25.5. Upgrading the Cluster Operator

Procedure

1. Take note of any configuration changes made to the existing Cluster Operator resources (in the /install/cluster-operator directory). Any changes will be overwritten by the new version of the Cluster Operator.
2. Update your custom resources to reflect the supported configuration options available for Streams for Apache Kafka version 2.7.

3. Update the Cluster Operator.

   1. Modify the installation files for the new Cluster Operator version according to the namespace the Cluster Operator is running in.

      On Linux, use:

      sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml

      On MacOS, use:

      sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml

   2. If you modified one or more environment variables in your existing Cluster Operator Deployment, edit the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file to use those environment variables.

4. When you have an updated configuration, deploy it along with the rest of the installation resources:

   oc replace -f install/cluster-operator

   Wait for the rolling updates to complete.

5. If the new Operator version no longer supports the Kafka version you are upgrading from, the Cluster Operator returns an error message to say the version is not supported. Otherwise, no error message is returned.

   • If the error message is returned, upgrade to a Kafka version that is supported by the new Cluster Operator version:

     1. Edit the Kafka custom resource.
     2. Change the spec.kafka.version property to a supported Kafka version.
   • If the error message is not returned, go to the next step. You will upgrade the Kafka version later.

6. Get the image for the Kafka pod to ensure the upgrade was successful:

   oc get pods my-cluster-kafka-0 -o jsonpath='{.spec.containers[0].image}'

   The image tag shows the new Streams for Apache Kafka version followed by the Kafka version:

   registry.redhat.io/amq-streams/strimzi-kafka-37-rhel9:2.7.0

   You can also check the upgrade has completed successfully from the status of the Kafka resource.

1. Upgrade to Streams for Apache Kafka 1.7.
2. Download the Red Hat Streams for Apache Kafka API Conversion Tool provided with Streams for Apache Kafka 1.8 from the Streams for Apache Kafka software downloads page.

3. Convert custom resources and CRDs to v1beta2.

   For more information, see the Streams for Apache Kafka 1.7 upgrade documentation.

4. In the OperatorHub, delete version 1.7 of the Streams for Apache Kafka Operator.

5. If it also exists, delete version 2.7 of the Streams for Apache Kafka Operator.

   If it does not exist, go to the next step.

   If the Approval Strategy for the Streams for Apache Kafka Operator was set to Automatic, version 2.7 of the operator might already exist in your cluster. If you did not convert custom resources and CRDs to the v1beta2 API version before release, the operator-managed custom resources and CRDs will be using the old API version. As a result, the 2.7 Operator is stuck in Pending status. In this situation, you need to delete version 2.7 of the Streams for Apache Kafka Operator as well as version 1.7.

   If you delete both operators, reconciliations are paused until the new operator version is installed. Follow the next steps immediately so that any changes to custom resources are not delayed.

6. In the OperatorHub, do one of the following:

   • Upgrade to version 1.8 of the Streams for Apache Kafka Operator (where the ControlPlaneListener feature gate is disabled by default).
   • Upgrade to version 2.0 or 2.2 of the Streams for Apache Kafka Operator (where the ControlPlaneListener feature gate is enabled by default) with the ControlPlaneListener feature gate disabled.

7. Upgrade to version 2.7 of the Streams for Apache Kafka Operator immediately.

   The installed 2.7 operator begins to watch the cluster and performs rolling updates. You might notice a temporary decrease in cluster performance during this process.