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Chapter 4. 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
config map.
Prerequisites
-
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 installed the OpenShift CLI (
oc
).
Procedure
Edit the
cluster-monitoring-config
config map in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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 areDoNotSchedule
andScheduleAnyway
. SpecifyDoNotSchedule
if you want themaxSkew
value to define the maximum difference allowed between the number of matching pods in the target topology and the global minimum. SpecifyScheduleAnyway
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
- Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
Additional resources
4.1. Storing and recording data for core platform monitoring
Store and record your metrics and alerting data, configure logs to specify which activities are recorded, control how long Prometheus retains stored data, and set the maximum amount of disk space for the data. These actions help you protect your data and use them for troubleshooting.
4.1.1. Configuring persistent storage
Run cluster monitoring with persistent storage to gain the following benefits:
- Protect your metrics and alerting data from data loss by storing them in a persistent volume (PV). As a result, they can survive pods being restarted or recreated.
- Avoid getting duplicate notifications and losing silences for alerts when the Alertmanager pods are restarted.
For production environments, it is highly recommended to configure persistent storage.
In multi-node clusters, you must configure persistent storage for Prometheus, Alertmanager, and Thanos Ruler to ensure high availability.
4.1.1.1. Persistent storage prerequisites
- Dedicate sufficient persistent storage to ensure that the disk does not become full.
Use
Filesystem
as the storage type value for thevolumeMode
parameter when you configure the persistent volume.Important-
Do not use a raw block volume, which is described with
volumeMode: Block
in thePersistentVolume
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.
-
Do not use a raw block volume, which is described with
4.1.1.2. Configuring a persistent volume claim
To use a persistent volume (PV) for monitoring components, you must configure a persistent volume claim (PVC).
Prerequisites
-
You have access to the cluster as a user with the
cluster-admin
cluster role. -
You have created the
cluster-monitoring-config
ConfigMap
object. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
cluster-monitoring-config
config map in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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
The following example configures a PVC that claims persistent storage for Prometheus:
Example PVC configuration
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
Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed and the new storage configuration is applied.
WarningWhen you update the config map with a PVC configuration, the affected
StatefulSet
object is recreated, resulting in a temporary service outage.
Additional resources
- Understanding persistent storage
- PersistentVolumeClaims (Kubernetes documentation)
4.1.1.3. Resizing a persistent volume
You can resize a persistent volume (PV) for monitoring components, such as Prometheus or Alertmanager. You need to manually expand a persistent volume claim (PVC), and then update the config map in which the component is configured.
You can only expand the size of the PVC. Shrinking the storage size is not possible.
Prerequisites
-
You have access to the cluster as a user with the
cluster-admin
cluster role. -
You have created the
cluster-monitoring-config
ConfigMap
object. - You have configured at least one PVC for core OpenShift Container Platform monitoring components.
-
You have installed the OpenShift CLI (
oc
).
Procedure
- Manually expand a PVC with the updated storage request. For more information, see "Expanding persistent volume claims (PVCs) with a file system" in Expanding persistent volumes.
Edit the
cluster-monitoring-config
config map in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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
The following example sets the new PVC request to 100 gigabytes for the Prometheus instance:
Example storage configuration for
prometheusK8s
apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | prometheusK8s: volumeClaimTemplate: spec: resources: requests: storage: 100Gi
Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
WarningWhen you update the config map with a new storage size, the affected
StatefulSet
object is recreated, resulting in a temporary service outage.
4.1.2. Modifying retention time and size for Prometheus metrics data
By default, Prometheus retains metrics data for 15 days for core platform monitoring. You can modify the retention time for the Prometheus instance to change when the data is deleted. You can also set the maximum amount of disk space the retained metrics data uses.
Data compaction occurs every two hours. Therefore, a persistent volume (PV) might fill up before compaction, potentially exceeding the retentionSize
limit. In such cases, the KubePersistentVolumeFillingUp
alert fires until the space on a PV is lower than the retentionSize
limit.
Prerequisites
-
You have access to the cluster as a user with the
cluster-admin
cluster role. -
You have created the
cluster-monitoring-config
ConfigMap
object. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
cluster-monitoring-config
config map in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
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), ory
(years). You can also combine time values for specific times, such as1h30m15s
. - 2
- The retention size: a number directly followed by
B
(bytes),KB
(kilobytes),MB
(megabytes),GB
(gigabytes),TB
(terabytes),PB
(petabytes), andEB
(exabytes).
The following example sets the retention time to 24 hours and the retention size to 10 gigabytes for the Prometheus instance:
Example of setting retention time for Prometheus
apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | prometheusK8s: retention: 24h retentionSize: 10GB
- Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
4.1.3. 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
4.1.4. Setting log levels for monitoring components
You can configure the log level for Alertmanager, Prometheus Operator, Prometheus, and Thanos Querier.
The following log levels can be applied to the relevant component in the cluster-monitoring-config
ConfigMap
object:
-
debug
. Log debug, informational, warning, and error messages. -
info
. Log informational, warning, and error messages. -
warn
. Log warning and error messages only. -
error
. Log error messages only.
The default log level is info
.
Prerequisites
-
You have access to the cluster as a user with the
cluster-admin
cluster role. -
You have created the
cluster-monitoring-config
ConfigMap
object. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
cluster-monitoring-config
config map in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
Add
logLevel: <log_level>
for a component underdata/config.yaml
:apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | <component>: 1 logLevel: <log_level> 2
- Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
Confirm that the log level has been applied by reviewing the deployment or pod configuration in the related project. The following example checks the log level for the
prometheus-operator
deployment:$ oc -n openshift-monitoring get deploy prometheus-operator -o yaml | grep "log-level"
Example output
- --log-level=debug
Check that the pods for the component are running. The following example lists the status of pods:
$ oc -n openshift-monitoring get pods
NoteIf an unrecognized
logLevel
value is included in theConfigMap
object, the pods for the component might not restart successfully.
4.1.5. 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.
Because log rotation is not supported, only enable this feature temporarily when you need to troubleshoot an issue. After you finish troubleshooting, disable query logging by reverting the changes you made to the ConfigMap
object to enable the feature.
Prerequisites
-
You have access to the cluster as a user with the
cluster-admin
cluster role. -
You have created the
cluster-monitoring-config
ConfigMap
object. -
You have installed the OpenShift CLI (
oc
).
Procedure
Edit the
cluster-monitoring-config
config map in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
Add the
queryLogFile
parameter for Prometheus underdata/config.yaml
:apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | prometheusK8s: queryLogFile: <path> 1
- 1
- Add the full path to the file in which queries will be logged.
- Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
Verify that the pods for the component are running. The following sample command lists the status of pods:
$ oc -n openshift-monitoring get pods
Example output
... prometheus-operator-567c9bc75c-96wkj 2/2 Running 0 62m prometheus-k8s-0 6/6 Running 1 57m prometheus-k8s-1 6/6 Running 1 57m thanos-querier-56c76d7df4-2xkpc 6/6 Running 0 57m thanos-querier-56c76d7df4-j5p29 6/6 Running 0 57m ...
Read the query log:
$ oc -n openshift-monitoring exec prometheus-k8s-0 -- cat <path>
ImportantRevert the setting in the config map after you have examined the logged query information.
Additional resources
4.1.6. 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.
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:
Edit the
cluster-monitoring-config
ConfigMap
object in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
Add a
thanosQuerier
section underdata/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
- Save the file to apply the changes. The pods affected by the new configuration are automatically redeployed.
Verification
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
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'
Run the following command to read the query log:
$ oc -n openshift-monitoring logs <thanos_querier_pod_name> -c thanos-query
NoteBecause the
thanos-querier
pods are highly available (HA) pods, you might be able to see logs in only one pod.-
After you examine the logged query information, disable query logging by changing the
enableRequestLogging
value tofalse
in the config map.
4.2. Configuring metrics for core platform monitoring
Configure the collection of metrics to monitor how cluster components and your own workloads are performing.
You can send ingested metrics to remote systems for long-term storage and add cluster ID labels to the metrics to identify the data coming from different clusters.
Additional resources
4.2.1. Configuring remote write storage
You can configure remote write storage to enable Prometheus to send ingested metrics to remote systems for long-term storage. Doing so has no impact on how or for how long Prometheus stores metrics.
Prerequisites
-
You have access to the cluster as a user with the
cluster-admin
cluster role. -
You have created the
cluster-monitoring-config
ConfigMap
object. -
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.
ImportantRed 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 theopenshift-monitoring
namespace.WarningTo reduce security risks, use HTTPS and authentication to send metrics to an endpoint.
Procedure
Edit the
cluster-monitoring-config
config map in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
Add a
remoteWrite:
section underdata/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.
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
andmy_metric_2
inmy_namespace
namespaceapiVersion: 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
- Save the file to apply the changes. The new configuration is applied automatically.
4.2.1.1. Supported remote write authentication settings
You can use different methods to authenticate with a remote write endpoint. Currently supported authentication methods are AWS Signature Version 4, basic authentication, authorization, OAuth 2.0, and TLS client. The following table provides details about supported authentication methods for use with remote write.
Authentication method | Config map field | Description |
---|---|---|
AWS Signature Version 4 |
| 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 |
| Basic authentication sets the authorization header on every remote write request with the configured username and password. |
authorization |
|
Authorization sets the |
OAuth 2.0 |
|
An OAuth 2.0 configuration uses the client credentials grant type. Prometheus fetches an access token from |
TLS client |
| A TLS client configuration specifies the CA certificate, the client certificate, and the client key file information used to authenticate with the remote write endpoint server using TLS. The sample configuration assumes that you have already created a CA certificate file, a client certificate file, and a client key file. |
4.2.1.2. Example remote write authentication settings
The following samples show different authentication settings you can use to connect to a remote write endpoint. Each sample also shows how to configure a corresponding Secret
object that contains authentication credentials and other relevant settings. Each sample configures authentication for use with default platform monitoring in the openshift-monitoring
namespace.
4.2.1.2.1. Sample YAML for AWS Signature Version 4 authentication
The following shows the settings for a sigv4
secret named sigv4-credentials
in the openshift-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
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.
4.2.1.2.2. Sample YAML for Basic authentication
The following shows sample Basic authentication settings for a Secret
object named rw-basic-auth
in the openshift-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
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
4.2.1.2.3. Sample YAML for authentication with a bearer token using a Secret
Object
The following shows bearer token settings for a Secret
object named rw-bearer-auth
in the openshift-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
4.2.1.2.4. Sample YAML for OAuth 2.0 authentication
The following shows sample OAuth 2.0 settings for a Secret
object named oauth2-credentials
in the openshift-monitoring
namespace:
apiVersion: v1 kind: Secret metadata: name: oauth2-credentials namespace: openshift-monitoring stringData: id: <oauth2_id> 1 secret: <oauth2_secret> 2 type: Opaque
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 thatClientId
can alternatively refer to aConfigMap
object, althoughclientSecret
must refer to aSecret
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
andclientSecret
. - 6
- The OAuth 2.0 scopes for the authorization request. These scopes limit what data the tokens can access.
- 7
- The OAuth 2.0 authorization request parameters required for the authorization server.
4.2.1.2.5. Sample YAML for TLS client authentication
The following shows sample TLS client settings for a tls
Secret
object named mtls-bundle
in the openshift-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
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 thatca
andcert
can alternatively refer to aConfigMap
object, thoughkeySecret
must refer to aSecret
object. - 2
- The key in the specified
Secret
object that contains the CA certificate for the endpoint. - 4
- The key in the specified
Secret
object that contains the client certificate for the endpoint. - 6
- The key in the specified
Secret
object that contains the client key secret.
4.2.1.3. Example remote write queue configuration
You can use the queueConfig
object for remote write to tune the remote write queue parameters. The following example shows the queue parameters with their default values for 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 to0s
, the parameter is ignored.
Additional resources
- Prometheus REST API reference for remote write
- Setting up remote write compatible endpoints (Prometheus documentation)
- Tuning remote write settings (Prometheus documentation)
- Understanding secrets
4.2.2. Creating cluster ID labels for metrics
You can create cluster ID labels for metrics by adding the write_relabel
settings for remote write storage in the cluster-monitoring-config
config map in the openshift-monitoring
namespace.
Prerequisites
-
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 installed the OpenShift CLI (
oc
). - You have configured remote write storage.
Procedure
Edit the
cluster-monitoring-config
config map in theopenshift-monitoring
project:$ oc -n openshift-monitoring edit configmap cluster-monitoring-config
In the
writeRelabelConfigs:
section underdata/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
The following sample shows how to forward a metric with the cluster ID label
cluster_id
: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.
- Save the file to apply the changes. The new configuration is applied automatically.
Additional resources
4.3. Configuring alerts and notifications for core platform monitoring
You can configure a local or external Alertmanager instance to route alerts from Prometheus to endpoint receivers. You can also attach custom labels to all time series and alerts to add useful metadata information.