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Merge pull request #100456 from openshift/revert-100033-OBSDOCS-2350
Revert "OBSDOCS-2350 Remove content in OCP and point at standalone docs"
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‎_topic_maps/_topic_map.yml‎

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Dir: cluster_observability_operator
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Distros: openshift-enterprise,openshift-origin
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Topics:
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- Name: Cluster Observability Operator release notes
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File: cluster-observability-operator-release-notes
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- Name: Cluster Observability Operator overview
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File: cluster-observability-operator-overview
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- Name: Installing the Cluster Observability Operator
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File: installing-the-cluster-observability-operator
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- Name: Configuring the Cluster Observability Operator to monitor a service
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File: configuring-the-cluster-observability-operator-to-monitor-a-service
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- Name: Observability UI plugins
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Dir: ui_plugins
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Distros: openshift-enterprise,openshift-origin
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Topics:
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- Name: Observability UI plugins overview
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File: observability-ui-plugins-overview
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- Name: Monitoring UI plugin
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File: monitoring-ui-plugin
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- Name: Logging UI plugin
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File: logging-ui-plugin
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- Name: Distributed tracing UI plugin
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File: distributed-tracing-ui-plugin
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- Name: Troubleshooting UI plugin
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File: troubleshooting-ui-plugin
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# - Name: Dashboard UI plugin
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# File: dashboard-ui-plugin
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- Name: Monitoring API reference
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File: api-monitoring-package
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# - Name: Observability API reference
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# File: api-observability-package
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- Name: Monitoring
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Dir: monitoring
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Distros: openshift-enterprise,openshift-origin

‎modules/coo-advantages.adoc‎

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// Module included in the following assemblies:
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// * observability/cluster_observability_operator/cluster-observability-operator-overview.adoc
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:_mod-docs-content-type: CONCEPT
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[id="coo-advantages_{context}"]
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= Key advantages of using {coo-short}
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Deploying {coo-short} helps you address monitoring requirements that are hard to achieve using the default monitoring stack.
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[id="coo-advantages-extensibility_{context}"]
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== Extensibility
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- You can add more metrics to a {coo-short}-deployed monitoring stack, which is not possible with core platform monitoring without losing support.
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- You can receive cluster-specific metrics from core platform monitoring through federation.
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- {coo-short} supports advanced monitoring scenarios like trend forecasting and anomaly detection.
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[id="coo-advantages-multi-tenancy_{context}"]
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== Multi-tenancy support
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- You can create monitoring stacks per user namespace.
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- You can deploy multiple stacks per namespace or a single stack for multiple namespaces.
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- {coo-short} enables independent configuration of alerts and receivers for different teams.
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[id="coo-advantages-scalability_{context}"]
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== Scalability
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- Supports multiple monitoring stacks on a single cluster.
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- Enables monitoring of large clusters through manual sharding.
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- Addresses cases where metrics exceed the capabilities of a single Prometheus instance.
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[id="coo-advantages-scalabilityflexibility_{context}"]
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== Flexibility
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- Decoupled from {product-title} release cycles.
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- Faster release iterations and rapid response to changing requirements.
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- Independent management of alerting rules.

‎modules/coo-dashboard-ui-plugin-configure.adoc‎

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// Module included in the following assemblies:
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// * observability/cluster_observability_operator/ui_plugins/dashboard-ui-plugin.adoc
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:_mod-docs-content-type: PROCEDURE
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[id="coo-dashboard-ui-plugin-configure-_{context}"]
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= Configuring a dashboard
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The dashboard UI plugin searches for datasources from `ConfigMap` resources in the `openshift-config-managed` namespace, that have the label `console.openshift.io/dashboard-datasource: 'true'`. The `ConfigMap` resource must define a datasource type and an in-cluster service where the data can be fetched.
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The examples in the following section are taken from link:https://github.com/openshift/console-dashboards-plugin[https://github.com/openshift/console-dashboards-plugin].
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.Prerequisites
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* You have access to the cluster as a user with the `cluster-admin` cluster role.
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* You have logged in to the {product-title} web console.
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* You have installed the {coo-full}.
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* You have installed the dashboard UI plugin.
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.Procedure
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. Create a `ConfigMap` resource in the `openshift-config-managed` namespace, with the label `console.openshift.io/dashboard-datasource: 'true'`. The example below is from link:https://github.com/openshift/console-dashboards-plugin/blob/main/docs/prometheus-datasource-example.yaml[prometheus-datasource-example.yaml]
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[source,yaml]
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----
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apiVersion: v1
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kind: ConfigMap
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metadata:
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name: cluster-prometheus-proxy
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namespace: openshift-config-managed
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labels:
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console.openshift.io/dashboard-datasource: "true"
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data:
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"dashboard-datasource.yaml": |-
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kind: "Datasource"
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metadata:
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name: "cluster-prometheus-proxy"
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project: "openshift-config-managed"
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spec:
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plugin:
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kind: "prometheus"
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spec:
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direct_url: "https://prometheus-k8s.openshift-monitoring.svc.cluster.local:9091"
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----
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. Configure a custom dashboard that connects to the datasource. The YAML for a sample dashboard is available at link:https://github.com/openshift/console-dashboards-plugin/blob/main/docs/prometheus-dashboard-example.yaml[prometheus-dashboard-example.yaml]. An excerpt from that file is shown below for demonstration purposes:
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.Extract from example dashboard, taken from prometheus-dashboard-example.yaml
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[%collapsible]
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====
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[source,yaml]
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----
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apiVersion: v1
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kind: ConfigMap
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metadata:
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name: dashboard-example
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namespace: openshift-config-managed
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labels:
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console.openshift.io/dashboard: "true"
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data:
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k8s-resources-workloads-namespace.json: |-
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{
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"annotations": {
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"list": [
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]
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},
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"editable": true,
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"gnetId": null,
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"graphTooltip": 0,
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"hideControls": false,
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"links": [
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],
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"refresh": "10s",
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"rows": [
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{
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"collapse": false,
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"height": "250px",
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"panels": [
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{
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"aliasColors": {
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},
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"bars": false,
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"dashLength": 10,
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"dashes": false,
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"datasource": {
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"name": "cluster-prometheus-proxy",
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"type": "prometheus"
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},
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"fill": 10,
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"id": 1,
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"interval": "1m",
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"legend": {
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"alignAsTable": true,
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"avg": false,
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"current": false,
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"max": false,
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"min": false,
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"rightSide": true,
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"show": true,
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"total": false,
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"values": false
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},
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"lines": true,
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"linewidth": 0,
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"links": [
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],
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"nullPointMode": "null as zero",
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"percentage": false,
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"pointradius": 5,
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"points": false,
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"renderer": "flot",
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"seriesOverrides": [
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{
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"alias": "quota - requests",
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"color": "#F2495C",
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"dashes": true,
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"fill": 0,
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"hiddenSeries": true,
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"hideTooltip": true,
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"legend": true,
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"linewidth": 2,
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"stack": false
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},
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{
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"alias": "quota - limits",
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"color": "#FF9830",
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"dashes": true,
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"fill": 0,
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"hiddenSeries": true,
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"hideTooltip": true,
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"legend": true,
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"linewidth": 2,
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"stack": false
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}
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],
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"spaceLength": 10,
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"span": 12,
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"stack": false,
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"steppedLine": false,
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"targets": [
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{
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"expr": "sum( node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{cluster=\"$cluster\", namespace=\"$namespace\"}* on(namespace,pod) group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload, workload_type)",
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"format": "time_series",
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"intervalFactor": 2,
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"legendFormat": "{{workload}} - {{workload_type}}",
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"legendLink": null,
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"step": 10
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},
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{
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"expr": "scalar(kube_resourcequota{cluster=\"$cluster\", namespace=\"$namespace\", type=\"hard\",resource=\"requests.cpu\"})",
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"format": "time_series",
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"intervalFactor": 2,
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"legendFormat": "quota - requests",
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"legendLink": null,
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"step": 10
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},
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{
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"expr": "scalar(kube_resourcequota{cluster=\"$cluster\", namespace=\"$namespace\", type=\"hard\",resource=\"limits.cpu\"})",
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"format": "time_series",
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"intervalFactor": 2,
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"legendFormat": "quota - limits",
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"legendLink": null,
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"step": 10
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}
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],
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"thresholds": [
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],
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"timeFrom": null,
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"timeShift": null,
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"title": "CPU Usage",
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"tooltip": {
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"shared": false,
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"sort": 2,
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"value_type": "individual"
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},
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"type": "graph",
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"xaxis": {
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"buckets": null,
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"mode": "time",
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"name": null,
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"show": true,
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"values": [
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]
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},
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...
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----
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====
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. Click *Observe* -> *Dashboards* and the custom dashboard is available with the title ++** DASHBOARD EXAMPLE **++, based on the configuration in `prometheus-dashboard-example.yaml`.
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image::coo-custom-dashboard.png[]
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You can set the namespace, time range and refresh interval for the dashboard in the UI.
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‎modules/coo-dashboard-ui-plugin-install.adoc‎

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// Module included in the following assemblies:
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// * observability/cluster_observability_operator/ui_plugins/dashboard-ui-plugin.adoc
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:_mod-docs-content-type: PROCEDURE
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[id="coo-dashboard-ui-plugin-install-_{context}"]
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= Installing the {coo-full} dashboard UI plugin
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.Prerequisites
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* You have access to the cluster as a user with the `cluster-admin` cluster role.
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* You have logged in to the {product-title} web console.
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* You have installed the {coo-full}.
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.Procedure
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. In the {product-title} web console, click *Ecosystem* -> *Installed Operators* and select {coo-full}.
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. Choose the *UI Plugin* tab (at the far right of the tab list) and press *Create UIPlugin*.
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. Select *YAML view*, enter the following content, and then press *Create*:
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[source,yaml]
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----
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apiVersion: observability.openshift.io/v1alpha1
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kind: UIPlugin
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metadata:
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name: dashboards
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spec:
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type: Dashboards
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----

‎modules/coo-distributed-tracing-ui-plugin-install.adoc‎

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// Module included in the following assemblies:
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// * observability/cluster_observability_operator/ui_plugins/distributed-tracing-ui-plugin.adoc
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:_mod-docs-content-type: PROCEDURE
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[id="coo-distributed-tracing-ui-plugin-install_{context}"]
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= Installing the {coo-full} distributed tracing UI plugin
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.Prerequisites
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* You have access to the cluster as a user with the `cluster-admin` cluster role.
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* You have logged in to the {product-title} web console.
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* You have installed the {coo-full}
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.Procedure
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. In the {product-title} web console, click *Ecosystem* -> *Installed Operators* and select {coo-full}
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. Choose the *UI Plugin* tab (at the far right of the tab list) and press *Create UIPlugin*
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. Select *YAML view*, enter the following content, and then press *Create*:
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[source,yaml]
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----
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apiVersion: observability.openshift.io/v1alpha1
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kind: UIPlugin
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metadata:
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name: distributed-tracing
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spec:
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type: DistributedTracing
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----

‎modules/coo-distributed-tracing-ui-plugin-using.adoc‎

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// Module included in the following assemblies:
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// * observability/cluster_observability_operator/ui_plugins/distributed-tracing-ui-plugin.adoc
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:_mod-docs-content-type: PROCEDURE
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[id="coo-distributed-tracing-ui-plugin-using_{context}"]
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= Using the {coo-full} distributed tracing UI plugin
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.Prerequisites
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* You have access to the cluster as a user with the `cluster-admin` cluster role.
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* You have logged in to the {product-title} web console.
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* You have installed the {coo-full}.
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* You have installed the {coo-full} distributed tracing UI plugin.
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* You have a `TempoStack` or `TempoMonolithic` multi-tenant instance in the cluster.
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.Procedure
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. In the {product-title} web console, click **Observe** → **Traces**.
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. Select a `TempoStack` or `TempoMonolithic` multi-tenant instance and set a time range and query for the traces to be loaded.
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The traces are displayed on a scatter-plot showing the trace start time, duration, and number of spans. Underneath the scatter plot, there is a list of traces showing information such as the `Trace Name`, number of `Spans`, and `Duration`.
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. Click on a trace name link.
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The trace detail page for the selected trace contains a Gantt Chart of all of the spans within the trace. Select a span to show a breakdown of the configured attributes.
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‎modules/coo-incident-detection-overview.adoc‎

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// Module included in the following assemblies:
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// * observability/cluster_observability_operator/ui_plugins/incident-detection-ui-plugin.adoc
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:_mod-docs-content-type: CONCEPT
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[id="coo-incident-detection-overview_{context}"]
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= {coo-full} incident detection overview
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Clusters can generate significant volumes of monitoring data, making it hard for you to distinguish critical signals from noise.
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Single incidents can trigger a cascade of alerts, and this results in extended time to detect and resolve issues.
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The {coo-full} incident detection feature groups related alerts into *incidents*. These incidents are then visualized as timelines that are color-coded by severity.
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Alerts are mapped to specific components, grouped by severity, helping you to identify root causes by focusing on high impact components first.
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You can then drill down from the incident timelines to individual alerts to determine how to fix the underlying issue.
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{coo-full} incident detection transforms the alert storm into clear steps for faster understanding and resolution of the incidents that occur on your clusters.

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