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Custom Metrics Autoscaler Operator release notes - Automatically scaling pods with the Custom Metrics Autoscaler Operator | Nodes | OpenShift Container Platform 4.10
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Custom Metrics Autoscaler Operator release notes

The release notes for the Custom Metrics Autoscaler Operator for Red Hat OpenShift describe new features and enhancements, deprecated features, and known issues.

The Custom Metrics Autoscaler Operator uses the Kubernetes-based Event Driven Autoscaler (KEDA) and is built on top of the OpenShift Container Platform horizontal pod autoscaler (HPA).

The Custom Metrics Autoscaler Operator for Red Hat OpenShift is provided as an installable component, with a distinct release cycle from the core OpenShift Container Platform. The Red Hat OpenShift Container Platform Life Cycle Policy outlines release compatibility.

Supported versions

The following table defines the Custom Metrics Autoscaler Operator versions for each OpenShift Container Platform version.

Version OpenShift Container Platform version General availability

2.10.1-267

4.13

General availability

2.10.1-267

4.12

General availability

2.10.1-267

4.11

General availability

2.10.1-267

4.10

General availability

Custom Metrics Autoscaler Operator 2.10.1-267 release notes

This release of the Custom Metrics Autoscaler Operator 2.10.1-267 provides new features and bug fixes for running the Operator in an OpenShift Container Platform cluster. The components of the Custom Metrics Autoscaler Operator 2.10.1-267 were released in RHBA-2023:4089.

Before installing this version of the Custom Metrics Autoscaler Operator, remove any previously installed Technology Preview versions or the community-supported version of KEDA.

Bug fixes

  • Previously, the custom-metrics-autoscaler and custom-metrics-autoscaler-adapter images did not contain time zone information. Because of this, scaled objects with cron triggers failed to work because the controllers were unable to find time zone information. With this fix, the image builds now include time zone information. As a result, scaled objects containing cron triggers now function properly. (OCPBUGS-15264)

  • Previously, the Custom Metrics Autoscaler Operator would attempt to take ownership of all managed objects, including objects in other namespaces and cluster-scoped objects. Because of this, the Custom Metrics Autoscaler Operator was unable to create the role binding for reading the credentials necessary to be an API server. This caused errors in the kube-system namespace. With this fix, the Custom Metrics Autoscaler Operator skips adding the ownerReference field to any object in another namespace or any cluster-scoped object. As a result, the role binding is now created without any errors. (OCPBUGS-15038)

  • Previously, the Custom Metrics Autoscaler Operator added an ownerReferences field to the openshift-keda namespace. While this did not cause functionality problems, the presence of this field could have caused confusion for cluster administrators. With this fix, the Custom Metrics Autoscaler Operator does not add the ownerReference field to the openshift-keda namespace. As a result, the openshift-keda namespace no longer has a superfluous ownerReference field. (OCPBUGS-15293)

  • Previously, if you used a Prometheus trigger configured with authentication method other than pod identity, and the podIdentity parameter was set to none, the trigger would fail to scale. With this fix, the Custom Metrics Autoscaler for OpenShift now properly handles the none pod identity provider type. As a result, a Prometheus trigger configured with authentication method other than pod identity, and the podIdentity parameter sset to none now properly scales. (OCPBUGS-15274)

Custom Metrics Autoscaler Operator 2.10.1 release notes

This release of the Custom Metrics Autoscaler Operator 2.10.1 provides new features and bug fixes for running the Operator in an OpenShift Container Platform cluster. The components of the Custom Metrics Autoscaler Operator 2.10.1 were released in RHEA-2023:3199.

Before installing this version of the Custom Metrics Autoscaler Operator, remove any previously installed Technology Preview versions or the community-supported version of KEDA.

New features and enhancements

Custom Metrics Autoscaler Operator general availability

The Custom Metrics Autoscaler Operator is now generally available as of Custom Metrics Autoscaler Operator version 2.10.1.

Scaling by using a scaled job 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 Technology Preview Features Support Scope.

Performance metrics

You can now use the Prometheus Query Language (PromQL) to query metrics on the Custom Metrics Autoscaler Operator.

Pausing the custom metrics autoscaling for scaled objects

You can now pause the autoscaling of a scaled object, as needed, and resume autoscaling when ready.

Replica fall back for scaled objects

You can now specify the number of replicas to fall back to if a scaled object fails to get metrics from the source.

Customizable HPA naming for scaled objects

You can now specify a custom name for the horizontal pod autoscaler in scaled objects.

Activation and scaling thresholds

Because the horizontal pod autoscaler (HPA) cannot scale to or from 0 replicas, the Custom Metrics Autoscaler Operator does that scaling, after which the HPA performs the scaling. You can now specify when the HPA takes over autoscaling, based on the number of replicas. This allows for more flexibility with your scaling policies.

Custom Metrics Autoscaler Operator 2.8.2-174 release notes

This release of the Custom Metrics Autoscaler Operator 2.8.2-174 provides new features and bug fixes for running the Operator in an OpenShift Container Platform cluster. The components of the Custom Metrics Autoscaler Operator 2.8.2-174 were released in RHEA-2023:1683.

The Custom Metrics Autoscaler Operator version 2.8.2-174 is a Technology Preview feature.

New features and enhancements

Operator upgrade support

You can now upgrade from a prior version of the Custom Metrics Autoscaler Operator. See "Changing the update channel for an Operator" in the "Additional resources" for information on upgrading an Operator.

must-gather support

You can now collect data about the Custom Metrics Autoscaler Operator and its components by using the OpenShift Container Platform must-gather tool. Currently, the process for using the must-gather tool with the Custom Metrics Autoscaler is different than for other operators. See "Gathering debugging data in the "Additional resources" for more information.

Custom Metrics Autoscaler Operator 2.8.2 release notes

This release of the Custom Metrics Autoscaler Operator 2.8.2 provides new features and bug fixes for running the Operator in an OpenShift Container Platform cluster. The components of the Custom Metrics Autoscaler Operator 2.8.2 were released in RHSA-2023:1042.

The Custom Metrics Autoscaler Operator version 2.8.2 is a Technology Preview feature.

New features and enhancements

Audit Logging

You can now gather and view audit logs for the Custom Metrics Autoscaler Operator and its associated components. Audit logs are security-relevant chronological sets of records that document the sequence of activities that have affected the system by individual users, administrators, or other components of the system.

Scale applications based on Apache Kafka metrics

You can now use the KEDA Apache kafka trigger/scaler to scale deployments based on an Apache Kafka topic.

Scale applications based on CPU metrics

You can now use the KEDA CPU trigger/scaler to scale deployments based on CPU metrics.

Scale applications based on memory metrics

You can now use the KEDA memory trigger/scaler to scale deployments based on memory metrics.