Monitoring


OpenShift Container Platform 4.17

Configuring and using the monitoring stack in OpenShift Container Platform

Red Hat OpenShift Documentation Team

Abstract

Use metrics and customized alerts provided by the monitoring stack to track the health and performance of your applications running on OpenShift Container Platform clusters.

Chapter 1. Monitoring overview

1.1. About OpenShift Container Platform monitoring

OpenShift Container Platform includes a preconfigured, preinstalled, and self-updating monitoring stack that provides monitoring for core platform components. You also have the option to enable monitoring for user-defined projects.

A cluster administrator can configure the monitoring stack with the supported configurations. OpenShift Container Platform delivers monitoring best practices out of the box.

A set of alerts are included by default that immediately notify administrators about issues with a cluster. Default dashboards in the OpenShift Container Platform web console include visual representations of cluster metrics to help you to quickly understand the state of your cluster. With the OpenShift Container Platform web console, you can view and manage metrics, alerts, and review monitoring dashboards.

In the Observe section of OpenShift Container Platform web console, you can access and manage monitoring features such as metrics, alerts, monitoring dashboards, and metrics targets.

After installing OpenShift Container Platform, cluster administrators can optionally enable monitoring for user-defined projects. By using this feature, cluster administrators, developers, and other users can specify how services and pods are monitored in their own projects. As a cluster administrator, you can find answers to common problems such as user metrics unavailability and high consumption of disk space by Prometheus in Troubleshooting monitoring issues.

1.2. Understanding the monitoring stack

The OpenShift Container Platform monitoring stack is based on the Prometheus open source project and its wider ecosystem. The monitoring stack includes the following:

  • Default platform monitoring components. A set of platform monitoring components are installed in the openshift-monitoring project by default during an OpenShift Container Platform installation. This provides monitoring for core cluster components including Kubernetes services. The default monitoring stack also enables remote health monitoring for clusters.

    These components are illustrated in the Installed by default section in the following diagram.

  • Components for monitoring user-defined projects. After optionally enabling monitoring for user-defined projects, additional monitoring components are installed in the openshift-user-workload-monitoring project. This provides monitoring for user-defined projects. These components are illustrated in the User section in the following diagram.

OpenShift Container Platform monitoring architecture

1.2.1. Default monitoring components

By default, the OpenShift Container Platform 4.17 monitoring stack includes these components:

Table 1.1. Default monitoring stack components
ComponentDescription

Cluster Monitoring Operator

The Cluster Monitoring Operator (CMO) is a central component of the monitoring stack. It deploys, manages, and automatically updates Prometheus and Alertmanager instances, Thanos Querier, Telemeter Client, and metrics targets. The CMO is deployed by the Cluster Version Operator (CVO).

Prometheus Operator

The Prometheus Operator (PO) in the openshift-monitoring project creates, configures, and manages platform Prometheus instances and Alertmanager instances. It also automatically generates monitoring target configurations based on Kubernetes label queries.

Prometheus

Prometheus is the monitoring system on which the OpenShift Container Platform monitoring stack is based. Prometheus is a time-series database and a rule evaluation engine for metrics. Prometheus sends alerts to Alertmanager for processing.

Metrics Server

The Metrics Server component (MS in the preceding diagram) collects resource metrics and exposes them in the metrics.k8s.io Metrics API service for use by other tools and APIs, which frees the core platform Prometheus stack from handling this functionality. Note that with the OpenShift Container Platform 4.16 release, Metrics Server replaces Prometheus Adapter.

Alertmanager

The Alertmanager service handles alerts received from Prometheus. Alertmanager is also responsible for sending the alerts to external notification systems.

kube-state-metrics agent

The kube-state-metrics exporter agent (KSM in the preceding diagram) converts Kubernetes objects to metrics that Prometheus can use.

monitoring-plugin

The monitoring-plugin dynamic plugin component deploys the monitoring pages in the Observe section of the OpenShift Container Platform web console. You can use Cluster Monitoring Operator config map settings to manage monitoring-plugin resources for the web console pages.

openshift-state-metrics agent

The openshift-state-metrics exporter (OSM in the preceding diagram) expands upon kube-state-metrics by adding metrics for OpenShift Container Platform-specific resources.

node-exporter agent

The node-exporter agent (NE in the preceding diagram) collects metrics about every node in a cluster. The node-exporter agent is deployed on every node.

Thanos Querier

Thanos Querier aggregates and optionally deduplicates core OpenShift Container Platform metrics and metrics for user-defined projects under a single, multi-tenant interface.

Telemeter Client

Telemeter Client sends a subsection of the data from platform Prometheus instances to Red Hat to facilitate Remote Health Monitoring for clusters.

All of the components in the monitoring stack are monitored by the stack and are automatically updated when OpenShift Container Platform is updated.

Note

All components of the monitoring stack use the TLS security profile settings that are centrally configured by a cluster administrator. If you configure a monitoring stack component that uses TLS security settings, the component uses the TLS security profile settings that already exist in the tlsSecurityProfile field in the global OpenShift Container Platform apiservers.config.openshift.io/cluster resource.

1.2.2. Default monitoring targets

In addition to the components of the stack itself, the default monitoring stack monitors additional platform components.

The following are examples of monitoring targets:

  • CoreDNS
  • etcd
  • HAProxy
  • Image registry
  • Kubelets
  • Kubernetes API server
  • Kubernetes controller manager
  • Kubernetes scheduler
  • OpenShift API server
  • OpenShift Controller Manager
  • Operator Lifecycle Manager (OLM)
Note
  • The exact list of targets can vary depending on your cluster capabilities and installed components.
  • Each OpenShift Container Platform component is responsible for its monitoring configuration. For problems with the monitoring of an OpenShift Container Platform component, open a Jira issue against that component, not against the general monitoring component.

Other OpenShift Container Platform framework components might be exposing metrics as well. For details, see their respective documentation.

1.2.3. Components for monitoring user-defined projects

OpenShift Container Platform includes an optional enhancement to the monitoring stack that enables you to monitor services and pods in user-defined projects. This feature includes the following components:

Table 1.2. Components for monitoring user-defined projects
ComponentDescription

Prometheus Operator

The Prometheus Operator (PO) in the openshift-user-workload-monitoring project creates, configures, and manages Prometheus and Thanos Ruler instances in the same project.

Prometheus

Prometheus is the monitoring system through which monitoring is provided for user-defined projects. Prometheus sends alerts to Alertmanager for processing.

Thanos Ruler

The Thanos Ruler is a rule evaluation engine for Prometheus that is deployed as a separate process. In OpenShift Container Platform , Thanos Ruler provides rule and alerting evaluation for the monitoring of user-defined projects.

Alertmanager

The Alertmanager service handles alerts received from Prometheus and Thanos Ruler. Alertmanager is also responsible for sending user-defined alerts to external notification systems. Deploying this service is optional.

Note

The components in the preceding table are deployed after monitoring is enabled for user-defined projects.

All of these components are monitored by the stack and are automatically updated when OpenShift Container Platform is updated.

1.2.4. Monitoring targets for user-defined projects

When monitoring is enabled for user-defined projects, you can monitor:

  • Metrics provided through service endpoints in user-defined projects.
  • Pods running in user-defined projects.

1.2.5. Understanding the monitoring stack in high-availability clusters

By default, in multi-node clusters, the following components run in high-availability (HA) mode to prevent data loss and service interruption:

  • Prometheus
  • Alertmanager
  • Thanos Ruler
  • Thanos Querier
  • Metrics Server
  • Monitoring plugin

The component is replicated across two pods, each running on a separate node. This means that the monitoring stack can tolerate the loss of one pod.

Prometheus in HA mode
  • Both replicas independently scrape the same targets and evaluate the same rules.
  • The replicas do not communicate with each other. Therefore, data might differ between the pods.
Alertmanager in HA mode
  • The two replicas synchronize notification and silence states with each other. This ensures that each notification is sent at least once.
  • If the replicas fail to communicate or if there is an issue on the receiving side, notifications are still sent, but they might be duplicated.
Important

Prometheus, Alertmanager, and Thanos Ruler are stateful components. To ensure high availability, you must configure them with persistent storage.

1.3. Glossary of common terms for OpenShift Container Platform monitoring

This glossary defines common terms that are used in OpenShift Container Platform architecture.

Alertmanager
Alertmanager handles alerts received from Prometheus. Alertmanager is also responsible for sending the alerts to external notification systems.
Alerting rules
Alerting rules contain a set of conditions that outline a particular state within a cluster. Alerts are triggered when those conditions are true. An alerting rule can be assigned a severity that defines how the alerts are routed.
Cluster Monitoring Operator
The Cluster Monitoring Operator (CMO) is a central component of the monitoring stack. It deploys and manages Prometheus instances such as, the Thanos Querier, the Telemeter Client, and metrics targets to ensure that they are up to date. The CMO is deployed by the Cluster Version Operator (CVO).
Cluster Version Operator
The Cluster Version Operator (CVO) manages the lifecycle of cluster Operators, many of which are installed in OpenShift Container Platform by default.
config map
A config map provides a way to inject configuration data into pods. You can reference the data stored in a config map in a volume of type ConfigMap. Applications running in a pod can use this data.
Container
A container is a lightweight and executable image that includes software and all its dependencies. Containers virtualize the operating system. As a result, you can run containers anywhere from a data center to a public or private cloud as well as a developer’s laptop.
custom resource (CR)
A CR is an extension of the Kubernetes API. You can create custom resources.
etcd
etcd is the key-value store for OpenShift Container Platform, which stores the state of all resource objects.
Fluentd

Fluentd is a log collector that resides on each OpenShift Container Platform node. It gathers application, infrastructure, and audit logs and forwards them to different outputs.

Note

Fluentd is deprecated and is planned to be removed in a future release. Red Hat provides bug fixes and support for this feature during the current release lifecycle, but this feature no longer receives enhancements. As an alternative to Fluentd, you can use Vector instead.

Kubelets
Runs on nodes and reads the container manifests. Ensures that the defined containers have started and are running.
Kubernetes API server
Kubernetes API server validates and configures data for the API objects.
Kubernetes controller manager
Kubernetes controller manager governs the state of the cluster.
Kubernetes scheduler
Kubernetes scheduler allocates pods to nodes.
labels
Labels are key-value pairs that you can use to organize and select subsets of objects such as a pod.
Metrics Server
The Metrics Server monitoring component collects resource metrics and exposes them in the metrics.k8s.io Metrics API service for use by other tools and APIs, which frees the core platform Prometheus stack from handling this functionality.
node
A worker machine in the OpenShift Container Platform cluster. A node is either a virtual machine (VM) or a physical machine.
Operator
The preferred method of packaging, deploying, and managing a Kubernetes application in an OpenShift Container Platform cluster. An Operator takes human operational knowledge and encodes it into software that is packaged and shared with customers.
Operator Lifecycle Manager (OLM)
OLM helps you install, update, and manage the lifecycle of Kubernetes native applications. OLM is an open source toolkit designed to manage Operators in an effective, automated, and scalable way.
Persistent storage
Stores the data even after the device is shut down. Kubernetes uses persistent volumes to store the application data.
Persistent volume claim (PVC)
You can use a PVC to mount a PersistentVolume into a Pod. You can access the storage without knowing the details of the cloud environment.
pod
The pod is the smallest logical unit in Kubernetes. A pod is comprised of one or more containers to run in a worker node.
Prometheus
Prometheus is the monitoring system on which the OpenShift Container Platform monitoring stack is based. Prometheus is a time-series database and a rule evaluation engine for metrics. Prometheus sends alerts to Alertmanager for processing.
Prometheus Operator
The Prometheus Operator (PO) in the openshift-monitoring project creates, configures, and manages platform Prometheus and Alertmanager instances. It also automatically generates monitoring target configurations based on Kubernetes label queries.
Silences
A silence can be applied to an alert to prevent notifications from being sent when the conditions for an alert are true. You can mute an alert after the initial notification, while you work on resolving the underlying issue.
storage
OpenShift Container Platform supports many types of storage, both for on-premise and cloud providers. You can manage container storage for persistent and non-persistent data in an OpenShift Container Platform cluster.
Thanos Ruler
The Thanos Ruler is a rule evaluation engine for Prometheus that is deployed as a separate process. In OpenShift Container Platform, Thanos Ruler provides rule and alerting evaluation for the monitoring of user-defined projects.
Vector
Vector is a log collector that deploys to each OpenShift Container Platform node. It collects log data from each node, transforms the data, and forwards it to configured outputs.
web console
A user interface (UI) to manage OpenShift Container Platform.

1.4. Additional resources

Chapter 2. Common monitoring configuration scenarios

After OpenShift Container Platform is installed, core platform monitoring components immediately begin collecting metrics, which you can query and view. The default in-cluster monitoring stack includes the core platform Prometheus instance that collects metrics from your cluster and the core Alertmanager instance that routes alerts, among other components. Depending on who will use the monitoring stack and for what purposes, as a cluster administrator, you can further configure these monitoring components to suit the needs of different users in various scenarios.

In addition to core platform monitoring, you can also optionally enable monitoring for user-defined projects for user workload monitoring. Users can then monitor their own services and workloads without the need for an additional monitoring solution.

2.1. Configuring core platform monitoring: Postinstallation steps

After OpenShift Container Platform is installed, cluster administrators typically configure core platform monitoring to suit their needs. These activities include setting up storage and configuring options for Prometheus, Alertmanager, and other monitoring components.

Note

By default, in a newly installed OpenShift Container Platform system, users can query and view collected metrics. You need only configure an alert receiver if you want users to receive alert notifications. Any other configuration options listed here are optional.

With the monitoring stack configured to suit your needs, Prometheus collects metrics from the specified services and stores these metrics according to your settings. You can go to the Observe pages in the OpenShift Container Platform web console to view and query collected metrics, manage alerts, identify performance bottlenecks, and scale resources as needed:

  • View dashboards to visualize collected metrics, troubleshoot alerts, and monitor other information about your cluster.
  • Query collected metrics by creating PromQL queries or using predefined queries.

2.2. Configuring monitoring for user-defined projects: Getting started

As a cluster administrator, you can optionally enable monitoring for user-defined projects in addition to core platform monitoring. Non-administrator users such as developers can then monitor their own projects outside of core platform monitoring.

Cluster administrators typically complete the following activities to configure user-defined projects so that users can view collected metrics, query these metrics, and receive alerts for their own projects:

After monitoring for user-defined projects is enabled and configured, developers and other non-administrator users can then perform the following activities to set up and use monitoring for their own projects:

Chapter 3. Configuring the monitoring stack

The OpenShift Container Platform installation program provides only a low number of configuration options before installation. Configuring most OpenShift Container Platform framework components, including the cluster monitoring stack, happens after the installation.

This section explains what configuration is supported, shows how to configure the monitoring stack, and demonstrates several common configuration scenarios.

Important

Not all configuration parameters for the monitoring stack are exposed. Only the parameters and fields listed in the Config map reference for the Cluster Monitoring Operator are supported for configuration.

3.1. Prerequisites

  • The monitoring stack imposes additional resource requirements. Consult the computing resources recommendations in Scaling the Cluster Monitoring Operator and verify that you have sufficient resources.

3.2. Maintenance and support for monitoring

Not all configuration options for the monitoring stack are exposed. The only supported way of configuring OpenShift Container Platform monitoring is by configuring the Cluster Monitoring Operator (CMO) using the options described in the Config map reference for the Cluster Monitoring Operator. Do not use other configurations, as they are unsupported.

Configuration paradigms might change across Prometheus releases, and such cases can only be handled gracefully if all configuration possibilities are controlled. If you use configurations other than those described in the Config map reference for the Cluster Monitoring Operator, your changes will disappear because the CMO automatically reconciles any differences and resets any unsupported changes back to the originally defined state by default and by design.

3.2.1. Support considerations for monitoring

Note

Backward compatibility for metrics, recording rules, or alerting rules is not guaranteed.

The following modifications are explicitly not supported:

  • Creating additional ServiceMonitor, PodMonitor, and PrometheusRule objects in the openshift-* and kube-* projects.
  • Modifying any resources or objects deployed in the openshift-monitoring or openshift-user-workload-monitoring projects. The resources created by the OpenShift Container Platform monitoring stack are not meant to be used by any other resources, as there are no guarantees about their backward compatibility.

    Note

    The Alertmanager configuration is deployed as the alertmanager-main secret resource in the openshift-monitoring namespace. If you have enabled a separate Alertmanager instance for user-defined alert routing, an Alertmanager configuration is also deployed as the alertmanager-user-workload secret resource in the openshift-user-workload-monitoring namespace. To configure additional routes for any instance of Alertmanager, you need to decode, modify, and then encode that secret. This procedure is a supported exception to the preceding statement.

  • Modifying resources of the stack. The OpenShift Container Platform monitoring stack ensures its resources are always in the state it expects them to be. If they are modified, the stack will reset them.
  • Deploying user-defined workloads to openshift-*, and kube-* projects. These projects are reserved for Red Hat provided components and they should not be used for user-defined workloads.
  • Enabling symptom based monitoring by using the Probe custom resource definition (CRD) in Prometheus Operator.
  • Manually deploying monitoring resources into namespaces that have the openshift.io/cluster-monitoring: "true" label.
  • Adding the openshift.io/cluster-monitoring: "true" label to namespaces. This label is reserved only for the namespaces with core OpenShift Container Platform components and Red Hat certified components.
  • Installing custom Prometheus instances on OpenShift Container Platform. A custom instance is a Prometheus custom resource (CR) managed by the Prometheus Operator.

3.2.2. Support policy for monitoring Operators

Monitoring Operators ensure that OpenShift Container Platform monitoring resources function as designed and tested. If Cluster Version Operator (CVO) control of an Operator is overridden, the Operator does not respond to configuration changes, reconcile the intended state of cluster objects, or receive updates.

While overriding CVO control for an Operator can be helpful during debugging, this is unsupported and the cluster administrator assumes full control of the individual component configurations and upgrades.

Overriding the Cluster Version Operator

The spec.overrides parameter can be added to the configuration for the CVO to allow administrators to provide a list of overrides to the behavior of the CVO for a component. Setting the spec.overrides[].unmanaged parameter to true for a component blocks cluster upgrades and alerts the administrator after a CVO override has been set:

Disabling ownership via cluster version overrides prevents upgrades. Please remove overrides before continuing.
Warning

Setting a CVO override puts the entire cluster in an unsupported state and prevents the monitoring stack from being reconciled to its intended state. This impacts the reliability features built into Operators and prevents updates from being received. Reported issues must be reproduced after removing any overrides for support to proceed.

3.2.3. Support version matrix for monitoring components

The following matrix contains information about versions of monitoring components for OpenShift Container Platform 4.12 and later releases:

Table 3.1. OpenShift Container Platform and component versions
OpenShift Container PlatformPrometheus OperatorPrometheusMetrics ServerAlertmanagerkube-state-metrics agentmonitoring-pluginnode-exporter agentThanos

4.17

0.75.2

2.53.1

0.7.1

0.27.0

2.13.0

1.0.0

1.8.2

0.35.1

4.16

0.73.2

2.52.0

0.7.1

0.26.0

2.12.0

1.0.0

1.8.0

0.35.0

4.15

0.70.0

2.48.0

0.6.4

0.26.0

2.10.1

1.0.0

1.7.0

0.32.5

4.14

0.67.1

2.46.0

N/A

0.25.0

2.9.2

1.0.0

1.6.1

0.30.2

4.13

0.63.0

2.42.0

N/A

0.25.0

2.8.1

N/A

1.5.0

0.30.2

4.12

0.60.1

2.39.1

N/A

0.24.0

2.6.0

N/A

1.4.0

0.28.1

Note

The openshift-state-metrics agent and Telemeter Client are OpenShift-specific components. Therefore, their versions correspond with the versions of OpenShift Container Platform.

3.3. Preparing to configure the monitoring stack

You can configure the monitoring stack by creating and updating monitoring config maps. These config maps configure the Cluster Monitoring Operator (CMO), which in turn configures the components of the monitoring stack.

3.3.1. Creating a cluster monitoring config map

You can configure the core OpenShift Container Platform monitoring components by creating the cluster-monitoring-config ConfigMap object in the openshift-monitoring project. The Cluster Monitoring Operator (CMO) then configures the core components of the monitoring stack.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Check whether the cluster-monitoring-config ConfigMap object exists:

    $ oc -n openshift-monitoring get configmap cluster-monitoring-config
  2. If the ConfigMap object does not exist:

    1. Create the following YAML manifest. In this example the file is called cluster-monitoring-config.yaml:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: cluster-monitoring-config
        namespace: openshift-monitoring
      data:
        config.yaml: |
    2. Apply the configuration to create the ConfigMap object:

      $ oc apply -f cluster-monitoring-config.yaml

3.3.2. Creating a user-defined workload monitoring config map

You can configure the user workload monitoring components with the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project. The Cluster Monitoring Operator (CMO) then configures the components that monitor user-defined projects.

Note
  • If you enable monitoring for user-defined projects, the user-workload-monitoring-config ConfigMap object is created by default.
  • When you save your changes to the user-workload-monitoring-config ConfigMap object, some or all of the pods in the openshift-user-workload-monitoring project might be redeployed. It can sometimes take a while for these components to redeploy.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Check whether the user-workload-monitoring-config ConfigMap object exists:

    $ oc -n openshift-user-workload-monitoring get configmap user-workload-monitoring-config
  2. If the user-workload-monitoring-config ConfigMap object does not exist:

    1. Create the following YAML manifest. In this example the file is called user-workload-monitoring-config.yaml:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
    2. Apply the configuration to create the ConfigMap object:

      $ oc apply -f user-workload-monitoring-config.yaml
      Note

      Configurations applied to the user-workload-monitoring-config ConfigMap object are not activated unless a cluster administrator has enabled monitoring for user-defined projects.

3.4. Granting users permissions for core platform monitoring

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 for core platform monitoring. You can grant the permissions by assigning one of the following monitoring roles or cluster roles:

NameDescriptionProject

cluster-monitoring-metrics-api

Users with this role have the ability to access Thanos Querier API endpoints. Additionally, it grants access to the core platform Prometheus API and user-defined Thanos Ruler API endpoints.

openshift-monitoring

cluster-monitoring-operator-alert-customization

Users with this role can manage AlertingRule and AlertRelabelConfig resources for core platform monitoring. These permissions are required for the alert customization feature.

openshift-monitoring

monitoring-alertmanager-edit

Users with this role can manage the Alertmanager API for core platform monitoring. They can also manage alert silences in the Administrator perspective of the OpenShift Container Platform web console.

openshift-monitoring

monitoring-alertmanager-view

Users with this role can monitor the Alertmanager API for core platform monitoring. They can also view alert silences in the Administrator perspective of the OpenShift Container Platform web console.

openshift-monitoring

cluster-monitoring-view

Users with this cluster role have the same access rights as cluster-monitoring-metrics-api role, with additional permissions, providing access to the /federate endpoint for the user-defined Prometheus.

Must be bound with ClusterRoleBinding to gain access to the /federate endpoint for the user-defined Prometheus.

3.5. Configuring the monitoring stack

In OpenShift Container Platform 4.17, you can configure the monitoring stack using the cluster-monitoring-config or user-workload-monitoring-config ConfigMap objects. Config maps configure the Cluster Monitoring Operator (CMO), which in turn configures the components of the stack.

Prerequisites

  • If you are configuring core OpenShift Container Platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Edit the ConfigMap object.

    • To configure core OpenShift Container Platform monitoring components:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Add your configuration under data/config.yaml as a key-value pair <component_name>: <component_configuration>:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>:
              <configuration_for_the_component>

        Substitute <component> and <configuration_for_the_component> accordingly.

        The following example ConfigMap object configures a persistent volume claim (PVC) for Prometheus. This relates to the Prometheus instance that monitors core OpenShift Container Platform components only:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s: 1
              volumeClaimTemplate:
                spec:
                  storageClassName: fast
                  volumeMode: Filesystem
                  resources:
                    requests:
                      storage: 40Gi
        1
        Defines the Prometheus component and the subsequent lines define its configuration.
    • To configure components that monitor user-defined projects:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. Add your configuration under data/config.yaml as a key-value pair <component_name>: <component_configuration>:

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

        Substitute <component> and <configuration_for_the_component> accordingly.

        The following example ConfigMap object configures a data retention period and minimum container resource requests for Prometheus. This relates to the Prometheus instance that monitors user-defined projects only:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            prometheus: 1
              retention: 24h 2
              resources:
                requests:
                  cpu: 200m 3
                  memory: 2Gi 4
        1
        Defines the Prometheus component and the subsequent lines define its configuration.
        2
        Configures a twenty-four hour data retention period for the Prometheus instance that monitors user-defined projects.
        3
        Defines a minimum resource request of 200 millicores for the Prometheus container.
        4
        Defines a minimum pod resource request of 2 GiB of memory for the Prometheus container.
        Note

        The Prometheus config map component is called prometheusK8s in the cluster-monitoring-config ConfigMap object and prometheus in the user-workload-monitoring-config ConfigMap object.

  2. Save the file to apply the changes to the ConfigMap object.

    Warning

    Different configuration changes to the ConfigMap object result in different outcomes:

    • The pods are not redeployed. Therefore, there is no service outage.
    • The affected pods are redeployed:

      • For single-node clusters, this results in temporary service outage.
      • For multi-node clusters, because of high-availability, the affected pods are gradually rolled out and the monitoring stack remains available.
      • Configuring and resizing a persistent volume always results in a service outage, regardless of high availability.

    Each procedure that requires a change in the config map includes its expected outcome.

Additional resources

3.6. Configurable monitoring components

This table shows the monitoring components you can configure and the keys used to specify the components in the cluster-monitoring-config and user-workload-monitoring-config ConfigMap objects.

Table 3.2. Configurable monitoring components
Componentcluster-monitoring-config config map keyuser-workload-monitoring-config config map key

Prometheus Operator

prometheusOperator

prometheusOperator

Prometheus

prometheusK8s

prometheus

Alertmanager

alertmanagerMain

alertmanager

kube-state-metrics

kubeStateMetrics

 

monitoring-plugin

monitoringPlugin

 

openshift-state-metrics

openshiftStateMetrics

 

Telemeter Client

telemeterClient

 

Metrics Server

metricsServer

 

Thanos Querier

thanosQuerier

 

Thanos Ruler

 

thanosRuler

Note

The Prometheus key is called prometheusK8s in the cluster-monitoring-config ConfigMap object and prometheus in the user-workload-monitoring-config ConfigMap object.

3.7. Using node selectors to move monitoring components

By using the nodeSelector constraint with labeled nodes, you can move any of the monitoring stack components to specific nodes. By doing so, you can control the placement and distribution of the monitoring components across a cluster.

By controlling placement and distribution of monitoring components, you can optimize system resource use, improve performance, and segregate workloads based on specific requirements or policies.

3.7.1. How node selectors work with other constraints

If you move monitoring components by using node selector constraints, be aware that other constraints to control pod scheduling might exist for a cluster:

  • Topology spread constraints might be in place to control pod placement.
  • Hard anti-affinity rules are in place for Prometheus, Thanos Querier, Alertmanager, and other monitoring components to ensure that multiple pods for these components are always spread across different nodes and are therefore always highly available.

When scheduling pods onto nodes, the pod scheduler tries to satisfy all existing constraints when determining pod placement. That is, all constraints compound when the pod scheduler determines which pods will be placed on which nodes.

Therefore, if you configure a node selector constraint but existing constraints cannot all be satisfied, the pod scheduler cannot match all constraints and will not schedule a pod for placement onto a node.

To maintain resilience and high availability for monitoring components, ensure that enough nodes are available and match all constraints when you configure a node selector constraint to move a component.

3.7.2. Moving monitoring components to different nodes

To specify the nodes in your cluster on which monitoring stack components will run, configure the nodeSelector constraint in the component’s ConfigMap object to match labels assigned to the nodes.

Note

You cannot add a node selector constraint directly to an existing scheduled pod.

Prerequisites

  • If you are configuring core OpenShift Container Platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. If you have not done so yet, add a label to the nodes on which you want to run the monitoring components:

    $ oc label nodes <node-name> <node-label>
  2. Edit the ConfigMap object:

    • To move a component that monitors core OpenShift Container Platform projects:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Specify the node labels for the nodeSelector constraint for the component under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>: 1
              nodeSelector:
                <node-label-1> 2
                <node-label-2> 3
                <...>
        1
        Substitute <component> with the appropriate monitoring stack component name.
        2
        Substitute <node-label-1> with the label you added to the node.
        3
        Optional: Specify additional labels. If you specify additional labels, the pods for the component are only scheduled on the nodes that contain all of the specified labels.
        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.

    • To move a component that monitors user-defined projects:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. 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.

  3. 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.

3.8. Assigning tolerations to monitoring components

You can assign tolerations to any of the monitoring stack components to enable moving them to tainted nodes.

Prerequisites

  • If you are configuring core OpenShift Container Platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Edit the ConfigMap object:

    • To assign tolerations to a component that monitors core OpenShift Container Platform projects:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Specify tolerations for the component:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>:
              tolerations:
                <toleration_specification>

        Substitute <component> and <toleration_specification> accordingly.

        For example, oc adm taint nodes node1 key1=value1:NoSchedule adds a taint 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 alertmanagerMain component to tolerate the example taint:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            alertmanagerMain:
              tolerations:
              - key: "key1"
                operator: "Equal"
                value: "value1"
                effect: "NoSchedule"
    • To assign tolerations to a component that monitors user-defined projects:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. 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"
  2. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.

Additional resources

3.9. Setting the body size limit for metrics scraping

By default, no limit exists for the uncompressed body size for data returned from scraped metrics targets. You can set a body size limit to help avoid situations in which Prometheus consumes excessive amounts of memory when scraped targets return a response that contains a large amount of data. In addition, by setting a body size limit, you can reduce the impact that a malicious target might have on Prometheus and on the cluster as a whole.

After you set a value for enforcedBodySizeLimit, the alert PrometheusScrapeBodySizeLimitHit fires when at least one Prometheus scrape target replies with a response body larger than the configured value.

Note

If metrics data scraped from a target has an uncompressed body size exceeding the configured size limit, the scrape fails. Prometheus then considers this target to be down and sets its up metric value to 0, which can trigger the TargetDown alert.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring namespace:

    $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
  2. Add a value for enforcedBodySizeLimit to data/config.yaml/prometheusK8s to limit the body size that can be accepted per target scrape:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: cluster-monitoring-config
      namespace: openshift-monitoring
    data:
      config.yaml: |-
        prometheusK8s:
          enforcedBodySizeLimit: 40MB 1
    1
    Specify the maximum body size for scraped metrics targets. This enforcedBodySizeLimit example limits the uncompressed size per target scrape to 40 megabytes. Valid numeric values use the Prometheus data size format: B (bytes), KB (kilobytes), MB (megabytes), GB (gigabytes), TB (terabytes), PB (petabytes), and EB (exabytes). The default value is 0, which specifies no limit. You can also set the value to automatic to calculate the limit automatically based on cluster capacity.
  3. Save the file to apply the changes. The new configuration is applied automatically.

3.10. Managing CPU and memory resources for monitoring components

You can ensure that the containers that run monitoring components have enough CPU and memory resources by specifying values for resource limits and requests for those components.

You can configure these limits and requests for core platform monitoring components in the openshift-monitoring namespace and for the components that monitor user-defined projects in the openshift-user-workload-monitoring namespace.

3.10.1. About specifying limits and requests for monitoring components

You can configure resource limits and request settings for core platform monitoring components and for the components that monitor user-defined projects, including the following components:

  • Alertmanager (for core platform monitoring and for user-defined projects)
  • kube-state-metrics
  • monitoring-plugin
  • node-exporter
  • openshift-state-metrics
  • Prometheus (for core platform monitoring and for user-defined projects)
  • Metrics Server
  • Prometheus Operator and its admission webhook service
  • Telemeter Client
  • Thanos Querier
  • Thanos Ruler

By defining resource limits, you limit a container’s resource usage, which prevents the container from exceeding the specified maximum values for CPU and memory resources.

By defining resource requests, you specify that a container can be scheduled only on a node that has enough CPU and memory resources available to match the requested resources.

3.10.2. Specifying limits and requests for monitoring components

To configure CPU and memory resources, specify values for resource limits and requests in the appropriate ConfigMap object for the namespace in which the monitoring component is located:

  • The cluster-monitoring-config config map in the openshift-monitoring namespace for core platform monitoring
  • The user-workload-monitoring-config config map in the openshift-user-workload-monitoring namespace for components that monitor user-defined projects

Prerequisites

  • If you are configuring core platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created a ConfigMap object named cluster-monitoring-config.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
  • You have installed the OpenShift CLI (oc).

Procedure

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

    $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
  2. Add values to define resource limits and requests for each core platform monitoring component you want to configure.

    Important

    Make sure that the value set for a limit is always higher than the value set for a request. Otherwise, an error will occur, and the container will not run.

    Example

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: cluster-monitoring-config
      namespace: openshift-monitoring
    data:
      config.yaml: |
        alertmanagerMain:
          resources:
            limits:
              cpu: 500m
              memory: 1Gi
            requests:
              cpu: 200m
              memory: 500Mi
        prometheusK8s:
          resources:
            limits:
              cpu: 500m
              memory: 3Gi
            requests:
              cpu: 200m
              memory: 500Mi
        prometheusOperator:
          resources:
            limits:
              cpu: 500m
              memory: 1Gi
            requests:
              cpu: 200m
              memory: 500Mi
        metricsServer:
          resources:
            requests:
              cpu: 10m
              memory: 50Mi
            limits:
              cpu: 50m
              memory: 500Mi
        kubeStateMetrics:
          resources:
            limits:
              cpu: 500m
              memory: 1Gi
            requests:
              cpu: 200m
              memory: 500Mi
        telemeterClient:
          resources:
            limits:
              cpu: 500m
              memory: 1Gi
            requests:
              cpu: 200m
              memory: 500Mi
        openshiftStateMetrics:
          resources:
            limits:
              cpu: 500m
              memory: 1Gi
            requests:
              cpu: 200m
              memory: 500Mi
        thanosQuerier:
          resources:
            limits:
              cpu: 500m
              memory: 1Gi
            requests:
              cpu: 200m
              memory: 500Mi
        nodeExporter:
          resources:
            limits:
              cpu: 50m
              memory: 150Mi
            requests:
              cpu: 20m
              memory: 50Mi
        monitoringPlugin:
          resources:
            limits:
              cpu: 500m
              memory: 1Gi
            requests:
              cpu: 200m
              memory: 500Mi
        prometheusOperatorAdmissionWebhook:
          resources:
            limits:
              cpu: 50m
              memory: 100Mi
            requests:
              cpu: 20m
              memory: 50Mi

  3. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.

3.11. Configuring persistent storage

Run cluster monitoring with persistent storage to gain the following benefits:

  • Protect your metrics and alerting data from data loss by storing them in a persistent volume (PV). As a result, they can survive pods being restarted or recreated.
  • Avoid getting duplicate notifications and losing silences for alerts when the Alertmanager pods are restarted.

For production environments, it is highly recommended to configure persistent storage.

Important

In multi-node clusters, you must configure persistent storage for Prometheus, Alertmanager, and Thanos Ruler to ensure high availability.

3.11.1. 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.

3.11.2. Configuring a persistent volume claim

To use a persistent volume (PV) for monitoring components, you must configure a persistent volume claim (PVC).

Prerequisites

  • If you are configuring core OpenShift Container Platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Edit the ConfigMap object:

    • To configure a PVC for a component that monitors core OpenShift Container Platform projects:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Add your PVC configuration for the component under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>: 1
              volumeClaimTemplate:
                spec:
                  storageClassName: <storage_class> 2
                  resources:
                    requests:
                      storage: <amount_of_storage> 3
        1
        Specify the core 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.

        See the Kubernetes documentation on PersistentVolumeClaims for information on how to specify volumeClaimTemplate.

        The following example configures a PVC that claims persistent storage for the Prometheus instance that monitors core OpenShift Container Platform components:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              volumeClaimTemplate:
                spec:
                  storageClassName: my-storage-class
                  resources:
                    requests:
                      storage: 40Gi
    • To configure a PVC for a component that monitors user-defined projects:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. 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 component for user-defined monitoring 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.

        See the Kubernetes documentation on PersistentVolumeClaims for information on how to specify volumeClaimTemplate.

        The following example configures a PVC that claims persistent storage for Thanos Ruler:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            thanosRuler:
              volumeClaimTemplate:
                spec:
                  storageClassName: my-storage-class
                  resources:
                    requests:
                      storage: 10Gi
        Note

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

  2. 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.

3.11.3. Resizing a persistent volume

You can resize a persistent volume (PV) for monitoring components, such as Prometheus, Thanos Ruler, or 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 installed the OpenShift CLI (oc).
  • If you are configuring core OpenShift Container Platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
    • You have configured at least one PVC for core OpenShift Container Platform monitoring components.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
    • You have configured at least one PVC for components that monitor user-defined projects.

Procedure

  1. 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.
  2. Edit the ConfigMap object:

    • If you are configuring core OpenShift Container Platform monitoring components:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Add a new storage size for the PVC configuration for the component under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>: 1
              volumeClaimTemplate:
                spec:
                  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 previous value.

        The following example sets the new PVC request to 100 gigabytes for the Prometheus instance that monitors core OpenShift Container Platform components:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              volumeClaimTemplate:
                spec:
                  resources:
                    requests:
                      storage: 100Gi
    • If you are configuring components that monitor user-defined projects:

      Note

      You can resize the volumes for the Thanos Ruler and for instances of Alertmanager and Prometheus that monitor user-defined projects.

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. Update the PVC configuration for the monitoring component under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            <component>: 1
              volumeClaimTemplate:
                spec:
                  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 previous value.

        The following example sets the new PVC request to 20 gigabytes for Thanos Ruler:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            thanosRuler:
              volumeClaimTemplate:
                spec:
                  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.

  3. 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.

3.11.4. Modifying the retention time and size for Prometheus metrics data

By default, Prometheus retains metrics data for the following durations:

  • Core platform monitoring: 15 days
  • Monitoring for user-defined projects: 24 hours

You can modify the retention time for Prometheus to change how soon the data is deleted. You can also set the maximum amount of disk space the retained metrics data uses. If the data reaches this size limit, Prometheus deletes the oldest data first until the disk space used is again below the limit.

Note the following behaviors of these data retention settings:

  • The size-based retention policy applies to all data block directories in the /prometheus directory, including persistent blocks, write-ahead log (WAL) data, and m-mapped chunks.
  • Data in the /wal and /head_chunks directories counts toward the retention size limit, but Prometheus never purges data from these directories based on size- or time-based retention policies. Thus, if you set a retention size limit lower than the maximum size set for the /wal and /head_chunks directories, you have configured the system not to retain any data blocks in the /prometheus data directories.
  • The size-based retention policy is applied only when Prometheus cuts a new data block, which occurs every two hours after the WAL contains at least three hours of data.
  • If you do not explicitly define values for either retention or retentionSize, retention time defaults to 15 days for core platform monitoring and 24 hours for user-defined project monitoring. Retention size is not set.
  • If you define values for both retention and retentionSize, both values apply. If any data blocks exceed the defined retention time or the defined size limit, Prometheus purges these data blocks.
  • If you define a value for retentionSize and do not define retention, only the retentionSize value applies.
  • If you do not define a value for retentionSize and only define a value for retention, only the retention value applies.
  • If you set the retentionSize or retention value to 0, the default settings apply. The default settings set retention time to 15 days for core platform monitoring and 24 hours for user-defined project monitoring. By default, retention size is not set.
Note

Data compaction occurs every two hours. Therefore, a persistent volume (PV) might fill up before compaction, potentially exceeding the retentionSize limit. In such cases, the KubePersistentVolumeFillingUp alert fires until the space on a PV is lower than the retentionSize limit.

Prerequisites

  • If you are configuring core OpenShift Container Platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Edit the ConfigMap object:

    • To modify the retention time and size for the Prometheus instance that monitors core OpenShift Container Platform projects:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Add the retention time and size configuration under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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 that monitors core OpenShift Container Platform components:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              retention: 24h
              retentionSize: 10GB
    • To modify the retention time and size for the Prometheus instance that monitors user-defined projects:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. Add the retention time and size configuration under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            prometheus:
              retention: <time_specification> 1
              retentionSize: <size_specification> 2
        1
        The retention time: a number directly followed by ms (milliseconds), s (seconds), m (minutes), h (hours), d (days), w (weeks), or y (years). You can also combine time values for specific times, such as 1h30m15s.
        2
        The retention size: a number directly followed by B (bytes), KB (kilobytes), MB (megabytes), GB (gigabytes), TB (terabytes), PB (petabytes), or EB (exabytes).

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            prometheus:
              retention: 24h
              retentionSize: 10GB
  2. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.

3.11.5. Modifying the retention time for Thanos Ruler metrics data

By default, for user-defined projects, Thanos Ruler automatically retains metrics data for 24 hours. You can modify the retention time to change how long this data is retained by specifying a time value in the user-workload-monitoring-config config map in the openshift-user-workload-monitoring namespace.

Prerequisites

  • You have access to the cluster as a user with the 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

  1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

    $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
  2. 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
  3. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.

3.12. Configuring remote write storage

You can configure remote write storage to enable Prometheus to send ingested metrics to remote systems for long-term storage. Doing so has no impact on how or for how long Prometheus stores metrics.

Prerequisites

  • If you are configuring core OpenShift Container Platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • 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 same namespace as the Prometheus object for which you configure remote write: the openshift-monitoring namespace for default platform monitoring or the openshift-user-workload-monitoring namespace for user workload monitoring.

    Warning

    To reduce security risks, use HTTPS and authentication to send metrics to an endpoint.

Procedure

  1. Edit the ConfigMap object:

    • To configure remote write for the Prometheus instance that monitors core OpenShift Container Platform projects:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Add a remoteWrite: section under data/config.yaml/prometheusK8s, as shown in the following example:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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.
      3. Add write relabel configuration values after the authentication credentials:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              remoteWrite:
              - url: "https://remote-write-endpoint.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: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              remoteWrite:
              - url: "https://remote-write-endpoint.example.com"
                writeRelabelConfigs:
                - sourceLabels: [__name__,namespace]
                  regex: '(my_metric_1|my_metric_2);my_namespace'
                  action: keep

    • To configure remote write for the Prometheus instance that monitors user-defined projects:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. 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 an Authorization request header, basic authentication, OAuth 2.0, and TLS client. See Supported remote write authentication settings below for sample configurations of supported authentication methods.
      3. 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

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

3.12.1. 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 methodConfig map fieldDescription

AWS Signature Version 4

sigv4

This method uses AWS Signature Version 4 authentication to sign requests. You cannot use this method simultaneously with authorization, OAuth 2.0, or Basic authentication.

Basic authentication

basicAuth

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

authorization

authorization

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

OAuth 2.0

oauth2

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

TLS client

tlsConfig

A TLS client configuration specifies the CA certificate, the client certificate, and the client key file information used to authenticate with the remote write endpoint server using TLS. The sample configuration assumes that you have already created a CA certificate file, a client certificate file, and a client key file.

3.12.2. 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 default platform monitoring in the openshift-monitoring namespace.

3.12.2.1. Sample YAML for AWS Signature Version 4 authentication

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

apiVersion: v1
kind: Secret
metadata:
  name: sigv4-credentials
  namespace: openshift-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-monitoring namespace:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      remoteWrite:
      - url: "https://authorization.example.com/api/write"
        sigv4:
          region: <AWS_region> 1
          accessKey:
            name: sigv4-credentials 2
            key: accessKey 3
          secretKey:
            name: sigv4-credentials 4
            key: secretKey 5
          profile: <AWS_profile_name> 6
          roleArn: <AWS_role_arn> 7
1
The AWS region.
2 4
The name of the Secret object containing the AWS API access credentials.
3
The key that contains the AWS API access key in the specified Secret object.
5
The key that contains the AWS API secret key in the specified Secret object.
6
The name of the AWS profile that is being used to authenticate.
7
The unique identifier for the Amazon Resource Name (ARN) assigned to your role.
3.12.2.2. Sample YAML for Basic authentication

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

apiVersion: v1
kind: Secret
metadata:
  name: rw-basic-auth
  namespace: openshift-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-monitoring namespace. It assumes that you have already set up authentication credentials for the endpoint.

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      remoteWrite:
      - url: "https://basicauth.example.com/api/write"
        basicAuth:
          username:
            name: rw-basic-auth 1
            key: user 2
          password:
            name: rw-basic-auth 3
            key: password 4
1 3
The name of the Secret object that contains the authentication credentials.
2
The key that contains the username in the specified Secret object.
4
The key that contains the password in the specified Secret object.
3.12.2.3. 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-monitoring namespace:

apiVersion: v1
kind: Secret
metadata:
  name: rw-bearer-auth
  namespace: openshift-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-monitoring namespace:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    enableUserWorkload: true
    prometheusK8s:
      remoteWrite:
      - url: "https://authorization.example.com/api/write"
        authorization:
          type: Bearer 1
          credentials:
            name: rw-bearer-auth 2
            key: token 3
1
The authentication type of the request. The default value is Bearer.
2
The name of the Secret object that contains the authentication credentials.
3
The key that contains the authentication token in the specified Secret object.
3.12.2.4. 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-monitoring namespace:

apiVersion: v1
kind: Secret
metadata:
  name: oauth2-credentials
  namespace: openshift-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-monitoring namespace:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      remoteWrite:
      - url: "https://test.example.com/api/write"
        oauth2:
          clientId:
            secret:
              name: oauth2-credentials 1
              key: id 2
          clientSecret:
            name: oauth2-credentials 3
            key: secret 4
          tokenUrl: https://example.com/oauth2/token 5
          scopes: 6
          - <scope_1>
          - <scope_2>
          endpointParams: 7
            param1: <parameter_1>
            param2: <parameter_2>
1 3
The name of the corresponding Secret object. Note that ClientId can alternatively refer to a ConfigMap object, although clientSecret must refer to a Secret object.
2 4
The key that contains the OAuth 2.0 credentials in the specified Secret object.
5
The URL used to fetch a token with the specified clientId and clientSecret.
6
The OAuth 2.0 scopes for the authorization request. These scopes limit what data the tokens can access.
7
The OAuth 2.0 authorization request parameters required for the authorization server.
3.12.2.5. Sample YAML for TLS client authentication

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

apiVersion: v1
kind: Secret
metadata:
  name: mtls-bundle
  namespace: openshift-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: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      remoteWrite:
      - url: "https://remote-write-endpoint.example.com"
        tlsConfig:
          ca:
            secret:
              name: mtls-bundle 1
              key: ca.crt 2
          cert:
            secret:
              name: mtls-bundle 3
              key: client.crt 4
          keySecret:
            name: mtls-bundle 5
            key: client.key 6
1 3 5
The name of the corresponding Secret object that contains the TLS authentication credentials. Note that ca and cert can alternatively refer to a ConfigMap object, though keySecret must refer to a Secret object.
2
The key in the specified Secret object that contains the CA certificate for the endpoint.
4
The key in the specified Secret object that contains the client certificate for the endpoint.
6
The key in the specified Secret object that contains the client key secret.

3.12.3. 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 default platform monitoring in the openshift-monitoring namespace.

Example configuration of remote write parameters with default values

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    prometheusK8s:
      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.

3.13. Adding cluster ID labels to metrics

If you manage multiple OpenShift Container Platform clusters and use the remote write feature to send metrics data from these clusters to an external storage location, you can add cluster ID labels to identify the metrics data coming from different clusters. You can then query these labels to identify the source cluster for a metric and distinguish that data from similar metrics data sent by other clusters.

This way, if you manage many clusters for multiple customers and send metrics data to a single centralized storage system, you can use cluster ID labels to query metrics for a particular cluster or customer.

Creating and using cluster ID labels involves three general steps:

  • Configuring the write relabel settings for remote write storage.
  • Adding cluster ID labels to the metrics.
  • Querying these labels to identify the source cluster or customer for a metric.

3.13.1. Creating cluster ID labels for metrics

You can create cluster ID labels for metrics for default platform monitoring and for user workload monitoring.

For default platform monitoring, you add cluster ID labels for metrics in the write_relabel settings for remote write storage in the cluster-monitoring-config config map in the openshift-monitoring namespace.

For user workload monitoring, you edit the settings 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

  • If you are configuring default platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
  • You have installed the OpenShift CLI (oc).
  • You have configured remote write storage.

Procedure

  1. Edit the ConfigMap object:

    • To create cluster ID labels for core OpenShift Container Platform metrics:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. In the writeRelabelConfigs: section under data/config.yaml/prometheusK8s/remoteWrite, add cluster ID relabel configuration values:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              remoteWrite:
              - url: "https://remote-write-endpoint.example.com"
                <endpoint_authentication_credentials>
                writeRelabelConfigs: 1
                  - <relabel_config> 2
        1
        Add a list of write relabel configurations for metrics that you want to send to the remote endpoint.
        2
        Substitute the label configuration for the metrics sent to the remote write endpoint.

        The following sample shows how to forward a metric with the cluster ID label cluster_id in default platform monitoring:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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.
    • To create cluster ID labels for user-defined project metrics:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. In the writeRelabelConfigs: section under data/config.yaml/prometheus/remoteWrite, add cluster ID relabel configuration values:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            prometheus:
              remoteWrite:
              - url: "https://remote-write-endpoint.example.com"
                <endpoint_authentication_credentials>
                writeRelabelConfigs: 1
                  - <relabel_config> 2
        1
        Add a list of write relabel configurations for metrics that you want to send to the remote endpoint.
        2
        Substitute the label configuration for the metrics sent to the remote write endpoint.

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            prometheus:
              remoteWrite:
              - url: "https://remote-write-endpoint.example.com"
                writeRelabelConfigs:
                - sourceLabels:
                  - __tmp_openshift_cluster_id__ 1
                  targetLabel: cluster_id 2
                  action: replace 3
        1
        The system initially applies a temporary cluster ID source label named __tmp_openshift_cluster_id__. This temporary label gets replaced by the cluster ID label name that you specify.
        2
        Specify the name of the cluster ID label for metrics sent to remote write storage. If you use a label name that already exists for a metric, that value is overwritten with the name of this cluster ID label. For the label name, do not use __tmp_openshift_cluster_id__. The final relabeling step removes labels that use this name.
        3
        The replace write relabel action replaces the temporary label with the target label for outgoing metrics. This action is the default and is applied if no action is specified.
  2. Save the file to apply the changes. The new configuration is applied automatically.

3.14. Configuring audit logs for Metrics Server

You can configure audit logs for Metrics Server to help you troubleshoot issues with the server. Audit logs record the sequence of actions in a cluster. It can record user, application, or control plane activities.

You can set audit log rules, which determine what events are recorded and what data they should include. This can be achieved with the following audit profiles:

  • Metadata (default): This profile enables the logging of event metadata including user, timestamps, resource, and verb. It does not record request and response bodies.
  • Request: This enables the logging of event metadata and request body, but it does not record response body. This configuration does not apply for non-resource requests.
  • RequestResponse: This enables the logging of event metadata, and request and response bodies. This configuration does not apply for non-resource requests.
  • None: None of the previously described events are recorded.

You can configure the audit profiles by modifying the cluster-monitoring-config config map. The following example sets the profile to Request, allowing the logging of event metadata and request body for Metrics Server:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    metricsServer:
      audit:
        profile: Request

3.15. Configuring metrics collection profiles

Important

Using a metrics collection profile is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see https://access.redhat.com/support/offerings/techpreview.

By default, Prometheus collects metrics exposed by all default metrics targets in OpenShift Container Platform components. However, you might want Prometheus to collect fewer metrics from a cluster in certain scenarios:

  • If cluster administrators require only alert, telemetry, and console metrics and do not require other metrics to be available.
  • If a cluster increases in size, and the increased size of the default metrics data collected now requires a significant increase in CPU and memory resources.

You can use a metrics collection profile to collect either the default amount of metrics data or a minimal amount of metrics data. When you collect minimal metrics data, basic monitoring features such as alerting continue to work. At the same time, the CPU and memory resources required by Prometheus decrease.

3.15.1. About metrics collection profiles

You can enable one of two metrics collection profiles:

  • full: Prometheus collects metrics data exposed by all platform components. This setting is the default.
  • minimal: Prometheus collects only the metrics data required for platform alerts, recording rules, telemetry, and console dashboards.

3.15.2. Choosing a metrics collection profile

To choose a metrics collection profile for core OpenShift Container Platform monitoring components, edit the cluster-monitoring-config ConfigMap object.

Prerequisites

  • You have installed the OpenShift CLI (oc).
  • You have enabled Technology Preview features by using the FeatureGate custom resource (CR).
  • You have created the cluster-monitoring-config ConfigMap object.
  • You have access to the cluster as a user with the cluster-admin cluster role.

Procedure

  1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

    $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
  2. Add the metrics collection profile setting under data/config.yaml/prometheusK8s:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: cluster-monitoring-config
      namespace: openshift-monitoring
    data:
      config.yaml: |
        prometheusK8s:
          collectionProfile: <metrics_collection_profile_name> 1
    1
    The name of the metrics collection profile. The available values are full or minimal. If you do not specify a value or if the collectionProfile key name does not exist in the config map, the default setting of full is used.

    The following example sets the metrics collection profile to minimal for the core platform instance of Prometheus:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: cluster-monitoring-config
      namespace: openshift-monitoring
    data:
      config.yaml: |
        prometheusK8s:
          collectionProfile: minimal
  3. Save the file to apply the changes. The new configuration is applied automatically.

Additional resources

3.16. Controlling the impact of unbound metrics attributes in user-defined projects

Developers can create labels to define attributes for metrics in the form of key-value pairs. The number of potential key-value pairs corresponds to the number of possible values for an attribute. An attribute that has an unlimited number of potential values is called an unbound attribute. For example, a customer_id attribute is unbound because it has an infinite number of possible values.

Every assigned key-value pair has a unique time series. The use of many unbound attributes in labels can result in an exponential increase in the number of time series created. This can impact Prometheus performance and can consume a lot of disk space.

Cluster administrators can use the following measures to control the impact of unbound metrics attributes in user-defined projects:

  • Limit the number of samples that can be accepted per target scrape in user-defined projects
  • Limit the number of scraped labels, the length of label names, and the length of label values
  • Create alerts that fire when a scrape sample threshold is reached or when the target cannot be scraped
Note

Limiting scrape samples can help prevent the issues caused by adding many unbound attributes to labels. Developers can also prevent the underlying cause by limiting the number of unbound attributes that they define for metrics. Using attributes that are bound to a limited set of possible values reduces the number of potential key-value pair combinations.

3.16.1. Setting scrape sample and label limits for user-defined projects

You can limit the number of samples that can be accepted per target scrape in user-defined projects. You can also limit the number of scraped labels, the length of label names, and the length of label values.

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

  1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

    $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
  2. Add the enforcedSampleLimit configuration to data/config.yaml to limit the number of samples that can be accepted per target scrape in user-defined projects:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: user-workload-monitoring-config
      namespace: openshift-user-workload-monitoring
    data:
      config.yaml: |
        prometheus:
          enforcedSampleLimit: 50000 1
    1
    A value is required if this parameter is specified. This enforcedSampleLimit example limits the number of samples that can be accepted per target scrape in user-defined projects to 50,000.
  3. Add the enforcedLabelLimit, enforcedLabelNameLengthLimit, and enforcedLabelValueLengthLimit configurations to data/config.yaml to limit the number of scraped labels, the length of label names, and the length of label values in user-defined projects:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: user-workload-monitoring-config
      namespace: openshift-user-workload-monitoring
    data:
      config.yaml: |
        prometheus:
          enforcedLabelLimit: 500 1
          enforcedLabelNameLengthLimit: 50 2
          enforcedLabelValueLengthLimit: 600 3
    1
    Specifies the maximum number of labels per scrape. The default value is 0, which specifies no limit.
    2
    Specifies the maximum length in characters of a label name. The default value is 0, which specifies no limit.
    3
    Specifies the maximum length in characters of a label value. The default value is 0, which specifies no limit.
  4. Save the file to apply the changes. The limits are applied automatically.

3.16.2. 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

  1. Create a YAML file with alerts that inform you when the targets are down and when the enforced sample limit is approaching. The file in this example is called monitoring-stack-alerts.yaml:

    apiVersion: monitoring.coreos.com/v1
    kind: PrometheusRule
    metadata:
      labels:
        prometheus: k8s
        role: alert-rules
      name: monitoring-stack-alerts 1
      namespace: ns1 2
    spec:
      groups:
      - name: general.rules
        rules:
        - alert: TargetDown 3
          annotations:
            message: '{{ printf "%.4g" $value }}% of the {{ $labels.job }}/{{ $labels.service
              }} targets in {{ $labels.namespace }} namespace are down.' 4
          expr: 100 * (count(up == 0) BY (job, namespace, service) / count(up) BY (job,
            namespace, service)) > 10
          for: 10m 5
          labels:
            severity: warning 6
        - alert: ApproachingEnforcedSamplesLimit 7
          annotations:
            message: '{{ $labels.container }} container of the {{ $labels.pod }} pod in the {{ $labels.namespace }} namespace consumes {{ $value | humanizePercentage }} of the samples limit budget.' 8
          expr: (scrape_samples_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.
  2. Apply the configuration to the user-defined project:

    $ oc apply -f monitoring-stack-alerts.yaml
  3. Additionally, you can check if a target has hit the configured limit:

    1. In the Administrator perspective of the web console, go to ObserveTargets 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.

Chapter 4. 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 core OpenShift Container Platform projects or 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

  • If you are configuring core OpenShift Container Platform monitoring components in the openshift-monitoring project:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config config map.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Edit the ConfigMap object.

    • To configure additional Alertmanagers for routing alerts from core OpenShift Container Platform projects:

      1. Edit the cluster-monitoring-config config map in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Add an additionalAlertmanagerConfigs: section under data/config.yaml/prometheusK8s.
      3. Add the configuration details for additional Alertmanagers in this section:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              additionalAlertmanagerConfigs:
              - <alertmanager_specification>

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              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
    • To configure additional Alertmanager instances for routing alerts from user-defined projects:

      1. Edit the user-workload-monitoring-config config map in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. Add a <component>/additionalAlertmanagerConfigs: section under data/config.yaml/.
      3. Add the configuration details for additional Alertmanagers in this section:

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

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

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            thanosRuler:
              additionalAlertmanagerConfigs:
              - scheme: https
                pathPrefix: /
                timeout: "30s"
                apiVersion: v1
                bearerToken:
                  name: alertmanager-bearer-token
                  key: token
                tlsConfig:
                  key:
                    name: alertmanager-tls
                    key: tls.key
                  cert:
                    name: alertmanager-tls
                    key: tls.crt
                  ca:
                    name: alertmanager-tls
                    key: tls.ca
                staticConfigs:
                - external-alertmanager1-remote.com
                - external-alertmanager1-remote2.com
  2. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.

Chapter 5. 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.

5.1. Adding a secret to the Alertmanager configuration

You can add secrets to the Alertmanager configuration for core platform monitoring components by editing the cluster-monitoring-config config map in the openshift-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

  • If you are configuring core OpenShift Container Platform monitoring components in the openshift-monitoring project:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config config map.
    • You have created the secret to be configured in Alertmanager in the openshift-monitoring project.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • You have created the secret to be configured in Alertmanager 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

  1. Edit the ConfigMap object.

    • To add a secret configuration to Alertmanager for core platform monitoring:

      1. Edit the cluster-monitoring-config config map in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Add a secrets: section under data/config.yaml/alertmanagerMain with the following configuration:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            alertmanagerMain:
              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: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            alertmanagerMain:
              secrets:
              - test-secret-basic-auth
              - test-secret-api-token
    • To add a secret configuration to Alertmanager for user-defined project monitoring:

      1. Edit the user-workload-monitoring-config config map in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. Add a secrets: section under data/config.yaml/alertmanager/secrets with the following configuration:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            alertmanager:
              secrets: 1
              - <secret_name_1> 2
              - <secret_name_2>
        1
        This section contains the secrets to be mounted into Alertmanager. The secrets must be located within the same namespace as the Alertmanager object.
        2
        The name of the Secret object that contains authentication credentials for the receiver. If you add multiple secrets, place each one on a new line.

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

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            alertmanager:
              enabled: true
              secrets:
              - test-secret
              - test-api-receiver-token
  2. Save the file to apply the changes. The new configuration is applied automatically.

5.2. 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

  • If you are configuring core OpenShift Container Platform monitoring components:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring components that monitor user-defined projects:

    • You have access to the cluster as a user with the cluster-admin cluster role, or as a user with the user-workload-monitoring-config-edit role in the openshift-user-workload-monitoring project.
    • A cluster administrator has enabled monitoring for user-defined projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Edit the ConfigMap object:

    • To attach custom labels to all time series and alerts leaving the Prometheus instance that monitors core OpenShift Container Platform projects:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Define a map of labels you want to add for every metric under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              externalLabels:
                <key>: <value> 1
        1
        Substitute <key>: <value> with a map of key-value pairs where <key> is a unique name for the new label and <value> is its value.
        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.

        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: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            prometheusK8s:
              externalLabels:
                region: eu
                environment: prod
      3. Save the file to apply the changes. The new configuration is applied automatically.
    • To attach custom labels to all time series and alerts leaving the Prometheus instance that monitors user-defined projects:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. Define a map of labels you want to add for every metric under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            prometheus:
              externalLabels:
                <key>: <value> 1
        1
        Substitute <key>: <value> with a map of key-value pairs where <key> is a unique name for the new label and <value> is its value.
        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 related to user-defined projects, use the following example:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            prometheus:
              externalLabels:
                region: eu
                environment: prod
      3. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.

Additional resources

Chapter 6. Using pod topology spread constraints for monitoring

You can use pod topology spread constraints to control how the monitoring pods are spread across a network topology when OpenShift Container Platform pods are deployed in multiple availability zones.

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

6.1. Configuring pod topology spread constraints

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

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

Prerequisites

  • If you are configuring pods for core OpenShift Container Platform monitoring:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are configuring pods for user-defined monitoring:

    • 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

  • To configure pod topology spread constraints for core OpenShift Container Platform monitoring:

    1. Edit the cluster-monitoring-config config map in the openshift-monitoring project:

      $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
    2. Add the following settings under the data/config.yaml field to configure pod topology spread constraints:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: cluster-monitoring-config
        namespace: openshift-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 Prometheus

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: cluster-monitoring-config
        namespace: openshift-monitoring
      data:
        config.yaml: |
          prometheusK8s:
            topologySpreadConstraints:
            - maxSkew: 1
              topologyKey: monitoring
              whenUnsatisfiable: DoNotSchedule
              labelSelector:
                matchLabels:
                  app.kubernetes.io/name: prometheus

    3. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
  • To configure pod topology spread constraints for user-defined monitoring:

    1. Edit the user-workload-monitoring-config config map in the openshift-user-workload-monitoring project:

      $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
    2. 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

    3. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.

6.2. Setting log levels for monitoring components

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

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

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

The default log level is info.

Prerequisites

  • If you are setting a log level for Alertmanager, Prometheus Operator, Prometheus, or Thanos Querier in the openshift-monitoring project:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are setting a log level for Prometheus Operator, Prometheus, or Thanos Ruler in the openshift-user-workload-monitoring project:

    • 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

  1. Edit the ConfigMap object:

    • To set a log level for a component in the openshift-monitoring project:

      1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

        $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
      2. Add logLevel: <log_level> for a component under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: cluster-monitoring-config
          namespace: openshift-monitoring
        data:
          config.yaml: |
            <component>: 1
              logLevel: <log_level> 2
        1
        The monitoring stack component for which you are setting a log level. For default platform monitoring, available component values are prometheusK8s, alertmanagerMain, prometheusOperator, and thanosQuerier.
        2
        The log level to set for the component. The available values are error, warn, info, and debug. The default value is info.
    • To set a log level for a component in the openshift-user-workload-monitoring project:

      1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

        $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
      2. Add logLevel: <log_level> for a component under data/config.yaml:

        apiVersion: v1
        kind: ConfigMap
        metadata:
          name: user-workload-monitoring-config
          namespace: openshift-user-workload-monitoring
        data:
          config.yaml: |
            <component>: 1
              logLevel: <log_level> 2
        1
        The monitoring stack component for which you are setting a log level. For user workload monitoring, available component values are alertmanager, prometheus, prometheusOperator, and thanosRuler.
        2
        The log level to apply to the component. The available values are error, warn, info, and debug. The default value is info.
  2. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
  3. Confirm that the log-level has been applied by reviewing the deployment or pod configuration in the related project. The following example checks the log level in the prometheus-operator deployment in the openshift-user-workload-monitoring project:

    $ oc -n openshift-user-workload-monitoring get deploy prometheus-operator -o yaml | grep "log-level"

    Example output

            - --log-level=debug

  4. Check that the pods for the component are running. The following example lists the status of pods in the openshift-user-workload-monitoring project:

    $ oc -n openshift-user-workload-monitoring get pods
    Note

    If an unrecognized logLevel value is included in the ConfigMap object, the pods for the component might not restart successfully.

6.3. 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. You can do so for default platform monitoring and for user-defined workload monitoring.

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

  • If you are enabling the query log file feature for Prometheus in the openshift-monitoring project:

    • You have access to the cluster as a user with the cluster-admin cluster role.
    • You have created the cluster-monitoring-config ConfigMap object.
  • If you are enabling the query log file feature for Prometheus in the openshift-user-workload-monitoring project:

    • 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

  • To set the query log file for Prometheus in the openshift-monitoring project:

    1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

      $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
    2. Add queryLogFile: <path> for prometheusK8s under data/config.yaml:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: cluster-monitoring-config
        namespace: openshift-monitoring
      data:
        config.yaml: |
          prometheusK8s:
            queryLogFile: <path> 1
      1
      The full path to the file in which queries will be logged.
    3. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
    4. Verify that the pods for the component are running. The following sample command lists the status of pods in the openshift-monitoring project:

      $ oc -n openshift-monitoring get pods
    5. Read the query log:

      $ oc -n openshift-monitoring exec prometheus-k8s-0 -- cat <path>
      Important

      Revert the setting in the config map after you have examined the logged query information.

  • To set the query log file for Prometheus in the openshift-user-workload-monitoring project:

    1. Edit the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project:

      $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
    2. Add queryLogFile: <path> for prometheus under data/config.yaml:

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: user-workload-monitoring-config
        namespace: openshift-user-workload-monitoring
      data:
        config.yaml: |
          prometheus:
            queryLogFile: <path> 1
      1
      The full path to the file in which queries will be logged.
    3. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
    4. Verify that the pods for the component are running. The following example command lists the status of pods in the openshift-user-workload-monitoring project:

      $ oc -n openshift-user-workload-monitoring get pods
    5. Read the query log:

      $ 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

6.4. Enabling query logging for Thanos Querier

For default platform monitoring in the openshift-monitoring project, you can enable the Cluster Monitoring Operator (CMO) to log all queries run by Thanos Querier.

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 installed the OpenShift CLI (oc).
  • You have access to the cluster as a user with the cluster-admin cluster role.
  • You have created the cluster-monitoring-config ConfigMap object.

Procedure

You can enable query logging for Thanos Querier in the openshift-monitoring project:

  1. Edit the cluster-monitoring-config ConfigMap object in the openshift-monitoring project:

    $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
  2. Add a thanosQuerier section under data/config.yaml and add values as shown in the following example:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: cluster-monitoring-config
      namespace: openshift-monitoring
    data:
      config.yaml: |
        thanosQuerier:
          enableRequestLogging: <value> 1
          logLevel: <value> 2
    1
    Set the value to true to enable logging and false to disable logging. The default value is false.
    2
    Set the value to debug, info, warn, or error. If no value exists for logLevel, the log level defaults to error.
  3. Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.

Verification

  1. Verify that the Thanos Querier pods are running. The following sample command lists the status of pods in the openshift-monitoring project:

    $ oc -n openshift-monitoring get pods
  2. Run a test query using the following sample commands as a model:

    $ token=`oc create token prometheus-k8s -n openshift-monitoring`
    $ oc -n openshift-monitoring exec -c prometheus prometheus-k8s-0 -- curl -k -H "Authorization: Bearer $token" 'https://thanos-querier.openshift-monitoring.svc:9091/api/v1/query?query=cluster_version'
  3. Run the following command to read the query log:

    $ oc -n openshift-monitoring logs <thanos_querier_pod_name> -c thanos-query
    Note

    Because the thanos-querier pods are highly available (HA) pods, you might be able to see logs in only one pod.

  4. After you examine the logged query information, disable query logging by changing the enableRequestLogging value to false in the config map.

Additional resources

Additional resources

6.5. Disabling the local Alertmanager

A local Alertmanager that routes alerts from Prometheus instances is enabled by default in the openshift-monitoring project of the OpenShift Container Platform monitoring stack.

If you do not need the local Alertmanager, you can disable it by configuring the cluster-monitoring-config config map in the openshift-monitoring project.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role.
  • You have created the cluster-monitoring-config config map.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Edit the cluster-monitoring-config config map in the openshift-monitoring project:

    $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
  2. Add enabled: false for the alertmanagerMain component under data/config.yaml:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: cluster-monitoring-config
      namespace: openshift-monitoring
    data:
      config.yaml: |
        alertmanagerMain:
          enabled: false
  3. Save the file to apply the changes. The Alertmanager instance is disabled automatically when you apply the change.

Chapter 7. 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.

7.1. 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

  1. Edit the cluster-monitoring-config ConfigMap object:

    $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
  2. 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.
  3. 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.

  4. 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:

    $ 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

7.2. Granting users permission to monitor 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 7.1. Monitoring roles
Role nameDescriptionProject

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 7.2. Monitoring cluster roles
Cluster role nameDescriptionProject

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

7.2.1. 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

  1. In the Administrator perspective of the OpenShift Container Platform web console, go to User ManagementRoleBindingsCreate binding.
  2. In the Binding Type section, select the Namespace Role Binding type.
  3. In the Name field, enter a name for the role binding.
  4. 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.

  5. Select a monitoring role or cluster role from the Role Name list.
  6. In the Subject section, select User.
  7. In the Subject Name field, enter the name of the user.
  8. Select Create to apply the role binding.

7.2.2. 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:

    $ 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:

    $ 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.

7.3. 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

  1. Assign the user-workload-monitoring-config-edit role to a user in the openshift-user-workload-monitoring project:

    $ oc -n openshift-user-workload-monitoring adm policy add-role-to-user \
      user-workload-monitoring-config-edit <user> \
      --role-namespace openshift-user-workload-monitoring
  2. Verify that the user is correctly assigned to the user-workload-monitoring-config-edit role by displaying the related role binding:

    $ oc describe rolebinding <role_binding_name> -n openshift-user-workload-monitoring

    Example command

    $ 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.

7.4. Accessing metrics from outside the cluster for custom applications

You can query Prometheus metrics from outside the cluster when monitoring your own services with user-defined projects. Access this data from outside the cluster by using the thanos-querier route.

This access only supports using a bearer token for authentication.

Prerequisites

  • You have deployed your own service, following the "Enabling monitoring for user-defined projects" procedure.
  • You are logged in to an account with the cluster-monitoring-view cluster role, which provides permission to access the Thanos Querier API.
  • You are logged in to an account that has permission to get the Thanos Querier API route.

    Note

    If your account does not have permission to get the Thanos Querier API route, a cluster administrator can provide the URL for the route.

Procedure

  1. Extract an authentication token to connect to Prometheus by running the following command:

    $ TOKEN=$(oc whoami -t)
  2. Extract the thanos-querier API route URL by running the following command:

    $ HOST=$(oc -n openshift-monitoring get route thanos-querier -ojsonpath={.status.ingress[].host})
  3. Set the namespace to the namespace in which your service is running by using the following command:

    $ NAMESPACE=ns1
  4. Query the metrics of your own services in the command line by running the following command:

    $ curl -H "Authorization: Bearer $TOKEN" -k "https://$HOST/api/v1/query?" --data-urlencode "query=up{namespace='$NAMESPACE'}"

    The output shows the status for each application pod that Prometheus is scraping:

    The formatted example output

    {
      "status": "success",
      "data": {
        "resultType": "vector",
        "result": [
          {
            "metric": {
              "__name__": "up",
              "endpoint": "web",
              "instance": "10.129.0.46:8080",
              "job": "prometheus-example-app",
              "namespace": "ns1",
              "pod": "prometheus-example-app-68d47c4fb6-jztp2",
              "service": "prometheus-example-app"
            },
            "value": [
              1591881154.748,
              "1"
            ]
          }
        ],
      }
    }

    Note
    • The formatted example output uses a filtering tool, such as jq, to provide the formatted indented JSON. See the jq Manual (jq documentation) for more information about using jq.
    • The command requests an instant query endpoint of the Thanos Querier service, which evaluates selectors at one point in time.

7.5. 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

  1. Add the label to the project namespace:

    $ oc label namespace my-project 'openshift.io/user-monitoring=false'
  2. To re-enable monitoring, remove the label from the namespace:

    $ 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.

7.6. 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

  1. Edit the cluster-monitoring-config ConfigMap object:

    $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
    1. 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
  2. Save the file to apply the changes. Monitoring for user-defined projects is then disabled automatically.
  3. 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:

    $ 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.

Chapter 8. Enabling alert routing for user-defined projects

In OpenShift Container Platform 4.17, a cluster administrator can enable alert routing for user-defined projects. This process consists of two general steps:

  • Enable alert routing for user-defined projects to use the default platform Alertmanager instance or, optionally, 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.

8.1. Understanding alert routing for user-defined projects

As a cluster administrator, you can enable alert routing for user-defined projects. With this feature, you can allow users with the alert-routing-edit role to configure alert notification routing and receivers for user-defined projects. These notifications are routed by the default Alertmanager instance or, if enabled, an optional Alertmanager instance dedicated to user-defined monitoring.

Users can then create and configure user-defined alert routing by creating or editing the AlertmanagerConfig objects for their user-defined projects without the help of an administrator.

After a user has defined alert routing for a user-defined project, user-defined alert notifications are routed as follows:

  • To the alertmanager-main pods in the openshift-monitoring namespace if using the default platform Alertmanager instance.
  • To the alertmanager-user-workload pods in the openshift-user-workload-monitoring namespace if you have enabled a separate instance of Alertmanager for user-defined projects.
Note

The following are limitations of alert routing for user-defined projects:

  • For user-defined alerting rules, user-defined 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.

8.2. 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

  1. Edit the cluster-monitoring-config ConfigMap object:

    $ oc -n openshift-monitoring edit configmap cluster-monitoring-config
  2. 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.
  3. Save the file to apply the changes. The new configuration is applied automatically.

8.3. 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

  1. Edit the user-workload-monitoring-config ConfigMap object:

    $ oc -n openshift-user-workload-monitoring edit configmap user-workload-monitoring-config
  2. 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.
  3. 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

8.4. 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:

    $ 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.

Chapter 9. Managing metrics

You can collect metrics to monitor how cluster components and your own workloads are performing.

9.1. Understanding metrics

In OpenShift Container Platform 4.17, cluster components are monitored by scraping metrics exposed through service endpoints. You can also configure metrics collection for user-defined projects. Metrics enable you to monitor how cluster components and your own workloads are performing.

You can define the metrics that you want to provide for your own workloads by using Prometheus client libraries at the application level.

In OpenShift Container Platform, metrics are exposed through an HTTP service endpoint under the /metrics canonical name. You can list all available metrics for a service by running a curl query against http://<endpoint>/metrics. For instance, you can expose a route to the prometheus-example-app example application and then run the following to view all of its available metrics:

$ curl http://<example_app_endpoint>/metrics

Example output

# HELP http_requests_total Count of all HTTP requests
# TYPE http_requests_total counter
http_requests_total{code="200",method="get"} 4
http_requests_total{code="404",method="get"} 2
# HELP version Version information about this binary
# TYPE version gauge
version{version="v0.1.0"} 1

9.2. 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.

9.2.1. 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

  1. Create a YAML file for the service configuration. In this example, it is called prometheus-example-app.yaml.
  2. 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.

  3. Apply the configuration to the cluster:

    $ oc apply -f prometheus-example-app.yaml

    It takes some time to deploy the service.

  4. You can check that the pod is running:

    $ oc -n ns1 get pod

    Example output

    NAME                                      READY     STATUS    RESTARTS   AGE
    prometheus-example-app-7857545cb7-sbgwq   1/1       Running   0          81m

9.2.2. 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

  1. Create a new YAML configuration file named example-app-service-monitor.yaml.
  2. 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.

  3. Apply the configuration to the cluster:

    $ oc apply -f example-app-service-monitor.yaml

    It takes some time to deploy the ServiceMonitor resource.

  4. Verify that the ServiceMonitor resource is running:

    $ oc -n <namespace> get servicemonitor

    Example output

    NAME                         AGE
    prometheus-example-monitor   81m

9.2.3. 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.

9.2.3.1. 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.

9.2.3.2. 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.
9.2.3.3. 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.

9.3. Viewing a list of available metrics

As a cluster administrator or as a user with view permissions for all projects, you can view a list of metrics available in a cluster and output the list in JSON format.

Prerequisites

  • You are a cluster administrator, or you have access to the cluster as a user with the cluster-monitoring-view cluster role.
  • You have installed the OpenShift Container Platform CLI (oc).
  • You have obtained the OpenShift Container Platform API route for Thanos Querier.
  • You are able to get a bearer token by using the oc whoami -t command.

    Important

    You can only use bearer token authentication to access the Thanos Querier API route.

Procedure

  1. If you have not obtained the OpenShift Container Platform API route for Thanos Querier, run the following command:

    $ oc get routes -n openshift-monitoring thanos-querier -o jsonpath='{.status.ingress[0].host}'
  2. Retrieve a list of metrics in JSON format from the Thanos Querier API route by running the following command. This command uses oc to authenticate with a bearer token.

    $ curl -k -H "Authorization: Bearer $(oc whoami -t)" https://<thanos_querier_route>/api/v1/metadata 1
    1
    Replace <thanos_querier_route> with the OpenShift Container Platform API route for Thanos Querier.

9.4. Querying metrics

The OpenShift Container Platform monitoring dashboard enables you to run Prometheus Query Language (PromQL) queries to examine metrics visualized on a plot. This functionality provides information about the state of a cluster and any user-defined workloads that you are monitoring.

As a cluster administrator, you can query metrics for all core OpenShift Container Platform and user-defined projects.

As a developer, you must specify a project name when querying metrics. You must have the required privileges to view metrics for the selected project.

9.4.1. Querying metrics for all projects as a cluster administrator

As a cluster administrator or as a user with view permissions for all projects, you can access metrics for all default OpenShift Container Platform and user-defined projects in the Metrics UI.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role or with view permissions for all projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. From the Administrator perspective in the OpenShift Container Platform web console, select ObserveMetrics.
  2. To add one or more queries, do any of the following:

    OptionDescription

    Create a custom query.

    Add your Prometheus Query Language (PromQL) query to the Expression field.

    As you type a PromQL expression, autocomplete suggestions appear in a drop-down list. These suggestions include functions, metrics, labels, and time tokens. You can use the keyboard arrows to select one of these suggested items and then press Enter to add the item to your expression. You can also move your mouse pointer over a suggested item to view a brief description of that item.

    Add multiple queries.

    Select Add query.

    Duplicate an existing query.

    Select the Options menu kebab next to the query, then choose Duplicate query.

    Disable a query from being run.

    Select the Options menu kebab next to the query and choose Disable query.

  3. To run queries that you created, select Run queries. The metrics from the queries are visualized on the plot. If a query is invalid, the UI shows an error message.

    Note

    Queries that operate on large amounts of data might time out or overload the browser when drawing time series graphs. To avoid this, select Hide graph and calibrate your query using only the metrics table. Then, after finding a feasible query, enable the plot to draw the graphs.

    Note

    By default, the query table shows an expanded view that lists every metric and its current value. You can select ˅ to minimize the expanded view for a query.

  4. Optional: The page URL now contains the queries you ran. To use this set of queries again in the future, save this URL.
  5. Explore the visualized metrics. Initially, all metrics from all enabled queries are shown on the plot. You can select which metrics are shown by doing any of the following:

    OptionDescription

    Hide all metrics from a query.

    Click the Options menu kebab for the query and click Hide all series.

    Hide a specific metric.

    Go to the query table and click the colored square near the metric name.

    Zoom into the plot and change the time range.

    Either:

    • Visually select the time range by clicking and dragging on the plot horizontally.
    • Use the menu in the left upper corner to select the time range.

    Reset the time range.

    Select Reset zoom.

    Display outputs for all queries at a specific point in time.

    Hold the mouse cursor on the plot at that point. The query outputs will appear in a pop-up box.

    Hide the plot.

    Select Hide graph.

Additional resources

9.4.2. Querying metrics for user-defined projects as a developer

You can access metrics for a user-defined project as a developer or as a user with view permissions for the project.

In the Developer perspective, the Metrics UI includes some predefined CPU, memory, bandwidth, and network packet queries for the selected project. You can also run custom Prometheus Query Language (PromQL) queries for CPU, memory, bandwidth, network packet and application metrics for the project.

Note

Developers can only use the Developer perspective and not the Administrator perspective. As a developer, you can only query metrics for one project at a time.

Prerequisites

  • You have access to the cluster as a developer or as a user with view permissions for the project that you are viewing metrics for.
  • You have enabled monitoring for user-defined projects.
  • You have deployed a service in a user-defined project.
  • You have created a ServiceMonitor custom resource definition (CRD) for the service to define how the service is monitored.

Procedure

  1. From the Developer perspective in the OpenShift Container Platform web console, select ObserveMetrics.
  2. Select the project that you want to view metrics for in the Project: list.
  3. Select a query from the Select query list, or create a custom PromQL query based on the selected query by selecting Show PromQL. The metrics from the queries are visualized on the plot.

    Note

    In the Developer perspective, you can only run one query at a time.

  4. Explore the visualized metrics by doing any of the following:

    OptionDescription

    Zoom into the plot and change the time range.

    Either:

    • Visually select the time range by clicking and dragging on the plot horizontally.
    • Use the menu in the left upper corner to select the time range.

    Reset the time range.

    Select Reset zoom.

    Display outputs for all queries at a specific point in time.

    Hold the mouse cursor on the plot at that point. The query outputs appear in a pop-up box.

Additional resources

9.5. Getting detailed information about a metrics target

In the Administrator perspective in the OpenShift Container Platform web console, you can use the Metrics targets page to view, search, and filter the endpoints that are currently targeted for scraping, which helps you to identify and troubleshoot problems. For example, you can view the current status of targeted endpoints to see when OpenShift Container Platform Monitoring is not able to scrape metrics from a targeted component.

The Metrics targets page shows targets for default OpenShift Container Platform projects and for user-defined projects.

Prerequisites

  • You have access to the cluster as an administrator for the project for which you want to view metrics targets.

Procedure

  1. In the Administrator perspective, select ObserveTargets. The Metrics targets page opens with a list of all service endpoint targets that are being scraped for metrics.

    This page shows details about targets for default OpenShift Container Platform and user-defined projects. This page lists the following information for each target:

    • Service endpoint URL being scraped
    • ServiceMonitor component being monitored
    • The up or down status of the target
    • Namespace
    • Last scrape time
    • Duration of the last scrape
  2. Optional: The list of metrics targets can be long. To find a specific target, do any of the following:

    OptionDescription

    Filter the targets by status and source.

    Select filters in the Filter list.

    The following filtering options are available:

    • Status filters:

      • Up. The target is currently up and being actively scraped for metrics.
      • Down. The target is currently down and not being scraped for metrics.
    • Source filters:

      • Platform. Platform-level targets relate only to default Red Hat OpenShift Service on AWS projects. These projects provide core Red Hat OpenShift Service on AWS functionality.
      • User. User targets relate to user-defined projects. These projects are user-created and can be customized.

    Search for a target by name or label.

    Enter a search term in the Text or Label field next to the search box.

    Sort the targets.

    Click one or more of the Endpoint Status, Namespace, Last Scrape, and Scrape Duration column headers.

  3. Click the URL in the Endpoint column for a target to navigate to its Target details page. This page provides information about the target, including the following:

    • The endpoint URL being scraped for metrics
    • The current Up or Down status of the target
    • A link to the namespace
    • A link to the ServiceMonitor details
    • Labels attached to the target
    • The most recent time that the target was scraped for metrics

Chapter 10. Managing alerts

In OpenShift Container Platform 4.17, the Alerting UI enables you to manage alerts, silences, and alerting rules.

  • Alerting rules. Alerting rules contain a set of conditions that outline a particular state within a cluster. Alerts are triggered when those conditions are true. An alerting rule can be assigned a severity that defines how the alerts are routed.
  • Alerts. An alert is fired when the conditions defined in an alerting rule are true. Alerts provide a notification that a set of circumstances are apparent within an OpenShift Container Platform cluster.
  • Silences. A silence can be applied to an alert to prevent notifications from being sent when the conditions for an alert are true. You can mute an alert after the initial notification, while you work on resolving the issue.
Note

The alerts, silences, and alerting rules that are available in the Alerting UI relate to the projects that you have access to. For example, if you are logged in as a user with the cluster-admin role, you can access all alerts, silences, and alerting rules.

10.1. Accessing the Alerting UI in the Administrator and Developer perspectives

The Alerting UI is accessible through the Administrator perspective and the Developer perspective of the OpenShift Container Platform web console.

  • In the Administrator perspective, go to ObserveAlerting. The three main pages in the Alerting UI in this perspective are the Alerts, Silences, and Alerting rules pages.
  • In the Developer perspective, go to Observe<project_name>Alerts. In this perspective, alerts, silences, and alerting rules are all managed from the Alerts page. The results shown in the Alerts page are specific to the selected project.
Note

In the Developer perspective, you can select from core OpenShift Container Platform and user-defined projects that you have access to in the Project: <project_name> list. However, alerts, silences, and alerting rules relating to core OpenShift Container Platform projects are not displayed if you are not logged in as a cluster administrator.

10.2. Searching and filtering alerts, silences, and alerting rules

You can filter the alerts, silences, and alerting rules that are displayed in the Alerting UI. This section provides a description of each of the available filtering options.

Understanding alert filters

In the Administrator perspective, the Alerts page in the Alerting UI provides details about alerts relating to default OpenShift Container Platform and user-defined projects. The page includes a summary of severity, state, and source for each alert. The time at which an alert went into its current state is also shown.

You can filter by alert state, severity, and source. By default, only Platform alerts that are Firing are displayed. The following describes each alert filtering option:

  • State filters:

    • Firing. The alert is firing because the alert condition is true and the optional for duration has passed. The alert continues to fire while the condition remains true.
    • Pending. The alert is active but is waiting for the duration that is specified in the alerting rule before it fires.
    • Silenced. The alert is now silenced for a defined time period. Silences temporarily mute alerts based on a set of label selectors that you define. Notifications are not sent for alerts that match all the listed values or regular expressions.
  • Severity filters:

    • Critical. The condition that triggered the alert could have a critical impact. The alert requires immediate attention when fired and is typically paged to an individual or to a critical response team.
    • Warning. The alert provides a warning notification about something that might require attention to prevent a problem from occurring. Warnings are typically routed to a ticketing system for non-immediate review.
    • Info. The alert is provided for informational purposes only.
    • None. The alert has no defined severity.
    • You can also create custom severity definitions for alerts relating to user-defined projects.
  • Source filters:

    • Platform. Platform-level alerts relate only to default OpenShift Container Platform projects. These projects provide core OpenShift Container Platform functionality.
    • User. User alerts relate to user-defined projects. These alerts are user-created and are customizable. User-defined workload monitoring can be enabled postinstallation to provide observability into your own workloads.
Understanding silence filters

In the Administrator perspective, the Silences page in the Alerting UI provides details about silences applied to alerts in default OpenShift Container Platform and user-defined projects. The page includes a summary of the state of each silence and the time at which a silence ends.

You can filter by silence state. By default, only Active and Pending silences are displayed. The following describes each silence state filter option:

  • State filters:

    • Active. The silence is active and the alert will be muted until the silence is expired.
    • Pending. The silence has been scheduled and it is not yet active.
    • Expired. The silence has expired and notifications will be sent if the conditions for an alert are true.
Understanding alerting rule filters

In the Administrator perspective, the Alerting rules page in the Alerting UI provides details about alerting rules relating to default OpenShift Container Platform and user-defined projects. The page includes a summary of the state, severity, and source for each alerting rule.

You can filter alerting rules by alert state, severity, and source. By default, only Platform alerting rules are displayed. The following describes each alerting rule filtering option:

  • Alert state filters:

    • Firing. The alert is firing because the alert condition is true and the optional for duration has passed. The alert continues to fire while the condition remains true.
    • Pending. The alert is active but is waiting for the duration that is specified in the alerting rule before it fires.
    • Silenced. The alert is now silenced for a defined time period. Silences temporarily mute alerts based on a set of label selectors that you define. Notifications are not sent for alerts that match all the listed values or regular expressions.
    • Not Firing. The alert is not firing.
  • Severity filters:

    • Critical. The conditions defined in the alerting rule could have a critical impact. When true, these conditions require immediate attention. Alerts relating to the rule are typically paged to an individual or to a critical response team.
    • Warning. The conditions defined in the alerting rule might require attention to prevent a problem from occurring. Alerts relating to the rule are typically routed to a ticketing system for non-immediate review.
    • Info. The alerting rule provides informational alerts only.
    • None. The alerting rule has no defined severity.
    • You can also create custom severity definitions for alerting rules relating to user-defined projects.
  • Source filters:

    • Platform. Platform-level alerting rules relate only to default OpenShift Container Platform projects. These projects provide core OpenShift Container Platform functionality.
    • User. User-defined workload alerting rules relate to user-defined projects. These alerting rules are user-created and are customizable. User-defined workload monitoring can be enabled postinstallation to provide observability into your own workloads.
Searching and filtering alerts, silences, and alerting rules in the Developer perspective

In the Developer perspective, the Alerts page in the Alerting UI provides a combined view of alerts and silences relating to the selected project. A link to the governing alerting rule is provided for each displayed alert.

In this view, you can filter by alert state and severity. By default, all alerts in the selected project are displayed if you have permission to access the project. These filters are the same as those described for the Administrator perspective.

10.3. Getting information about alerts, silences, and alerting rules

The Alerting UI provides detailed information about alerts and their governing alerting rules and silences.

Prerequisites

  • You have access to the cluster as a developer or as a user with view permissions for the project that you are viewing alerts for.

Procedure

To obtain information about alerts in the Administrator perspective:

  1. Open the OpenShift Container Platform web console and go to the ObserveAlertingAlerts page.
  2. Optional: Search for alerts by name by using the Name field in the search list.
  3. Optional: Filter alerts by state, severity, and source by selecting filters in the Filter list.
  4. Optional: Sort the alerts by clicking one or more of the Name, Severity, State, and Source column headers.
  5. Click the name of an alert to view its Alert details page. The page includes a graph that illustrates alert time series data. It also provides the following information about the alert:

    • A description of the alert
    • Messages associated with the alert
    • Labels attached to the alert
    • A link to its governing alerting rule
    • Silences for the alert, if any exist

To obtain information about silences in the Administrator perspective:

  1. Go to the ObserveAlertingSilences page.
  2. Optional: Filter the silences by name using the Search by name field.
  3. Optional: Filter silences by state by selecting filters in the Filter list. By default, Active and Pending filters are applied.
  4. Optional: Sort the silences by clicking one or more of the Name, Firing alerts, State, and Creator column headers.
  5. Select the name of a silence to view its Silence details page. The page includes the following details:

    • Alert specification
    • Start time
    • End time
    • Silence state
    • Number and list of firing alerts

To obtain information about alerting rules in the Administrator perspective:

  1. Go to the ObserveAlertingAlerting rules page.
  2. Optional: Filter alerting rules by state, severity, and source by selecting filters in the Filter list.
  3. Optional: Sort the alerting rules by clicking one or more of the Name, Severity, Alert state, and Source column headers.
  4. Select the name of an alerting rule to view its Alerting rule details page. The page provides the following details about the alerting rule:

    • Alerting rule name, severity, and description.
    • The expression that defines the condition for firing the alert.
    • The time for which the condition should be true for an alert to fire.
    • A graph for each alert governed by the alerting rule, showing the value with which the alert is firing.
    • A table of all alerts governed by the alerting rule.

To obtain information about alerts, silences, and alerting rules in the Developer perspective:

  1. Go to the Observe<project_name>Alerts page.
  2. View details for an alert, silence, or an alerting rule:

    • Alert details can be viewed by clicking a greater than symbol (>) next to an alert name and then selecting the alert from the list.
    • Silence details can be viewed by clicking a silence in the Silenced by section of the Alert details page. The Silence details page includes the following information:

      • Alert specification
      • Start time
      • End time
      • Silence state
      • Number and list of firing alerts
    • Alerting rule details can be viewed by clicking the kebab menu next to an alert in the Alerts page and then clicking View Alerting Rule.
Note

Only alerts, silences, and alerting rules relating to the selected project are displayed in the Developer perspective.

Additional resources

10.4. Managing silences

You can create a silence for an alert in the OpenShift Container Platform web console in both the Administrator and Developer perspectives. After you create a silence, you will not receive notifications about an alert when the alert fires.

Creating silences is useful in scenarios where you have received an initial alert notification, and you do not want to receive further notifications during the time in which you resolve the underlying issue causing the alert to fire.

When creating a silence, you must specify whether it becomes active immediately or at a later time. You must also set a duration period after which the silence expires.

After you create silences, you can view, edit, and expire them.

Note

When you create silences, they are replicated across Alertmanager pods. However, if you do not configure persistent storage for Alertmanager, silences might be lost. This can happen, for example, if all Alertmanager pods restart at the same time.

Additional resources

10.4.1. Silencing alerts

You can silence a specific alert or silence alerts that match a specification that you define.

Prerequisites

  • If you are a cluster administrator, you have access to the cluster as a user with the cluster-admin role.
  • If you are a non-administrator user, you have access to the cluster as a user with the following user roles:

    • The cluster-monitoring-view cluster role, which allows you to access Alertmanager.
    • The monitoring-alertmanager-edit role, which permits you to create and silence alerts in the Administrator perspective in the web console.
    • The monitoring-rules-edit cluster role, which permits you to create and silence alerts in the Developer perspective in the web console.

Procedure

To silence a specific alert in the Administrator perspective:

  1. Go to ObserveAlertingAlerts in the OpenShift Container Platform web console.
  2. For the alert that you want to silence, click kebab and select Silence alert to open the Silence alert page with a default configuration for the chosen alert.
  3. Optional: Change the default configuration details for the silence.

    Note

    You must add a comment before saving a silence.

  4. To save the silence, click Silence.

To silence a specific alert in the Developer perspective:

  1. Go to Observe<project_name>Alerts in the OpenShift Container Platform web console.
  2. If necessary, expand the details for the alert by selecting a greater than symbol (>) next to the alert name.
  3. Click the alert message in the expanded view to open the Alert details page for the alert.
  4. Click Silence alert to open the Silence alert page with a default configuration for the alert.
  5. Optional: Change the default configuration details for the silence.

    Note

    You must add a comment before saving a silence.

  6. To save the silence, click Silence.

To silence a set of alerts by creating a silence configuration in the Administrator perspective:

  1. Go to ObserveAlertingSilences in the OpenShift Container Platform web console.
  2. Click Create silence.
  3. On the Create silence page, set the schedule, duration, and label details for an alert.

    Note

    You must add a comment before saving a silence.

  4. To create silences for alerts that match the labels that you entered, click Silence.

To silence a set of alerts by creating a silence configuration in the Developer perspective:

  1. Go to Observe<project_name>Silences in the OpenShift Container Platform web console.
  2. Click Create silence.
  3. On the Create silence page, set the duration and label details for an alert.

    Note

    You must add a comment before saving a silence.

  4. To create silences for alerts that match the labels that you entered, click Silence.

10.4.2. Editing silences

You can edit a silence, which expires the existing silence and creates a new one with the changed configuration.

Prerequisites

  • If you are a cluster administrator, you have access to the cluster as a user with the cluster-admin role.
  • If you are a non-administrator user, you have access to the cluster as a user with the following user roles:

    • The cluster-monitoring-view cluster role, which allows you to access Alertmanager.
    • The monitoring-alertmanager-edit role, which permits you to create and silence alerts in the Administrator perspective in the web console.
    • The monitoring-rules-edit cluster role, which permits you to create and silence alerts in the Developer perspective in the web console.

Procedure

To edit a silence in the Administrator perspective:

  1. Go to ObserveAlertingSilences.
  2. For the silence you want to modify, click kebab and select Edit silence.

    Alternatively, you can click Actions and select Edit silence on the Silence details page for a silence.

  3. On the Edit silence page, make changes and click Silence. Doing so expires the existing silence and creates one with the updated configuration.

To edit a silence in the Developer perspective:

  1. Go to Observe<project_name>Silences.
  2. For the silence you want to modify, click kebab and select Edit silence.

    Alternatively, you can click Actions and select Edit silence on the Silence details page for a silence.

  3. On the Edit silence page, make changes and click Silence. Doing so expires the existing silence and creates one with the updated configuration.

10.4.3. Expiring silences

You can expire a single silence or multiple silences. Expiring a silence deactivates it permanently.

Note

You cannot delete expired, silenced alerts. Expired silences older than 120 hours are garbage collected.

Prerequisites

  • If you are a cluster administrator, you have access to the cluster as a user with the cluster-admin role.
  • If you are a non-administrator user, you have access to the cluster as a user with the following user roles:

    • The cluster-monitoring-view cluster role, which allows you to access Alertmanager.
    • The monitoring-alertmanager-edit role, which permits you to create and silence alerts in the Administrator perspective in the web console.
    • The monitoring-rules-edit cluster role, which permits you to create and silence alerts in the Developer perspective in the web console.

Procedure

To expire a silence or silences in the Administrator perspective:

  1. Go to ObserveAlertingSilences.
  2. For the silence or silences you want to expire, select the checkbox in the corresponding row.
  3. Click Expire 1 silence to expire a single selected silence or Expire <n> silences to expire multiple selected silences, where <n> is the number of silences you selected.

    Alternatively, to expire a single silence you can click Actions and select Expire silence on the Silence details page for a silence.

To expire a silence in the Developer perspective:

  1. Go to Observe<project_name>Silences.
  2. For the silence or silences you want to expire, select the checkbox in the corresponding row.
  3. Click Expire 1 silence to expire a single selected silence or Expire <n> silences to expire multiple selected silences, where <n> is the number of silences you selected.

    Alternatively, to expire a single silence you can click Actions and select Expire silence on the Silence details page for a silence.

10.5. Managing alerting rules for core platform monitoring

OpenShift Container Platform 4.17 monitoring ships with a large set of default alerting rules for platform metrics. As a cluster administrator, you can customize this set of rules in two ways:

  • Modify the settings for existing platform alerting rules by adjusting thresholds or by adding and modifying labels. For example, you can change the severity label for an alert from warning to critical to help you route and triage issues flagged by an alert.
  • Define and add new custom alerting rules by constructing a query expression based on core platform metrics in the openshift-monitoring namespace.

Core platform alerting rule considerations

  • New alerting rules must be based on the default OpenShift Container Platform monitoring metrics.
  • You must create the AlertingRule and AlertRelabelConfig objects in the openshift-monitoring namespace.
  • You can only add and modify alerting rules. You cannot create new recording rules or modify existing recording rules.
  • If you modify existing platform alerting rules by using an AlertRelabelConfig object, your modifications are not reflected in the Prometheus alerts API. Therefore, any dropped alerts still appear in the OpenShift Container Platform web console even though they are no longer forwarded to Alertmanager. Additionally, any modifications to alerts, such as a changed severity label, do not appear in the web console.

10.5.1. Tips for optimizing alerting rules for core platform monitoring

If you customize core platform alerting rules to meet your organization’s specific needs, follow these guidelines to help ensure that the customized rules are efficient and effective.

  • Minimize the number of new rules. Create only rules that are essential to your specific requirements. By minimizing the number of rules, you create a more manageable and focused alerting system in your monitoring environment.
  • Focus on symptoms rather than causes. Create rules that notify users of symptoms instead of underlying causes. This approach ensures that users are promptly notified of a relevant symptom so that they can investigate the root cause after an alert has triggered. This tactic also significantly reduces the overall number of rules you need to create.
  • Plan and assess your needs before implementing changes. First, decide what symptoms are important and what actions you want users to take if these symptoms occur. Then, assess existing rules and decide if you can modify any of them to meet your needs instead of creating entirely new rules for each symptom. By modifying existing rules and creating new ones judiciously, you help to streamline your alerting system.
  • Provide clear alert messaging. When you create alert messages, describe the symptom, possible causes, and recommended actions. Include unambiguous, concise explanations along with troubleshooting steps or links to more information. Doing so helps users quickly assess the situation and respond appropriately.
  • Include severity levels. Assign severity levels to your rules to indicate how a user needs to react when a symptom occurs and triggers an alert. For example, classifying an alert as Critical signals that an individual or a critical response team needs to respond immediately. By defining severity levels, you help users know how to respond to an alert and help ensure that the most urgent issues receive prompt attention.

10.5.2. Creating new alerting rules

As a cluster administrator, you can create new alerting rules based on platform metrics. These alerting rules trigger alerts based on the values of chosen metrics.

Note
  • If you create a customized AlertingRule resource based on an existing platform alerting rule, silence the original alert to avoid receiving conflicting alerts.
  • To help users understand the impact and cause of the alert, ensure that your alerting rule contains an alert message and severity value.

Prerequisites

  • You have access to the cluster as a user that has the cluster-admin cluster role.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Create a new YAML configuration file named example-alerting-rule.yaml.
  2. Add an AlertingRule resource to the YAML file. The following example creates a new alerting rule named example, similar to the default Watchdog alert:

    apiVersion: monitoring.openshift.io/v1
    kind: AlertingRule
    metadata:
      name: example
      namespace: openshift-monitoring 1
    spec:
      groups:
      - name: example-rules
        rules:
        - alert: ExampleAlert 2
          for: 1m 3
          expr: vector(1) 4
          labels:
            severity: warning 5
          annotations:
            message: This is an example alert. 6
    1
    Ensure that the namespace is openshift-monitoring.
    2
    The name of the alerting rule you want to create.
    3
    The duration for which the condition should be true before an alert is fired.
    4
    The PromQL query expression that defines the new rule.
    5
    The severity that alerting rule assigns to the alert.
    6
    The message associated with the alert.
    Important

    You must create the AlertingRule object in the openshift-monitoring namespace. Otherwise, the alerting rule is not accepted.

  3. Apply the configuration file to the cluster:

    $ oc apply -f example-alerting-rule.yaml

10.5.3. Modifying core platform alerting rules

As a cluster administrator, you can modify core platform alerts before Alertmanager routes them to a receiver. For example, you can change the severity label of an alert, add a custom label, or exclude an alert from being sent to Alertmanager.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Create a new YAML configuration file named example-modified-alerting-rule.yaml.
  2. Add an AlertRelabelConfig resource to the YAML file. The following example modifies the severity setting to critical for the default platform watchdog alerting rule:

    apiVersion: monitoring.openshift.io/v1
    kind: AlertRelabelConfig
    metadata:
      name: watchdog
      namespace: openshift-monitoring 1
    spec:
      configs:
      - sourceLabels: [alertname,severity] 2
        regex: "Watchdog;none" 3
        targetLabel: severity 4
        replacement: critical 5
        action: Replace 6
    1
    Ensure that the namespace is openshift-monitoring.
    2
    The source labels for the values you want to modify.
    3
    The regular expression against which the value of sourceLabels is matched.
    4
    The target label of the value you want to modify.
    5
    The new value to replace the target label.
    6
    The relabel action that replaces the old value based on regex matching. The default action is Replace. Other possible values are Keep, Drop, HashMod, LabelMap, LabelDrop, and LabelKeep.
    Important

    You must create the AlertRelabelConfig object in the openshift-monitoring namespace. Otherwise, the alert label will not change.

  3. Apply the configuration file to the cluster:

    $ oc apply -f example-modified-alerting-rule.yaml

Additional resources

10.6. Managing alerting rules for user-defined projects

OpenShift Container Platform monitoring ships with a set of default alerting rules. As a cluster administrator, you can view the default alerting rules.

In OpenShift Container Platform 4.17, you can create, view, edit, and remove alerting rules in user-defined projects.

Alerting rule considerations

  • The default alerting rules are used specifically for the OpenShift Container Platform cluster.
  • Some alerting rules intentionally have identical names. They send alerts about the same event with different thresholds, different severity, or both.
  • Inhibition rules prevent notifications for lower severity alerts that are firing when a higher severity alert is also firing.

10.6.1. Optimizing alerting for user-defined projects

You can optimize alerting for your own projects by considering the following recommendations when creating alerting rules:

  • Minimize the number of alerting rules that you create for your project. Create alerting rules that notify you of conditions that impact you. It is more difficult to notice relevant alerts if you generate many alerts for conditions that do not impact you.
  • Create alerting rules for symptoms instead of causes. Create alerting rules that notify you of conditions regardless of the underlying cause. The cause can then be investigated. You will need many more alerting rules if each relates only to a specific cause. Some causes are then likely to be missed.
  • Plan before you write your alerting rules. Determine what symptoms are important to you and what actions you want to take if they occur. Then build an alerting rule for each symptom.
  • Provide clear alert messaging. State the symptom and recommended actions in the alert message.
  • Include severity levels in your alerting rules. The severity of an alert depends on how you need to react if the reported symptom occurs. For example, a critical alert should be triggered if a symptom requires immediate attention by an individual or a critical response team.

Additional resources

10.6.2. About creating alerting rules for user-defined projects

If you create alerting rules for a user-defined project, consider the following key behaviors and important limitations when you define the new rules:

  • A user-defined alerting rule can include metrics exposed by its own project in addition to the default metrics from core platform monitoring. You cannot include metrics from another user-defined project.

    For example, an alerting rule for the ns1 user-defined project can use metrics exposed by the ns1 project in addition to core platform metrics, such as CPU and memory metrics. However, the rule cannot include metrics from a different ns2 user-defined project.

  • To reduce latency and to minimize the load on core platform monitoring components, you can add the openshift.io/prometheus-rule-evaluation-scope: leaf-prometheus label to a rule. This label forces only the Prometheus instance deployed in the openshift-user-workload-monitoring project to evaluate the alerting rule and prevents the Thanos Ruler instance from doing so.

    Important

    If an alerting rule has this label, your alerting rule can use only those metrics exposed by your user-defined project. Alerting rules you create based on default platform metrics might not trigger alerts.

10.6.3. Creating alerting rules for user-defined projects

You can create alerting rules for user-defined projects. Those alerting rules will trigger alerts based on the values of the chosen metrics.

Note
  • When you create an alerting rule, a project label is enforced on it even if a rule with the same name exists in another project.
  • To help users understand the impact and cause of the alert, ensure that your alerting rule contains an alert message and severity value.

Prerequisites

  • You have enabled monitoring for user-defined projects.
  • You are logged in as a user that has the monitoring-rules-edit cluster role for the project where you want to create an alerting rule.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Create a YAML file for alerting rules. In this example, it is called example-app-alerting-rule.yaml.
  2. Add an alerting rule configuration to the YAML file. The following example creates a new alerting rule named example-alert. The alerting rule fires an alert when the version metric exposed by the sample service becomes 0:

    apiVersion: monitoring.coreos.com/v1
    kind: PrometheusRule
    metadata:
      name: example-alert
      namespace: ns1
    spec:
      groups:
      - name: example
        rules:
        - alert: VersionAlert 1
          for: 1m 2
          expr: version{job="prometheus-example-app"} == 0 3
          labels:
            severity: warning 4
          annotations:
            message: This is an example alert. 5
    1
    The name of the alerting rule you want to create.
    2
    The duration for which the condition should be true before an alert is fired.
    3
    The PromQL query expression that defines the new rule.
    4
    The severity that alerting rule assigns to the alert.
    5
    The message associated with the alert.
  3. Apply the configuration file to the cluster:

    $ oc apply -f example-app-alerting-rule.yaml

Additional resources

  • See Monitoring overview for details about OpenShift Container Platform 4.17 monitoring architecture.

10.6.4. Accessing alerting rules for user-defined projects

To list alerting rules for a user-defined project, you must have been assigned the monitoring-rules-view cluster role for the project.

Prerequisites

  • You have enabled monitoring for user-defined projects.
  • You are logged in as a user that has the monitoring-rules-view cluster role for your project.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. To list alerting rules in <project>:

    $ oc -n <project> get prometheusrule
  2. To list the configuration of an alerting rule, run the following:

    $ oc -n <project> get prometheusrule <rule> -o yaml

10.6.5. Listing alerting rules for all projects in a single view

As a cluster administrator, you can list alerting rules for core OpenShift Container Platform and user-defined projects together in a single view.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin role.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. In the Administrator perspective, navigate to ObserveAlertingAlerting rules.
  2. Select the Platform and User sources in the Filter drop-down menu.

    Note

    The Platform source is selected by default.

10.6.6. Removing alerting rules for user-defined projects

You can remove alerting rules for user-defined projects.

Prerequisites

  • You have enabled monitoring for user-defined projects.
  • You are logged in as a user that has the monitoring-rules-edit cluster role for the project where you want to create an alerting rule.
  • You have installed the OpenShift CLI (oc).

Procedure

  • To remove rule <foo> in <namespace>, run the following:

    $ oc -n <namespace> delete prometheusrule <foo>

Additional resources

10.7. Sending notifications to external systems

In OpenShift Container Platform 4.17, firing alerts can be viewed in the Alerting UI. Alerts are not configured by default to be sent to any notification systems. You can configure OpenShift Container Platform to send alerts to the following receiver types:

  • PagerDuty
  • Webhook
  • Email
  • Slack
  • Microsoft Teams

Routing alerts to receivers enables you to send timely notifications to the appropriate teams when failures occur. For example, critical alerts require immediate attention and are typically paged to an individual or a critical response team. Alerts that provide non-critical warning notifications might instead be routed to a ticketing system for non-immediate review.

Checking that alerting is operational by using the watchdog alert

OpenShift Container Platform monitoring includes a watchdog alert that fires continuously. Alertmanager repeatedly sends watchdog alert notifications to configured notification providers. The provider is usually configured to notify an administrator when it stops receiving the watchdog alert. This mechanism helps you quickly identify any communication issues between Alertmanager and the notification provider.

10.7.1. Configuring alert receivers

You can configure alert receivers to ensure that you learn about important issues with your cluster.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role.

Procedure

  1. In the Administrator perspective, go to AdministrationCluster SettingsConfigurationAlertmanager.

    Note

    Alternatively, you can go to the same page through the notification drawer. Select the bell icon at the top right of the OpenShift Container Platform web console and choose Configure in the AlertmanagerReceiverNotConfigured alert.

  2. Click Create Receiver in the Receivers section of the page.
  3. In the Create Receiver form, add a Receiver name and choose a Receiver type from the list.
  4. Edit the receiver configuration:

    • For PagerDuty receivers:

      1. Choose an integration type and add a PagerDuty integration key.
      2. Add the URL of your PagerDuty installation.
      3. Click Show advanced configuration if you want to edit the client and incident details or the severity specification.
    • For webhook receivers:

      1. Add the endpoint to send HTTP POST requests to.
      2. Click Show advanced configuration if you want to edit the default option to send resolved alerts to the receiver.
    • For email receivers:

      1. Add the email address to send notifications to.
      2. Add SMTP configuration details, including the address to send notifications from, the smarthost and port number used for sending emails, the hostname of the SMTP server, and authentication details.
      3. Select whether TLS is required.
      4. Click Show advanced configuration if you want to edit the default option not to send resolved alerts to the receiver or edit the body of email notifications configuration.
    • For Slack receivers:

      1. Add the URL of the Slack webhook.
      2. Add the Slack channel or user name to send notifications to.
      3. Select Show advanced configuration if you want to edit the default option not to send resolved alerts to the receiver or edit the icon and username configuration. You can also choose whether to find and link channel names and usernames.
  5. By default, firing alerts with labels that match all of the selectors are sent to the receiver. If you want label values for firing alerts to be matched exactly before they are sent to the receiver, perform the following steps:

    1. Add routing label names and values in the Routing labels section of the form.
    2. Click Add label to add further routing labels.
  6. Click Create to create the receiver.

10.7.2. 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.

10.7.3. Creating 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

  1. Create a YAML file for alert routing. The example in this procedure uses a file called example-app-alert-routing.yaml.
  2. 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
    Note

    For user-defined alerting rules, user-defined routing is scoped to the namespace in which the resource is defined. For example, a routing configuration defined in the AlertmanagerConfig object for namespace ns1 only applies to PrometheusRules resources in the same namespace.

  3. Save the file.
  4. Apply the resource to the cluster:

    $ oc apply -f example-app-alert-routing.yaml

    The configuration is automatically applied to the Alertmanager pods.

10.8. Configuring Alertmanager to send notifications

You can configure Alertmanager to send notifications by editing the alertmanager-main secret for default platform alerts or alertmanager-user-workload secret for user-defined alerts.

Note

All features of a supported version of upstream Alertmanager are also supported in an OpenShift Alertmanager configuration. To check all the configuration options of a supported version of upstream Alertmanager, see Alertmanager configuration.

10.8.1. Configuring notifications for default platform alerts

You can configure Alertmanager to send notifications. Customize where and how Alertmanager sends notifications about default platform alerts by editing the default configuration in the alertmanager-main secret in the openshift-monitoring namespace.

Important

Alertmanager does not send notifications by default. It is recommended to configure Alertmanager to receive notifications by setting up notifications details in the alertmanager-main secret configuration file.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role.

Procedure

  1. Open the Alertmanager YAML configuration file:

    • To open the Alertmanager configuration from the CLI:

      1. Print the currently active Alertmanager configuration from the alertmanager-main secret into alertmanager.yaml file:

        $ oc -n openshift-monitoring get secret alertmanager-main --template='{{ index .data "alertmanager.yaml" }}' | base64 --decode > alertmanager.yaml
      2. Open the alertmanager.yaml file.
    • To open the Alertmanager configuration from the OpenShift Container Platform web console:

      1. Go to the AdministrationCluster SettingsConfigurationAlertmanagerYAML page of the web console.
  2. Edit the Alertmanager configuration by updating parameters in the YAML:

    global:
      resolve_timeout: 5m
    route:
      group_wait: 30s 1
      group_interval: 5m 2
      repeat_interval: 12h 3
      receiver: default
      routes:
      - matchers:
        - "alertname=Watchdog"
        repeat_interval: 2m
        receiver: watchdog
      - matchers:
        - "service=<your_service>" 4
        routes:
        - matchers:
          - <your_matching_rules> 5
          receiver: <receiver> 6
    receivers:
    - name: default
    - name: watchdog
    - name: <receiver>
      <receiver_configuration> 7
    1
    Specify how long Alertmanager waits while collecting initial alerts for a group of alerts before sending a notification.
    2
    Specify how much time must elapse before Alertmanager sends a notification about new alerts added to a group of alerts for which an initial notification was already sent.
    3