$ oc -n ns1 get service prometheus-example-app -o yaml
OpenShift Container Platform includes a pre-configured, pre-installed, and self-updating monitoring stack that provides monitoring for core platform components. In OpenShift Container Platform 4.7, cluster administrators can optionally enable monitoring for user-defined projects.
You can follow these procedures if your own metrics are unavailable or if Prometheus is consuming a lot of disk space.
ServiceMonitor
resources enable you to determine how to use the metrics exposed by a service in user-defined projects. Follow the steps outlined in this procedure if you have created a ServiceMonitor
resource but cannot see any corresponding metrics in the Metrics UI.
You have access to the cluster as a user with the cluster-admin
role.
You have installed the OpenShift CLI (oc
).
You have enabled and configured monitoring for user-defined workloads.
You have created the user-workload-monitoring-config
ConfigMap
object.
You have created a ServiceMonitor
resource.
Check that the corresponding labels match in the service and ServiceMonitor
resource configurations.
Obtain the label defined in the service. The following example queries the prometheus-example-app
service in the ns1
project:
$ oc -n ns1 get service prometheus-example-app -o yaml
labels:
app: prometheus-example-app
Check that the matchLabels
app
label in the ServiceMonitor
resource configuration matches the label output in the preceding step:
$ oc -n ns1 get servicemonitor prometheus-example-monitor -o yaml
spec: endpoints: - interval: 30s port: web scheme: http selector: matchLabels: app: prometheus-example-app
You can check service and |
Inspect the logs for the Prometheus Operator in the openshift-user-workload-monitoring
project.
List the pods in the openshift-user-workload-monitoring
project:
$ oc -n openshift-user-workload-monitoring get pods
NAME READY STATUS RESTARTS AGE
prometheus-operator-776fcbbd56-2nbfm 2/2 Running 0 132m
prometheus-user-workload-0 5/5 Running 1 132m
prometheus-user-workload-1 5/5 Running 1 132m
thanos-ruler-user-workload-0 3/3 Running 0 132m
thanos-ruler-user-workload-1 3/3 Running 0 132m
Obtain the logs from the prometheus-operator
container in the prometheus-operator
pod. In the following example, the pod is called prometheus-operator-776fcbbd56-2nbfm
:
$ oc -n openshift-user-workload-monitoring logs prometheus-operator-776fcbbd56-2nbfm -c prometheus-operator
If there is a issue with the service monitor, the logs might include an error similar to this example:
level=warn ts=2020-08-10T11:48:20.906739623Z caller=operator.go:1829 component=prometheusoperator msg="skipping servicemonitor" error="it accesses file system via bearer token file which Prometheus specification prohibits" servicemonitor=eagle/eagle namespace=openshift-user-workload-monitoring prometheus=user-workload
Review the target status for your project in the Prometheus UI directly.
Establish port-forwarding to the Prometheus instance in the openshift-user-workload-monitoring
project:
$ oc port-forward -n openshift-user-workload-monitoring pod/prometheus-user-workload-0 9090
Open http://localhost:9090/targets in a web browser and review the status of the target for your project directly in the Prometheus UI. Check for error messages relating to the target.
Configure debug level logging for the Prometheus Operator in the openshift-user-workload-monitoring
project.
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
Add logLevel: debug
for prometheusOperator
under data/config.yaml
to set the log level to debug
:
apiVersion: v1
kind: ConfigMap
metadata:
name: user-workload-monitoring-config
namespace: openshift-user-workload-monitoring
data:
config.yaml: |
prometheusOperator:
logLevel: debug
Save the file to apply the changes.
The |
Confirm that the debug
log-level has been applied to 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"
- --log-level=debug
Debug level logging will show all calls made by the Prometheus Operator.
Check that the prometheus-operator
pod is running:
$ oc -n openshift-user-workload-monitoring get pods
If an unrecognized Prometheus Operator |
Review the debug logs to see if the Prometheus Operator is using the ServiceMonitor
resource. Review the logs for other related errors.
See Specifying how a service is monitored for details on how to create a service monitor or pod monitor
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.
You can use the following measures when Prometheus consumes a lot of disk:
Check the number of scrape samples that are being collected.
Check the time series database (TSDB) status in the Prometheus UI for more information on which labels are creating the most time series. This requires cluster administrator privileges.
Reduce the number of unique time series that are created by reducing the number of unbound attributes that are assigned to user-defined metrics.
Using attributes that are bound to a limited set of possible values reduces the number of potential key-value pair combinations. |
Enforce limits on the number of samples that can be scraped across user-defined projects. This requires cluster administrator privileges.
You have access to the cluster as a user with the cluster-admin
role.
You have installed the OpenShift CLI (oc
).
In the Administrator perspective, navigate to Monitoring → Metrics.
Run the following Prometheus Query Language (PromQL) query in the Expression field. This returns the ten metrics that have the highest number of scrape samples:
topk(10,count by (job)({__name__=~".+"}))
Investigate the number of unbound label values assigned to metrics with higher than expected scrape sample counts.
If the metrics relate to a user-defined project, review the metrics key-value pairs assigned to your workload. These are implemented through Prometheus client libraries at the application level. Try to limit the number of unbound attributes referenced in your labels.
If the metrics relate to a core OpenShift Container Platform project, create a Red Hat support case on the Red Hat Customer Portal.
Check the TSDB status in the Prometheus UI.
In the Administrator perspective, navigate to Networking → Routes.
Select the openshift-monitoring
project in the Project list.
Select the URL in the prometheus-k8s
row to open the login page for the Prometheus UI.
Choose Log in with OpenShift to log in using your OpenShift Container Platform credentials.
In the Prometheus UI, navigate to Status → TSDB Status.
See Setting a scrape sample limit for user-defined projects for details on how to set a scrape sample limit and create related alerting rules