Deploying and Upgrading AMQ Streams on OpenShift

Red Hat AMQ 2020.Q4

For use with AMQ Streams 1.6 on OpenShift Container Platform

Abstract

This guide provides instructions for deploying and upgrading AMQ Streams

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.

If the topic is reconfigured or reassigned to different Kafka nodes, 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.

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

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

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 versions

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 3.11 and later

OperatorHub

Use the AMQ Streams Operator in the OperatorHub to deploy the Cluster Operator to a single namespace or all namespaces.

OpenShift 4.x only

For the greatest flexibility, choose the installation artifacts method. Choose the OperatorHub method if you want to install AMQ Streams to OpenShift 4 in a standard configuration using the OpenShift 4 web console. The OperatorHub also allows you to take advantage of automatic updates.

In the case of both methods, the Cluster Operator is deployed to your OpenShift cluster, ready for you to deploy the other components of AMQ Streams, starting with a Kafka cluster, using the YAML example files provided.

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

In 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 4 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 (project) namespace or all (projects) namespaces, and managed by the OLM. Engineering teams can then independently manage the software in development, test, and production environments using the OLM.

Note

The OperatorHub is not available in versions of OpenShift earlier than version 4.

AMQ Streams Operator

The 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.

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 3.11 and later cluster is available.

    AMQ Streams is based on Strimzi 0.20.x.

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

AMQ Streams supports some features that are specific to OpenShift, where such integration benefits OpenShift users and there is no equivalent implementation using standard Kubernetes.

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 4.1.1, “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. 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-26-rhel7:1.6.4
  • registry.redhat.io/amq7/amq-streams-kafka-25-rhel7:1.6.4

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-rhel7-operator:1.6.4

AMQ Streams image for running the operators:

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

Kafka Bridge

  • registry.redhat.io/amq7/amq-streams-bridge-rhel7:1.6.4

AMQ Streams image for running the AMQ Streams Kafka Bridge

3.4. 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 apply -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

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 0.20.x. This section describes the procedures to deploy AMQ Streams on OpenShift 3.11 and later.

Note

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

4.1. Create the Kafka cluster

In order to create your Kafka cluster, you deploy the Cluster Operator to manage the Kafka cluster, then deploy the Kafka cluster.

When deploying the Kafka cluster using the Kafka resource, you can deploy the Topic Operator and User Operator at the same time. Alternatively, if you are using a non-AMQ Streams Kafka cluster, you can deploy the Topic Operator and User Operator as standalone components.

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.

4.1.1. 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:

4.1.1.1. 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.

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

    oc get deployments

4.1.1.3. 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-rhel7-operator:1.6.4
            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 apply -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace
    oc apply -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace
    oc apply -f install/cluster-operator/032-RoleBinding-strimzi-cluster-operator-topic-operator-delegation.yaml -n watched-namespace
  4. Deploy the Cluster Operator:

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

    oc get deployments

4.1.1.4. 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-rhel7-operator:1.6.4
            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
    oc create clusterrolebinding strimzi-cluster-operator-topic-operator-delegation --clusterrole=strimzi-topic-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 apply -f install/cluster-operator -n my-cluster-operator-namespace
  5. Verify that the Cluster Operator was successfully deployed:

    oc get deployments

4.1.1.5. Deploying the Cluster Operator from the OperatorHub

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

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 4 web console, click Operators > OperatorHub.
  2. Search or browse for the AMQ Streams Operator, in the Streaming & Messaging category.

    Image: The AMQ Streams Operator in the OperatorHub in OpenShift 4
  3. Click the AMQ Streams 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:

    • Installation Mode: Choose to install the AMQ Streams Operator to all (projects) namespaces in the cluster (the default option) or a specific (project) 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.

    Installed Operators in OpenShift 4

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

4.1.2. 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.

4.1.2.1. 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 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.

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
# ...

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

4.1.2.2. 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.

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 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/v1beta1
    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>

4.1.2.3. 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.

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 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/v1beta1
    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>

4.1.3. Alternative standalone deployment options for AMQ Streams Operators

When deploying a Kafka cluster using the Cluster Operator, you can also deploy the Topic Operator and User Operator. Alternatively, you can perform a standalone deployment.

A standalone deployment means the Topic Operator and User Operator can operate with a Kafka cluster that is not managed by AMQ Streams.

4.1.3.1. Deploying the standalone Topic Operator

This procedure shows how to deploy the Topic Operator as a standalone component.

A standalone deployment requires configuration of environment variables, and is more complicated than deploying the Topic Operator using the Cluster Operator. However, a standalone deployment is more flexible as the Topic Operator can operate with any Kafka cluster, not necessarily one deployed by the Cluster Operator.

Prerequisites

  • You need an existing Kafka cluster for the Topic Operator to connect to.

Procedure

  1. Edit the Deployment.spec.template.spec.containers[0].env properties in the install/topic-operator/05-Deployment-strimzi-topic-operator.yaml file by setting:

    1. STRIMZI_KAFKA_BOOTSTRAP_SERVERS to list the bootstrap brokers in your Kafka cluster, given as a comma-separated list of hostname:‍port pairs.
    2. STRIMZI_ZOOKEEPER_CONNECT to list the ZooKeeper nodes, given as a comma-separated list of hostname:‍port pairs. This should be the same ZooKeeper cluster that your Kafka cluster is using.
    3. STRIMZI_NAMESPACE to the OpenShift namespace in which you want the operator to watch for KafkaTopic resources.
    4. STRIMZI_RESOURCE_LABELS to the label selector used to identify the KafkaTopic resources managed by the operator.
    5. STRIMZI_FULL_RECONCILIATION_INTERVAL_MS to specify the interval between periodic reconciliations, in milliseconds.
    6. STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS to specify the number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation could take more time due to the number of partitions or replicas. Default 6.
    7. STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS to the ZooKeeper session timeout, in milliseconds. For example, 10000. Default 20000 (20 seconds).
    8. STRIMZI_TOPICS_PATH to the Zookeeper node path where the Topic Operator stores its metadata. Default /strimzi/topics.
    9. STRIMZI_TLS_ENABLED to enable TLS support for encrypting the communication with Kafka brokers. Default true.
    10. STRIMZI_TRUSTSTORE_LOCATION to the path to the truststore containing certificates for enabling TLS based communication. Mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.
    11. STRIMZI_TRUSTSTORE_PASSWORD to the password for accessing the truststore defined by STRIMZI_TRUSTSTORE_LOCATION. Mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.
    12. STRIMZI_KEYSTORE_LOCATION to the path to the keystore containing private keys for enabling TLS based communication. Mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.
    13. STRIMZI_KEYSTORE_PASSWORD to the password for accessing the keystore defined by STRIMZI_KEYSTORE_LOCATION. Mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.
    14. STRIMZI_LOG_LEVEL to the level for printing logging messages. The value can be set to: ERROR, WARNING, INFO, DEBUG, and TRACE. Default INFO.
    15. STRIMZI_JAVA_OPTS (optional) to the Java options used for the JVM running the Topic Operator. An example is -Xmx=512M -Xms=256M.
    16. STRIMZI_JAVA_SYSTEM_PROPERTIES (optional) to list the -D options which are set to the Topic Operator. An example is -Djavax.net.debug=verbose -DpropertyName=value.
  2. Deploy the Topic Operator:

    oc apply -f install/topic-operator
  3. Verify that the Topic Operator has been deployed successfully:

    oc describe deployment strimzi-topic-operator

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

    Note

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

4.1.3.2. Deploying the standalone User Operator

This procedure shows how to deploy the User Operator as a standalone component.

A standalone deployment requires configuration of environment variables, and is more complicated than deploying the User Operator using the Cluster Operator. However, a standalone deployment is more flexible as the User Operator can operate with any Kafka cluster, not necessarily one deployed by the Cluster Operator.

Prerequisites

  • You need an existing Kafka cluster for the User Operator to connect to.

Procedure

  1. Edit the following Deployment.spec.template.spec.containers[0].env properties in the install/user-operator/05-Deployment-strimzi-user-operator.yaml file by setting:

    1. STRIMZI_KAFKA_BOOTSTRAP_SERVERS to list the Kafka brokers, given as a comma-separated list of hostname:‍port pairs.
    2. STRIMZI_ZOOKEEPER_CONNECT to list the ZooKeeper nodes, given as a comma-separated list of hostname:‍port pairs. This must be the same ZooKeeper cluster that your Kafka cluster is using. Connecting to ZooKeeper nodes with TLS encryption is not supported.
    3. STRIMZI_NAMESPACE to the OpenShift namespace in which you want the operator to watch for KafkaUser resources.
    4. STRIMZI_LABELS to the label selector used to identify the KafkaUser resources managed by the operator.
    5. STRIMZI_FULL_RECONCILIATION_INTERVAL_MS to specify the interval between periodic reconciliations, in milliseconds.
    6. STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS to the ZooKeeper session timeout, in milliseconds. For example, 10000. Default 20000 (20 seconds).
    7. STRIMZI_CA_CERT_NAME to point to an OpenShift Secret that contains the public key of the Certificate Authority for signing new user certificates for TLS client authentication. The Secret must contain the public key of the Certificate Authority under the key ca.crt.
    8. STRIMZI_CA_KEY_NAME to point to an OpenShift Secret that contains the private key of the Certificate Authority for signing new user certificates for TLS client authentication. The Secret must contain the private key of the Certificate Authority under the key ca.key.
    9. STRIMZI_CLUSTER_CA_CERT_SECRET_NAME to point to an OpenShift Secret containing the public key of the Certificate Authority used for signing Kafka brokers certificates for enabling TLS-based communication. The Secret must contain the public key of the Certificate Authority under the key ca.crt. This environment variable is optional and should be set only if the communication with the Kafka cluster is TLS based.
    10. STRIMZI_EO_KEY_SECRET_NAME to point to an OpenShift Secret containing the private key and related certificate for TLS client authentication against the Kafka cluster. The Secret must contain the keystore with the private key and certificate under the key entity-operator.p12, and the related password under the key entity-operator.password. This environment variable is optional and should be set only if TLS client authentication is needed when the communication with the Kafka cluster is TLS based.
    11. STRIMZI_CA_VALIDITY the validity period for the Certificate Authority. Default is 365 days.
    12. STRIMZI_CA_RENEWAL the renewal period for the Certificate Authority.
    13. STRIMZI_LOG_LEVEL to the level for printing logging messages. The value can be set to: ERROR, WARNING, INFO, DEBUG, and TRACE. Default INFO.
    14. STRIMZI_GC_LOG_ENABLED to enable garbage collection (GC) logging. Default true. Default is 30 days to initiate certificate renewal before the old certificates expire.
    15. STRIMZI_JAVA_OPTS (optional) to the Java options used for the JVM running User Operator. An example is -Xmx=512M -Xms=256M.
    16. STRIMZI_JAVA_SYSTEM_PROPERTIES (optional) to list the -D options which are set to the User Operator. An example is -Djavax.net.debug=verbose -DpropertyName=value.
  2. Deploy the User Operator:

    oc apply -f install/user-operator
  3. Verify that the User Operator has been deployed successfully:

    oc describe deployment strimzi-user-operator

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

    Note

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

4.2. 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.

4.2.1. 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. For a Kafka cluster with 3 or more brokers, use the examples/connect/kafka-connect.yaml file. For a Kafka cluster with less than 3 brokers, use the examples/connect/kafka-connect-single-node-kafka.yaml file.

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

    oc get deployments

4.2.2. 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 4.1. 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 Cluster Operator can also use images that you have created to deploy a Kafka Connect cluster to your OpenShift cluster.

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.

4.2.2.1. 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-26-rhel7:1.6.4 as the base image:

    FROM registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.4
    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/v1beta1
      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:

4.2.2.2. 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-26-rhel7:1.6.4 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.

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.

4.2.3. 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 tasks for a connector instance
  • Restart failed tasks (not supported by KafkaConnector resource)
  • Pause a connector instance
  • Resume a previously paused connector instance
  • Delete a connector instance

4.2.3.1. 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.

You manage a running connector instance by updating its corresponding KafkaConnector, and then applying the updates. 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.

4.2.3.2. 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.

4.2.4. Deploying a KafkaConnector resource to Kafka Connect

This procedure describes how to deploy the example KafkaConnector to a Kafka Connect cluster.

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

Prerequisites

Procedure

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

    apiVersion: kafka.strimzi.io/v1alpha1
    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"
        topic: my-topic
        # ...
    1
    Enter a name for the KafkaConnector resource. This will be used as the name of the connector within Kafka Connect. You can choose any name that is valid for an OpenShift resource.
    2
    Enter the name of the Kafka Connect cluster in which to create the connector.
    3
    The name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
    4
    The maximum number of tasks that the connector can create.
    5
    Configuration settings for the connector. Available configuration options depend on the connector class.
  2. Create the KafkaConnector in your OpenShift cluster:

    oc apply -f examples/connect/source-connector.yaml
  3. Check that the resource was created:

    oc get kctr --selector strimzi.io/cluster=my-connect-cluster -o name

4.3. 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.

4.3.1. 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

4.4. Deploy Kafka Bridge

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

4.4.1. 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 5. 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.

5.1. 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-26-rhel7:1.6.4 --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-26-rhel7:1.6.4 --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.

5.2. 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 or SCRAM-SHA-512 authentication, both of which can be used with TLS encryption. If you are using an authorization server, you can use token-based OAuth 2.0 authentication and OAuth 2.0 authorization. Open Policy Agent (OPA) authorization is also supported as a Kafka authorization option.

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 format (.p12) and PEM (.crt) formats.

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/v1beta1
      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
      Authoization 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. Extract the public cluster CA certificate and password from the generated KAFKA-CLUSTER-NAME-cluster-ca-cert Secret.

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

    Use the certificate and password in your Kafka client to connect to the Kafka cluster with TLS encryption.

    Note

    Cluster CA certificates renew automatically by default. If you are using your own Kafka listener certificates, you will need to renew the certificates manually.

  5. 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/v1beta1
      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).
  6. 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
  7. Configure your client to connect to the Kafka cluster with the properties required to make a secure connection to the Kafka cluster.

    1. Add the authentication details for the public cluster certificates:

      security.protocol: SSL 1
      ssl.truststore.location: PATH-TO/ssl/keys/truststore 2
      ssl.truststore.password: CLUSTER-CA-CERT-PASSWORD 3
      ssl.truststore.type=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.

    2. Add the bootstrap address and port for connecting to the Kafka cluster:

      bootstrap.servers: BOOTSTRAP-ADDRESS:PORT
    3. Add the authentication details for the public user certificates:

      ssl.keystore.location: PATH-TO/ssl/keys/user1.keystore 1
      ssl.keystore.password: USER-CERT-PASSWORD 2
      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.

Chapter 6. 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

6.1. 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.

6.1.1. 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.

6.1.2. 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 6.1. 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

6.2. 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:

6.2.1. 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.

6.2.2. 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

6.3. 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:

6.3.1. 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 6.3, “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

6.3.2. 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

6.3.3. 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

6.3.4. 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

6.3.5. 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 6.3.3, “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.

6.4. 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.

6.4.1. 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.

6.4.2. 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.

6.4.2.1. 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.

6.4.2.2. 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).

6.4.2.3. 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

6.4.3. 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.

6.4.3.1. 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:

6.4.3.2. 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.

6.4.3.3. 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.

6.4.3.4. 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):

    oc create secret generic alertmanager-alertmanager --from-file=alertmanager.yaml=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

6.4.4. Setting up Grafana

Grafana provides visualizations of Prometheus metrics.

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

6.4.4.1. 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.

6.4.4.2. Enabling the example Grafana dashboards

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

  • strimzi-kafka.json
  • strimzi-zookeeper.json
  • strimzi-kafka-connect.json
  • strimzi-kafka-mirror-maker-2.json
  • strimzi-operators.json
  • strimzi-kafka-bridge.json
  • strimzi-cruise-control.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 6.1. 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 6.2. 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 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

6.5. 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.

6.5.1. 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.

6.5.2. 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.

6.5.3. 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.

The data extracted is described here.

Table 6.2. Broker metrics output

NameInformation

kafka_brokers

Number of brokers in the Kafka cluster

Table 6.3. 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 6.4. 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

6.5.4. 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 the sample Kafka YAML 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/v1beta1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      # ...
      kafkaExporter:
        image: my-org/my-image: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.

6.5.5. 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.

6.6. 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

6.6.1. 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/v1beta1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  bootstrapServers: my-cluster-kafka:9092
  http:
    # ...
  enableMetrics: true
  # ...

6.6.2. 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

6.7. 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

6.7.1. Configuring Cruise Control

You can enable the Cruise Control metrics in the Kafka resource using the cruiseControl.metrics property that contains the JMX exporter configuration about the metrics to expose.

For example:

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  kafka:
    # ...
  zookeeper:
    # ...
  cruiseControl:
    metrics:
      lowercaseOutputName: true
      rules:
      - pattern: kafka.cruisecontrol<name=(.+)><>(\w+)
        name: kafka_cruisecontrol_$1_$2
        type: GAUGE

6.7.2. 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 7. Upgrading AMQ Streams

AMQ Streams can be upgraded with no cluster downtime. Each version of AMQ Streams supports one or more versions of Apache Kafka. You can upgrade to a higher Kafka version as long as it is supported by your version of AMQ Streams.

Newer versions of AMQ Streams may support newer versions of Kafka, but you need to upgrade AMQ Streams before you can upgrade to a higher supported Kafka version.

To upgrade AMQ Streams Operators, you can use the Operator Lifecycle Manager (OLM) on your OpenShift Container Platform cluster.

Important

If applicable, Resource upgrades must be performed after upgrading AMQ Streams and Kafka.

7.1. AMQ Streams and Kafka upgrades

Upgrading AMQ Streams is a two-stage process. To upgrade brokers and clients without downtime, you must complete the upgrade procedures in the following order:

  1. Update your Cluster Operator to the latest 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.
    • 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 the new AMQ Streams version.

      Depending on your chosen upgrade strategy, following the channel update either:

      • An automatic upgrade is initiated
      • A manual upgrade then requires approval before the installation begins

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

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

7.1.1. Kafka versions

Kafka’s log message format version and inter-broker protocol version specify the log format version appended to messages and the version of protocol used in a cluster. As a result, the upgrade process involves making configuration changes to existing Kafka brokers and code changes to client applications (consumers and producers) to ensure the correct versions are used.

The following table shows the differences between Kafka versions:

Kafka versionInterbroker protocol versionLog message format versionZooKeeper version

2.5.0

2.5

2.5

3.5.8

2.6.0

2.6

2.6

3.5.8

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 include a version identifying 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.

7.1.2. Upgrading the Cluster Operator

The steps to upgrade your Cluster Operator deployment to use AMQ Streams 1.6 are outlined in this section.

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.

7.1.2.1. Upgrading the Cluster Operator to a later version

This procedure describes how to upgrade a Cluster Operator deployment to a later 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 the Cluster Operator.

    1. Modify the installation files for the new 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-cluster-operator.yaml file to use those environment variables.
  3. When you have an updated configuration, deploy it along with the rest of the installation resources:

    oc apply -f install/cluster-operator

    Wait for the rolling updates to complete.

  4. Get the image for the Kafka pod to ensure the upgrade was successful:

    oc get po 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 AMQ Streams version>-kafka-<Current Kafka version>.

  5. Update existing resources to handle deprecated custom resource properties.

You now have an updated Cluster Operator, but the version of Kafka running in the cluster it manages is unchanged.

What to do next

Following the Cluster Operator upgrade, you can perform a Kafka upgrade.

7.1.3. Upgrading Kafka

After you have upgraded your Cluster Operator to 1.6, 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.

7.1.3.1. Kafka version and image mappings

When upgrading Kafka, consider your settings for the STRIMZI_KAFKA_IMAGES and Kafka.spec.kafka.version properties.

  • 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.

7.1.3.2. Strategies for upgrading clients

The best 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.

7.1.3.3. 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 7.1.1, “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 2.5.0 to 2.6.0:

    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.5.0
        config:
          log.message.format.version: "2.5"
          inter.broker.protocol.version: "2.5"
          # ...

    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 2.5.0 to 2.6.0:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.6.0 1
        config:
          log.message.format.version: "2.5" 2
          inter.broker.protocol.version: "2.5" 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 2.6.0:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.6.0
        config:
          log.message.format.version: "2.5"
          inter.broker.protocol.version: "2.6"
          # ...
  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 2.6.0:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.6.0
        config:
          log.message.format.version: "2.6"
          inter.broker.protocol.version: "2.6"
          # ...
  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:

7.1.3.4. Updating listener configuration

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

GenericKafkaListener replaces the KafkaListeners schema, which is deprecated.

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 uses sub-properties for plain, tls and external listeners, with fixed ports for each. After the Kafka upgrade, you can 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.

7.1.3.5. Upgrading consumers and Kafka Streams applications 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

7.2. AMQ Streams resource upgrades

The kafka.strimzi.io/v1alpha1 API version is deprecated for the following AMQ Streams resources:

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

Update these resources to use the kafka.strimzi.io/v1beta1 API version.

This section describes the upgrade steps for the resources.

Important

The upgrade of resources must be performed after upgrading the Cluster Operator, so the Cluster Operator can understand the resources.

What if the resource upgrade does not take effect?

If the upgrade does not take effect, a warning is given in the logs on reconciliation to indicate that the resource cannot be updated until the apiVersion is updated.

To trigger the update, make a cosmetic change to the custom resource, such as adding an annotation.

Example annotation:

metadata:
  # ...
  annotations:
    upgrade: "Upgraded to kafka.strimzi.io/v1beta1"

The following procedures describe the steps to update specific resources to use the kafka.strimzi.io/v1beta1 API version:

7.2.1. Upgrading Kafka resources

Prerequisites

  • A Cluster Operator supporting the v1beta1 API version is up and running.

Procedure

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

  1. Update the Kafka resource in an editor.

    oc edit kafka my-cluster
  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta1
  3. If the Kafka resource has:

    Kafka.spec.topicOperator

    Replace it with:

    Kafka.spec.entityOperator.topicOperator

    For example, replace:

    spec:
      # ...
      topicOperator: {}

    with:

    spec:
      # ...
      entityOperator:
        topicOperator: {}
  4. If present, move:

    Kafka.spec.entityOperator.affinity
    Kafka.spec.entityOperator.tolerations

    to:

    Kafka.spec.entityOperator.template.pod.affinity
    Kafka.spec.entityOperator.template.pod.tolerations

    For example, move:

    spec:
      # ...
      entityOperator:
        affinity {}
        tolerations {}

    to:

    spec:
      # ...
      entityOperator:
        template:
          pod:
            affinity {}
            tolerations {}
  5. If present, move:

    Kafka.spec.kafka.affinity
    Kafka.spec.kafka.tolerations

    to:

    Kafka.spec.kafka.template.pod.affinity
    Kafka.spec.kafka.template.pod.tolerations

    For example, move:

    spec:
      # ...
      kafka:
        affinity {}
        tolerations {}

    to:

    spec:
      # ...
      kafka:
        template:
          pod:
            affinity {}
            tolerations {}
  6. If present, move:

    Kafka.spec.zookeeper.affinity
    Kafka.spec.zookeeper.tolerations

    to:

    Kafka.spec.zookeeper.template.pod.affinity
    Kafka.spec.zookeeper.template.pod.tolerations

    For example, move:

    spec:
      # ...
      zookeeper:
        affinity {}
        tolerations {}

    to:

    spec:
      # ...
      zookeeper:
        template:
          pod:
            affinity {}
            tolerations {}
  7. Save the file, exit the editor and wait for the updated resource to be reconciled.

7.2.2. Upgrading Kafka Connect resources

Prerequisites

  • A Cluster Operator supporting the v1beta1 API version is up and running.

Procedure

Execute the following steps for each KafkaConnect resource in your deployment.

  1. Update the KafkaConnect resource in an editor.

    oc edit kafkaconnect my-connect
  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta1
  3. 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 {}
  4. Save the file, exit the editor and wait for the updated resource to be reconciled.

7.2.3. Upgrading Kafka Connect S2I resources

Prerequisites

  • A Cluster Operator supporting the v1beta1 API version is up and running.

Procedure

Execute the following steps for each KafkaConnectS2I resource in your deployment.

  1. Update the KafkaConnectS2I resource in an editor.

    oc edit kafkaconnects2i my-connect
  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta1
  3. 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 {}
  4. Save the file, exit the editor and wait for the updated resource to be reconciled.

7.2.4. Upgrading Kafka MirrorMaker resources

Prerequisites

  • A Cluster Operator supporting the v1beta1 API version is up and running.

Procedure

Execute the following steps for each KafkaMirrorMaker resource in your deployment.

  1. Update the KafkaMirrorMaker resource in an editor.

    oc edit kafkamirrormaker my-connect
  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta1
  3. If present, move:

    KafkaConnectMirrorMaker.spec.affinity
    KafkaConnectMirrorMaker.spec.tolerations

    to:

    KafkaConnectMirrorMaker.spec.template.pod.affinity
    KafkaConnectMirrorMaker.spec.template.pod.tolerations

    For example, move:

    spec:
      # ...
      affinity {}
      tolerations {}

    to:

    spec:
      # ...
      template:
        pod:
          affinity {}
          tolerations {}
  4. Save the file, exit the editor and wait for the updated resource to be reconciled.

7.2.5. Upgrading Kafka Topic resources

Prerequisites

  • A Topic Operator supporting the v1beta1 API version is up and running.

Procedure

Execute the following steps for each KafkaTopic resource in your deployment.

  1. Update the KafkaTopic resource in an editor.

    oc edit kafkatopic my-topic
  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta1
  3. Save the file, exit the editor and wait for the updated resource to be reconciled.

7.2.6. Upgrading Kafka User resources

Prerequisites

  • A User Operator supporting the v1beta1 API version is up and running.

Procedure

Execute the following steps for each KafkaUser resource in your deployment.

  1. Update the KafkaUser resource in an editor.

    oc edit kafkauser my-user
  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion: kafka.strimzi.io/v1beta1
  3. Save the file, exit the editor and wait for the updated resource to be reconciled.

Chapter 8. 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.

8.1. 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 apply -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-STREAMS-VERSION-kafka-CURRENT-KAFKA-VERSION.

Your Cluster Operator was downgraded to the previous version.

8.2. Downgrading Kafka

Kafka version downgrades are performed by the Cluster Operator.

8.2.1. 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 2.5.0 because the log.message.format.version has not been changed:

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  # ...
  kafka:
    version: 2.6.0
    config:
      log.message.format.version: "2.5"
      # ...

The downgrade would not be possible if the log.message.format.version was set at "2.6" or a value was absent (so that the parameter took the default value for a 2.6.0 broker of 2.6).

8.2.2. 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 2.6.0 to 2.5.0.

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 in an editor, as required.

    oc edit kafka my-cluster
  2. Change the Kafka.spec.kafka.version to specify the previous version.

    For example, if downgrading from Kafka 2.6.0 to 2.5.0:

    apiVersion: kafka.strimzi.io/v1beta1
    kind: Kafka
    spec:
      # ...
      kafka:
        version: 2.5.0 1
        config:
          log.message.format.version: "2.5" 2
          inter.broker.protocol.version: "2.5" 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 7.1.3.1, “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.

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 JBOSS 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 2021-05-17 16:27:44 UTC

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