-defined project from monitoring Individual user-defined projects can be excluded from user workload monitoring. To do so, add the openshift.io/user-monitoring label to the project's namespace with a value of false . Procedure Add the label to the project namespace: USD oc label namespace my-project 'openshift.io/user-monitoring=false' To re-enable monitoring, remove the label from the namespace: USD oc label namespace my-project 'openshift.io/user-monitoring-' Note If there were any active monitoring targets for the project, it may take a few minutes for Prometheus to stop scraping them after adding the label. 4.1.6. Disabling monitoring for user-defined projects After enabling monitoring for user-defined projects, you can disable it again by setting enableUserWorkload: false in the cluster monitoring ConfigMap object. Note Alternatively, you can remove enableUserWorkload: true to disable monitoring for user-defined projects. Procedure Edit the cluster-monitoring-config ConfigMap object: USD oc -n openshift-monitoring edit configmap cluster-monitoring-config Set enableUserWorkload: to false under data/config.yaml : apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | enableUserWorkload: false Save the file to apply the changes. Monitoring for user-defined projects is then disabled automatically. Check that the prometheus-operator , prometheus-user-workload and thanos-ruler-user-workload pods are terminated in the openshift-user-workload-monitoring project. This might take a short while: USD oc -n openshift-user-workload-monitoring get pod Example output No resources found in openshift-user-workload-monitoring project. Note The user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project is not automatically deleted when monitoring for user-defined projects is disabled. This is to preserve any custom configurations that you may have created in the ConfigMap object. 4.2. Configuring performance and scalability for user workload monitoring You can configure the monitoring stack to optimize the performance and scale of your clusters. The following documentation provides information about how to distribute the monitoring components and control the impact of the monitoring stack on CPU and memory resources. 4.2.1. Controlling the placement and distribution of monitoring components You can move the monitoring stack components to specific nodes: Use the nodeSelector constraint with labeled nodes to move any of the monitoring stack components to specific nodes. Assign tolerations to enable moving components to tainted nodes. By doing so, you 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. Additional resources Using node selectors to move monitoring components 4.2.1.1. 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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 Enabling monitoring for user-defined projects Understanding how to update labels on nodes Placing pods on specific nodes using node selectors nodeSelector (Kubernetes documentation) 4.2.1.2. 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 cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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 Enabling monitoring for user-defined projects Controlling pod placement using node taints Taints and Tolerations (Kubernetes documentation) 4.2.2. 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 monitoring components that monitor user-defined projects in the openshift-user-workload-monitoring namespace. 4.2.2.1. 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. Additional resources About specifying limits and requests for monitoring components Kubernetes requests and limits documentation (Kubernetes documentation) 4.2.3. Controlling the impact of unbound metrics attributes in user-defined projects Cluster administrators can use the following measures to control the impact of unbound metrics attributes in user-defined projects: Limit the number of samples that can be accepted per target scrape in user-defined projects 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. Additional resources Controlling the impact of unbound metrics attributes in user-defined projects Enabling monitoring for user-defined projects Determining why Prometheus is consuming a lot of disk space 4.2.3.1. 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 cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. 4.2.3.2. Creating scrape sample alerts You can create alerts that notify you when: The target cannot be scraped or is not available for the specified for duration A scrape sample threshold is reached or is exceeded for the specified for duration Prerequisites You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. You have limited the number of samples that can be accepted per target scrape in user-defined projects, by using enforcedSampleLimit . You have installed the OpenShift CLI ( oc ). Procedure Create a YAML file with alerts that inform you when the targets are down and when the enforced sample limit is approaching. The file in this example is called monitoring-stack-alerts.yaml : apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: labels: prometheus: k8s role: alert-rules name: monitoring-stack-alerts 1 namespace: ns1 2 spec: groups: - name: general.rules rules: - alert: TargetDown 3 annotations: message: '{{ printf "%.4g" USDvalue }}% of the {{ USDlabels.job }}/{{ USDlabels.service }} targets in {{ USDlabels.namespace }} namespace are down.' 4 expr: 100 * (count(up == 0) BY (job, namespace, service) / count(up) BY (job, namespace, service)) > 10 for: 10m 5 labels: severity: warning 6 - alert: ApproachingEnforcedSamplesLimit 7 annotations: message: '{{ USDlabels.container }} container of the {{ USDlabels.pod }} pod in the {{ USDlabels.namespace }} namespace consumes {{ USDvalue | humanizePercentage }} of the samples limit budget.' 8 expr: (scrape_samples_post_metric_relabeling / (scrape_sample_limit > 0)) > 0.9 9 for: 10m 10 labels: severity: warning 11 1 Defines the name of the alerting rule. 2 Specifies the user-defined project where the alerting rule is deployed. 3 The TargetDown alert fires if the target cannot be scraped and is not available for the for duration. 4 The message that is displayed when the TargetDown alert fires. 5 The conditions for the TargetDown alert must be true for this duration before the alert is fired. 6 Defines the severity for the TargetDown alert. 7 The ApproachingEnforcedSamplesLimit alert fires when the defined scrape sample threshold is exceeded and lasts for the specified for duration. 8 The message that is displayed when the ApproachingEnforcedSamplesLimit alert fires. 9 The threshold for the ApproachingEnforcedSamplesLimit alert. In this example, the alert fires when the number of ingested samples exceeds 90% of the configured limit. 10 The conditions for the ApproachingEnforcedSamplesLimit alert must be true for this duration before the alert is fired. 11 Defines the severity for the ApproachingEnforcedSamplesLimit alert. Apply the configuration to the user-defined project: USD oc apply -f monitoring-stack-alerts.yaml Additionally, you can check if a target has hit the configured limit: In the Administrator perspective of the web console, go to Observe Targets and select an endpoint with a Down status that you want to check. The Scrape failed: sample limit exceeded message is displayed if the endpoint failed because of an exceeded sample limit. 4.2.4. 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. Additional resources About pod topology spread constraints for monitoring Controlling pod placement by using pod topology spread constraints Pod Topology Spread Constraints (Kubernetes documentation) 4.3. Storing and recording data for user workload monitoring Store and record your metrics and alerting data, configure logs to specify which activities are recorded, control how long Prometheus retains stored data, and set the maximum amount of disk space for the data. These actions help you protect your data and use them for troubleshooting. 4.3.1. 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. 4.3.1.1. Persistent storage prerequisites Dedicate sufficient persistent storage to ensure that the disk does not become full. Use Filesystem as the storage type value for the volumeMode parameter when you configure the persistent volume. Important Do not use a raw block volume, which is described with volumeMode: Block in the PersistentVolume resource. Prometheus cannot use raw block volumes. Prometheus does not support file systems that are not POSIX compliant. For example, some NFS file system implementations are not POSIX compliant. If you want to use an NFS file system for storage, verify with the vendor that their NFS implementation is fully POSIX compliant. 4.3.1.2. 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 cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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 Understanding persistent storage PersistentVolumeClaims (Kubernetes documentation) 4.3.1.3. Resizing a persistent volume You can resize a persistent volume (PV) for the instances of Prometheus, Thanos Ruler, and Alertmanager. You need to manually expand a persistent volume claim (PVC), and then update the config map in which the component is configured. Important You can only expand the size of the PVC. Shrinking the storage size is not possible. 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. A cluster administrator has enabled monitoring for user-defined projects. You have configured at least one PVC for components that monitor user-defined projects. You have installed the OpenShift CLI ( oc ). Procedure Manually expand a PVC with the updated storage request. For more information, see "Expanding persistent volume claims (PVCs) with a file system" in Expanding persistent volumes . 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 new storage size for the 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: resources: requests: storage: <amount_of_storage> 2 1 The component for which you want to change the storage size. 2 Specify the new size for the storage volume. It must be greater than the value. The following example sets the new PVC request to 20 gigabytes for Thanos Ruler: Example storage configuration for thanosRuler apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | thanosRuler: volumeClaimTemplate: spec: resources: requests: storage: 20Gi Note Storage requirements for the thanosRuler component depend on the number of rules that are evaluated and how many samples each rule generates. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed. Warning When you update the config map with a new storage size, the affected StatefulSet object is recreated, resulting in a temporary service outage. Additional resources Prometheus database storage requirements Expanding persistent volume claims (PVCs) with a file system 4.3.2. 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 cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. 4.3.2.1. 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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 Retention time and size for Prometheus metrics Enabling monitoring for user-defined projects Prometheus database storage requirements Recommended configurable storage technology Understanding persistent storage Optimizing storage 4.3.3. 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. 4.3.4. 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. Additional resources Enabling monitoring for user-defined projects 4.4. Configuring metrics for user workload monitoring Configure the collection of metrics to monitor how cluster components and your own workloads are performing. You can send ingested metrics to remote systems for long-term storage and add cluster ID labels to the metrics to identify the data coming from different clusters. Additional resources Understanding metrics 4.4.1. 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. 4.4.1.1. 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. 4.4.1.2. 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. 4.4.1.2.1. 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. 4.4.1.2.2. 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. 4.4.1.2.3. 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. 4.4.1.2.4. 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. 4.4.1.2.5. 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. 4.4.1.3. 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 Prometheus REST API reference for remote write Setting up remote write compatible endpoints (Prometheus documentation) Tuning remote write settings (Prometheus documentation) Understanding secrets 4.4.2. 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. Note When Prometheus scrapes user workload targets that expose a namespace label, the system stores this label as exported_namespace . This behavior ensures that the final namespace label value is equal to the namespace of the target pod. You cannot override this default configuration by setting the value of the honorLabels field to true for PodMonitor or ServiceMonitor objects. 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. A cluster administrator has enabled monitoring for user-defined projects. 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 Adding cluster ID labels to metrics Obtaining your cluster ID 4.4.3. Setting up metrics collection for user-defined projects You can create a ServiceMonitor resource to scrape metrics from a service endpoint in a user-defined project. This assumes that your application uses a Prometheus client library to expose metrics to the /metrics canonical name. This section describes how to deploy a sample service in a user-defined project and then create a ServiceMonitor resource that defines how that service should be monitored. 4.4.3.1. Deploying a sample service To test monitoring of a service in a user-defined project, you can deploy a sample service. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role or as a user with administrative permissions for the namespace. Procedure Create a YAML file for the service configuration. In this example, it is called prometheus-example-app.yaml . Add the following deployment and service configuration details to the file: apiVersion: v1 kind: Namespace metadata: name: ns1 --- apiVersion: apps/v1 kind: Deployment metadata: labels: app: prometheus-example-app name: prometheus-example-app namespace: ns1 spec: replicas: 1 selector: matchLabels: app: prometheus-example-app template: metadata: labels: app: prometheus-example-app spec: containers: - image: ghcr.io/rhobs/prometheus-example-app:0.4.2 imagePullPolicy: IfNotPresent name: prometheus-example-app --- apiVersion: v1 kind: Service metadata: labels: app: prometheus-example-app name: prometheus-example-app namespace: ns1 spec: ports: - port: 8080 protocol: TCP targetPort: 8080 name: web selector: app: prometheus-example-app type: ClusterIP This configuration deploys a service named prometheus-example-app in the user-defined ns1 project. This service exposes the custom version metric. Apply the configuration to the cluster: USD oc apply -f prometheus-example-app.yaml It takes some time to deploy the service. You can check that the pod is running: USD oc -n ns1 get pod Example output NAME READY STATUS RESTARTS AGE prometheus-example-app-7857545cb7-sbgwq 1/1 Running 0 81m 4.4.3.2. Specifying how a service is monitored To use the metrics exposed by your service, you must configure OpenShift Container Platform monitoring to scrape metrics from the /metrics endpoint. You can do this using a ServiceMonitor custom resource definition (CRD) that specifies how a service should be monitored, or a PodMonitor CRD that specifies how a pod should be monitored. The former requires a Service object, while the latter does not, allowing Prometheus to directly scrape metrics from the metrics endpoint exposed by a pod. This procedure shows you how to create a ServiceMonitor resource for a service in a user-defined project. Prerequisites You have access to the cluster as a user with the cluster-admin cluster role or the monitoring-edit cluster role. You have enabled monitoring for user-defined projects. For this example, you have deployed the prometheus-example-app sample service in the ns1 project. Note The prometheus-example-app sample service does not support TLS authentication. Procedure Create a new YAML configuration file named example-app-service-monitor.yaml . Add a ServiceMonitor resource to the YAML file. The following example creates a service monitor named prometheus-example-monitor to scrape metrics exposed by the prometheus-example-app service in the ns1 namespace: apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: prometheus-example-monitor namespace: ns1 1 spec: endpoints: - interval: 30s port: web 2 scheme: http selector: 3 matchLabels: app: prometheus-example-app 1 Specify a user-defined namespace where your service runs. 2 Specify endpoint ports to be scraped by Prometheus. 3 Configure a selector to match your service based on its metadata labels. Note A ServiceMonitor resource in a user-defined namespace can only discover services in the same namespace. That is, the namespaceSelector field of the ServiceMonitor resource is always ignored. Apply the configuration to the cluster: USD oc apply -f example-app-service-monitor.yaml It takes some time to deploy the ServiceMonitor resource. Verify that the ServiceMonitor resource is running: USD oc -n <namespace> get servicemonitor Example output NAME AGE prometheus-example-monitor 81m 4.4.3.3. Example service endpoint authentication settings You can configure authentication for service endpoints for user-defined project monitoring by using ServiceMonitor and PodMonitor custom resource definitions (CRDs). The following samples show different authentication settings for a ServiceMonitor resource. Each sample shows how to configure a corresponding Secret object that contains authentication credentials and other relevant settings. 4.4.3.3.1. Sample YAML authentication with a bearer token The following sample shows bearer token settings for a Secret object named example-bearer-auth in the ns1 namespace: Example bearer token secret apiVersion: v1 kind: Secret metadata: name: example-bearer-auth namespace: ns1 stringData: token: <authentication_token> 1 1 Specify an authentication token. The following sample shows bearer token authentication settings for a ServiceMonitor CRD. The example uses a Secret object named example-bearer-auth : Example bearer token authentication settings apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: prometheus-example-monitor namespace: ns1 spec: endpoints: - authorization: credentials: key: token 1 name: example-bearer-auth 2 port: web selector: matchLabels: app: prometheus-example-app 1 The key that contains the authentication token in the specified Secret object. 2 The name of the Secret object that contains the authentication credentials. Important Do not use bearerTokenFile to configure bearer token. If you use the bearerTokenFile configuration, the ServiceMonitor resource is rejected. 4.4.3.3.2. Sample YAML for Basic authentication The following sample shows Basic authentication settings for a Secret object named example-basic-auth in the ns1 namespace: Example Basic authentication secret apiVersion: v1 kind: Secret metadata: name: example-basic-auth namespace: ns1 stringData: user: <basic_username> 1 password: <basic_password> 2 1 Specify a username for authentication. 2 Specify a password for authentication. The following sample shows Basic authentication settings for a ServiceMonitor CRD. The example uses a Secret object named example-basic-auth : Example Basic authentication settings apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: prometheus-example-monitor namespace: ns1 spec: endpoints: - basicAuth: username: key: user 1 name: example-basic-auth 2 password: key: password 3 name: example-basic-auth 4 port: web selector: matchLabels: app: prometheus-example-app 1 The key that contains the username in the specified Secret object. 2 4 The name of the Secret object that contains the Basic authentication. 3 The key that contains the password in the specified Secret object. 4.4.3.3.3. Sample YAML authentication with OAuth 2.0 The following sample shows OAuth 2.0 settings for a Secret object named example-oauth2 in the ns1 namespace: Example OAuth 2.0 secret apiVersion: v1 kind: Secret metadata: name: example-oauth2 namespace: ns1 stringData: id: <oauth2_id> 1 secret: <oauth2_secret> 2 1 Specify an Oauth 2.0 ID. 2 Specify an Oauth 2.0 secret. The following sample shows OAuth 2.0 authentication settings for a ServiceMonitor CRD. The example uses a Secret object named example-oauth2 : Example OAuth 2.0 authentication settings apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: prometheus-example-monitor namespace: ns1 spec: endpoints: - oauth2: clientId: secret: key: id 1 name: example-oauth2 2 clientSecret: key: secret 3 name: example-oauth2 4 tokenUrl: https://example.com/oauth2/token 5 port: web selector: matchLabels: app: prometheus-example-app 1 The key that contains the OAuth 2.0 ID in the specified Secret object. 2 4 The name of the Secret object that contains the OAuth 2.0 credentials. 3 The key that contains the OAuth 2.0 secret in the specified Secret object. 5 The URL used to fetch a token with the specified clientId and clientSecret . Additional resources Enabling monitoring for user-defined projects Scrape Prometheus metrics using TLS in ServiceMonitor configuration (Red Hat Customer Portal article) PodMonitor API ServiceMonitor API 4.5. Configuring alerts and notifications for user workload monitoring You can configure a local or external Alertmanager instance to route alerts from Prometheus to endpoint receivers. You can also attach custom labels to all time series and alerts to add useful metadata information. 4.5.1. Configuring external Alertmanager instances The OpenShift Container Platform 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. 4.5.2. Configuring secrets for Alertmanager The OpenShift Container Platform 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. 4.5.2.1. 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. 4.5.3. 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 cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project. A cluster administrator has enabled monitoring for user-defined projects. 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. Additional resources Enabling monitoring for user-defined projects 4.5.4. Configuring alert notifications In OpenShift Container Platform, an administrator can enable alert routing for user-defined projects with one of the following methods: Use the default platform Alertmanager instance. Use a separate Alertmanager instance only for user-defined projects. Developers and other users with the alert-routing-edit cluster role can configure custom alert notifications for their user-defined projects by configuring alert receivers. Note Review the following limitations of alert routing for user-defined projects: User-defined alert routing is scoped to the namespace in which the resource is defined. For example, a routing configuration in namespace ns1 only applies to PrometheusRules resources in the same namespace. When a namespace is excluded from user-defined monitoring, AlertmanagerConfig resources in the namespace cease to be part of the Alertmanager configuration. Additional resources Understanding alert routing for user-defined projects Sending notifications to external systems PagerDuty (PagerDuty official site) Prometheus Integration Guide (PagerDuty official site) Support version matrix for monitoring components Enabling alert routing for user-defined projects 4.5.4.1. Configuring alert routing for user-defined projects If you are a non-administrator user who has been given the alert-routing-edit cluster role, you can create or edit alert routing for user-defined projects. Prerequisites A cluster administrator has enabled monitoring for user-defined projects. A cluster administrator has enabled alert routing for user-defined projects. You are logged in as a user that has the alert-routing-edit cluster role for the project for which you want to create alert routing. You have installed the OpenShift CLI ( oc ). Procedure Create a YAML file for alert routing. The example in this procedure uses a file called example-app-alert-routing.yaml . Add an AlertmanagerConfig YAML definition to the file. For example: apiVersion: monitoring.coreos.com/v1beta1 kind: AlertmanagerConfig metadata: name: example-routing namespace: ns1 spec: route: receiver: default groupBy: [job] receivers: - name: default webhookConfigs: - url: https://example.org/post Save the file. Apply the resource to the cluster: USD oc apply -f example-app-alert-routing.yaml The configuration is automatically applied to the Alertmanager pods. 4.5.4.2. Configuring alert routing for user-defined projects with the Alertmanager secret If you have enabled a separate instance of Alertmanager that is dedicated to user-defined alert routing, you can customize where and how the instance sends notifications by editing the alertmanager-user-workload secret in the openshift-user-workload-monitoring namespace. Note All features of a supported version of upstream Alertmanager are also supported in an OpenShift Container Platform Alertmanager configuration. To check all the configuration options of a supported version of upstream Alertmanager, see Alertmanager configuration (Prometheus documentation). Prerequisites You have access to the cluster as a user with the cluster-admin cluster role. You have enabled a separate instance of Alertmanager for user-defined alert routing. You have installed the OpenShift CLI ( oc ). Procedure Print the currently active Alertmanager configuration into the file alertmanager.yaml : USD oc -n openshift-user-workload-monitoring get secret alertmanager-user-workload --template='{{ index .data "alertmanager.yaml" }}' | base64 --decode > alertmanager.yaml Edit the configuration in alertmanager.yaml : global: http_config: proxy_from_environment: true 1 route: receiver: Default group_by: - name: Default routes: - matchers: - "service = prometheus-example-monitor" 2 receiver: <receiver> 3 receivers: - name: Default - name: <receiver> <receiver_configuration> 4 1 If you configured an HTTP cluster-wide proxy, set the proxy_from_environment parameter to true to enable proxying for all alert receivers. 2 Specify labels to match your alerts. This example targets all alerts that have the service="prometheus-example-monitor" label. 3 Specify the name of the receiver to use for the alerts group. 4 Specify the receiver configuration. Apply the new configuration in the file: USD oc -n openshift-user-workload-monitoring create secret generic alertmanager-user-workload --from-file=alertmanager.yaml --dry-run=client -o=yaml | oc -n openshift-user-workload-monitoring replace secret --filename=- 4.5.4.3. Configuring different alert receivers for default platform alerts and user-defined alerts You can configure different alert receivers for default platform alerts and user-defined alerts to ensure the following results: All default platform alerts are sent to a receiver owned by the team in charge of these alerts. All user-defined alerts are sent to another receiver so that the team can focus only on platform alerts. You can achieve this by using the openshift_io_alert_source="platform" label that is added by the Cluster Monitoring Operator to all platform alerts: Use the openshift_io_alert_source="platform" matcher to match default platform alerts. Use the openshift_io_alert_source!="platform" or 'openshift_io_alert_source=""' matcher to match user-defined alerts. Note This configuration does not apply if you have enabled a separate instance of Alertmanager dedicated to user-defined alerts.

Commands:
oc -n openshift-monitoring edit configmap cluster-monitoring-config
apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | enableUserWorkload: true 1
oc -n openshift-user-workload-monitoring get pod
NAME READY STATUS RESTARTS AGE prometheus-operator-6f7b748d5b-t7nbg 2/2 Running 0 3h prometheus-user-workload-0 4/4 Running 1 3h prometheus-user-workload-1 4/4 Running 1 3h thanos-ruler-user-workload-0 3/3 Running 0 3h thanos-ruler-user-workload-1 3/3 Running 0 3h
oc -n openshift-user-workload-monitoring adm policy add-role-to-user user-workload-monitoring-config-edit <user> --role-namespace openshift-user-workload-monitoring
oc describe rolebinding <role_binding_name> -n openshift-user-workload-monitoring
oc describe rolebinding user-workload-monitoring-config-edit -n openshift-user-workload-monitoring
Name: user-workload-monitoring-config-edit Labels: <none> Annotations: <none> Role: Kind: Role Name: user-workload-monitoring-config-edit Subjects: Kind Name Namespace ---- ---- --------- User user1 1
oc -n openshift-monitoring edit configmap cluster-monitoring-config
apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | # alertmanagerMain: enableUserAlertmanagerConfig: true 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: | alertmanager: enabled: true 1 enableAlertmanagerConfig: true 2
oc -n openshift-user-workload-monitoring get alertmanager
NAME VERSION REPLICAS AGE user-workload 0.24.0 2 100s
oc -n <namespace> adm policy add-role-to-user alert-routing-edit <user> 1
oc adm policy add-role-to-user <role> <user> -n <namespace> --role-namespace <namespace> 1
oc adm policy add-cluster-role-to-user <cluster-role> <user> -n <namespace> 1
oc label namespace my-project 'openshift.io/user-monitoring=false'
oc label namespace my-project 'openshift.io/user-monitoring-'
oc -n openshift-monitoring edit configmap cluster-monitoring-config
apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | enableUserWorkload: false
oc -n openshift-user-workload-monitoring get pod
No resources found in openshift-user-workload-monitoring project.
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: | prometheus: enforcedSampleLimit: 50000 1 enforcedLabelLimit: 500 2 enforcedLabelNameLengthLimit: 50 3 enforcedLabelValueLengthLimit: 600 4 scrapeInterval: 1m30s 5 evaluationInterval: 1m15s 6
apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: labels: prometheus: k8s role: alert-rules name: monitoring-stack-alerts 1 namespace: ns1 2 spec: groups: - name: general.rules rules: - alert: TargetDown 3 annotations: message: '{{ printf "%.4g" USDvalue }}% of the {{ USDlabels.job }}/{{ USDlabels.service }} targets in {{ USDlabels.namespace }} namespace are down.' 4 expr: 100 * (count(up == 0) BY (job, namespace, service) / count(up) BY (job, namespace, service)) > 10 for: 10m 5 labels: severity: warning 6 - alert: ApproachingEnforcedSamplesLimit 7 annotations: message: '{{ USDlabels.container }} container of the {{ USDlabels.pod }} pod in the {{ USDlabels.namespace }} namespace consumes {{ USDvalue | humanizePercentage }} of the samples limit budget.' 8 expr: (scrape_samples_post_metric_relabeling / (scrape_sample_limit > 0)) > 0.9 9 for: 10m 10 labels: severity: warning 11
oc apply -f monitoring-stack-alerts.yaml
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 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: | <component>: 1 volumeClaimTemplate: spec: resources: requests: storage: <amount_of_storage> 2
apiVersion: v1 kind: ConfigMap metadata: name: user-workload-monitoring-config namespace: openshift-user-workload-monitoring data: config.yaml: | thanosRuler: volumeClaimTemplate: spec: resources: requests: storage: 20Gi
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: | <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>
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
apiVersion: v1 kind: Namespace metadata: name: ns1 --- apiVersion: apps/v1 kind: Deployment metadata: labels: app: prometheus-example-app name: prometheus-example-app namespace: ns1 spec: replicas: 1 selector: matchLabels: app: prometheus-example-app template: metadata: labels: app: prometheus-example-app spec: containers: - image: ghcr.io/rhobs/prometheus-example-app:0.4.2 imagePullPolicy: IfNotPresent name: prometheus-example-app --- apiVersion: v1 kind: Service metadata: labels: app: prometheus-example-app name: prometheus-example-app namespace: ns1 spec: ports: - port: 8080 protocol: TCP targetPort: 8080 name: web selector: app: prometheus-example-app type: ClusterIP
oc apply -f prometheus-example-app.yaml
oc -n ns1 get pod
NAME READY STATUS RESTARTS AGE prometheus-example-app-7857545cb7-sbgwq 1/1 Running 0 81m
apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: prometheus-example-monitor namespace: ns1 1 spec: endpoints: - interval: 30s port: web 2 scheme: http selector: 3 matchLabels: app: prometheus-example-app
oc apply -f example-app-service-monitor.yaml
oc -n <namespace> get servicemonitor
NAME AGE prometheus-example-monitor 81m
apiVersion: v1 kind: Secret metadata: name: example-bearer-auth namespace: ns1 stringData: token: <authentication_token> 1
apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: prometheus-example-monitor namespace: ns1 spec: endpoints: - authorization: credentials: key: token 1 name: example-bearer-auth 2 port: web selector: matchLabels: app: prometheus-example-app
apiVersion: v1 kind: Secret metadata: name: example-basic-auth namespace: ns1 stringData: user: <basic_username> 1 password: <basic_password> 2
apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: prometheus-example-monitor namespace: ns1 spec: endpoints: - basicAuth: username: key: user 1 name: example-basic-auth 2 password: key: password 3 name: example-basic-auth 4 port: web selector: matchLabels: app: prometheus-example-app
apiVersion: v1 kind: Secret metadata: name: example-oauth2 namespace: ns1 stringData: id: <oauth2_id> 1 secret: <oauth2_secret> 2
apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: prometheus-example-monitor namespace: ns1 spec: endpoints: - oauth2: clientId: secret: key: id 1 name: example-oauth2 2 clientSecret: key: secret 3 name: example-oauth2 4 tokenUrl: https://example.com/oauth2/token 5 port: web selector: matchLabels: app: prometheus-example-app
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
apiVersion: monitoring.coreos.com/v1beta1 kind: AlertmanagerConfig metadata: name: example-routing namespace: ns1 spec: route: receiver: default groupBy: [job] receivers: - name: default webhookConfigs: - url: https://example.org/post
oc apply -f example-app-alert-routing.yaml
oc -n openshift-user-workload-monitoring get secret alertmanager-user-workload --template='{{ index .data "alertmanager.yaml" }}' | base64 --decode > alertmanager.yaml
global: http_config: proxy_from_environment: true 1 route: receiver: Default group_by: - name: Default routes: - matchers: - "service = prometheus-example-monitor" 2 receiver: <receiver> 3 receivers: - name: Default - name: <receiver> <receiver_configuration> 4
oc -n openshift-user-workload-monitoring create secret generic alertmanager-user-workload --from-file=alertmanager.yaml --dry-run=client -o=yaml | oc -n openshift-user-workload-monitoring replace secret --filename=-