Supertubes automates Schema Registry deployment by introducing a new custom resource called SchemaRegistry. Supertubes provides two modes to manage the schemas:

Both methods use the SchemaRegistry Custom Resource under the hood to manage Schema registry instances. ​

Imperative management of Schema Registry instances πŸ”—︎

​ The Supertubes CLI provides commands to deploy Schema Registry instances with either default or custom settings with ease.

Note: If you install Supertubes with the following command, it sets up everything needed to start playing with Kafka and Schema Registry on Kubernetes, which is ideal for testing: supertubes install -a ​ To deploy additional Schema Registry instances or manage existing ones, run the supertubes cluster schemaregistry create and supertubes cluster schemaregistry update commands. Both of these commands expect a Schema Registry descriptor.

Declarative management of Schema Registry instances πŸ”—︎

​ Managing Schema Registry instances with Supertubes is as simple as creating and updating the SchemaRegistry custom resource. Supertubes automatically monitors the Schema Registry deployment and configuration settings specified using the SchemaRegistry custom resource (for details, see the description of the custom resource).

These will perform the necessary steps to spin up new Schema Registry instances or reconfigure existing ones with the desired configuration. For details ​​

Schema Registry API endpoints πŸ”—︎

​ The deployed Schema Registry instances are reachable at the schema-registry-svc-<schema-registry-name>.<namespace>.svc:<servicePort> endpoint from within the Kubernetes cluster. ​ Client applications connecting from outside the Kubernetes cluster can reach Schema Registry instances at the public IP address of the schema-registry-meshgateway-<schema-registry-name> LoadBalancer-type service. This public endpoint is made available when the Kafka cluster that the Schema Registry is bound to becomes exposed externally. ​

Security πŸ”—︎

​ All participants that connect to Schema Registry are authenticated using mTLS by default. This can be disabled via the MTLS field of the SchemaRegistry custom resource. ​

Schema Registry Kafka ACLs πŸ”—︎

​ When ACLs are enabled in the Kafka cluster that the particular Schema Registry is bound to, Supertubes automatically creates the required Kafka ACLs for Schema Registry. ​

Registering schemas declaratively πŸ”—︎

By default, client applications automatically register new schemas. When they produce new messages to the topic, they automatically try to register new schemas. This behavior can be useful during development, but should be avoided in production environments. ​ Aside from registering schemas through Schema Registry’s API endpoint, Supertubes also makes it possible to register schemas declaratively, through Kubernetes ConfigMaps that hold schema definitions. Supertubes watches ConfigMaps with specific labels to read schema definitions from, and registers them into the Schema Registry running in that namespace. This behavior is in line with the GitOps and Configuration as Code trends.

Supertubes SchemaRegistry structure ​ See the following example:

apiVersion: v1
kind: ConfigMap
  name: my-topic-value-schema
  labels: my-schema-registry my-topic-value
  schema.json: |-
       "namespace": "io.examples",
       "type": "record",
       "name": "Payment",
       "fields": [
            "name": "id",
            "type": "string"
           "name": "amount",
           "type": "double"

​ Notice the two labels on ConfigMap: ​

  • - The name of the SchemaRegistry custom resource that represents the Schema Registry deployment where the schema should be registered.
  • - The name of the subject where the schema should be registered. ​ The schema specification should be under the schema.json key in the ConfigMap. Any updates made to the schema specifications in the ConfigMap create new versions of the underlying schema in the Schema Registry. ​ When the ConfigMap is deleted, all versions related to the corresponding schema are deleted from their Schema Registry.

The SchemaRegistry custom resource πŸ”—︎

kind: SchemaRegistry
  name: my-schema-registry
  namespace: kafka
    # Name of the KafkaCluster custom resource that represents the Kafka cluster this Schema Registry instance will connect to
    name: kafka
  # The port Schema registry listens on for API requests (default: 8081)
  servicePort: 8081

  # Labels to be applied to the schema registry pod

  # Annotations to be applied to the schema registry pod

  # Annotations to be applied to the service that exposes the Schema registry API on port `ServicePort`

  # Labels to be applied to the service that exposes the Schema Registry API on port `ServicePort`

  # Service account for schema registry pod

  # Description of compute resource requirements
  # requests:
  #   cpu: 200m
  #   mem: 800mi
  # limits:
  #   cpu: 1
  #   mem: 1.2Gi
  # Minimum number of replicas (default: 1)
  minReplicas: 1
  # Maximum number of replicas (default: 5) Horizontal Pod Autoscaler can upscale to
  maxReplicas: 5
  # Controls whether mTLS is enforced between Schema Registry and client applications
  # as well as Schema registry instances
  # (default: true)
  MTLS: true
  # Heap settings for Schema Registry (default: -Xms512M -Xmx1024M)
  heapOpts: -Xms512M -Xmx1024M
  # Defines the config values for schema registry in the form of key-value pairs

​ The following SchemaRegistry configurations are computed and maintained by Supertubes, and cannot be overridden: ​

  • listeners
  • kafkastore.bootstrap.servers
  • master.eligibility - always true
  • kafkastore.topic

Example: Schema Registry with a demo application πŸ”—︎

For a detailed example on managing Schema Registry with a demo Spring Boot application, see our Kafka Schema Registry on Kubernetes blog post.