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To collect metrics from a service in OpenShift, expose Prometheus-formatted data at /metrics, enable user-workload monitoring, and create a ServiceMonitor that selects the Kubernetes Service in the same namespace. Use a PodMonitor instead when you need Prometheus to discover pods directly and there is no Service. The examples below cover endpoints represented by OpenShift workload resources; a remote system with no corresponding Service or pod is a different discovery case.
What OpenShift needs to scrape
The user-workload monitoring stack scrapes a Prometheus-compatible HTTP metrics endpoint. The expected exposition path for custom metrics is /metrics. Before changing OpenShift resources, check that the endpoint responds and returns metric data. For example, run curl -i http://metrics.example.internal:8080/metrics using the real host and port for your endpoint. Red Hat’s OpenShift documentation says services must be configured for monitoring to scrape their /metrics endpoint.
A successful response from a workstation does not by itself prove that the monitoring components can reach the endpoint. The endpoint must also be reachable from the cluster monitoring stack, and its transport and authentication requirements must match the scrape configuration. Red Hat’s sample uses HTTP and notes that the sample service does not support TLS; do not copy that transport choice without checking your target.
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Choose ServiceMonitor or PodMonitor
| Resource | Use it when | Discovery target |
|---|---|---|
ServiceMonitor |
A Kubernetes Service fronts the metrics endpoint. | Services selected by labels; the selected Service must expose the named port used in the monitor endpoint. |
PodMonitor |
You want to scrape a pod directly and do not have a Service for it. | Pods selected directly. |
Red Hat states that a ServiceMonitor requires a Service, whereas a PodMonitor does not. For ordinary applications exposed through a Service, prefer ServiceMonitor: it follows Service selection rather than binding monitoring to individual pod targets. Choose PodMonitor when direct pod discovery is the intended setup. The manifest and namespace example below is specifically for ServiceMonitor.
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Enable user-workload monitoring and check access
User-defined projects are not monitored by the user-workload stack until that monitoring is enabled. Have a cluster administrator enable it using the procedure for your OpenShift version. You also need permissions to manage monitoring resources: the documented options include cluster-admin, monitoring-edit, or equivalent administrative access.
Metrics visibility is separately protected by RBAC. Grant only the access needed to create or view monitoring resources and query collected data. Red Hat identifies roles such as cluster-monitoring-view and cluster-monitoring-metrics-api for monitoring access; use the role appropriate to the operation rather than granting broad cluster privileges by default.
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Create a ServiceMonitor in the workload namespace
Use the service’s actual selector labels, named port, namespace, and transport scheme. This follows Red Hat’s documented example pattern; its 30s interval is sample configuration, not a required interval.
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apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: prometheus-example-monitor
namespace: ns1
spec:
endpoints:
- interval: 30s
port: web
scheme: http
selector:
matchLabels:
app: prometheus-example-appHere, selector.matchLabels must match labels on the Service, and endpoints.port must name a Service port. Adapt ns1, the labels, port, scheme, and interval to the real workload. If the endpoint requires HTTPS or authentication, configure and verify those settings for the actual service instead of copying the HTTP example.
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- Save the resource as a YAML file, for example
service-monitor.yaml. - Apply it with
oc apply -f service-monitor.yaml. - Confirm it exists in the workload namespace with
oc -n ns1 get servicemonitor, replacingns1with that namespace.
Understand the namespace boundary
A ServiceMonitor placed in a user-defined namespace discovers Services only in that same namespace. Red Hat specifically notes that namespaceSelector is ignored for a ServiceMonitor in a user-defined namespace. Put the monitor alongside the Service it selects; adding a cross-namespace selector there will not extend discovery.
Verify collection and troubleshoot missing metrics
- Check the endpoint first. Request the real
/metricsURL withcurl. Confirm that it returns Prometheus-formatted metrics, not an error page, redirect, or authentication challenge. - Confirm user-workload monitoring is enabled. If the workload is in a user-defined project and the feature is off, its metrics will not be collected by that stack.
- Check the resource and namespace. Run
oc -n <namespace> get servicemonitorand verify the monitor is in the same namespace as its Service. Replace<namespace>with the real namespace when running the command. - Compare labels and port names. The monitor’s selector must match the Service labels, and the configured endpoint port must correspond to a named Service port. A selector that matches no Service, or a port name that does not match, prevents the expected target from being selected.
- Check reachability and scrape settings. Verify that the monitoring stack can reach the endpoint and that the configured scheme, TLS, and authentication requirements agree with the service’s configuration.
- Query with authorized access. Use the documented Prometheus endpoints or OpenShift monitoring interface with an account granted an appropriate monitoring role. RBAC can prevent access to query results even when scraping is configured.
Do not assume installed operators are scraped automatically
User-installed operators are not automatically scraped by the default monitoring stack. If an operator exposes metrics, configure monitoring for its endpoint explicitly and confirm it is within the scope of user-workload monitoring.
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