Deploying and Upgrading AMQ Streams on OpenShift

Red Hat AMQ 2021.q3

For use with AMQ Streams 1.8 on OpenShift Container Platform

Abstract

This guide provides instructions for deploying and upgrading AMQ Streams

Making open source more inclusive

Red Hat is committed to replacing problematic language in our code, documentation, and web properties. We are beginning with these four terms: master, slave, blacklist, and whitelist. Because of the enormity of this endeavor, these changes will be implemented gradually over several upcoming releases. For more details, see our CTO Chris Wright’s message.

Chapter 1. Deployment overview

AMQ Streams simplifies the process of running Apache Kafka in an OpenShift cluster.

This guide provides instructions on all the options available for deploying and upgrading AMQ Streams, describing what is deployed, and the order of deployment required to run Apache Kafka in an OpenShift cluster.

As well as describing the deployment steps, the guide also provides pre- and post-deployment instructions to prepare for and verify a deployment. Additional deployment options described include the steps to introduce metrics. Upgrade instructions are provided for AMQ Streams and Kafka upgrades.

AMQ Streams is designed to work on all types of OpenShift cluster regardless of distribution, from public and private clouds to local deployments intended for development.

1.1. How AMQ Streams supports Kafka

AMQ Streams provides container images and Operators for running Kafka on OpenShift. AMQ Streams Operators are fundamental to the running of AMQ Streams. The Operators provided with AMQ Streams are purpose-built with specialist operational knowledge to effectively manage Kafka.

Operators simplify the process of:

  • Deploying and running Kafka clusters
  • Deploying and running Kafka components
  • Configuring access to Kafka
  • Securing access to Kafka
  • Upgrading Kafka
  • Managing brokers
  • Creating and managing topics
  • Creating and managing users

1.2. AMQ Streams Operators

AMQ Streams supports Kafka using Operators to deploy and manage the components and dependencies of Kafka to OpenShift.

Operators are a method of packaging, deploying, and managing an OpenShift application. AMQ Streams Operators extend OpenShift functionality, automating common and complex tasks related to a Kafka deployment. By implementing knowledge of Kafka operations in code, Kafka administration tasks are simplified and require less manual intervention.

Operators

AMQ Streams provides Operators for managing a Kafka cluster running within an OpenShift cluster.

Cluster Operator
Deploys and manages Apache Kafka clusters, Kafka Connect, Kafka MirrorMaker, Kafka Bridge, Kafka Exporter, and the Entity Operator
Entity Operator
Comprises the Topic Operator and User Operator
Topic Operator
Manages Kafka topics
User Operator
Manages Kafka users

The Cluster Operator can deploy the Topic Operator and User Operator as part of an Entity Operator configuration at the same time as a Kafka cluster.

Operators within the AMQ Streams architecture

Operators within the AMQ Streams architecture

1.2.1. Cluster Operator

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

  • Kafka (including ZooKeeper, Entity Operator, Kafka Exporter, and Cruise Control)
  • Kafka Connect
  • Kafka MirrorMaker
  • Kafka Bridge

Custom resources are used to deploy the clusters.

For example, to deploy a Kafka cluster:

  • A Kafka resource with the cluster configuration is created within the OpenShift cluster.
  • The Cluster Operator deploys a corresponding Kafka cluster, based on what is declared in the Kafka resource.

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

  • A Topic Operator to provide operator-style topic management through KafkaTopic custom resources
  • A User Operator to provide operator-style user management through KafkaUser custom resources

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

Example architecture for the Cluster Operator

The Cluster Operator creates and deploys Kafka and ZooKeeper clusters

1.2.2. Topic Operator

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

Example architecture for the Topic Operator

The Topic Operator manages topics for a Kafka cluster via KafkaTopic 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:

  • Created, the Topic Operator creates the topic
  • Deleted, the Topic Operator deletes the topic
  • Changed, the Topic Operator updates the topic

Working in the other direction, if a topic is:

  • Created within the Kafka cluster, the Operator creates a KafkaTopic
  • Deleted from the Kafka cluster, the Operator deletes the KafkaTopic
  • Changed in the Kafka cluster, the Operator updates the KafkaTopic

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.

The Topic Operator maintains information about each topic in a topic store, which is continually synchronized with updates from Kafka topics or OpenShift KafkaTopic custom resources. Updates from operations applied to a local in-memory topic store are persisted to a backup topic store on disk. If a topic is reconfigured or reassigned to other brokers, the KafkaTopic will always be up to date.

1.2.3. User 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:

  • Created, the User Operator creates the user it describes
  • Deleted, the User Operator deletes the user it describes
  • Changed, the User Operator updates the user it describes

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.

1.2.4. Feature gates in AMQ Streams Operators

You can enable and disable some features of operators using feature gates.

Feature gates are set in the operator configuration and have three stages of maturity: alpha, beta, or General Availability (GA).

For more information, see Feature gates.

1.3. AMQ Streams custom resources

A deployment of Kafka components to an OpenShift cluster using AMQ Streams is highly configurable through the application of custom resources. Custom resources are created as instances of APIs added by Custom resource definitions (CRDs) to extend OpenShift resources.

CRDs act as configuration instructions to describe the custom resources in an OpenShift cluster, and are provided with AMQ Streams for each Kafka component used in a deployment, as well as users and topics. CRDs and custom resources are defined as YAML files. Example YAML files are provided with the AMQ Streams distribution.

CRDs also allow AMQ Streams resources to benefit from native OpenShift features like CLI accessibility and configuration validation.

1.3.1. AMQ Streams custom resource example

CRDs require a one-time installation in a cluster to define the schemas used to instantiate and manage AMQ Streams-specific resources.

After a new custom resource type is added to your cluster by installing a CRD, you can create instances of the resource based on its specification.

Depending on the cluster setup, installation typically requires cluster admin privileges.

Note

Access to manage custom resources is limited to AMQ Streams administrators. For more information, see Designating AMQ Streams administrators in the Deploying and Upgrading AMQ Streams on OpenShift guide.

A CRD defines a new kind of resource, such as kind:Kafka, within an OpenShift cluster.

The Kubernetes API server allows custom resources to be created based on the kind and understands from the CRD how to validate and store the custom resource when it is added to the OpenShift cluster.

Warning

When CRDs are deleted, custom resources of that type are also deleted. Additionally, the resources created by the custom resource, such as pods and statefulsets are also deleted.

Each AMQ Streams-specific custom resource conforms to the schema defined by the CRD for the resource’s kind. The custom resources for AMQ Streams components have common configuration properties, which are defined under spec.

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

Kafka topic CRD

apiVersion: kafka.strimzi.io/v1beta2
kind: CustomResourceDefinition
metadata: 1
  name: kafkatopics.kafka.strimzi.io
  labels:
    app: strimzi
spec: 2
  group: kafka.strimzi.io
  versions:
    v1beta2
  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
      # ...

1
The metadata for the topic CRD, its name and a label to identify the CRD.
2
The specification for this CRD, including the group (domain) name, the plural name and the supported schema version, which are used in the URL to access the API of the topic. The other names are used to identify instance resources in the CLI. For example, oc get kafkatopic my-topic or oc get kafkatopics.
3
The shortname can be used in CLI commands. For example, oc get kt can be used as an abbreviation instead of oc get kafkatopic.
4
The information presented when using a get command on the custom resource.
5
The current status of the CRD as described in the schema reference for the resource.
6
openAPIV3Schema validation provides validation for the creation of topic custom resources. For example, a topic requires at least one partition and one replica.
Note

You can identify the CRD YAML files supplied with the AMQ Streams installation files, because the file names contain an index number followed by ‘Crd’.

Here is a corresponding example of a KafkaTopic custom resource.

Kafka topic custom resource

apiVersion: kafka.strimzi.io/v1beta2
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
  / ...

1
The kind and apiVersion identify the CRD of which the custom resource is an instance.
2
A label, applicable only to KafkaTopic and KafkaUser resources, that defines the name of the Kafka cluster (which is same as the name of the Kafka resource) to which a topic or user belongs.
3
The spec shows the number of partitions and replicas for the topic as well as the configuration parameters for the topic itself. In this example, the retention period for a message to remain in the topic and the segment file size for the log are specified.
4
Status conditions for the KafkaTopic resource. The type condition changed to Ready at the lastTransitionTime.

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.

1.4. AMQ Streams installation methods

There are two ways to install AMQ Streams on OpenShift.

Installation methodDescriptionSupported platform

Installation artifacts (YAML files)

Download the amq-streams-x.y.z-ocp-install-examples.zip file from the AMQ Streams download site. Next, deploy the YAML installation artifacts to your OpenShift cluster using oc. You start by deploying the Cluster Operator from install/cluster-operator to a single namespace, multiple namespaces, or all namespaces.

OpenShift 4.6 and 4.8

OperatorHub

Use the Red Hat Integration - AMQ Streams Operator in the OperatorHub to deploy AMQ Streams to a single namespace or all namespaces.

OpenShift 4.6 and 4.8

For the greatest flexibility, choose the installation artifacts method. Choose the OperatorHub method if you want to install AMQ Streams to OpenShift 4.6 and 4.8 in a standard configuration using the web console. The OperatorHub also allows you to take advantage of automatic updates.

Both methods install the Cluster Operator to your OpenShift cluster. Use the same method to deploy the other components, starting with the Kafka cluster. If you are using the installation artifacts method, example YAML files are provided. If you are using the OperatorHub, the AMQ Streams Operator makes Kafka components available for installation from the OpenShift web console.

AMQ Streams installation artifacts

The AMQ Streams installation artifacts contain various YAML files that can be deployed to OpenShift, using oc, to create custom resources, including:

  • Deployments
  • Custom resource definitions (CRDs)
  • Roles and role bindings
  • Service accounts

YAML installation files are provided for the Cluster Operator, Topic Operator, User Operator, and the Strimzi Admin role.

OperatorHub

Starting with OpenShift 4, the Operator Lifecycle Manager (OLM) helps cluster administrators to install, update, and manage the lifecycle of all Operators and their associated services running across their clusters. The OLM is part of the Operator Framework, an open source toolkit designed to manage Kubernetes-native applications (Operators) in an effective, automated, and scalable way.

The OperatorHub is part of the OpenShift web console. Cluster administrators can use it to discover, install, and upgrade Operators. Operators can be pulled from the OperatorHub, installed on the OpenShift cluster to a single namespace or all namespaces, and managed by the OLM. Engineering teams can then independently manage the software in development, test, and production environments using the OLM.

Red Hat Integration - AMQ Streams Operator

The Red Hat Integration - AMQ Streams Operator is available to install from the OperatorHub. Once installed, the AMQ Streams Operator deploys the Cluster Operator to your OpenShift cluster, along with the necessary CRDs and role-based access control (RBAC) resources. You still need to install the Kafka components from the OpenShift web console.

Additional resources

Installing AMQ Streams using the installation artifacts:

Installing AMQ Streams from the OperatorHub:

Chapter 2. What is deployed with AMQ Streams

Apache Kafka components are provided for deployment to OpenShift with the AMQ Streams distribution. The Kafka components are generally run as clusters for availability.

A typical deployment incorporating Kafka components might include:

  • Kafka cluster of broker nodes
  • ZooKeeper cluster of replicated ZooKeeper instances
  • Kafka Connect cluster for external data connections
  • Kafka MirrorMaker cluster to mirror the Kafka cluster in a secondary cluster
  • Kafka Exporter to extract additional Kafka metrics data for monitoring
  • Kafka Bridge to make HTTP-based requests to the Kafka cluster

Not all of these components are mandatory, though you need Kafka and ZooKeeper as a minimum. Some components can be deployed without Kafka, such as MirrorMaker or Kafka Connect.

2.1. Order of deployment

The required order of deployment to an OpenShift cluster is as follows:

  1. Deploy the Cluster Operator to manage your Kafka cluster
  2. Deploy the Kafka cluster with the ZooKeeper cluster, and include the Topic Operator and User Operator in the deployment
  3. Optionally deploy:

    • The Topic Operator and User Operator standalone if you did not deploy them with the Kafka cluster
    • Kafka Connect
    • Kafka MirrorMaker
    • Kafka Bridge
    • Components for the monitoring of metrics

2.2. Additional deployment configuration options

The deployment procedures in this guide describe a deployment using the example installation YAML files provided with AMQ Streams. The procedures highlight any important configuration considerations, but they do not describe all the configuration options available.

You can use custom resources to refine your deployment.

You may wish to review the configuration options available for Kafka components before you deploy AMQ Streams. For more information on the configuration through custom resources, see Deployment configuration in the Using AMQ Streams on OpenShift guide.

2.2.1. Securing Kafka

On deployment, the Cluster Operator automatically sets up TLS certificates for data encryption and authentication within your cluster.

AMQ Streams provides additional configuration options for encryption, authentication and authorization, which are described in the Using AMQ Streams on OpenShift guide:

2.2.2. Monitoring your deployment

AMQ Streams supports additional deployment options to monitor your deployment.

Chapter 3. Preparing for your AMQ Streams deployment

This section shows how you prepare for a AMQ Streams deployment, describing:

Note

To run the commands in this guide, your cluster user must have the rights to manage role-based access control (RBAC) and CRDs.

3.1. Deployment prerequisites

To deploy AMQ Streams, make sure that:

  • An OpenShift 4.6 and 4.8 cluster is available.

    AMQ Streams is based on AMQ Streams Strimzi 0.24.x.

  • The oc command-line tool is installed and configured to connect to the running cluster.

3.2. Downloading AMQ Streams release artifacts

To install AMQ Streams, download and extract the release artifacts from the amq-streams-<version>-ocp-install-examples.zip file from the AMQ Streams download site.

AMQ Streams release artifacts include sample YAML files to help you deploy the components of AMQ Streams to OpenShift, perform common operations, and configure your Kafka cluster.

Use oc to deploy the Cluster Operator from the install/cluster-operator folder of the downloaded ZIP file. For more information about deploying and configuring the Cluster Operator, see Section 5.1.1, “Deploying the Cluster Operator”.

In addition, if you want to use standalone installations of the Topic and User Operators with a Kafka cluster that is not managed by the AMQ Streams Cluster Operator, you can deploy them from the install/topic-operator and install/user-operator folders.

Note

Additionally, AMQ Streams container images are available through the Red Hat Ecosystem Catalog. However, we recommend that you use the YAML files provided to deploy AMQ Streams.

3.3. Authenticating with the container registry for Kafka Connect S2I

You need to configure authentication with the Red Hat container registry (registry.redhat.io) before creating a container image using OpenShift builds and Source-to-Image (S2I).

The container registry is used to store AMQ Streams container images on the Red Hat Ecosystem Catalog. The Catalog contains a Kafka Connect builder image with S2I support. The OpenShift build pulls this builder image, together with your source code and binaries, and uses it to build the new container image.

Note

Authentication with the Red Hat container registry is only required if using Kafka Connect S2I. It is not required for the other AMQ Streams components.

Prerequisites

Procedure

  1. If needed, log in to your OpenShift cluster as an administrator:

    oc login --user system:admin --token=my-token --server=https://my-cluster.example.com:6443
  2. Open the project that will contain the Kafka Connect S2I cluster:

    oc project CLUSTER-NAME
    Note

    You might have already deployed the Kafka Connect S2I cluster.

  3. Create a docker-registry secret using your Red Hat Customer Portal account, replacing PULL-SECRET-NAME with the secret name to create:

    oc create secret docker-registry PULL-SECRET-NAME \
     --docker-server=registry.redhat.io \
     --docker-username=CUSTOMER-PORTAL-USERNAME \
     --docker-password=CUSTOMER-PORTAL-PASSWORD \
     --docker-email=EMAIL-ADDRESS

    You should see the following output:

    secret/PULL-SECRET-NAME created
    Important

    You must create this docker-registry secret in every OpenShift project that will authenticate to registry.redhat.io.

  4. Link the secret to your service account to use the secret for pulling images. The service account name must match the name that the OpenShift pod uses.

    oc secrets link SERVICE-ACCOUNT-NAME PULL-SECRET-NAME --for=pull

    For example, using the default service account and a secret named my-secret:

    oc secrets link default my-secret --for=pull
  5. Link the secret to the builder service account to use the secret for pushing and pulling build images:

    oc secrets link builder PULL-SECRET-NAME
    Note

    If you do not want to use your Red Hat username and password to create the pull secret, you can create an authentication token using a registry service account.

3.4. Pushing container images to your own registry

Container images for AMQ Streams are available in the Red Hat Ecosystem Catalog. The installation YAML files provided by AMQ Streams will pull the images directly from the Red Hat Ecosystem Catalog.

If you do not have access to the Red Hat Ecosystem Catalog or want to use your own container repository:

  1. Pull all container images listed here
  2. Push them into your own registry
  3. Update the image names in the installation YAML files
Note

Each Kafka version supported for the release has a separate image.

Container imageNamespace/RepositoryDescription

Kafka

  • registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0
  • registry.redhat.io/amq7/amq-streams-kafka-27-rhel8:1.8.0

AMQ Streams image for running Kafka, including:

  • Kafka Broker
  • Kafka Connect / S2I
  • Kafka Mirror Maker
  • ZooKeeper
  • TLS Sidecars

Operator

  • registry.redhat.io/amq7/amq-streams-rhel8-operator:1.8.0

AMQ Streams image for running the operators:

  • Cluster Operator
  • Topic Operator
  • User Operator
  • Kafka Initializer

Kafka Bridge

  • registry.redhat.io/amq7/amq-streams-bridge-rhel8:1.8.0

AMQ Streams image for running the AMQ Streams Kafka Bridge

3.5. Designating AMQ Streams administrators

AMQ Streams provides custom resources for configuration of your deployment. By default, permission to view, create, edit, and delete these resources is limited to OpenShift cluster administrators. AMQ Streams provides two cluster roles that you can use to assign these rights to other users:

  • strimzi-view allows users to view and list AMQ Streams resources.
  • strimzi-admin allows users to also create, edit or delete AMQ Streams resources.

When you install these roles, they will automatically aggregate (add) these rights to the default OpenShift cluster roles. strimzi-view aggregates to the view role, and strimzi-admin aggregates to the edit and admin roles. Because of the aggregation, you might not need to assign these roles to users who already have similar rights.

The following procedure shows how to assign a strimzi-admin role that allows non-cluster administrators to manage AMQ Streams resources.

A system administrator can designate AMQ Streams administrators after the Cluster Operator is deployed.

Prerequisites

Procedure

  1. Create the strimzi-view and strimzi-admin cluster roles in OpenShift.

    oc create -f install/strimzi-admin
  2. If needed, assign the roles that provide access rights to users that require them.

    oc create clusterrolebinding strimzi-admin --clusterrole=strimzi-admin --user=user1 --user=user2

Chapter 4. Deploying AMQ Streams from the OperatorHub

Use the Red Hat Integration - AMQ Streams Operator to deploy AMQ Streams from the OperatorHub.

The procedures in this section show how to:

4.1. Using the Red Hat Integration Operator to install the AMQ Streams Operator

The Red Hat Integration Operator allows you to choose and install the Operators that manage your Red Hat Integration components. If you have more than one Red Hat Integration subscription, you can use the Red Hat Integration Operator to install and update the AMQ Streams Operator, as well as the Operators for all subscribed Red Hat Integration components.

As with the AMQ Streams Operator, you can use the Operator Lifecycle Manager (OLM) to install the Red Hat Integration Operator on an OpenShift Container Platform (OCP) cluster from the OperatorHub in the OCP console.

Additional resources

For more information on installing and using the Red Hat Integration Operator, see Installing the Red Hat Integration Operator on OpenShift.

4.2. Deploying the AMQ Streams Operator from the OperatorHub

You can deploy the Cluster Operator to your OpenShift cluster by installing the AMQ Streams Operator from the OperatorHub.

Warning

Make sure you use the appropriate update channel. If you are on a supported version of the OpenShift, installing AMQ Streams from the default stable channel is safe. However, if you are using a version of the OpenShift that is unsupported, installing AMQ Streams from the stable channel is unsafe, especially when automatic updates are enabled, as the cluster will receive automatic updates with new components that are unsupported by the OpenShift release.

Prerequisites

  • The Red Hat Operators OperatorSource is enabled in your OpenShift cluster. If you can see Red Hat Operators in the OperatorHub, the correct OperatorSource is enabled. For more information, see the Operators guide.
  • Installation requires a user with sufficient privileges to install Operators from the OperatorHub.

Procedure

  1. In the OpenShift web console, click Operators > OperatorHub.
  2. Search or browse for the AMQ Streams Operator, in the Streaming & Messaging category.
  3. Click the Red Hat Integration - AMQ Streams Operator tile and then, in the sidebar on the right, click Install.
  4. On the Create Operator Subscription screen, choose from the following installation and update options:

    • Update Channel: Choose the update channel for the AMQ Streams Operator.

      • The (default) stable channel contains all the latest updates and releases, including major, minor, and micro releases, which are assumed to be well tested and stable.
      • An amq-streams-X.x channel contains the minor and micro release updates for a major release, where X is the major release version number.
      • An amq-streams-X.Y.x channel contains the micro release updates for a minor release, where X is the major release version number and Y is the minor release version number.
    • Installation Mode: Choose to install the AMQ Streams Operator to all namespaces in the cluster (the default option) or a specific namespace. It is good practice to use namespaces to separate functions. We recommend that you dedicate a specific namespace to the Kafka cluster and other AMQ Streams components.
    • Approval Strategy: By default, the AMQ Streams Operator is automatically upgraded to the latest AMQ Streams version by the Operator Lifecycle Manager (OLM). Optionally, select Manual if you want to manually approve future upgrades. For more information, see the Operators guide in the OpenShift documentation.
  5. Click Subscribe; the AMQ Streams Operator is installed to your OpenShift cluster.

    The AMQ Streams Operator deploys the Cluster Operator, CRDs, and role-based access control (RBAC) resources to the selected namespace, or to all namespaces.

  6. On the Installed Operators screen, check the progress of the installation. The AMQ Streams Operator is ready to use when its status changes to InstallSucceeded.

Next, you can use the AMQ Streams Operator to deploy the Kafka components, starting with a Kafka cluster.

4.3. Deploying Kafka components using the AMQ Streams Operator

When installed on an Openshift Container Platform, the AMQ Streams Operator makes Kafka components available for installation from the user interface.

Kafka components available for installation:

  • Kafka
  • Kafka Connect
  • Kafka Connect Source to Image (S2I)
  • Kafka MirrorMaker
  • Kafka MirrorMaker 2
  • Kafka Topic
  • Kafka User
  • Kafka Bridge
  • Kafka Connector
  • Kafka Rebalance

Prerequisites

Procedure

  1. Navigate to Installed Operators and click Red Hat Integration - AMQ Streams Operator to display the Operator details page.
  2. From Provided APIs, click Create Instance for the Kafka component you wish to install.

    The default configuration for each component is encapsulated in a CRD spec property.

  3. (Optional) Configure the installation specification from the form or YAML views before you perform the installation.
  4. Click Create to start the installation of the selected component.

    Wait until the status changes to Succeeded.

Chapter 5. Deploying AMQ Streams using installation artifacts

As an alternative to using the OperatorHub to deploy AMQ Streams using the AMQ Streams Operator, you can use the installation artifacts. Having prepared your environment for a deployment of AMQ Streams, this section shows:

The procedures assume an OpenShift cluster is available and running.

AMQ Streams is based on AMQ Streams Strimzi 0.24.x. This section describes the procedures to deploy AMQ Streams on OpenShift 4.6 and 4.8.

Note

To run the commands in this guide, your cluster user must have the rights to manage role-based access control (RBAC) and CRDs.

5.1. Create the Kafka cluster

To be able to manage a Kafka cluster with the Cluster Operator, you must deploy it as a Kafka resource. AMQ Streams provides example deployment files to do this. You can use these files to deploy the Topic Operator and User Operator at the same time.

If you haven’t deployed a Kafka cluster as a Kafka resource, you can’t use the Cluster Operator to manage it. This applies, for example, to a Kafka cluster running outside of OpenShift. But you can deploy and use the Topic Operator and User Operator as standalone components.

Note

The Cluster Operator can watch one, multiple, or all namespaces in an OpenShift cluster. The Topic Operator and User Operator watch for KafkaTopics and KafkaUsers in the single namespace of the Kafka cluster deployment.

Deploying a Kafka cluster with the Topic Operator and User Operator

Perform these deployment steps if you want to use the Topic Operator and User Operator with a Kafka cluster managed by AMQ Streams.

  1. Deploy the Cluster Operator
  2. Use the Cluster Operator to deploy the:

Deploying a standalone Topic Operator and User Operator

Perform these deployment steps if you want to use the Topic Operator and User Operator with a Kafka cluster that is not managed by AMQ Streams.

5.1.1. Deploying the Cluster Operator

The Cluster Operator is responsible for deploying and managing Apache Kafka clusters within an OpenShift cluster.

The procedures in this section show:

5.1.1.1. Watch options for a Cluster Operator deployment

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

You can choose to deploy the Cluster Operator to watch Kafka resources from:

  • A single namespace (the same namespace containing the Cluster Operator)
  • Multiple namespaces
  • All namespaces
Note

AMQ Streams provides example YAML files to make the deployment process easier.

The Cluster Operator watches for changes to the following resources:

  • Kafka for the Kafka cluster.
  • KafkaConnect for the Kafka Connect cluster.
  • KafkaConnectS2I for the Kafka Connect cluster with Source2Image support.
  • KafkaConnector for creating and managing connectors in a Kafka Connect cluster.
  • KafkaMirrorMaker for the Kafka MirrorMaker instance.
  • KafkaBridge for the Kafka Bridge instance

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.

5.1.1.2. Deploying the Cluster Operator to watch a single namespace

This procedure shows how to deploy the Cluster Operator to watch AMQ Streams resources in a single namespace in your OpenShift cluster.

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    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. Deploy the Cluster Operator:

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  3. Verify that the Cluster Operator was successfully deployed:

    oc get deployments

5.1.1.3. Deploying the Cluster Operator to watch multiple namespaces

This procedure shows how to deploy the Cluster Operator to watch AMQ Streams resources across multiple namespaces in your OpenShift cluster.

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    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. Edit the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file to add a list of all the namespaces the Cluster Operator will watch to the STRIMZI_NAMESPACE environment variable.

    For example, in this procedure the Cluster Operator will watch the namespaces watched-namespace-1, watched-namespace-2, watched-namespace-3.

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq7/amq-streams-rhel8-operator:1.8.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: watched-namespace-1,watched-namespace-2,watched-namespace-3
  3. For each namespace listed, install the RoleBindings.

    In this example, we replace watched-namespace in these commands with the namespaces listed in the previous step, repeating them for watched-namespace-1, watched-namespace-2, watched-namespace-3:

    oc create -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace
    oc create -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace
  4. Deploy the Cluster Operator:

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  5. Verify that the Cluster Operator was successfully deployed:

    oc get deployments

5.1.1.4. Deploying the Cluster Operator to watch all namespaces

This procedure shows how to deploy 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.

Prerequisites

  • This procedure requires use of an OpenShift user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the OpenShift cluster usually means that permission to create, edit, and delete these resources is limited to OpenShift cluster administrators, such as system:admin.

Procedure

  1. Edit the AMQ Streams installation files to use the namespace the Cluster Operator is going to be installed into.

    For example, in this procedure the Cluster Operator is installed into the namespace my-cluster-operator-namespace.

    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. Edit the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file to set the value of the STRIMZI_NAMESPACE environment variable to *.

    apiVersion: apps/v1
    kind: Deployment
    spec:
      # ...
      template:
        spec:
          # ...
          serviceAccountName: strimzi-cluster-operator
          containers:
          - name: strimzi-cluster-operator
            image: registry.redhat.io/amq7/amq-streams-rhel8-operator:1.8.0
            imagePullPolicy: IfNotPresent
            env:
            - name: STRIMZI_NAMESPACE
              value: "*"
            # ...
  3. Create ClusterRoleBindings that grant cluster-wide access for all namespaces to the Cluster Operator.

    oc create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
    oc create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator

    Replace my-cluster-operator-namespace with the namespace you want to install the Cluster Operator into.

  4. Deploy the Cluster Operator to your OpenShift cluster.

    oc create -f install/cluster-operator -n my-cluster-operator-namespace
  5. Verify that the Cluster Operator was successfully deployed:

    oc get deployments

5.1.2. Deploying Kafka

Apache Kafka is an open-source distributed publish-subscribe messaging system for fault-tolerant real-time data feeds.

The procedures in this section show:

When installing Kafka, AMQ Streams also installs a ZooKeeper cluster and adds the necessary configuration to connect Kafka with ZooKeeper.

5.1.2.1. Deploying the Kafka cluster

This procedure shows how to deploy a Kafka cluster to your OpenShift using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a Kafka resource.

AMQ Streams provides example YAMLs files for deployment in examples/kafka/:

kafka-persistent.yaml
Deploys a persistent cluster with three ZooKeeper and three Kafka nodes.
kafka-jbod.yaml
Deploys a persistent cluster with three ZooKeeper and three Kafka nodes (each using multiple persistent volumes).
kafka-persistent-single.yaml
Deploys a persistent cluster with a single ZooKeeper node and a single Kafka node.
kafka-ephemeral.yaml
Deploys an ephemeral cluster with three ZooKeeper and three Kafka nodes.
kafka-ephemeral-single.yaml
Deploys an ephemeral cluster with three ZooKeeper nodes and a single Kafka node.

In this procedure, we use the examples for an ephemeral and persistent Kafka cluster deployment:

Ephemeral cluster
In general, an ephemeral (or temporary) Kafka cluster is suitable for development and testing purposes, not for production. This deployment uses emptyDir volumes for storing broker information (for ZooKeeper) and topics or partitions (for Kafka). Using an emptyDir volume means that its content is strictly related to the pod life cycle and is deleted when the pod goes down.
Persistent cluster
A persistent Kafka cluster uses PersistentVolumes to store ZooKeeper and Kafka data. The PersistentVolume is acquired using a PersistentVolumeClaim to make it independent of the actual type of the PersistentVolume. For example, it can use Amazon EBS volumes in Amazon AWS deployments without any changes in the YAML files. The PersistentVolumeClaim can use a StorageClass to trigger automatic volume provisioning.

The example YAML files specify the latest supported Kafka version, and configuration for its supported log message format version and inter-broker protocol version. Updates to these properties are required when upgrading Kafka.

The example clusters are named my-cluster by default. The cluster name is defined by the name of the resource and cannot be changed after the cluster has been deployed. To change the cluster name before you deploy the cluster, edit the Kafka.metadata.name property of the Kafka resource in the relevant YAML file.

Default cluster name and specified Kafka versions

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    version: 2.8.0
    #...
    config:
      #...
      log.message.format.version: 2.8
      inter.broker.protocol.version: 2.8
  # ...

For more information about configuring the Kafka resource, see Kafka cluster configuration in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Create and deploy an ephemeral or persistent cluster.

    For development or testing, you might prefer to use an ephemeral cluster. You can use a persistent cluster in any situation.

    • To create and deploy an ephemeral cluster:

      oc apply -f examples/kafka/kafka-ephemeral.yaml
    • To create and deploy a persistent cluster:

      oc apply -f examples/kafka/kafka-persistent.yaml
  2. Verify that the Kafka cluster was successfully deployed:

    oc get deployments

5.1.2.2. Deploying the Topic Operator using the Cluster Operator

This procedure describes how to deploy the Topic Operator using the Cluster Operator.

You configure the entityOperator property of the Kafka resource to include the topicOperator. By default, the Topic Operator watches for KafkaTopics in the namespace of the Kafka cluster deployment.

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 about configuring the entityOperator and topicOperator properties, see Configuring the Entity Operator in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Edit the entityOperator properties of the Kafka resource to include topicOperator:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the Topic Operator spec using the properties described in EntityTopicOperatorSpec schema reference.

    Use an empty object ({}) if you want all properties to use their default values.

  3. Create or update the resource:

    Use oc apply:

    oc apply -f <your-file>

5.1.2.3. Deploying the User Operator using the Cluster Operator

This procedure describes how to deploy the User Operator using the Cluster Operator.

You configure the entityOperator property of the Kafka resource to include the userOperator. By default, the User Operator watches for KafkaUsers in the namespace of the Kafka cluster deployment.

If you want to use the User Operator with a Kafka cluster that is not managed by AMQ Streams, you must deploy the User Operator as a standalone component.

For more information about configuring the entityOperator and userOperator properties, see Configuring the Entity Operator in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Edit the entityOperator properties of the Kafka resource to include userOperator:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      #...
      entityOperator:
        topicOperator: {}
        userOperator: {}
  2. Configure the User Operator spec using the properties described in EntityUserOperatorSpec schema reference in the Using AMQ Streams on OpenShift guide.

    Use an empty object ({}) if you want all properties to use their default values.

  3. Create or update the resource:

    oc apply -f <your-file>

5.1.3. Alternative standalone deployment options for AMQ Streams Operators

You can perform a standalone deployment of the Topic Operator and the User Operator instead of deploying these operators using the Cluster Operator. Consider a standalone deployment if you want to use the Topic Operator and the User Operator with a Kafka cluster that is not managed by the Cluster Operator.

In a standalone deployment, Kafka can be running outside of OpenShift. For example, you might be using Kafka as a managed service. You adjust the deployment configuration for the standalone operators to match the address of your Kafka cluster.

Note

In a standalone deployment, the Topic Operator and User Operator will not connect to an AMQ Streams Kafka cluster (that is, a Kafka cluster deployed using the Cluster Operator). The operators will not work, even if a connection with the cluster is established.

5.1.3.1. Deploying the standalone Topic Operator

This procedure shows how to deploy the Topic Operator as a standalone component for topic management. You can use a standalone Topic Operator with a Kafka cluster that is not managed by the Cluster Operator.

A standalone deployment can operate with any Kafka cluster.

Standalone deployment files are provided. Configure the 05-Deployment-strimzi-topic-operator.yaml deployment file to add the environment variables that enable the Topic Operator to connect to a Kafka cluster.

Prerequisites

  • You are running a Kafka cluster for the Topic Operator to connect to.

Procedure

  1. Edit the env properties in the install/topic-operator/05-Deployment-strimzi-topic-operator.yaml standalone deployment file.

    Example standalone Topic Operator deployment configuration

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: strimzi-topic-operator
      labels:
        app: strimzi
    spec:
      # ...
      template:
        # ...
        spec:
          # ...
          containers:
            - name: strimzi-topic-operator
              # ...
              env:
                - name: STRIMZI_NAMESPACE 1
                  valueFrom:
                    fieldRef:
                      fieldPath: metadata.namespace
                - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS 2
                  value: my-kafka-bootstrap-address:9092
                - name: STRIMZI_RESOURCE_LABELS 3
                  value: "strimzi.io/cluster=my-cluster"
                - name: STRIMZI_ZOOKEEPER_CONNECT 4
                  value: my-cluster-zookeeper-client:2181
                - name: STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS 5
                  value: "18000"
                - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS 6
                  value: "120000"
                - name: STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS 7
                  value: "6"
                - name: STRIMZI_LOG_LEVEL 8
                  value: INFO
                - name: STRIMZI_TLS_ENABLED 9
                  value: "false"
                - name: STRIMZI_JAVA_OPTS 10
                  value: "-Xmx=512M -Xms=256M"
                - name: STRIMZI_JAVA_SYSTEM_PROPERTIES 11
                  value: "-Djavax.net.debug=verbose -DpropertyName=value"

    1
    The OpenShift namespace for the Topic Operator to watch for KafkaTopic resources. Specify the namespace of the Kafka cluster.
    2
    The host and port pair of the bootstrap broker address to discover and connect to all brokers in the Kafka cluster. Use a comma-separated list to specify two or three broker addresses in case a server is down.
    3
    The label selector to identify the KafkaTopic resources managed by the Topic Operator.
    4
    The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
    5
    The ZooKeeper session timeout, in milliseconds. The default is 18000 (18 seconds).
    6
    The interval between periodic reconciliations, in milliseconds. The default is 120000 (2 minutes).
    7
    The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential backoff. Consider increasing this value when topic creation takes more time due to the number of partitions or replicas. The default is 6 attempts.
    8
    The level for printing logging messages. You can set the level to ERROR, WARNING, INFO, DEBUG, or TRACE.
    9
    Enables TLS support for encrypted communication with the Kafka brokers.
    10
    (Optional) The Java options used by the JVM running the Topic Operator.
    11
    (Optional) The debugging (-D) options set for the Topic Operator.
  2. If you enabled TLS with the STRIMZI_TLS_ENABLED environment variable, specify the keystore and truststore used to authenticate connection to the Kafka cluster.

    Example TLS configuration

    # ....
    env:
      - name: STRIMZI_TRUSTSTORE_LOCATION 1
        value: "/path/to/truststore.p12"
      - name: STRIMZI_TRUSTSTORE_PASSWORD 2
        value: "TRUSTSTORE-PASSWORD"
      - name: STRIMZI_KEYSTORE_LOCATION 3
        value: "/path/to/keystore.p12"
      - name: STRIMZI_KEYSTORE_PASSWORD 4
        value: "KEYSTORE-PASSWORD"
    # ..."

    1
    The truststore contains the public key (ca.crt) value of the Certificate Authority used to sign new user certificates for TLS client authentication.
    2
    The password for accessing the truststore.
    3
    The keystore contains the private key (ca.key) of the Certificate Authority for signing new user certificates for TLS client authentication.
    4
    The password for accessing the keystore.
  3. Deploy the standalone Topic Operator.

    oc create -f install/topic-operator
  4. Verify that the standalone Topic Operator has been deployed successfully.

    oc describe deployment strimzi-topic-operator

    The standalone Topic Operator is deployed when the Replicas entry shows 1 available.

    Note

    You might experience a delay with the deployment if you have a slow connection to the OpenShift cluster and the Topic Operator images have not been downloaded before.

5.1.3.2. Deploying the standalone User Operator

This procedure shows how to deploy the User Operator as a standalone component for user management. You can use a standalone User Operator with a Kafka cluster that is not managed by the Cluster Operator.

A standalone deployment can operate with any Kafka cluster.

Standalone deployment files are provided. Edit the 05-Deployment-strimzi-user-operator.yaml deployment file to add the environment variables that enable the User Operator to connect to a Kafka cluster.

Prerequisites

  • You are running a Kafka cluster for the User Operator to connect to.

Procedure

  1. Edit the following env properties in the install/user-operator/05-Deployment-strimzi-user-operator.yaml standalone deployment file.

    Example standalone User Operator deployment configuration

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: strimzi-user-operator
      labels:
        app: strimzi
    spec:
      # ...
      template:
        # ...
        spec:
          # ...
          containers:
            - name: strimzi-user-operator
              # ...
              env:
                - name: STRIMZI_NAMESPACE 1
                  valueFrom:
                    fieldRef:
                      fieldPath: metadata.namespace
                - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS 2
                  value: my-kafka-bootstrap-address:9092
                - name: STRIMZI_CA_CERT_NAME 3
                  value: my-cluster-clients-ca-cert
                - name: STRIMZI_CA_KEY_NAME 4
                  value: my-cluster-clients-ca
                - name: STRIMZI_ZOOKEEPER_CONNECT 5
                  value: my-cluster-zookeeper-client:2181
                - name: STRIMZI_LABELS 6
                  value: "strimzi.io/cluster=my-cluster"
                - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS 7
                  value: "120000"
                - name: STRIMZI_ZOOKEEPER_CONNECT 8
                  value: my-cluster-zookeeper-client:2181
                - name: STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS 9
                  value: "18000"
                - name: STRIMZI_LOG_LEVEL 10
                  value: INFO
                - name: STRIMZI_GC_LOG_ENABLED 11
                  value: "true"
                - name: STRIMZI_CA_VALIDITY 12
                  value: "365"
                - name: STRIMZI_CA_RENEWAL 13
                  value: "30"
                - name: STRIMZI_JAVA_OPTS 14
                  value: "-Xmx=512M -Xms=256M"
                - name: STRIMZI_JAVA_SYSTEM_PROPERTIES 15
                  value: "-Djavax.net.debug=verbose -DpropertyName=value"

    1
    The OpenShift namespace for the User Operator to watch for KafkaUser resources. Only one namespace can be specified.
    2
    The host and port pair of the bootstrap broker address to discover and connect to all brokers in the Kafka cluster. Use a comma-separated list to specify two or three broker addresses in case a server is down.
    3
    The OpenShift Secret that contains the public key (ca.crt) value of the Certificate Authority that signs new user certificates for TLS client authentication.
    4
    The OpenShift Secret that contains the private key (ca.key) value of the Certificate Authority that signs new user certificates for TLS client authentication.
    5
    The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
    6
    The label selector used to identify the KafkaUser resources managed by the User Operator.
    7
    The interval between periodic reconciliations, in milliseconds. The default is 120000 (2 minutes).
    8
    The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
    9
    The ZooKeeper session timeout, in milliseconds. The default is 18000 (18 seconds).
    10
    The level for printing logging messages. You can set the level to ERROR, WARNING, INFO, DEBUG, or TRACE.
    11
    Enables garbage collection (GC) logging. The default is true.
    12
    The validity period for the Certificate Authority. The default is 365 days.
    13
    The renewal period for the Certificate Authority. The renewal period is measured backwards from the expiry date of the current certificate. The default is 30 days to initiate certificate renewal before the old certificates expire.
    14
    (Optional) The Java options used by the JVM running the User Operator
    15
    (Optional) The debugging (-D) options set for the User Operator
  2. If you are using TLS to connect to the Kafka cluster, specify the secrets used to authenticate the connection. Otherwise, go to the next step.

    Example TLS configuration

    # ....
    env:
      - name: STRIMZI_CLUSTER_CA_CERT_SECRET_NAME 1
        value: my-cluster-cluster-cert
      - name: STRIMZI_EO_KEY_SECRET_NAME 2
        value: my-cluster-cluster-ca
    # ..."

    1
    The OpenShift Secret that contains the public key (ca.crt) value of the Certificate Authority that signs Kafka broker certificates for TLS client authentication.
    2
    The OpenShift Secret that contains the keystore (entity-operator_.p12) with the private key and certificate for TLS authentication against the Kafka cluster. The Secret must also contain the password (entity-operator_.password) for accessing the keystore.
  3. Deploy the standalone User Operator.

    oc create -f install/user-operator
  4. Verify that the standalone User Operator has been deployed successfully.

    oc describe deployment strimzi-user-operator

    The standalone User Operator is deployed when the Replicas entry shows 1 available.

    Note

    You might experience a delay with the deployment if you have a slow connection to the OpenShift cluster and the User Operator images have not been downloaded before.

5.2. Deploy Kafka Connect

Kafka Connect is a tool for streaming data between Apache Kafka and external systems.

In AMQ Streams, Kafka Connect is deployed in distributed mode. Kafka Connect can also work in standalone mode, but this is not supported by AMQ Streams.

Using the concept of connectors, Kafka Connect provides a framework for moving large amounts of data into and out of your Kafka cluster while maintaining scalability and reliability.

Kafka Connect is typically used to integrate Kafka with external databases and storage and messaging systems.

The procedures in this section show how to:

Note

The term connector is used interchangeably to mean a connector instance running within a Kafka Connect cluster, or a connector class. In this guide, the term connector is used when the meaning is clear from the context.

5.2.1. Deploying Kafka Connect to your OpenShift cluster

This procedure shows how to deploy a Kafka Connect cluster to your OpenShift cluster using the Cluster Operator.

A Kafka Connect cluster is implemented as a Deployment with a configurable number of nodes (also called workers) that distribute the workload of connectors as tasks so that the message flow is highly scalable and reliable.

The deployment uses a YAML file to provide the specification to create a KafkaConnect resource.

In this procedure, we use the example file provided with AMQ Streams:

  • examples/connect/kafka-connect.yaml

For information about configuring the KafkaConnect resource (or the KafkaConnectS2I resource with Source-to-Image (S2I) support), see Kafka Connect cluster configuration in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Deploy Kafka Connect to your OpenShift cluster. Use the examples/connect/kafka-connect.yaml file to deploy Kafka Connect.

    oc apply -f examples/connect/kafka-connect.yaml
  2. Verify that Kafka Connect was successfully deployed:

    oc get deployments

5.2.2. Kafka Connect configuration for multiple instances

If you are running multiple instances of Kafka Connect, you have to change the default configuration of the following config properties:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: connect-cluster 1
    offset.storage.topic: connect-cluster-offsets 2
    config.storage.topic: connect-cluster-configs 3
    status.storage.topic: connect-cluster-status  4
    # ...
# ...
1
Kafka Connect cluster group that the instance belongs to.
2
Kafka topic that stores connector offsets.
3
Kafka topic that stores connector and task status configurations.
4
Kafka topic that stores connector and task status updates.
Note

Values for the three topics must be the same for all Kafka Connect instances with the same group.id.

Unless you change the default settings, each Kafka Connect instance connecting to the same Kafka cluster is deployed with the same values. What happens, in effect, is all instances are coupled to run in a cluster and use the same topics.

If multiple Kafka Connect clusters try to use the same topics, Kafka Connect will not work as expected and generate errors.

If you wish to run multiple Kafka Connect instances, change the values of these properties for each instance.

5.2.3. Extending Kafka Connect with connector plug-ins

The AMQ Streams container images for Kafka Connect include two built-in file connectors for moving file-based data into and out of your Kafka cluster.

Table 5.1. File connectors

File ConnectorDescription

FileStreamSourceConnector

Transfers data to your Kafka cluster from a file (the source).

FileStreamSinkConnector

Transfers data from your Kafka cluster to a file (the sink).

The procedures in this section show how to add your own connector classes to connector images by:

Important

You create the configuration for connectors directly using the Kafka Connect REST API or KafkaConnector custom resources.

5.2.3.1. Creating a new container image automatically using AMQ Streams

This procedure shows how to configure Kafka Connect so that AMQ Streams automatically builds a new container image with additional connectors. You define the connector plugins using the .spec.build.plugins property of the KafkaConnect custom resource. AMQ Streams will automatically download and add the connector plugins into a new container image. The container is pushed into the container repository specified in .spec.build.output and automatically used in the Kafka Connect deployment.

Prerequisites

You need to provide your own container registry where images can be pushed to, stored, and pulled from. AMQ Streams supports private container registries as well as public registries such as Quay or Docker Hub.

Procedure

  1. Configure the KafkaConnect custom resource by specifying the container registry in .spec.build.output, and additional connectors in .spec.build.plugins:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: my-connect-cluster
    spec: 1
      #...
      build:
        output: 2
          type: docker
          image: my-registry.io/my-org/my-connect-cluster:latest
          pushSecret: my-registry-credentials
        plugins: 3
          - name: debezium-postgres-connector
            artifacts:
              - type: tgz
                url: https://repo1.maven.org/maven2/io/debezium/debezium-connector-postgres/1.3.1.Final/debezium-connector-postgres-1.3.1.Final-plugin.tar.gz
                sha512sum: 962a12151bdf9a5a30627eebac739955a4fd95a08d373b86bdcea2b4d0c27dd6e1edd5cb548045e115e33a9e69b1b2a352bee24df035a0447cb820077af00c03
          - name: camel-telegram
            artifacts:
              - type: tgz
                url: https://repo.maven.apache.org/maven2/org/apache/camel/kafkaconnector/camel-telegram-kafka-connector/0.7.0/camel-telegram-kafka-connector-0.7.0-package.tar.gz
                sha512sum: a9b1ac63e3284bea7836d7d24d84208c49cdf5600070e6bd1535de654f6920b74ad950d51733e8020bf4187870699819f54ef5859c7846ee4081507f48873479
      #...
    1
    2
    (Required) Configuration of the container registry where new images are pushed.
    3
    (Required) List of connector plugins and their artifacts to add to the new container image. Each plugin must be configured with at least one artifact.
  2. Create or update the resource:

    $ oc apply -f KAFKA-CONNECT-CONFIG-FILE
  3. Wait for the new container image to build, and for the Kafka Connect cluster to be deployed.
  4. Use the Kafka Connect REST API or the KafkaConnector custom resources to use the connector plugins you added.

Additional resources

See the Using AMQ Streams on OpenShift guide for more information on:

5.2.3.2. Creating a Docker image from the Kafka Connect base image

This procedure shows how to create a custom image and add it to the /opt/kafka/plugins directory.

You can use the Kafka container image on Red Hat Ecosystem Catalog as a base image for creating your own custom image with additional connector plug-ins.

At startup, the AMQ Streams version of Kafka Connect loads any third-party connector plug-ins contained in the /opt/kafka/plugins directory.

Procedure

  1. Create a new Dockerfile using registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0 as the base image:

    FROM registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0
    USER root:root
    COPY ./my-plugins/ /opt/kafka/plugins/
    USER 1001

    Example plug-in file

    $ tree ./my-plugins/
    ./my-plugins/
    ├── debezium-connector-mongodb
    │   ├── bson-3.4.2.jar
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mongodb-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mongodb-driver-3.4.2.jar
    │   ├── mongodb-driver-core-3.4.2.jar
    │   └── README.md
    ├── debezium-connector-mysql
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mysql-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mysql-binlog-connector-java-0.13.0.jar
    │   ├── mysql-connector-java-5.1.40.jar
    │   ├── README.md
    │   └── wkb-1.0.2.jar
    └── debezium-connector-postgres
        ├── CHANGELOG.md
        ├── CONTRIBUTE.md
        ├── COPYRIGHT.txt
        ├── debezium-connector-postgres-0.7.1.jar
        ├── debezium-core-0.7.1.jar
        ├── LICENSE.txt
        ├── postgresql-42.0.0.jar
        ├── protobuf-java-2.6.1.jar
        └── README.md

  2. Build the container image.
  3. Push your custom image to your container registry.
  4. Point to the new container image.

    You can either:

    • Edit the KafkaConnect.spec.image property of the KafkaConnect custom resource.

      If set, this property overrides the STRIMZI_KAFKA_CONNECT_IMAGES variable in the Cluster Operator.

      apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
      spec: 1
        #...
        image: my-new-container-image 2
        config: 3
          #...
      1
      2
      The docker image for the pods.
      3
      Configuration of the Kafka Connect workers (not connectors).

      or

    • In the install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml file, edit the STRIMZI_KAFKA_CONNECT_IMAGES variable to point to the new container image, and then reinstall the Cluster Operator.

Additional resources

See the Using AMQ Streams on OpenShift guide for more information on:

5.2.3.3. Creating a container image using OpenShift builds and Source-to-Image

This procedure shows how to use OpenShift builds and the Source-to-Image (S2I) framework to create a new container image.

An OpenShift build takes a builder image with S2I support, together with source code and binaries provided by the user, and uses them to build a new container image. Once built, container images are stored in OpenShift’s local container image repository and are available for use in deployments.

A Kafka Connect builder image with S2I support is provided on the Red Hat Ecosystem Catalog as part of the registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0 image. This S2I image takes your binaries (with plug-ins and connectors) and stores them in the /tmp/kafka-plugins/s2i directory. It creates a new Kafka Connect image from this directory, which can then be used with the Kafka Connect deployment. When started using the enhanced image, Kafka Connect loads any third-party plug-ins from the /tmp/kafka-plugins/s2i directory.

Important

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 and will be removed after AMQ Streams 1.8. To prepare for this change, you can migrate Kafka Connect S2I instances to Kafka Connect instances.

Procedure

  1. On the command line, use the oc apply command to create and deploy a Kafka Connect S2I cluster:

    oc apply -f examples/connect/kafka-connect-s2i.yaml
  2. Create a directory with Kafka Connect plug-ins:

    $ tree ./my-plugins/
    ./my-plugins/
    ├── debezium-connector-mongodb
    │   ├── bson-3.4.2.jar
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mongodb-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mongodb-driver-3.4.2.jar
    │   ├── mongodb-driver-core-3.4.2.jar
    │   └── README.md
    ├── debezium-connector-mysql
    │   ├── CHANGELOG.md
    │   ├── CONTRIBUTE.md
    │   ├── COPYRIGHT.txt
    │   ├── debezium-connector-mysql-0.7.1.jar
    │   ├── debezium-core-0.7.1.jar
    │   ├── LICENSE.txt
    │   ├── mysql-binlog-connector-java-0.13.0.jar
    │   ├── mysql-connector-java-5.1.40.jar
    │   ├── README.md
    │   └── wkb-1.0.2.jar
    └── debezium-connector-postgres
        ├── CHANGELOG.md
        ├── CONTRIBUTE.md
        ├── COPYRIGHT.txt
        ├── debezium-connector-postgres-0.7.1.jar
        ├── debezium-core-0.7.1.jar
        ├── LICENSE.txt
        ├── postgresql-42.0.0.jar
        ├── protobuf-java-2.6.1.jar
        └── README.md
  3. Use the oc start-build command to start a new build of the image using the prepared directory:

    oc start-build my-connect-cluster-connect --from-dir ./my-plugins/
    Note

    The name of the build is the same as the name of the deployed Kafka Connect cluster.

  4. When the build has finished, the new image is used automatically by the Kafka Connect deployment.

5.2.4. Creating and managing connectors

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.

A connector is an instance of a particular connector class that knows how to communicate with the relevant external system in terms of messages. Connectors are available for many external systems, or you can create your own.

You can create source and sink types of connector.

Source connector
A source connector is a runtime entity that fetches data from an external system and feeds it to Kafka as messages.
Sink connector
A sink connector is a runtime entity that fetches messages from Kafka topics and feeds them to an external system.

AMQ Streams provides two APIs for creating and managing connectors:

  • KafkaConnector resources (referred to as KafkaConnectors)
  • Kafka Connect REST API

Using the APIs, you can:

  • Check the status of a connector instance
  • Reconfigure a running connector
  • Increase or decrease the number of connector tasks for a connector instance
  • Restart connectors
  • Restart connector tasks, including failed tasks
  • Pause a connector instance
  • Resume a previously paused connector instance
  • Delete a connector instance

5.2.4.1. KafkaConnector resources

KafkaConnectors allow you to create and manage connector instances for Kafka Connect in an OpenShift-native way, so an HTTP client such as cURL is not required. Like other Kafka resources, you declare a connector’s desired state in a KafkaConnector YAML file that is deployed to your OpenShift cluster to create the connector instance. KafkaConnector resources must be deployed to the same namespace as the Kafka Connect cluster they link to.

You manage a running connector instance by updating its corresponding KafkaConnector resource, and then applying the updates. Annotations are used to manually restart connector instances and connector tasks. You remove a connector by deleting its corresponding KafkaConnector.

To ensure compatibility with earlier versions of AMQ Streams, KafkaConnectors are disabled by default. To enable them for a Kafka Connect cluster, you must use annotations on the KafkaConnect resource. For instructions, see Configuring Kafka Connect in the Using AMQ Streams on OpenShift guide.

When KafkaConnectors are enabled, the Cluster Operator begins to watch for them. It updates the configurations of running connector instances to match the configurations defined in their KafkaConnectors.

AMQ Streams includes an example KafkaConnector, named examples/connect/source-connector.yaml. You can use this example to create and manage a FileStreamSourceConnector and a FileStreamSinkConnector as described in Section 5.2.5, “Deploying the example KafkaConnector resources”.

5.2.4.2. Availability of the Kafka Connect REST API

The Kafka Connect REST API is available on port 8083 as the <connect-cluster-name>-connect-api service.

If KafkaConnectors are enabled, manual changes made directly using the Kafka Connect REST API are reverted by the Cluster Operator.

The operations supported by the REST API are described in the Apache Kafka documentation.

5.2.5. Deploying the example KafkaConnector resources

AMQ Streams includes an example KafkaConnector in examples/connect/source-connector.yaml. This creates a basic FileStreamSourceConnector instance that sends each line of the Kafka license file (an example file source) to a single Kafka topic.

This procedure describes how to create:

  • A FileStreamSourceConnector that reads data from the Kafka license file (the source) and writes the data as messages to a Kafka topic.
  • A FileStreamSinkConnector that reads messages from the Kafka topic and writes the messages to a temporary file (the sink).
Note

In a production environment, you prepare container images containing your desired Kafka Connect connectors, as described in Section 5.2.3, “Extending Kafka Connect with connector plug-ins”.

The FileStreamSourceConnector and FileStreamSinkConnector are provided as examples. Running these connectors in containers as described here is unlikely to be suitable for production use cases.

Prerequisites

Procedure

  1. Edit the examples/connect/source-connector.yaml file:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-source-connector 1
      labels:
        strimzi.io/cluster: my-connect-cluster 2
    spec:
      class: org.apache.kafka.connect.file.FileStreamSourceConnector 3
      tasksMax: 2 4
      config: 5
        file: "/opt/kafka/LICENSE" 6
        topic: my-topic 7
        # ...
    1
    Name of the KafkaConnector resource, which is used as the name of the connector. Use any name that is valid for an OpenShift resource.
    2
    Name of the Kafka Connect cluster to create the connector instance in. Connectors must be deployed to the same namespace as the Kafka Connect cluster they link to.
    3
    Full name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
    4
    Maximum number of Kafka Connect Tasks that the connector can create.
    5
    Connector configuration as key-value pairs.
    6
    This example source connector configuration reads data from the /opt/kafka/LICENSE file.
    7
    Kafka topic to publish the source data to.
  2. Create the source KafkaConnector in your OpenShift cluster:

    oc apply -f examples/connect/source-connector.yaml
  3. Create an examples/connect/sink-connector.yaml file:

    touch examples/connect/sink-connector.yaml
  4. Paste the following YAML into the sink-connector.yaml file:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: my-sink-connector
      labels:
        strimzi.io/cluster: my-connect
    spec:
      class: org.apache.kafka.connect.file.FileStreamSinkConnector 1
      tasksMax: 2
      config: 2
        file: "/tmp/my-file" 3
        topics: my-topic 4
    1
    Full name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
    2
    Connector configuration as key-value pairs.
    3
    Temporary file to publish the source data to.
    4
    Kafka topic to read the source data from.
  5. Create the sink KafkaConnector in your OpenShift cluster:

    oc apply -f examples/connect/sink-connector.yaml
  6. Check that the connector resources were created:

    oc get kctr --selector strimzi.io/cluster=MY-CONNECT-CLUSTER -o name
    
    my-source-connector
    my-sink-connector

    Replace MY-CONNECT-CLUSTER with your Kafka Connect cluster.

  7. In the container, execute kafka-console-consumer.sh to read the messages that were written to the topic by the source connector:

    oc exec MY-CLUSTER-kafka-0 -i -t -- bin/kafka-console-consumer.sh --bootstrap-server MY-CLUSTER-kafka-bootstrap.NAMESPACE.svc:9092 --topic my-topic --from-beginning
Source and sink connector configuration options

The connector configuration is defined in the spec.config property of the KafkaConnector resource.

The FileStreamSourceConnector and FileStreamSinkConnector classes support the same configuration options as the Kafka Connect REST API. Other connectors support different configuration options.

Table 5.2. Configuration options for the FileStreamSource connector class

NameTypeDefault valueDescription

file

String

Null

Source file to write messages to. If not specified, the standard input is used.

topic

List

Null

The Kafka topic to publish data to.

Table 5.3. Configuration options for FileStreamSinkConnector class

NameTypeDefault valueDescription

file

String

Null

Destination file to write messages to. If not specified, the standard output is used.

topics

List

Null

One or more Kafka topics to read data from.

topics.regex

String

Null

A regular expression matching one or more Kafka topics to read data from.

5.2.6. Performing a restart of a Kafka connector

This procedure describes how to manually trigger a restart of a Kafka connector by using an OpenShift annotation.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaConnector custom resource that controls the Kafka connector you want to restart:

    oc get KafkaConnector
  2. To restart the connector, annotate the KafkaConnector resource in OpenShift. For example, using oc annotate:

    oc annotate KafkaConnector KAFKACONNECTOR-NAME strimzi.io/restart=true
  3. Wait for the next reconciliation to occur (every two minutes by default).

    The Kafka connector is restarted, as long as the annotation was detected by the reconciliation process. When Kafka Connect accepts the restart request, the annotation is removed from the KafkaConnector custom resource.

Additional resources

5.2.7. Performing a restart of a Kafka connector task

This procedure describes how to manually trigger a restart of a Kafka connector task by using an OpenShift annotation.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Find the name of the KafkaConnector custom resource that controls the Kafka connector task you want to restart:

    oc get KafkaConnector
  2. Find the ID of the task to be restarted from the KafkaConnector custom resource. Task IDs are non-negative integers, starting from 0.

    oc describe KafkaConnector KAFKACONNECTOR-NAME
  3. To restart the connector task, annotate the KafkaConnector resource in OpenShift. For example, using oc annotate to restart task 0:

    oc annotate KafkaConnector KAFKACONNECTOR-NAME strimzi.io/restart-task=0
  4. Wait for the next reconciliation to occur (every two minutes by default).

    The Kafka connector task is restarted, as long as the annotation was detected by the reconciliation process. When Kafka Connect accepts the restart request, the annotation is removed from the KafkaConnector custom resource.

Additional resources

5.3. Deploy Kafka MirrorMaker

The Cluster Operator deploys one or more Kafka MirrorMaker replicas to replicate data between Kafka clusters. This process is called mirroring to avoid confusion with the Kafka partitions replication concept. MirrorMaker consumes messages from the source cluster and republishes those messages to the target cluster.

5.3.1. Deploying Kafka MirrorMaker to your OpenShift cluster

This procedure shows how to deploy a Kafka MirrorMaker cluster to your OpenShift cluster using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a KafkaMirrorMaker or KafkaMirrorMaker2 resource depending on the version of MirrorMaker deployed.

In this procedure, we use the example files provided with AMQ Streams:

  • examples/mirror-maker/kafka-mirror-maker.yaml
  • examples/mirror-maker/kafka-mirror-maker-2.yaml

For information about configuring KafkaMirrorMaker or KafkaMirrorMaker2 resources, see Kafka MirrorMaker cluster configuration in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Deploy Kafka MirrorMaker to your OpenShift cluster:

    For MirrorMaker:

    oc apply -f examples/mirror-maker/kafka-mirror-maker.yaml

    For MirrorMaker 2.0:

    oc apply -f examples/mirror-maker/kafka-mirror-maker-2.yaml
  2. Verify that MirrorMaker was successfully deployed:

    oc get deployments

5.4. Deploy Kafka Bridge

The Cluster Operator deploys one or more Kafka bridge replicas to send data between Kafka clusters and clients via HTTP API.

5.4.1. Deploying Kafka Bridge to your OpenShift cluster

This procedure shows how to deploy a Kafka Bridge cluster to your OpenShift cluster using the Cluster Operator.

The deployment uses a YAML file to provide the specification to create a KafkaBridge resource.

In this procedure, we use the example file provided with AMQ Streams:

  • examples/bridge/kafka-bridge.yaml

For information about configuring the KafkaBridge resource, see Kafka Bridge cluster configuration in the Using AMQ Streams on OpenShift guide.

Procedure

  1. Deploy Kafka Bridge to your OpenShift cluster:

    oc apply -f examples/bridge/kafka-bridge.yaml
  2. Verify that Kafka Bridge was successfully deployed:

    oc get deployments

Chapter 6. Setting up client access to the Kafka cluster

After you have deployed AMQ Streams, the procedures in this section explain how to:

  • Deploy example producer and consumer clients, which you can use to verify your deployment
  • Set up external client access to the Kafka cluster

    The steps to set up access to the Kafka cluster for a client outside OpenShift are more complex, and require familiarity with the Kafka component configuration procedures described in the Using AMQ Streams on OpenShift guide.

6.1. Deploying example clients

This procedure shows how to deploy example producer and consumer clients that use the Kafka cluster you created to send and receive messages.

Prerequisites

  • The Kafka cluster is available for the clients.

Procedure

  1. Deploy a Kafka producer.

    oc run kafka-producer -ti --image=registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0 --rm=true --restart=Never -- bin/kafka-console-producer.sh --broker-list cluster-name-kafka-bootstrap:9092 --topic my-topic
  2. Type a message into the console where the producer is running.
  3. Press Enter to send the message.
  4. Deploy a Kafka consumer.

    oc run kafka-consumer -ti --image=registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0 --rm=true --restart=Never -- bin/kafka-console-consumer.sh --bootstrap-server cluster-name-kafka-bootstrap:9092 --topic my-topic --from-beginning
  5. Confirm that you see the incoming messages in the consumer console.

6.2. Setting up access for clients outside of OpenShift

This procedure shows how to configure client access to a Kafka cluster from outside OpenShift.

Using the address of the Kafka cluster, you can provide external access to a client on a different OpenShift namespace or outside OpenShift entirely.

You configure an external Kafka listener to provide the access.

The following external listener types are supported:

  • route to use OpenShift Route and the default HAProxy router
  • loadbalancer to use loadbalancer services
  • nodeport to use ports on OpenShift nodes
  • ingress to use OpenShift Ingress and the NGINX Ingress Controller for Kubernetes

The type chosen depends on your requirements, and your environment and infrastructure. For example, loadbalancers might not be suitable for certain infrastructure, such as bare metal, where node ports provide a better option.

In this procedure:

  1. An external listener is configured for the Kafka cluster, with TLS encryption and authentication, and Kafka simple authorization is enabled.
  2. A KafkaUser is created for the client, with TLS authentication and Access Control Lists (ACLs) defined for simple authorization.

You can configure your listener to use TLS, SCRAM-SHA-512 or OAuth 2.0 authentication. TLS always uses encryption, but it is recommended to also use encryption with SCRAM-SHA-512 and OAuth 2.0 authentication.

You can configure simple, OAuth 2.0, OPA or custom authorization for Kafka brokers. When enabled, authorization is applied to all enabled listeners.

When you configure the KafkaUser authentication and authorization mechanisms, ensure they match the equivalent Kafka configuration:

  • KafkaUser.spec.authentication matches Kafka.spec.kafka.listeners[*].authentication
  • KafkaUser.spec.authorization matches Kafka.spec.kafka.authorization

You should have at least one listener supporting the authentication you want to use for the KafkaUser.

Note

Authentication between Kafka users and Kafka brokers depends on the authentication settings for each. For example, it is not possible to authenticate a user with TLS if it is not also enabled in the Kafka configuration.

AMQ Streams operators automate the configuration process:

  • The Cluster Operator creates the listeners and sets up the cluster and client certificate authority (CA) certificates to enable authentication within the Kafka cluster.
  • The User Operator creates the user representing the client and the security credentials used for client authentication, based on the chosen authentication type.

In this procedure, the certificates generated by the Cluster Operator are used, but you can replace them by installing your own certificates. You can also configure your listener to use a Kafka listener certificate managed by an external Certificate Authority.

Certificates are available in PKCS #12 (.p12) and PEM (.crt) formats. This procedure shows PKCS #12 certificates.

Prerequisites

  • The Kafka cluster is available for the client
  • The Cluster Operator and User Operator are running in the cluster
  • A client outside the OpenShift cluster to connect to the Kafka cluster

Procedure

  1. Configure the Kafka cluster with an external Kafka listener.

    • Define the authentication required to access the Kafka broker through the listener
    • Enable authorization on the Kafka broker

      For example:

      apiVersion: kafka.strimzi.io/v1beta2
      kind: Kafka
      metadata:
        name: my-cluster
        namespace: myproject
      spec:
        kafka:
          # ...
          listeners: 1
          - name: external 2
            port: 9094 3
            type: LISTENER-TYPE 4
            tls: true 5
            authentication:
              type: tls 6
            configuration:
              preferredNodePortAddressType: InternalDNS 7
              bootstrap and broker service overrides 8
              #...
          authorization: 9
            type: simple
            superUsers:
              - super-user-name 10
        # ...
      1
      Configuration options for enabling external listeners are described in the Generic Kafka listener schema reference.
      2
      Name to identify the listener. Must be unique within the Kafka cluster.
      3
      Port number used by the listener inside Kafka. The port number has to be unique within a given Kafka cluster. Allowed port numbers are 9092 and higher with the exception of ports 9404 and 9999, which are already used for Prometheus and JMX. Depending on the listener type, the port number might not be the same as the port number that connects Kafka clients.
      4
      External listener type specified as route, loadbalancer, nodeport or ingress. An internal listener is specified as internal.
      5
      Enables TLS encryption on the listener. Default is false. TLS encryption is not required for route listeners.
      6
      Authentication specified as tls.
      7
      (Optional, for nodeport listeners only) Configuration to specify a preference for the first address type used by AMQ Streams as the node address.
      8
      (Optional) AMQ Streams automatically determines the addresses to advertise to clients. The addresses are automatically assigned by OpenShift. You can override bootstrap and broker service addresses if the infrastructure on which you are running AMQ Streams does not provide the right address. Validation is not performed on the overrides. The override configuration differs according to the listener type. For example, you can override hosts for route, DNS names or IP addresses for loadbalancer, and node ports for nodeport.
      9
      Authorization specified as simple, which uses the AclAuthorizer Kafka plugin.
      10
      (Optional) Super users can access all brokers regardless of any access restrictions defined in ACLs.
      Warning

      An OpenShift Route address comprises the name of the Kafka cluster, the name of the listener, and the name of the namespace it is created in. For example, my-cluster-kafka-listener1-bootstrap-myproject (CLUSTER-NAME-kafka-LISTENER-NAME-bootstrap-NAMESPACE). If you are using a route listener type, be careful that the whole length of the address does not exceed a maximum limit of 63 characters.

  2. Create or update the Kafka resource.

    oc apply -f KAFKA-CONFIG-FILE

    The Kafka cluster is configured with a Kafka broker listener using TLS authentication.

    A service is created for each Kafka broker pod.

    A service is created to serve as the bootstrap address for connection to the Kafka cluster.

    A service is also created as the external bootstrap address for external connection to the Kafka cluster using nodeport listeners.

    The cluster CA certificate to verify the identity of the kafka brokers is also created with the same name as the Kafka resource.

  3. Find the bootstrap address and port from the status of the Kafka resource.

    oc get kafka KAFKA-CLUSTER-NAME -o jsonpath='{.status.listeners[?(@.type=="external")].bootstrapServers}'

    Use the bootstrap address in your Kafka client to connect to the Kafka cluster.

  4. Create or modify a user representing the client that requires access to the Kafka cluster.

    • Specify the same authentication type as the Kafka listener.
    • Specify the authorization ACLs for simple authorization.

      For example:

      apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaUser
      metadata:
        name: my-user
        labels:
          strimzi.io/cluster: my-cluster 1
      spec:
        authentication:
          type: tls 2
        authorization:
          type: simple
          acls: 3
            - resource:
                type: topic
                name: my-topic
                patternType: literal
              operation: Read
            - resource:
                type: topic
                name: my-topic
                patternType: literal
              operation: Describe
            - resource:
                type: group
                name: my-group
                patternType: literal
              operation: Read
      1
      The label must match the label of the Kafka cluster for the user to be created.
      2
      Authentication specified as tls.
      3
      Simple authorization requires an accompanying list of ACL rules to apply to the user. The rules define the operations allowed on Kafka resources based on the username (my-user).
  5. Create or modify the KafkaUser resource.

    oc apply -f USER-CONFIG-FILE

    The user is created, as well as a Secret with the same name as the KafkaUser resource. The Secret contains a private and public key for TLS client authentication.

    For example:

    apiVersion: v1
    kind: Secret
    metadata:
      name: my-user
      labels:
        strimzi.io/kind: KafkaUser
        strimzi.io/cluster: my-cluster
    type: Opaque
    data:
      ca.crt: PUBLIC-KEY-OF-THE-CLIENT-CA
      user.crt: USER-CERTIFICATE-CONTAINING-PUBLIC-KEY-OF-USER
      user.key: PRIVATE-KEY-OF-USER
      user.p12: P12-ARCHIVE-FILE-STORING-CERTIFICATES-AND-KEYS
      user.password: PASSWORD-PROTECTING-P12-ARCHIVE
  6. Extract the public cluster CA certificate to the desired certificate format:

    oc get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
  7. Extract the password from the password file:

    oc get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.password}' | base64 -d > ca.password
  8. Configure your client with the authentication details for the public cluster certificates:

    Sample client code

    properties.put("security.protocol","SSL"); 1
    properties.put(SslConfigs.SSL_TRUSTSTORE_LOCATION_CONFIG,"/path/to/ca.p12"); 2
    properties.put(SslConfigs.SSL_TRUSTSTORE_PASSWORD_CONFIG,CA-PASSWORD); 3
    properties.put(SslConfigs.SSL_TRUSTSTORE_TYPE_CONFIG,"PKCS12"); 4

    1
    Enables TLS encryption (with or without TLS client authentication).
    2
    Specifies the truststore location where the certificates were imported.
    3
    Specifies the password for accessing the truststore. This property can be omitted if it is not needed by the truststore.
    4
    Identifies the truststore type.
    Note

    Use security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.

  9. Extract the user CA certificate from the user Secret to the desired certificate format:

    oc get secret USER-NAME -o jsonpath='{.data.user\.p12}' | base64 -d > user.p12
  10. Extract the password from the password file:

    oc get secret USER-NAME -o jsonpath='{.data.user\.password}' | base64 -d > user.password
  11. Configure your client with the authentication details for the user CA certificate:

    Sample client code

    properties.put(SslConfigs.SSL_KEYSTORE_LOCATION_CONFIG,"/path/to/user.p12"); 1
    properties.put(SslConfigs.SSL_KEYSTORE_PASSWORD_CONFIG,"<user.password>"); 2
    properties.put(SslConfigs.SSL_KEYSTORE_TYPE_CONFIG,"PKCS12"); 3

    1
    Specifies the keystore location where the certificates were imported.
    2
    Specifies the password for accessing the keystore. This property can be omitted if it is not needed by the keystore. The public user certificate is signed by the client CA when it is created.
    3
    Identifies the keystore type.
  12. Add the bootstrap address and port for connecting to the Kafka cluster:

    bootstrap.servers: BOOTSTRAP-ADDRESS:PORT

Additional resources

Chapter 7. Setting up metrics and dashboards for AMQ Streams

You can monitor your AMQ Streams deployment by viewing key metrics on dashboards and setting up alerts that trigger under certain conditions. Metrics are available for Kafka, ZooKeeper, and the other components of AMQ Streams.

To provide metrics information, AMQ Streams uses Prometheus rules and Grafana dashboards.

When configured with a set of rules for each component of AMQ Streams, Prometheus consumes key metrics from the pods that are running in your cluster. Grafana then visualizes those metrics on dashboards. AMQ Streams includes example Grafana dashboards that you can customize to suit your deployment.

On OpenShift Container Platform 4.x, AMQ Streams employs monitoring for user-defined projects (an OpenShift feature) to simplify the Prometheus setup process.

On OpenShift Container Platform 3.11, you need to deploy the Prometheus and Alertmanager components to your cluster separately.

Regardless of your OpenShift Container Platform version, you have to start by deploying the Prometheus metrics configuration for AMQ Streams.

Next, follow the instructions for your OpenShift Container Platform version:

With Prometheus and Grafana set up, you can use the example Grafana dashboards and alerting rules to monitor your Kafka cluster.

Additional monitoring options

Kafka Exporter is an optional component that provides additional monitoring related to consumer lag. If you want to use Kafka Exporter with AMQ Streams, see Configure the Kafka resource to deploy Kafka Exporter with your Kafka cluster.

You can also configure your deployment to track messages end-to-end by setting up distributed tracing. For more information, see Distributed tracing in the Using AMQ Streams on OpenShift guide.

Additional resources

7.1. Example metrics files

You can find example Grafana dashboards and other metrics configuration files in the examples/metrics directory. As indicated in the following list, some files are only used with OpenShift Container Platform 3.11, and not with OpenShift Container Platform 4.x.

Example metrics files provided with AMQ Streams

metrics
├── grafana-dashboards 1
│   ├── strimzi-cruise-control.json
│   ├── strimzi-kafka-bridge.json
│   ├── strimzi-kafka-connect.json
│   ├── strimzi-kafka-exporter.json
│   ├── strimzi-kafka-mirror-maker-2.json
│   ├── strimzi-kafka.json
│   ├── strimzi-operators.json
│   └── strimzi-zookeeper.json
├── grafana-install
│   └── grafana.yaml 2
├── prometheus-additional-properties
│   └── prometheus-additional.yaml - OPENSHIFT 3.11 ONLY 3
├── prometheus-alertmanager-config
│   └── alert-manager-config.yaml 4
├── prometheus-install
│    ├── alert-manager.yaml - OPENSHIFT 3.11 ONLY 5
│    ├── prometheus-rules.yaml 6
│    ├── prometheus.yaml - OPENSHIFT 3.11 ONLY 7
│    ├── strimzi-pod-monitor.yaml 8
├── kafka-bridge-metrics.yaml 9
├── kafka-connect-metrics.yaml 10
├── kafka-cruise-control-metrics.yaml 11
├── kafka-metrics.yaml 12
└── kafka-mirror-maker-2-metrics.yaml 13

1
Example Grafana dashboards.
2
Installation file for the Grafana image.
3
OPENSHIFT 3.11 ONLY: Additional Prometheus configuration to scrape metrics for CPU, memory, and disk volume usage, which comes directly from the OpenShift cAdvisor agent and kubelet on the nodes.
4
Hook definitions for sending notifications through Alertmanager.
5
OPENSHIFT 3.11 ONLY: Resources for deploying and configuring Alertmanager.
6
Alerting rules examples for use with Prometheus Alertmanager.
7
OPENSHIFT 3.11 ONLY: Installation resource file for the Prometheus image.
8
PodMonitor definitions translated by the Prometheus Operator into jobs for the Prometheus server to be able to scrape metrics data directly from pods.
9
Kafka Bridge resource with metrics enabled.
10
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka Connect.
11
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Cruise Control.
12
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka and ZooKeeper.
13
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka Mirror Maker 2.0.

7.1.1. Example Grafana dashboards

Example Grafana dashboards are provided for monitoring the following resources:

AMQ Streams Kafka

Shows metrics for:

  • Brokers online count
  • Active controllers in the cluster count
  • Unclean leader election rate
  • Replicas that are online
  • Under-replicated partitions count
  • Partitions which are at their minimum in sync replica count
  • Partitions which are under their minimum in sync replica count
  • Partitions that do not have an active leader and are hence not writable or readable
  • Kafka broker pods memory usage
  • Aggregated Kafka broker pods CPU usage
  • Kafka broker pods disk usage
  • JVM memory used
  • JVM garbage collection time
  • JVM garbage collection count
  • Total incoming byte rate
  • Total outgoing byte rate
  • Incoming messages rate
  • Total produce request rate
  • Byte rate
  • Produce request rate
  • Fetch request rate
  • Network processor average time idle percentage
  • Request handler average time idle percentage
  • Log size
AMQ Streams ZooKeeper

Shows metrics for:

  • Quorum Size of Zookeeper ensemble
  • Number of alive connections
  • Queued requests in the server count
  • Watchers count
  • ZooKeeper pods memory usage
  • Aggregated ZooKeeper pods CPU usage
  • ZooKeeper pods disk usage
  • JVM memory used
  • JVM garbage collection time
  • JVM garbage collection count
  • Amount of time it takes for the server to respond to a client request (maximum, minimum and average)
AMQ Streams Kafka Connect

Shows metrics for:

  • Total incoming byte rate
  • Total outgoing byte rate
  • Disk usage
  • JVM memory used
  • JVM garbage collection time
AMQ Streams Kafka MirrorMaker 2

Shows metrics for:

  • Number of connectors
  • Number of tasks
  • Total incoming byte rate
  • Total outgoing byte rate
  • Disk usage
  • JVM memory used
  • JVM garbage collection time
AMQ Streams Operators

Shows metrics for:

  • Custom resources
  • Successful custom resource reconciliations per hour
  • Failed custom resource reconciliations per hour
  • Reconciliations without locks per hour
  • Reconciliations started hour
  • Periodical reconciliations per hour
  • Maximum reconciliation time
  • Average reconciliation time
  • JVM memory used
  • JVM garbage collection time
  • JVM garbage collection count

Dashboards are also provided for the Kafka Bridge and Cruise Control components of AMQ Streams.

All the dashboards provide JVM metrics, as well as metrics that are specific to each component. For example, the Operators dashboard provides information on the number of reconciliations or custom resources that are being processed.

7.1.2. Example Prometheus metrics configuration

AMQ Streams uses the Prometheus JMX Exporter to expose JMX metrics using an HTTP endpoint, which is then scraped by Prometheus.

Grafana dashboards are dependent on Prometheus JMX Exporter relabeling rules, which are defined for AMQ Streams components as custom resource configuration.

A label is a name-value pair. Relabeling is the process of writing a label dynamically. For example, the value of a label might be derived from the name of a Kafka server and client ID.

AMQ Streams provides example custom resource configuration YAML files with the relabeling rules already defined. When deploying Prometheus metrics configuration, you can deploy the example custom resources or copy the metrics configuration to your own custom resource definitions.

Table 7.1. Example custom resources with metrics configuration

ComponentCustom resourceExample YAML file

Kafka and ZooKeeper

Kafka

kafka-metrics.yaml

Kafka Connect

KafkaConnect and KafkaConnectS2I

kafka-connect-metrics.yaml

Kafka MirrorMaker 2.0

KafkaMirrorMaker2

kafka-mirror-maker-2-metrics.yaml

Kafka Bridge

KafkaBridge

kafka-bridge-metrics.yaml

Cruise Control

Kafka

kafka-cruise-control-metrics.yaml

Additional resources

7.2. Deploying Prometheus metrics configuration

AMQ Streams provides example custom resource configuration YAML files with relabeling rules.

To apply metrics configuration of relabeling rules, do one of the following:

7.2.1. Copying Prometheus metrics configuration to a custom resource

To use Grafana dashboards for monitoring, copy the example metrics configuration to a custom resource.

In this procedure, the Kafka resource is updated, but the procedure is the same for all components that support monitoring.

Procedure

Perform the following steps for each Kafka resource in your deployment.

  1. Update the Kafka resource in an editor.

    oc edit kafka KAFKA-CONFIG-FILE
  2. Copy the example configuration in kafka-metrics.yaml to your own Kafka resource definition.
  3. Save the file, and wait for the updated resource to be reconciled.

7.2.2. Deploying a Kafka cluster with Prometheus metrics configuration

To use Grafana dashboards for monitoring, you can deploy an example Kafka cluster with metrics configuration.

In this procedure, The kafka-metrics.yaml file is used for the Kafka resource.

Procedure

7.3. Viewing Kafka metrics and dashboards in OpenShift 4

When AMQ Streams is deployed to OpenShift Container Platform 4.x, metrics are provided through monitoring for user-defined projects. This OpenShift feature gives developers access to a separate Prometheus instance for monitoring their own projects (for example, a Kafka project).

If monitoring for user-defined projects is enabled, the openshift-user-workload-monitoring project contains the following components:

  • A Prometheus Operator
  • A Prometheus instance (automatically deployed by the Prometheus Operator)
  • A Thanos Ruler instance

AMQ Streams uses these components to consume metrics.

A cluster administrator must enable monitoring for user-defined projects and then grant developers and other users permission to monitor applications within their own projects.

Grafana deployment

You can deploy a Grafana instance to the project containing your Kafka cluster. The example Grafana dashboards can then be used to visualize Prometheus metrics for AMQ Streams in the Grafana user interface.

Important

The openshift-monitoring project provides monitoring for core platform components. Do not use the Prometheus and Grafana components in this project to configure monitoring for AMQ Streams on OpenShift Container Platform 4.x.

Grafana version 6.3 is the minimum supported version.

Prerequisites

Procedure outline

To set up AMQ Streams monitoring in OpenShift Container Platform 4.x, follow these procedures in order:

7.3.1. Deploying the Prometheus resources

Note

Use this procedure when running AMQ Streams on OpenShift Container Platform 4.x.

To enable Prometheus to consume Kafka metrics, you configure and deploy the PodMonitor resources in the example metrics files. The PodMonitors scrape data directly from pods for Apache Kafka, ZooKeeper, Operators, the Kafka Bridge, and Cruise Control.

Then, you deploy the example alerting rules for Alertmanager.

Prerequisites

Procedure

  1. Check that monitoring for user-defined projects is enabled:

    oc get pods -n openshift-user-workload-monitoring

    If enabled, pods for the monitoring components are returned. For example:

    NAME                                   READY   STATUS    RESTARTS   AGE
    prometheus-operator-5cc59f9bc6-kgcq8   1/1     Running   0          25s
    prometheus-user-workload-0             5/5     Running   1          14s
    prometheus-user-workload-1             5/5     Running   1          14s
    thanos-ruler-user-workload-0           3/3     Running   0          14s
    thanos-ruler-user-workload-1           3/3     Running   0          14s

    If no pods are returned, monitoring for user-defined projects is disabled. See the Prerequisites in Section 7.3, “Viewing Kafka metrics and dashboards in OpenShift 4”.

  2. Multiple PodMonitor resources are defined in examples/metrics/prometheus-install/strimzi-pod-monitor.yaml.

    For each PodMonitor resource, edit the spec.namespaceSelector.matchNames property:

    apiVersion: monitoring.coreos.com/v1
    kind: PodMonitor
    metadata:
      name: cluster-operator-metrics
      labels:
        app: strimzi
    spec:
      selector:
        matchLabels:
          strimzi.io/kind: cluster-operator
      namespaceSelector:
        matchNames:
          - PROJECT-NAME 1
      podMetricsEndpoints:
      - path: /metrics
        port: http
    # ...
    1
    The project where the pods to scrape the metrics from are running, for example, Kafka.
  3. Deploy the strimzi-pod-monitor.yaml file to the project where your Kafka cluster is running:

    oc apply -f strimzi-pod-monitor.yaml -n MY-PROJECT
  4. Deploy the example Prometheus rules to the same project:

    oc apply -f prometheus-rules.yaml -n MY-PROJECT

Additional resources

7.3.2. Creating a Service Account for Grafana

Note

Use this procedure when running AMQ Streams on OpenShift Container Platform 4.x.

Your Grafana instance for AMQ Streams needs to run with a Service Account that is assigned the cluster-monitoring-view role.

Procedure

  1. Create a ServiceAccount for Grafana. Here the resource is named grafana-serviceaccount.

    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: grafana-serviceaccount
      labels:
        app: strimzi
  2. Deploy the ServiceAccount to the project containing your Kafka cluster:

    oc apply -f GRAFANA-SERVICEACCOUNT -n MY-PROJECT
  3. Create a ClusterRoleBinding resource that assigns the cluster-monitoring-view role to the Grafana ServiceAccount. Here the resource is named grafana-cluster-monitoring-binding.

    apiVersion: rbac.authorization.k8s.io/v1
    kind: ClusterRoleBinding
    metadata:
      name: grafana-cluster-monitoring-binding
      labels:
        app: strimzi
    subjects:
      - kind: ServiceAccount
        name: grafana-serviceaccount
        namespace: MY-PROJECT 1
    roleRef:
      kind: ClusterRole
      name: cluster-monitoring-view
      apiGroup: rbac.authorization.k8s.io
    1
    Name of your project.
  4. Deploy the ClusterRoleBinding to the project containing your Kafka cluster:

    oc apply -f GRAFANA-CLUSTER-MONITORING-BINDING -n MY-PROJECT

7.3.3. Deploying Grafana with a Prometheus datasource

Note

Use this procedure when running AMQ Streams on OpenShift Container Platform 4.x.

This procedure describes how to deploy a Grafana application that is configured for the OpenShift Container Platform 4.x monitoring stack.

OpenShift Container Platform 4.x includes a Thanos Querier instance in the openshift-monitoring project. Thanos Querier is used to aggregate platform metrics.

To consume the required platform metrics, your Grafana instance requires a Prometheus data source that can connect to Thanos Querier. To configure this connection, you create a Config Map that authenticates, by using a token, to the oauth-proxy sidecar that runs alongside Thanos Querier. A datasource.yaml file is used as the source of the Config Map.

Finally, you deploy the Grafana application with the Config Map mounted as a volume to the project containing your Kafka cluster.

Procedure

  1. Get the access token of the Grafana ServiceAccount:

    oc serviceaccounts get-token grafana-serviceaccount -n MY-PROJECT

    Copy the access token to use in the next step.

  2. Create a datasource.yaml file containing the Thanos Querier configuration for Grafana.

    Paste the access token into the httpHeaderValue1 property as indicated.

    apiVersion: 1
    
    datasources:
    - name: Prometheus
      type: prometheus
      url: https://thanos-querier.openshift-monitoring.svc.cluster.local:9091
      access: proxy
      basicAuth: false
      withCredentials: false
      isDefault: true
      jsonData:
        timeInterval: 5s
        tlsSkipVerify: true
        httpHeaderName1: "Authorization"
      secureJsonData:
        httpHeaderValue1: "Bearer ${GRAFANA-ACCESS-TOKEN}" 1
      editable: true
    1
    GRAFANA-ACCESS-TOKEN: The value of the access token for the Grafana ServiceAccount.
  3. Create a Config Map named grafana-config from the datasource.yaml file:

    oc create configmap grafana-config --from-file=datasource.yaml -n MY-PROJECT
  4. Create a Grafana application consisting of a Deployment and a Service.

    The grafana-config Config Map is mounted as a volume for the datasource configuration.

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: grafana
      labels:
        app: strimzi
    spec:
      replicas: 1
      selector:
        matchLabels:
          name: grafana
      template:
        metadata:
          labels:
            name: grafana
        spec:
          serviceAccountName: grafana-serviceaccount
          containers:
          - name: grafana
            image: grafana/grafana:6.3.0
            ports:
            - name: grafana
              containerPort: 3000
              protocol: TCP
            volumeMounts:
            - name: grafana-data
              mountPath: /var/lib/grafana
            - name: grafana-logs
              mountPath: /var/log/grafana
            - name: grafana-config
              mountPath: /etc/grafana/provisioning/datasources/datasource.yaml
              readOnly: true
              subPath: datasource.yaml
            readinessProbe:
              httpGet:
                path: /api/health
                port: 3000
              initialDelaySeconds: 5
              periodSeconds: 10
            livenessProbe:
              httpGet:
                path: /api/health
                port: 3000
              initialDelaySeconds: 15
              periodSeconds: 20
          volumes:
          - name: grafana-data
            emptyDir: {}
          - name: grafana-logs
            emptyDir: {}
          - name: grafana-config
            configMap:
              name: grafana-config
    ---
    apiVersion: v1
    kind: Service
    metadata:
      name: grafana
      labels:
        app: strimzi
    spec:
      ports:
      - name: grafana
        port: 3000
        targetPort: 3000
        protocol: TCP
      selector:
        name: grafana
      type: ClusterIP
  5. Deploy the Grafana application to the project containing your Kafka cluster:

    oc apply -f GRAFANA-APPLICATION -n MY-PROJECT

Additional resources

7.3.4. Creating a Route to the Grafana Service

Note

Use this procedure when running AMQ Streams on OpenShift Container Platform 4.x.

You can access the Grafana user interface through a Route that exposes the Grafana Service.

Procedure

  • Create an edge route to the grafana service:

    oc create route edge MY-GRAFANA-ROUTE --service=grafana --namespace=KAFKA-NAMESPACE

7.3.5. Importing the example Grafana dashboards

Note

Use this procedure when running AMQ Streams on OpenShift Container Platform 4.x.

Import the example Grafana dashboards using the Grafana user interface.

Procedure

  1. Get the details of the Route to the Grafana Service. For example:

    oc get routes
    
    NAME               HOST/PORT                         PATH  SERVICES
    MY-GRAFANA-ROUTE   MY-GRAFANA-ROUTE-amq-streams.net        grafana
  2. In a web browser, access the Grafana login screen using the URL for the Route host and port.
  3. Enter your user name and password, and then click Log In.

    The default Grafana user name and password are both admin. After logging in for the first time, you can change the password.

  4. In Configuration > Data Sources, check that the Prometheus data source was created. The data source was created in Section 7.3.3, “Deploying Grafana with a Prometheus datasource”.
  5. Click Dashboards > Manage, and then click Import.
  6. In examples/metrics/grafana-dashboards, copy the JSON of the dashboard to import.
  7. Paste the JSON into the text box, and then click Load.
  8. Repeat steps 1 -7 for the other example Grafana dashboards.

The imported Grafana dashboards are available to view from the Dashboards home page.

7.4. Viewing Kafka metrics and dashboards in OpenShift 3.11

When AMQ Streams is deployed to OpenShift Container Platform 3.11, you can use Prometheus to provide monitoring data for the example Grafana dashboards provided with AMQ Streams. You need to manually deploy the Prometheus components to your cluster.

In order to run the example Grafana dashboards, you must:

Note

The resources referenced in this section are intended as a starting point for setting up monitoring, but they are provided as examples only. If you require further support on configuring and running Prometheus or Grafana in production, try reaching out to their respective communities.

7.4.1. Prometheus support

The Prometheus server is not supported when AMQ Streams is deployed to OpenShift Container Platform 3.11. However, the Prometheus endpoint and the Prometheus JMX Exporter used to expose the metrics are supported.

For your convenience, we supply detailed instructions and example metrics configuration files should you wish to use Prometheus for monitoring.

7.4.2. Setting up Prometheus

Note

Use these procedures when running AMQ Streams on OpenShift Container Platform 3.11.

Prometheus provides an open source set of components for systems monitoring and alert notification.

Here we describe how to use the provided Prometheus image and configuration files to run and manage a Prometheus server when AMQ Streams is deployed to OpenShift Container Platform 3.11.

Prerequisites

  • You have deployed compatible versions of Prometheus and Grafana to your OpenShift Container Platform 3.11 cluster.
  • The service account used for running the Prometheus server pod has access to the OpenShift API server. This allows the service account to retrieve the list of pods in the cluster from which it gets metrics.

    For more information, see Discovering services.

7.4.2.1. Prometheus configuration

AMQ Streams provides example configuration files for the Prometheus server.

A Prometheus image is provided for deployment:

  • prometheus.yaml

Additional Prometheus-related configuration is also provided in the following files:

  • prometheus-additional.yaml
  • prometheus-rules.yaml
  • strimzi-pod-monitor.yaml

For Prometheus to obtain monitoring data, you must have deployed a compatible version of Prometheus to your OpenShift Container Platform 3.11 cluster.

Then, use the configuration files to Deploy Prometheus.

7.4.2.2. Prometheus resources

When you apply the Prometheus configuration, the following resources are created in your OpenShift cluster and managed by the Prometheus Operator:

  • A ClusterRole that grants permissions to Prometheus to read the health endpoints exposed by the Kafka and ZooKeeper pods, cAdvisor and the kubelet for container metrics.
  • A ServiceAccount for the Prometheus pods to run under.
  • A ClusterRoleBinding which binds the ClusterRole to the ServiceAccount.
  • A Deployment to manage the Prometheus Operator pod.
  • A PodMonitor to manage the configuration of the Prometheus pod.
  • A Prometheus to manage the configuration of the Prometheus pod.
  • A PrometheusRule to manage alerting rules for the Prometheus pod.
  • A Secret to manage additional Prometheus settings.
  • A Service to allow applications running in the cluster to connect to Prometheus (for example, Grafana using Prometheus as datasource).

7.4.2.3. Deploying Prometheus

To obtain monitoring data in your Kafka cluster, you can use your own Prometheus deployment or deploy Prometheus by applying the example installation resource file for the Prometheus docker image and the YAML files for Prometheus-related resources.

The deployment process creates a ClusterRoleBinding and discovers an Alertmanager instance in the namespace specified for the deployment.

Prerequisites

Procedure

  1. Modify the Prometheus installation file (prometheus.yaml) according to the namespace Prometheus is going to be installed into:

    On Linux, use:

    sed -i 's/namespace: .*/namespace: my-namespace/' prometheus.yaml

    On MacOS, use:

    sed -i '' 's/namespace: .*/namespace: my-namespace/' prometheus.yaml
  2. Edit the PodMonitor resource in strimzi-pod-monitor.yaml to define Prometheus jobs that will scrape the metrics data from pods.

    Update the namespaceSelector.matchNames property with the namespace where the pods to scrape the metrics from are running.

    PodMonitor is used to scrape data directly from pods for Apache Kafka, ZooKeeper, Operators, the Kafka Bridge and Cruise Control.

  3. Edit the prometheus.yaml installation file to include additional configuration for scraping metrics directly from nodes.

    The Grafana dashboards provided show metrics for CPU, memory and disk volume usage, which come directly from the OpenShift cAdvisor agent and kubelet on the nodes.

    1. Create a Secret resource from the configuration file (prometheus-additional.yaml in the examples/metrics/prometheus-additional-properties directory):

      oc apply -f prometheus-additional.yaml
    2. Edit the additionalScrapeConfigs property in the prometheus.yaml file to include the name of the Secret and the prometheus-additional.yaml file.
  4. Deploy the Prometheus resources:

    oc apply -f strimzi-pod-monitor.yaml
    oc apply -f prometheus-rules.yaml
    oc apply -f prometheus.yaml

7.4.3. Setting up Prometheus Alertmanager

Prometheus Alertmanager is a plugin for handling alerts and routing them to a notification service. Alertmanager supports an essential aspect of monitoring, which is to be notified of conditions that indicate potential issues based on alerting rules.

7.4.3.1. Alertmanager configuration

AMQ Streams provides example configuration files for Prometheus Alertmanager.

A configuration file defines the resources for deploying Alertmanager:

  • alert-manager.yaml

An additional configuration file provides the hook definitions for sending notifications from your Kafka cluster.

  • alert-manager-config.yaml

For Alertmanger to handle Prometheus alerts, use the configuration files to:

7.4.3.2. Alerting rules

Alerting rules provide notifications about specific conditions observed in the metrics. Rules are declared on the Prometheus server, but Prometheus Alertmanager is responsible for alert notifications.

Prometheus alerting rules describe conditions using PromQL expressions that are continuously evaluated.

When an alert expression becomes true, the condition is met and the Prometheus server sends alert data to the Alertmanager. Alertmanager then sends out a notification using the communication method configured for its deployment.

Alertmanager can be configured to use email, chat messages or other notification methods.

Additional resources

For more information about setting up alerting rules, see Configuration in the Prometheus documentation.

7.4.3.3. Alerting rule examples

Example alerting rules for Kafka and ZooKeeper metrics are provided with AMQ Streams for use in a Prometheus deployment.

General points about the alerting rule definitions:

  • A for property is used with the rules to determine the period of time a condition must persist before an alert is triggered.
  • A tick is a basic ZooKeeper time unit, which is measured in milliseconds and configured using the tickTime parameter of Kafka.spec.zookeeper.config. For example, if ZooKeeper tickTime=3000, 3 ticks (3 x 3000) equals 9000 milliseconds.
  • The availability of the ZookeeperRunningOutOfSpace metric and alert is dependent on the OpenShift configuration and storage implementation used. Storage implementations for certain platforms may not be able to supply the information on available space required for the metric to provide an alert.

Kafka alerting rules

UnderReplicatedPartitions
Gives the number of partitions for which the current broker is the lead replica but which have fewer replicas than the min.insync.replicas configured for their topic. This metric provides insights about brokers that host the follower replicas. Those followers are not keeping up with the leader. Reasons for this could include being (or having been) offline, and over-throttled interbroker replication. An alert is raised when this value is greater than zero, providing information on the under-replicated partitions for each broker.
AbnormalControllerState
Indicates whether the current broker is the controller for the cluster. The metric can be 0 or 1. During the life of a cluster, only one broker should be the controller and the cluster always needs to have an active controller. Having two or more brokers saying that they are controllers indicates a problem. If the condition persists, an alert is raised when the sum of all the values for this metric on all brokers is not equal to 1, meaning that there is no active controller (the sum is 0) or more than one controller (the sum is greater than 1).
UnderMinIsrPartitionCount
Indicates that the minimum number of in-sync replicas (ISRs) for a lead Kafka broker, specified using min.insync.replicas, that must acknowledge a write operation has not been reached. The metric defines the number of partitions that the broker leads for which the in-sync replicas count is less than the minimum in-sync. An alert is raised when this value is greater than zero, providing information on the partition count for each broker that did not achieve the minimum number of acknowledgments.
OfflineLogDirectoryCount
Indicates the number of log directories which are offline (for example, due to a hardware failure) so that the broker cannot store incoming messages anymore. An alert is raised when this value is greater than zero, providing information on the number of offline log directories for each broker.
KafkaRunningOutOfSpace
Indicates the remaining amount of disk space that can be used for writing data. An alert is raised when this value is lower than 5GiB, providing information on the disk that is running out of space for each persistent volume claim. The threshold value may be changed in prometheus-rules.yaml.

ZooKeeper alerting rules

AvgRequestLatency
Indicates the amount of time it takes for the server to respond to a client request. An alert is raised when this value is greater than 10 (ticks), providing the actual value of the average request latency for each server.
OutstandingRequests
Indicates the number of queued requests in the server. This value goes up when the server receives more requests than it can process. An alert is raised when this value is greater than 10, providing the actual number of outstanding requests for each server.
ZookeeperRunningOutOfSpace
Indicates the remaining amount of disk space that can be used for writing data to ZooKeeper. An alert is raised when this value is lower than 5GiB., providing information on the disk that is running out of space for each persistent volume claim.

7.4.3.4. Deploying Alertmanager

To deploy Alertmanager, apply the example configuration files.

The sample configuration provided with AMQ Streams configures the Alertmanager to send notifications to a Slack channel.

The following resources are defined on deployment:

  • An Alertmanager to manage the Alertmanager pod.
  • A Secret to manage the configuration of the Alertmanager.
  • A Service to provide an easy to reference hostname for other services to connect to Alertmanager (such as Prometheus).

Procedure

  1. Create a Secret resource from the Alertmanager configuration file (alert-manager-config.yaml in the examples/metrics/prometheus-alertmanager-config directory):

    oc apply -f alert-manager-config.yaml
  2. Update the alert-manager-config.yaml file to replace the:

    • slack_api_url property with the actual value of the Slack API URL related to the application for the Slack workspace
    • channel property with the actual Slack channel on which to send notifications
  3. Deploy Alertmanager:

    oc apply -f alert-manager.yaml

7.4.4. Setting up Grafana

Grafana provides visualizations of Prometheus metrics.

You can deploy and enable the example Grafana dashboards provided with AMQ Streams.

7.4.4.1. Deploying Grafana

To provide visualizations of Prometheus metrics, you can use your own Grafana installation or deploy Grafana by applying the grafana.yaml file provided in the examples/metrics directory.

Procedure

  1. Deploy Grafana:

    oc apply -f grafana.yaml
  2. Enable the Grafana dashboards.

7.4.4.2. Enabling the example Grafana dashboards

AMQ Streams provides example dashboard configuration files for Grafana. Example dashboards are provided in the examples/metrics/grafana-dashboards directory as JSON files:

  • strimzi-kafka.json
  • strimzi-zookeeper.json
  • strimzi-operators.json
  • strimzi-kafka-connect.json
  • strimzi-kafka-mirror-maker-2.json
  • strimzi-kafka-bridge.json
  • strimzi-cruise-control.json
  • strimzi-kafka-exporter.json

The example dashboards are a good starting point for monitoring key metrics, but they do not represent all available metrics. You can modify the example dashboards or add other metrics, depending on your infrastructure.

After setting up Prometheus and Grafana, you can visualize the AMQ Streams data on the Grafana dashboards.

Note

No alert notification rules are defined.

When accessing a dashboard, you can use the port-forward command to forward traffic from the Grafana pod to the host.

Note

The name of the Grafana pod is different for each user.

Procedure

  1. Get the details of the Grafana service:

    oc get service grafana

    For example:

    NAMETYPECLUSTER-IPPORT(S)

    grafana

    ClusterIP

    172.30.123.40

    3000/TCP

    Note the port number for port forwarding.

  2. Use port-forward to redirect the Grafana user interface to localhost:3000:

    oc port-forward svc/grafana 3000:3000
  3. Point a web browser to http://localhost:3000.

    The Grafana Log In page appears.

  4. Enter your user name and password, and then click Log In.

    The default Grafana user name and password are both admin. After logging in for the first time, you can change the password.

  5. Add Prometheus as a data source.

    • Specify a name
    • Add Prometheus as the type
    • Specify a Prometheus server URL (http://prometheus-operated:9090)

      Save and test the connection when you have added the details.

      Add Prometheus data source
  6. From DashboardsImport, upload the example dashboards or paste the JSON directly.
  7. On the top header, click the dashboard drop-down menu, and then select the dashboard you want to view.

    When the Prometheus server has been collecting metrics for a AMQ Streams cluster for some time, the dashboards are populated.

Figure 7.1. Dashboard selection options

AMQ Streams dashboard selection
AMQ Streams Kafka

Shows metrics for:

  • Brokers online count
  • Active controllers in the cluster count
  • Unclean leader election rate
  • Replicas that are online
  • Under-replicated partitions count
  • Partitions which are at their minimum in sync replica count
  • Partitions which are under their minimum in sync replica count
  • Partitions that do not have an active leader and are hence not writable or readable
  • Kafka broker pods memory usage
  • Aggregated Kafka broker pods CPU usage
  • Kafka broker pods disk usage
  • JVM memory used
  • JVM garbage collection time
  • JVM garbage collection count
  • Total incoming byte rate
  • Total outgoing byte rate
  • Incoming messages rate
  • Total produce request rate
  • Byte rate
  • Produce request rate
  • Fetch request rate
  • Network processor average time idle percentage
  • Request handler average time idle percentage
  • Log size

    Figure 7.2. AMQ Streams Kafka dashboard

    Kafka dashboard
AMQ Streams ZooKeeper

Shows metrics for:

  • Quorum Size of Zookeeper ensemble
  • Number of alive connections
  • Queued requests in the server count
  • Watchers count
  • ZooKeeper pods memory usage
  • Aggregated ZooKeeper pods CPU usage
  • ZooKeeper pods disk usage
  • JVM memory used
  • JVM garbage collection time
  • JVM garbage collection count
  • Amount of time it takes for the server to respond to a client request (maximum, minimum and average)
AMQ Streams Operators

Shows metrics for:

  • Custom resources
  • Successful custom resource reconciliations per hour
  • Failed custom resource reconciliations per hour
  • Reconciliations without locks per hour
  • Reconciliations started hour
  • Periodical reconciliations per hour
  • Maximum reconciliation time
  • Average reconciliation time
  • JVM memory used
  • JVM garbage collection time
  • JVM garbage collection count
AMQ Streams Kafka Connect

Shows metrics for:

  • Total incoming byte rate
  • Total outgoing byte rate
  • Disk usage
  • JVM memory used
  • JVM garbage collection time
AMQ Streams Kafka MirrorMaker 2

Shows metrics for:

  • Number of connectors
  • Number of tasks
  • Total incoming byte rate
  • Total outgoing byte rate
  • Disk usage
  • JVM memory used
  • JVM garbage collection time
AMQ Streams Kafka Bridge
See Section 7.6, “Monitor Kafka Bridge”.
AMQ Streams Cruise Control
See Section 7.7, “Monitor Cruise Control”.
AMQ Streams Kafka Exporter
See Section 7.5.5, “Enabling the Kafka Exporter Grafana dashboard”.

7.5. Add Kafka Exporter

Kafka Exporter is an open source project to enhance monitoring of Apache Kafka brokers and clients. Kafka Exporter is provided with AMQ Streams for deployment with a Kafka cluster to extract additional metrics data from Kafka brokers related to offsets, consumer groups, consumer lag, and topics.

The metrics data is used, for example, to help identify slow consumers.

Lag data is exposed as Prometheus metrics, which can then be presented in Grafana for analysis.

If you are already using Prometheus and Grafana for monitoring of built-in Kafka metrics, you can configure Prometheus to also scrape the Kafka Exporter Prometheus endpoint.

AMQ Streams includes an example Kafka Exporter dashboard in examples/metrics/grafana-dashboards/strimzi-kafka-exporter.json.

7.5.1. Monitoring Consumer lag

Consumer lag indicates the difference in the rate of production and consumption of messages. Specifically, consumer lag for a given consumer group indicates the delay between the last message in the partition and the message being currently picked up by that consumer.

The lag reflects the position of the consumer offset in relation to the end of the partition log.

Consumer lag between the producer and consumer offset

Consumer lag

This difference is sometimes referred to as the delta between the producer offset and consumer offset: the read and write positions in the Kafka broker topic partitions.

Suppose a topic streams 100 messages a second. A lag of 1000 messages between the producer offset (the topic partition head) and the last offset the consumer has read means a 10-second delay.

The importance of monitoring consumer lag

For applications that rely on the processing of (near) real-time data, it is critical to monitor consumer lag to check that it does not become too big. The greater the lag becomes, the further the process moves from the real-time processing objective.

Consumer lag, for example, might be a result of consuming too much old data that has not been purged, or through unplanned shutdowns.

Reducing consumer lag

Typical actions to reduce lag include:

  • Scaling-up consumer groups by adding new consumers
  • Increasing the retention time for a message to remain in a topic
  • Adding more disk capacity to increase the message buffer

Actions to reduce consumer lag depend on the underlying infrastructure and the use cases AMQ Streams is supporting. For instance, a lagging consumer is less likely to benefit from the broker being able to service a fetch request from its disk cache. And in certain cases, it might be acceptable to automatically drop messages until a consumer has caught up.

7.5.2. Example Kafka Exporter alerting rules

If you performed the steps to introduce metrics to your deployment, you will already have your Kafka cluster configured to use the alert notification rules that support Kafka Exporter.

The rules for Kafka Exporter are defined in prometheus-rules.yaml, and are deployed with Prometheus. For more information, see Prometheus.

The sample alert notification rules specific to Kafka Exporter are as follows:

UnderReplicatedPartition
An alert to warn that a topic is under-replicated and the broker is not replicating to enough partitions. The default configuration is for an alert if there are one or more under-replicated partitions for a topic. The alert might signify that a Kafka instance is down or the Kafka cluster is overloaded. A planned restart of the Kafka broker may be required to restart the replication process.
TooLargeConsumerGroupLag
An alert to warn that the lag on a consumer group is too large for a specific topic partition. The default configuration is 1000 records. A large lag might indicate that consumers are too slow and are falling behind the producers.
NoMessageForTooLong
An alert to warn that a topic has not received messages for a period of time. The default configuration for the time period is 10 minutes. The delay might be a result of a configuration issue preventing a producer from publishing messages to the topic.

Adapt the default configuration of these rules according to your specific needs.

7.5.3. Exposing Kafka Exporter metrics

Lag information is exposed by Kafka Exporter as Prometheus metrics for presentation in Grafana.

Kafka Exporter exposes metrics data for brokers, topics and consumer groups. These metrics are displayed on the example strimzi-kafka-exporter dashboard.

The data extracted is described here.

Table 7.2. Broker metrics output

NameInformation

kafka_brokers

Number of brokers in the Kafka cluster

Table 7.3. Topic metrics output

NameInformation

kafka_topic_partitions

Number of partitions for a topic

kafka_topic_partition_current_offset

Current topic partition offset for a broker

kafka_topic_partition_oldest_offset

Oldest topic partition offset for a broker

kafka_topic_partition_in_sync_replica

Number of in-sync replicas for a topic partition

kafka_topic_partition_leader

Leader broker ID of a topic partition

kafka_topic_partition_leader_is_preferred

Shows 1 if a topic partition is using the preferred broker

kafka_topic_partition_replicas

Number of replicas for this topic partition

kafka_topic_partition_under_replicated_partition

Shows 1 if a topic partition is under-replicated

Table 7.4. Consumer group metrics output

NameInformation

kafka_consumergroup_current_offset

Current topic partition offset for a consumer group

kafka_consumergroup_lag

Current approximate lag for a consumer group at a topic partition

Consumer group metrics are only displayed on the Kafka Exporter dashboard if at least one consumer group has a lag greater than zero.

7.5.4. Configuring Kafka Exporter

This procedure shows how to configure Kafka Exporter in the Kafka resource through KafkaExporter properties.

For more information about configuring the Kafka resource, see Kafka cluster configuration in the Using AMQ Streams on OpenShift guide.

The properties relevant to the Kafka Exporter configuration are shown in this procedure.

You can configure these properties as part of a deployment or redeployment of the Kafka cluster.

Prerequisites

  • An OpenShift cluster
  • A running Cluster Operator

Procedure

  1. Edit the KafkaExporter properties for the Kafka resource.

    The properties you can configure are shown in this example configuration:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      # ...
      kafkaExporter:
        image: my-registry.io/my-org/my-exporter-cluster:latest 1
        groupRegex: ".*" 2
        topicRegex: ".*" 3
        resources: 4
          requests:
            cpu: 200m
            memory: 64Mi
          limits:
            cpu: 500m
            memory: 128Mi
        logging: debug 5
        enableSaramaLogging: true 6
        template: 7
          pod:
            metadata:
              labels:
                label1: value1
            imagePullSecrets:
              - name: my-docker-credentials
            securityContext:
              runAsUser: 1000001
              fsGroup: 0
            terminationGracePeriodSeconds: 120
        readinessProbe: 8
          initialDelaySeconds: 15
          timeoutSeconds: 5
        livenessProbe: 9
          initialDelaySeconds: 15
          timeoutSeconds: 5
    # ...
    1
    ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
    2
    A regular expression to specify the consumer groups to include in the metrics.
    3
    A regular expression to specify the topics to include in the metrics.
    4
    5
    Logging configuration, to log messages with a given severity (debug, info, warn, error, fatal) or above.
    6
    Boolean to enable Sarama logging, a Go client library used by Kafka Exporter.
    7
    8
    9
  2. Create or update the resource:

    oc apply -f kafka.yaml

What to do next

After configuring and deploying Kafka Exporter, you can enable Grafana to present the Kafka Exporter dashboards.

7.5.5. Enabling the Kafka Exporter Grafana dashboard

AMQ Streams provides example dashboard configuration files for Grafana. The Kafka Exporter dashboard is provided in the examples/metrics directory as a JSON file:

  • strimzi-kafka-exporter.json

If you deployed Kafka Exporter with your Kafka cluster, you can visualize the metrics data it exposes on the Grafana dashboard.

This procedure assumes you already have access to the Grafana user interface and Prometheus has been added as a data source. If you are accessing the user interface for the first time, see Grafana.

Procedure

  1. Access the Grafana user interface.
  2. Select the Strimzi Kafka Exporter dashboard.

    When metrics data has been collected for some time, the Kafka Exporter charts are populated.

    AMQ Streams Kafka Exporter

    Shows metrics for:

    • Topic count
    • Partition count
    • Replicas count
    • In-sync replicas count
    • Under-replicated partitions count
    • Partitions which are at their minimum in sync replica count
    • Partitions which are under their minimum in sync replica count
    • Partitions not on a preferred node
    • Messages in per second from topics
    • Messages consumed per second from topics
    • Messages consumed per minute by consumer groups
    • Lag by consumer group
    • Number of partitions
    • Latest offsets
    • Oldest offsets

Use the Grafana charts to analyze lag and to check if actions to reduce lag are having an impact on an affected consumer group. If, for example, Kafka brokers are adjusted to reduce lag, the dashboard will show the Lag by consumer group chart going down and the Messages consumed per minute chart going up.

7.6. Monitor Kafka Bridge

If you are already using Prometheus and Grafana for monitoring of built-in Kafka metrics, you can configure Prometheus to also scrape the Kafka Bridge Prometheus endpoint.

The example Grafana dashboard for the Kafka Bridge provides:

  • Information about HTTP connections and related requests to the different endpoints
  • Information about the Kafka consumers and producers used by the bridge
  • JVM metrics from the bridge itself

7.6.1. Configuring Kafka Bridge

You can enable the Kafka Bridge metrics in the KafkaBridge resource using the enableMetrics property.

You can configure this property as part of a deployment or redeployment of the Kafka Bridge.

For example:

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  bootstrapServers: my-cluster-kafka:9092
  http:
    # ...
  enableMetrics: true
  # ...

7.6.2. Enabling the Kafka Bridge Grafana dashboard

If you deployed Kafka Bridge with your Kafka cluster, you can enable Grafana to present the metrics data it exposes.

A Kafka Bridge dashboard is provided in the examples/metrics directory as a JSON file:

  • strimzi-kafka-bridge.json

When metrics data has been collected for some time, the Kafka Bridge charts are populated.

Kafka Bridge

Shows metrics for:

  • HTTP connections to the Kafka Bridge count
  • HTTP requests being processed count
  • Requests processed per second grouped by HTTP method
  • The total request rate grouped by response codes (2XX, 4XX, 5XX)
  • Bytes received and sent per second
  • Requests for each Kafka Bridge endpoint
  • Number of Kafka consumers, producers, and related opened connections used by the Kafka Bridge itself
  • Kafka producer:

    • The average number of records sent per second (grouped by topic)
    • The number of outgoing bytes sent to all brokers per second (grouped by topic)
    • The average number of records per second that resulted in errors (grouped by topic)
  • Kafka consumer:

    • The average number of records consumed per second (grouped by clientId-topic)
    • The average number of bytes consumed per second (grouped by clientId-topic)
    • Partitions assigned (grouped by clientId)
  • JVM memory used
  • JVM garbage collection time
  • JVM garbage collection count

7.7. Monitor Cruise Control

If you are already using Prometheus and Grafana for monitoring of built-in Kafka metrics, you can configure Prometheus to also scrape the Cruise Control Prometheus endpoint.

The example Grafana dashboard for Cruise Control provides:

  • Information about optimization proposals computation, goals violation, cluster balancedness, and more
  • Information about REST API calls for rebalance proposals and actual rebalance operations
  • JVM metrics from Cruise Control itself

7.7.1. Configuring Cruise Control

Enable Cruise Control metrics using the cruiseControl.metricsConfig property in the Kafka resource to provide a reference to a ConfigMap that contains JMX exporter configuration for the metrics to expose.

For example:

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

7.7.2. Enabling the Cruise Control Grafana dashboard

If you deployed Cruise Control with your Kafka cluster with the metrics enabled, you can enable Grafana to present the metrics data it exposes.

A Cruise Control dashboard is provided in the examples/metrics directory as a JSON file:

  • strimzi-cruise-control.json

When metrics data has been collected for some time, the Cruise Control charts are populated.

Cruise Control

Shows metrics for:

  • Number of snapshot windows that are monitored by Cruise Control
  • Number of time windows considered valid because they contain enough samples to compute an optimization proposal
  • Number of ongoing executions running for proposals or rebalances
  • Current balancedness score of the Kafka cluster as calculated by the anomaly detector component of Cruise Control (every 5 minutes by default)
  • Percentage of monitored partitions
  • Number of goal violations reported by the anomaly detector (every 5 minutes by default)
  • How often a disk read failure happens on the brokers
  • Rate of metric sample fetch failures
  • Time needed to compute an optimization proposal
  • Time needed to create the cluster model
  • How often a proposal request or an actual rebalance request is made through the Cruise Control REST API
  • How often the overall cluster state and the user tasks state are requested through the Cruise Control REST API
  • JVM memory used
  • JVM garbage collection time
  • JVM garbage collection count

Chapter 8. Upgrading AMQ Streams

Action required: Convert custom resources and CRDs to the v1beta2 API version before you upgrade the AMQ Streams Operator to version 1.8.0.

From AMQ Streams version 1.8 onwards, the Red Hat Integration - AMQ Streams Operator supports v1beta2 custom resources only. Before you upgrade the AMQ Streams Operator to version 1.8.0 in the OperatorHub UI, you must use the API conversion tool to upgrade custom resources to v1beta2. The API conversion tool requires Java 11.

The recommended approach for OperatorHub upgrades is as follows:

  1. First, upgrade to AMQ Streams 1.7, if you have not already done so.
  2. Download the Red Hat AMQ Streams 1.8.0 API Conversion Tool from the AMQ Streams download site.
  3. Use the API conversion tool to convert custom resources and CRDs to v1beta2

    To fully complete the conversion, you must run the following two commands in order:

    1. convert-resource to convert AMQ Streams custom resources into a format applicable to v1beta2. See Converting custom resources directly using the API conversion tool.
    2. crd-upgrade to update spec.versions in the CRDs to declare v1beta2 as the storage API version. See Upgrading CRDs to v1beta2 using the API conversion tool.
  4. In the OperatorHub UI, delete version 1.7.0 of the Red Hat Integration - AMQ Streams Operator.
  5. Perform this step if version 1.8.0 of the Red Hat Integration - AMQ Streams Operator already exists in your cluster. If not, go to step 6.

    If the Approval Strategy for the AMQ Streams Operator was set to Automatic, version 1.8.0 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 1.8.0 Operator is stuck in the "Pending" status.

    In this situation, delete version 1.8.0 of the Red Hat Integration - AMQ Streams Operator as well as version 1.7.0.

    Note

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

  6. In the OperatorHub UI, install version 1.8.0 of the Red Hat Integration - AMQ Streams Operator immediately.

    The installed 1.8.0 Operator begins to watch the cluster and performs rolling updates. You might notice a temporary decrease in cluster performance during this process.

Upgrades overview

AMQ Streams can be upgraded to version 1.8 to take advantage of new features and enhancements, performance improvements, and security options.

As part of the upgrade, you upgrade Kafka to the latest supported version. Each Kafka release introduces new features, improvements, and bug fixes to your AMQ Streams deployment.

AMQ Streams can be downgraded to the previous version if you encounter issues with the newer version.

Released versions of AMQ Streams are listed in the Product Downloads section of the Red Hat Customer Portal.

Upgrade paths

Two upgrade paths are possible:

Incremental
Upgrading AMQ Streams from the previous minor version to version 1.8.
Multi-version

Upgrading AMQ Streams from an old version to version 1.8 within a single upgrade (skipping one or more intermediate versions).

For example, upgrading from AMQ Streams 1.5 directly to AMQ Streams 1.7.

Upgrading from a version earlier than 1.7

The v1beta2 API version for all custom resources was introduced with AMQ Streams 1.7. For AMQ Streams 1.8, the v1alpha1 and v1beta1 API versions were removed from all AMQ Streams custom resources apart from KafkaTopic and KafkaUser.

If you are upgrading from an AMQ Streams version prior to version 1.7:

  1. Upgrade AMQ Streams to 1.7
  2. Convert the custom resources to v1beta2
  3. Upgrade AMQ Streams to 1.8 or newer
Note

As an alternative, you can install the custom resources from version 1.7, convert the resources, and then upgrade to 1.8 or newer.

Kafka version support

The Kafka versions table lists the supported Kafka versions for AMQ Streams 1.8. In the table:

  • The latest Kafka version is supported for production use.
  • The previous Kafka version is supported only for the purpose of upgrading to AMQ Streams 1.8.

Decide which Kafka version to upgrade to before beginning the AMQ Streams upgrade process.

Note

You can upgrade to a higher Kafka version as long as it is supported by your version of AMQ Streams. In some cases, you can also downgrade to a previous supported Kafka version.

Downtime and availability

If topics are configured for high availability, upgrading AMQ Streams should not cause any downtime for consumers and producers that publish and read data from those topics. Highly available topics have a replication factor of at least 3 and partitions distributed evenly among the brokers.

Upgrading AMQ Streams triggers rolling updates, where all brokers are restarted in turn, at different stages of the process. During rolling updates, not all brokers are online, so overall cluster availability is temporarily reduced. A reduction in cluster availability increases the chance that a broker failure will result in lost messages.

8.1. Required upgrade sequence

To upgrade brokers and clients without downtime, you must complete the Strimzi upgrade procedures in the following order:

  1. Update existing custom resources to support the v1beta2 API version.

    Do this after upgrading to AMQ Streams 1.7, but before upgrading to AMQ Streams 1.8 or newer. For a multi-version upgrade from a version prior to version 1.7:

    1. Skip this step and continue with the following steps to upgrade to version 1.7.
    2. Return to this step and perform all the steps in this upgrade sequence to upgrade to 1.8 or newer.
  2. Update your Cluster Operator to a new AMQ Streams version.

    The approach you take depends on how you deployed the Cluster Operator.

    • If you deployed the Cluster Operator using the installation YAML files, perform your upgrade by modifying the Operator installation files, as described in Upgrading the Cluster Operator.
    • If you deployed the Cluster Operator from the OperatorHub, use the Operator Lifecycle Manager (OLM) to change the update channel for the AMQ Streams Operators to a new AMQ Streams version.

      Depending on your chosen upgrade strategy, after updating the channel, either:

      • An automatic upgrade is initiated
      • A manual upgrade will require approval before the installation begins

        For more information on using the OperatorHub to upgrade Operators, see Upgrading installed Operators in the OpenShift documentation.

  3. Upgrade all Kafka brokers and client applications to the latest supported Kafka version.

Optional: incremental cooperative rebalance upgrade

Consider upgrading consumers and Kafka Streams applications to use the incremental cooperative rebalance protocol for partition rebalances.

8.2. AMQ Streams custom resource upgrades

Before upgrading AMQ Streams to 1.8, you must ensure that your custom resources are using API version v1beta2. You can do this any time after upgrading to AMQ Streams 1.7, but the upgrades must be completed before upgrading to AMQ Streams 1.8 or newer.

Important

Upgrade of the custom resources to v1beta2 must be performed before upgrading the Cluster Operator, so the Cluster Operator can understand the resources.

Note

Upgrade of the custom resources to v1beta2 prepares AMQ Streams for a move to OpenShift CRD v1, which is required for OpenShift v1.22.

CLI upgrades to custom resources

AMQ Streams provides an API conversion tool with its release artifacts.

You can download its ZIP or TAR.GZ from AMQ Streams download site. To use the tool, extract it and use the scripts in the bin directory.

From its CLI, you can then use the tool to convert the format of your custom resources to v1beta2 in one of two ways:

After the conversion of your custom resources, you must set v1beta2 as the storage API version in your CRDs:

Manual upgrades to custom resources

Instead of using the API conversion tool to update custom resources to v1beta2, you can manually update each custom resource to use v1beta2:

Update the Kafka custom resource, including the configurations for the other components:

Update the other custom resources that apply to your deployment:

The manual procedures show the changes that are made to each custom resource. After these changes, you must use the API conversion tool to upgrade your CRDs.

8.2.1. API versioning

Custom resources are edited and controlled using APIs added to OpenShift by CRDs. Put another way, CRDs extend the Kubernetes API to allow the creation of custom resources. CRDs are themselves resources within OpenShift. They are installed in an OpenShift cluster to define the versions of API for the custom resource. Each version of the custom resource API can define its own schema for that version. OpenShift clients, including the AMQ Streams Operators, access the custom resources served by the Kubernetes API server using a URL path (API path), which includes the API version.

The introduction of v1beta2 updates the schemas of the custom resources. The v1alpha1 and v1beta1 versions have been removed.

The v1alpha1 API version is no longer used for the following AMQ Streams custom resources:

  • Kafka
  • KafkaConnect
  • KafkaConnectS2I
  • KafkaConnector
  • KafkaMirrorMaker
  • KafkaMirrorMaker2
  • KafkaTopic
  • KafkaUser
  • KafkaBridge
  • KafkaRebalance

The v1beta1 API version is no longer used for the following AMQ Streams custom resources:

  • Kafka
  • KafkaConnect
  • KafkaConnectS2I
  • KafkaMirrorMaker
  • KafkaTopic
  • KafkaUser

8.2.2. Converting custom resources configuration files using the API conversion tool

This procedure describes how to use the API conversion tool to convert YAML files describing the configuration for AMQ Streams custom resources into a format applicable to v1beta2. To do so, you use the convert-file (cf) command.

The convert-file command can convert YAML files containing multiple documents. For a multi-document YAML file, all the AMQ Streams custom resources it contains are converted. Any non-AMQ Streams OpenShift resources are replicated unmodified in the converted output file.

After you have converted the YAML file, you must apply the configuration to update the custom resource in the cluster. Alternatively, if the GitOps synchronization mechanism is being used for updates on your cluster, you can use it to apply the changes. The conversion is only complete when the custom resource is updated in the OpenShift cluster.

Alternatively, you can use the convert-resource procedure to convert custom resources directly.

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.
  • The API conversion tool, which is provided with the release artifacts.
  • The tool requires Java 11.

Use the CLI help for more information on the API conversion tool, and the flags available for the convert-file command:

bin/api-conversion.sh help
bin/api-conversion.sh help convert-file

Use bin/api-conversion.cmd for this procedure if you are using Windows.

Table 8.1. Flags for YAML file conversion

FlagDescription

-f, --file=NAME-OF-YAML-FILE

Specifies the YAML file for the AMQ Streams custom resource being converted

-o, --output=NAME-OF-CONVERTED-YAML-FILE

Creates an output YAML file for the converted custom resource

--in-place

Updates the original source file with the converted YAML

Procedure

  1. Run the API conversion tool with the convert-file command and appropriate flags.

    Example 1, converts a YAML file and displays the output, though the file does not change:

    bin/api-conversion.sh convert-file --file input.yaml

    Example 2, converts a YAML file, and writes the changes into the original source file:

    bin/api-conversion.sh convert-file --file input.yaml --in-place

    Example 3, converts a YAML file, and writes the changes into a new output file:

    bin/api-conversion.sh convert-file --file input.yaml --output output.yaml
  2. Update the custom resources using the converted configuration file.

    oc apply -f CONVERTED-CONFIG-FILE
  3. Verify that the custom resources have been converted.

    oc get KIND CUSTOM-RESOURCE-NAME -o yaml

8.2.3. Converting custom resources directly using the API conversion tool

This procedure describes how to use the API conversion tool to convert AMQ Streams custom resources directly in the OpenShift cluster into a format applicable to v1beta2. To do so, you use the convert-resource (cr) command. The command uses Kubernetes APIs to make the conversions.

You can specify one or more of types of AMQ Streams custom resources, based on the kind property, or you can convert all types. You can also target a specific namespace or all namespaces for conversion. When targeting a namespace, you can convert all custom resources in that namespace, or convert a single custom resource by specifying its name and kind.

Alternatively, you can use the convert-file procedure to convert and apply the YAML files describing the custom resources.

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.
  • The API conversion tool, which is provided with the release artifacts.
  • The tool requires Java 11 (OpenJDK).
  • The steps require a user admin account with RBAC permission to:

    • Get the AMQ Streams custom resources being converted using the --name option
    • List the AMQ Streams custom resources being converted without using the --name option
    • Replace the AMQ Streams custom resources being converted

Use the CLI help for more information on the API conversion tool, and the flags available for the convert-resource command:

bin/api-conversion.sh help
bin/api-conversion.sh help convert-resource

Use bin/api-conversion.cmd for this procedure if you are using Windows.

Table 8.2. Flags for converting custom resources

FlagDescription

-k, --kind

Specifies the kinds of custom resources to be converted, or converts all resources if not specified

-a, --all-namespaces

Converts custom resources in all namespaces

-n, --namespace

Specifies an OpenShift namespace or OpenShift project, or uses the current namespace if not specified

--name

If --namespace and a single custom resource --kind is used, specifies the name of the custom resource being converted

Procedure

  1. Run the API conversion tool with the convert-resource command and appropriate flags.

    Example 1, converts all AMQ Streams resources in current namespace:

    bin/api-conversion.sh convert-resource

    Example 2, converts all AMQ Streams resources in all namespaces:

    bin/api-conversion.sh convert-resource --all-namespaces

    Example 3, converts all AMQ Streams resources in the my-kafka namespace:

    bin/api-conversion.sh convert-resource --namespace my-kafka

    Example 4, converts only Kafka resources in all namespaces:

    bin/api-conversion.sh convert-resource --all-namespaces --kind Kafka

    Example 5, converts Kafka and Kafka Connect resources in all namespaces:

    bin/api-conversion.sh convert-resource --all-namespaces --kind Kafka --kind KafkaConnect

    Example 6, converts a Kafka custom resource named my-cluster in the my-kafka namespace:

    bin/api-conversion.sh convert-resource --kind Kafka --namespace my-kafka --name my-cluster
  2. Verify that the custom resources have been converted.

    oc get KIND CUSTOM-RESOURCE-NAME -o yaml

8.2.4. Upgrading CRDs to v1beta2 using the API conversion tool

This procedure describes how to use the API conversion tool to convert the CRDs that define the schemas used to instantiate and manage AMQ Streams-specific resources in a format applicable to v1beta2. To do so, you use the crd-upgrade command.

Perform this procedure after converting all AMQ Streams custom resources in the whole OpenShift cluster to v1beta2. If you upgrade your CRDs first, and then convert your custom resources, you will need to run this command again.

The command updates spec.versions in the CRDs to declare v1beta2 as the storage API version. The command also updates custom resources so they are stored under v1beta2. New custom resource instances are created from the specification of the storage API version, so only one API version is ever marked as the storage version.

When you have upgraded the CRDs to use v1beta2 as the storage version, you should only use v1beta2 properties in your custom resources.

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.
  • The API conversion tool, which is provided with the release artifacts.
  • The tool requires Java 11 (OpenJDK).
  • Custom resources have been converted to v1beta2.
  • The steps require a user admin account with RBAC permission to:

    • List the AMQ Streams custom resources in all namespaces
    • Replace the AMQ Streams custom resources being converted
    • Update CRDs
    • Replace the status of the CRDs

Use the CLI help for more information on the API conversion tool:

bin/api-conversion.sh help

Use bin/api-conversion.cmd for this procedure if you are using Windows.

Procedure

  1. If you have not done so, convert your custom resources to use v1beta2.

    You can use the API conversion tool to do this in one of two ways:

  2. Run the API conversion tool with the crd-upgrade command.

    bin/api-conversion.sh crd-upgrade
  3. Verify that the CRDs have been upgraded so that v1beta2 is the storage version.

    For example, for the Kafka topic CRD:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: CustomResourceDefinition
    metadata:
      name: kafkatopics.kafka.strimzi.io
      #...
    spec:
      group: kafka.strimzi.io
      #...
      versions:
      - name: v1beta2
        served: true
        storage: true
        #...
    status:
      #...
      storedVersions:
      - v1beta2

8.2.5. Upgrading Kafka resources to support v1beta2

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.

Procedure

Perform the following steps for each Kafka custom resource in your deployment.

  1. Update the Kafka custom resource in an editor.

    oc edit kafka KAFKA-CLUSTER
  2. If you have not already done so, update .spec.kafka.listener to the new generic listener format, as described in Section 8.2.6, “Updating listeners to the generic listener configuration”.

    Warning

    The old listener format is not supported in API version v1beta2.

  3. If present, move affinity from .spec.kafka.affinity to .spec.kafka.template.pod.affinity.
  4. If present, move tolerations from .spec.kafka.tolerations to .spec.kafka.template.pod.tolerations.
  5. If present, remove .spec.kafka.template.tlsSidecarContainer.
  6. If present, remove .spec.kafka.tlsSidecarContainer.
  7. If either of the following policy configurations exist:

    • .spec.kafka.template.externalBootstrapService.externalTrafficPolicy
    • .spec.kafka.template.perPodService.externalTrafficPolicy

      1. Move the configuration to .spec.kafka.listeners[].configuration.externalTrafficPolicy, for both type: loadbalancer and type: nodeport listeners.
      2. Remove .spec.kafka.template.externalBootstrapService.externalTrafficPolicy or .spec.kafka.template.perPodService.externalTrafficPolicy.
  8. If either of the following loadbalancer listener configurations exist:

    • .spec.kafka.template.externalBootstrapService.loadBalancerSourceRanges
    • .spec.kafka.template.perPodService.loadBalancerSourceRanges

      1. Move the configuration to .spec.kafka.listeners[].configuration.loadBalancerSourceRanges, for type: loadbalancer listeners.
      2. Remove .spec.kafka.template.externalBootstrapService.loadBalancerSourceRanges or .spec.kafka.template.perPodService.loadBalancerSourceRanges.
  9. If type: external logging is configured in .spec.kafka.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j.properties
  10. If the .spec.kafka.metrics field is used to enable metrics:

    1. Create a new ConfigMap that stores the YAML configuration for the JMX Prometheus exporter under a key. The YAML must match what is currently in the .spec.kafka.metrics field.

      kind: ConfigMap
      apiVersion: v1
      metadata:
        name: kafka-metrics
        labels:
          app: strimzi
      data:
        kafka-metrics-config.yaml: |
            <YAML>
    2. Add a .spec.kafka.metricsConfig property that points to the ConfigMap and key:

      metricsConfig:
        type: jmxPrometheusExporter
        valueFrom:
          configMapKeyRef:
            name: kafka-metrics
            key: kafka-metrics-config.yaml
    3. Delete the old .spec.kafka.metrics field.
  11. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

What to do next

For each Kafka custom resource, upgrade the configurations for ZooKeeper, Topic Operator, Entity Operator, and Cruise Control (if deployed) to support version v1beta2. This is described in the following procedures.

When all Kafka configurations are updated to support v1beta2, you can upgrade the Kafka custom resource to v1beta2.

8.2.6. Updating listeners to the generic listener configuration

AMQ Streams provides a GenericKafkaListener schema for the configuration of Kafka listeners in a Kafka resource.

GenericKafkaListener replaces the KafkaListeners schema, which has been removed from AMQ Streams.

With the GenericKafkaListener schema, you can configure as many listeners as required, as long as their names and ports are unique. The listeners configuration is defined as an array, but the deprecated format is also supported.

For clients inside the OpenShift cluster, you can create plain (without encryption) or tls internal listeners.

For clients outside the OpenShift cluster, you create external listeners and specify a connection mechanism, which can be nodeport, loadbalancer, ingress or route.

The KafkaListeners schema used sub-properties for plain, tls and external listeners, with fixed ports for each. At any stage in the upgrade process, you must convert listeners configured using the KafkaListeners schema into the format of the GenericKafkaListener schema.

For example, if you are currently using the following configuration in your Kafka configuration:

Old listener configuration

listeners:
  plain:
    # ...
  tls:
    # ...
  external:
    type: loadbalancer
    # ...

Convert the listeners into the new format using:

New listener configuration

listeners:
  #...
  - name: plain
    port: 9092
    type: internal
    tls: false 1
  - name: tls
    port: 9093
    type: internal
    tls: true
  - name: external
    port: 9094
    type: EXTERNAL-LISTENER-TYPE 2
    tls: true

1
The TLS property is now required for all listeners.
2
Options: ingress, loadbalancer, nodeport, route.

Make sure to use the exact names and port numbers shown.

For any additional configuration or overrides properties used with the old format, you need to update them to the new format.

Changes introduced to the listener configuration:

  • overrides is merged with the configuration section
  • dnsAnnotations has been renamed annotations
  • preferredAddressType has been renamed preferredNodePortAddressType
  • address has been renamed alternativeNames
  • loadBalancerSourceRanges and externalTrafficPolicy move to the listener configuration from the now deprecated template

For example, this configuration:

Old additional listener configuration

listeners:
  external:
    type: loadbalancer
    authentication:
      type: tls
    overrides:
      bootstrap:
        dnsAnnotations:
          #...

Changes to:

New additional listener configuration

listeners:
    #...
  - name: external
    port: 9094
    type:loadbalancer
    tls: true
    authentication:
      type: tls
    configuration:
      bootstrap:
        annotations:
          #...

Important

The name and port numbers shown in the new listener configuration must be used for backwards compatibility. Using any other values will cause renaming of the Kafka listeners and OpenShift services.

For more information on the configuration options available for each type of listener, see the GenericKafkaListener schema reference.

8.2.7. Upgrading ZooKeeper to support v1beta2

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.

Procedure

Perform the following steps for each Kafka custom resource in your deployment.

  1. Update the Kafka custom resource in an editor.

    oc edit kafka KAFKA-CLUSTER
  2. If present, move affinity from .spec.zookeeper.affinity to .spec.zookeeper.template.pod.affinity.
  3. If present, move tolerations from .spec.zookeeper.tolerations to .spec.zookeeper.template.pod.tolerations.
  4. If present, remove .spec.zookeeper.template.tlsSidecarContainer.
  5. If present, remove .spec.zookeeper.tlsSidecarContainer.
  6. If type: external logging is configured in .spec.kafka.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j.properties
  7. If the .spec.zookeeper.metrics field is used to enable metrics:

    1. Create a new ConfigMap that stores the YAML configuration for the JMX Prometheus exporter under a key. The YAML must match what is currently in the .spec.zookeeper.metrics field.

      kind: ConfigMap
      apiVersion: v1
      metadata:
        name: kafka-metrics
        labels:
          app: strimzi
      data:
        zookeeper-metrics-config.yaml: |
            <YAML>
    2. Add a .spec.zookeeper.metricsConfig property that points to the ConfigMap and key:

      metricsConfig:
        type: jmxPrometheusExporter
        valueFrom:
          configMapKeyRef:
            name: kafka-metrics
            key: zookeeper-metrics-config.yaml
    3. Delete the old .spec.zookeeper.metrics field.
  8. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.8. Upgrading the Topic Operator to support v1beta2

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.

Procedure

Perform the following steps for each Kafka custom resource in your deployment.

  1. Update the Kafka custom resource in an editor.

    oc edit kafka KAFKA-CLUSTER
  2. If Kafka.spec.topicOperator is used:

    1. Move affinity from .spec.topicOperator.affinity to .spec.entityOperator.template.pod.affinity.
    2. Move tolerations from .spec.topicOperator.tolerations to .spec.entityOperator.template.pod.tolerations.
    3. Move .spec.topicOperator.tlsSidecar to .spec.entityOperator.tlsSidecar.
    4. After moving affinity, tolerations, and tlsSidecar, move the remaining configuration in .spec.topicOperator to .spec.entityOperator.topicOperator.
  3. If type: external logging is configured in .spec.topicOperator.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j2.properties
    Note

    You can also complete this step as part of the Entity Operator upgrade.

  4. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.9. Upgrading the Entity Operator to support v1beta2

Prerequisites

Procedure

Perform the following steps for each Kafka custom resource in your deployment.

  1. Update the Kafka custom resource in an editor.

    oc edit kafka KAFKA-CLUSTER
  2. Move affinity from .spec.entityOperator.affinity to .spec.entityOperator.template.pod.affinity.
  3. Move tolerations from .spec.entityOperator.tolerations to .spec.entityOperator.template.pod.tolerations.
  4. If type: external logging is configured in .spec.entityOperator.userOperator.logging or .spec.entityOperator.topicOperator.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j2.properties
  5. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.10. Upgrading Cruise Control to support v1beta2

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.
  • Cruise Control is configured and deployed. See Deploying Cruise Control in the Using AMQ Streams on OpenShift guide.

Procedure

Perform the following steps for each Kafka.spec.cruiseControl configuration in your Kafka cluster.

  1. Update the Kafka custom resource in an editor.

    oc edit kafka KAFKA-CLUSTER
  2. If type: external logging is configured in .spec.cruiseControl.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j2.properties
  3. If the .spec.cruiseControl.metrics field is used to enable metrics:

    1. Create a new ConfigMap that stores the YAML configuration for the JMX Prometheus exporter under a key. The YAML must match what is currently in the .spec.cruiseControl.metrics field.

      kind: ConfigMap
      apiVersion: v1
      metadata:
        name: kafka-metrics
        labels:
          app: strimzi
      data:
        cruise-control-metrics-config.yaml: |
            <YAML>
    2. Add a .spec.cruiseControl.metricsConfig property that points to the ConfigMap and key:

      metricsConfig:
        type: jmxPrometheusExporter
        valueFrom:
          configMapKeyRef:
            name: kafka-metrics
            key: cruise-control-metrics-config.yaml
    3. Delete the old .spec.cruiseControl.metrics field.
  4. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.11. Upgrading the API version of Kafka resources to v1beta2

Prerequisites

Procedure

Perform the following steps for each Kafka custom resource in your deployment.

  1. Update the Kafka custom resource in an editor.

    oc edit kafka KAFKA-CLUSTER
  2. Update the apiVersion of the Kafka custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1beta1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  3. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.12. Upgrading Kafka Connect resources to v1beta2

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.

Procedure

Perform the following steps for each KafkaConnect custom resource in your deployment.

  1. Update the KafkaConnect custom resource in an editor.

    oc edit kafkaconnect KAFKA-CONNECT-CLUSTER
  2. If present, move:

    KafkaConnect.spec.affinity
    KafkaConnect.spec.tolerations

    to:

    KafkaConnect.spec.template.pod.affinity
    KafkaConnect.spec.template.pod.tolerations

    For example, move:

    spec:
      # ...
      affinity:
        # ...
      tolerations:
        # ...

    to:

    spec:
      # ...
      template:
        pod:
          affinity:
            # ...
          tolerations:
            # ...
  3. If type: external logging is configured in .spec.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j.properties
  4. If the .spec.metrics field is used to enable metrics:

    1. Create a new ConfigMap that stores the YAML configuration for the JMX Prometheus exporter under a key. The YAML must match what is currently in the .spec.metrics field.

      kind: ConfigMap
      apiVersion: v1
      metadata:
        name: kafka-connect-metrics
        labels:
          app: strimzi
      data:
        connect-metrics-config.yaml: |
            <YAML>
    2. Add a .spec.metricsConfig property that points to the ConfigMap and key:

      metricsConfig:
        type: jmxPrometheusExporter
        valueFrom:
          configMapKeyRef:
            name: kafka-connect-metrics
            key: connect-metrics-config.yaml
    3. Delete the old .spec.metrics field.
  5. Update the apiVersion of the KafkaConnect custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1beta1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  6. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.13. Upgrading Kafka Connect S2I resources to v1beta2

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.

Procedure

Perform the following steps for each KafkaConnectS2I custom resource in your deployment.

  1. Update the KafkaConnectS2I custom resource in an editor.

    oc edit kafkaconnects2i S2I-CLUSTER
  2. If present, move:

    KafkaConnectS2I.spec.affinity
    KafkaConnectS2I.spec.tolerations

    to:

    KafkaConnectS2I.spec.template.pod.affinity
    KafkaConnectS2I.spec.template.pod.tolerations

    For example, move:

    spec:
      # ...
      affinity:
        # ...
      tolerations:
        # ...

    to:

    spec:
      # ...
      template:
        pod:
          affinity:
            # ...
          tolerations:
            # ...
  3. If type: external logging is configured in .spec.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j.properties
  4. If the .spec.metrics field is used to enable metrics:

    1. Create a new ConfigMap that stores the YAML configuration for the JMX Prometheus exporter under a key. The YAML must match what is currently in the .spec.metrics field.

      kind: ConfigMap
      apiVersion: v1
      metadata:
        name: kafka-connect-s2i-metrics
        labels:
          app: strimzi
      data:
        connect-s2i-metrics-config.yaml: |
            <YAML>
    2. Add a .spec.metricsConfig property that points to the ConfigMap and key:

      metricsConfig:
        type: jmxPrometheusExporter
        valueFrom:
          configMapKeyRef:
            name: kafka-connect-s2i-metrics
            key: connect-s2i-metrics-config.yaml
    3. Delete the old .spec.metrics field
  5. Update the apiVersion of the KafkaConnectS2I custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1beta1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  6. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.14. Upgrading Kafka MirrorMaker resources to v1beta2

Prerequisites

Procedure

Perform the following steps for each KafkaMirrorMaker custom resource in your deployment.

  1. Update the KafkaMirrorMaker custom resource in an editor.

    oc edit kafkamirrormaker MIRROR-MAKER
  2. If present, move:

    KafkaMirrorMaker.spec.affinity
    KafkaMirrorMaker.spec.tolerations

    to:

    KafkaMirrorMaker.spec.template.pod.affinity
    KafkaMirrorMaker.spec.template.pod.tolerations

    For example, move:

    spec:
      # ...
      affinity:
        # ...
      tolerations:
        # ...

    to:

    spec:
      # ...
      template:
        pod:
          affinity:
            # ...
          tolerations:
            # ...
  3. If type: external logging is configured in .spec.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j.properties
  4. If the .spec.metrics field is used to enable metrics:

    1. Create a new ConfigMap that stores the YAML configuration for the JMX Prometheus exporter under a key. The YAML must match what is currently in the .spec.metrics field.

      kind: ConfigMap
      apiVersion: v1
      metadata:
        name: kafka-mm-metrics
        labels:
          app: strimzi
      data:
        mm-metrics-config.yaml: |
            <YAML>
    2. Add a .spec.metricsConfig property that points to the ConfigMap and key:

      metricsConfig:
        type: jmxPrometheusExporter
        valueFrom:
          configMapKeyRef:
            name: kafka-mm-metrics
            key: mm-metrics-config.yaml
    3. Delete the old .spec.metrics field.
  5. Update the apiVersion of the KafkaMirrorMaker custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1beta1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  6. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.15. Upgrading Kafka MirrorMaker 2.0 resources to v1beta2

Prerequisites

Procedure

Perform the following steps for each KafkaMirrorMaker2 custom resource in your deployment.

  1. Update the KafkaMirrorMaker2 custom resource in an editor.

    oc edit kafkamirrormaker2 MIRROR-MAKER-2
  2. If present, move affinity from .spec.affinity to .spec.template.pod.affinity.
  3. If present, move tolerations from .spec.tolerations to .spec.template.pod.tolerations.
  4. If type: external logging is configured in .spec.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j.properties
  5. If the .spec.metrics field is used to enable metrics:

    1. Create a new ConfigMap that stores the YAML configuration for the JMX Prometheus exporter under a key. The YAML must match what is currently in the .spec.metrics field.

      kind: ConfigMap
      apiVersion: v1
      metadata:
        name: kafka-mm2-metrics
        labels:
          app: strimzi
      data:
        mm2-metrics-config.yaml: |
            <YAML>
    2. Add a .spec.metricsConfig property that points to the ConfigMap and key:

      metricsConfig:
        type: jmxPrometheusExporter
        valueFrom:
          configMapKeyRef:
            name: kafka-mm2-metrics
            key: mm2-metrics-config.yaml
    3. Delete the old .spec.metrics field.
  6. Update the apiVersion of the KafkaMirrorMaker2 custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  7. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.16. Upgrading Kafka Bridge resources to v1beta2

Prerequisites

Procedure

Perform the following steps for each KafkaBridge resource in your deployment.

  1. Update the KafkaBridge custom resource in an editor.

    oc edit kafkabridge KAFKA-BRIDGE
  2. If type: external logging is configured in KafkaBridge.spec.logging:

    Replace the name of the ConfigMap containing the logging configuration:

    logging:
      type: external
      name: my-config-map

    With the valueFrom.configMapKeyRef field, and specify both the ConfigMap name and the key under which the logging is stored:

    logging:
      type: external
      valueFrom:
        configMapKeyRef:
          name: my-config-map
          key: log4j2.properties
  3. Update the apiVersion of the KafkaBridge custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  4. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.17. Upgrading Kafka User resources to v1beta2

Prerequisites

  • A User Operator supporting the v1beta2 API version is up and running.

Procedure

Perform the following steps for each KafkaUser custom resource in your deployment.

  1. Update the KafkaUser custom resource in an editor.

    oc edit kafkauser KAFKA-USER
  2. Update the apiVersion of the KafkaUser custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1beta1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  3. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.18. Upgrading Kafka Topic resources to v1beta2

Prerequisites

  • A Topic Operator supporting the v1beta2 API version is up and running.

Procedure

Perform the following steps for each KafkaTopic custom resource in your deployment.

  1. Update the KafkaTopic custom resource in an editor.

    oc edit kafkatopic KAFKA-TOPIC
  2. Update the apiVersion of the KafkaTopic custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1beta1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  3. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.19. Upgrading Kafka Connector resources to v1beta2

Prerequisites

Procedure

Perform the following steps for each KafkaConnector custom resource in your deployment.

  1. Update the KafkaConnector custom resource in an editor.

    oc edit kafkaconnector KAFKA-CONNECTOR
  2. Update the apiVersion of the KafkaConnector custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  3. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.2.20. Upgrading Kafka Rebalance resources to v1beta2

Prerequisites

  • A Cluster Operator supporting the v1beta2 API version is up and running.
  • Cruise Control is configured and deployed. See Deploying Cruise Control in the Using AMQ Streams on OpenShift guide.

Procedure

Perform the following steps for each KafkaRebalance custom resource in your deployment.

  1. Update the KafkaRebalance custom resource in an editor.

    oc edit kafkarebalance KAFKA-REBALANCE
  2. Update the apiVersion of the KafkaRebalance custom resource to v1beta2:

    Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta2
  3. Save the file, exit the editor and wait for the updated custom resource to be reconciled.

8.3. Upgrading the Cluster Operator

This procedure describes how to upgrade a Cluster Operator deployment to use AMQ Streams 1.8.

Follow this procedure if you deployed the Cluster Operator using the installation YAML files rather than OperatorHub.

The availability of Kafka clusters managed by the Cluster Operator is not affected by the upgrade operation.

Note

Refer to the documentation supporting a specific version of AMQ Streams for information on how to upgrade to that version.

Prerequisites

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 AMQ Streams version 1.8.
  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 a "Version not found" error message. Otherwise, no error message is returned.

    For example:

    "Version 2.4.0 is not supported. Supported versions are: 2.6.0, 2.6.1, 2.7.0."
    • 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 Operator version. For example:

    registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:{ContainerVersion}

Your Cluster Operator was upgraded to version 1.8 but the version of Kafka running in the cluster it manages is unchanged.

Following the Cluster Operator upgrade, you must perform a Kafka upgrade.

8.4. Upgrading Kafka

After you have upgraded your Cluster Operator to 1.8, the next step is to upgrade all Kafka brokers to the latest supported version of Kafka.

Kafka upgrades are performed by the Cluster Operator through rolling updates of the Kafka brokers.

The Cluster Operator initiates rolling updates based on the Kafka cluster configuration.

If Kafka.spec.kafka.config contains…​The Cluster Operator initiates…​

Both the inter.broker.protocol.version and the log.message.format.version.

A single rolling update. After the update, the inter.broker.protocol.version must be updated manually, followed by log.message.format.version. Changing each will trigger a further rolling update.

Either the inter.broker.protocol.version or the log.message.format.version.

Two rolling updates.

No configuration for the inter.broker.protocol.version or the log.message.format.version.

Two rolling updates.

As part of the Kafka upgrade, the Cluster Operator initiates rolling updates for ZooKeeper.

  • A single rolling update occurs even if the ZooKeeper version is unchanged.
  • Additional rolling updates occur if the new version of Kafka requires a new ZooKeeper version.

8.4.1. Kafka versions

Kafka’s log message format version and inter-broker protocol version specify, respectively, the log format version appended to messages and the version of the Kafka protocol used in a cluster. To ensure the correct versions are used, the upgrade process involves making configuration changes to existing Kafka brokers and code changes to client applications (consumers and producers).

The following table shows the differences between Kafka versions:

Kafka versionInterbroker protocol versionLog message format versionZooKeeper version

2.7.0

2.7

2.7

3.5.8

2.7.1

2.7

2.7

3.5.9

2.8.0

2.8

2.8

3.5.9

Inter-broker protocol version

In Kafka, the network protocol used for inter-broker communication is called the inter-broker protocol. Each version of Kafka has a compatible version of the inter-broker protocol. The minor version of the protocol typically increases to match the minor version of Kafka, as shown in the preceding table.

The inter-broker protocol version is set cluster wide in the Kafka resource. To change it, you edit the inter.broker.protocol.version property in Kafka.spec.kafka.config.

Log message format version

When a producer sends a message to a Kafka broker, the message is encoded using a specific format. The format can change between Kafka releases, so messages specify which version of the format they were encoded with. You can configure a Kafka broker to convert messages from newer format versions to a given older format version before the broker appends the message to the log.

In Kafka, there are two different methods for setting the message format version:

  • The message.format.version property is set on topics.
  • The log.message.format.version property is set on Kafka brokers.

The default value of message.format.version for a topic is defined by the log.message.format.version that is set on the Kafka broker. You can manually set the message.format.version of a topic by modifying its topic configuration.

The upgrade tasks in this section assume that the message format version is defined by the log.message.format.version.

8.4.2. Strategies for upgrading clients

The right approach to upgrading your client applications (including Kafka Connect connectors) depends on your particular circumstances.

Consuming applications need to receive messages in a message format that they understand. You can ensure that this is the case in one of two ways:

  • By upgrading all the consumers for a topic before upgrading any of the producers.
  • By having the brokers down-convert messages to an older format.

Using broker down-conversion puts extra load on the brokers, so it is not ideal to rely on down-conversion for all topics for a prolonged period of time. For brokers to perform optimally they should not be down converting messages at all.

Broker down-conversion is configured in two ways:

  • The topic-level message.format.version configures it for a single topic.
  • The broker-level log.message.format.version is the default for topics that do not have the topic-level message.format.version configured.

Messages published to a topic in a new-version format will be visible to consumers, because brokers perform down-conversion when they receive messages from producers, not when they are sent to consumers.

There are a number of strategies you can use to upgrade your clients:

Consumers first
  1. Upgrade all the consuming applications.
  2. Change the broker-level log.message.format.version to the new version.
  3. Upgrade all the producing applications.

    This strategy is straightforward, and avoids any broker down-conversion. However, it assumes that all consumers in your organization can be upgraded in a coordinated way, and it does not work for applications that are both consumers and producers. There is also a risk that, if there is a problem with the upgraded clients, new-format messages might get added to the message log so that you cannot revert to the previous consumer version.

Per-topic consumers first

For each topic:

  1. Upgrade all the consuming applications.
  2. Change the topic-level message.format.version to the new version.
  3. Upgrade all the producing applications.

    This strategy avoids any broker down-conversion, and means you can proceed on a topic-by-topic basis. It does not work for applications that are both consumers and producers of the same topic. Again, it has the risk that, if there is a problem with the upgraded clients, new-format messages might get added to the message log.

Per-topic consumers first, with down conversion

For each topic:

  1. Change the topic-level message.format.version to the old version (or rely on the topic defaulting to the broker-level log.message.format.version).
  2. Upgrade all the consuming and producing applications.
  3. Verify that the upgraded applications function correctly.
  4. Change the topic-level message.format.version to the new version.

    This strategy requires broker down-conversion, but the load on the brokers is minimized because it is only required for a single topic (or small group of topics) at a time. It also works for applications that are both consumers and producers of the same topic. This approach ensures that the upgraded producers and consumers are working correctly before you commit to using the new message format version.

    The main drawback of this approach is that it can be complicated to manage in a cluster with many topics and applications.

Other strategies for upgrading client applications are also possible.

Note

It is also possible to apply multiple strategies. For example, for the first few applications and topics the "per-topic consumers first, with down conversion" strategy can be used. When this has proved successful another, more efficient strategy can be considered acceptable to use instead.

8.4.3. Kafka version and image mappings

When upgrading Kafka, consider your settings for the STRIMZI_KAFKA_IMAGES environment variable and the Kafka.spec.kafka.version property.

  • Each Kafka resource can be configured with a Kafka.spec.kafka.version.
  • The Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable provides a mapping between the Kafka version and the image to be used when that version is requested in a given Kafka resource.

    • If Kafka.spec.kafka.image is not configured, the default image for the given version is used.
    • If Kafka.spec.kafka.image is configured, the default image is overridden.
Warning

The Cluster Operator cannot validate that an image actually contains a Kafka broker of the expected version. Take care to ensure that the given image corresponds to the given Kafka version.

8.4.4. Upgrading Kafka brokers and client applications

This procedure describes how to upgrade a AMQ Streams Kafka cluster to the latest supported Kafka version.

Compared to your current Kafka version, the new version might support a higher log message format version or inter-broker protocol version, or both. Follow the steps to upgrade these versions, if required. For more information, see Section 8.4.1, “Kafka versions”.

You should also choose a strategy for upgrading clients. Kafka clients are upgraded in step 6 of this procedure.

Prerequisites

For the Kafka resource to be upgraded, check that:

  • The Cluster Operator, which supports both versions of Kafka, is up and running.
  • The Kafka.spec.kafka.config does not contain options that are not supported in the new Kafka version.

Procedure

  1. Update the Kafka cluster configuration:

    oc edit kafka my-cluster
  2. If configured, ensure that Kafka.spec.kafka.config has the log.message.format.version and inter.broker.protocol.version set to the defaults for the current Kafka version.

    For example, if upgrading from Kafka version 2.7.0 to 2.8.0:

    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.7.0
        config:
          log.message.format.version: "2.7"
          inter.broker.protocol.version: "2.7"
          # ...

    If log.message.format.version and inter.broker.protocol.version are not configured, AMQ Streams automatically updates these versions to the current defaults after the update to the Kafka version in the next step.

    Note

    The value of log.message.format.version and inter.broker.protocol.version must be strings to prevent them from being interpreted as floating point numbers.

  3. Change the Kafka.spec.kafka.version to specify the new Kafka version; leave the log.message.format.version and inter.broker.protocol.version at the defaults for the current Kafka version.

    Note

    Changing the kafka.version ensures that all brokers in the cluster will be upgraded to start using the new broker binaries. During this process, some brokers are using the old binaries while others have already upgraded to the new ones. Leaving the inter.broker.protocol.version unchanged ensures that the brokers can continue to communicate with each other throughout the upgrade.

    For example, if upgrading from Kafka 2.7.0 to 2.8.0:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.8.0 1
        config:
          log.message.format.version: "2.7" 2
          inter.broker.protocol.version: "2.7" 3
          # ...
    1
    Kafka version is changed to the new version.
    2
    Message format version is unchanged.
    3
    Inter-broker protocol version is unchanged.
    Warning

    You cannot downgrade Kafka if the inter.broker.protocol.version for the new Kafka version changes. The inter-broker protocol version determines the schemas used for persistent metadata stored by the broker, including messages written to __consumer_offsets. The downgraded cluster will not understand the messages.

  4. If the image for the Kafka cluster is defined in the Kafka custom resource, in Kafka.spec.kafka.image, update the image to point to a container image with the new Kafka version.

    See Kafka version and image mappings

  5. Save and exit the editor, then wait for rolling updates to complete.

    Check the progress of the rolling updates by watching the pod state transitions:

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

    The rolling updates ensure that each pod is using the broker binaries for the new version of Kafka.

  6. Depending on your chosen strategy for upgrading clients, upgrade all client applications to use the new version of the client binaries.

    If required, set the version property for Kafka Connect and MirrorMaker as the new version of Kafka:

    1. For Kafka Connect, update KafkaConnect.spec.version.
    2. For MirrorMaker, update KafkaMirrorMaker.spec.version.
    3. For MirrorMaker 2.0, update KafkaMirrorMaker2.spec.version.
  7. If configured, update the Kafka resource to use the new inter.broker.protocol.version version. Otherwise, go to step 9.

    For example, if upgrading to Kafka 2.8.0:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.8.0
        config:
          log.message.format.version: "2.7"
          inter.broker.protocol.version: "2.8"
          # ...
  8. Wait for the Cluster Operator to update the cluster.
  9. If configured, update the Kafka resource to use the new log.message.format.version version. Otherwise, go to step 10.

    For example, if upgrading to Kafka 2.8.0:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.8.0
        config:
          log.message.format.version: "2.8"
          inter.broker.protocol.version: "2.8"
          # ...
  10. Wait for the Cluster Operator to update the cluster.

    • The Kafka cluster and clients are now using the new Kafka version.
    • The brokers are configured to send messages using the inter-broker protocol version and message format version of the new version of Kafka.

Following the Kafka upgrade, if required, you can:

8.5. Upgrading consumers to cooperative rebalancing

You can upgrade Kafka consumers and Kafka Streams applications to use the incremental cooperative rebalance protocol for partition rebalances instead of the default eager rebalance protocol. The new protocol was added in Kafka 2.4.0.

Consumers keep their partition assignments in a cooperative rebalance and only revoke them at the end of the process, if needed to achieve a balanced cluster. This reduces the unavailability of the consumer group or Kafka Streams application.

Note

Upgrading to the incremental cooperative rebalance protocol is optional. The eager rebalance protocol is still supported.

Prerequisites

Procedure

To upgrade a Kafka consumer to use the incremental cooperative rebalance protocol:

  1. Replace the Kafka clients .jar file with the new version.
  2. In the consumer configuration, append cooperative-sticky to the partition.assignment.strategy. For example, if the range strategy is set, change the configuration to range, cooperative-sticky.
  3. Restart each consumer in the group in turn, waiting for the consumer to rejoin the group after each restart.
  4. Reconfigure each consumer in the group by removing the earlier partition.assignment.strategy from the consumer configuration, leaving only the cooperative-sticky strategy.
  5. Restart each consumer in the group in turn, waiting for the consumer to rejoin the group after each restart.

To upgrade a Kafka Streams application to use the incremental cooperative rebalance protocol:

  1. Replace the Kafka Streams .jar file with the new version.
  2. In the Kafka Streams configuration, set the upgrade.from configuration parameter to the Kafka version you are upgrading from (for example, 2.3).
  3. Restart each of the stream processors (nodes) in turn.
  4. Remove the upgrade.from configuration parameter from the Kafka Streams configuration.
  5. Restart each consumer in the group in turn.

Additional resources

Chapter 9. Downgrading AMQ Streams

If you are encountering issues with the version of AMQ Streams you upgraded to, you can revert your installation to the previous version.

You can perform a downgrade to:

  1. Revert your Cluster Operator to the previous AMQ Streams version.

  2. Downgrade all Kafka brokers and client applications to the previous Kafka version.

If the previous version of AMQ Streams does not support the version of Kafka you are using, you can also downgrade Kafka as long as the log message format versions appended to messages match.

9.1. Downgrading the Cluster Operator to a previous version

If you are encountering issues with AMQ Streams, you can revert your installation.

This procedure describes how to downgrade a Cluster Operator deployment to a previous version.

Prerequisites

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 previous version of the Cluster Operator.
  2. Revert your custom resources to reflect the supported configuration options available for the version of AMQ Streams you are downgrading to.
  3. Update the Cluster Operator.

    1. Modify the installation files for the previous 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. Get the image for the Kafka pod to ensure the downgrade was successful:

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

    The image tag shows the new AMQ Streams version followed by the Kafka version. For example, NEW-STRIMZI-VERSION-kafka-CURRENT-KAFKA-VERSION.

Your Cluster Operator was downgraded to the previous version.

9.2. Downgrading Kafka

Kafka version downgrades are performed by the Cluster Operator.

9.2.1. Kafka version compatibility for downgrades

Kafka downgrades are dependent on compatible current and target Kafka versions, and the state at which messages have been logged.

You cannot revert to the previous Kafka version if that version does not support any of the inter.broker.protocol.version settings which have ever been used in that cluster, or messages have been added to message logs that use a newer log.message.format.version.

The inter.broker.protocol.version determines the schemas used for persistent metadata stored by the broker, such as the schema for messages written to __consumer_offsets. If you downgrade to a version of Kafka that does not understand an inter.broker.protocol.version that has (ever) been previously used in the cluster the broker will encounter data it cannot understand.

If the target downgrade version of Kafka has:

  • The same log.message.format.version as the current version, the Cluster Operator downgrades by performing a single rolling restart of the brokers.
  • A different log.message.format.version, downgrading is only possible if the running cluster has always had log.message.format.version set to the version used by the downgraded version. This is typically only the case if the upgrade procedure was aborted before the log.message.format.version was changed. In this case, the downgrade requires:

    • Two rolling restarts of the brokers if the interbroker protocol of the two versions is different
    • A single rolling restart if they are the same

Downgrading is not possible if the new version has ever used a log.message.format.version that is not supported by the previous version, including when the default value for log.message.format.version is used. For example, this resource can be downgraded to Kafka version 2.7.0 because the log.message.format.version has not been changed:

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
  # ...
  kafka:
    version: 2.8.0
    config:
      log.message.format.version: "2.7"
      # ...

The downgrade would not be possible if the log.message.format.version was set at "2.8" or a value was absent (so that the parameter took the default value for a 2.8.0 broker of 2.8).

9.2.2. Downgrading Kafka brokers and client applications

This procedure describes how you can downgrade a AMQ Streams Kafka cluster to a lower (previous) version of Kafka, such as downgrading from 2.8.0 to 2.7.0.

Prerequisites

For the Kafka resource to be downgraded, check:

  • IMPORTANT: Compatibility of Kafka versions.
  • The Cluster Operator, which supports both versions of Kafka, is up and running.
  • The Kafka.spec.kafka.config does not contain options that are not supported by the Kafka version being downgraded to.
  • The Kafka.spec.kafka.config has a log.message.format.version and inter.broker.protocol.version that is supported by the Kafka version being downgraded to.

Procedure

  1. Update the Kafka cluster configuration.

    oc edit kafka KAFKA-CONFIGURATION-FILE
  2. Change the Kafka.spec.kafka.version to specify the previous version.

    For example, if downgrading from Kafka 2.8.0 to 2.7.0:

    apiVersion: kafka.strimzi.io/v1beta2
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.7.0 1
        config:
          log.message.format.version: "2.7" 2
          inter.broker.protocol.version: "2.7" 3
          # ...
    1
    Kafka version is changed to the previous version.
    2
    Message format version is unchanged.
    3
    Inter-broker protocol version is unchanged.
    Note

    You must format the value of log.message.format.version and inter.broker.protocol.version as a string to prevent it from being interpreted as a floating point number.

  3. If the image for the Kafka version is different from the image defined in STRIMZI_KAFKA_IMAGES for the Cluster Operator, update Kafka.spec.kafka.image.

    See Section 8.4.3, “Kafka version and image mappings”

  4. Save and exit the editor, then wait for rolling updates to complete.

    Check the update in the logs or by watching the pod state transitions:

    oc logs -f CLUSTER-OPERATOR-POD-NAME | grep -E "Kafka version downgrade from [0-9.]+ to [0-9.]+, phase ([0-9]+) of \1 completed"
    oc get pod -w

    Check the Cluster Operator logs for an INFO level message:

    Reconciliation #NUM(watch) Kafka(NAMESPACE/NAME): Kafka version downgrade from FROM-VERSION to TO-VERSION, phase 1 of 1 completed
  5. Downgrade all client applications (consumers) to use the previous version of the client binaries.

    The Kafka cluster and clients are now using the previous Kafka version.

  6. If you are reverting back to a version of AMQ Streams earlier than 0.22, which uses ZooKeeper for the storage of topic metadata, delete the internal topic store topics from the Kafka cluster.

    oc run kafka-admin -ti --image=registry.redhat.io/amq7/amq-streams-kafka-28-rhel8:1.8.0 --rm=true --restart=Never -- ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi-topic-operator-kstreams-topic-store-changelog --delete && ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi_store_topic --delete

Additional resources

Appendix A. Using your subscription

AMQ Streams is provided through a software subscription. To manage your subscriptions, access your account at the Red Hat Customer Portal.

Accessing Your Account

  1. Go to access.redhat.com.
  2. If you do not already have an account, create one.
  3. Log in to your account.

Activating a Subscription

  1. Go to access.redhat.com.
  2. Navigate to My Subscriptions.
  3. Navigate to Activate a subscription and enter your 16-digit activation number.

Downloading Zip and Tar Files

To access zip or tar files, use the customer portal to find the relevant files for download. If you are using RPM packages, this step is not required.

  1. Open a browser and log in to the Red Hat Customer Portal Product Downloads page at access.redhat.com/downloads.
  2. Locate the Red Hat AMQ Streams entries in the INTEGRATION AND AUTOMATION category.
  3. Select the desired AMQ Streams product. The Software Downloads page opens.
  4. Click the Download link for your component.

Revised on 2021-09-23 14:51:14 UTC

Legal Notice

Copyright © 2021 Red Hat, Inc.
The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/. In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version.
Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law.
Red Hat, Red Hat Enterprise Linux, the Shadowman logo, the Red Hat logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries.
Linux® is the registered trademark of Linus Torvalds in the United States and other countries.
Java® is a registered trademark of Oracle and/or its affiliates.
XFS® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries.
MySQL® is a registered trademark of MySQL AB in the United States, the European Union and other countries.
Node.js® is an official trademark of Joyent. Red Hat is not formally related to or endorsed by the official Joyent Node.js open source or commercial project.
The OpenStack® Word Mark and OpenStack logo are either registered trademarks/service marks or trademarks/service marks of the OpenStack Foundation, in the United States and other countries and are used with the OpenStack Foundation's permission. We are not affiliated with, endorsed or sponsored by the OpenStack Foundation, or the OpenStack community.
All other trademarks are the property of their respective owners.