Chapter 2. Getting started with AMQ Streams

Each AMQ Streams-specific custom resource conforms to the schema defined by the CRD for the resource’s kind.

To understand the relationship between a CRD and a custom resource, let’s look at a sample of the CRD for a Kafka topic.

apiVersion: kafka.strimzi.io/v1beta1
kind: CustomResourceDefinition
metadata: 1
  name: kafkatopics.kafka.strimzi.io
  labels:
    app: strimzi
spec: 2
  group: kafka.strimzi.io
  versions:
    v1beta1
  scope: Namespaced
  names:
    # ...
    singular: kafkatopic
    plural: kafkatopics
    shortNames:
    - kt 3
  additionalPrinterColumns: 4
      # ...
  subresources:
    status: {} 5
  validation: 6
    openAPIV3Schema:
      properties:
        spec:
          type: object
          properties:
            partitions:
              type: integer
              minimum: 1
            replicas:
              type: integer
              minimum: 1
              maximum: 32767
      # ...

Here is a corresponding example of a KafkaTopic custom resource.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic 1
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster 2
spec: 3
  partitions: 1
  replicas: 1
  config:
    retention.ms: 7200000
    segment.bytes: 1073741824
status:
  conditions: 4
    lastTransitionTime: "2019-08-20T11:37:00.706Z"
    status: "True"
    type: Ready
  observedGeneration: 1
  / ...

Custom resources can be applied to a cluster through the platform CLI. When the custom resource is created, it uses the same validation as the built-in resources of the Kubernetes API.

After a KafkaTopic custom resource is created, the Topic Operator is notified and corresponding Kafka topics are created in AMQ Streams.

The status property of a AMQ Streams custom resource publishes information about the resource to users and tools that need it.

Several resources have a status property, as described in the following table.

The status property of a resource provides information on the resource’s:

The status property also provides resource-specific information. For example:

A resource’s current state is useful for tracking progress related to the resource achieving its desired state, as defined by the spec property. The status conditions provide the time and reason the state of the resource changed and details of events preventing or delaying the operator from realizing the resource’s desired state.

The last observed generation is the generation of the resource that was last reconciled by the Cluster Operator. If the value of observedGeneration is different from the value of metadata.generation, the operator has not yet processed the latest update to the resource. If these values are the same, the status information reflects the most recent changes to the resource.

AMQ Streams creates and maintains the status of custom resources, periodically evaluating the current state of the custom resource and updating its status accordingly. When performing an update on a custom resource using oc edit, for example, its status is not editable. Moreover, changing the status would not affect the configuration of the Kafka cluster.

Here we see the status property specified for a Kafka custom resource.

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
spec:
  # ...
status:
  conditions: 1
  - lastTransitionTime: 2019-07-23T23:46:57+0000
    status: "True"
    type: Ready 2
  observedGeneration: 4 3
  listeners: 4
  - addresses:
    - host: my-cluster-kafka-bootstrap.myproject.svc
      port: 9092
    type: plain
  - addresses:
    - host: my-cluster-kafka-bootstrap.myproject.svc
      port: 9093
    certificates:
    - |
      -----BEGIN CERTIFICATE-----
      ...
      -----END CERTIFICATE-----
    type: tls
  - addresses:
    - host: 172.29.49.180
      port: 9094
    certificates:
    - |
      -----BEGIN CERTIFICATE-----
      ...
      -----END CERTIFICATE-----
    type: external
    # ...

AMQ Streams uses the Cluster Operator to deploy and manage clusters for:

Custom resources are used to deploy the clusters.

For example, to deploy a Kafka cluster:

The Cluster Operator can also deploy (through configuration of the Kafka resource):

The Topic Operator and User Operator function within the Entity Operator on deployment.

When the Cluster Operator is running, it starts to watch for updates of Kafka resources.

Depending on the deployment, the Cluster Operator can watch Kafka resources from:

The Cluster Operator watches for changes to the following resources:

When one of these resources is created in the OpenShift cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary OpenShift resources, such as StatefulSets, Services and ConfigMaps.

Each time a Kafka resource is updated, the operator performs corresponding updates on the OpenShift resources that make up the cluster for the resource.

Resources are either patched or deleted, and then recreated in order to make the cluster for the resource reflect the desired state of the cluster. This operation might cause a rolling update that might lead to service disruption.

When a resource is deleted, the operator undeploys the cluster and deletes all related OpenShift resources.

You can configure the Cluster Operator to watch AMQ Streams resources across all namespaces in your OpenShift cluster. When running in this mode, the Cluster Operator automatically manages clusters in any new namespaces that are created.

You can deploy the Cluster Operator to your OpenShift cluster by installing the AMQ Streams Operator from the OperatorHub. The OperatorHub is available in OpenShift 4 only.

Next, you can deploy the other components of AMQ Streams, starting with a Kafka cluster, using the YAML example files.

You can deploy an ephemeral or persistent Kafka cluster to OpenShift on the command line.

You can deploy a Kafka Connect cluster to your OpenShift cluster by using the Cluster Operator.

The AMQ Streams container images for Kafka Connect include the two built-in file connectors: FileStreamSourceConnector and FileStreamSinkConnector. You can add your own connectors by:

When you have created a container image for your connector plug-in, you need to create a connector instance in your Kafka Connect cluster. You can then configure, monitor, and manage a running connector instance.

AMQ Streams provides two APIs for creating and managing connectors:

Using the APIs, you can:

Deploy the example KafkaConnector to a Kafka Connect cluster. The example YAML will create a FileStreamSourceConnector to send each line of the license file to Kafka as a message in a topic named my-topic.

You can deploy a Kafka Bridge cluster to your OpenShift cluster by using the Cluster Operator.

The Topic Operator provides a way of managing topics in a Kafka cluster through OpenShift resources.

The role of the Topic Operator is to keep a set of KafkaTopic OpenShift resources describing Kafka topics in-sync with corresponding Kafka topics.

Specifically, if a KafkaTopic is:

Working in the other direction, if a topic is:

This allows you to declare a KafkaTopic as part of your application’s deployment and the Topic Operator will take care of creating the topic for you. Your application just needs to deal with producing or consuming from the necessary topics.

If the topic is reconfigured or reassigned to different Kafka nodes, the KafkaTopic will always be up to date.

This procedure describes how to deploy the Topic Operator using the Cluster Operator. If you want to use the Topic Operator with a Kafka cluster that is not managed by AMQ Streams, you must deploy the Topic Operator as a standalone component. For more information, see Section 4.2.6, “Deploying the standalone Topic Operator”.

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser resources that describe Kafka users, and ensuring that they are configured properly in the Kafka cluster.

For example, if a KafkaUser is:

Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the OpenShift resources. Kafka topics can be created by applications directly in Kafka, but it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator.

The User Operator allows you to declare a KafkaUser resource as part of your application’s deployment. You can specify the authentication and authorization mechanism for the user. You can also configure user quotas that control usage of Kafka resources to ensure, for example, that a user does not monopolize access to a broker.

When the user is created, the user credentials are created in a Secret. Your application needs to use the user and its credentials for authentication and to produce or consume messages.

In addition to managing credentials for authentication, the User Operator also manages authorization rules by including a description of the user’s access rights in the KafkaUser declaration.