Release Notes for AMQ Streams 1.7 on OpenShift

AMQ Streams 1.7 is supported on OpenShift Container Platform 4.6 and 4.7.

For more information about the supported platform versions, see the Red Hat Knowledgebase article Red Hat AMQ 7 Supported Configurations.

AMQ Streams now supports Apache Kafka version 2.7.0.

AMQ Streams uses Kafka 2.7.0. Only Kafka distributions built by Red Hat are supported.

You must upgrade the Cluster Operator to AMQ Streams version 1.7 before you can upgrade brokers and client applications to Kafka 2.7.0. For upgrade instructions, see Upgrading AMQ Streams.

Refer to the Kafka 2.6.0 and Kafka 2.7.0 Release Notes for additional information.

For more information on supported versions, see the Red Hat AMQ 7 Component Details Page on the Customer Portal.

Kafka 2.7.0 requires the same ZooKeeper version as Kafka 2.6.x (ZooKeeper version 3.5.8). Therefore, the Cluster Operator does not perform a ZooKeeper upgrade when you upgrade from AMQ Streams 1.6 to AMQ Streams 1.7.

AMQ Streams 1.7 introduces the v1beta2 API version, which updates the schemas of the AMQ Streams custom resources. Older API versions are now deprecated.

After you have upgraded to AMQ Streams 1.7, you must upgrade your custom resources to use API version v1beta2. You can do this any time after upgrading, but the upgrades must be completed before the next AMQ Streams minor version update (AMQ Streams 1.8).

The Red Hat AMQ Streams 1.7.0 API Conversion Tool is provided to support the upgrade of custom resources. You can download the API conversion tool from the AMQ Streams download site. Instructions for using the tool are included in the documentation and the provided readme.

Upgrading custom resources to v1beta2 prepares AMQ Streams for Kubernetes CRD v1, which will be required for Kubernetes 1.22.

You can now upgrade from a previous AMQ Streams version directly to the latest AMQ Streams version within a single upgrade. For example, upgrading from AMQ Streams 1.5 directly to AMQ Streams 1.7, skipping intermediate versions.

See Upgrading AMQ Streams

You can now use build configuration so that AMQ Streams automatically builds a container image with the connector plugins you require for your data connections.

For AMQ Streams to create the new image automatically, the build configuration requires output properties to reference a container registry that stores the container image, and plugins properties to list the connector plugins and their artifacts to add to the image.

A build configuration can also reference an imagestream. Imagestreams reference a container image stored in OpenShift Container Platform’s integrated registry.

The output properties describe the type and name of the image, and optionally the name of the Secret containing the credentials needed to access the container registry. The plugins properties describe the type of artifact and the URL from which the artifact is downloaded. Additionally, you can specify a SHA-512 checksum to verify the artifact before unpacking it.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect-cluster
spec:
  # ...
  build:
    output:
      type: docker
      image: my-registry.io/my-org/my-connect-cluster:latest
      pushSecret: my-registry-credentials
    plugins:
      - name: debezium-postgres-connector
        artifacts:
          - type: tgz
            url: https://ARTIFACT-ADDRESS.tgz
            sha512sum: HASH-NUMBER-TO-VERIFY-ARTIFACT
      # ...
  #...

See:

Metrics are now configured using a ConfigMap that is automatically created when a custom resource is deployed.

Use the metricsConfig property to enable and configure Prometheus metrics for Kafka components. The metricsConfig property contains a reference to a ConfigMap containing additional configuration for the Prometheus JMX exporter.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metricsConfig:
      type: jmxPrometheusExporter
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: my-key
    # ...
  zookeeper:
    # ...

The ConfigMap stores the YAML configuration for the JMX Prometheus exporter under a key.

kind: ConfigMap
apiVersion: v1
metadata:
  name: my-configmap
data:
  my-key: |
    lowercaseOutputName: true
    rules:
    # Special cases and very specific rules
    - pattern: kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value
      name: kafka_server_$1_$2
      type: GAUGE
      labels:
       clientId: "$3"
       topic: "$4"
       partition: "$5"
    # further configuration

To enable Prometheus metrics export without further configuration, you can reference a ConfigMap containing an empty file under metricsConfig.valueFrom.configMapKeyRef.key. When referencing an empty file, all metrics are exposed as long as they have not been renamed.

The spec.metrics property is deprecated, as described in Chapter 4, Deprecated features.

See Common configuration properties

Red Hat Debezium is a distributed change data capture platform. It captures row-level changes in databases, creates change event records, and streams the records to Kafka topics. Debezium is built on Apache Kafka. You can deploy and integrate Debezium with AMQ Streams. Following a deployment of AMQ Streams, you deploy Debezium as a connector configuration through Kafka Connect. Debezium passes change event records to AMQ Streams on OpenShift. Applications can read these change event streams and access the change events in the order in which they occurred.

Debezium has multiple uses, including:

Debezium provides connectors (based on Kafka Connect) for the following common databases:

For more information on deploying Debezium with AMQ Streams, refer to the product documentation.

You can use Service Registry as a centralized store of service schemas for data streaming. For Kafka, you can use Service Registry to store Apache Avro or JSON schema.

Service Registry provides a REST API and a Java REST client to register and query the schemas from client applications through server-side endpoints.

Using Service Registry decouples the process of managing schemas from the configuration of client applications. You enable an application to use a schema from the registry by specifying its URL in the client code.

For example, the schemas to serialize and deserialize messages can be stored in the registry, which are then referenced from the applications that use them to ensure that the messages that they send and receive are compatible with those schemas.

Kafka client applications can push or pull their schemas from Service Registry at runtime.

For more information on using Service Registry with AMQ Streams, refer to the product documentation.

For an overview of the enhancements introduced with Kafka 2.7.0, refer to the Kafka 2.7.0 Release Notes.

You can now configure the Deployment strategy for Kafka Connect, MirrorMaker, and the Kafka Bridge.

The RollingUpdate strategy is used by default for all resources. During a rolling update of a Kafka cluster, the old and new pods in the Deployment are run in parallel. This is the optimal strategy for most use cases.

To reduce resource consumption, you can choose the Recreate strategy. With this strategy, during a rolling update, the old pods in the Deployment are terminated before any new pods are created.

You set the Deployment strategy in spec.template.deployment in the KafkaConnect, KafkaMirrorMaker, KafkaMirrorMaker2, and KafkaBridge resources.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect-cluster
spec:
  #...
  template:
    deployment:
      deploymentStrategy: Recreate
  #...

If spec.template.deployment is not configured, the RollingUpdate strategy is used.

See DeploymentTemplate schema reference

Cluster and Client CA Secrets are created with an ownerReference field, which is set to the Kafka custom resource.

Now, you can disable the CA Secrets ownerReference by adding the generateSecretOwnerReference: false property to your Kafka cluster configuration. If the ownerReference for a CA Secret is disabled, the Secret is not deleted by OpenShift when the corresponding Kafka custom resource is deleted. The CA Secret is then available for reuse with a new Kafka cluster.

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
# ...
spec:
# ...
  clusterCa:
    generateCertificateAuthority: true
    generateSecretOwnerReference: false
  clientsCa:
    generateCertificateAuthority: true
    generateSecretOwnerReference: false
# ...

See Disabling ownerReference in the CA Secrets

You can now use the secretPrefix property to configure the User Operator, which adds a prefix to all secret names created for a KafkaUser resource.

For example, this configuration:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    userOperator:
      secretPrefix: kafka-
    # ...

Creates a secret named kafka-my-user for a user named my-user.

See EntityUserOperatorSpec schema reference

Using an annotation, you can manually trigger a rolling update of an existing pod that is part of the Kafka cluster or ZooKeeper cluster StatefulSets. When multiple pods from the same StatefulSet are annotated at the same time, consecutive rolling updates are performed within the same reconciliation run.

See Performing a rolling update using a Pod annotation

AMQ Streams no longer uses ZooKeeper to store topic metadata. Topic metadata is now brought into the Kafka cluster, and under the control of the Topic Operator.

This change is required to prepare AMQ Streams for the future removal of ZooKeeper as a Kafka dependency.

The Topic Operator now uses persistent storage to store topic metadata describing topic configuration as key-value pairs. Topic metadata is accessed locally in-memory. Updates from operations applied to the local in-memory topic store are persisted to a backup topic store on disk. The topic store is continually synchronized with updates from Kafka topics.

When upgrading to AMQ Streams 1.7, the transition to Topic Operator control of the topic store is seamless. Metadata is found and migrated from ZooKeeper, and the old store is cleansed.

See Topic Operator topic store

The JAAS configuration string in the sasl.jaas.config property has been added to the generated secrets for a KafkaUser with SCRAM-SHA-512 authentication.

See SCRAM-SHA-512 Authentication

The KafkaStatus schema is updated to include the clusterId to identify a Kafka cluster. The status property of the Kafka resource provides status information on a Kafka cluster.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
  # ...
status:
  conditions:
    lastTransitionTime: "YEAR-MONTH-20T11:37:00.706Z"
    status: "True"
    type: Ready
  observedGeneration: 1
  clusterId: CLUSTER-ID
  # ...

When you retrieve the status of a Kafka resource, the id of the Kafka cluster is also returned:

oc get kafka MY-KAFKA-CLUSTER -o jsonpath='{.status}'

You can also retrieve only the cluster id for the Kafka resource:

oc get kafka MY-KAFKA-CLUSTER -o jsonpath='{.status.clusterId}'

See KafkaStatus schema reference and Finding the status of a custom resource

When you retrieve the status of a KafkaConnector resource, the list of topics used by the connector is now returned in the topics property.

See KafkaConnectorStatus schema reference and Finding the status of a custom resource

You can now run AMQ Streams with a read-only root file system. Additional volume has been added so that temporary files are written to a mounted /tmp file. Previously, the /tmp directory was used directly from the container.

In this way, the container file system does not need to be modified, and AMQ Streams can run unimpeded from a read-only root file system.

The example Kafka configuration files provided with AMQ Streams now specify the inter.broker.protocol.version. The inter.broker.protocol.version and log.message.format.version properties for the Kafka config are the versions supported by the specified Kafka version (spec.kafka.version). The properties represent the log format version appended to messages and the version of protocol used in a Kafka cluster. Updates to these properties are required when upgrading your Kafka version.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    version: 2.7.0
    #...
    config:
      #...
      log.message.format.version: 2.7
      inter.broker.protocol.version: 2.7

See Upgrading Kafka

The Cluster Operator can run in the same namespace as the resources it manages, or in a separate namespace. Two new environment variables now control which namespaces can access the Cluster Operator.

By default, the STRIMZI_OPERATOR_NAMESPACE environment variable is configured to use the Kubernetes Downward API to find which namespace the Cluster Operator is running in. If the Cluster Operator is running in the same namespace as the resources, only local access is required, and allowed by Strimzi.

If the Cluster Operator is running in a separate namespace to the resources it manages, any namespace in the Kubernetes cluster is allowed access to the Cluster Operator unless network policy is configured. Use the optional STRIMZI_OPERATOR_NAMESPACE_LABELS environment variable to establish network policy for the Cluster Operator using namespace labels. By adding namespace labels, access to the Cluster Operator is restricted to the namespaces specified.

#...
env:
  - name: STRIMZI_OPERATOR_NAMESPACE_LABELS
    value: label1=value1,label2=value2
  #...

See Cluster Operator configuration

By configuring the clusterCaCert template property in the Kafka custom resource, you can add custom labels and annotations to the Cluster CA Secrets created by the Cluster Operator. Labels and annotations are useful for identifying objects and adding contextual information. You configure template properties in Strimzi custom resources.

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    template:
      clusterCaCert:
        metadata:
          labels:
            label1: value1
            label2: value2
          annotations:
            annotation1: value1
            annotation2: value2
    # ...

See Customizing OpenShift resources

You can pause the reconciliation of a custom resource by setting the strimzi.io/pause-reconciliation annotation to true in its configuration. For example, you can apply the annotation to the KafkaConnect resource so that reconciliation by the Cluster Operator is paused.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  annotations:
    strimzi.io/pause-reconciliation: "true"
    strimzi.io/use-connector-resources: "true"
  creationTimestamp: 2021-03-12T10:47:11Z
  #...
spec:
  # ...
status:
  conditions:
  - lastTransitionTime: 2021-03-12T10:47:41.689249Z
    status: "True"
    type: ReconciliationPaused

See Pausing reconciliation of custom resources

Connector instances and their tasks can now be restarted by using Kubernetes annotations on the relevant custom resources.

You can restart connectors for both Kafka Connect and MirrorMaker 2.0, which uses the Kafka Connect framework to replicate data between the source and target Kafka clusters.

The annotations can also be used to restart a specified task for a connector.

For Kafka Connect, see Performing a restart of a Kafka connector and Performing a restart of a Kafka connector task.

For MirrorMaker 2.0, see Performing a restart of a Kafka MirrorMaker 2.0 connector and Performing a restart of a Kafka MirrorMaker 2.0 connector task.

This release includes the following enhancements to OAuth 2.0 token-based authentication and authorization in AMQ Streams.

Checks on JWT access tokens

You can now configure two additional checks on JWT access tokens. Both of these checks are configured in the OAuth 2.0 configuration for Kafka broker listeners.

If an access token does not contain the necessary data, it is rejected. When using introspection endpoint token validation, the custom check is applied to the introspection endpoint response JSON.

To configure custom claim checks, add the customClaimCheck option and define a JsonPath filter query. Custom claim checks are disabled by default.

See Configuring OAuth 2.0 support for Kafka brokers

When audience checks are enabled, the Kafka broker rejects tokens that do not contain the broker’s clientId in their aud claims.

To enable audience checks, set the checkAudience option to true. Audience checks are disabled by default.

See Configuring OAuth 2.0 support for Kafka brokers

Support for OAuth 2.0 over SASL PLAIN authentication

You can now configure the PLAIN mechanism for OAuth 2.0 authentication between Kafka clients and Kafka brokers. Previously, the only supported authentication mechanism was OAUTHBEARER.

PLAIN is a simple authentication mechanism used by all Kafka client tools (including developer tools such as kafkacat). AMQ Streams includes server-side callbacks that enable PLAIN to be used with OAuth 2.0 authentication. These capabilities are referred to as OAuth 2.0 over PLAIN.

When used with the provided OAuth 2.0 over PLAIN callbacks, Kafka clients can authenticate with Kafka brokers using either of the following methods:

To use PLAIN, you must enable it in the oauth listener configuration for the Kafka broker. Three new configuration options are now supported:

  # ...
  name: external
  port: 9094
  type: loadbalancer
  tls: true
  authentication:
    type: oauth
    # ...
    checkIssuer: false
    fallbackUserNameClaim: client_id
    fallbackUserNamePrefix: client-account-
    validTokenType: bearer
    userInfoEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/userinfo
    enableOauthBearer: false 1
    enablePlain: true 2
    tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token 3
    #...

See OAuth 2.0 authentication mechanisms and Configuring OAuth 2.0 support for Kafka brokers

A new rack property is now available for Kafka Connect. Rack awareness is configured to spread replicas across different racks. By configuring a rack for a Kafka Connect cluster, consumers are allowed to fetch data from the closest replica. This is useful when a Kafka cluster spans multiple datacenters.

A topology key must match the label of a cluster node.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
#...
spec:
  #...
  rack:
    topologyKey: topology.kubernetes.io/zone

See KafkaConnectSpec schema reference and KafkaConnectS2ISpec schema reference

Pod topology spread constraints are now supported for the following AMQ Streams custom resources:

Pod topology spread constraints allow you to distribute Kafka related pods across nodes, zones, regions, or other user-defined domains. You can use them together with the existing affinity and tolerations properties for pod scheduling.

Constraints are specified in the template.pod.topologySpreadConstraints property in the relevant custom resource.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
#...
spec:
  # ...
  template:
    pod:
      topologySpreadConstraints:
      - maxSkew: "1"
        whenUnsatisfiable: DoNotSchedule
        labelSelector:
          matchLabels:
            label1: value1
#...

See:

You can deploy Cruise Control and use it to rebalance your Kafka cluster using optimization goals — defined constraints on CPU, disk, network load, and more. In a balanced Kafka cluster, the workload is more evenly distributed across the broker pods.

Cruise Control is configured and deployed as part of a Kafka resource. You can use the default optimization goals or modify them to suit your requirements. Example YAML configuration files for Cruise Control are provided in examples/cruise-control/.

When Cruise Control is deployed, you can create KafkaRebalance custom resources to:

Other Cruise Control features are not currently supported, including anomaly detection, notifications, write-your-own goals, and changing the topic replication factor.

See Cruise Control for cluster rebalancing.

AMQ Streams 1.7 introduces build configuration to the KafkaConnect resource, as described in Chapter 1, Features. With the introduction of build configuration to the KafkaConnect resource, AMQ Streams can now automatically build a container image with the connector plugins you require for your data connections.

As a result, support for Kafka Connect with Source-to-Image (S2I) is deprecated.

To prepare for this change, you can migrate Kafka Connect S2I instances to Kafka Connect instances.

See Migrating from Kafka Connect with S2I to Kafka Connect

Metrics configuration is now specified as a ConfigMap for Kafka components. Previously, the spec.metrics property was used.

To update the configuration, and enable Prometheus metrics export, a new ConfigMap must be created that matches the configuration for the .spec.metrics property. The .spec.metricsConfig property is used to specify the ConfigMap, as described in Chapter 1, Features.

See Upgrading AMQ Streams

The introduction of v1beta2 updates the schemas of the custom resources. Older API versions are deprecated.

The v1alpha1 API version is deprecated for the following AMQ Streams custom resources:

The v1beta1 API version is deprecated for the following AMQ Streams custom resources:

See AMQ Streams custom resource upgrades.

The following annotations are deprecated, and will be removed in AMQ Streams 1.8.0:

The AMQ Streams Cluster Operator does not start on Internet Protocol version 6 (IPv6) clusters.

There are two workarounds for this issue.

Red Hat 3scale cannot discover the Kafka Bridge service as documented in Deploying 3scale for the Kafka Bridge.