4.2. Operator deployment best practices
Potential issues can arise from installing more than one Streams for Apache Kafka operator in the same OpenShift cluster, especially when using different versions. Each Streams for Apache Kafka operator manages a set of resources in an OpenShift cluster. When you install multiple Streams for Apache Kafka operators, they may attempt to manage the same resources concurrently. This can lead to conflicts and unpredictable behavior within your cluster. Conflicts can still occur even if you deploy Streams for Apache Kafka operators in different namespaces within the same OpenShift cluster. Although namespaces provide some degree of resource isolation, certain resources managed by the Streams for Apache Kafka operator, such as Custom Resource Definitions (CRDs) and roles, have a cluster-wide scope.
Additionally, installing multiple operators with different versions can result in compatibility issues between the operators and the Kafka clusters they manage. Different versions of Streams for Apache Kafka operators may introduce changes, bug fixes, or improvements that are not backward-compatible.
To avoid the issues associated with installing multiple Streams for Apache Kafka operators in an OpenShift cluster, the following guidelines are recommended:
- Install the Streams for Apache Kafka operator in a separate namespace from the Kafka cluster and other Kafka components it manages, to ensure clear separation of resources and configurations.
- Use a single Streams for Apache Kafka operator to manage all your Kafka instances within an OpenShift cluster.
- Update the Streams for Apache Kafka operator and the supported Kafka version as often as possible to reflect the latest features and enhancements.
By following these best practices and ensuring consistent updates for a single Streams for Apache Kafka operator, you can enhance the stability of managing Kafka instances in an OpenShift cluster. This approach also enables you to make the most of Streams for Apache Kafka’s latest features and capabilities.
As Streams for Apache Kafka is based on Strimzi, the same issues can also arise when combining Streams for Apache Kafka operators with Strimzi operators in an OpenShift cluster.
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